bluuebunny/crossref_metadata_2025 huggingface dataset:
Neuro-adaptive hierarchical sliding mode control for a 3-wheeled mobile robot with disturbance rejection
Authors: Vizarreta Mamani Jose Carlos , Vera Huamani Bladimir Leonel , Torres Raul Sulla
Published: January 2025

High tracking accuracy and fast convergence are essential features in the control of wheeled mobile robots for autonomous navigation applications. However, the design of a control system for these robots faces significant challenges due to the inherent complexity of their nonlinear dynamics and their non-holonomic underactuated nature. This article introduces a novel control framework for trajectory tracking of a three-wheeled mobile robot (3WMR), considering external and internal disturbances. To address the uncertain nonlinear and underactuated non-holonomic dynamics of the 3WMR, an adaptive hierarchical fast terminal sliding mode control (AHFTSMC) strategy is proposed. In this approach, an adaptive neural network scheme adjusts the sliding surface coefficients in real time to minimize tracking errors and mitigate the chattering phenomenon. In addition, a finite time disturbance observer (FTDO) is designed to accurately estimate and compensate for unknown lumped disturbances, which helps to improve the disturbance rejection capability. The stability of the closed-loop system is demonstrated using Lyapunov theory. The proposed control approach is validated by numerical simulations, and a comparative analysis of recent hierarchical sliding mode controllers (SMC) is performed. The results demonstrate that the proposed approach achieves superior performance in terms of fast convergence and tracking accuracy, which are crucial features for the autonomous navigation of mobile robots.
Authenticated image encryption using robust chaotic maps and enhanced advanced encryption standard
Authors: Chothe Rupaliben V. , Ugale Sunita P. , Chandwadkar Dinesh M. , Shelke Shraddha V.
Published: January 2025

The ability of advanced encryption standard (AES) algorithm to protect information systems has given cryptography a new dimension. Recent encryption approaches to enhance randomness include the use of chaotic algorithms, which provide resistance to differential attacks. We have proposed the application of robust chaotic maps in the block cipher to design a secure authenticated encryption scheme to get advantages of both. The chaotic sequence is generated using hyperbolic tangent map and added to input image initially to increase randomness. The basic 256-bit AES key is generated using the robust Renyi modulo map. An additional 128-bit key enhances security. Instead of static values used in AES, dynamic initialization vector (IV), different for every image will be generated. The results are mathematically verified using various security parameters. The algorithm provides lower values of peak signal-to-noise ratio (PSNR) (7.81 to 9.10 dB) for encrypted images and higher dissimilarities between input and encrypted image histograms. Thus, it is highly resistant to statistical attacks. The experimental results and their comparison prove the superiority of our proposed cryptosystem against statistical, differential and brute-force attacks. Thus, the novel multi-chaotic AES-GCM (galois/counter mode) algorithm can be used for color image encryption in military and industrial applications demanding high data security and authentication.
Convolutional neural network for speech emotion recognition in the Moroccan Arabic dialect language
Authors: Ouali Soufiyan , El Garouani Said
Published: January 2025

Extracting the speaker's emotional state has become an active research topic lately due to the demand for more human interactive applications. This field of research has noted significant advancement, especially in the English language, owing to the availability of massive speech-labeled corpora. However, the progress of analogous methodologies in the Arabic language is still in its infancy stages. This paper presents a new massive natural speech emotion dataset and a speech recognition model for the Moroccan Arabic language. Four primary emotion labels were selected: happy, sad, angry, and neutral. Various spectral features, such as the mel-frequency cepstral coefficient (MFCC), were extracted and tested to determine the optimal feature combination. A convolutional neural networks (CNNs) model was built and trained on our dataset. The results were compared between spectral features individually and combined with the CNN model resulting in the selection of MFCC, root-mean-square (RMS), mel-scaled spectrogram, and spectral, as optimal spectral features for our dataset. This selection yielded significant results, with an accuracy of 99.55% for emotion recognition, outperforming the existing research.
Sustainable supply chain modeling: a review based on the application of the system dynamics approach
Authors: Kurniasih Julia , Abas Zuraida Abal , Asmai Siti Azirah , Wibowo Agung Budhi
Published: January 2025

Sustainable supply chains, evolving with supply chain 5.0 revolution, are crucial for achieving sustainable development goals (SDGs) by balancing economic growth, environmental protection, and social responsibility. They help reduce environmental impacts, promote ethical labor practices, and ensure financial viability. Sustainable supply chains involve complex interactions and external influences. The system dynamics approach effectively captures these intricate interactions through feedback loops and non-linear relationships. This review seeks to identify issues in modeling sustainable supply chains using system dynamics and offer insights for developing sustainable, flexible, responsive, and resilient models. This paper reviews literature from 2020 to 2023 using thematic analysis. It examines dynamics, behaviors, management, sustainability strategies, decision-making, and future directions for sustainable supply chain modeling. The findings suggest that a comprehensive framework can enhance management practices, support policymaking, and promote sustainability. Integrated risk management is essential for resilient, adaptable supply chains, while financial viability and scalability are essential for the widespread adoption of sustainability practices. Understanding the roles of various actors and integrating supply chain components can improve support systems, and exploring green energy, technology adoption, and consumer behavior can advance sustainability goals. Future research should also better integrate sustainability aspects and explore a broader range of literature for deeper insights.
Automated adversarial detection in mobile apps using API calls and permissions
Authors: Somanathan Pillai Sanjaikanth E Vadakkethil , Vallabhaneni Rohith , Vaddadi Srinivas A , Addula Santosh Reddy , Ananthan Bhuvanesh
Published: January 2025

Android mobile phones’ growing popularity has led to developers creating more malicious apps, which can be included in third-party arcades as protected applications. Detecting these malware applications is challenging due to time-consuming and high-cost techniques. This study proposes a robust deep learning (DL) model for detecting adversarial third-party apps using adaptive feature learning. The strategy involves preprocessing raw apk files, extracting permission behavioral features, and using the proposed spatial dropout-assisted convolutional autoencoder (SD_ConvAE) model to determine if the app is benign or malignant. The approach is simulated using a Python tool and assessed using various measures like accuracy, recall, weighted F-score (W-FS), false discovery rate (FDR), and kappa coefficient. The overall accuracies achieved by the developed techniques are about 99.6% and 99% for detecting benign and malignant apps, respectively.
Classification of traditional Joha rice using deep neural network
Authors: Kalita Hemanta , Rahman Mirzanur
Published: January 2025

Rice is the major food for the most of the people in the world. It is grown comfortably in a rain fed area. Rice is called a kharif crop in India. According to the International Rice Research Institute (IRRI) classification rice is classified as Ahu, Sali, Boro and Hill rice. In north eastern India the Joha rice is mainly cultivated in the state of Assam. There are many varieties of Joha rice like Kunkuni, Rampal, Manipuri, Tulshi, and Keteki. But it is quite complex for the farmers as well as the common people to differentiate these type of Joha rice due to their morphological structure. Initially this type of classification done by naked eyes or used some laboratory experiments. But due to the tiredness and some external factors it is not fruitful. Some of the image classification technologies are used in classification as well identification of rice in different research work using MATLAB. At present deep learning plays a tremendous role in image analysis in agricultural domain. Here in this paper we take two verities of Joha rice which are closer in morphological structure and cannot be separated by our naked eyes. Using deep neural network, binary classification is done in these Joha rice.
Visual treatment with AR for children with dysphasia
Authors: Amado Misael Lazo , Paico Campos Meyluz Monica
Published: January 2025

The language disorder known as dysphasia significantly affects the ability to communicate effectively, presenting challenges in both comprehension and expression of language. To address this issue, the development of a visual treatment using augmented reality (AR) specifically designed for children with dysphasia has been proposed. The methodology selected for this project is analysis, design, development, implementation, and evaluation (ADDIE), an innovative methodology that encompasses analysis, design, development, implementation and evaluation. This methodology is perfectly adapted to the needs of the project, allowing a systematic and complete approach at all stages of the process of creating the visual treatment. The results obtained show that the visual treatment with AR has been positively evaluated by development experts and dysphasia specialists. Its innovative capacity to assist children with this disorder in health and educational settings is highlighted. This approach provides an effective tool to improve the communication and language development of children affected by dysphasia, offering new opportunities for their learning and growth. Its implementation in healthcare and educational settings could have a significant impact on the quality of life and development of these children.
Deep learning techniques for satellite image classification
Authors: Musali Suresh Kumar , Janthakal Rajeshwari , Rajasekhar Nuvvusetty
Published: January 2025

Because of its wide range of uses in computer vision applications, including image retrieval, remote sensing, object recognition, scene analysis, and surveillance, image classification has attracted a lot of attention. Assigning appropriate class labels to images according to their contents is the primary objective of image classification. In the domain of remote sensing, image classification and analysis play crucial roles in both military and civil applications. Conventional methods for scene analysis and remote sensing depended on low-level representations of features, such as those of color and texture. However, recent advancements have shifted towards the use of convolutional neural networks (CNNs), which have shown promising results in remote sensing and scene classification tasks. In light of effectiveness of deep learning (DL) models, this research aims to develop four DL models by fine tuning already existing DL models-CNNs, residual neural network (ResNet), visual geometry group (VGG-19), network mobile net V2 based model and classifies satellite images of RSI-CB256 data set in to four classes namely cloudy, desert, green_area and water. For the RSI-CB256 dataset, appropriate network structures are explored in this research to get good performance. The CNN, ResNet and VGG-19 base models achieved an accuracy of 90.48, 92.68 and 91.18 respectively. While the mobile net V2 based model outperformed the other three models with 96.83% accuracy.
Implementation of perspective-n-point techniques and YOLOv5 algorithm based on surveillance camera for localization
Authors: Pawako Siripong , Khaewnak Nopparut , Srisertpol Jiraphon
Published: January 2025

The technology of processors has advanced significantly, resulting in smaller and more powerful devices with much processing capability. Particularly, camera technology has witnessed extensive research in utilizing images for various applications. Currently, surveillance cameras are widely used for security purposes when abnormal events occur. In this research, the benefits of utilizing data from surveillance cameras are explored to assist in determining the position of a moving robot using the perspective-n-point (PnP) technique. the scale factor, which varies, has been improved by integrating checks with the YOLOv5 algorithm. This algorithm employs a custom model to specifically detect the robot of interest, enabling the determination of its real-world position using multiple surveillance cameras. These cameras have different perspectives within the same area. Considering the deviation caused by determining the position from a single viewpoint, multiple cameras are employed to mitigate this issue.
Tomato leaf disease detection using Taguchi-based Pareto optimized lightweight CNN
Authors: Das Bappaditya , Raghuvanshi C. S.
Published: January 2025

The prospect of food security becoming a global danger by 2050 due to the exponential growth of the world population. An increase in production is indispensable to satisfy the escalating demand for food. Considering the scarcity of arable land, safeguarding crops against disease is the best alternative to maximize agricultural output. The conventional method of visually detecting agricultural diseases by skilled farmers is time-consuming and vulnerable to inaccuracies. Technology-driven agriculture is an integral strategy for effectively addressing this matter. However, orthodox lightweight convolutional neural network (CNN) models for early crop disease detection require fine-tuning to enhance the precision and robustness of the models. Discovering the optimal combination of several hyperparameters might be an exhaustive process. Most researchers use trial and error to set hyperparameters in deep learning (DL) networks. This study introduces a new systematic approach for developing a less sensitive CNN for crop leaf disease detection by hyperparameter tuning in DL networks. Hyperparameter tuning using a Taguchi-based orthogonal array (OA) emphasizes the S/N ratio as a performance metric primarily dependent on the model’s accuracy. The multi-objective Pareto optimization technique accomplished the selection of a robust model. The experimental results demonstrated that the suggested approach achieved a high level of accuracy of 99.846% for tomato leaf disease detection. This approach can generate a set of optimal CNN models’ configurations to classify leaf disease with limited resources accurately.
Leveraging image fusion and transfer learning for enhanced tumor diagnosis
Authors: Narisetty Nirmalajyothi , Babu Kunda Suresh , Banala Sneha , Kandi Akash Reddy , Nukarapu Sreeja , Mekala Jagruthi
Published: January 2025

In India, the number of brain tumor cases are increasing rapidly. Compared to the current treatment approaches there is a need for efficient and advanced diagnostic approaches. Doctor primarily use magnetic resonance imaging (MRI) and computed tomography (CT) scans for diagnosis, each with its own set of advantages and limitations. Accurate diagnosis of brain tumors requires detailed information about the tumor’s type, size, location, and proliferation rate. But all this information must be estimated accurately and precisely. Even though MRI and CT provide anatomical information regarding tumors, they may not always correctly classify the tumor hence, often requiring the additional biopsy for more detailed analysis. This paper aims to demonstrate the fusion of MRI and CT scans by generating a fused image using transfer learning with convolution neural networks (CNN) that possesses all the important information from both modalities yields better results than using MRI and CT scans alone. This fused scan has the potential to highlight subtle details that may be overlooked on individual scans. This proposed system retains all the best possible functionalities for medical approaches to brain tumor detection. It paves the way to offer a much more impactful healthcare approach to patients by delivering accurate and reliable statistics, enhancing the diagnostic accuracy (96.43%) precedent to any single modality.
A review based on sentimental analysis for Hindi language
Authors: Vasani Vaibhav Prakash , Ambhaikar Asha
Published: January 2025

The 'Mother Tongue' of India is considered to be Hindi. The demand for Hindi content rises as the Indian population grows. Hindi is typically used when Indians express their opinions about something. This generates data in Hindi that may later be analyzed. Sentiment analysis (SA) is a category for one of the analyses. SA examines how a speaker or author's emotions, sentiments, and attitudes are expressed in a particular text. SA, occasionally referred to as "Opinion Mining," is a sort of contextual mining that uses algorithmic recognition and categorization to detect the viewpoints expressed in a text and determine whether they are positive, negative, or neutral. Word polarity, that SA gives, enables us to assess the text's impact and decide if it is good or negative. It is possible to implement SA using a variety of methods. There was a lot of study on SA in the English language, but not as much for Hindi. In this essay, SA studies regarding the Hindi language are reviewed and analyzed.
Anomaly based detection in time series data on IoT systems using statistical models
Authors: Boujrad Mouna , Kasmi Mohammed Amine , Ouerdi Noura , El Mazoui Nadori Yasser Lamlili
Published: January 2025

The internet of things (IoT) has become a real revolution that represents technological innovation in all domains, it becomes more and more integrated into human activities starting from personal needs, to professional eras including industry, logistics, healthcare. Yet this technology didn’t only bring advantages to all industries, it also creates new challenges mostly security-related, due to the specifications of the IoT environment: its heterogeneity, the restricted resources, and the continuous enormous sensitive data generated and exchanged on the IoT ecosystem. In this paper, we propose a study of statistical models (autoregression, moving-average, autoregression-movingaverage, autoregression integrated movingaverage, seasonal autoregression integrated movingaverage) to build an anomaly-based detection model for times series data, to detect abnormal behavior that can be explained by a sensor failure, or a compromised sensor. The proposed anomaly-based detection relies on defining the data behavior and creating a profile on the assumed normal state, the created profile will be used as knowledge to predict future values to which real records will be compared, any deviation from the predicted data will be considered as abnormal, that indicates an anomaly has occurred on the sensor or the exchanged data. The proposed approach will help improve accuracy, reliability, trustworthiness, and data integrity.
Conception of speech emotion recognition methods: a review
Authors: Benzirar Abdelkader , Hamidi Mohamed , Bouami Mouncef Filali
Published: January 2025

In recent years, speech emotion recognition (SER) has emerged as a pivotal tool for understanding and enhancing human-computer interaction (HCI), thus garnering significant attention from researchers due to its diverse range of applications. However, SER systems encounter numerous challenges, particularly concerning the selection of appropriate features and classifiers for emotion recognition. This paper provides a concise survey of the field of speech emotion recognition, elucidating its classification algorithms and various feature extraction techniques across multiple languages. Additionally, it explores the limitations and weaknesses inherent in speech emotion recognition systems. Furthermore, the paper endeavors to categorize recent research endeavors in Arabic speech emotion recognition, employing diverse modeling approaches and extraction methods.
Value group classifier model for ethical decision-making
Authors: Bhatnagar Shweta , Agrawal Rashmi
Published: January 2025

Decision-makers refer to ethics or moral philosophy during times of ethical dilemma. Dilemmas are situations of inner conflict, which require a methodical approach. Diversity in viewpoints on moral decisions ensures there cannot be a fixed solution for ethical dilemmas as in the case of numerical problems. Existing ethical and sustainable decision models for businesses are not automated because of a lack of a comprehensive list of dilemmas. To resolve this gap, an AI model was trained to classify all dilemmas into three value groups by using a support vector classifier (SVC). The model provided scaffolding to the ethical decision-maker by suggesting relevant human values applicable to the dilemma. The design works on the ethical theory of stakeholder management, which includes sustainable business goals. The study was conducted with 30 students and 30 adults to identify their dilemmas. The dilemma dataset was used to train an ethical decision-support tool, called the value group classification (VGC) model. The model achieved a score of 0.52 on performance. The VGC model overcomes the black-box biases of similar machine-learning models by allowing human autonomy in ethical decisions.
Empower BreastNet: breast cancer detection with transfer learning VGG Net-19
Authors: Joshi Vaishali M. , Dandavate Prajkta P. , Ramamurthy Rashmi , Mirajkar Riddhi , Thune Neeta N. , Shinde Gitanjali R.
Published: January 2025

Breast cancer is a major cause of death among women globally, making early detection crucial for effective treatment. This study introduces a new deep learning (DL) method using transfer learning (TL) to automatically detect and diagnose breast cancer. TL improves performance on new tasks by using knowledge from previous tasks. In this study, we use pre-trained convolutional neural networks (CNNs) like AlexNet, ResNet50, visual geometry group (VGG)-16, and VGG-19 to extract features from the breast cancer wisconsin (BCW) diagnostic dataset. We measure the model's success with accuracy, sensitivity, specificity, precision, and F-score. The results show that the VGG-19 model, when applied with TL, performs best for diagnosing breast cancer, achieving an overall accuracy of 98.75%, sensitivity of 97.38%, specificity of 98.35%, precision of 97.35%, and an F-score of 97.66%.
Application of quantum annealing solvers along with machine learning algorithms to identify online deception
Authors: Rao Borra Surya Prasada , Reddy Bhargavi Peddi , Prasad Paruchuri Baba Venkata Nageswara , Venkata Ramana Rachakulla Sai , Srinivas Onteru , Rathod Lakshmi
Published: January 2025

The rising frequency of online transactions has heightened the potential of online fraud, posing significant concerns for consumers, organizations, and financial institutions. Conventional fraud detection systems frequently inadequately handle the dynamic and shifting characteristics of fraudulent activity. The increasing menace of online fraud requires novel strategies to improve the effectiveness of fraud detection systems. This study has developed and implemented a detection framework utilizing a quantum machine learning (QML) technique that integrates support vector machines (SVM) with quantum annealing solvers. We assessed its detection performance by comparing the QML application's efficacy against twelve distinct ML techniques. This study examines the integration of classical ML algorithms with quantum annealing solutions as an innovative approach to enhance online fraud detection. This study examines the possible integration of ML and quantum computing to tackle the rising issues of fraudulent activities in online transactions, as existing solutions are inadequate. This work seeks to illustrate the viability and efficacy of using these technologies, including quantum annealing to enhance the intricate decision-making processes involved in fraud detection. We offer insights on the performance, speed, and adaptability of the integrated model, highlighting its potential to transform online fraud detection and enhance cyber security measures.
Forecasting bitcoin price fluctuations: a time series analysis approach for predictive modelling
Authors: Batsi Amine , Biniz Mohamed , El Ayachi Rachid
Published: January 2025

The recent fluctuations in the price of Bitcoin, marked by both significant increases and subsequent decreases, has attracted media and public attention. Consequently, many researchers have explored various factors influencing Bitcoin’s price and the underlying patterns of its fluctuations. This paper aims to predict and analyses the factors affecting Bitcoin’s price by creating a unique dataset with nearly 40 features and deriving two child datasets using correlation and mutual information as feature selection techniques. Additionally, we train machine learning models, including linear regression (LR), extreme gradient boosting (XGBoost), support vector regression (SVR), Facebook Prophet (FB Prophet), and bidirectional gated recurrent unit (BI-GRU), to predict Bitcoin’s next-day price. The model’s performance is evaluated using mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R2 score metrics. Our findings indicate that machine learning techniques are effective in predicting Bitcoin’s price and could be valuable for investors seeking to maximize profits.
Linguistic feature selection for personality trait identification from textual data
Authors: Singh Angad , Maheshwary Priti , Mishra Nitin Kumar , Malche Timothy
Published: January 2025

Personality identification is a common and central problem in text processing. Sensing personality is helpful for various purposes; for example, estimating users' personalities before providing them with any service is necessary. Individuality is essential in a person's nature in every outlook, for instance, in text writing. But, this remains a core challenge because of the low accuracy achieved. The proposed study solves this problem and presents a big five trait identification technique from text data, which applies a feature selection method to increase accuracy. This technique is called linguistic feature selection for personality trait identification (LFSPTI). This technique first finds features based on mutual information (MI), F-statistic, principal component analysis (PCA), and chi-square, then uses the genetic algorithm (GA) to select high-ranked features from all feature subsets. These four parameters provide various forms of the dataset. The experimental results exhibit that the LFSPTI method enhances the classification accuracy against the best of the competing methods by 1.18%, 0.83%, 1.61%., 1.15%, 1.82%, and 1.39% for extraversion (EXT), neuroticism (NEU), agreeableness (AGR), conscientiousness (CON), openness (OPN), and mean overall personality traits, respectively.
Strengthening resilience against cyberattacks in Moroccan Universities through AHP, TOPSIS, and ITIL v4
Authors: Chahid Abdelilah , Ahriz Souad , El Guemmat Kamal , Mansouri Khalifa
Published: January 2025

This study addresses the complex challenges of digital transformation in higher education by enhancing IT governance to combat cyber threats in Moroccan universities. By adopting a hybrid multi-criteria decision-making (MCDM) framework, the research combines the analytic hierarchy process (AHP) and the TOPSIS method to evaluate fourteen IT governance criteria, categorized into structural, procedural, and relational dimensions. Using TOPSIS, the study identifies the most relevant SVC services from the ITIL v4 value chain for each category, with the aim of developing an optimized strategic approach against cyberattacks. The input from ten academic experts was crucial in prioritizing these services. The results show that SVC services A5 and A2 are fundamental for optimizing the resources of structural and procedural mechanisms, while A4 and A2 play a key role in relational mechanisms. This strategic alignment enhances the resilience of Moroccan universities to cyber threats by ensuring a more efficient allocation of security resources and providing a robust defense against potential attacks.
Optimized dense convolutional network with conditional autoregressive value-at-risk for chronic kidney disease detection through group-based search
Authors: Aher Chetan Nimba , Date Archana Rajesh , Vasekar Shridevi S. , Tupe-Waghmare Priyanka , Chavan Amrapali Shivajirao
Published: January 2025

Chronic kidney disease (CKD) is the gradual decrease in renal functionality that leads to kidney failure or damage. This disease is the most severe worldwide health condition that kills numerous people every year as an outcome of hereditary factors and worse lifestyles. As CKD progresses, it becomes difficult to diagnose. Utilizing regular doctor consultation data for evaluating diverse phases of CKD can assist in earlier detection and timely inference. Furthermore, effectual detection methods are vital owing to an increased count of patients with CKD. Here, group search conditional autoregressive value-at-risk based dense convolutional network (GSCAViaR-DenseNet) is introduced for CKD detection. Firstly, chronic data is acquired from the dataset and Min-Max normalization is utilized to pre-process considered chronic kidney data. Thereafter, feature selection (FS) is performed based on Topsoe similarity. Lastly, CKD detection is executed by dense convolutional network (DenseNet) and group search conditional autoregressive value-at-risk (GSCAViaR) is employed to train DenseNet. However, GSCAViaR is designed by incorporating a group search optimizer (GSO) with a conditional autoregressive value-at-risk (CAViaR) model. Additionally, GSCAViaR-DenseNet acquired a maximal accuracy of about 91.5%, sensitivity of about 92.8% and specificity of about 90.7%.
Assessing fingerprinting and machine learning approaches for wireless indoor localization
Authors: Pratama Azkario Rizky , Anindya Wahyuaji Muhammad Evan , Nur Hidayat Muhammad Fadhil , Hantono Bimo Sunarfri , Siddiq Nur Abdillah
Published: January 2025

This paper presents a comparative analysis of fingerprinting and machine learning techniques for bluetooth low energy (BLE)-based localization. Two fingerprinting algorithms, namely fingerprint feature extraction (FPFE) and Bayesian estimation (BE), along with various machine learning approaches including support vector regression (SVR), ensemble learning, and instance-based learning, are investigated. The selection of techniques depends on the availability of training data or the fingerprint database, explored in both ideal scenario and real-world scenario. In ideal scenario where the system administrator can collect fingerprint data through users’ devices, FPFE emerges as the preferred algorithm, achieving superior performance with a mean error of 0.50 m. In the context of real-world scenario, where data collection from multiple devices is limited, the system administrator may gather fingerprint data for localization using one or a few specific devices. Our experiments reveal that when there is a scarcity of fingerprint data, BE and SVR exhibit acceptable performance, reaching a mean error of 1.785 m and 1.965 m, respectively.
Multifaceted approach for anticipating learner performance using parameter weightage and ensemble algorithm fusion
Authors: S Jahagirdar Shabnam Ara , Ramachandraiah Tanuja
Published: January 2025

Anticipating student performance has garnered significant attention in education research for offering early insights that enable timely interventions and personalized support, ultimately improving student success and retention rates. This research focuses on enhancing the accuracy and efficiency of student performance prediction models by employing a hybrid ensemble framework that integrates weighted feature selection with meta-learner-based approaches. A weighted feature selection method was employed to prioritize the most influential of the 23 parameters in the dataset, enhancing prediction accuracy while reducing the computational burden. These parameters were then used to build a hybrid ensemble model by combining base learners with meta-learners, systematically tuned using hyperparameter optimization. This approach aimed to further improve prediction accuracy by fusing multiple base learners, leveraging the strengths of different algorithms for more accurate predictions. The proposed hybrid model was validated across different features selected based on feature importance using random forest (RF). An accuracy of 98.38% was achieved when all 23 features were considered and an accuracy of 97.13 % was achieved when the top 10 features were used. The research highlights the significance of early prediction for prompt intervention and demonstrates how feature weighting can boost model efficacy.
BanSpEmo: a Bangla audio dataset for speech emotion recognition and its baseline evaluation
Authors: Sultana Babe , Hussain Md Gulzar , Rahman Mahmuda
Published: January 2025

Speech interfaces provide a natural and comfortable way for humans to communicate with machines. Recognizing emotions from acoustic signals is essential in audio and speech processing. Detection of emotion in speech is critical to the next generation of human-computer interaction (HCI) fields. However, a lack of large-scale datasets has hampered the progress of relevant research. In this study, we prepare BANSpEmo, a demanding Bangla speech emotion dataset consisting of 792 audio recordings totaling more than 1 hour and 23 minutes. The recordings feature 22 native speakers and each speaker uttered two sets of sentences representing six emotions: disgust, happiness, anger, sadness, surprise, and fear. The dataset consists of 12 Bangla sentences, each expressed in these six emotions. Furthermore, a series of investigations are carried out to assess the baseline performance of the support vector machine (SVM), logistic regression (LR), and multinomial Naive Bayes models on the BANSpEmo dataset presented in this study. The studies found that SVM performed best on this dataset, with an accuracy of 87.18%.
Predicting student status using machine learning by analyzing classroom behaviors with X-API data
Authors: Elouafi Abdelamine , Tammouch Ilyas , Eddarouich Souad , Touahni Raja
Published: January 2025

We explore the emergence and growing significance of educational data mining, a field dedicated to extracting valuable insights from vast datasets gathered from diverse educational environments. Utilizing the experience API (XAPI) and the Kalboard 360 online learning platform, our research presents a novel behaviorally based student performance model that evaluates the influence of student interactions on academic results. We create reliable models for precisely projecting academic success by utilizing machine learning techniques including logistic regression, k-nearest neighbors (KNNs), support vector machines (SVM), decision trees, random forests (RF), and XGBoost. The outcomes show a notable increase in categorization accuracy. Through the personalization of instruction, formative assessment support, and proactive identification of each student's unique needs to maximize their learning experience, this approach holds the potential to improve educational processes.
Investigation on OMYA with single superstrate layer
Authors: Quzwain Kamelia , Ardiansyah Oki , Nur Levy Olivia , Raniprima Sevierda , Ismail Alyani
Published: January 2025

The need for wireless communication technology is increasing rapidly. Many people are already using internet data services offered by providers or using wireless fidelity (Wi-Fi) services. One of the things that must be considered in Wi-Fi technology is that it requires antenna characteristics that have a relatively small shape and light mass. This paper aimed to improve gain and to analyze the performance of octagon microstrip Yagi antenna (OMYA) with a single superstrate layer. The antenna was designed, simulated, and measured. The experimental result presents that this concept is capable to produce a gain of 11.80 dB, a return loss of -23.24 dB, and the bandwidth is up to 800 MHz with total dimensions of 70×75 mm.
Metabolic Syndrome in Patients with Depressive Disorder: A Cross-sectional Study
Authors: Moorthy Renasre , A John Dinesh , Annamalai Melody Munusamy
Published: January 2025

Background:

Depression not only fosters the development of metabolic syndrome through behavioral, physiological, genetic, and treatment-related factors, but it also doubles the risk of experiencing metabolic syndrome. The objectives were to assess the sociodemographic and clinical profile of patients with depressive disorder, to assess the various metabolic parameters of metabolic syndrome in patients with depressive disorder, and to study the association between the severity of depression and metabolic syndrome.

Methods:

A cross-sectional study was conducted among patients diagnosed with depression (n = 160) attending the Psychiatry outpatient department of a tertiary healthcare facility in Puducherry. The Hamilton Depression Rating Scale (HAM-D) and modified National Cholesterol Education Program—Adult Treatment Panel-III (NCEP ATP-III) criteria were used to assess the severity of depression and diagnose metabolic syndrome, respectively.

Results:

The mean age at onset of depression was 31.4 years (+11.3); the duration of depression was 41.2 months (+32.5); and the severity of depression as assessed using the HAM-D was 17.9 (+6.3). The results showed that 27.5% of patients had metabolic syndrome. Factors associated with higher rates of metabolic syndrome included increasing age, female gender (79.5%), being single (25.0%), belonging to upper socioeconomic class (65.9%), non-Hindu religion (20.5%), and urban residence (72.7%) ( P < .05). Patients with metabolic syndrome had later onset (36.4 years) and longer duration (51.6 months) of depression, more severe symptoms (18.2), and were more likely to have recurrent depressive disorder or dysthymia (88.6%) ( P < .05). Furthermore, the current use of psychotropic medications (59.1%) and obesity (93.2%) were significantly associated with metabolic syndrome ( P < .05).

Conclusion:

This study reveals a high prevalence of metabolic syndrome among patients with depressive disorders linked to factors such as age, gender, marital status, socioeconomic status, religion, and urban residence. Integrated care approaches, including comprehensive screening and targeted interventions, are crucial for improving both mental and metabolic health outcomes.
A Reconsideration of Small Sample Studies and Absence of Equipoise as Ethical Issues in Research
Authors: Andrade Chittaranjan
Published: January 2025

In studies such as randomized controlled trials (RCTs), ethical requirements, inter alia, are that the study should be powered to detect the smallest clinically significant difference between treatments, and that the treatment groups should be in equipoise at the start of the trial. This article provides examples of circumstances where small sample, potentially underpowered studies may be justifiable, and where RCTs of treatments that are not in equipoise may be considered appropriate. The concepts presented may be extendable from RCTs to other study designs, as well.
Application of Mixed Methods in Childhood Trauma Research: A Study Example of Divorce-Related Trauma Research from India
Authors: Nambiar Priyanka P. , Jangam Kavita V. , Seshadri Shekhar P.
Published: January 2025

Background:

The use of mixed methods research (MMR) has become progressively important in the field of behavioral and social sciences. This article makes an argument for the combination of quantitative and qualitative methods in the study of childhood trauma. It focuses on the role of MMR in effectively exploring the traumatic life experiences and narratives of children and adolescents.

Methods:

The mixed method approach is beneficial for multi-dimensional research strategies, thereby surpassing the quantitative-qualitative divide. We also discuss the key characteristics of the mixed methods approach applicable to these behavioral studies.

Results:

This article concludes with a study example from the author’s MMR-driven doctoral research on the topic of divorce-related child custody issues in India.

Conclusion:

The implications for MMR use in future childhood trauma research are discussed.
Comparison of C-reactive Protein Versus White Blood Cell Count in Early Detection of Major Complications Post Whipple Procedure—A Prospective Study
Authors: Jayapal Loganathan , Balachandar Tirupporur Govindaswamy , Swain Sudeepta Kumar
Published: January 2025

Introduction:

Whipple’s (pancreatoduodenectomy) surgery carries significant post-operative morbidity even in specialised centres. We share our findings by comparing the inflammatory markers C-reactive protein (CRP) and white blood cell count (WBC) in early detection of post-operative complications.

Material and Methods:

In this prospective study, 56 patients who underwent the Whipple procedure in our institution between January 2021 and December 2022 were evaluated. Demographic characteristics, pre-and post-operative CRP and WBC levels, intraoperative parameters like pancreatic texture and duct diameter, intraoperative blood loss, postoperative complications, length of stay and final histopathology were evaluated. The receiver operator characteristics curve (ROC) was drawn, and the area under the curve was calculated.

Results:

Among the 32 (57.2%) patients who developed complications, 6 (10.7%) had major and 26 (46.4%) had minor complications. ROC curves revealed greatest area under curve value for POD5 CRP (AUC-1.000; P <.01) followed by POD3 (AUC-0.999; P < .01) and then POD7 (AUC-0.995; P < .01). For WBC, the greatest value was found only on POD5 (AUC-0.742; P = .003) followed by POD7 (AUC-0.716; P < .009), but not on POD3 (AUC-0.607; P = .19).

Conclusion:

C-reactive protein was better than white blood cell counts in the early detection of postoperative complications.
Some Tumours Can Drive You Nuts: An Enigmatic Case of Metastatic Lymph Node
Authors: Muthukumarappan Kumudavalli , Lakshmi Swetha , Subramanyan Annapurneswari , Mani C. S.
Published: January 2025

Introduction:

NUT carcinoma (NC) is a rare aggressive undifferentiated malignant neoplasm. It can arise from midline and non-midline structures with no predilection for age and gender. NC is due to NUT gene rearrangement (Chromosomal translocation). The diagnosis is established by NUTM1 protein expression by IHC / FISH / NGS RNA sequencing Given the rarity and novelty, herein we discuss our case of NC.

Aim:

To establish a diagnosis of NUT carcinoma and its diagnostic challenges.

Clinical History:

A 23-year-old young male presented with complaints of right neck swelling.

Results:

Histopathology revealed a poorly differentiated malignant neoplasm. Immunohistochemistry showed diffuse positivity for NUTM1 protein, hence was reported as NUT carcinoma.

Conclusion:

It is an aggressive malignant neoplasm with a dismal prognosis. An integrated approach by clinicians, radiologists and pathologists is required for accurate diagnosis and timely management.
Experimental investigation of rotor wound multi disc magneto-rheological fluid brake
Authors: Bhat Shrivatsa H , A Abinesh , Naveen Shivarama , Kumar Hemantha , M Arun
Published: January 2025

Magneto-Rheological fluid (MRF), known for changing properties under a magnetic field, is ideal for brakes and dampers in magnetically controlled devices. This research presents a novel design for a 10-disc MR brake using in-house Magneto-Rheological Fluid (MRF), distinguished by its integration of electromagnet windings directly onto the brake shaft. Magneto-static analysis, performed using Finite Element Method Magnetics (FEMM) software, optimized the material selection and dimensions, enhancing the magnetic field distribution across the MRF gap and maximizing braking torque. The design, with rotor windings and a consistent MRF gap, generates a uniform magnetic field, significantly boosting performance. Theoretical braking torque was estimated using Bingham plastic model for MRF characterization, aligning well with experimental results. The compact 10-disc MR brake design, weighing 1.19 kg, shows robust torque performance across varying current levels. Remarkably, prior research had not integrated electromagnet windings directly on the rotor of MR brake, marking this study as pioneering in advancing MR brake performance.
An Empirical Investigation of Secondary Transfer Effects of Contact Among Five Ethnic-Cultural Minority Groups in the Netherlands
Authors: Dierckx Kim , Vezzali Loris
Published: January 2025

Although the relationship between intergroup contact and prejudice has received pervasive scholarly attention, research on secondary transfer effects (STEs)—whereby positive contact experiences with primary outgroups promote tolerance toward uninvolved secondary outgroups—is still in the early stages. In particular, only a few studies have explored interminority STEs (i.e., primary-to-secondary-minority-outgroup transfer effects). Therefore, concerns about the robustness, the generality, and the underlying psychological mechanisms remain unaddressed. To tackle these gaps in the literature, we investigated STEs in a Dutch representative sample of five ethnic-cultural minority groups ( N = 5,157) and an additional national majority group sample ( N = 1,046). Our meta-analytic integrations revealed a small but robust overall interminority STE. It was further shown that attitude generalization (i.e., enhanced attitudes toward the primary outgroup) was an explanatory process variable. Follow-up analyses additionally demonstrated significant intergroup STE variation. Taken together, our findings contribute to literature by showing the robustness of interminority STEs in the Dutch context.
Clinical diagnosis of cerebral amyloid angiopathy related hemorrhage in China: Simplified Edinburgh criteria and Boston criteria version 2.0
Authors: Zhang Mengke , Che Ruiwen , Liu Xin , Hou Chengbei , Wang Zhongyue , Hu Sen , Fu Shengqi , Kan Yuan , Sun Hailiang , Xu Jianmin , Ma Shiliang , Li Sijie , Ren Changhong , Zhao Wenbo , Jia Milan , Wang Jingang , Wu Chuanjie , Ji Xunming
Published: January 2025

Background:

Accurate diagnosis of cerebral amyloid angiopathy (CAA) in surviving patients is indispensable for making treatment decisions and conducting clinical trials. We aimed to evaluate the diagnostic value and clinical utility of the simplified Edinburgh computed tomography (CT) criteria for CAA-related hemorrhage in Chinese patients.

Methods:

We analyzed 212 patients with lobar hemorrhage who underwent brain CT and magnetic resonance imaging (MRI) from a multicentre cohort. Using the Boston criteria version 2.0 (v2.0) as the gold standard, we assessed the application value of the simplified Edinburgh CT criteria, and investigated whether the Edinburgh CT criteria predict patient outcomes.

Results:

Patients with probable CAA accounted for 36.6% according to the Boston criteria v2.0. The Edinburgh CT criteria indicated an area under the receiver operating characteristic curves (AUC) of 0.735 for the diagnosis of probable CAA, and it performed better when there was a high-risk threshold of CAA in the decision curve analysis. Patients with a high risk of CAA based on the Edinburgh CT criteria had poorer outcomes at 90-day after adjusting for confounding factors ( p = 0.034). Finger-like projections in the Edinburgh CT criteria were associated with lobar microbleeds, cortical superficial siderosis, and multispot white matter hyperintensity according to the Boston criteria.

Conclusions:

Taking the Boston criteria v2.0 as the gold standard, the Edinburgh CT criteria demonstrated good diagnostic value and predicted outcomes well at 90-day in Chinese patients with lobar hemorrhage. Further studies with larger sample sizes are required to confirm these findings.