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The IETE Journal of Research's first issue of Volume 70 covers a wide range of topics in communications, electromagnetics, opto-electronics, computers and computing, control engineering, electronic circuits, devices and components, instrumentation and measurement, medical electronics, and power electronics. The articles in this issue discuss various subjects, including improved network performance in wireless sensor networks, machine learning-based classification of cervical cancer, optimal energy reduction in 5G communication systems, and secure data transmission in the Internet of Things. The issue also includes papers on photonic sensors, detection of diabetic retinopathy, optimisation models for home health care scheduling and wind-integrated power systems, and efficient task scheduling in cloud computing environments. One research paper introduces a smartwatch-based machine learning model for monitoring epilepsy and detecting seizures, achieving a high accuracy of 99.40% in cross-validation. The document provides a summary of articles published in the journal, with authors who are experts in their fields and provide experimental findings and simulations to support their research. [Extracted from the article]

Data security concerns safeguarding the data from unauthorized users. Numerous research works have been conducted using Reversible Data Hiding (RDH) technique, but not been found to be fooproof. In this paper, a novel reversible data hiding method, based on an Artificial Neural Network, called Histogram Transpose and Naor Pseduorandom Arnold (HT-NPA) data security system for handling health care reports and securely transferring patient information, is proposed to evaluate the embedding capacity, improve the visual quality of the recovered image and ensure security via duplex scramble. This entire process is performed by means of an Artificial Neural Network (i.e. multilayer perceptron). The experimental results reveal that the HT-NPA performs better with 29%, 16%, 19%, and 9% improvement in embedding rate, PSNR, SSIM, entropy, and NPCR, and 26% reduction of bit error rate, respectively for retrieved medical images compared to existing works. [ABSTRACT FROM AUTHOR]