bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficient reduction of computational complexity in video surveillance using hybrid machine learning for event recognition
Authors: Honnegowda Jyothi , Mallikarjunaiah Komala , Srikantaswamy Mallikarjunaswamy
Published: November 2024

This paper addresses the challenge of high computational complexity in video surveillance systems by proposing an efficient model that integrates hybrid machine learning algorithms (HML) for event recognition. Conventional surveillance methods struggle with processing vast amounts of video data in real-time, leading to scalability, and performance issues. Our proposed approach utilizes convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to enhance the accuracy and efficiency of detecting events. By comparing our model with conventional surveillance techniques motion detection, background subtraction, and frame differencing. We demonstrate significant improvements in frame processing time, object detection speed, energy efficiency, and anomaly detection accuracy. The integration of dynamic model scaling and edge computing further optimizes computational resource usage, making our method a scalable and effective solution for real-time surveillance needs. This research highlights the potential of machine learning to revolutionize video surveillance, offering insights into developing more intelligent and responsive security systems. The results of your simulation analysis, indicating performance improvements in accuracy by 0.25%, 0.35%, and 0.45% for the motion detection algorithm, background subtraction, and frame differencing respectively, and in real-time data processing by 5.65%, 4.45%, and 6.75% for the motion detection algorithm, background subtraction, and frame differencing respectively, highlight the potential of machine learning to transform video surveillance into a more intelligent and responsive system.
SQL-CB-GuArd: a deep learning mechanism for structured query language injection attack detection
Authors: Sirmulla AsifIqbal , Manickam Prabhakar
Published: November 2024

Structured query language (SQL) injection attacks, which take advantage of input field vulnerabilities to introduce malicious code into database queries, are a serious danger to database-driven programs and systems. Intruders can now alter, recover, or remove sensitive data because of illegal access. Strong artificial intelligence (AI) based security solutions are required to reduce SQL injection threats, as these assaults' significance highlights. This study's main goal is to create automated AI-based techniques that can identify structured query language injection attack (SQLIA) in real time eliminating the need for human intervention. Although machine learning (ML) and deep learning-based techniques have received a lot of interest in this field, MLbased techniques have problems with accuracy and false negatives. Deep learning (DL) is therefore commonly used in these text data processing and natural language processing (NLP) applications. We have introduced a hybrid DL approach for SQLIA detection in this paper. The pre-processing step performs decoding, generalization, and tokenization to improve the learning performance. The proposed approach uses combination of convolutional neural network (CNN), bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU) with attention mechanism. The combination helps to improve the pattern learning capacity. The proposed approach is validated on publically available data and experimental analysis reported that the proposed SQL-CB-GuArd achieves better accuracy of SQLIA detection.
New method for assessing suicide ideation based on an attention mechanism and spiking neural network
Authors: Francis Corrine , Al-Hababi Abdulrazak Yahya Saleh
Published: November 2024

The COVID-19 pandemic has had a substantial effect on global mental health, leading to increased depression and suicide ideation (SI), particularly among young adults. This study introduces a novel method for enhancing SI assessment in young adults with depression, utilizing machine learning (ML) techniques applied to structural magnetic resonance imaging (SMRI) data. SMRI data from 20 individuals with depression and 60 healthy controls were analyzed. A hybrid ML algorithm, integrating self-attention mechanism and evolving spiking neural networks, successfully classified depression with 94% accuracy, 100% sensitivity, 92% specificity, and an area under the curve of 0.96. These results offer potential for enhancing mental health intervention and support in the context of the ongoing and post-pandemic period influenced by COVID-19.
Prediction of metabolic syndrome in mexicans using machine learning
Authors: Pineda-Rico Zaira , Rojas Mendoza Diana Luz de los Angeles , Pineda-Rico Ulises
Published: November 2024

Metabolic syndrome (MetS) is a compelling public health issue in Mexico, with high prevalence rates of overweight, obesity, arterial hypertension, diabetes, high triglycerides, low high-density lipoprotein cholesterol, and high total cholesterol. Despite this, predictive models tailored for under-researched professional groups with sedentary habits are scarce. This study introduces a novel predictive model for MetS using data from the National Center for Health Statistics and a unique dataset of higher education staff. By employing and comparing machine learning algorithms such as decision trees, random forest, artificial neural networks, and adaptive boosting, the research provides new insights into gender and race-specific aspects of MetS. The data was labeled using standards from the International Diabetes Federation and the National Cholesterol Education Program Adult Treatment Panel III to create classification models, which were tested on the higher education staff dataset. Model predictions were assessed using F1-score, accuracy and area under the curve - receiver operating characteristic (AUC-ROC), with random forest, decision tree, and adaptive boosting performing best. The key predictive features identified for MetS prediction include triglycerides, glucose, high-density lipoprotein cholesterol, waist-to-height ratio, and body mass index. 
A systematic analysis on machine learning classifiers with data pre-processing to detect anti-pattern from source code
Authors: Akhter Nazneen , Khatun Afrina , Rahman Md. Sazzadur , Sanwar Hosen A. S. M. , Shahidul Islam Mohammad
Published: November 2024

Automatic detection of anti-patterns from source code can reduce software maintenance costs massively. Nowadays, machine learning approaches are very commonly used to identify anti-patterns. Hence, it is very crucial to choose a classifier that can be useful for detecting anti-patterns. This work aims to help practitioners to choose a suitable classifier to detect anti-patterns. In this paper, we highlight 16 classifiers in four different categories to detect anti-patterns. Furthermore, the performance of these classifiers is identified with the data pre-processing (DPP) to detect four commonly occurring anti-patterns from the three commonly used open-source Java projects’ source code. The accuracy of Dagging classifiers is 98.4%. Kernel logistic regression (KLR) also performs well i.e., 97%. In the case of time complexity, naive Bayes (NB), decision trees (DT), support vector machines (SVM), library for support vector machines (LibSVM), logistic, and LightGBM (LB) have less time complexity to build a model in all the projects.
Machine learning for mental health: predicting transitions from addiction to illness
Authors: Alkhazraji Ali , Alsafi Fatima , Dbouk Mohamed , Ibrahim Zein Al Abidin , Sbeity Ihab
Published: November 2024

The increasing prevalence of infection-causing diseases due to environmental factors and lifestyle choices has strained the healthcare system, necessitating advanced techniques to save lives. Disease prediction plays a crucial role in identifying individuals at risk, enabling early treatment, and benefiting governments and health insurance providers. The collaboration between biomedicine and data science, particularly artificial intelligence and machine learning, has led to significant advancements in this field. However, researchers face challenges related to data availability and quality. Clinical and hospital data, crucial for accurate predictions, are often confidential and not freely accessible. Moreover, healthcare data is predominantly unstructured, requiring extensive cleaning, preprocessing, and labeling. This study aims to predict the likelihood of patients transitioning to mental illness by monitoring addiction conditions and constructing treatment protocols, with the goal of modifying these protocols accordingly. We focus on predicting such transformations to illuminate the underlying factors behind shifts in mental health. To achieve this objective, data from an Iraqi hospital has been collected and analyzed yielding promising results. 
Automatic diabetes prediction with explainable machine learning techniques
Authors: Haque Adiba , Islam Sanjida , Mim Nusrat Rahim , Meem Sabrina Mannan , Saha Ananya , Khan Riasat
Published: November 2024

Diabetes is a metabolic disorder caused by various genetic, physiological and behavioral factors. It occurs due to an imbalance in the body’s insulin processing, which results in elevated blood sugar levels. Its early diagnosis can alleviate the risk of other deadly diseases. The onset and accurate detection of diabetes can decrease the progression of different complications and dysfunction of tissues. The principal objective of this article is to utilize machine learning approaches to predict the existence of diabetes in female patients at a primary stage. Multiple machine learning, including ensemble classifiers with the Pima Indian dataset and a private dataset obtained from a local Bangladeshi hospital, are used in this work. We employed feature scaling, synthetic oversampling technique (SMOTE), and hyperparameter optimization with GridSearchCV to get the best performance from different machine learning algorithms. The support vector machine (SVM) with the SMOTE framework and default hyperparameters achieved the accuracy and F1 score of 87% and 91%, respectively. The accuracy and F1 score of the SVM model improved to 95% and 91%, respectively, with hyperparameter optimization. Finally, explainable artificial intelligence with the local interpretable model-agnostic explanations (LIME) is employed to illustrate the predictability of the SVM technique.
Weighted nearest neighbors and radius oversampling for imbalanced data classification
Authors: Pradipta Gede Angga , Wulaning Ayu Putu Desiana , Liandana Made , Hostiadi Dandy Pramana
Published: November 2024

The challenges associated with high-dimensional and imbalanced datasets were observed to often lead to a degradation in the performance of classical machine learning algorithms. In the case of high dimensional data, not all features contribute significantly and are considered relevant to the performance of the model. Therefore, this study introduced a novel method called feature weighted variance analysis-nearest neighbors (WFVANN) which was developed on the foundation of k-nearest neighbors (KNN). The process involved modifying the calculation of the Euclidean distance by fully considering the relevance and contribution levels of features based on their Fvalue. WFVANN at the algorithmic level processing and radius-synthetic minority oversampling technique (R-SMOTE) at the data level processing used as the oversampling method later became the proposed model to solve the aforementioned issues. Moreover, extensive experiments were conducted on two distinct types of data including the high-dimensional and imbalanced by comparing WFVANN with the state-of-art KNN-based and synthetic minority oversampling technique (SMOTE)-based methods. The results showed that the proposed method had the highest accuracy, precision, recall, and F1-measure values across the majority of test datasets and outperformed the other methods.
Detection of location-specific intra-cranial brain tumors
Authors: Usharani Shola , Lakshmanan Rama Parvathy , Rajakumaran Gayathri , Basu Aritra , Nandam Anjana Devi , Depuru Sivakumar
Published: November 2024

Mutations or abnormalities in genes can occasionally cause cells to grow uncontrolled, resulting in a tumor, which is very dangerous. These are the most prevalent cancer causes. They are caused by significant damage to genes in a specific cell during a person's existence. Brain tumors are increasing rapidly, majorly brain tumor cases in the US are projected to rise from 27,000 in 2020 to 31,000 in 2023 at an annual growth rate of 1.5%, all the cases are rising because of the detection of the tumors in the late phase. Thus, it needs the hour to create something which can solve this anomaly and help us detect the tumor rapidly and efficiently. While major research papers on brain tumor detection mainly focus on the detection and classification of the tumors, the presented research aims to first detect the tumor using pre-recognized photos using machine learning object detection models. Then after successful detection of the tumor, the study team plans to determine its precise coordinates and display the tumor and its location in the picture.
Machine learning for the detection of soil pH, macronutrients, and micronutrients with crop and fertilizer recommendations
Authors: Montañez John Joshua , Sarmiento Jeffrey
Published: November 2024

The study aims to determine the levels of soil parameters such as soil pH, macronutrients, and micronutrients. After determining said parameters, the system appropriately recommends crops and fertilizers suitable for the soil samples. For soil pH and macronutrient levels, i.e., nitrogen, phosphorus, and potassium, these parameters can be detected using the soil test kit. Meanwhile, for soil micronutrients, i.e., copper, iron, and zinc, there is a need for the development of appropriate assays for colorimetric processes that can be done for the appropriate determination of said micronutrients. Comparison of available machine learning such as support vector machine algorithm, naïve Bayes algorithms, and K-nearest neighbor algorithm is a must to determine the well-fit algorithm that is considered fast and has high predictive power in classification and regression. The outputs of the colorimetric and spectrometric processes are the inputs in the machine learning activities intended for crop and fertilizer recommendation.
User sentiment dynamics in social media: a comparative analysis of X and Threads
Authors: Khairunnas Rezki , Pagua Jeri Apriansyah , Fitriya Ghina , Ruldeviyani Yova
Published: November 2024

This research examines the dynamics of user sentiment and its correlation with the usage factors of applications in the context of the competition between X (formerly Twitter) and Threads, a social media application under the umbrella of Meta. Through sentiment analysis of user reviews on the Google Play Store and App Store, the study aims to identify the key factors contributing to a significant decline in user engagement with Threads and the return of users to X. The method employed in this research is the support vector machine (SVM) for sentiment classification of reviews. The study then correlates the classified sentiments with application usage factors: usability, features, design, and support. The research findings indicate user sentiment influences user engagement, especially in features and design. The research concludes with insights regarding implications for application developers and suggests directions for future research.
Machine learning methods for classification and prediction information security risk assessment
Authors: Muhammad Alva Hendi , Nasiri Asro , Harimurti Agung
Published: November 2024

Information is an essential company asset that must be protected. The value of information assets depends on the type and scale of the business and its role in delivering services. One of the primary programs that can help identify areas of improvement and guide the development of security awareness programs is risk assessment. Managing cybersecurity risks is critical to protecting enterprises from developing cyber threats and promoting resilience. This includes detecting, assessing, and mitigating risks to protect sensitive data, systems, and networks. While cybersecurity risk management is challenging, organizations may improve their security posture. This paper seeks to contribute to the field of information security risk assessment by leveraging the power of machine learning to provide quick, cost-effective, and individualized risk assessments for small and medium enterprises. Specifically, we extend the evaluation for security level classification by utilizing a support vector machine, random forest, and gradient boosting algorithms. The results demonstrate how well the model detects significant cases while reducing false positives. The model’s exceptional precision ensures that its identifications are dependable, while the high recall demonstrates that it accurately detects relevant data. Precision is critical in security risk assessment because a false positive result might have profound effects.
Balancing and metaheuristic techniques for improving machine learning models in brain stroke prediction
Authors: Aouragh Abd Allah , Bahaj Mohamed , Toufik Fouad
Published: November 2024

A brain stroke, medically referred to as a stroke, represents a critical condition triggered by the disruption of blood flow to a region of the brain. Early detection of stroke is crucial to prevent fatal complications. In this study, we worked with an unbalanced dataset of 4981 entries on stroke, which we balanced using the K-means synthetic minority over-sampling technique (KMeansSMOTE) algorithm. We then employed five machine learning algorithms: decision tree, random forest, support vector machine, K-nearest neighbors, and gradient boosting. We compared the hyperparameter optimization of these algorithms using four metaheuristic techniques: gray wolf optimization, particle swarm optimization, genetic algorithm, and artificial bee colony. The models' effectiveness was evaluated using multiple metrics, such as accuracy, recall, precision, F1-score, and area under the receiver operating characteristic curve. Our findings indicate that the random forest optimized by the genetic algorithm achieved the best performance, with an accuracy of 97.39% and an F1-score of 97.35%. This study highlights the effectiveness of balancing and metaheuristics techniques in optimizing machine learning models for stroke forecasting.
Classification of Bharatanatyam postures using tailored features and artificial neural network
Authors: Bhandage Venkatesh , Anami Basavaraj , J Andrew , Hadimani Balachandra
Published: November 2024

Bharatanatyam is a classical dance form of India that upholds the rich culture of India. This dance is learned under the supervision of Guru, the teacher traditionally called in India. The scarcity of experts resulted in the decline of people practicing this dance. There is a need for leveraging technology in preserving and promoting this traditional dance and propagating it amongst the youth. In this research, it is attempted to develop a methodology for automated classification of Bharatanatyam dance postures. The methodology involves extraction of existing features such as speeded up robust features (SURF) and histogram of oriented gradients (HOG), which are used to train and test an artificial neural network (ANN). The results are corroborated with deep learning architectures such as AlexNet and GoogleNet. The proposed methodology has yielded a classification accuracy of 99.85% as compared with 93.10% and 94.25% of AlexNet and GoogleNet respectively. The proposed method finds applications such as assistance to Bharatanatyam dance teachers, e-learning of dance, and evaluating the correctness of the postures.
Hybrid intrusion detection model for hierarchical wireless sensor network using federated learning
Authors: Mani Sathishkumar , Kishoreraja Parasuram Chandrasekaran , Joseph Christeena , Manoharan Reji , Theerthagiri Prasannavenkatesan
Published: November 2024

The applications of wireless sensor networks are vast and popular in today’s technology world. These networks consist of small, independent sensors that are capable of measuring various physical quantities. Deployment of wireless sensor networks increased due to immense applications which are susceptible to different types of attacks in an unprotected and open region. Intrusion detection systems (IDS) play a vital part in any secured environment for any network. IDS using federated learning have the potential to achieve better classification accuracy. Usually, all the data is stored in centralized server in order to communicate between the systems. On the other hand, federated learning is a distributed learning technique that does not transfer data but trains models locally and transfers the parameters to the centralized server. The proposed research uses a hybrid IDS for wireless sensor networks using federating learning. The detection takes place in real-time through detailed analysis of attacks at different levels in a decentralized manner. Hybrid IDS are designed for node level, cluster level and the base station where federated learning acts as a client and aggregated server.
Enhancing traffic flow through multi-agent reinforcement learning for adaptive traffic light duration control
Authors: Faqir Nada , Boumhidi Jaouad , Loqman Chakir , Oubenaalla Youness
Published: November 2024

This study addresses urban traffic congestion through deep learning for traffic signal control (TSC). In contrast to previous research on single traffic light controllers, our approach is tailored to the TSC challenge within a network of two intersections. Employing convolutional neural networks (CNN) in a deep Q-network (DQN) model, our method adopts centralized training and distributed execution (CTDE) within a multi-agent reinforcement learning (MARL) framework. The primary aim is to optimize traffic flow in a twointersection setting, comparing outcomes with baseline strategies. Overcoming scalability and partial observability challenges, our approach demonstrates the efficacy of the CTDE-based MARL framework. Experiments using urban mobility simulation (SUMO) exhibit a 68% performance enhancement over basic traffic light control systems, validating our solution across diverse scenarios. While the study focuses on two intersections, it hints at broader applications in complex settings, presenting a promising avenue for mitigating urban traffic congestion. The research underscores the importance of collaboration within MARL frameworks, contributing significantly to the advancement of adaptive traffic signal control (ATSC) in urban environments for sustainable transportation solutions.
A revolutionary convolutional neural network architecture for more accurate lung cancer classification
Authors: Muliadi Muliadi , Windarto Agus Perdana , Solikhun Solikhun , Alkhairi Putrama
Published: November 2024

This research aimed to investigate a breakthrough in convolutional neural network (CNN) architecture with the potential to revolutionize lung cancer classification. The proposed method is a comparative optimization model of ResNet architecture, with accuracy rate of 99.68% in identifying and categorizing lung cancer types. The results showed that the use of innovative methods in CNN architecture, such as multi-dimensional convolutional layers and the integration of specific lung cancer features, effectively provided highly accurate and reliable outcomes. These showed a positive impact on the development of medical diagnostic technology, offering promising potential to enhance prognosis and response to treatment for lung cancer patients. With high accuracy rate, this breakthrough presents opportunities for further advancements in lung cancer management through artificial intelligence-based methods.
Hadamard Walsh space based hybrid technique for image data augmentation
Authors: Suryawanshi Vaishali , Sarode Tanuja K.
Published: November 2024

Image data augmentation (IDA) is common when deep learning is used for image classification to address the issue of overfitting. Overfitting occurs when the datasets are small and the deep learning models have a huge capacity. Overfitting models have low training errors but high validation errors and result in poor generalization. Several methods have been researched in this context, but frequency domain-based methods are less explored. In this research, we have explored the Hadamard and Walsh space and developed two hybrid technique for IDA. The proposed techniques use a combination of Hadamard/Walsh transform and geometrical transformations. Empirical study is carried out using the VGG-16 model for image classification on the CIFAR-10 dataset and the results are compared with existing methods. The analysis of the results shows that the proposed techniques improve the evaluation parameters significantly. Further, analysis of training loss vs. validation loss shows that the proposed Hadamard-based hybrid methods have better generalization ability than the proposed Walsh-based hybrid method.
Overcoming imbalanced rice seed germination classification: enhancing accuracy for effective seedling identification
Authors: Mara Muhlasah Novitasari , Hidayat Sidiq Syamsul , Putri Farika Tono , Rahmawati Dwi , Wahyuni Sri , Prabowo Muhamad Cahyo Ardi , Kabir Noer Ni'mat Syamsu , Indra Ragil Tri
Published: November 2024

This study aimed to automatically classify rice seedling germination on day seven using image analysis. The categories included normal, abnormal, and dead seeds. Due to the rarity of abnormal seedlings, capturing their images resulted in imbalanced data. To address this, abnormal categories were combined into a single class. We compared logistic regression, random forest, and deep learning models (VGG19, VGG16, Alex Net) for classification. Surprisingly, logistic regression achieved the highest accuracy (93.89%) and F1-scores (0.96 normal, 0.81 abnormal) despite the imbalanced data and complex task. The effectiveness of logistic regression for rice seedling classification with imbalanced data has been demonstrated in this novel research. Historically, deep learning models dominate image recognition, but our findings suggest simpler models can excel in specific scenarios, especially with limited data availability. This highlights the importance of selecting models based on data characteristics. The urgency for this research stems from the need for efficient and accurate rice seedling evaluation. Improved classification can enhance agricultural practices and optimize resource allocation.
A robust penalty regression function-based deep convolutional neural network for accurate cardiac arrhythmia classification using electrocardiogram signals
Authors: Pratima Anniah , Kanathur Gopalakrishna , Prasad Sarappadi Narasimha
Published: November 2024

Cardiac arrhythmias are a leading cause of morbidity and mortality worldwide, necessitating accurate, and timely diagnosis. This paper presents a novel approach for the classification of cardiac arrhythmias using a penalty regression function (PRF)-based deep convolutional neural network (DCNN). The proposed model integrates advanced preprocessing techniques, including frechet with fitness rank distribution-based anas platyrhynchos optimization (FFRD-APO) for feature selection and ensemble empirical mode decomposition (EEMD) for signal decomposition. Utilizing the St. Petersburg INCART 12-lead arrhythmia database, the PRF-DCNN model achieved superior performance metrics: an area under the curve-receiver operating characteristic (AUC-ROC) of 0.97, accuracy of 0.95, precision of 0.93, recall of 0.92, specificity of 0.97, and an F1 score of 0.93. The PRF effectively mitigated overfitting, ensuring robust and reliable classification across varied patient demographics. The model demonstrated significant improvements over traditional methods, offering an efficient solution for real-time cardiac monitoring and diagnosis. This study underscores the potential of PRF-DCNN in enhancing automated arrhythmia detection and lays the groundwork for future research to optimize and validate this approach in diverse clinical settings.
A novel model to detect and categorize objects from images by using a hybrid machine learning model
Authors: Sethi Nilambar , Rama Raju Vetukuri Venkata Siva , Lokavarapu Venkata Srinivas , Devareddi Ravi Babu , Reddy Shiva Shankar , Nrusimhadri Silpa
Published: November 2024

As humans, we can easily recognize and distinguish different features of objects in images due to our brain’s ability to unconsciously learn from a set of images. The objectives of this effort are to develop a model that is capable of identifying and categorizing objects that are present within images. We imported the dataset from Keras and loaded it using data loaders to achieve this. We then utilized various deep learning algorithms, such as visual geometry group (VGG)-16 and a simple net-random forest hybrid model, to classify the objects. After classification, the accuracy obtained by VGG16 and the hybrid model was 84.7% and 89.6%, respectively. Therefore, the proposed model successfully detects objects in images using a simple net as a feature extractor and a random forest for object classification, achieving better accuracy than VGG16.
Towards efficient knowledge extraction: Natural language processing-based summarization of research paper introductions
Authors: Chaudhari Nikita , Vora Deepali , Kadam Payal , Khairnar Vaishali , Patil Shruti , Kotecha Ketan
Published: November 2024

Academic and research papers serve as valuable platforms for disseminating expertise and discoveries to diverse audiences. The growing volume of academic papers, with nearly 7 million new publications annually, presents a formidable challenge for students and researchers alike. Consequently, the development of research paper summarization tools has become crucial to distilling crucial insights efficiently. This study examines the effectiveness of pre-trained models like text-to-text transfer transformer (T5), bidirectional encoder representations from transformers (BERT), bidirectional and auto-regressive transformer (BART), and pre-training with extracted gap-sentences for abstractive summarization (PEGASUS) on research papers, introducing a novel hybrid model merging extractive and abstractive techniques. Comparative analysis of summaries, recall-oriented understudy for gisting evaluation (ROUGE) and bilingual evaluation understudy (BLEU) score evaluations and author evaluation help evaluate the quality and accuracy of the generated summaries. This advancement contributes to enhancing the accessibility and efficiency of assimilating complex academic content, emphasizing the importance of advanced summarization tools in promoting the accessibility of academic knowledge.
Financial text embeddings for the Russian language: a global vectors-based approach
Authors: Malyshenko Kostyantyn A. , Anashkin Dmitriy
Published: November 2024

The article presents a software implementation of the linguistic embedding method for the Russian language, based on the global vectors for word representation (GloVe) model. The GloVe method allows to obtain word vectors that reflect their semantic and syntactic properties. The resulting vector model can be used in various natural language processing (NLP) tasks, such as machine translation and text clustering. The article describes the architecture of software that implements a method similar to the GloVe algorithm for Russian-language financial texts. The mechanisms used to train the model as well as to compute word vectors are described. Testing with typical classification methods demonstrated that the developed program generates accurate vector representations of Russian-language texts, proving effective in various NLP tasks. This work is one of the first studies devoted to the software implementation of the GloVe method for the Russian language using learning algorithms based on sparse matrices. The results of this study can be used in various NLP tasks, such as machine translation and text clustering.
Smart contracts vulnerabilities detection using ensemble architecture of graphical attention model distillation and inference network
Authors: Preethi Preethi , Ulla Mohammed Mujeer , Anni Ashwitha , Murthy Pavithra Narasimha , Renukaradhya Sapna
Published: November 2024

Smart contracts are automated agreements executed on a blockchain, offering reliability through their immutable and distributed nature. Yet, their unalterable deployment necessitates precise preemptive security checks, as vulnerabilities could lead to substantial financial damages henceforth testing for vulnerabilities is necessary prior to deployment. This paper presents the graphical attention model distillation and inference network (GAMDI-Net), a pioneering methodology that significantly enhances smart contract vulnerability detection. GAMDI-Net introduces a unique graphical learning module that employs attention mechanism networks to transform complex contract code into a smart graphical representation. In addition to this a dual-modality model distillation and mutual modality learning mechanism, GAMDI-Net excels in synthesizing semantic and control flow data to predict absent bytecode embeddings with high accuracy. This methodology not only improves the precision of vulnerability detection but also addresses scalability and efficiency challenges, reinforcing trust in the deployment of secure smart contracts within the blockchain ecosystem.
Bring your own device readiness and productivity framework: a structured partial least square approach
Authors: Nik Rosli Nik Nur Izzati , Mohamad Rosman Mohamad Rahimi , Alias Noor Rahmawati , Mohd Shukry Amira Idayu , Razlan Noor Masliana , Alimin Noor Azreen
Published: November 2024

Bring your own device (BYOD) is defined as the practice that allow users to bring their private owned devices to organizations or institutions. BYOD bring benefits to both organizations and education sector in terms of cost efficient and productivity enhancement. However, there is a dearth of research on the determinant and impact of BYOD adoption in the context of educational sector. Therefore, the purpose of this study is to develop a BYOD readiness and impact framework based on four dimensions, namely technological readiness, individual readiness, contextual readiness, and organisational readiness. This study employed a quantitative research approach, utilizing an online survey questionnaire as the primary research instrument. Findings were analysed based on descriptive and inferential statistics using statistical package for social sciences (SPSS) version 26 and SmartPLS version 4.0. The findings shows that individual readiness, contextual readiness, and organisational readiness have a positive and significant relationship with BYOD adoption, while technological readiness proof to be an insignificant predictor. Subsequently, BYOD adoption also proven as a positive and significant predictor capable to improve user’s productivity.
Mobile robot localization using visual odometry in indoor environments with TurtleBot4
Authors: Singh Gurpreet , Goyal Deepam , Kumar Vijay
Published: November 2024

Accurate localization is crucial for mobile robots to navigate autonomously in indoor environments. This article presents a novel visual odometry (VO) approach for localizing a TurtleBot4 mobile robot in indoor settings using only an onboard red green blue – depth (RGB-D) camera. Motivated by the challenges posed by slippery floors and the limitations of traditional wheel odometry, an attempt has been made to develop a reliable, accurate, and low-cost localization solution. The present method extracts oriented FAST and rotated BRIEF (ORB) features for feature extraction and matching using brute-force matching with Hamming distance. The essential matrix is then computed using the 5-point algorithm and decomposed to recover the relative rotation and translation between poses. The absolute pose is obtained by chaining the incremental motions estimated from VO. Through experimentation and comparison with wheel odometry, the findings demonstrate the effectiveness of our VO system, achieving a positional accuracy with minimal error of 4-5%. The article also compares VO with wheel odometry and shows the advantages of using a visual approach, especially in environments with slippery floors where wheel slippage causes large odometry errors. Overall, this work presents an effective VO system for reliable, accurate, and low-cost localization of TurtleBot4 in indoor environments without relying on external infrastructure.
Method for developing and partitioning graph-based data warehouses using association rules
Authors: Labzioui Redouane , Letrache Khadija , Ramdani Mohammed
Published: November 2024

The evolution of modern databases has led to a variety of not only structured query language (NoSQL) models, particularly graph-oriented-databases. This growth has encouraged businesses to explore graph-based business intelligence (BI) solutions. This paper explores three essential aspects in the domain of graph warehouse: the establishment of efficient graph warehouses, the significance of data historization, and the development of effective strategies for graph partitioning. It starts by building a BI system within a graph database. Subsequently, the paper emphasizes the pivotal role of data historization, highlighting the slowly graph changing dimension (SGCD) approach as a versatile framework for accommodating varied dimensional changes, additionally; the paper introduces a novel partitioning strategy utilizing association rules algorithms, for optimized and scalable graph warehouse management.
A review and bibliometric analysis of traceability system development in the agricultural and food sector in Indonesia
Authors: Siregar Yusnan Hasani , Purwandoko Pradeka Brilyan , Harsonowati Wiwiek , Nanda Muhammad Achirul , Tjahjohutomo Rudy , Budiman Diana Atma , Rahmawati Laila , Susanti Novita Dwi
Published: November 2024

Several technologies and methods for traceability systems in the agriculture and food sectors have developed rapidly in recent decades. There has been an increase in traceability system research in many developing countries, including Indonesia. Our review collects data from the Scopus database to study the development and dynamics of research on traceability systems and to identify emerging technological trends in the field. This paper uses bibliometric analysis by VOSviewer to find out studies regarding traceability. Our findings reveal traceability system research in Indonesia encompasses 1,264 documents within the Scopus database from 1998 to 2022. The number of studies on traceability systems has increased significantly after 2016. Most scholarly articles on traceability technology are disseminated as conference proceedings. These traceability systems have been established and are widely adopted to ensure the quality and safety of agricultural and food products, monitor species diversity, and oversee environmental parameters. The objective of the user influences the development of the traceability system. Technologies such as deoxyribonucleic acid (DNA) barcoding, unmanned aerial vehicles (UAVs), satellites, wireless sensor networks (WSNs), blockchain, product tagging, spectroscopy, and smart packaging rapidly advance to enhance traceability capabilities.
Lung cancer prediction with advanced graph neural networks
Authors: Moozhippurath Bineesh , Natarajan Jayapandian
Published: November 2024

This research aims to enhance lung cancer prediction using advanced machine learning techniques. The major finding is that integrating graph convolutional networks (GCNs) with graph attention networks (GATs) significantly improves predictive accuracy. The problem addressed is the need for early and accurate detection of lung cancer, leveraging a dataset from Kaggle's "Lung Cancer Prediction Dataset," which includes 309 instances and 16 attributes. The proposed A-GCN with GAT model is meticulously engineered with multiple layers and hidden units, optimized through hyperparameter adjustments, early stopping mechanisms, and Adam optimization techniques. Experimental results demonstrate the model's superior performance, achieving an accuracy of 0.9454, precision of 0.9213, recall of 0.9743, and an F1 score of 0.9482. These findings highlight the model's efficacy in capturing intricate patterns within patient data, facilitating early interventions and personalized treatment plans. This research underscores the potential of graph-based methodologies in medical research, particularly for lung cancer prediction, ultimately aiming to improve patient outcomes and survival rates through proactive healthcare interventions.
Kimball data warehouse for the sales analysis process in a manufacturing business in Perú
Authors: Vidal Carlos Palomino , Obregon Patricia Condori
Published: November 2024

The main goal of this research is to demonstrate that the use of innovative technology like business intelligence (BI) in a specific type of business significantly impacts their sales processes, enhancing decision-making, promotional strategies, and consequently customer loyalty and sales growth. The case study is a manufacturing business located in Lima, Peru. The information requirements of this business were analyzed, and a data mart model was created using the Kimball methodology. This multidimensional model enabled the comparison of client sales trends to propose new promotions and marketing strategies. The data analysis used to evaluate the results included hypothesis testing, analysis of employee responses to questionnaires to measure the impact of technology use on sales processes, and data reviews to assess sales increases both before and after the implementation of this technology. In both cases, the approval of the BI technology by the employees was satisfactory, and the increase in sales quantity was significant.
Efficient deep learning approach for enhancing plant leaf disease classification
Authors: Belmir Meroua , Difallah Wafa , Ghazli Abdelkader
Published: November 2024

The widespread occurrence of plant diseases is a major factor in the reduction of agricultural output, affecting both crop quality and quantity. These diseases typically begin on the leaves, influenced by alterations in plant structure and growing techniques, and can eventually spread over the entire plant. This results in a notable decrease in crop variety and yield. Successfully managing these diseases depends on accurately classifying and detecting leaf infections early, which is essential for controlling their spread and ensuring healthy plant growth. To address these challenges, this paper introduces an efficient approach for detecting plant leaf diseases. A concatenation of pre-trained convolutional neural networks (CNN) for enhanced plant leaf disease using transfer learning technique is implemented, with a specific focus on accurate early detection, utilizing the comprehensive new plant diseases dataset. The combined residual network-50 (ResNet-50) with densely connected convolutional network-121 (DenseNet-121) architecture aims to provide an efficient and reliable solution to these critical agricultural concerns. Various evaluation metrics were utilized to evaluate the robustness of the proposed hybrid model. The proposed ResNet-50 with the DenseNet-121 hybrid model achieved a rate of accuracy of 99.66%.
An evaluation model of website testing framework based on ISO 25010 performance efficiency
Authors: Kurniasari Dias Tri , Rochimah Siti
Published: November 2024

Testing is an important aspect of software development. Automation testing is now widely used to achieve better and more efficient results. Various automation testing frameworks are available in the market. However, one of the major challenges is determining which automation testing framework is suitable for testing. This study proposes an evaluation model for evaluating web automation testing frameworks based on seven performance efficiency factors to address this issue. The model evaluates five types of transactions commonly used on the web; CRUD, Get Massive Data, search, file upload, and file download. In addition, the tested frameworks are categorized as good, medium, and low. To measure the success of the research, expert weighting was also used. Based on the results obtained for all types of transactions, almost all classifications between the experimental results and weighting were in the same class. Although the model was found to be effective with a 100% accuracy rate, it had an accuracy rate of 80% for upload transactions. The outcomes of this study serve as a valuable reference for choosing suitable software for both tested frameworks and other software applications. In future studies focus on narrowing the selection based on not only performance but also functionality and ease of use.
Recognizing geographical locations using a GAN-based text-to-image approach
Authors: Ibrahim Dina M. , Al-Shargabi Amal A.
Published: November 2024

Generating photo-realistic images that align with the text descriptions is the goal of the text-to-image generation (T2I) model. They can assist in visualizing the descriptions thanks to advancements in machine learning algorithms. Using text as a source, generative adversarial networks (GANs) can generate a series of pictures that serve as descriptions. Recent GANs have allowed oldest T2I models to achieve remarkable gains. However, they have some limitations. The main target of this study is to address these limitations to enhance the text-to-image generation models to enhance location services. To produce high-quality photos utilizing a multi-step approach, we build an attentional generating network called AttnGAN. The fine-grained image-text matching loss needed to train the AttnGAN’s generator is computed using our multimodal similarity model. With an inception score of 4.81 on the PatternNet dataset, our AttnGAN model achieves an impressive R-precision value of 70.61 percent. Because the PatternNet dataset comprises photographs, we’ve added verbal descriptions to each one to make it a text-based dataset instead. Many experiments have shown that AttnGAN’s proposed attention procedures, which are critical for text-to-image production in complex circumstances, are effective.
Machine learning based prediction of production using real time data of a point bottom sealing and cutting machine
Authors: Mary Diana Fathima Rani Irudaya , Rajendran Subha , Muthusamy Selvadass
Published: November 2024

The packaging sector utilizes polypropylene based flexible materials for diverse product packaging with customization options in size and design achieved through advanced flexographic printing and point bottom sealing and cutting machines. Accurately estimating production time and quantity is vital for efficient planning and cost estimation, with factors like material dimensions, thickness, and cutting machine speed influencing production output. Understanding the intricate relationship between these parameters is essential for comprehending their impact on production time and quantity. Predicting production quantity before production begins helps in determining machine runtime and associated costs. In large-scale production systems, machine learning (ML) has proven to be a useful tool for resource allocation and predictive scheduling. An attempt has been made in this paper to develop an intelligent model for predicting the yield of a cutting machine using artificial neural network (ANN), support vector regression (SVR), regression tree ensemble (RTE) and gaussian process regression (GPR). The most crucial features for prediction were identified and the hyperparameters of the ML models were optimized to create efficient models for prediction. A comparative analysis of the four models revealed that the GPR model was simple and effective with least training time and prediction error.