This paper proposes security technique for the confidential data which is the combination of three techniques, first is image compression that is based on wavelet transformation which will compress confidential image and reduce the size of the image, second is cryptography that is based on symmetric key which will encrypt the confidential image, and third is steganography that is based on least significant bit (LSB) which will embedded encrypted information inside a cover image. Therefore the purpose of the proposed technique is the high security and quality of the reconstructed cover image.

Heart disease is a leading cause of mortality worldwide, and early detection and accurate prediction of heart disease can significantly improve patient outcomes. Machine learning techniques have shown great promise in assisting healthcare professionals in diagnosing and predicting heart disease. The diagnosis and prognosis of heart disease must be improved, refined, and accurate, because a small mistake can cause weakness or death. According to a recent World Health Organization study, 17.5 million people die each year. By 2030, this number will increase to 75 million.[2] This document explains how to enable online KSRM capabilities. The KSRM smart system allows users to report heart-related problems. This research paper aims to explore the use of machine learning algorithms for effective heart disease prediction classification with Ada boost for improve the accuracy of algorithm.

Mobile ad hoc networks (MANET) is the most useful network in present and it is used in many application. In the MANET prevention concept is not sufficient from the security point of view, so the detection concept is added. Intrusion Detection System (IDS) is another concept to provide security in the network. Basically IDS is used to identify selfish and malevolent node in the network. Generally IDS concept is used in wired networks but incase of wireless network then it can no longer sufficient if we used it directly. Here we present the concept of MANET, IDS and we have discussed various existing IDS in MANET environment along with their pros and cons.

Feature selection is critical for improving model performance and managing high-dimensional data, yet existing methods often face limitations such as inefficiency and suboptimal results. This study addresses these challenges by introducing a novel approach using the white shark optimization (WSO) algorithm and its binary variants to enhance feature selection. The proposed methods are evaluated on various datasets, including “Dorothea,” “Breast Cancer,” and “Arrhythmia,” focusing on classification accuracy, the number of features selected, and fitness values. Results demonstrate that the WSO algorithms significantly outperform traditional methods, offering notable improvements in accuracy and efficiency. Specifically, the WSO variants consistently achieve higher accuracy and better fitness values while effectively reducing the number of selected features. This research contributes to the field by providing a more effective optimization approach for feature selection, addressing existing inefficiencies, and suggesting future directions for further refinement and broader application. The findings highlight the potential of advanced optimization techniques in enhancing data analysis and model performance, offering valuable insights for practitioners and researchers.