Enhancing Model Accuracy through Explainable Error
Analysis: A Decision Tree-Based Approach for XAI

**Communication Info**

**Authors:**

Sidi Mohamed DAFALI
Mohamed KISSI
Omar EL BEGGAR

Faculty of Sciences and Techniques
of Mohammed University
hassan2 of Casablanca, Morocco

Faculty of Sciences and Techniques
of Mohammed University
hassan2 of Casablanca, Morocco

Faculty of Sciences and Techniques
of Mohammed University
hassan2 of Casablanca, Morocco

**Keywords:**

(1) eXplainable Artificial
Intelligence (XAI)

(2) Decision Trees

(3) Model Explainability

(4) Error Analysis

(5) Machine Learning
Transparency

**Abstract**

The transparency and explainability of machine
learning models have become key challenges in the
development of trustworthy and ethical artificial
intelligence (AI)[1]. Although often highly performant,
the most complex models, such as deep neural
networks, frequently exhibit opacity, complicating the
understanding of their functioning and errors[2]. In this
paper, we propose an original approach based on
decision trees to identify and explain classification
errors of predictive models. By segmenting the data
into correct and incorrect predictions, we train a
decision tree on misclassified examples to enable the
discovery of precise patterns explaining the initial
error. On one hand, the explanations provided by this
method are interpretable due to their simple rule-based
format[3]. On the other hand, they allow for the
improvement of the initial model's learning process. We
evaluate our approach on several diverse datasets,
demonstrating its effectiveness in explaining a
significant number of errors and showing that when
these insights are used to refine the model, they lead to
tangible performance improvements. Our work aligns
with current initiatives within Explainable Artificial
Intelligence (XAI) and meets the transparency and
reliability thresholds required for critical applications
(healthcare, finance, justice[3]). By integrating this
method directly into the model development
framework, we contribute to building end-user trust
without compromising the goal of high model
performance[4].

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] Ribeiro, M. T., Singh, S., & Guestrin, C. "Why Should I Trust You?" Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016.

[2] Lipton, Z. C., The Mythos of Model Interpretability, 2018, Queue, 16(3), 31-57.

[3] Molnar, C., Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. 2020

[4] Lundberg, S. M., & Lee, S. I., *A Unified Approach to Interpreting Model Predictions*. Advances in Neural Information Processing Systems. 2017.
L'Utilisation de l'IA et du Machine Learning dans les Prévisions de Ventes et l'Optimisation des Performances Commerciales dans le domaine des OPCI

**Communication Info**

**Authors:**

Yassir DAHBI¹
Aissam BEKKARI ²
Aberrazzak NEJEOUI ³
Mustapha OUJAOURA ⁴

¹ LMCS Laboratory ENSA,
Marrakech, Morocco

**Keywords:**

(1) OPCI

(2) Appels clients

(3) Processus de prévision

(3) Apprentissage automatique

**Abstract**

Cet article aborde le manque d'études empiriques sur le processus de génération de ventes au sein des Organismes de Placement Collectif en Immobilier (OPCI) en se concentrant sur l'impact des appels clients sur la clôture des opportunités. Dans ce papier nous utilisons des techniques d'analyse prédictive, y compris la sélection de caractéristiques et l'optimisation des hyperparamètres, afin de développer un modèle d'apprentissage supervisé adapté aux dynamiques uniques des processus de vente des OPCI.

L'objectif étant d'identifier les attributs critiques au sein de l'entonnoir de vente qui influencent significativement le processus de prévision, en mettant particulièrement l'accent sur le rôle des appels clients dans la conclusion des opportunités.

Le document explore différents types d'apprentissage automatique, avec un accent particulier sur l'apprentissage supervisé, et son applicabilité dans la prise de décision et la prévision commerciale dans le contexte des OPCI.

La méthodologie proposée est présentée dans le cadre de CRISP-DM, englobant des phases telles que la préparation des données, le nettoyage, la transformation et la modélisation. Les défis liés aux données manquantes et catégorielles, ainsi que l'importance de la sélection des caractéristiques et de leur encodage dans la prévision des ventes des OPCI sont également examinés.

La nouvelle approche proposée permet de prévoir efficacement la probabilité de succès dans les ventes des OPCI en analysant l'impact des appels clients, et en identifiant les attributs critiques au sein de l'entonnoir de vente.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] Bohanec, M., Kljajić Borštnar, M., & Robnik-Sikonja, M. (2017). Explaining machine learning models in sales predictions. Expert Systems with Applications, 71, 416-428.

[2] Chapman, P., Clinton, J., & Khabaza, T. (2000). CRISP-DM 1.0: Step-by-step data mining guide. The CRISP-DM Consortium.

[3] Gurnani, M., Jain, R., & Shukla, V. (2017). Forecasting of sales by using fusion of machine learning techniques. ICDMAI, 1-6.
# Machine learning and remote sensing for urban sprawl detection: A case study of Casablanca

## Communication Info

**Authors:**

Hafsa OUCHRA¹

Abdessamad BELANGOUR²

Allae ERRAISSI³

¹ Laboratory of Information Technology and Modeling LTIM, Hassan II University, Faculty of Sciences Ben M'sik, Casablanca, Morocco

² Laboratory of Information Technology and Modeling LTIM, Hassan II University, Faculty of Sciences Ben M'sik, Casablanca, Morocco

³ Chouaib Doukkali University, EL Jadida, Morocco

**Keywords:**

(1) Remote sensing

(2) Machine learning

(3) Urbain planning

## Abstract

Rapid and uncontrolled urbanization represents a major challenge for large metropolises, particularly Casablanca, where urban expansion significantly affects the environment and territorial resource management. This work proposes an approach based on machine learning and remote sensing [1] to analyze the evolution of urban sprawl over a 20-year period (2000-2020). By exploiting Landsat OLI/TIRS satellite images [2] via Google Earth Engine (GEE) and using supervised classification algorithms [3], [4] such as Random Forest (RF), Support Vector Machine (SVM) and CART, we were able to identify and quantify changes in land use [5]. The results show a significant increase in the urban area (from 21.8% in 2000 to 38% in 2020), accompanied by a reduction in vegetated surfaces and bare soil. A comparative evaluation of the algorithms revealed that Random Forest offered the best performance, with overall accuracy reaching 94% in 2010 and a Kappa coefficient greater than 0.85 over all the periods studied.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] A. Erraissi, H. Ouchra, and M. Banane, "Integrating Remote Sensing and Machine Learning for Accurate Detection of Agricultural Zones in El Jadida, Morocco," *Communications in Computer and Information Science*, vol. 2281 CCIS, pp. 35-49, 2024, doi: 10.1007/978-3-031-77432-4_3.

[2] H. Ouchra, A. Belangour, A. Erraissi, and M. Labied, "New MDA Transformation Process from Urban Satellite Image Classification to Specific Urban Landsat Satellite Image Classification," *Journal of Environmental & Earth Sciences*, vol. 7, no. 1, pp. 81-91, Oct. 2024, doi: 10.30564/jees.v7i1.7145.

[3] H. Ouchra, A. Belangour, and A. Erraissi, "Land cover mapping using Landsat 8 data and supervised learning classifiers in Casablanca region, Morocco," 2023 4th International Conference on Data Analytics for Business and Industry, ICDABI 2023, pp. 12-17, 2023, doi: 10.1109/ICDABI60145.2023.10629339.

[4] H. Ouchra, A. Belangour, and A. Erraissi, "Analyzing the performance of supervised and unsupervised learning for satellite image classification: A case study of Casablanca," 2023 4th International Conference on Data Analytics for Business and Industry, ICDABI 2023, pp. 23-29, 2023, doi: 10.1109/ICDABI60145.2023.10629343.

[5] H. Ouchra, A. Belangour, A. Erraissi, and M. Labied, "Data to Cartography: New MDE-Based Approach for Urban Satellite Image Classification," *Journal of Environmental & Earth Sciences*, vol. 7, no. 1, pp. 18-28, Oct. 2024, doi: 10.30564/jees.v7i1.7054.
<figure><img src="image_103.png" /></figure>

Noise-Resilient Segmentation: A Novel Framework for Medical
Images

**Communication Info**

**Authors:**
Chaimae OUCHICHA¹

¹ Department of Management
Assistance Techniques, Hassan
1st University (UH1), ENCG,
Settat, Morocco

**Keywords:**
(1) Image processing
(2) Brain tissue segmentation
(3) Noise
(4) Fuzzy clustering
(5) Convergence analysis.

**Abstract**

Image segmentation is one of the most important and challenging processes in medical image analysis, particularly for Magnetic Resonance Imaging (MRI) brain scans. Accurate segmentation of brain tissues, such as gray matter, white matter and cerebrospinal fluid, is essential for diagnosing neurological disorders and conducting research. However, traditional segmentation methods often struggle with noise, intensity inhomogeneity, and the overlapping nature of tissue intensities in MRI data. In this context, we propose a novel approach to MRI brain segmentation using fuzzy clustering, which addresses these challenges by incorporating spatial information and handling uncertainty in voxel classification. Experiments were conducted on publicly available MRI datasets, and results show that the proposed fuzzy clustering approach outperforms traditional FCM and other state-of-the art methods in terms of segmentation accuracy and robustness to noise. The proposed algorithm demonstrates significant potential for clinical applications where accurate brain tissue segmentation is crucial.
**Communication Info**

**Authors:**

Imane Mhiliss<fnref n="1" />

Rajaa Sebihi<fnref n="1" />

Abdessamad Didi<fnref n="2" />

**Keywords:**

(1) Radiotherapy

(2) Hadrontherapy

(3) Bragg Peak

(4) Charged particles

(5) Energy deposition

(6) Monte Carlo simulation

(7) Geant4

**Abstract**

Radiotherapy is one of the most common methods for treating cancer, typically involving the irradiation of tumours with high-energy photons. However, this approach often damages healthy tissues along the irradiation path. Hadron therapy, an innovative form of radiotherapy, uses charged particles such as protons or carbon-12 ions to target tumours more precisely. It is particularly effective in treating radio-resistant tumours near critical organs while minimising side effects due to its ability to deliver highly concentrated energy at the tumour site through the Bragg Peak. This study aims to model a detector based on a 50 × 50 × 50 cm³ water phantom and to simulate, using the Monte Carlo tool Geant4, the interactions of charged heavy particles within an energy range of 300 to 400 MeV. The objective is to analyse the mechanisms responsible for energy deposition beyond the Bragg Peak. This work contributes to a better understanding of the physical processes involved in hadron therapy.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] B.Ille, M. C. Galez, F. Haas, M. Boutemeur, C. Morel, and P. Lautesse, 'Contribution à l'étude des processus nucléaires intervenant en hadronthérapie et de leur impact sur la délocalisation du dépôt de dose'.

[2] https://geant4.web.cern.ch/

[3] C. Jacobs, 'Simulation of a proton beam through a phantom of water using Geant4'.

<footnote n="1">¹ LPHE-MS, Faculty of Sciences, Mohammed V University, Rabat.</footnote>

<footnote n="2">² National Centre for Energy, Science, and Nuclear Techniques -CNESTEN-, Rabat, Morocco.</footnote>
<figure><img src="image_104.png" /></figure>

# A Comprehensive Review of Data Mining Techniques for Analyzing Factors Influencing Graduates' Employability

## Communication Info

**Authors:**

Brahim BELLA<fnref n="1" />

Yman CHEMLAL<fnref n="2" />

Mohamed AZZOUAZI<fnref n="3" />

Abderrahmane DAIF<fnref n="4" />

**Keywords:**

(1) Data Mining

(2) Graduates' Employability

(3) Predictive Analysis

(4) Employability Factors

(5) Higher education

## Abstract

Graduate employability is a key concern for higher education institutions and governments. This review examines data mining techniques used to study the factors influencing graduates' transition to employment. It synthesizes findings from various studies on methods like classification, clustering, and association rule analysis for predicting employability outcomes. Key factors influencing employability include academic performance, soft skills, technical skills, and demographics [1]. The review evaluates the effectiveness of algorithms such as decision trees, Naive Bayes, SVM, and neural networks in predicting employment results [2]. It also explores the role of educational data mining (EDM) and learning analytics (LA) in understanding student behaviors and identifying employability patterns [3]. Challenges like data imbalance and the need for qualitative studies are discussed [4]. Future research will focus on explainable AI (XAI) for improved model interpretability and examining social and economic factors on employability. This review aims to provide insights for educators, policymakers, and researchers to enhance graduate employability through data-driven strategies, guiding targeted interventions for improved employment outcomes [5].

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

## References

[1] Khadragy, S. A. G., & Abdallah, S. (2021). Adaptive Network Based Fuzzy Inference System and the Future of Employability. *Network and Complex Systems*, 12, 1-XX. ISSN 2224-610X (Print), ISSN 2225-0603 (Online).

[2] Othman, Z., Shan, S. W., Yusoff, I., & Kee, C. P. (2018). Classification techniques for predicting graduate employability. *International Journal on Advanced Science, Engineering and Information Technology*, 8(4-2), 1712-1720.

[3] Crasta, L. C., & Shailashri, V. T. (2023). A Systematic Review on the Employability Prediction Model for the Management Students. *International Journal of Case Studies in Business, IT, and Education*, 7(1).

[4] Noori, M. I., & Azmi, F. T. (2021). Students Perceived Employability: A Systematic Literature Search and Bibliometric Analysis. *Eurasian Journal of Business and Economics*, 14(28), 37-56.

[5] Cruz, M. E. L. T., & Encarnacion, R. E. (2021). Analysis and prediction of students' academic performance and employability using data mining techniques: A research travelogue. *The Eurasia Proceedings of Science, Technology, Engineering & Mathematics (EPSTEM)*, 16, 117-131.

[6] Fang, F. (2023). A Study on the Application of Data Mining Techniques in the Management of Sustainable Education for Employment. *Data Science Journal*, 22(23), 1-13.

[7] Mezhoudi, N., Alghamdi, R., Aljunaid, R., Krichna, G., & Düştegör, D. (2021). Employability prediction: a survey of current approaches, research challenges and applications. *Journal of Ambient Intelligence and Humanized Computing*, 14(3).

<footnote n="1">¹ Faculty of Sciences Ben M'Sick / Hassan II University of Casablanca, Casablanca, Morocco</footnote>

<footnote n="2">² Faculty of Sciences Ben M'Sick / Hassan II University of Casablanca, Casablanca, Morocco</footnote>

<footnote n="3">³ Faculty of Sciences Ben M'Sick / Hassan II University of Casablanca, Casablanca, Morocco</footnote>

<footnote n="4">⁴ Faculty of Sciences Ben M'Sick / Hassan II University of Casablanca, Casablanca, Morocco</footnote>
VAE-Based Anomaly Detection for Load Balancing in Cloud Computing

**Communication Info**

**Authors:**

Salma YAMARI¹
El Habib BEN LAHMAR 1

¹ HASSAN II University-
Mohammedia-Casablanca,
Faculty of Sciences Ben M'sik.

¹ Laboratory of Information
Technology and Modelling
Casablanca Morocco

**Keywords:**

(1) Variational Autoencoder
(VAE)

(2) Load Balancing

(3) Anomaly Detection

(4) Generative AI

(5) Cloud Computing

**Abstract**

In the face of dynamic and unpredictable workloads in cloud computing environments, having an effective load-balancing method is essential in addressing the relationship between cloud resources and performing effective resource utilization [1]. This research introduces an anomaly detection framework leveraging a Variational Autoencoder (VAE) trained to detect overloaded machines in cloud environments by utilizing Google's Borg cluster dataset [2]. By learning normal operational patterns, the VAE detects anomalies in CPU and memory usage, aiding an intelligent load balancer in making real-time decisions. The framework is integrated in the CloudSim simulation environment [3], demonstrating significant improvements in detection accuracy and system stability. This approach extends existing adaptive anomaly detection models such as the one proposed by Jiang [4], exploring the potential benefits of generative AI techniques like VAE [5] in workload management.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] S. Simaiya et al., "A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques," *Sci. Rep.*, vol. 14, no. 1, p. 1337, Jan. 2024, doi: 10.1038/s41598-024-51466-0.

[2] A. Verma, L. Pedrosa, M. Korupolu, D. Oppenheimer, E. Tune, and J. Wilkes, "Large-scale cluster management at Google with Borg," in *Proceedings of the Tenth European Conference on Computer Systems*, Bordeaux France: ACM, Apr. 2015, pp. 1-17. doi: 10.1145/2741948.2741964.

[3] Y. Hao, G. Liu, and J. Lu, "Three Levels Load Balancing on Cloudsim," *Int. J. Grid Distrib. Comput.*, vol. 7, no. 3, pp. 71-88, Jun. 2014, doi: 10.14257/ijgdc.2014.7.3.08.

[4] G. Jiang, "Artificial intelligence-based adaptive anomaly detection technology for IaaS cloud virtual machines," *J. Eng. Appl. Sci.*, vol. 71, no. 1, p. 102, Dec. 2024, doi: 10.1186/s44147-024-00439-1.

[5] D. P. Kingma and M. Welling, "Auto-Encoding Variational Bayes," 2013, *arXiv*. doi: 10.48550/ARXIV.1312.6114.
# A Comparative Study of NLP Techniques for Bias Detection in Recruitment Processes

**Communication Info**

**Authors:**

Fatima Zahra ABBOUR<fnref n="1" />
Soufiane ARDCHIR<fnref n="2" />
Soumaya OUNACER<fnref n="1" />
Mohamed AZZOUAZI<fnref n="1" />
Khadija MAHJOUBI<fnref n="1" />

**Keywords:**

(1) Natural Language
Processing

(2) Bias Detection

(3) Recruitment Processes

**Abstract**

This study has a comparative overview of Natural Language Processing (NLP) techniques for identifying bias in recruitment text. We discuss the application of word embedding, sentiment analysis, and transformer models to identify potentially discriminatory language in job advertisements and resumes in terms of gender, race, and age biases. We have a qualitative overview of the techniques in light of their mechanisms and suitability in identifying bias. We point out the pragmatic effects and theoretic differences among these NLP approaches. This analysis aims to provide a comprehensive description of how different NLP techniques can be used to detect and address bias in recruitment processes, and their strengths and pitfalls for pragmatic purposes respectively.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] R. Frissen, K. J. Adebayo, and R. Nanda, "A machine learning approach to recognize bias and discrimination in job advertisements," *AI Soc.*, vol. 38, no. 2, pp. 1025-1038, 2023.

[2] D. Quer, A. Via, M. Serra-Vidal, L. Nadal, and D. Fortuny, "Analyzing bias and discrimination in an algorithmic hiring use case," 2024.

[3] R. Mao, L. Tan, and R. Moieni, "Developing a large-scale language model to unveil and alleviate gender and age biases in Australian job ads," in *2023 IEEE International Conference on Big Data (BigData)*, IEEE, 2023, pp. 4176-4185.

[4] N. H. Koh, J. Plata, and J. Chai, "BAD: BiAs Detection for Large Language Models in the context of candidate screening," *ArXiv Prepr. ArXiv230510407*, 2023.

[5] M. Dikshit, H. Bouamor, and N. Habash, "Investigating Gender Bias in STEM Job Advertisements," in *Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)*, 2024, pp. 179-189.

<footnote n="1">¹ Faculty of sciences Ben M'sik,
Hassan II University,
Casablanca, Morocco</footnote>

<footnote n="2">² National School of Business
and Management, Hassan II
University, Casablanca,
Morocco</footnote>
**Communication Info**

**Authors:**

Fatimazahra Ouahouda<fnref n="1" />
Khadija Achtaich <fnref n="2" />

**Keywords:**

(1) STEAM education

(2) Primary education

(3) Scratch

**Abstract**

The STEAM (Science, Technology, Engineering, Arts, and Mathematics) approach is increasingly recognized as an effective educational model for fostering creativity, critical thinking, and problem-solving skills in primary education. This paper explores the integration of STEAM in primary classrooms, focusing on the contributions of mathematics and computer science to enhance student engagement and learning outcomes. By incorporating interactive activities, digital tools, and coding platforms such as Scratch, this approach encourages hands-on learning and interdisciplinary connections. The study highlights the benefits of STEAM-based instruction in developing computational thinking, logical reasoning, and collaborative skills among young learners. Challenges and best practices for successful implementation in primary schools are also discussed.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] Yakman, G. "STEAM Education: An Overview of Creating a Model of Integrative Education." Invention Journal of Research Technology in Engineering & Management, 1, 2008, 1-8.

[2] Johnston, C., & Mooney, A. "STEAM and Coding in Primary Education: Fostering Computational Thinking through Interdisciplinary Learning." Computers & Education, 140, 2019, 103601.

[3] Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., & Kafai, Y. "Scratch: Programming for All." Communications of the ACM, 52, 2009, 60-67.

[4] Honey, M., Pearson, G., & Schweingruber, H. "STEM Integration in K-12 Education: Status, Prospects, and an Agenda for Research." National Academies Press, 2014.

[5] Liao, C., Chen, D. T., & Shih, M. "The Interplay of Computational Thinking and STEAM Education." Journal of Science Education and Technology, 27, 2018, 421-437.

<footnote n="1">¹ FSBM, Casablanca, Morocco</footnote>

<footnote n="2">² Fsbm, Casablanca, Morocco</footnote>
# PRÉDICTION DE LA RÉSURGENCE DE DIX
MALADIES INFECTIEUSES SOUS SURVEILLANCE

## Communication Info

**Authors:**

Abdourahmane NDAO¹
Cheikh Tidiane SECK ¹
Boly DIOP ²

¹ Department of Mathematics,
Alioune Diop University,
Bambey, Senegal

² Prevention Department,
Ministry of Health, Dakar,
Senegal

**Keywords:**

(1) Forêt aléatoire binaire
multiple

(2) Arbre de décision multi-
output

(3) Critère d'inertie

(2) Keyword2

(3) Keyword3

## Abstract

Dans ce travail, nous proposons deux nouveaux modèles de prédiction de la résurgence de dix maladies infectieuses sous surveillance épidémique au Sénégal [1]. Le premier modèle est une forêt aléatoire binaire multiple (FABM) utilisant l'algorithme de forêt aléatoire « ranger » avec le critère de Gini, et permet de prédire séparément chacune de ces dix maladies infectieuses en tenant compte des interdépendances avec les autres [2]. Le second modèle est un arbre de décision « multi-output » (ADMO), utilisant le critère d'inertie (basé sur la distance du khi-deux) comme mesure d'impureté des nœuds, et permet de prédire simultanément l'ensemble de dix maladies infectieuses en fournissant leur probabilité de réapparition [3]. Les données sont issues de la base globale de données de surveillance des maladies au Sénégal et concernent, entre autres, des informations sur 68 698 cas confirmés des dix maladies ciblées et des caractéristiques intrinsèques aux districts de surveillance [4]. Les résultats ont montré que ces pathologies ont une probabilité de résurgence moyenne

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

## References

[1] Santangelo, OE; Gentile, V.; Pizzo, S.; Giordano, D.; Cedrone, F, une revue systématique. *Mach. Apprendre.* *Connaître.* *Extr*, 5, 2023, 175-198.

[2] Wright, M. N., & Ziegler, A, Journal of Statistical Software, 77(1), 2017, 1–17.

[3] Garcia A., Lopez M., Rodriguez J., & Martinez E, Journal of Vector-borne Diseases, 57(4), 2020, 321-335.

[4] NDAO A., SECK CT., DIOP B., NDIAYE AA, African Journal of Applied Statistics, 11(1), 2024, 1535-1551.

[5] Villi Dane M. Go, HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE, 13(2), 2023, 45-54.
A hybrid approach for feature selection based on
graph partitioning and TOPSIS

**Communication Info**

**Authors:**

Abdelali OUBAOUZINE
Tayeb OUADERHMAN
Hasna CHAMLAL

*Computer Science and Systems*
*Laboratory (LIS), Faculty of*
*Sciences Ain Chock, Hassan II*
*University of Casablanca,*
*Morocco*

**Keywords:**

(1) Feature selection

(2) Graph partitioning

(3) TOPSIS

**Abstract**

Feature selection plays a key role in dimensionality reduction by extracting essential variables from large datasets. This work proposes an innovative method for feature selection in high-dimensional datasets, combining the mrMR algorithm, a graph partitioning technique and TOPSIS approach. After an initial filtering phase aimed at selecting the K best variables, a graph-based model integrates relevance and redundancy through a new weighting of edges to extract meaningful subgraphs using a graph partitioning technique. The final selection optimizes modularity, accuracy and diameter, ensuring an effective and compact subset of variables by the TOPSIS method. Experimental results on high dimensional datasets validate the effectiveness and superiority of this approach over existing methods.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] Azadifar S, Rostami M, Berahmand K, Moradi P, Oussalah M, Graph-based relevancy-redundancy gene selection method for cancer diagnosis, *Computers in Biology and Medicine*, vol. 147, 2022, p. 105766.

[2] El Akadi A, Amine A, El Ouardighi A, Aboutajdine D, A two-stage gene selection scheme utilizing MRMR filter and GA wrapper, *Knowledge and Information Systems*, vol. 26, 2011, p. 487-500.

[3] Ding C, Peng H, Minimum redundancy feature selection from microarray gene expression data, *Journal of bioinformatics and computational biology*, vol. 3, 2005, p. 185-205.
Analysis of Global Discourse on the Palestinian
War: Text Classification through transfer

Communication Info

Authors:

Yassir MATRANE¹

Zineb ELLAKY 2

Faouzia BENABBOU 3

¹ Faculty of sciences Ben M'Sick / University Hassan II of Casablanca, Morocco

² Faculty of sciences Ben M'Sick / University Hassan II of Casablanca, Morocco

³ Faculty of sciences Ben M'Sick / University Hassan II of Casablanca, Morocco

Keywords:

(1) Text Classification

(2) Political Discourse Analysis

(3) Natural Language
Processing

Abstract

The Palestinian-Israeli conflict generates diverse online
reactions, with social media becoming a critical arena
for public discourse beyond traditional media
narratives. This research develops an automated
classification system to identify pro- and anti-
Palestinian sentiments in online discussions. Building
on approaches in [1] and [2], we fine-tuned
transformer-based models including BERT, DistilBERT,
and XLM-RoBERTa. The used dataset of more than 6,000
comments follows methodological insights from [3].
XLM-RoBERTa achieved 95% accuracy, outperforming
other models and showing better cross-cultural
applicability than in [4]. These findings extend [5] by
contributing to the growing body of work on automated
stance detection in complex geopolitical discussions,
providing insights into global public opinion beyond
mainstream media narratives.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

References

[1] Bo Pang; Lillian Lee, Opinion Mining and Sentiment Analysis, now, 2008, doi: 10.1561/1500000011.

[2] E. Cambria, B. Schuller, Y. Xia, and C. Havasi, "New Avenues in Opinion Mining and Sentiment Analysis," IEEE Intell. Syst., vol. 28, no. 2, pp. 15-21, Mar. 2013, doi: 10.1109/MIS.2013.30.

[3] W. F. Al-Sarraj and H. M. Lubbad, "Bias Detection of Palestinian/Israeli Conflict in Western Media: A Sentiment Analysis Experimental Study," in 2018 International Conference on Promising Electronic Technologies (ICPET), Oct. 2018, pp. 98-103. doi: 10.1109/ICPET.2018.00024.

[4] A. Gangwar and T. Mehta, "Sentiment Analysis of Political Tweets for Israel using Machine Learning," Apr. 12, 2022, arXiv: arXiv:2204.06515. doi: 10.48550/arXiv.2204.06515.

[5] A. Liyih, S. Anagaw, M. Yibeyin, and Y. Tehone, "Sentiment analysis of the Hamas-Israel war on YouTube comments using deep learning," Sci. Rep., vol. 14, no. 1, p. 13647, Jun. 2024, doi: 10.1038/s41598-024-63367-3.
# Solving Reaction-Diffusion Equations Using Kolmogorov-Arnold Neural Networks: A Comparative Study with Exact Solution

## Communication Info

**Authors:**

Imane HARIRI<fnref n="1" />

Atika RADID <fnref n="1" />

Karim RHOFIR <fnref n="2" />

**Keywords:**

(1) Kolmogorov-Arnold networks

(2) Reaction-diffusion equations

(3) Deep Learning

## Abstract

In this work, we investigate the effectiveness of Kolmogorov-Arnold Neural Networks (KANs) in solving reaction-diffusion partial differential equations (PDEs). These equations describe various physical and biological processes[1], making their accurate numerical resolution crucial. We propose a framework based on KANs to approximate the solutions of selected reaction-diffusion PDEs and evaluate their performance by comparing the predicted solutions with the corresponding exact solutions. The accuracy of the proposed approach is assessed using the L2 error metric. Our study builds upon recent advancements in physics-informed machine learning [2] and function approximation theories [3], demonstrating the potential of KANs as an alternative to traditional numerical methods for solving complex PDEs.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

## References

[1] Murray, J.D. Mathematical Biology. Springer, 2002.

[2] Raissi, M., Perdikaris, P., & Karniadakis, G.E. "Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations." Journal of Computational Physics, 378, 2019, pp. 686-707.

[3] Arnold, V.I. "On functions of three variables." Doklady Akademii Nauk SSSR, 114(4), 1957, pp. 679-681.

<footnote n="1">¹ FSAC, Casablanca, Morocco</footnote>

<footnote n="2">² ENSAK, Khouribga, Morocco</footnote>
Optimisation des systems temps reel dans les
véhicules autonomes

Communication Info

Authors:

Meryem SAQALLI¹

¹ ENSEM School Hassan II
University, Casablanca, Maroc

Keywords:

(1) Système temps réel

(2) Système embarqué

(3) Système distribué

(4) Système multi-agents

(3) Intéligence Artificielle

Abstract

Actuellement, les systèmes temps réels sont de plus en plus
présents dans le quotidien, on les trouve dans
l'aéronautique, transport ferroviaire, l'automobile,
l'électroménager ou le multimédia.

Les voitures autonomes utilisent des systèmes embarqués pour contrôler la perception (capteurs, caméras, LIDAR, etc.) et l'actionnement (freins, direction, accélération). Ces systèmes doivent respecter des contraintes temporelles rigides pour garantir la sécurité, la réactivité et la stabilité des décisions.

Dans cet article nous proposons une nouvelle Plateforme
temps réel « Timer » pour une architecture distribuée basé sur
le système multi-agents et nous abordons les défis
rencontrés par les véhicules autonomes pour respecter les
contraintes temporelles strictes imposées par les systèmes
embarqués. Les conclusions de cette étude soulignent
l'importance d'une gestion optimale des ressources pour
garantir une réactivité en temps réel dans des situations
complexes et imprévisibles, telles que les interventions
d'urgence sur la route.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

References

[1] M.Saqalli, H.Medromi, New Approach TIMER for the Control of the Distributed Systems with the Multi-Agents Systems, ID: pxc3874766, International Journal of Computer Applications (IJCA), 2011, Number 9.

[2] M.Saqalli, H.Medromi, New Platform TIMER For The Temporal Control Of The Systems Distributed with the Multi-Agents Systems -Application to Systems M2M, Paper Reference ID: IJEIT201209_883, International Journal of Engineering and Innovative Technology IJEIT, 2012.

[3] P. Chevochot and I. Puaut, An approach for fault-tolerance in hard-time distributed systems, Proc. 18th IEEE Symposium on Reliable Distributed Systems, 1999, Pages 292-293.

[4] E. Grolleau, Ordonnancement temps réel hors-ligne optimal à l'aide de réseaux de Petri en environnement monoprocesseur et multiprocesseur, Thèse, ENSMA-Université Poitiers, 1999, Pages 152-260.
Comparative Study of Text Classification Using Deep Learning Models

**Communication Info**

**Authors :**

Youssef ETTAFSSAOUI¹

Omar BENCHAREF 2

1,2 Cadi Ayyad University, Faculty
of Science and Technology, L2IS
Laboratory, Marrakech, Morocco.

**Keywords:**

(1) Text Classification

(2) Natural Language Processing

(3) Deep Learning

(4) Embedding Model

(5) Conv1D

(6) Long Short-Term Memory

(7) Transformer

**Abstract**

In natural language processing, text classification is a basic job for
which many deep learning architectures fight to get best
performance. Aiming to find the most efficient strategy involving
accuracy, recall, and F1-score while considering computational
complexity and training efficiency, this work compares four
different deep learning models for text classification using the BBC
News dataset. Extensive data preparation, hyperparameter tuning
[1], and a standardized performance evaluation system [2] were
among the strict approaches used here. With a good trade-off
between accuracy and computational requirements, the findings
show that embedding-based models outperformed more intricate
designs like Transformers [3]. These results complement earlier
studies stressing the effectiveness of simpler models in text
classification problems [4]. The work emphasizes the need of
balancing model complexity and interpretability in real-world
applications, therefore supporting the possible use of embedding-
based strategies [5].
<figure><img src="image_105.png" /></figure>

# Adaptive Boundary-Aware Progressive Ensemble for Imbalanced Classification

## Communication Info

**Authors:**

Hajar KAMEL
Hasna CHAMLAL
Tayeb OUADERHMAN

Computer Science and Systems
Laboratory (LIS), Faculty of
Sciences Ain Chock, Hassan II
University of Casablanca,
Morocco

**Keywords:**

(1) Imbalanced Data

(2) Class overlap

(3) Clustering

(4) Classification

(5) Boundary Sampling

## Abstract

Classifying imbalanced data with high class overlap remains a challenge [1][2]. Traditional methods suffer from noise in oversampling or information loss in undersampling, failing to address overlapping distributions [3]. We propose Boundary-Focused Adaptive Ensemble (BFAE), integrating dynamic boundary sampling, progressive learning, and adaptive ensemble weighting. BFAE prioritizes high-impact samples near decision boundaries and uses Elastic Weight Consolidation to prevent catastrophic forgetting. Its adaptive ensemble framework refines classifier weights in real time. Extensive experiments show BFAE outperforms state-of-the-art methods [4], achieving high accuracy, precision, and recall, making it a robust solution for imbalanced classification.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

## References

[1] CHAMLAL, Hasna, KAMEL, Hajar, et OUADERHMAN, Tayeb. A hybrid multi-criteria meta-learner based classifier for imbalanced data. *Knowledge-based systems*, 2024, vol. 285, p. 111367

[2] OZTOPRAK, Kasim et HASSANPOUR, Reza. Efficient Dynamic Federated Learning for Imbalanced Data. In : 2023 IEEE International Conference on Big Data (BigData). IEEE, 2023. p. 5877-5879.

[3] LIU, Yongxu et LIU, Yan. Position-aware safe boundary interpolation oversampling. In : 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. p. 5519-5526.

[4] LANGEVIN, A., CODY, T., ADAMS, S., et al. Synthetic data augmentation of imbalanced datasets with generative adversarial networks under varying distributional assumptions: A case study in credit card fraud detection. Journal of the Operational Research Society, 2021, vol. 72, no 1, p. 1-28.
Improving Security in Intelligent Transportation
Systems: CNN Model for Multi-Class Intrusion
Detection in VANETS

**Communication Info**

**Authors:**

Khadija MOUATASSIM¹

Abdelfettah MABROUK 2

Chaimae OUCHICHA 2

¹ Chouaïb Doukkali University,
El Jadida, Morocco

² Chouaïb Doukkali University,
El Jadida, Morocco

² Hassan 1st University (UH1),
Settat, Morocco

**Keywords:**

(1) Artificial Intelligence

(2) vehicular ad hoc networks (VANETs)

(3) Intrusion detection system

(4) Security

**Abstract**

intelligent transportation systems (ITS) [1] are
booming, offering innovative services to improve the
safety and efficiency of transportation networks. An
essential component of these systems is the vehicular
ad-hoc network (VANET) [2], which plays a key role in
collision prevention and real-time data security [3],
However, VANETs face significant security challenges,
making Intrusion Detection Systems (IDS) [4], essential
for detecting malicious activities. The integration of
Artificial Intelligence (AI) [5] has notably improved
attack detection. in this paper we propose a new
network intrusion detection model using Convolutional
Neural Networks (CNN) for multi-class classification
and the Experimental results show that the proposed
model is capable of automatically identifying and
classifying multiple types of intrusions.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] Wilbur, M., Sivagnanam, A., Ayman, A., Samaranayeke, S., Dubey, A., & Laszka, A. "Artificial Intelligence for Smart Transportation." arXiv, Vol. 2308.07457, 2023, pp. 1-15.

[2] M. Sohail. et al, “Routing protocols in Vehicular Ad hoc Networks (VANETs): A comprehensive survey comprehensive survey”, Vol. 100837, 2023, pp.1-22

[3] Van Wyk, F., Wang, Y., Khojandi, A., Masoud, N. "Real-time sensor anomaly detection and identification in automated vehicles". IEEE Trans. Intell. Transp. Syst., vol. 21, 2020, pp.1-20.

[4] Jishiya, P. A., & Swaraj, K. P. "Intrusion Detection System Using Machine Learning for SDN Environment." AIP Conference Proceedings, Vol. 3037, 2024, pp. 020035.

[5] Boukela, L., Zhang, G., Yacoub, M., & Bouzefrane, S. "A Near-Autonomous and Incremental Intrusion Detection System Through Active Learning of Known and Unknown Attacks." arXiv, Vol. 2310.17430, 2023, pp. 1-15. 3, pp. 1264-1276
**Communication Info**

**Authors:**

Mohamed El Amine CHAFIKI¹

Oumaima STITINI ¹,²

Soulaimane KALOUN ¹

¹ Faculty of Science and
Technology, Laboratory of
Computer and System
Engineering (L2IS), Cadi Ayyad
University, Marrakesh,
Morocco

² Ecole Normale Superieure,
Marrakech, Morocco

**Keywords:**

(1) Context-Aware

Recommendation System

(2) Smart Tourism

(3) Resource Allocation

(4) Sustainable Tourism
Management

(5) Multi-Criteria Decision
Support

**Abstract**

Tourism makes a basis for local wealth and fast growing economic, but the ever-increasing number of tourists that land in a region will always pressure local amenities such as water or electric networks. Which remains detrimental for over-populated, damaging for the environment and also makes visitors unsatisfactory in their tour experience. To address these issues, we propose an Internet of Things-enabled, context-aware recommendation system that utilizes environmental and users' data in real time for the optimization of tourism resources. We propose a retrieval augmentation mechanism by utilizing historical and real-time data to optimize decision-making and a Multi-Criteria Decision Support System to ensure balanced resource allocation by addressing visitor satisfaction, sustainability, and operational efficiency. Initial results show that our model enhances resource usage, maximizes facility oversight, and assists tourism bodies in evidence-based decision making.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] TAJER, ELNAZ, and SARA DEMİR. "MULTI-CRITERIA DECISION-MAKING METHODS IN SUSTAINABLE TOURISM." Scientific approaches in Landscape Architecture (2023): 185.

[2] Arbolino, Roberta, et al. "Multi-objective optimization technique: A novel approach in tourism sustainability planning." Journal of Environmental Management 285 (2021): 112016.

[3] Wang, Ning. "Using the Fuzzy Method and Multi-Criteria Decision Making to Analyze the Impact of Digital Economy on Urban Tourism." International Journal of Computational Intelligence Systems 17.1 (2024): 122.

[4] Zhu, Shuo, and Yan Liu. "Analysis of human resource allocation model for tourism industry based on improved BP neural network." Journal of Mathematics 2022.1 (2022): 1332829.

[5] Chen, Guo. "Tourism management strategies under the intelligent tourism IoT service platform." Computational Intelligence and Neuroscience 2022.1 (2022): 7750098.
Urban digital twin towards city digital twin: Framework
development.

**Communication Info**

**Authors:**

Zayneb MIRAOUI<fnref n="1" />

Abdelkader NASSER<fnref n="2" />

Mohcine KODAD<fnref n="3" />

**Keywords:**

(1) City digitalization

(2) Urban management

(3) Smart cities

**Abstract**

As the global population continues to expand and more
individuals migrate to urban areas, the urban management of
cities is becoming increasingly challenging[1]. In response,
recent efforts have been made to adopt smart city initiatives,
which aim to integrate digital information and connect
management tasks with inhabitants[2], The key objectives of
these initiatives are to enhance city functions, provide a
high-quality of life to citizens, and maintain a good harmony
between technology evolution and nature[3].

The role of the digital twin (DT) technology[4] in smart city
development is a significant area of research. which will
provide a high visual modulization of the city and full
autonomous bidirectional connectivity of data [5] between
real physical city entities and their virtual counterparts.

This paper aims to highlight the role of the digital twin
technology in city development, in the related work section,
analyze multiple frameworks of urban digital twin (UDT)
concept, and digital twin city (DTC) system, then discuss
basic similarities and current challenges. to propose as a
result, a structural model design of the DTC system based on
urban digital twin concept.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] A. Abu-Rayash et I. Dincer, « Development of integrated sustainability performance indicators for better management of smart cities », *Sustain. Cities Soc.*, vol. 67, p. 102704, avr. 2021, doi: 10.1016/j.scs.2020.102704.

[2] R. Giffinger et H. Gudrun, « Smart cities ranking: an effective instrument for the positioning of the cities? », *ACE Archit. City Environ.*, vol. 4, n° 12, p. 7-26, févr. 2010, doi: 10.5821/ace.v4i12.2483.

[3] J. Mondschein, A. Clark-Ginsberg, et A. Kuehn, « Smart cities as large technological systems: Overcoming organizational challenges in smart cities through collective action », *Sustain. Cities Soc.*, vol. 67, p. 102730, avr. 2021, doi: 10.1016/j.scs.2021.102730.

[4] M. Grieves et J. Vickers, « Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems », in *Transdisciplinary Perspectives on Complex Systems*, F.-J. Kahlen, S. Flumerfelt, et A. Alves, Éd., Cham: Springer International Publishing, 2017, p. 85-113. doi: 10.1007/978-3-319-38756-7_4.

[5] W. Kritzinger, M. Karner, G. Traar, J. Henjes, et W. Sihn, « Digital Twin in manufacturing: A categorical literature review and classification », IFAC-Pap., vol. 51, n° 11, p. 1016-1022, 2018, doi: 10.1016/j.ifacol.2018.08.474.

<footnote n="1">¹ Laboratory LaMoN2E, FSO,
Team MEGCE HSTO University
Mohamed I, Oujda, Maroc.</footnote>

<footnote n="2">² Laboratory LaMoN2E, FSO,
Team MEGCE HSTO University
Mohamed I, Oujda, Maroc.</footnote>

<footnote n="3">³ Laboratory MATSI, HSTO,
University Mohamed I, Oujda,</footnote>
<figure><img src="image_106.png" /></figure>

Blockchain et Marque Employeur dans le Commerce International :
Une Analyse Structurelle de son Impact et de ses Enjeux

Communication Info

Authors:

GUERBAZ RABY
RAFIK TAHA

¹ FSJES AIN SEBAA,
CASABLANCA, MORROCO

² FSJES AIN SEBAA,
CASABLANCA, MORROCO

Keywords:

(1) Blockchain

(2) Marque employeur

(3) Équations structurelles

Abstract

Dans un monde de plus en plus numérique et interconnecté, la blockchain se positionne comme un outil stratégique qui affecte la réputation de l'employeur dans le monde du commerce international. Cette recherche propose un modèle d'équations structurelles (SEM) pour examiner les effets de la blockchain sur la marque employeur, en mettant l'accent sur sa contribution à l'attractivité de l'entreprise (Y2) et à la confiance des salariés (Y3). Le modèle structurel repose sur quatre hypothèses principales :

(1) la blockchain améliore la transparence des compétences (X1 → Y1).

(2) une meilleure transparence des compétences accroît l'attractivité de l'entreprise (Y1 → Y2).

(3) la blockchain renforce directement la confiance des employés (X1 → Y3), et (4) la confiance des employés joue un rôle médiateur dans l'attractivité de l'entreprise (Y3 → Y2).

Les résultats démontrent que la blockchain influence de manière importante la transparence des compétences, un facteur clé pour renforcer l'attractivité et la confiance des employés. Effectivement, les entités qui mettent en œuvre la blockchain constatent une amélioration de la transparence des compétences, ce qui se traduit par une plus grande attraction pour les talents et une confiance accumulée de leurs employés. Ces conclusions valident que la blockchain est un outil clé pour optimiser les avantages de la marque employeur dans l'administration des ressources humaines. Cette étude enrichit la littérature en présentant un modèle SEM solide pour évaluer l'influence de la blockchain sur la marque employeur. Elle présente également des conséquences concrètes pour les décideurs qui souhaitent renforcer leur attractivité et leur compétitivité sur le marché mondial des compétences. Les recherches à venir pourraient se pencher sur l'association de la blockchain avec d'autres technologies innovantes, telles que l'intelligence artificielle et le big data, dans le mais d'améliorer encore la gestion des ressources humaines.

References

[1] Kline, R.B., Principles and Practice of Structural Equation Modeling, Guilford Press, Vol. 3, 2015, pp. 1-534.

[2] Nakamoto, S., Bitcoin: A Peer-to-Peer Electronic Cash System, Journal of Cryptology, Vol. 1, 2008, pp. 1-9.

[3] Tapscott, D., Tapscott, A., Blockchain Revolution: How the Technology Behind Bitcoin is Changing Money, Business, and the World, Harvard Business Review, Vol. 94, 2016, pp. 121-132.

[4] Ambler, T., Barrow, S., The Employer Brand, Journal of Brand Management, Vol. 4, 1996, pp. 185-206.

[5] Swan, M., Blockchain: Blueprint for a New Economy, O'Reilly Media, Vol. 1, 2015, pp. 1-152.
# Application of Advanced Transformer Neural Networks for Honey Quality Assessment through Thermal Imaging

## Communication Info

**Authors:**

Ilias BOULBARJ<fnref n="1" />

Abdelaziz BOUKLOUZE <fnref n="2" />

Samira DOUZI <fnref n="3" />

Hassan DOUZI <fnref n="1" />

**Keywords:**

(1) Honey Adulteration

(2) Transformers

(3) Attention mechanism

(4) Thermal imaging

## Abstract

This study proposes a new method for detecting honey adulteration using the Vision Transformer (ViT) model [1] combined with thermal imaging. Honey adulteration is a significant health and economic issue, and conventional detection methods are slow and not very sensitive. Thermal imaging offers a unique advantage by detecting temperature variations in honey, which can indicate differences in sugar content, moisture, and the presence of adulterants. A large dataset was created with thermal images of 9 pure honey samples and 84 adulterated samples at various contamination levels, used to train and calibrate the model. The results showed a 99.9% accuracy, 99.5% sensitivity, and 100% specificity. This research demonstrates that thermal imaging with Transformers [2] can effectively and accurately identify honey adulteration, offering a promising tool for quality control in the honey industry.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

## References

[1] Dosovitskiy A., An image is worth 16x16 words: Transformers for image recognition at scale, arXiv preprint, 2020, arXiv:2010.11929.

[2] Vaswani A., Attention is all you need, Advances in Neural Information Processing Systems, 2017.

<footnote n="1">¹ Faculty of Sciences Ibn Zohr,
Agadir, Morocco</footnote>

<footnote n="2">² Faculty of Medicine and
Pharmacy Mohammed V,
Rabat, Morocco</footnote>

<footnote n="3">³ Faculty of Medicine and
Pharmacy Mohammed V,
Rabat, Morocco</footnote>
<figure><img src="image_107.png" /></figure>

# A Dominant sets Approach to Interactive Image Segmentation

**Communication Info**

**Authors:**

Abdelbasset MANSOURI <fnref n="1" />

My Driss AOURAGH <fnref n="2" />

**Keywords:**

(1) Dominant sets

(2) Interactive image segmentation

(3) Pairwise constraints

**Abstract**

Interactive image segmentation remains a very interesting topic for computer vision community because it is an essential step in different applications such as image editing, medical image analysis, etc.

Many of modern interactive image segmentation methods model the image using a graph [1], such as Graph cuts algorithm [2], Random walker [3] and Shortest path models [4].

Unlike these algorithms [2-4] which express user annotations using individual constraints. In this work, we propose a novel approach to interactive image segmentation by incorporating user annotations using pairwise constraints in Dominant sets [5]. Experimental results on a well-known benchmark dataset show the effectiveness of our method compared to other algorithms.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] Peng, B., Zhang, L., Zhang, D., A survey of graph theoretical approaches to image segmentation Pattern Recognition, 46, 2013, 1020-1038.

[2] Boykov, Y., Jolly, M., Interactive graph cuts for optimal boundary and region segmentation of objects in nd images IEEE International Conference on Computer Vision, 2001, 105-112.

[3] Grady, L., Random walks for image segmentation IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, 2006, 1768-1783.

[4] Bai, X., Sapiro, G., A geodesic framework for fast interactive image and video segmentation and matting IEEE International Conference on Computer Vision, 2007, 1-8.

[5] Pavan, M., Pelillo, M., Dominant sets and pairwise clustering IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, 2007, 167-172.

<footnote n="1">¹ FST Moulay-Ismail University,
Errachidia, Morocco</footnote>

<footnote n="2">² EST Moulay-Ismail University,
Meknès, Morocco</footnote>
<figure><img src="image_108.png" /></figure>

IA Générative pour une Irrigation Durable : Combler le
Manque de Données dans l'Agriculture Marocaine

Communication Info

Authors:

Mohamed MOURAFIK
Laila Moussaid

Keywords:

(1) Smart irrigation

(2) generative IA

(3) Données synthétiques

Abstract

L'optimisation de l'irrigation au Maroc fait face à un obstacle majeur : le manque de données précises et à jour sur les conditions agricoles, les ressources en eau, et les besoins des cultures. Pour pallier cette lacune, la technologie de l'IA générative est utilisée pour produire des données synthétiques, capables de simuler des scénarios réalistes et variés pour enrichir les bases de données agricoles [1]. Ces données permettent une modélisation plus précise des systèmes d'irrigation, améliorant ainsi la gestion des ressources hydriques dans des contextes de pénurie d'eau [2]. En effet, l'IA générative peut générer des ensembles de données à partir de paramètres climatiques et environnementaux limités, comblant ainsi les écarts de données réelles [3]. Au Maroc, où les défis liés à la sécheresse sont exacerbés par le changement climatique, ces données synthétiques permettent de mieux prévoir les besoins en eau des cultures et de concevoir des stratégies d'irrigation plus adaptées et durables [4]. Cette approche innovante a le potentiel de réduire la consommation d'eau tout en augmentant les rendements agricoles, soutenant ainsi les efforts du pays en matière de gestion durable des ressources [5].

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

References

[1] Alvaro Figueira, Bruno Vaz, Mathematics 2022, 10(15), 2733.

[2] Youness Tace, Mohamed Tabaa Energy Reports Volume 8, Supplement 9, November 2022, 1025-1036.

[2] Emmanuel Abiodun, Oliver Hensel, AgriEngineering 2022, 4(1), 70-103.

[3] Yuzhen Lu, Dong Chen Computers and Electronics in Agriculture Volume 200, September 2022, 107208.

[4] Loubna Rabhi, Noureddine Falih, Indonesian Journal of Electrical Engineering and Computer Science Vol. 24, No. 1, October 2021, pp. 581~589.

[5] Ahlem Ferchichi, Mejda Chihaoui Expert Systems with Applications Volume 238, Part F, 15 March 2024, 122211.

<footnote n="1">¹ ENSEM, Casablanca, Maroc</footnote>

<footnote n="2">² ENSEM, Casablanca, Maroc</footnote>
<figure><img src="image_109.png" /></figure>

# Fine-tuning AraBERT for Arabic Text Extraction and Classification via Chatbot

## Communication Info

**Authors:**

Monir EL OUARROUDI<fnref n="1" />
Abdelilah JRAIFI<fnref n="2" />
Driss HARZALLA <fnref n="3" />

**Keywords:**

(1) AraBERT

(2) Fine-tuning

(3) Natural Language
Processing (NLP)

## Abstract

This article proposes an innovative approach to Arabic document extraction and classification by combining AraBERT with a conversational interface. The study aims to automate these tasks while addressing the linguistic challenges specific to Arabic. Our methodology follows a three-step process: (1) specialized document preprocessing (handling diacritics and dialectal variations), (2) fine-tuning AraBERT for domain-specific understanding, and (3) integrating an extraction and classification framework into a natural language chatbot. This approach enhances accuracy compared to generic language models, achieving optimal performance on various document types (contracts, administrative forms, academic texts). It contributes to the advancement of Arabic NLP by bridging the gap between advanced language models and practical applications, potentially reducing document processing time by up to 78%.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

## References

[1] Khalid N. Elmadani, Mukhtar Elgezouli, Anas Showk, "BERT Fine-tuning For Arabic Text Summarization," arXiv preprint arXiv:2004.14135, 2020, pp. 1-4.

ARXIV.ORG

[2] Mohammed A. Al-Qurishi, Mohammed A. Al-Gaphari, "Leveraging BERT Language Model for Arabic Long Document Classification," arXiv preprint arXiv:2305.03519, 2023, pp. 1-8.

ARXIV.ORG

[3] Moustafa Al-Hajj, Mustafa Jarrar, "ArabGlossBERT: Fine-Tuning BERT on Context-Gloss Pairs for WSD," arXiv preprint arXiv:2205.09685, 2022, pp. 1-8.

ARXIV.ORG

[4] Mohammad Sammoudi, Ahmad Habaybeh, Huthaifa I. Ashqar, Mohammed Elhenawy, "Question-Answering (QA) Model for a Personalized Learning Assistant for Arabic Language," arXiv preprint arXiv:2406.08519, 2024, pp. 1-7.

ARXIV.ORG

[5] Wissam Antoun, Fady Baly, Hazem Hajj, "AraBERT: Transformer-based Model for Arabic Language Understanding," arXiv preprint arXiv:2003.00104, 2020, pp. 1-8.

<footnote n="1">¹ FST, Marrakech, Doctorant</footnote>

<footnote n="2">² ENSA, Safi, Morocco Dept IRT
Labo MISCOM</footnote>

<footnote n="3">³ ENSA, Safi, Dept IRT Labo
MISCOM</footnote>
Nash game theory combined with convolutional
neural network for similar images retrieval

**Communication Info**

**Authors:**

Meriem MANSOUHI
Samira EL MOUMEN
Noureddine MOUSSAID

¹ Université Hassan II de Casablanca, (FSAC), LMFA, Casablanca, Morocco

² Université Hassan II de Casablanca, (FSAC), LMFA, Casablanca, Morocco

³ Université Hassan II de Casablanca, FSTM, LMCSA, Mohammedia, Morocco

**Keywords:**

(1) image retrieval

(2) convolutional neural network

(3) Game theory

**Abstract**

Content-based image retrieval (CBIR) relies on feature extraction techniques such as color, shape, and texture to retrieve visually similar images [1]. Traditional CBIR methods have difficulty effectively extracting these different features, resulting in unsatisfactory retrieval results [2]. In this study, we introduce a novel approach combining **Nash game theory** with **convolutional neural networks (CNNs)** [3] to enhance retrieval performance. **Our method** transforms the problem into a multi-player game where each feature extraction method is treated as an independent player in a non-cooperative game [4]. A Nash equilibrium is reached when no player can improve the results.

We test our method on the **Oxford5k dataset** (Philbin et al., 2007) [5] and show competitive performance compared to Content-based image retrieval traditional methods.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] Smeulders, A. W. M., et al. "CBIR at the end of the early years," *IEEE TPAMI*, 2000.

[2] Elmoumen, S., Moussaid, N., & Aboulaich, R. (2021). *Mathematical Modeling and Computing, 8*(4), 646–657.

[3] Vakalopoulou et al. (2023), *Deep Learning: Basics and CNNs*, Springer.

[4] Nash, J. "Non-cooperative games." *Annals of Mathematics*, 1951.

[5] Philbin et al. (2007), CVPR.
Hybrid Content-Based Recommendation System Using
Machine Learning Models

Communication Info

**Authors:**

El mehdi LGHAOUCH<fnref n="1" />
Soumaya OUNACER<fnref n="1" />
Soufiane ARDCHIR<fnref n="2" />
Mohamed AZZOUAZI<fnref n="1" />

**Keywords:**

(1) Recommendation system.

(2) Machine learning.

(3) Content-based
recommendations.

Abstract

Recommender systems help users discover items that match their preferences [1], such as food products from large review datasets. While deep learning is prominent, traditional machine learning models like Support Vector Machines (SVMs), Random Forest, and Logistic Regression remain valuable for their simplicity, efficiency, and interpretability [2][3]. This study evaluates these models in a content-based recommendation framework using food review data, measuring performance through accuracy, precision, recall, MAE, and RMSE [4]. Results show that traditional models achieve competitive performance, especially in resource-constrained settings. The findings highlight their suitability for real-world food recommendation systems, balancing effectiveness and practicality. This work provides guidance for selecting algorithms tailored to specific operational needs.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

References

[1] V. Shashank H., "Machine Learning to Be Used in Recommendation Systems," International Journal for Research in Applied Science and Engineering Technology, 2022, pp. 1-8.
https://doi.org/10.22214/ijraset.2022.45578

[2] Vashisth, P., and Meehan, K., "Gender Classification using Twitter Text Data," Irish Signals and Systems Conference, 2020, pp. 1-5. https://doi.org/10.1109/ISSC49989.2020.9180161

[3] Nawrocka, A., Kot, A., and Nawrocki, M., "Application of machine learning in recommendation systems," International Conference on Innovative Computing and Cloud Computing, 2018, pp. 1-6.
https://doi.org/10.1109/CARPATHIANCC.2018.8399650

[4] B, G., Balamurugan, K., Balamurugan, A., and Balamurugan, A., "Optimizing Recommendation Systems: A Hybrid Approach for Improved Accuracy," International Conference on Intelligent Data Communication Technologies and Internet of Things, 2024, pp. 1-6. https://doi.org/10.1109/ICICI62254.2024.00084

<footnote n="1">¹ Faculty of Sciences Ben M'Sik/
Hassan II University,
Casablanca, Morocco.</footnote>

<footnote n="2">² National School of Business
and Management/Hassan II
University, Casablanca,
Morocco.</footnote>
**Communication Info**

**Authors:**

Kaoutar AJMILI¹
Houda EL HADAF ²
Abdelilah JRAIFI ³

¹MISCOM-Laboratory. ENSA-S,
University Cadi Ayyad, Safi,
Morocco

²Department of Industrial
Engineering/EIGSI Casablanca,
Morocco

³MISCOM-Laboratory. ENSA-S,
University Cadi Ayyad, Safi,
Morocco

**Keywords:**

(1) Q-Learning Algorithm

(2) Weibull low

(3) Spare parts

(4) Inventory management

**Abstract**

Inventory replenishment is an ongoing process that maintains products in inventory to meet customer demand [4]. The (s, S) policy is a widely used method of inventory control in dynamic systems with random demand. According to this policy, if the initial inventory level of a period is equal to or less than the reorder point, an order is issued for replenishment to bring the stock level to level S [3]. This paper introduces a Markov decision process-based mathematical model for one-echelon system inventory control with the cases of failure that follow the Weibull low [1][2]. The Q-Learning algorithm is both theoretically and practically well-suited to addressing stochastic transitions and reward structures when there is an unknown model environment a priori [5]. The proposed approach is to determine the optimum order quantity of spare parts per period and reduce overall costs of inventory management through the incorporation of various cost considerations in decision-making.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] Elhadaf H, Tkiouat M, Benmansour R, Artiba A: A joint maintenance and production strategy to minimize the makespan, In Proceedings of 2013 International Conference on Industrial Engineering and Systems Management (IESM) (pp. 1-4). IEEE, 2013

[2] H. Elhadaf R. Benmansour H. Allaoui M. Tkiouat, Artiba A: Preventive maintenance scheduling for a multi-component system and spare parts inventory, Asset Management & Maintenance Journal, Vol.25, No.2, 2012

[3] Sethi, Suresh P, and Feng Cheng, Optimality of (s, S) policies in inventory models with Markovian demand, Operations Research 45.6, 931-939, 1997

[4] Wang, Hao, et al: Dynamic inventory replenishment strategy for aerospace manufacturing supply chain: combining reinforcement learning and multi-agent simulation, International Journal of Production Research 60.13, 4117-4136, 2022.

[5] Watkins, Christopher JCH, and Peter Dayan.: Q-Learning. Machine learning 8.3, 279-292, 1992
Data-Centric AI for Enhancing Data Quality in
Healthcare: Big Data and IoT Applications

**Communication Info**

**Authors:**

Imane RAFIQ<fnref n="1" />

Zahi JARIR<fnref n="2" />

Hiba ASRI<fnref n="3" />

**Keywords:**

(1) Data-Centric AI

(2) IoT

(3) Big Data

(4) Healthcare

(5) Data Quality

(6) Bias Mitigation

(7) Privacy

(8) Disease Prediction

(9) Machine Learning

**Abstract**

The integration of Artificial Intelligence (AI), the Internet of Things (IoT), and Big Data is transforming healthcare by enabling predictive diagnostics, real-time monitoring, and personalized treatment through data-driven analytics and intelligent decision-making [1]. However, data quality issues, including missing values, noise, bias, and inconsistencies, hinder the effectiveness of AI-driven healthcare systems [2]. Ensuring reliable AI applications requires shifting from model-centric AI, which prioritizes algorithm refinement, to Data-Centric AI (DCAI), which emphasizes systematic data improvements [3], [4]. Our study explores DCAI as a solution to healthcare data challenges, enhancing data completeness, standardization, and bias mitigation. We highlight the METRIC model for systematic data assessment and propose future research directions to improve scalability and trustworthiness in AI-driven healthcare, integrating advanced AI techniques such as generative AI and multimodal frameworks with DCAI principles for more ethical AI applications [5].

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] A. Banerjee, C. Chakraborty, A. Kumar, et D. Biswas, « Chapter 5 - Emerging trends in IoT and big data analytics for biomedical and health care technologies », Elsevier in *Handbook of Data Science Approaches for Biomedical Engineering*, vol. 5, 2020, p. 121-152.

[2] D. Schwabe et K. Becker, « The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic review », *NPJ Digital Medicine*, vol. 7, nº 203, 2024.

[3] N. Bhatt, N. Bhatt, P. Prajapati, V. Sorathiya, S. Alshathri, et W. El-Shafai, « A Data-Centric Approach to improve performance of deep learning models », *Scientific Reports*, vol. 14, nº 1, 2024, p. 22329.

[4] D. Malerba et V. Pasquadibisceglie, « Data-Centric AI », *Journal of Intelligent Information Systems*, vol. 39, nº 4, 2024.

[5] M. H. Jarrahi, A. Memariani, et S. Guha, « The Principles of Data-Centric AI », *Communications of the ACM*, vol. 66, nº 8, 2023, p. 84-92.

<footnote n="1">¹ FSSM Cadi Ayyad University,
Marrakech, Morocco</footnote>

<footnote n="2">² FSSM Cadi Ayyad University,
Marrakech, Morocco</footnote>

<footnote n="3">³ FSSM Cadi Ayyad University,
Marrakech, Morocco</footnote>
Olive Yield Prediction in the Mediterranean Region Through
Integrating Satellite Data, IoT, and Deep Learning: A
Comprehensive Review

**Communication Info**

**Authors:**

Abderrahmane SADIQ¹
Jilali ANTARI¹
Abdelouafi BOUKHRIS¹
Asma EL MEJDI¹

¹ Laboratory of Computer
Systems Engineering,
Mathematics and Applications
(ISIMA), Polydisciplinary
Faculty of Taroudant, Ibnou
Zohr University, Morocco

**Keywords:**

(1) Yield prediction

(2) Satellite imagery

(3) Internet of Things

(4) Deep learning

**Abstract**

Predicting olive yield in the Mediterranean region can be significantly advanced through the integration of satellite imagery, Internet of Things, and deep learning [1]. This review explores how key vegetation indices derived from multi-spectral satellite imagery (e.g. Sentinel-2) [2], combined with IoT-generated data, enhance yield prediction models. To analyse these heterogeneous datasets, this review investigates the combined use of advanced models—namely Transformers, CNNs, and RNNs—to capture and integrate spatial, temporal, and contextual patterns across the data [3]. In this context, integrating multi-source satellite with IoT inputs can enhance the accuracy of yield prediction models designed for the specific agricultural challenges of the Mediterranean region [4]. This review highlights advancements, evaluates the strengths and limitations of these approaches, and discusses their potential to optimize resource management, and support sustainable olive farming practices. Challenges such as data fusion, model interpretability, and regional adaptability are also addressed [5], with a focus on developing scalable and generalizable solutions.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] Hayajneh, A.M.; Batayneh, S.; Alzoubi, E.; Alwedyan, M., TinyML Olive Fruit Variety Classification by Means of Convolutional Neural Networks on IoT Edge Devices. *AgriEngineering 5, 2023*, 2266-2283.

[2] Ramos MI, Cubillas JJ, Córdoba RM, Ortega LM, Improving early prediction of crop yield in Spanish olive groves using satellite imagery and machine learning. PLoS ONE 20(1), 2025.

[3] Joshi, A.; Pradhan, B.; Gite, S.; Chakraborty, S., Remote-Sensing Data and Deep-Learning Techniques in Crop Mapping and Yield Prediction: A Systematic Review. *Remote Sens.*, **15**, *2023*, 2023.

[4] El Sakka, M.; Ivanovici, M.; Chaari, L.; Mothe, J., A Review of CNN Applications in Smart Agriculture Using Multimodal Data. Sensors, **25**, 2025, 472.

[5] Villalobos FJ, López-Bernal Á, García-Tejera O and Testi L, Is olive crop modelling ready to assess the impacts of global change? *Front. Plant Sci.* **14**, 2023. doi: 10.3389/fpls.2023.1249793
# The Impact of Socio-Cultural Variations on Sentiment Analysis in Dialect

## Communication Info

**Authors:**

IKBAL CHABBAKI

Faculty of Sciences Ben M'Sik, Casablanca, Morocco

**Keywords:**

(1) Moroccan Dialect

(2) Linguistic Variation

(3) Machine Learning

(4) Lexicon-Based Methods

(5) Natural Language Processing (NLP)

(6) Demographic Influence

(7) Cultural and Linguistic Diversity

## Abstract

Sentiment analysis in the Moroccan dialect presents unique challenges due to social and cultural diversity, linguistic change, and the interaction between dialectal Arabic, Classical Arabic, and French. This study aims to explore how factors such as age, geographical location, and social class influence sentiment expression.

Using a mixed-methods approach, we analyzed a variety of texts from social media, online forums, and interviews with native speakers. To achieve this, we applied sentiment analysis techniques, including lexicon-based methods and machine learning models, to measure sentiment while accounting for the impact of linguistic change.

Preliminary results indicate significant variation in sentiment expression across different demographic groups. These findings highlight the need to develop sentiment analysis models that consider the cultural and linguistic characteristics of the dialect to better capture the complexity and richness of emotions expressed within this community.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

## References

[1] beling, and model construction ElMehdi Boujou, Hamza Chataoui, Abdellah El Mekki, Saad Benjelloun, Ikram Chairi, and Ismail Berrada

[2] Bechikh Ali, C., Mulki, H., & Haddad, H. (2018). *Impact du prétraitement linguistique sur l'analyse de sentiment du dialecte tunisien*

[3] Abidi, K., & Smaïli, K. (2021). *Creating multi-scripts sentiment analysis lexicons for Algerian, Moroccan and Tunisian dialects*. In *Advances in Machine Learning, Data Mining and Computing*.

[4] Hdioud, B., & Tirari, M. (2021). *Sentiment analysis of Moroccan dialect using deep learning*. In *Proceedings of the International Conference on Big Data and Internet of Things*.

[5] Jbel, M., Jabrane, M., Hafidi, I., & Metrane, A. (2023). *Sentiment analysis dataset in Moroccan dialect: Bridging the gap between Arabic and Latin scripted dialect*. *Language Resources and Evaluation*.
Improvement of Convolutional Neural Networks for Brain Tumor Detection through Bayesian Optimization

**Communication Info**

**Authors:**

Fatima FATIH¹
Zakariae EN-NAIMANI ²
Khalid HADDOUCH ¹

¹ Laboratory of Applied Sciences and Emerging Technologies, ENSA, Sidi Mohamed Ben Abdellah University, Morocco

² Laboratory Computer Science, Artificial Intelligence and Cyber Security (2IACS), ENSET, Mohammeddia, University of Hassan II Casablanca, Morocco

**Keywords:**

(1) Hyperparameter Tuning

(2) Bayesian Optimization

(3) Convolutional Neural Networks

**Abstract**

Early detection of brain tumors is crucial for improving survival rates. However, their diversity in size and shape complicates the analysis of medical images and can lead to human errors. Artificial intelligence, particularly convolutional neural networks (CNNs), offers a promising solution due to their ability to capture complex image features [1]. These models, inspired by the human visual cortex, can outperform experts in tumor classification. However, their performance depends on tuning hyperparameters such as dropout rates and L2 regularization, which influence the model's generalization [2][3]. Poor tuning can lead to overfitting, reducing the model's reliability on new data. Techniques such as dropout and batch normalization help improve the robustness of CNNs. Bayesian optimization is used to fine-tune these hyperparameters efficiently by targeting the most promising combinations [4][5]. Various regularization scenarios are tested. The study demonstrates that Bayesian optimization enhances CNN performance by minimizing overfitting and maximizing accuracy. These advancements are essential for strengthening the reliability of brain tumor detection models and improving medical diagnosis.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] J Naranjo-Torres, M Mora, R Hernandez-García, RJ Barrientos, C Fredesand A Valenzuela, A review of convolutional neural network applied to fruit image processing; Applied Sciences 10(10), 3443 (2020).

[2] Andrew Y. Ng, Feature selection, l 1 vs. l 2 regularization, and rotational invariance. In: Proceedings of the Twenty-first International Conference on Machine Learning; n: Proceedings of the Twenty-first International Conference on Machine Learning, p. 78 (2004) 29.

[3] Christian Garbin, Xingquan Zhu, and Oge Marques, Dropout vs. batch normalization: an empirical study of their impact on deep learning.; Multimedia tools and applications 79(19), 12777-12815 (2020).

[4] Li Yang and Abdallah Shami, On hyperparameter optimization of machine learning algorithms: Theory and practice; Neurocomputing 415, 295-316 (2020)

[5] Jasper Snoek, Hugo Larochelle and Ryan P. Adams, Practical Bayesian optimization of machine learning algorithms; Advances in neural information processing systems 25 (2012).
<figure><img src="image_110.png" /></figure>

Meshless and Monte Carlo Method for Solving the Drug transport
equations with random coefficients

**Communication Info**

**Authors:**

Saad eddine Essarrout ¹
Said Raghay ²
Zouhir Mahani ³

¹ department of science
computing University Ibn Zohr,
Agadir, Morocco

² faculty of science and
techniques, cadi Ayyad
University Marrakesh, Morocco

³ department of science
computing University ibn Zohr,
Agadir, Morocco

**Keywords:**

(1) Monte Carlo Method

(2) Meshless Method

(3) Drug Transport Equation

(4) Random Coefficients

**Abstract**

A combination of the Meshless techniques and the Monte Carlo method has been developed for drug transport problems. The governing two-dimensional pharmacokinetic equation with random coefficients are simulated in complex geometric domains. For spatial discretization the meshless technique and the Monte Carlo method are integrated in our effective approach. For accurate shape function construction, the Random point interpolation Method (RPIM) employed polynomial basis function. Thorough testing on our problem reveals that decreasing the time step led to an algorithm's efficiency. The successful integration underscores the potential of the present method, providing a hopeful pathway for tackling pharmacokinetic challenges across diverse scientific applications.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] S. Essarrout, S. Raghay, and Z. Mahani. (2022)"Quantifying uncertainty of a mathematical model of drug transport in tumors.", Mathematical modeling and computation, 9, Num. 3, 567-578
Damage and Dynamic Behavior of Civil
Engineering Structures up to Failure

**Communication Info**

**Authors:**

Zakaria LAGHRIB ¹
Benaissa KISSI ¹
Hamza KHATIB ¹
Hassan AAYA ²

¹ National School of Arts and
Crafts (ENSAM), Casablanca,
Morocco

² International University of
Casablanca (UIC), Casablanca,
Morocco

**Keywords:**

(1) Civil engineering
structures

(2) Dynamic properties

(3) Numerical modeling

(4) Finite element model

(5) Material degradation

**Abstract**

Civil engineering structures, such as bridges, buildings, and dams, are exposed to static and dynamic loads, as well as material degradation (cracking, deformation, corrosion). These factors alter dynamic properties, increasing the risk of failure.

This study investigates damage evolution, variations in dynamic properties, and early failure indicators using numerical modeling and experimental validation. Finite element models simulate structural deterioration, incorporating crack propagation and material degradation. The analysis provides insights into damage progression and its effects on structural behavior under various loads.

The findings contribute to improved structural monitoring strategies, enabling early detection of critical damage and enhancing predictive maintenance approaches.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] M. Khebizi, H. Guenfoud, M. Guenfoud, "Contribution to modeling damage in reinforced concrete structures", Computers and Concrete, 15, 2015, 547-562.

[2] A. M. Alshoaibi, Y. A. Fageehi, "Advances in Finite Element Modeling of Fatigue Crack Propagation", Applied Sciences, 14, 2024, 9297.

[3] K. J. Kim, S. T. Yi, "Experimental Study of Crack Propagation in Concrete Beams under Repeated Loading", Journal of Engineering Mechanics, 128, 2002, 1049-1058.
Advances in Feature Selection for Gene
Expression Data: A Systematic Review of 2024

**Communication Info**

**Authors:**

Sara BAALI¹
Mohammed HAMIM ¹
Hicham MOUTACHAQUIK¹
Mustapha HAIN¹

¹ ENSAM, Casablanca, Morocco

**Keywords:**

(1) Feature Selection

(2) Gene Expression Data

(3) Classification

**Abstract**

The complexity of high-dimensional gene expression data necessitates effective feature selection to enhance cancer classification accuracy and efficiency[1]. This review examines recent feature selection methods proposed in 2024, emphasizing the dominance of hybrid filter Wrapper approaches[2]. Support Vector Machines (SVM) remain the most effective classifier, while KNN and AdaBoost also demonstrate competitive performance[3]. Deep learning-assisted techniques are emerging as promising solutions for improving classification accuracy and model robustness[4]. Additionally, 10-fold cross-validation remains the most widely adopted evaluation method, ensuring reliability and generalizability. Despite these advancements, challenges persist in scalability, computational efficiency, and dataset variability. Addressing these limitations through optimized feature selection strategies is essential for improving diagnostic precision and facilitating clinical applications[5].

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] J. Hua, W. D. Tembe, and E. R. Dougherty, "Performance of feature-selection methods in the classification of high-dimension data," *Pattern Recognit.*, vol. 42, no. 3, pp. 409-424, 2009, doi: 10.1016/j.patcog.2008.08.001.

[2] K. Yu, W. Li, W. Xie, and L. Wang, "A Hybrid Feature-Selection Method Based on mRMR and Binary Differential Evolution for Gene Selection," *Processes*, vol. 12, no. 2, p. 313, Feb. 2024, doi: 10.3390/pr12020313.

[3] H. AlShamlan and H. AlMazrua, "Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection," *Comput. Mater. Contin.*, vol. 79, no. 1, pp. 675-694, 2024, doi: 10.32604/cmc.2024.048146.

[4] A. Das, N. Neelima, K. Deepa, and T. Özer, "Gene Selection Based Cancer Classification With Adaptive Optimization Using Deep Learning Architecture," *IEEE Access*, vol. 12, pp. 62234-62255, 2024, doi: 10.1109/ACCESS.2024.3392633.

[5] S. Baali, M. Hamim, H. Moutachaouik, M. Hain, and I. EL Moudden, "Feature Selection for High-Dimensional Gene Expression Data: A Review," in *Smart Applications and Data Analysis*, M. Hamlich, F. Dornaika, C. Ordonez, L. Bellatreche, and H. Moutachaouik, Eds., Cham: Springer Nature Switzerland, 2024, pp. 74-92. doi: 10.1007/978-3-031-77040-1_6.
<figure><img src="image_111.png" /></figure>

# Risk Management in Supply Chains with the Use of Industry 4.0 Technologies

## Communication Info

**Authors:**

Meriem BENHAFID <fnref n="1" />
Aziz AKHIATE <fnref n="2" />

**Keywords:**

(1) Industry 4.0 technologies

(2) Risk management

(3) Blockchain

(4) IoT

(5) Proactive risk management

(6) Cybersecurity

## Abstract

Risk management in supply chains is crucial in a globalized environment. Industry 4.0 technologies, such as IoT, AI, blockchain, and Big Data, enable proactive risk management. IoT ensures real-time monitoring of inventory and equipment, while AI, through predictive analytics, anticipates disruptions. Blockchain guarantees traceability and transparency, reducing the risk of fraud. Big Data analyzes large amounts of data to identify emerging risks. These technologies not only help companies respond to potential threats but also enhance collaboration among supply chain partners. By improving communication and sharing real-time data, they enable more efficient problem-solving. Additionally, they allow for faster recovery from disruptions by providing valuable insights for contingency planning. However, these technologies present challenges, including cost and cybersecurity concerns. Nevertheless, their adoption strengthens the resilience of supply chains, allowing for more reactive and informed risk management.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

## References

[1] Ivanov, D., & Sokolov, B., *Supply Chain Risk Management and Resilience: A Literature Review*, Journal of Business Research, Volume 69, Year 2016, Pages 2278-2287.

[2] Tian, Z., & Wei, C., *The Impact of Industry 4.0 Technologies on Supply Chain Resilience*, International Journal of Physical Distribution & Logistics Management, Volume 50, Year 2020, Pages 597-618.

[3] Helo, P., & Xu, J., *IoT-based Supply Chain Risk Management*, Journal of Manufacturing Systems, Volume 51, Year 2019, Pages 16-27.

[4] Fahimnia, B., & Jabbarzadeh, A., *A Big Data-driven Approach for Supply Chain Risk Management*, European Journal of Operational Research, Volume 267, Year 2018, Pages 757-769.

[5] Alfalla-Luque, R., & Medina-Lopez, C., *The Impact of Industry 4.0 on Supply Chain Risk Management*, Journal of Manufacturing Technology Management, Volume 30, Year 2019, Pages 7.

<footnote n="1">¹ ENSAM, Casablanca, Morocco</footnote>

<footnote n="2">² ENSAM, Casablanca, Morocco</footnote>
Offensive Text Detection Using Natural Language Processing (NLP)

**Communication Info**

**Authors:**

Tarik HADDANE¹
Said BAHASSINE¹
Abdelali ZAKRANI¹

¹ Artificial Intelligence,
Modeling and Computational
Engineering (AIMCE), ENSAM,
CASABLANCA, MOROCCO

**Keywords:**

(1) offensive text detection

(2) natural language processing

(3) machine learning

**Abstract**

Given the rapid explosion of content on the web, which leads to major societal issues due to offensive content, offensive language is defined as any form of verbal, written, or symbolic expression that targets an individual or group, often due to prejudice, insults, or discriminatory content. Its forms and meanings can vary depending on context, language, and time period [1]. The automatic processing of such texts relies on natural language processing (NLP) techniques. Our study aims to synthesize existing approaches to provide a comprehensive comparison in terms of methods, datasets, and metrics[3]. We base our analysis on research articles from the past four years to outline the direction of our study and define the main focus of our contribution. This involves examining different perspectives to determine key aspects such as: **What types of offensive text? In which language? Using which data sources? And with what evaluation metrics?**

Furthermore, we consider the major challenges related to offensive text detection, such as language ambiguity, linguistic evolution, and algorithmic biases [2].

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] Marwa Khairy et al., " Comparative performance of ensemble machine learning for Arabic cyberbullying and offensive language detection", Springer, 23 July 2023.

[2] Khouloud Mnassri et al., " A survey on multi-lingual offensive language detection ", PeerJ Computer Science, 18 February 2024.

[3] Saminu MohammadAliyu et al., "BEYOND ENGLISH: OFFENSIVE LANGUAGE DETECTION IN LOW-RESOURCE NIGERIAN LANGUAGES", la 5e conférence AfricaNLP, 2024.
<figure><img src="image_112.png" /></figure>

# QSAR-Guided Ebola Virus Glycoprotein Inhibitors Design

## Communication Info

**Authors:**

Nenhaila Ait Lahcen<fnref n="1" />

Wissal Liman <fnref n="1" />,<fnref n="2" />

Mehdi Oubahmane <fnref n="1" />,<fnref n="2" />

Ismail Hdoufane<fnref n="1" />

Mohammed M. Alanazi <fnref n="3" />

Youssef Habibi <fnref n="2" />

Mohamed Maatallah <fnref n="1" />

Driss Cherqaoui <fnref n="1" />,<fnref n="2" />

**Keywords:**

(1) Quantitative Structure-Activity Relationship

(2) QSAR models

(3) Ebola Virus glycoprotein

## Abstract

Ebola virus disease (EVD) is a deadly infection, with historical fatality rates reaching up to 90% [1,2]. A critical therapeutic target for halting viral replication is the Ebola Virus glycoprotein (EBOV-GP), which mediates entry of the virus into human cells [3]. This study applies Quantitative Structure-Activity Relationship (QSAR) models, a family of mathematical methods that relate molecular structures to biological activity, to design novel inhibitors of EBOV-GP. Two QSAR models were developed using a dataset of amodiaquine derivatives, achieving robust predictive performance. The first model is SMILES-based and employs a Monte Carlo approach, whereas the second uses a genetic algorithm coupled with multiple linear regression (GA-MLR) [4,5]. Both models highlight quantitative correlations between molecular descriptors and anti-EBOV-GP activity, illustrating how structural features can be translated into predictive mathematical relationships. According to the insights provided by the CORAL model, five new drug candidates were mathematically designed by modifying structural fragments associated with higher inhibitory potential; their predicted activities were then independently confirmed through the GA-MLR model. To further validate these computational predictions, molecular docking and molecular dynamics simulations were conducted to explore ligand-protein interactions, alongside ADMET evaluations to confirm favorable pharmacological profiles. Overall, this work demonstrates the power of QSAR as a mathematical framework for accelerating the discovery of promising EBOV-GP inhibitors and underscores the role of data-driven modeling in guiding targeted therapeutic development.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

## References

[1] Yh. Ma, X. Hong, F. Wu et al. Inhibiting the transcription and replication of Ebola viruses by disrupting the nucleoprotein and VP30 protein interaction with small molecules. Acta Pharmacol Sin 44, 2023, 1487-1499.

[2] O. Cenciarelli, S. Pietropaoli, A. Malizia et al. Ebola virus disease 2013–2014 outbreak in West Africa: an analysis of the epidemic spread and response. Int J Microbiol. 2015, 2015:769121.

[3] JE. Lee, EO Saphire, Ebolavirus glycoprotein structure and mechanism of entry. Future Virology, 4(6), 2009, 621-635.

[4] H. Hamzehali, S. Lotfi, S. Ahmadi et al. Quantitative structure-activity relationship modeling for prediction of inhibition potencies of imatinib derivatives using SMILES attributes. Sci Rep 12, 2022, 21708.

[5] E. Dadfar, F. Shafiei and TM. Isfahani, Structural Relationship Study of Octanol-Water Partition Coefficient of Some Sulfa Drugs Using GA-MLR and GA-ANN Methods, Current Computer - Aided Drug Design, 16, 2020, 207-221.

<footnote n="1">¹ Cadi Ayyad University, Marrakech, Morocco.</footnote>

<footnote n="2">² University Mohammed VI Polytechnic, Benguerir, Morocco.</footnote>

<footnote n="3">³ King Saud University, Riyadh, Saudi Arabia.</footnote>
Optimizing CNN Hyper-parameters via Simulated Annealing for Enhanced Brain Tumor Classification

**Communication Info**

**Authors:**

Youssef Smili¹

Youssef Fakhri²

Sofia El Amoury¹

¹ ENIC, FSTS, Hassan First
University, Settat, Morocco

² Laboratory RI, Faculty of
Sciences of Kenitra, Ibn Tofail
University, Kenitra, Morocco

**Keywords:**

(1) Brain tumor classification

(2) Convolutional Neural
Networks (CNN)

(3) Hyper-parameter
Optimization

(4) Simulated Annealing (SA)

**Abstract**

Accurate classification of brain tumors is vital in medical imaging due to the complex and variable nature of tumor structures. Although Magnetic Resonance Imaging (MRI) offers detailed anatomical information, manually distinguishing normal from abnormal tissues remains challenging and time-consuming, highlighting the need for automated diagnostic systems [1]. Convolutional Neural Networks (CNNs) have proven effective in automating feature extraction and enhancing diagnostic accuracy [2]. However, their performance is highly dependent on optimal hyper-parameter tuning. In this study, we employ the Simulated Annealing (SA) algorithm to optimize the hyper-parameters of a CNN tailored for brain tumor classification using MRI scans. Our approach utilizes direct hyper-parameter encoding along with a robust perturbation strategy to efficiently explore the solution space. The SA-optimized CNN achieves a validation accuracy that outperforms those obtained using Particle Swarm Optimization and Genetic Algorithms [3,4]. These findings underscore SA's potential as an efficient tool for hyper-parameter optimization in complex medical imaging applications.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] A. Tiwari, S. Srivastava, and M. Pant, "Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019," Pattern Recognition Letters, vol. 131, 2020, pp. 244-260.

[2] G. Litjens, T. Kooi, B. E. Bejnordi, A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. Van Der Laak, B. Van Ginneken, and C. I. Sanchez, "A survey on deep learning in medical image analysis," Medical image analysis, vol. 42, 2017, pp. 60-88.

[3] El Amoury, Sofia, Youssef Smili, and Youssef Fakhri, "Design of an Optimal Convolutional Neural Network Architecture for MRI Brain Tumor Classification by Exploiting Particle Swarm Optimization" Journal of Imaging, vol. 11, no. 2: 31. 2025. https://doi.org/10.3390/jimaging11020031.

[4] A. K. Anaraki, M. Ayati, and F. Kazemi, "Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms," biocybernetics and biomedical engineering, vol. 39, no. 1, 2019, pp. 63-74.
# Generative AI for Software Quality Assessment: A Systematic Review and Analysis of Applications in Code Quality, Technical Debt, and Bug Detection

## Communication Info

**Authors:**

Yassine BOUSDOG<fnref n="1" />
Abdelali ZAKRANI<fnref n="1" />
Said NOUH <fnref n="2" />

**Keywords:**

Generative AI, Software
Quality, LLMs, Code Quality,
Technical Debt, Bug Detection,
Defect Prediction, Code
Review, AI in SE, Model
Interpretability, AI Reliability,
Trust in AI, Human-AI
Collaboration, Context
Awareness, Static Analysis,
Scalability, ML for Code,
Software Maintenance,
Explainability, AI Validation.

## Abstract

Ensuring high software quality remains a challenge in software engineering. Traditional methods like static analysis and manual reviews struggle with precision and scalability. Recently, Generative AI (GenAI), including LLMs (e.g., GPT-4, Codex, CodeBERT), has emerged for code analysis, defect prediction, and quality improvement. However, its reliability and interpretability are still under investigation.

This paper reviews GenAI applications in (1) Code Quality Assessment, (2) Technical Debt Detection, and (3) Bug Detection. Analyzing multiple studies (2020-2024), we find that while LLMs provide superior contextual analysis, they face challenges like data dependence, domain-specific variability, and limited explainability. Key research gaps include model generalization, trust, and human-AI collaboration. While GenAI shows promise for software quality assurance, rigorous validation is needed before widespread adoption.

<footnote n="1">¹ENSAM, AIMCE Laboratory,
Casablanca, Morocco</footnote>

<footnote n="2">² LTIM Laboratory, Faculty of
Sciences Ben M'Sick, Hassan II
University of Casablanca,
Casablanca, Morocco</footnote>
A Comparative Study of Bagging and Boosting Techniques for
Correcting Data Transmission Errors

**Communication Info**

**Authors:**

El Assad Mohammed¹

Saïd Nouh¹

Seddiq El Kasmi Alaoui²

Azzouazi Mohamed¹

¹ LTIM Laboratory, Faculty of Sciences Ben M'Sick, Hassan II University of Casablanca

² LIDSI Lab, Ain Chock Sciences Faculty, Hassan II University of Casablanca

**Keywords:**

(1) Bagging

(2) Boosting

(3) Error Correcting Codes

(4) Machine Learning

(5) Joundan codes

(6) BCH codes

(7) Bit Error Rate (BER)

(8) Signal to Noise Ratio (SNR)

**Abstract**

In a previous work [1], we have used the boosting technique in our ELBoostDec model for decoding Bose, Ray-Chaudhuri, and Hocquinghem (BCH) codes, Joundan codes, and Quadratic Residue (QR) codes. In this article, we present a comparative study between two ensemble learning techniques: bagging and boosting, which combine predictions from multiple diverse and complementary models for decoding Joundan codes et al. [2]. We have designed, developed, and tested a new decoder ELBaggDec, based on Bagging. The results show that ELBoostDec significantly outperforms ELBaggDec in terms of bit error rate (BER) and also in terms of time complexity. With the same number of trees and the same maximum depth, ELBoostDec consistently delivered good results on the studied codes using two decision trees with a maximum depth of 13 and at the same SNR=8 dB.: for the BCH(15,7,5) code, ELBoostDec achieved a BER=4.8x10⁻⁵, whereas ELBaggDec yielded a BER=7.1x10⁻³, which is significantly lower. For the Joundan(26,13,7) code, ELBoostDec achieved a BER=2.51x10⁻⁶, while ELBaggDec yielded a BER=5.81x10⁻³, which is also significantly lower.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] Mohammed El Assad, Said Nouh, Imrane Chemseddine Idrissi, Seddiq El Kasmi Alaoui, Bouchaib Aylaj, Mohamed Azzouazi, A new efficient decoder of linear block codes based on ensemble learning methods; AES International Journal of Artificial Intelligence (IJ-AI); ISSN:2252-8938 Volume:13 N°:2 Année:2024
Doi:http://doi.org/10.11591/ijai.v13.i2.pp2236-2246

[2] Issamabderrahman Joundan, Said NOUH and Abdelwahed Namir "Design of good linear codes for a decoder based on majority voting procedure", IEEE international conference on advanced communication systems and information security (acosis'16), Marrakesh, Morocco, October 17-19, 2016.
Computational Design of Potent Dimeric
Phenylthiazole NS5A Inhibitors for Hepatitis C Virus

Communication Info

Authors:

Wissal LIMAN ¹

Nouhaila AIT LAHCEN 2

Driss CHERQAOUI 2

Achraf EL ALLALI 1

¹ Bioinformatics Laboratory, College of
Computing, University Mohammed VI
Polytechnic, Morocco.

² Department of Chemistry, Faculty of
Sciences Semlalia, BP, 2390
Marrakech, Morocco.

Keywords:

(1) Quantitative Structure-Activity
Relationship

(2) HCV

(3) NS5A

Abstract

Hepatitis C Virus (HCV) remains a major global health concern due to its widespread prevalence and the lack of an effective vaccine. Although significant advancements have been made in therapeutic treatments since the virus was first identified, its persistence necessitates the development of novel intervention strategies [1]. The nonstructural protein NS5A plays a vital role in the HCV life cycle, contributing to both viral replication and assembly, making it a key target in all currently approved HCV combination therapies [2]. In this study, a quantitative structure-activity relationship (QSAR) analysis was conducted to design new inhibitors with improved potency against HCV. A dataset of 82 phenylthiazole derivatives was utilized to develop a QSAR model using the Monte Carlo optimization method [3]. This model provided crucial insights into the structural features influencing inhibitory activity. Based on these findings, novel NS5A inhibitors were designed. To further evaluate their potential, molecular docking was performed to estimate the binding affinity of the newly designed compounds within the NS5A protein [4]. Molecular dynamics simulations were then carried out to analyze their stability and interactions over time. Additionally, MMGBSA (Molecular Mechanics Generalized Born Surface Area) calculations were used to determine the binding free energies, offering further insights into their binding strength and stability. Lastly, an ADMET (absorption, distribution, metabolism, excretion, and toxicity) analysis was conducted to assess the pharmacokinetic and toxicity profiles of the proposed inhibitors. This integrated computational approach provides a comprehensive understanding of the designed inhibitors' efficacy, stability, and safety, offering valuable insights for their potential development as effective therapeutic agents against HCV [5].

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

References

[1] Hepatitis C. https://www.who.int/news-room/fact-sheets/detail/hepatitis-c.

[2] J.M. Pawlotsky, NS5A inhibitors in the treatment of hepatitis C, J Hepatol 59, 2013, 375-382.

[3] P. Kumar, A. Kumar, Monte Carlo Method Based QSAR Studies of Mer Kinase Inhibitors in Compliance with OECD Principles, Drug Res 68, 2018, 189-195.

[4] J. de Ruyck, G. Brysbaert, R. Blossey, M.F. Lensink, Molecular docking as a popular tool in drug design, an in silico travel, Advances and Applications in Bioinformatics and Chemistry 9, 2016, 1-11.

[5] W. Liman, M. Oubahmane, I. Hdoufane, I. Bjij, D. Villemin, R. Daoud, D. Cherqaoui, A. El Allali, Monte Carlo Method and GA-MLR-Based QSAR Modeling of NS5A Inhibitors against the Hepatitis C Virus, Molecules 27, 2022, 2729.
# A Review of Machine Learning and Deep Learning for Stock Market Crash Prediction

## Communication Info

**Authors:**

Wafae SAIFI¹

Said NOUH¹

Said BAHASSINE²

Imrane CHEMSEDDINE

IDRISSI¹

¹TIM Laboratory, Faculty of Sciences Ben M'Sick, Hassan II University, Casablanca, Morocco

²AIMCE Laboratory, ENSAM, Hassan II University, Casablanca, Morocco

**Keywords:**

(1) Digital finance

(2) Stock market crash

(3) Machine learning

(4) Deep learning

(5) Financial indicators

(6) Macroeconomic indicators

(7) Sentiment analysis

(7) Systematic literature review

## Abstract

Digital finance has been an important factor in the stabilization of financial markets, enabling the application of artificial intelligence in predicting and mitigating high risks such as stock market crashes with significant consequences [1-2]. This systematic literature review (SLR) aims to examine relevant studies on the Machine Learning (ML) and Deep Learning (DL) approaches that have been employed in predicting stock market crashes. From the examination of 20 studies published from 2018 to 2025, it reflects the diversity of approaches used, with neural networks [3], Support Vector Machines (SVM), and XGBoost models being among the most common [4-5]. The results further show that the use of various indicators, such as financial, macroeconomic, and sentiment data, enhances the predictability and trustworthiness of prediction systems [4][6]. The review also identifies gaps in current approaches, particularly with regard to the integration of textual elements and the explanation of models, thus guiding future research to address these gaps.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

## References

[1] Ai Y., Sun G., Kong T., Digital finance and stock price crash risk, International Review of Economics and Finance, 88 (2023), 607-619.

[2] Qin D., Kuruoglu E. E., Graph Learning Based Financial Market Crash Identification and Prediction, 2024 IEEE Conference on Artificial Intelligence (CAI), 2024, pp. 1-6.

[3] Changala R., Yadav S., Santosh K., Sawant P. D., Vuyyuru V. A., Bala B. K., Proactive Market Crash Prediction: Investigating GNN-LSTM Networks for Early Detection in Stock Markets, IEEE - 61001, 2024, pp. 1-8.

[4] Dichtl H., Drobetz W., Otto T., Forecasting Stock Market Crashes via Machine Learning, Journal of Financial Stability, 65 (2023), 101099.

[5] Chen Y., Andrew X., Supasanya S., CRISIS ALERT: Forecasting Stock Market Crisis Events Using Machine Learning Methods, arXiv:2401.06172 [q-fin.ST], 2024, 14.

[6] Naeem M., Jassim H. S., Korsah D., The Application of Machine Learning Techniques to Predict Stock Market Crises in Africa, J. Risk Financial Manag., 17 (2024), 554.
Long-Term Electricity Demand Forecasting with Deep Learning
Techniques

**Communication Info**

**Authors:**

Ibtissam LAHJILI¹

Aziz LMAKRI¹

Badr HIRCHOUA¹

Mustapha HAIN¹

¹ AICSE Laboratory, ENSAM,
Hassan II University in
Casablanca

**Keywords:**

(1) Time series

(2) Forecasting

(3) Electricity

(4) Deep learning

(5) LSTM

(6) GRU

**Abstract**

Electricity demand forecasting is essential for the efficient management of electrical grids and energy resources. Many studies have demonstrated the effectiveness of deep learning techniques in addressing this challenge, particularly recurrent neural networks such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), which have shown superior performance in capturing temporal dependencies in electricity consumption patterns [1]. These models are widely used due to their ability to model long-term dependencies and handle complex fluctuations in time series data [2]. Their specialized architecture enables efficient processing of sequential data and learning of nonlinear temporal relationships, making them suitable for electricity demand forecasting [3]. In this study, we explore the application of LSTM and GRU models to predict electricity demand not only for historical and current periods but also for long-term future trends [4]. The results demonstrate that these models can provide accurate and reliable forecasts, supporting optimized energy management strategies and proactive decision-making [5].

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] Abumohsen, M., Owda, A.Y., Owda, M. Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms Energies, 16(5), 2023, 2283.

[2] Manowska, A, Using the LSTM Network to Forecast the Demand for Electricity in Poland, Applied Sciences, 2020.

[3] M. Gul and W. A. Qureshi, Long term electricity demand forecasting in residential sector of Pakistan, 2012 IEEE Power and Energy Society General Meeting, San Diego, CA, USA, 2012, pp. 1-7.

[4] Son, N., Yang, S., & Na, J. Deep Neural Network and Long Short-Term Memory for Electric Power Load Forecasting. Applied Sciences. 2020.

[5] Anand, A., Suganthi, L. Forecasting of Electricity Demand by Hybrid ANN-PSO. 2017.
# Enhancing Water Sustainability: Transformer Networks for Evapotranspiration Forecasting

**Communication Info**

**Authors:**

Abdelouahed TRICHA

Laila MOUSSAID

National High School for
Electricity and Mechanics
(ENSEM), Hassan II University
of Casablanca, Casablanca,
Morocco

**Keywords:**

(1) Transformer Networks

(2) Evapotranspiration
Forecasting

(3) Smart Water Management

**Abstract**

Accurate reference evapotranspiration (ET₀) forecasting is vital for optimizing agricultural water use in Mediterranean climates, where seasonal droughts and variable rainfall strain water resources[1]. While the FAO-56 Penman-Monteith equation remains the gold standard [2], its reliance on solar radiation and wind speed data limits its applicability in regions with sparse meteorological infrastructure. This study proposes a Transformer-based deep learning model [3] to forecast ET₀ using only temperature, humidity, and wind speed—parameters widely available in developing regions. Trained on a 40-year NASA POWER dataset for Casablanca, Morocco, the Transformer leverages self-attention mechanisms to identify long-term climatic patterns, outperforming traditional Long Short-Term Memory (LSTM) networks and FAO-56 under missing data conditions. Results demonstrate improved accuracy over conventional methods, enabling reliable ET₀ estimates with minimal inputs. These findings suggest that Transformer-based architectures can significantly enhance predictive capabilities in agricultural water management, leading to more informed and efficient irrigation practices [4], [5].

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] L. B. Ferreira and F. F. da Cunha, "New approach to estimate daily reference evapotranspiration based on hourly temperature and relative humidity using machine learning and deep learning," *Agric Water Manag*, vol. 234, May 2020.

[2] L. S. Pereira, "Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56," 1998.

[3] A. Vaswani et al., "Attention Is All You Need," Advances in Neural Information Processing Systems (NeurIPS), 2017.

[4] D. H. Jung, T. S. Lee, K. G. Kim, and S. H. Park, "A Deep Learning Model to Predict Evapotranspiration and Relative Humidity for Moisture Control in Tomato Greenhouses," *Agronomy*, vol. 12, no. 9, Sep. 2022.

[5] H. Shi and X. Cai, "Extrapolability improvement of machine learning-based evapotranspiration models via domain-adversarial neural networks," *Environmental Modelling & Software*, vol. 187, p. 106383, 2025.
# Approche de Classification Hiérarchique Descendante des Variables autour des Composantes Latentes : Applications aux données de pêche maritime Mauritaniennes

## Communication Info

**Authors:**

Brahim Mohamed Tfeil ¹,²

Evelyne Vigneau ³

¹ Institut Mauritanien de
Recherches Océanographiques
et des Pêches, Nouadhibou,
Mauritanie

² Université de Nouakchott,
Mauritanie

³ Oniris, Nantes, France

**Keywords:**

(1) Classification hiérarchique

(2) composante latente

(3) groupe de variable

## Abstract

A l'instar d'une approche de classification ascendante des variables (CLV) (Vigneau et al. 2006), on propose une nouvelle approche de classification descendante autour des composantes latentes. La conception se base sur le principe qu'à l'étape initiale, étape zéro, toutes les variables sont regroupées en une seule classe, ensuite on procède à un processus de division de l'une de deux premières classes suivant un critère T de homogénéités de sorte que chaque variable soit liée de manière optimale à la composante du groupe auquel elle est affectée. $T= n \sum_{k=1}^{K} \sum_{j=1}^{P} \delta_{kj} \text{Cov}^2(x_{kj}, C_k)$, p nombre de variables, $C_k$ la composante latente du groupe $G_k$, $c'_k c_k = 1$ où $\delta_{kj}=1$ si la jème variable appartient à la classe $G_k$ et $\delta_{kj}= 0$ sinon. A l'étape i, la division d'une classe de variables candidate ($G_i$), en deux classes, $C_1$ et $C_2$ produit une variation du critère T donnée par $\Delta= T_{i-1} - T_i = \lambda_1^{(C1)} + \lambda_1^{(C2)} - \lambda_1^{(C1UC2)}$ où $\lambda_1^{(C1)}$ et $\lambda_1^{(C2)}$ sont les plus grandes valeurs propres associées avec les matrices de covariance des variables dans le groupe $C_1$, $C_2$ et $C1UC2$. Toutefois, avant de procéder à une division d'une classe candidate $G_k$, on effectue une consolidation des classes obtenues par l'algorithme *K_menas*. Une démarche de tests de faisabilité-fonctionnalité de cette approche a permis d'explorer une dizaine de cas de figures de données simulées à donné des résultats presques identiques avec l'approche CLV, avec un gain de temps d'exécution important (27 secondes contre 9 minutes) en faveur de l'approche descendante pour données volumineuses (au delà de 6000 variables).

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

## References

[1] Jean-Michel Roger, Alessandra Biancolillo, Bénédicte Favreau, Federico Marini, G-CovSel: Covariance oriented variable clustering, Chemometrics and Intelligent Laboratory Systems, volume 254, 2024, 105223.

[2] Evelyne Vigneau, Mingkun Chen, Qannari Moustapah, ClustVarLV: An R Package for the Clustering of Variables Around Latent Variables, The R Journal, 557, 2015, Pages.

[3] Vigneau et al, CLASSIFICATION DE VARIABLES AUTOUR DE COMPOSANTES LATENTES, Revue de statistique appliquée, tome 54, no 1 (2006), p. 27-45.

[3] AL-KANDARI N. M., JOLLIFFE I. T., Variable selection and interpretation of covariance principal components, Communications in Statistics : Simulation and Computation, (2001, 30, 339-354

[4] ABDALLAH H., SAPORTA G., Classification d'un ensemble de variables qualitatives, Revue de Statistique Appliquée, (1998), XLVI(4), 5-26
Artificial Intelligence in Thermal Energy Storage:
A Bibliometric Analysis

**Communication Info**

**Authors:**

Jaouad Zerhouni¹
Mohamed Hosni¹
M'Barek Bakkas²

¹ IEST Research Team, AIDTM Laboratory, ENSAM, University Moulay Ismail of Meknes, Morocco

² Department of Energy Engineering, ENSAM, University Moulay Ismail of Meknes, Morocco

**Keywords:**

(1) Thermal Energy Storage

(2) Bibliometric

(3) Machine Learning

(4) Artificial Intelligence

(5) Solar Energy

**Abstract**

Energy storage plays a fundamental role in improving the efficiency, reliability, and sustainability of modern energy systems. Among various energy storage technologies, Thermal Energy Storage (TES) has found widespread applications in diverse fields, including renewable energy integration, industrial processes, and heating and cooling systems [1]. TES systems help mitigate energy supply fluctuations, enhance energy utilization, and reduce overall energy consumption. However, optimizing the design, performance, and operational efficiency of TES systems remains a complex challenge. In recent years, the integration of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), has emerged as a powerful approach to advancing TES technologies [2]. AI-driven methods enable predictive modeling, real-time optimization, and intelligent control strategies, significantly improving the efficiency and adaptability of TES applications. The growing intersection of AI and TES research highlights the need for a comprehensive assessment of the scientific landscape in this domain.

This study presents a bibliometric analysis to investigate research trends and scholarly contributions at the intersection of AI and TES. A bibliographic dataset comprising 2,941 scientific publications from the period 2000 to 2024 was sourced from the Scopus digital library. The analysis employs key bibliometric indicators to evaluate publication trends, author and source productivity, patterns of scientific collaboration, and keyword co-occurrence, following the guidelines proposed by Anders [3]. The analysis was performed using the **Biblioshiny** tool [4]. This study provides insights into the evolution of AI applications in TES research, identifying influential authors, highly cited works, and emerging research directions.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] S. Chavan, R. Rudrapati, S. Manickam, A comprehensive review on current advances of thermal energy storage and its applications, Alexandria Eng. J. 61 (2022) 5455-5463. https://doi.org/10.1016/j.aej.2021.11.003.

[2] M.N.I. Maruf, G. Morales-España, J. Sijm, N. Helistö, J. Kiviluoma, Classification, potential role, and modeling of power-to-heat and thermal energy storage in energy systems: A review, Sustain. Energy Technol. Assessments. 53 (2022). https://doi.org/10.1016/j.seta.2022.102553.

[3] A. Andres, Measuring Academic Research, Meas. Acad. Res. (2009). https://doi.org/10.1533/9781780630182.

[4] M. Aria, C. Cuccurullo, bibliometrix: An R-tool for comprehensive science mapping analysis, J. Informetr. 11 (2017) 959-975. https://doi.org/10.1016/j.joi.2017.08.007.
Robotic Control through Liquid Neural
Networks and Hindsight Experience Replay

**Communication Info**

**Authors:**

Kaouthar OUBBAD<fnref n="1" />
Abdelilah JRAIFI <fnref n="1" />
Ilias ELMOUKI <fnref n="1" />
Mohammed MADIAFI<fnref n="1" />

**Keywords:**

(1) Liquid Time Constant

(2) Reinforcement learning

(3) Robot Control

(4) Hindsight Experience Replay

**Abstract**

In robotic control, a robot must learn from its environment to act appropriately and perform tasks effectively [4]. This process is achieved through Reinforcement Learning (RL) [1]. To adapt to dynamic environments and learn from real-world data, we used Liquid Time Constant (LTC) networks [2]. Here, we introduce LTC networks because they offer advantages such as interpretability and robustness to noise. However, the agent often encounters sparse or binary rewards during training, which can limit its learning potential. To overcome this challenge, we use in this work, the Hindsight Experience Replay (HER) [3].

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] LIU, Rongrong, NAGEOTTE, Florent, ZANNE, Philippe, et al. Deep reinforcement learning for the control of robotic manipulation: a focussed mini-review. Robotics, vol. 10, 2021, p. 22.

[2] HASANI, Ramin, LECHNER, Mathias, AMINI, Alexander, et al. Liquid time-constant networks, In: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35, 2021, p. 7657-7666.

[3] ANDRYCHOWICZ, Marcin, WOLSKI, Filip, RAY, Alex, et al. Hindsight experience replay, Advances in neural information processing systems, vol. 30, 2017.

[4] MNIH, Volodymyr. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013.

<footnote n="1">¹ ENSA, Safi, Morocco</footnote>
# L'Impact de l'Intelligence Artificielle Générative sur le Génie Logiciel : Analyse de Modèles Open-Source

## Communication Info

**Authors:**

Zakaria MOUHIB¹

Said NOUH¹

Abdelali ZAKRANI²

¹TIM laboratory, Faculty of Sciences Ben M'Sick, Hassan II University, Casablanca, Morocco
²AIMCE Laboratory, ENSAM, Hassan II University, Casablanca, Morocco

**Keywords:**

SE LLM, Software Engineering
Large Language Model, Software
Engineering LLM, SE Large
Language Model, Code LLM,
Code Large Language Model

## Abstract

L'intelligence artificielle générative occupe aujourd'hui une place prépondérante dans divers domaines, tels que la création de contenu, le génie logiciel, la santé, l'éducation, et la finance. En particulier, elle a transformé le génie logiciel, en dépit des défis techniques et de la complexité associés à son utilisation. Cette révolution est due à la capacité de ces modèles à effectuer des tâches variées, allant de la rédaction de code à la détection d'erreurs, en passant par l'amélioration des processus de développement logiciel.

Le génie logiciel ne se limite pas uniquement à la programmation ; il englobe plusieurs étapes essentielles telles que la documentation, la conception, le développement, les tests, et la maintenance. Cette étude explore les avancées récentes dans ce domaine, en se concentrant sur quatre modèles open-source : Granite Code Models [1], Stable Code [2], CodeGemma [3], et DeepSeek-CodeR [4]. Ces modèles, entraînés sur des ensembles de données vastes et variés – combinant code, langage naturel et jeux de données mathématiques renforçant le raisonnement logique – exploitent des architectures sophistiquées, telles que les **transformers** et le modèle **Fill-in-the-Middle**, pour mieux comprendre le contexte et répondre aux besoins des utilisateurs.

Ces modèles se distinguent par leur performance dans des tâches spécifiques, telles que la complétion de code, la modernisation d'applications, la correction d'erreurs, la traduction entre langages et l'explication du code. Ils ont été évalués à travers plusieurs benchmarks reconnus, tels que **HumanEval**, **MBPP**, **RepoBench** et **LeetCode Contest**, et ont surpassé des modèles propriétaires tels que **GPT-3.5** et **CodeLlama**, bien que leur nombre de paramètres soit inférieur. Les versions optimisées de ces modèles, qui sont adaptées pour suivre des consignes en langage naturel, améliorent encore la qualité des résultats ainsi que l'interaction avec les développeurs.

De plus, ces modèles sont disponibles sous des licences open-source permissives, ce qui les rend facilement adoptables par les entreprises et les chercheurs. En conséquence, ces modèles offrent des avantages considérables pour optimiser le développement logiciel, réduire les coûts et accélérer les cycles de production.

## References

[1] M. S. G. Z. Y. S. Mayank Mishra, "Granite Code Models: A Family of Open Foundation Models for Code Intelligence," p. 28, 7 MAY 2024.

[2] R. A. D. P. J. T. Nikhil Pinnaparaju, "Stable Code Technical Report," p. 14, 1 Apr 2024.

[3] J. H. J. H. N. N. Heri Zhao, "CodeGemma: Open Code Models Based on Gemma," p. 11, 19 JUN 2024.

[4] Q. Z. ., D. Y. ., Z. X. Daya Guo, "DeepSeek-Coder: When the Large Language Model Meets," p. 23, 26 JUN 2024.
Advanced AI Techniques for Solving Partial Differential Equations: A
Case Study on the Black-Scholes Model

**Communication Info**

**Authors:**

Zakaria ELBAYED¹
Abdelmjid QADI ELIDRISSI ²

¹ ² Laboratory for Analysis and Modeling of Systems and Decision Support, Center for Doctoral Studies in Science and Technology and Medical Sciences, Hassan First University, National School of Applied Sciences, Berrchid, Morocco

**Keywords:**

Partial differential equations, Artificial intelligence, Deep learning, PINNs, FNOs, Transformers, Black-Scholes model, Computational finance, Noise robustness, Model hybridization.

**Abstract**

The resolution of partial differential equations (PDEs) is a critical challenge across various scientific and engineering domains. Traditional numerical methods, while effective, often struggle with high-dimensional problems and noisy datasets. Recent advances in artificial intelligence, particularly deep learning models, offer promising alternatives to conventional techniques. This study conducts a comprehensive quantitative and qualitative analysis of three advanced AI-driven approaches—Physics-Informed Neural Networks (PINNs), Fourier Neural Operators (FNOs), and Transformers—in solving the Black-Scholes PDE, a canonical model in financial mathematics. Through rigorous experimentation on synthetic datasets under both ideal and noisy conditions, our results demonstrate that Transformers exhibit superior robustness to noise and adaptability to complex boundary conditions, outperforming other models in terms of accuracy and stability. The study also highlights the distinct advantages of PINNs in handling physical constraints and outliers, while FNOs show exceptional scalability in high-dimensional settings. These findings not only underscore the potential of AI models in advancing PDE resolution but also provide valuable insights into optimizing model selection and hybridization strategies for future research. This work paves the way for more efficient and scalable AI-based solutions to address real-world challenges in scientific computing and beyond.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

1.Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations. Journal of Computational Physics, 378, 686-707. https://doi.org/10.1016/j.jcp.2018.10.045

2.Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A., & Anandkumar, A. (2020). Fourier Neural Operator for Parametric Partial Differential Equations. arXiv preprint arXiv:2010.08895.

3.Chen, Y., Liu, X., Zhang, Z., Yin, D., & Wang, S. (2021). Learning to Solve Nonlinear PDEs with Transformers. Advances in Neural Information Processing Systems, 34, 15547-15558.

4.Wang, Y., Sun, J., Bai, J., Anitescu, C., Eshaghi, M. S., Zhuang, X., Rabczuk, T., & Liu, Y. (2023). Kolmogorov-Arnold-Informed Neural Networks (KAI-NN): A Deep Learning Framework for Solving Partial Differential Equations Based on Kolmogorov-Arnold Networks. Journal of Computational Physics, 467, 111334.

5.Lu, L., Meng, X., Mao, Z., & Karniadakis, G. E. (2021). DeepONet: Learning Nonlinear Operators for Identifying Differential Equations Based on Universal Approximation Theory. Nature Machine Intelligence, 3, 218-229.

6.Kovachki, N. B., Li, Z., Azizzadenesheli, K., Liu, B., Stuart, A., & Anandkumar, A. (2021). Neural Operator: Learning Maps Between Function Spaces. Journal of Machine Learning Research, 22, 1-75.

7.Tegmark, M., Lin, H. W., & Wu, L. (2020). Analyzing Machine Learning for Solving Partial Differential Equations. Proceedings of the National Academy of Sciences, 117(48), 30040-30050. https://doi.org/10.1073/pnas.2010906117

8.Sun, L., Gao, H., Pan, S., & Wang, Y. (2020). Surrogate Modeling for Fluid Flows Based on Physics-Constrained Deep Learning Without Simulation Data. Computer Methods in Applied Mechanics and Engineering, 361, 112732. https://doi.org/10.1016/j.cma.2019.112732
Artificial neural networks based-optimal control
of nonlinear epidemic model

**Communication Info**

**Authors:**
BIDAH AYMANE
ZAKARY OMAR

**Keywords:**
Artificial intelligence,
Artificial neural networks,
Deep learning

SIR model

**Abstract**

This research explores the use of two control strategies in a nonlinear SIR epidemic model with vaccination: the Pontryagin's Minimum Principle (PMP), a traditional control method, and Artificial Neural Networks (ANNs), a more modern data-driven approach. The study shows that ANNs outperform the traditional PMP in managing the epidemic.

The key findings highlight that, with ANN-based optimal control, the number of recovered individuals increases faster than in the previous controlled or uncontrolled systems. Additionally, the number of susceptible individuals drops sharply in the first few days and continues to decrease at a slower rate, achieving a more efficient eradication of the disease. This rapid reduction in infected individuals, alongside an early and significant recovery rate, illustrates the effectiveness of prioritizing vaccination in the initial stages of the epidemic.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] Russell, S., & Norvig, P. (2010). Artificial intelligence: A modern approach (3rd ed.). Prentice Hall Series in Artificial Intelligence.

[2] Zakary, O., Rachik, M., & Elmouki, I. (2017). A multi-regional epidemic model for controlling the spread of ebola: Awareness, treatment, and travel-blocking optimal control approaches. Mathematical Methods in the Applied Sciences, 40(4), 1265–1279. https://doi.org/10.1002/mma. 4048

[3] Lhous, M., Zakary, O., Rachik, M., Magri, E. M., & Tridane, A. (2020). Optimal containment control strategy of the second phase of the covid-19 lockdown in morocco. Applied Sciences, 10(21). https://doi.org/10.3390/app10217559.

[4] Zakary, O., Bidah, S., Rachik, M., & Ferjouchia, H. (2020). Mathematical model to estimate and predict the covid-19 infections in morocco: Optimal control strategy. Journal of Applied Mathematics, 2020,
# Impact of Data Preprocessing on Classification
Model Performance

## Communication Info

**Authors:**

Mouataz IDRISSI KHALDI¹
Allae ERRAISSI ²
Mustapha HAIN ¹
Mouad BANANE¹

¹ Laboratory of Artificial
Intelligence and Complex
Systems Engineering, ENSAM
,Hassan II University,
Casablanca, Morocco

² Chouaib Doukkali University,
El Jadida, Morocco

**Keywords:**

(1) Data pre-processing

(2) Supervised classification

(3) Feature selection

## Abstract

Data preprocessing is essential for optimizing the performance of supervised classification models. This paper presents a review of the main pre-processing techniques (cleaning, normalization, augmentation, missing value management) and an ongoing experimental analysis to assess their impact on Naive Bayes, Decision Tree, Random Forest, kNN and SVM algorithms. Existing research shows that cleaning and normalization improve accuracy [1], missing value management enhances robustness [2], and data augmentation promotes generalization [3]. Our study anticipates a significant improvement in performance metrics (accuracy, F1-score, ROC-AUC) thanks to optimal selection of preprocessing methods [4], supporting the hypothesis that careful data preparation enhances model robustness [5].

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

## References

[1] Smith et al., Data Preprocessing Techniques, Journal of Data Science, 12, 2021, 102-115.

[2] Johnson et al., Impact of Missing Data Handling, Machine Learning Journal, 24, 2020, 55-70.

[3] Lee et al., Data Augmentation in Classification, AI Research, 15, 2022, 78-90.

[4] Martinez et al., Preprocessing Strategies and Model Performance, Journal of Artificial Intelligence, 18, 2023, 200-215.

[5] Kumar et al., Evaluating Model Robustness, Computational Intelligence Journal, 10, 2022, 140-155.
Effectiveness of Transfer Learning-Based Deep
Learning Model in Facial Expression Recognition
Compared to a Sequential Model

**Communication Info**

**Authors:**

Fouad LEHLOU¹

Adil EL MAKRANI²

¹ Laboratory of Research in
Informatics, FS, UIT,, Kenitra,
Morocco

² Department of Informatics,
Laboratory of Research in
Informatics, FS, UIT,, Kenitra,
Morocco

**Keywords:**

(1) Deep Learning

(2) Transfer Learning

(3) Fer2013

**Abstract**

Facial expression recognition (FER) [1] is a growing field within computer vision and machine learning, with applications in psychology, education, human-computer interaction, and market research. Accurately detecting facial expressions is essential for improving interactions between humans and intelligent systems [2]. This study examines emotion classification using the FER-2013 dataset, focusing on identifying facial expressions across seven categories: anger, fear, disgust, happiness, surprise, sadness, and neutral. To achieve this, we compare the performance of different convolutional neural network (CNN) architectures [3], including a Sequential model. Our results emphasize the importance of transfer learning, which utilizes pre-trained models to enhance accuracy and efficiency. Fine-tuning a deep learning model proves to be more effective than training from scratch, demonstrating the benefits of leveraging prior knowledge for facial expression recognition.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] Ekman, P., Keltner, D.: Universal facial expressions of emotion. California Mental Health Research Digest 8(4), (1970), 151-158.

[2] Li, J., Lam, E.Y.: Facial expression recognition using deep neural networks. In: Imaging Systems and Techniques (IST), IEEE International Conference on, (2015), pp. 1-6. Doi:10.1109/IST.2015.7294547

[3] Agarwal, A., Susan, S.: Emotion Recognition from Masked Faces using Inception-v3. In: 5th International Conference on Recent Advances in Information Technology (RAIT), IEEE, Dhanbad (2023), pp. 1-6. Doi: 10.1109/RAIT57693.2023.10126777.
# Balancing Performance and Efficiency in Sentiment Analysis: Traditional and Neural Approaches

## Communication Info

**Authors:**

Kamal WALJI<fnref n="1" />
Allae ERRAISSI <fnref n="2" />
Abdelali ZAKRANI and Mouad
Banane <fnref n="1" />

**Keywords:**

Sentiment Analysis
Natural Language Processing
(NLP)
Traditional Machine Learning
Neural Networks
Logistic Regression
LSTM
Computational Efficiency

## Abstract

The Sentiment analysis in natural language processing (NLP) extracts valuable insights from textual data. Traditional machine learning models (e.g., Logistic Regression, Naïve Bayes, Random Forest) offer computational efficiency but often struggle with complex semantics. Advanced neural networks (e.g., CNNs, LSTMs) provide superior accuracy at higher computational costs.

This study fills a research gap by comparing these models using a multi-criteria framework that evaluates accuracy, computational efficiency, robustness to noisy data, and adaptability to diverse text structures. Experiments on the IMDB sentiment analysis dataset show that while LSTM achieved the highest accuracy (0.88), simpler models like Logistic Regression offered competitive performance (0.87) with significantly lower computational costs.

These findings highlight scenarios where simpler models may outperform complex architectures in resource-constrained environments. Future research could explore hybrid models that blend traditional efficiency with the advanced contextual analysis of modern neural architectures, including Transformers.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

## References

[1] Zhang, T.: LSTM network-based sentiment analysis for textual data. Journal of Information Technology Research 28(4), 305–320 (2023).

[2] Feng, Z., Zhao, J., Chen, L.: Sentiment analysis of user comments using LSTM and Word2Vec embeddings. Data Science Journal 12(2), 200–215 (2023).

[3] Abia, V.M., Johnson, T.K.: A comparative study of sentiment analysis techniques for Nigerian opinions using random forest and logistic regression. Applied Intelligence 42(1), 112–130 (2024).

[4] Bahtiar, N., Rahman, A., Putri, S.R.: Comparison of logistic regression and naive Bayes for sentiment analysis on Indonesian marketplaces. Procedia Computer Science 210, 1520–1528 (2023).

[5] Negi, A., Pandey, S., Ranjan, K.: Aspect-based sentiment analysis using CNN-LSTM. Computational Linguistics and Natural Language Processing 8(3), 180–195 (2022).

[6] Shan, L.: Social media sentiment analysis using CNN-BiGRU. International Journal of Computational Social Sciences 23, 94–100 (2023).

<footnote n="1">¹ Laboratory of Artificial Intelligence &
Complex Systems Engineering, Hassan II
University, ENSAM, Casablanca, Morocco.</footnote>

<footnote n="2">² Chouaib Doukkali University, El Jadida,
Morocco;</footnote>
# Integration of Artificial Intelligence in Wastewater Treatment Processes: A Comprehensive Analysis of Emerging Trends

## Communication Info

**Authors:**

Mourad ARRACH¹
Noureddine El Baraka¹
Abderrahmane SADIQ²

¹ Laboratory of Analysis, Modeling, Engineering, Natural Substances and Environment, Polydisciplinary Faculty of Taroudant, UIZ, Agadir, Morocco

² Laboratory of Computer Systems Engineering, Mathematics and Applications(ISIMA) Polydisciplinary Faculty of Taroudant, UIZ, Agadir, Morocco

**Keywords:**

(1) Machine Learning

(2) Wastewater Treatment

(3) Environmental Compliance

## Abstract

This research explores the integration of Artificial Intelligence into wastewater treatment processes to enhance efficiency, reduce costs, and ensure environmental compliance of the process [1]. Wastewater treatment plants in production units often face challenges with undesirable outcomes in treated water, impacting the quality of reused water and the environment [2]. This study explores the application of AI techniques through machine learning, leveraging real-time and historical data to optimize treatment processes, and address emerging trends in wastewater management [3]. Through an analysis of current applications and innovations, the research underscores the transformative potential of AI in revolutionizing the field, offering sustainable solutions to global water treatment challenges. Furthermore, this study aims also to explore the latest advancements to identify solutions applicable in contexts with socio-economic conditions like those of Morocco and comparable regions [4].

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

## References

[1] Wang, Y.; Cheng, Y.; Liu, H.; Guo, Q.; Dai, C.; Zhao, M.; Liu, D. A Review on Applications of Artificial Intelligence in Wastewater Treatment. *Sustainability* 15, 2023, 13557. https://doi.org/10.3390/su151813557

[2] Yang, Wenqi and Haiyan Li. Machine Learning in Wastewater Treatment: A Comprehensive Bibliometric Review. *ACS ES&T Water* (2025).

[3] El-Ghammad, O., Mabrouki, J., Slaoui, M., Azrour, M., Integration of AI for Intelligent Monitoring of Wastewater Treatment Plants. In: Azrour, M., Mabrouki, J., Alabdulatif, A., Guezzaz, A., Amounas, F. (eds) Smart Internet of Things for Environment and Healthcare. Studies in Computational Intelligence, vol 1165, 2025.

[4] Anthony Njuguna Matheri, Belaid Mohamed, Freeman Ntuli, Esther Nabadda, Jane Catherine Ngila, Sustainable circularity and intelligent data-driven operations and control of the wastewater treatment plant, Physics and Chemistry of the Earth, Parts A/B/C, Volume 126, 2022, 103152
# A Multi-Agent System for AI-Powered Contract Validation Under Moroccan Law

**Communication Info**

**Authors:**

Aymane MALIH¹

Omar BENCHAREF¹

*Laboratory of Computer and Systems Engineering (L2IS),*
*Cadi Ayaad University,*
*Marrakech, Morocco*

**Keywords:**

(1) Multi-Agent Systems

(2) Contract Validation

(3) Large language models

(4) Moroccan Law

**Abstract**

A legal system like Moroccan law is complex, and contract validation under legal guidelines is a time-consuming task, especially if expert knowledge is required, as in the case of Moroccan law [1]. Introducing a multi-agent system using language models to simplify and automate contract validation for non-experts is the purpose of this paper. The system is built around three key agents: Clause Extractor, Compliance Checker, and Explanation Agent. The system supports multiple contract types in both French and Arabic to be aligned with Morocco's linguistic and legal context [2]. By integrating language models with a multi-agent approach, the system is able to address complex legal challenges and thereby enables businesses and individuals to confidently manage their contracts [3]. The testing results show that the architecture is able to provide accurate and practical feedback to help users detect and solve problems before finalizing agreements. From the research, the above-outlined goals are achieved, and hence this paper contributes to the growing field of AI-driven legal technology, offering a practical and easily implementable solution for contract validation under the laws of Morocco.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] A. LawJournal, "Ethical and Legal Regulation of Using Artificial Intelligence in Morocco," Law Journal of Digital Studies, vol. 12, no. 3, pp. 45-58, 2023.

[2] M. Khalifa and A. Benjelloun, "Large Language Models as Legal Translators of Arabic Legislation," Proceedings of the Arabic NLP Conference, vol. 8, no. 1, pp. 102-114, 2024.

[3] Y. Alami, R. Said, and K. Ouchen, "ArabLegalEval: A Multitask Benchmark for Assessing Arabic Legal Capabilities," IEEE Transactions on Artificial Intelligence, vol. 6, no. 2, pp. 215-230, 2024.
<figure><img src="image_113.png" /></figure>

# Enhancing Breast Cancer Diagnosis using Blood Vessel Segmentation in Breast MRI

## Communication Info

**Authors:**

Laila EL JIANI¹
Sanaa EL FILALI¹
El Habib BENLAHMAR¹
Fatima-Zahra ALAOUI¹
Zouheir BANOU¹

¹ Hassan II University,
Casablanca, Morocco

**Keywords:**

(1) Breast MRI

(2) Breast Cancer

(3) Medical Imaging

(4) Angiogenesis

(5) Blood Vessel Segmentation

(6) Computer Vision

## Abstract

Magnetic Resonance Imaging (MRI) is a sensitive, non-invasive technique for monitoring and identifying lesions, establishing it as standard clinical practice [1,2]. However, its effectiveness in visualizing blood vessels in breast tissue requires further investigation [3]. Blood vessel analysis provides valuable insights into tumor progression and information that can be correlated with the underlying tumor biology [4,5]. This paper offers an extensive review of techniques and methodologies. A significant contribution of this work is the introduction of a unified workflow that combines the advantages of the different approaches examined, providing a more cohesive solution for blood vessel segmentation in breast MRI. The paper also examines the challenges and limitations in this field, including image quality, algorithmic constraints, anatomical complexities, and data scarcity. Our study identifies ongoing issues, particularly the need for robust evaluation metrics and standardized datasets. Addressing these issues is essential for driving future advancements in breast MRI vessel segmentation and improving clinical outcomes.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

## References

[1] GALATI, Francesca, MOFFA, Giuliana, et PEDICONI, Federica. Breast imaging: Beyond the detection. *European Journal of Radiology*, 2022, vol. 146, p. 110051.

[2] WEKKING, Demi, PORCU, Michele, DE SILVA, Pushpamali, et al. Breast MRI: clinical indications, recommendations, and future applications in breast cancer diagnosis. *Current oncology reports*, 2023, vol. 25, no 4, p. 257-267.

[3] LEW, Christopher O., HAROUNI, Majid, KIRKSEY, Ella R., et al. A publicly available deep learning model and dataset for segmentation of breast, fibroglandular tissue, and vessels in breast MRI. *Scientific reports*, 2024, vol. 14, no 1, p. 5383.

[4] STEEG, Patricia S. The blood-tumour barrier in cancer biology and therapy. *Nature Reviews Clinical Oncology*, 2021, vol. 18, no 11, p. 696-714.

[5] HUANG, Xinqun, CAO, Jingsong, et ZU, Xuyu. Tumor-associated macrophages: An important player in breast cancer progression. *Thoracic Cancer*, 2022, vol. 13, no 3, p. 269-276.
L'intelligence artificiel dans le contexte de la
Cyber-sécurité

**Communication Info**

**Authors:**
Zakariae CHEDDOUR

Department of Mathematic,
University Abdelmalek Essaadi,
Faculty of Sciences and Technology
Al Hoceima, BP 34. Ajdir 32003 Al
Hoceima, Morocco.

**Keywords:**
(1) Cybersécurité

**Abstract**

Avec l'augmentation exponentielle de la propagation
des menaces associée à la diffusion quotidienne de
nouveaux logiciels malveillants, il est particulièrement
impossible d'envisager de traiter efficacement ces
menaces en se basant uniquement sur des analyses
effectuées par les analystes. Il est nécessaire
d'introduire des algorithmes qui permettent
d'automatiser la phase d'introduction de l'analyse
appelée triage. C'est-à-dire d'effectuer un tri
préliminaire des menaces à soumettre à l'attention des
professionnels de la cybersécurité, ce qui permet de
répondre de manière rapide et efficace aux attaques
en cours.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] Jinting Zhu, Julian Jang-Jaccard, Ian Welch, Harith Al-Sahaf, Seyit Camtepe, Malware Triage Approach using a Task Memory based on Meta-Transfer Learning Framework, arXiv, 2023.

[2] Clemens Sauerwein, Alexander Pfohl, Towards Automated Classification of Attackers' TTPs by combining NLP with ML Techniques, arXiv, 2022.

[3] Giuseppe Laurenza, Riccardo Lazzeretti, Luca Mazzotti, Malware triage for early identification of Advanced Persistent Threat activities, arXiv, 2018.
Advanced Phonocardiogram (PCG) Signal Processing Using
VMD

**Communication Info**

**Authors:**

JAMAL ISKNAN<fnref n="1" />

SAMIR ELOUAHAM 2

**Keywords:**

(1) Phonocardiograms
(PCG)

(2) Variational mode
decomposition (VMD)

(3) Empirical mode
decomposition (EMD)

(4) Discrete wavelet
transform (DWT)

**Abstract**

Heart murmurs are biosignals used for early diagnosis of cardiovascular diseases. Digital heart sound recordings called phonocardiograms (PCG) are essential for identifying and automatically classifying possible heart diseases, leading to more efficient and accurate diagnosis. Variational mode decomposition (VMD) is an advanced signal processing technique that can decompose broadband signals into multiple narrowband components and improve feature extraction. However, there are challenges in selecting the appropriate number of modes and penalty coefficients, which can affect the accuracy of the decomposition. To address this issue, empirical mode decomposition (EMD) was proposed to extract sub-band signals as eigenmodes. Discrete wavelet transform (DWT) is also used to improve signal analysis and noise reduction. The results show that VMD is an innovative method that provides superior performance and is a more effective solution due to its lower RMSE error compared to other methods. This makes VMD a promising tool for improving heart sound analysis and early detection of cardiovascular diseases.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1]. Özkan Arslan, Mustafa Karhan, “Effect of Hilbert-Huang transform on classification of PCG signals using machine learning”, Journal of King Saud University - Computer and Information Sciences, Volume 34, Issue 10, Part B, 2022, Pages 9915-9925, https://doi.org/10.1016/j.jksuci.2021.12.019.

[2]. Xu Zhang, Congxiao Xu, Yan Fang, Wei Li, “An ultrasonic detection method based on the HT integrated with improved VMD for CFRP laminates with delamination defects,” Applied Acoustics, Volume 231, 2025, 110477, https://doi.org/10.1016/j.apacoust.2024.110477.

[3]. Xiaozhong Geng, Linen Wang, Ping Yu, Weixin Hu, Qipeng Liang, Xintong Zhang, Cheng Chen, Xi Zhang, “A method of EEG signal feature extraction based on hybrid DWT and EMD”, Alexandria Engineering Journal, Volume 113, 2025, Pages 195-204, https://doi.org/10.1016/j.aej.2024.11.056.

<footnote n="1">¹ ENSA, AGADIR, MOROCCO</footnote>

<footnote n="2">² ENSA, AGADIR, MOROCCO</footnote>
# Enhancing Blockchain PoW with Fisher-Yates Shuffle and a Merkle-Damgård-Inspired Hash

## Communication Info

**Authors:**

Asmaa CHERKAOUI¹

Seddik ABDELALIM ²

Abdelkarim LKOAIZA ³

Ilias ELMOUKI ⁴

¹,²,³,⁴ Institution, City, Country
Laboratory of Fundamental,
And Applied Mathematics,
Faculty of Sciences
Ain Chock, University Hassan II
of Casablanca,
Casablanca, Morocco

**Keywords:**

(1) Merkle-Damgård Hash

(2) Fisher-Yates shuffle

(3) Blockchain

(4) Proof of Work

## Abstract

In this talk, we introduce a Fisher-Yates shuffle for the development of the Merkle-Damgård construction [2] while using our own programming functions and without a call from any hashing library. Since the National Institute of Standards and Technology (NIST) computer scientist Chris Celi recommended that the Secure Hash Algorithm 1 (SHA-1) to be phased out in 2030 \cite{b14} in spite of their use in many digital signatures, we focus in our work, on getting inspired by the currently unbreakable Secure Hash Algorithm 2 (SHA-2) [1]. To the best of our knowledge, this is the first time that such an interesting suffling is implemented in this family and this could reinforce the security of our hash. Finally, we provide a practical explanation of how the use of our approach could be beneficial for the Proof-of-Work (PoW) in blockchain.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

## References

[1] Penard, W., amp; van Werkhove, T. (2008). On the secure hash algorithm family. Cryptography in context, 1-18.

[2] Merkle, R. C. (1979). Secrecy, authentication, and public key systems. Stanford university.

[3] NIST Retires SHA-1 Cryptographic Algorithm. (2022). (Press release). NIST. 2022-12-15.

[4] Islam, M. R., Rahman, M. M., Mahmud, M., Rahman, M. A., and Mohamad, M. H. S. (2021, August). A review on blockchain

[5] . K. Sharma, S. K. Mittal, and S. Mittal, "Attacks on Cryptographic Hash Functions and Advances," vol. 5, no. November, pp. 89-96, 2018.

security issues and challenges. In 2021 IEEE 12th control and system graduate research colloquium (ICSGRC) (pp. 227-232). IEEE.
# Agriculture 5.0: Plant Disease Detection Using Deep learning - Comparison study between CNN and VGG16

## Communication Info

**Authors:**

ALAOUI Fatima-zahra<fnref n="1" />

EL JIANI Laila<fnref n="1" />

ELFILALI SANAA<fnref n="1" />

AIT ABDELOUAHDI Rachida<fnref n="1" />

Zouheir Banou<fnref n="1" />

**Keywords:**

(1) Plant disease detection,

(2) Deep Learning,

(3) CNN,

(4) Transfer Learning,

(5) VGG16,

## Abstract

Integrating artificial intelligence methods, such as deep learning, holds significant potential for enhancing the accuracy and efficiency of plant disease detection. In this article, we provide a comprehensive overview of the application of deep learning in the field of plant disease detection, utilizing a Convolutional Neural Networks (CNN) model. The utilization of Convolutional Neural Networks (CNN), a deep learning model tailored for image analysis, has proven to be highly effective in the detection of plant diseases. Leveraging extensive datasets related to plant diseases and transfer learning techniques, CNNs consistently achieved accuracy rates exceeding 98%. This article underscores the potential of deep learning, particularly the CNN model, as a powerful and effective tool for plant disease detection. Furthermore, we demonstrate that using transfer learning models, such as VGG-16, resulted in an accuracy rate of 95%.

© ICRAMCS 2024 Proceedings ISSN: 2605-7700

## References

[1] Mohanty, S., Hughes, D. & Salath'e, M. Using deep learning for image-based plant disease detection. Frontiers In Plant Science. 7 pp. 1419 (2016)

[2] Raj, M., Gupta, S., Chamola, V., Elhence, A., Garg, T., Atiquzzaman, M. & Niyato, D. A survey on the role of Internet of Things for adopting and promoting Agriculture 4.0. Journal Of Network And Computer Applications. 187 pp. 103107(2021)

[3] Anand, T., Sinha, S., Mandal, M., Chamola, V. & Yu, F. AgriSegNet: Deep aerial semantic segmentation framework for IoT-assisted precision agriculture. IEEE Sensors Journal. 21, 17581-17590 (2021).

[4] Akbar, M., Ullah, M., Shah, B., Khan, R., Hussain, T., Ali, F., Alenezi, F., Syed, I. & Kwak, K. An effective deep learning approach for the classification of Bacteriosis in peach leaves. Frontiers In Plant Science. 13 pp. 1064854 (2022)

[5] Anand, T., Sinha, S., Mandal, M., Chamola, V. & Yu, F. AgriSegNet: Deep aerial semantic segmentation framework for IoT-assisted precision agriculture. IEEE Sensors Journal. 21, 17581-17590 (2021)

<footnote n="1">¹ University Hassan II</footnote>

<footnote>Casablanca, Faculty of Science</footnote>

<footnote>Ben M'sick, Casablanca,</footnote>

<footnote>Morocco</footnote>
# Adopting the Grendel Approach in Blockchain Signature

**Communication Info**

**Authors:**

Abdelkarim LKOAIZA¹
Seddik ABDELALIM²
Asmaa CHERKAOUI³
Ilias ELMOUKI4⁴

¹Laboratory of Fundamental and Applied
Mathematics, Faculty of Sciences Ain Chock,
University Hassan II of Casablanca,
Casablanca, Morocco

**Keywords:**

(1) Grendel hashing

(2) Blockchain signature

(3) KeITT family,

**Abstract**

In this presentation, we introduce our own custom-developed programming to generate a sponge-based function without relying on any hashing libraries. We then explore its implementation in a blockchain signature, drawing inspiration from Keccak methods, including the recently robust Secure Hash Algorithm 3 (SHA-3). However, our main contribution lies in introducing the Grendel permutation instead of the Ke permutation, as both utilize sponge-based techniques but differ in their shuffling process. To the best of our knowledge, this is the first time such an approach has been applied to blockchain signatures.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] Albrecht, M.R, Grassi, L, Rechberger, C, Roy, A, Tiessen, T Mimc: Efficient encryption and cryptographic hashing with minimal multiplicative complexity. In: Cheon, J.H., Takagi, T (eds) Advances in Cryptology - ASIACRYPT 2016 - 22nd International Conference on the Theory and Application of Cryptology and Information Security, Hanoi, Vietnam, December 4-8, 2016, Proceedings, Part I. Lecture Notes in Computer Science, vol. 10031, pp. 191219 (2016)..

[2] Szepieniec, A. (2021). On the use of the legendre symbol in symmetric cipher design. Cryptology ePrint Archive.

[3] Sauer, J.F., Szepieniec, A.: Sok: Grobner basis algorithms for arithmetization oriented ciphers. Cryptology ePrint Archive, Report 2021/870 (2021)

[4] G. Bertoni, J. Daemen, M. Peeters, and G. Van Assche, The KECCAK SHA-3 submission, Version 3, January 2011

[5] Nakamoto, S., and Bitcoin, A. (2008). A peer-to-peer electronic cash system. Bitcoin.-URL: https://bitcoin. org/bitcoin. pdf, 4(2), 15.

[6] Narayanan, A., Bonneau, J., Felten, E., Miller, A., Goldfeder, S. (2016). Bitcoin and cryptocurrency technologies: a comprehensive introduction. Princeton University Press.

[7] Zhang, R., Xue, R., Liu, L. (2019). Security and privacy on blockchain. ACM Computing Surveys (CSUR), 52(3), 1-34.

[8] Gupta, S., Sadoghi, M. (2021). Blockchain transaction processing. arXiv preprint arXiv:2107.11592.
# Stock Trends Forecasting with FinBERT and Bi-LSTM

## Communication Info

**Authors:**

Mohamed Rida SAHIB¹
Hamza ELKINA ¹
Taher ZAKI ¹

¹ Faculty of Applied Sciences,
Ait Melloul, Morocco

**Keywords:**

(1) Stock Price Prediction

(2) Sentiment Analysis

(3) FinBERT

(4) Bi-LSTM

## Abstract

This study proposes a hybrid model combining FinBERT for financial sentiment analysis with Bi-LSTM for stock trend forecasting [1]. FinBERT [2] extracts sentiment from financial news, which, when integrated with historical stock data, helps the Bi-LSTM model capture temporal dependencies. The model is evaluated using stock price and news datasets, showing improved predictive accuracy over traditional models. Results highlight the effectiveness of integrating sentiment analysis with deep learning [3] for better stock market predictions, benefiting investors and analysts.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

## References

[1] Aluvala, S., Balamurugan, V., Jeyasudha, J., Kumar, R.D. and Kumar, P., 2023, October. Decoding Financial Markets: Unleashing the Power of Bi-LSTM in Sentiment Analysis for Cutting-Edge Stock Price Prediction. In 2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT) (pp. 1-7). IEEE.

[2] Huang, Y., Lin, R., Li, J., Li, Y., Chen, J., Liang, J., Feng, G. and Lv, J., 2024, July. A FinBERT Framework for Sentiment Analysis of Chinese Financial News. In 2024 4th International Symposium on Computer Technology and Information Science (ISCTIS) (pp. 796-799). IEEE.

[3] Mohamed Rida, S.A.H.I.B., Hamza, E.L.K.I.N.A. and Taher, Z.A.K.I., 2024. From Technical Indicators to Trading Decisions: A Deep Learning Model Combining CNN and LSTM. International Journal of Advanced Computer Science & Applications, 15(6).
A Practical Approach of DevSecOps Integration
in Multi-Tier Web Applications

**Communication Info**

**Authors:**

Abderrahim RIDA¹

Ayoub AIT LAHCEN¹

¹ Engineering Sciences
Laboratory, National School of
Applied Sciences, Ibn Tofail
University, Kenitra, Morocco

**Keywords:**

(1) DevOps

(2) DevSecOps

(3) CI/CD pipelines

(4) Multi-tier Web
Applications.

**Abstract**

DevSecOps pipelines are diverse and the configurations of each DevSecOps team may be unique. While the used methodologies may be different, they have a common objective driven toward fast and secure delivery, supported with efficient communication between Development, Operations, and Security teams [1]. With the use of multiple DevSecOps pipelines, It would be crucial to adjust a specific pipeline intended for Multi-tier Web Applications, even with the potential challenges that can arise [2]. The practical pipeline, described here, can be used as a template for DevSecOps teams working on Multi-tier Web Applications, whether such applications are structured as monolithic architectures or realized using micro-services [3]. The purpose is to share this novel pipeline to determine its effectiveness, hence establishing a baseline CI/CD that DevSecOps teams can adapt to meet their specific requirements [4]. This approach aligns with the growing need for integrating security into DevOps practices to ensure robust and secure software delivery [5].

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] M. C. Paulk et al., "Capability Maturity Model for Software," Software Engineering Institute, CMU/SEI-93-TR-24, 1993, pp. 1-50.

[2] J. Humble and D. Farley, "Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation," Addison-Wesley Professional, 1st Edition, 2010, pp. 1-500.

[3] N. Forsgren, J. Humble, and G. Kim, "Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations," IT Revolution Press, 2018, pp. 1-288.

[4] S. Myrbakken and R. Colomo-Palacios, "DevSecOps: A Multivocal Literature Review," International Conference on Software Process Improvement and Capability Determination, 2017, pp. 17-29.

[5] G. Kim et al., "The DevOps Handbook: How to Create World-Class Agility, Reliability, and Security in Technology Organizations," IT Revolution Press, 2016, pp. 1-480.
# Ensemble Classification Methods for Stock Market Movement: A Review

## Communication Info

**Authors:**

Ilyas Elkinani¹

Mohamed Hosni ¹

Ibtissam Medarhri ²

Tawfik Masrour³

¹ IEST Research Team, AIDTM Laboratory, ENSAM, University Moulay Ismail of Meknes, Morocco

² MMCS Research Team, LMAID, ENSMR, Rabat, Morocco

³ IASI Research Team, AIDTM Laboratory, ENSAM, University Moulay Ismail of Meknes, Morocco

**Keywords:**

Ensemble Methods

Machine Learning

Stock Market

Classification

## Abstract

Ensemble methods combine multiple models to improve prediction accuracy and overcome the limitations of individual techniques [1]. They are widely used in various fields, including stock market forecasting, to enhance prediction tasks in both classification and regression [2,3]. Given the complexity of market characteristics, these methods provide more stable and reliable predictions [2]. The goal of this study is to analyze the state of the art in ensemble classification methods applied to stock market prediction concerning seven aspects: publication venues, stock market tasks addressed, types of ensembles proposed, individual techniques used to construct the ensembles, combiners adopted in ensemble methods, validation frameworks used to evaluate the proposed ensembles, and tools utilized to build the ensembles.

A total of 60 journal papers, published between 2000 and 2024, were outsourced from Scopus and Web of Science digital libraries after a selection process. The study found that *Expert Systems with Applications* is the most frequently used source venue and that, among the three existing stock market tasks, stock price prediction was the most frequently addressed. The homogeneous ensemble type was the most widely used for performing the classification task. Regarding individual techniques, this review found that decision trees, support vector machines, and neural networks were the most frequently adopted for building ensemble classifiers. For combination strategies, majority voting was the most commonly used rule. As for the evaluation framework, the S&P 500 index dataset was the most commonly used by researchers for their experiments, with accuracy being the most frequently used metric to assess the predictive capability of ensemble methods. The holdout method was the most widely adopted validation method. The review reveals that various tools can be used for experiments with ensemble classification methods, including Python and WEKA software. This study provides an in-depth analysis of ensemble methods in stock market prediction, identifying research gaps.

The findings show that most studies report higher accuracy for ensemble classifiers compared to individual models, highlighting the need for a systematic review and meta-analysis to confirm their superiority.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

## References

[1] Z.-H. Zhou, Ensemble Methods, CRC Press, 2012.

[2] M.M. Kumbure, C. Lohrmann, P. Luukka, J. Porras, Machine learning techniques and data for stock market forecasting: A literature review, Expert Syst. Appl. 197 (2022) 116659. https://doi.org/10.1016/j.eswa.2022.116659

[3] C.F. Tsai, Y.C. Lin, D.C. Yen, Y.M. Chen, Predicting stock returns by classifier ensembles, Appl. Soft Comput. J. 11 (2011) 2452–2459. https://doi.org/10.1016/j.asoc.2010.10.001
Predictive Maintenance in Industrial Systems
Using Machine Learning: A Review

**Communication Info**

**Authors:**

Oussama Benmansour¹

Ibtissam Medarhri¹

Mohamed Hosni²

¹MMCS Research Team, LMAID, ENSMR,

Rabat, Morocco

2IEST Research Team, AIDTM Laboratory,

ENSAM, University Moulay Ismail of Meknes

**Keywords:**

(1) Machine Learning

(2) Predictive Maintenance

(3) Artificial intelligence

(4) Review

(5) Industrial

**Abstract**

Predictive maintenance (PdM) has emerged as a crucial strategy in modern industrial settings, leveraging machine learning (ML) to enhance equipment reliability and reduce unplanned downtimes [1]. Unlike traditional maintenance strategies that rely on reactive or scheduled interventions, PdM enables real-time anomaly detection and failure prediction through sensor data and historical records [2]. Recent studies highlight the effectiveness of ML techniques in optimizing maintenance schedules and improving asset reliability.

This study presents a systematic mapping of ML classification techniques in industrial predictive maintenance. A total of 199 articles, after a selection process, in industry and manufacturing published between 2000 and 2024 were outsourced from the Scopus digital library. Findings indicate that fault diagnosis is the most frequently investigated task, with Support Vector Machines and decision trees being the predominant ML techniques. Additionally, recent research highlights the increasing role of deep learning in PdM, with architectures such as convolutional neural networks (CNNs) and long short-term memory (LSTMs) proving effective in anomaly detection and remaining useful life (RUL) estimation. Furthermore, a comparative analysis of ML models in real-world industrial applications demonstrates that ensemble methods like XGBoost provide an optimal balance between predictive accuracy and computational efficiency [3]. The datasets analyzed are primarily derived from real-world sensor data and maintenance records, ensuring practical applicability.

These findings emphasize the need for further exploration of hybrid approaches and alternative classification techniques to advance industrial AI applications.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

**References**

[1] T.P. Carvalho, F.A.A.M.N. Soares, R. Vita, R. da P. Francisco, J.P. Basto, S.G.S. Alcalá, A systematic literature review of machine learning methods applied to predictive maintenance, Comput. Ind. Eng. 137 (2019) 106024.
https://doi.org/10.1016/j.cie.2019.106024.

[2] S. Elkateb, A. Métwalli, A. Shendy, A.E.B. Abu-Elanien, Machine learning and IoT - Based predictive maintenance approach for industrial applications, Alexandria Eng. J. 88 (2024) 298-309.
https://doi.org/10.1016/j.aej.2023.12.065.

[3] U. Farooq, M. Ademola, A. Shaalan, Comparative Analysis of Machine Learning Models for Predictive Maintenance of Ball Bearing Systems, Electron. 13 (2024). https://doi.org/10.3390/electronics13020438.
# Dynamique des Bibliothèques Python Pour Les Matrices issues des Problèmes Inverses Mal Posés.

## Communication Info

**Authors:**

Aziz SOUKRI¹

Omar EL BANNAY¹

Karim GHOFIR¹

¹ ENSA, Khouribga, Maroc

**Keywords:**

(1) Matrices denses ;

(2) Matrices creuses ;

(3) Matrices de convolution ;

(4) Décomposition DVS ;

(5) Régularisation.

## Abstract

En mathématiques analytiques et en ingénierie des traitements d'images, Les problèmes inverses mal posés constituent un défi majeur. Les travaux de recherche menés dans ce sens sont principalement focalisés sur des algorithmes concentrés sur les propriétés de la décomposition en valeurs singulières (SVD) et particulièrement dans les techniques de régularisation (comme par exemple la régularisation de TIKHONOV, la régularisation TSVD ou encore la Totale Variation) [1,2,3,4]. L'objectif de ce travail consiste dans un premier temps à développer des algorithmes permettant de réduire la dynamique (vitesse) de création des matrices de convolution, et d'effectuer une comparaison avec d'autres algorithmes basés sur des bibliothèques prédéfinies ; et dans un deuxième temps, selon les propriétés de la matrice de convolution, une étude comparative des différentes méthodes d'inversion ou de décomposition en valeurs singulières. L'étude comparative menée a révélé que l'application de ces algorithmes, réduisent significativement le temps nécessaire aux traitements adéquats à un système désiré. Quelques exemples seront présentés pour valider l'étude.

© ICRAMCS 2025 Proceedings ISSN: 2605-7700

## References

[1] Aijie Qiao and Xingwu Zhang and Jiawei Gao and Ruonan Liu and Xuefeng Chen, Sparse deconvolution for the large-scale ill-posed inverse problem of impact force reconstruction, Mechanical Systems and Signal Processing, 83, 2017, 93-115.

[2] Brianna R. et O'Leary, Dianne P. A Guide to gide 5.1: A GUI for Graphical Image Deblurring Exploration. 2015.

[3] S. Lefkimmiatis, A. Bourquard and M. Unser, "Hessian-Based Norm Regularization for Image Restoration With Biomedical Applications," *IEEE Transactions on Image Processing*, 21, March 2012, 983-995,

[4] D. Calvetti and S. Morigi and L. Reichel and F. Sgallari, Tikhonov regularization and the L-curve for large discrete ill-posed problems, *Journal of Computational and Applied Mathematics*, 123, 2000, 423-446.