M. Kaliappan

TL;DR: The artificial intelligence has been used with Naive Bayes classification and random forest classification algorithm to classify many disease datasets like diabetes, heart disease, and cancer to check whether the patient is affected by that disease or not.

TL;DR: The simulation experiments showcase that the throughput, lifetime and jamming prediction is analyzed and enhances the energy using the MOACO, when compared to the artificial bee colony and genetic algorithm.

TL;DR: This work aims at using cervical cancer risk features to build classification model using Random Forest classification technique with the synthetic minority oversampling technique (SMOTE) and two feature reduction techniques recursive feature elimination and principle component analysis (PCA).

TL;DR: An attention‐based convolution neural network long short‐term memory (CNN‐LSTM), a multistep prediction model, which helps to identify the near term traffic details such as speed that is very important for predicting the future value of flow.

TL;DR: An experimental result reveals that the proposed ABC scheme reduces the execution time and classification error for selecting optimal clusters and gives a better performance than PSO and DE in terms of time efficiency.