Menakadevi Thangavelu

TL;DR: A general architecture has been proposed for predicting the disease in the healthcare industry using improved SVM-Radial bias kernel method and this system has compared with other machine learning techniques such with accuracy, misclassification rate, precision, sensitivity and specificity.

TL;DR: The simulation results show that the EHACORP has improved packet delivery rate, throughput, end-to-end delay, routing overhead, and packet loss rate compared to Fuzzy based ant colony optimization (F-ANT), Ad hoc on-demand distance vector (AODV), ant colony optimize routing algorithm (ARA), and AntNet routing protocols.

TL;DR: The proposed IDBACOR determines intervehicular distance, and it is triggered by modified ant colony optimization (modified ACO), a metaheuristic approach, motivated by the natural behavior of ants, which indicates that the overall performance of the proposed scheme is better than ant colony optimized routing (ACO), opposition‐based ant colonies optimization (OACO, and greedy routing with ant colony optimize (GRACO) in terms of throughput.

TL;DR: A speed prediction model using auto‐regressive integrated moving average (ARIMA) and neural networks for estimating the futuristic speed of the nodes in mobile ad hoc networks (MANETs) and results indicate that the ARIMA forecasted speed values almost match with the RWM observed speed values than RNN values.

TL;DR: The simulation results prove that the overall performance of EDR‐CAACO is better than Improved Distance Based Ant Colony Optimization Routing (IDBACOR), Fuzzy‐based Ant colony optimization (F‐ANT), ant colony optimization routing algorithm (ARA), and AntNet in terms of throughput, routing overhead, packet delivery ratio, end to end delay, and packet loss rate.