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The Impact of AI Tools on Enhancing EFL Learners' Engagement in Higher Education Using HubSVM Models
BL has become prevalent in higher education as a means of delivering information, managing activities, and executing lessons, thanks in large part to the proliferation of COVID-19 and other technological developments in education. By combining online and offline learning, BL encourages students to be more engaged and flexible than in a typical classroom setting. Engaged learners are crucial for psychometric analysis; they are like energy in action, full of life, focus, and determination. By encouraging mental and physical exertion towards studying, it significantly improves EFL students' involvement in higher education. Using MinMax for feature scaling and the HubSVM, which, similar to the L1-norm SVM, allows automatic feature selection, this study analyses and improves engagement. By highlighting highly connected features, HubSVM improves the selection process and makes computing the complete solution path easier. The results show that when dealing with highly correlated variables, HubSVM performs better than L1-norm SVM. The suggested classifier outperforms the competition with an accuracy of 95.65%. The results show that the concept works well to make BL settings more engaging for students. This research helps make higher education more engaging for EFL learners by incorporating modern machine learning techniques, which means they will have a better, more effective learning experience. 2025 IEEE. -
Enhancing Stock Market Price Prediction with Advanced Machine Learning Techniques: A Comparative Study
The non-linearity and intrinsic volatility of financial markets make accurate stock price prediction an important but challenging undertaking. This research proposes a Gated Recurrent Unit (GRU)-based model to forecast the stock prices of Tata Consultancy Services (TCS) using 18 years of historical data sourced from Yahoo Finance, comprising features such as Date, Open, High, Low, Close, Adjusted Close, and Volume. The methodology includes data preprocessing steps such as feature selection using Recursive Feature Elimination (RFE), normalization with standard scaling, and data splitting into 70% training and 30% testing sets. The proposed GRU model was evaluated and benchmarked against existing models including Long Short-Term Memory (LSTM), Linear Regression (LR), and Decision Tree (DT), using performance metrics such as Root Mean Squared Error (RMSE) and R2 score. Experimental outcomes revealed that the GRU model achieved the best performance with an RMSE of 0.045, outperforming LSTM (38.19), LR (8.66), and DT (5.22). The study's findings have important implications for algorithmic trading and well-informed investment choices, since the GRU model effectively captures temporal trends in stock data while minimizing prediction mistakes. 2025 IEEE. -
Bridging Traditional NLP and Deep Learning: Comparative Study on Text Categorization Performance
Text categorization is an important area of Natural Language Processing (NLP) that is used to automatically organize textual information into a set of specific categories. This study is a comparative study of models that use statistical features and models that use transformers, using the example of DistilBERT-base-uncased fine-tuned and LoRA (Parameter-Efficient Fine-Tuning, PEFT). The data extracted on the Kaggle site is presented in the form of labeled text samples of five classes Business, Entertainment, Sport, Tech, and Politics. Conventional models such as Logistic Regression, Random Forest and XGBoost were trained on manually crafted word-level features (word count, mean word length and punctuation ratio) and had precisions up to 94.7%. Comparatively, the given DistilBERT-LoRA model used semantic embeddings to find the contextual dependencies and managed to reach the total accuracy of 97, precision of 97, and the recall of 96. The training and validation loss curves showed the stable convergence without overfitting, and the confusion matrix showed the consistent performance at all the classes with minimum misclassification. Comparative analysis indicated that semantic embeddings are much better than statistical models because they enhance contextual perception and strength of classification. The findings confirm the effectiveness and scalability of the LoRA-based fine-tuning, which offers an efficient but lightweight strategy in the context of real-world settings to achieve high-performance text categorization. 2025 IEEE. -
A Lightweight LCDECG Model for Cardiovascular Diagnostics Using ECG Features
Cardiovascular disease (CVD) is among the leading causes of death around the world, requiring accurate and reliable diagnostics, and early detection. This project aims at the development of an efficient and accurate, lightweight model to classify heart rhythms based on an ECG.. In this paper, we propose the LCDECG (Lightweight Cardiac Diagnostic ECG) model, which integrates deep morphological feature extraction from ECG with clinically relevant handcrafted features. With MobileNetV2 used as a feature extractor and statistical descriptors of ECG signals, both the pathways are combined at the feature level for multi-class classification of cardiac conditions. Experiments conducted on the Dataset demonstrate better classification performance with 97.8% accuracy, 96.4% precision, 97.1% recall, and 96.7% F1-score over traditional neural networks alone or only statistical methods. The model is able to achieve the desired results as it only utilizes 2.43M parameters in its architecture, and therefore is amenable to real-time deployment in resource-scarce environments. Its use is advantageous for facilitating timely and early detection, which is necessary to improve patient survival and reduce healthcare costs through preventative treatment. Current ECG readings are based on manual assessment by trained cardiologists, which can be time-consuming and potentially subjective, depending on several professionals in the medical field evaluating the tracing. Due to the increased incidence of cardiovascular disease globally, and the limited number of professionals, particularly in developing countries, there is even greater need for automated convenient and trustworthy ECG tracing for diagnostic support. 2025 IEEE. -
Real-Time Video Text Spotting with OpenCV and OCR Powered by Deep Learning
The identification of text in video places huge challenges, and translating them into target form demands high-level expert skills in computer vision and deep learning. This system can grab and supervise the real-time video process of text elements by utilizing OpenCV for text recognition and extraction. The proposed model was employed diverse machine translation models to guarantee high-quality results for translation. Based on wide testing and assessment, the goal is to make the apporach fast and precise, offering valuable tool for instructors, content creators, and overall users. This novel approach solves problems relating to language difficulty problems. Key elements include video processing, text detection and recognition, and machine translation. In addition to these essential functionalities, sophisticated preprocessing methods are applied to make text stand out from diverse backgrounds to render high performance in diverse environments. Deep learning algorithms improves the accuracy of text detection, especially for occluded or distorted characters. Finally, the cloud-based translation service offers real-time multilingual support to enable maximal adaptability to user needs. For the first time, this innovative technology finally enables streamlining access to the content and facilitates cross-cultural communication in multimedia contexts with nigh-guaranteed linguistic barriers broken down. 2025 IEEE. -
Balancing the Cart: Evaluating Imbalance-Aware Machine-Learning Pipelines for Predicting E-Commerce Purchases
We present a comprehensive investigation into predicting purchase conversions in e-commerce sessions, addressing the challenges of severe class imbalance and complex user behavior signals. Using a real-world dataset of 12,330 user sessions described by 24 features (interaction counts, durations, bounce/exit rates, page values, temporal and device metadata), we first conduct exploratory analysis to reveal seasonal peaks in conversion and strong correlations between page value metrics and purchase likelihood. To mitigate the low positive-class rate (10.8%), we embed SMOTE oversampling within our training pipelines, ensuring balanced learning for all classifiers. We then perform a head-to-head comparison of twelve algorithmsranging from linear and generative methods (Logistic Regression, LDA, Gaussian NB), instance-based learners (KNN, SVM), bagging ensembles (Random Forest, Extra Trees, AdaBoost), gradient boosters (XGBoost, LightGBM, CatBoost), to a feed-forward neural network (MLP). Evaluation on a stratified 80/20 holdout set uses overall accuracy plus precision, recall, and F1-score for the purchase class, alongside ROC AUC. Our results demonstrate that ensemble tree methods dramatically outperform simpler models: LightGBM achieves the highest F1 (0.694) and ROC AUC (0.924), with Extra Trees closely following (F1 0.678, AUC 0.926). Simpler classifiers, despite SMOTE, lag markedly in recall and F1, underscoring the importance of powerful nonlinear learners. These findings establish a new benchmark for imbalance-aware conversion prediction and recommend SMOTE-augmented gradient boosting and randomized tree ensembles as the methods of choice for future research and practical deployments. 2025 IEEE. -
Scribble and Learn-A Leaner Centric Approach
This innovative practice full paper describes a creative learning method that support collaborative, creative visual leaning by improving the happiness index in classroom environment. Teamwork and creativity are limited in traditional classrooms that emphasize individual learning and technology dependence. This study offers 'Scribble and Learn,' a novel instructional tool for Cognitive Psychology and artificial intelligence courses to fill this need. 'Scribble and Learn' uses unique scribbling to encourage active participation and collaboration. Team research with limited technology is done by students. This constraint promotes teamwork and communication as they uncover and study memory performance determinants. Teams use keywords and images to communicate their findings on a huge writing surface after this research phase. This 'scribbling' approach accommodates varied learning styles and stimulates creative knowledge expression, creating a dynamic learning environment beyond lectures. Teams present and discuss their findings in a moderated class discussion. This conversation improves Cognitive Psychology and AI memory performance theory knowledge. 'Scribble and Learn' improves teamwork, creativity, and memory. Positive student feedback shows that the exercise promotes active learning and participation. In a class discussion that is moderated, teams present and discuss the discoveries that they have uncovered. The knowledge of cognitive psychology and artificial intelligence memory performance theory is improved by this discourse. The fact that the activity gets positive feedback from students demonstrates that it encourages active learning and involvement. 2025 IEEE. -
An Efficent Deep Learning Framework for Cyberbullying Detection Using DistilBERT and Sentiment Analysis
Particularly because of the complex and changing character of online communication, which hampers conventional detection strategies, the frequency of cyberbullying presents a significant threat to mental health and well-being in the digital age. This article presents a fast deep learning approach to improve cyberbullying detection by combining sentiment analysis with a lightweight transformer model, DistilBERT. This work intends to increase classification performance by using sentiment-based features and using DistilBERT's language and contextual awareness. Unlike conventional approaches and simpler machine learning methods, which can depend on feature extraction techniques like Bag of Words (BOW) or TF-IDF, the proposed model directly leverages contextual embeddings. Moreover, DistilBERT provides a balance between speed and performance unlike deep learning models like CNN, BLSTM, and LSTM, which could suffer with computational efficiency. Experimental results demonstrating remarkable accuracy and recall on many different datasets indicate the effectiveness of our hybrid approach. demonstrating a significant rise in cyberbullying detection over conventional methods, to evaluate performance criteria including computational efficiency, accuracy, and F1-score. With an outstanding 93.7 % accuracy, the proposed model exceeded earlier evaluated methods on this dataset. 2025 IEEE. -
Advance Data Ingestion Framework - Integration, Processing, Transformation, and Loading
The research introduces a new concept known as the Advanced Data Ingestion Framework, which is aimed at enhancing the process of getting into stored information through some intelligent methods like data preprocessing, transformation and loading. By making use of Azure services, the platform considers distributed computing and parallel processing so that structured as well as unstructured data can be incorporated from various origins without any difficulty. To begin with, the proposed framework starts with setting up scalable Azure infrastructure and integrating SAP S4/HANA for secure and efficient data transfer purposes. Within Azure Data Factory the ingestion occurs while Delta Lake ensures proper housekeeping & integrity within the system. It includes creating Power BI dashboards which allow users to see patterns easily and make better decisions based on what they know or can learn. The study brings out the flaws of current data input solutions and emphasizes the urgent requirement for a highly scalable low latency system that can support real time data processing efficiently. It tests the framework under different performance environments showing that it can effectively manage modern data within it. Finally, there is discussion about future improvements such as incorporating more sophisticated analytics or ML models thereby strengthening the decisionmaking process based on available facts. 2025 IEEE. -
Microgrid Energy Management- An Optimization Approach for Operational Cost Reduction
Optimization-based energy management and the related economic viability analysis of replacing a diesel generator alone-based microgrid with a PV, battery, fuel cell and diesel generator-based hybrid microgrid to minimize the operations cost, considering battery degradation and emissions, are presented in this work. The fuel cell considered here is the hydroplus fuel cell, which has minimum emissions, and eliminates the need for hydrogen storage. Mathematical modeling is based on experimentally obtained parameters for fuel consumption coefficients and emissions. The study considers an islanded mode of operation with two different scenarios of microgrid configuration, the first case with the hybrid microgrid working under optimal power conditions, and the second case with only diesel generator available to meet the load demand, for four representative months of the year. Comparing the operations cost under the two scenarios, replacement of a diesel generator-based microgrid, with a PV/battery/hydroplus fuel cell/diesel generator based microgrid resulted in reduction of daily operations cost by 54.06%, 35.25%, 34.38% and 32.71% for the months of January, April, July and October respectively. A sensitivity analysis with varied costs of fuel, operation and maintenance, and battery degradation proved that irrespective of these costs, a considerable amount of reduction in the operations cost is achieved. The results presented here are highly beneficial for application in microgrids worldwide. 2025 IEEE. -
Financial Behaviour Analysis for Payment Bank Adoption Using Random Forest and PCA: An Indian Perspective
The gradual acceptance of payment banks in India constitutes an important challenge to equitable financial development, especially for underbanked rural communities. The proposed study handles the difficulty by examining financial behaviour through a hybrid machine learning methodology that integrates Principal Component Analysis (PCA) for dimensionality reduction with a Random Forest classifier for predictive modelling. The study utilises datasets obtained from Kaggle that reflect demographic, behavioural, and digital engagement variables. PCA preserves approximately 9 0% of variance while reducing feature complexity, allowing the Random Forest to effectively characterise adoption behaviour. In comparison to conventional classifiers such as Logistic Regression, SVM, and Decision Trees, the suggested model improved performance, attaining 96.7% accuracy, 95.8% precision, 97.1% recall, 96.4% F 1-score, and an AUC-ROC of 0.982. The findings exceed all chosen baselines, demonstrating the system's resilience and reliability. The approach provides behavioural insights essential for policy formulation and strategic engagement by pinpointing the most significant adoption determinants. This research greatly advances the digital banking sector by integrating data science with social impact, providing a clear, high-performing solution to inform financial inclusion policies. It establishes a basis for the development of future real-time and personalised adoption prediction systems utilising advanced AI methodologies. 2025 IEEE. -
Fuzzy Logic-AHP Hybrid Model for Faculty Performance Evaluation to Enhance Educational Quality in Higher Education
Guaranteeing equitable and precise evaluation of teacher performance is a continual challenge in higher education, as subjective discrimination, uneven metrics, and absence of cohesive frameworks frequently obstruct informed decision-making. A hybrid Fuzzy Logic-Analytic Hierarchy Process (AHP) model is developed to integrate systematic requirement weighting through AHP with the uncertainty management features of fuzzy logic. The method assesses faculty performance across various dimensions, including classroom effectiveness, research output, service involvement, and professional advancement. The integration guarantees impartiality in criterion weighing and adaptability in managing qualitative assessments, resulting in a balanced and thorough evaluation method. The proposed hybrid framework, in contrast to standard models, reduces subjectivity, improves interpretability, and provides greater accuracy in prediction. Experimental findings indicate that the model attains an Accuracy of 96.8%, Precision of 97.2%, Recall of 96.5%, F1score of 96.8%, and AUC of 0.98, surpassing baseline methods like Decision Trees, Logistic Regression, and Support Vector Machines. These findings confirm the resilience and flexibility of the proposed methodology in practical teacher evaluation contexts. The research enhances educational quality and facilitates the integration of hybrid decision-support systems into institutional policy-making and future academic performance evaluations. 2025 IEEE. -
Advancing Astronomical Science - Machine Learning-Based Classification of Variable Stars for Scientific Innovation and Research
Variable star classification is an important part of astrophysics and gives astrophysicists a way of studying stellar evolution, structure and dynamics. Due to the availability of large scale surveys such as Gaia DR3, machine learning techniques are used in automation of the classification process. In this study, RF, SVM, MLP and XGBoost (XGBClassifier) models are evaluated for classification of variable stars. The data set used in this work was collected from Gaia DR3 using Astroquery and the ability of these models is evaluated for different star classes. The result shows that the XGBoost had the best accuracy of 91% compared to RF (89.98%), MLP (88%) and SVM (83%). A comparison of various metrics such as precision, recall and F1-score of each method is also provided to address their strengths and weaknesses. This work further emphasises the need of sophisticated machine learning techniques in astrophysical data analysis and discusses problems of certain kinds of variable star classification. KeywordsGaia, variable stars, Classification, Machine Learning, Random Forest, XGBoost, Multi-Layer Perceptron, Support Vector Machine. 2025 IEEE. -
Intelligent Research Summarization: Enhancing Academic Productivity through AI for a More Sustainable Future
The efficiency of researchers is often hindered by the enormous volume of academic research, which hinders the extraction of useful insights in a timely manner. This paper proposes an extension for summarizing research papers using artificial intelligence to ease the process through automated concise summarization. Our system leverages cutting-edge natural language processing tools, including PyPDF2 for text extraction, LangChains text chunking method, FAISS for similarity-based information retrieval, and Googles Gemini AI for high-quality summarization. We evaluated the performance of the model using a variety of metrics, including Rouge scores in conjunction with human judgments. Experimental results show that the suggested approach significantly improves research efficiency, reducing reading time by 60% without compromising high accuracy rates. 2025 IEEE. -
Attention-Enhanced Vision Transformer Model for Precise Skin Cancer Detection
Skin cancer is one of the most prevalent and potentially fatal diseases, requiring early and accurate detection for effective treatment. Recent advances in deep learning have significantly improved automated skin lesion classification, but traditional Convolutional Neural Networks (CNNs) struggle with capturing long-range dependencies in dermoscopic images. To address this limitation, we propose a Preprocessing-Optimized Vision Transformer (ViT) Model that enhances lesion detection using attention-based feature fusion. Our methodology includes contrast enhancement (CLAHE), hair removal (DullRazor), lesion segmentation (K-Means + Otsus Thresholding), and data augmentation, ensuring robust model training. The proposed Attention-Enhanced ViT Model effectively learns global contextual features from dermoscopic images through self-attention mechanisms. The proposed model is evaluated our model on the ISIC Skin Cancer Dataset, achieving an accuracy of 94.6%, precision of 92.8%, recall of 93.5%, and an AUC-ROC score of 0.97, outperforming traditional CNN-based models such as ResNet50 (92.1% accuracy) and EfficientNet-B0 (93.3% accuracy). Our results demonstrate that integrating preprocessing techniques with Vision Transformers significantly enhances classification performance, making this approach a viable solution for real-world computer-aided dermatology. 2025 IEEE. -
Deep Learning Approaches for Detection and Classification of Microplastics in Water for Clean Water Management
Microplastic pollution is a growing environmental concern, threatening aquatic ecosystems and human health. This study presents a dual deep learning approach for microplastic detection and classification using two datasets. For water microplastics, YOLOv8 and YOLOv11 were employed for object detection. InceptionV3, VGG19, ResNet50, ResNet152, DenseNet121, EfficientNetB0, and a custom CNN were applied for classification, classifying three distinct microplastic types in non-aquatic environments. Experimental findings display high accuracy, and indicate the potential of AI-enabled solutions for environmental monitoring. This research contributes to SDG 6 Clean Water and Sanitation, promoting sustainable management of water. 2025 IEEE. -
Graph Convolutional Networks for Predicting Postpartum Depression: A Symptom-Based Analysis
Postpartum Depression (PPD) is a serious mental health condition affecting new mothers and aligns with the United Nations Sustainable Development Goal (SDG) 3: Good Health and Well-being, which stresses early detection and intervention. This research investigates the use of Graph Neural Networks (GNNs)specifically, Graph Convolutional Networks (GCNs)to predict PPD by modeling the interdependencies among symptoms. A preprocessed dataset of 1,503 records was utilized, involving categorical encoding, missing value imputation, and feature standardization to enhance model reliability. The GCN model was built using a K-Nearest Neighbors (KNN)-based graph structure, enabling the network to learn intricate relationships between symptoms. Experimental results showed that the GCN achieved an accuracy of 89%, identifying key predictors such as trouble sleeping, guilt, irritability, difficulty concentrating, and anxiety. The use of SHAP explainability tools further validated these predictors, enhancing interpretability and revealing the models decision-making process. While traditional models like Random Forest achieved higher classification accuracy (95%), GCN offered valuable insights into the underlying structure and relationships among symptoms, supporting its potential in mental health diagnostics. Future work may explore hybrid architectures and larger datasets to further improve the models predictive performance and contribute to AI-driven early screening strategies for PPD. 2025 IEEE. -
Dynamic Financial Portfolio Optimization Using Temporal Convolutional Networks and Real-Time Data Analysis
This paper presents an integrated framework for AI-driven portfolio optimization combining temporal convolutional networks (TCNs) with conditional value-at-risk (CVaR) minimization. Our system processes real-time market data through an automated pipeline implementing volatility-adjusted feature engineering and walk-forward validation. The architecture employs dilated causal convolutions for temporal pattern extraction combined with Ledoit-Wolf shrinkage covariance estimation for robust portfolio optimization. Experimental results demonstrate an 18.7% annualized return with 22.3% volatility, outperforming traditional mean-variance optimization by 14.2% in risk-adjusted returns. The implementation addresses key challenges in numerical stability and computational efficiency through eigenvalue clamping and gradient checkpointing. 2025 IEEE. -
Artificial Intelligence and Machine Learning in Financial Fraud Research: A Bibliometric Analysis of Trends and Collaborations
This study conducts a bibliometric analysis of the financial fraud research domain, synthesizing publication metadata to uncover collaboration structures, thematic hotspots, and temporal trends. The research utilizes the bibliometric tool Vos viewer to retrieve the insights on the various aspects of financial fraud detection using the emerging technologies. The study analyses the integration of AI and machine learning techniques on the detection of financial fraud. Using co-authorship networks, we identify influential authors and collaboration clusters, while keyword frequency and co-occurrence patterns reveal core topics and emerging fronts such as anomaly detection, machine learning, insider trading, and regulatory analytics. Temporal profiling of publications highlights growth phases and shifts in emphasis across years and venues. The analysis provides an integrated view of the field's intellectual structure, enabling scholars and practitioners to locate key contributors, benchmark thematic coverage, and identify gaps for future inquiry. Results offer actionable insights for forming research collaborations, prioritizing topics, and designing evidence- based strategies against financial fraud. 2025 IEEE. -
Detecting Student Depression Using Non-Clinical Measures with Explainable Predictive Modeling
Depression among students is a serious global mental health concern, affecting academic performance, emotional wellbeing, and long-term development. While traditional diagnostic tools like self-reported questionnaires and clinical interviews are useful, they often suffer from subjectivity, recall bias, and limited scalability. This study introduces a data driven, interpretable machine learning approach to predict student depression using both academic and nonacademic factors, without relying on clinical indicators. The dataset comprises student information on demographics, academic workload, lifestyle habits, social interactions, financial stress, and emotional state. Following thorough preprocessing including handling missing values, encoding variables, correlation-based feature reduction, and SMOTE to address class imbalance, ten supervised machine learning models were trained and assessed. Among them, a SMOTE enhanced XGBoost model achieved the highest test ROC AUC score of 0.95. To maintain transparency, SHAP (Shapley Additive Explanations) was employed to interpret the model's predictions, highlighting key risk factors such as academic pressure, poor sleep quality, financial difficulties, and low social support. These findings can help guide early interventions and build trust with stakeholders. Future work may involve incorporating longitudinal and multimodal data, deploying real time solutions in educational settings, and addressing ethical considerations around privacy, fairness, and consent in AI based mental health systems. 2025 IEEE.