Volume 10, Issue 3 - IJIRTM
May - June (2026)
Impact Factor: 5.86 | Volume 10 | Issue 3
Performance Analysis of Liver Diseases Classification using Machine Learning Classifier
👥 Neetiraj Singh Lodhi, Dr.Rajneesh Choubey
📙 Abstract : Early detection through supervised learning techniques applied to diverse datasets is crucial in reducing mortality rates. With ongoing advancements in medicine, a significant amount of data has been collected, particularly in healthcare, where extensive data generation occurs. This data is processed and analyzed through data mining applications for knowledge extraction. Mining algorithms effectively predict patient diseases by utilizing appropriate learning strategies. Chronic kidney disease (CKD), hepatitis, cancer, and diabetes represent major global health concerns, making their prediction a significant focus for researchers. This dissertation primarily analyzes various classification algorithms, including Support Vector Machines (SVM), K Nearest Neighbor (KNN), and Extra Tree Classifier, by comparing their performance utilizing liver patient data. The study employs different machine learning classification techniques to diagnose liver conditions at an early stage, yielding performance metrics such as accuracy and other relevant parameters.
🔖 Keywords :️ Accuracy, support vector machines, Supervised machine learning, Random forest.
Detection and Prevention of Distributed Denial of Service (DDoS) Attacks in Cloud environments: A Comprehensive Survey
👥 Rajeev Ranjan, Darshna Rai, Chetan Agrawal
📙 Abstract : Cloud computing is the dominant paradigm for scalable IT services, but its openness and resource-sharing approach make it a target for DDoS attacks. This paper is a comprehensive survey of DDoS attacks targeting cloud infrastructures, the evolution of detection and prevention mechanisms, the role of machine learning and deep learning in automated threat mitigation, and the growing importance of SDN as a defence platform. We categorise 60+ research studies from 2018 to 2025 by approach and effectiveness and critically evaluate their strengths and weaknesses. Our assessment provides a uniform taxonomy, comparison tables, and architectural diagrams to help researchers and practitioners navigate this complicated world. Federated learning, explainable AI, and zero-trust cloud DDoS protection architectures are our final research problems and intriguing future prospects.
🔖 Keywords :️ DDoS attacks, cloud computing, intrusion detection, machine learning, deep learning, SDN, botnet, mitigation, network security, traffic classification.
Educational Data Mining for Student Performance Prediction: A Systematic Review
👥 Mirza Hufeza Baig, Prof.Aashish Kumar Tiwari
📙 Abstract : Educational Data Mining (EDM) has become a rapidly growing interdisciplinary research domain that applies data mining, machine learning, and artificial intelligence techniques to extract valuable insights from educational datasets. Among its various applications, student performance prediction has gained significant attention due to its potential to enhance academic achievement, identify at-risk students, and support informed educational decision-making. This review paper provides a comprehensive analysis of recent advancements in student performance prediction using Educational Data Mining approaches. Various predictive models, including Decision Trees, Naïve Bayes, Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest, Ensemble Learning, and Deep Learning techniques, are critically examined and compared. Furthermore, the study explores the key academic, demographic, and behavioral factors that influence student performance. Current challenges, existing research gaps, and emerging trends in the field are also discussed. The review highlights that hybrid machine learning frameworks, advanced feature selection techniques, and intelligent learning analytics significantly enhance prediction accuracy and contribute to the development of effective educational support systems. The findings of this study provide valuable insights for researchers, educators, and policymakers seeking to improve student success and institutional performance through data-driven educational strategies.
🔖 Keywords :️ Educational Data Mining, Student Performance Prediction, Machine Learning, Learning Analytics, Classification, Artificial Intelligence.
Genetic Algorithm and Neural Network in Educational Data Mining
👥 Mirza Hufeza Baig, Prof.Aashish Kumar Tiwari
📙 Abstract : Educational Data Mining (EDM) has become an effective research area for extracting meaningful knowledge from educational datasets and improving student learning outcomes. Accurate prediction of student performance plays a vital role in identifying academically weak students and supporting educational decision-making. This paper proposes a Behavior-Based Student Classification System (SCS-B) that integrates Genetic Algorithm (GA) and Back Propagation Neural Network (BP-NN) for student performance prediction and classification. Student data are collected through a structured Student Questionnaire (SQ) comprising learning techniques, personal information, student behavior, intellectual factors, and comprehensive abilities. The collected data undergo preprocessing through outlier detection and dimensionality reduction using Singular Value Decomposition (SVD). Genetic Algorithm is employed for optimal feature selection, while BP-NN is used for classification and prediction. The proposed model classifies students into four categories: Class A, Class B, Class C, and Class D based on their academic and behavioral characteristics. Experimental evaluation demonstrates that the proposed SCS-B model achieves superior classification accuracy compared with Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Multi-Tier Student Performance Evaluation Model (MTSPEM). The results confirm that integrating behavioral factors with machine learning techniques significantly enhances student performance prediction and educational decision support.
🔖 Keywords :️ Educational Data Mining, Student Performance Prediction, Genetic Algorithm, Neural Network, Behavior Analysis, Machine Learning, Student Classification.