Ranjit Panigrahi

TL;DR: An improved version of the random sampling mechanism called Supervised Relative Random Sampling has been proposed to generate a balanced sample from a high-class imbalanced dataset at the detector’s pre-processing stage, and an improved multi-class feature selection mechanism has been designed and developed as a filter component to generate the IDS datasets’ ideal outstanding features for efficient intrusion detection.

TL;DR: The current literature status in the field of network intrusion detection is analyzed, highlighting the number of classifiers used, dataset size, performance outputs, inferences, and research gaps and a robust classifier is proposed as the ideal classifier for designing IDSs.

TL;DR: In this paper , the authors presented a novel and enhanced deep-learning-based Mask R-CNN model for the identification of laryngeal cancer and its related symptoms by utilizing diverse image datasets and CT images in real time.

TL;DR: Three popular J48 group classifiers, namely J48, J48Consolidated and J48Graft are evaluated using both binary and multi-class datasets across thirteen performance matrices, which is unique in its area.

TL;DR: In this article , a hybrid of Decision Table and Naive Bayes models were combined to train and detect intrusions, which achieved an accuracy of 96.8% using five features of CICIDS2017, which is higher than the accuracy of methods discussed in the literatures.