The booming usage of Electric Vehicles (EV) led to the augmentation of Lithium-Ion batteries. This is due to its charging speed, ease of maintenance, lower operational cost, and high specific energy. Yet, these batteries are under scrutiny due to the significant amount of fire accidents in recent years. These accidents result from thermal runaway, a chain reaction that may lead to a catastrophic explosion. Approximately 60% of EV battery fire incidents stem from internal thermal runaway while charging accidents account for 30%. This paper envisages an ensemble machine learning (ML) approach for early prediction of fire in Li-ion batteries by quantitatively analyzing the volume of gases released during venting. The methodology is based upon previous research conducted and employs a dataset curated from previous studies to serve as a benchmark. Generative AI (Conditional Generative Adversarial Network) is used to generate synthetic data samples. The validity of the dataset has been rigorously verified using Kernal Density plots, ensuring its reliability for predictive modeling. The random forest algorithm proves its efficacy and can predict the time before the thermal runaway to alert the user. Performance evaluation metrics, including 97.83% accuracy, 96.97% precision, 98.63% recall, and a 97.8% F1 score, highlight its robustness. These findings represent a significant advancement over previous research, establishing a reliable framework for enhancing EV battery safety.

Summary Brain cancer is one of the leading diseases of death in the world caused due to unwanted proliferation of cells. If left untreated, this growth can spread into other human body parts. So early detection helps to enhance long‐term survival and reduce the mortality rate. A lot of research has been done in the field of brain tumors. There have been numerous previous studies conducted using traditional approaches. So far, few review studies are available on brain tumor segmentation and classification and moreover, they were quite limited in explaining the connections of Generative Adversarial Networks (GAN) for brain tumor segmentation and classification. This review mainly focuses on the GAN architectures for brain tumor segmentation and classification. Therefore, first, an overview of brain tumors, GAN‐based publications, approaches to brain tumor segmentation and classification, data sets, software platforms, and performance parameters, are presented. Finally, GAN applications and current trends are discussed. This thorough survey will aid researchers and beginners in the future in creating a better decision support system.

Summary Every drug manufacturer is legally bound to display the expiry date on all pharmaceutical products as medicines may not be safe or as effective after the expiry date. The problem is that any pharmaceutical drug has all the important information such as expiry date, batch no., manufacturing date written on a small corner of the packaging. Once that part is rubbed or torn out while taking a pill from the leaflet or otherwise, there is no way to know that crucial information making that whole drug a complete waste as one cannot take risks with their health. This article proposes a solution to find out this crucial information even after it is lost from the leaflet. Therefore, this research work is focused to design an application (app) based on the features of optical character recognition, database management system, auto‐classification, aiming at helping people to use it with ease. One just need to scan the medicine once and the app will autosave all the required credentials that can be accessed later on by scanning it again. Compared to the existing technologies, this app does not depend on QR code as it can also be torn out while taking any pill.

Image forgery detection using traditional algorithms takes much time to find forgeries. The new emerging methods for the detection of image forgery use a deep neural network algorithm. A hybrid deep learning (DL) and machine learning‐based approach is used in this study for passive image forgery detection. A DL algorithm classifies images into the forged and not forged categories, whereas colour illumination localises forgery. The simulated results are compared to other algorithms on public datasets. The simulated results achieved 99% accuracy for CASIA1.0, 98% accuracy for CASIA2.0, 98% accuracy for BSDS300, 97% accuracy for DVMM, and 99% accuracy for CMFD image manipulation dataset.