AN ADVANCED MULTIMODAL AI FRAMEWORK FOR EARLY BRAIN STROKE DETECTION USING HYBRID FEATURE SELECTION, ENSEMBLE MODELS, AND REINFORCEMENT LEARNING
The detection of stroke is vital since any delay in diagnosis may lead to significant disability or the loss of life. The existing predictive models fail to capture stroke symptoms with accuracy because of low complexity, and the ability to be used in the real-time situation in the clinical setting. In the following paper, an AI-based system of ear...
By D. Ushasree, A.V. Praveen Krishna, Ch. Mallikarjuna Rao, D.V. Lalita Parameswari
MEDIAN ATTRIBUTE HYBRID CLUSTERED MODEL USING PARTICLE SWARM OPTIMIZATION FOR NETWORK INTRUSION DETECTION
The rapid growth of cloud-based and large-scale network infrastructures has increased the complexity and frequency of cyber-attacks, demanding efficient and scalable intrusion detection systems (IDS). This paper will also attempt to create a better Network Intrusion Detection System (NIDS) by incorporating a Hybrid Median Attribute Clustering model...
By Rajasekhar Kaseebhotla, Raghava Rao, Mallikarjuna Rao