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.