Computer-aided diagnosis permits biopsy specimen analysis by creating quantitative images of brain diseases which enable the pathologists to examine the data properly. It has been observed from other image classification algorithms that the Extreme Learning Machine (ELM) demonstrates superior performance in terms of computational efforts. In this study, to classify the brain Magnetic Resonance Images as either normal or diseased, a hybridized Salp Swarm Algorithm-based ELM (ELM-SSA) is proposed. The SSA is employed to optimize the parameters associated with ELM model, whereas the Discrete Wavelet Transformation and Principal Component Analysis have been used for the feature extraction and reduction, respectively. The performance of the proposed “ELM-SSA” is evaluated through simulation study and compared with the standard classifiers such as Back-Propagation Neural Network, Functional Link Artificial Neural Network, and Radial Basis Function Network. All experimental validations have been carried out using two different brain disease datasets: Alzheimer’s and Hemorrhage. The simulation results demonstrate that the “ELM-SSA” is potentially superior to other hybrid methods in terms of ROC, AUC, and accuracy. To achieve better performance, reduce randomness, and overfitting, each algorithm has been run multiple times and a k-fold stratified cross-validation strategy has been used.

Machine learning (ML) is the most prominent topic, with many applications in video processing, language processing, visual diagnosis of illnesses, drug development, biochemistry, and biomedical. Healthcare systems and medical science-related applications are rapidly growing among these applications. Large data expansion, the Internet of Things (IoT), networked devices, and high-performance computers with graphics processing units are all contributing to the growing popularity of ML. Healthcare in IoT (HIoT), and electronic health record (EHR) data are the key data resources for neural network (NN) models. Several possible challenges, including security and quality of service improvement, are important to ML. Based on the comprehensive literature survey (CLS), ML for HIoT is examined in this research.

The convergence of renewable energy integration and the Internet‐of‐Things (IoT) technologies is transforming modern power grids into highly interconnected cyber‐physical systems. While this evolution improves observability, flexibility, and automation, it also introduces new and poorly understood vulnerability pathways that expose renewable‐driven IoT grids to malicious cyber and cyber‐physical attacks. Unlike conventional power systems, these grids exhibit inverter‐dominated dynamics, reduced inertia, distributed intelligence, device heterogeneity, and strong coupling between cyber, communication, and physical layers, which collectively amplify systemic risk under adversarial conditions. This review provides an integrative synthesis of vulnerability assessment approaches and resilience enhancement strategies for renewable energy–driven IoT–enabled smart grids under malicious attacks. In contrast to prior surveys that address cybersecurity, physical resilience, or renewable integration in isolation, the paper systematically links attack classes to cross‐layer vulnerability propagation mechanisms and corresponding resilience responses across detection, control, architectural design, and recovery stages. By adopting a unified cyber‐physical perspective tailored to inverter‐based and renewable‐dominant grids, this review clarifies how vulnerabilities emerge and escalate across layers, and why traditional grid security paradigms are insufficient. Key research gaps and future directions are identified, highlighting the need for holistic, adaptive, and multilayer resilience frameworks to support secure and reliable operation of next‐generation sustainable energy systems.

The transition toward carbon‐neutral integrated energy systems requires intelligent architectures capable of managing renewable intermittency, multi‐energy sector coupling, storage coordination, hydrogen integration, and dynamic carbon accounting while preserving stability and reliability. Although artificial intelligence has improved renewable forecasting, load prediction, and optimal dispatch, many existing approaches remain purely data‐driven and lack embedded physical constraints, limiting robustness under extreme operating conditions and reducing extrapolation capability. Carbon‐aware optimization and hydrogen‐integrated scheduling also often remain disconnected from stability modeling and system dynamics, creating fragmentation across planning, control, and sustainability assessment. Based on a structured review of recent studies, this paper identifies five essential operational layers for physics‐informed intelligent energy management: real‐time observability, physics‐based stability modeling, constraint‐embedded learning, AI optimization and control, and carbon intelligence. It then proposes a unified multi‐layer physics‐informed AI architecture for carbon‐neutral integrated energy systems. The framework integrates system monitoring, physical feasibility, adaptive learning, reinforcement‐learning‐driven control, and carbon‐intensity intelligence within a coherent operational paradigm. By linking energy system physics, advanced machine learning, and sustainability‐driven optimization, this review provides a structured roadmap toward resilient, scalable, and carbon‐aware intelligent energy infrastructures.