Predicting heart disease is regarded as one of the most difficult challenges in the health-care profession. To predict cardiac disease, researchers employed a variety of algorithms including LDA, RF, GBC, DT, SVM, and KNN, as well as the feature selection algorithm SFS. For verification, the system employs the K-fold cross-validation approach. These six strategies were used to conduct the comparative study. The Dataset for Cleveland, Hungry, Switzerland, and Long Beach V, as well as the Dataset Heart statlog Cleveland hungry, are used to assess the models’ performance. For both hungry, switzerland & Long Beach V and Heart statlog cleveland hungry Dataset, Random Forest Classifier sfs and Decision Tree Classifier sfs produced the highest and almost identical accuracy values (100%, 99.40% and 100%, 99.76 %, respectively). The findings were compared to previous research that focused on cardiac prediction. In the future, we hope to extend the model even further so that it may be used with various feature selection techniques; another possibility is to use a random forest classifier. The major goal of this study is to improve on previous work by developing a new and unique technique for creating the model, as well as to make the model relevant and easy to use in real-world situations.

This research investigates implementing and optimizing microgrid energy management systems (EMS) utilizing artificial intelligence (AI). Inspired by the need for efficient resource utilization and the limitations of traditional control methods, it addresses essential aspects of microgrid design, such as cost-effectiveness, system capacity, power generation mix, and customer satisfaction. The primary goals are to optimize energy management, control techniques, and AI applications in microgrids. The study critically examines the classification of energy management systems, various EMS applications, and their associated challenges. Additionally, it discusses different optimization techniques relevant to EMS, highlighting their applications, benefits, and challenges. The research emphasizes the importance of hybrid systems, demand-side management, and energy storage in addressing the intermittency of renewable energy sources. AI techniques, such as unsupervised learning (USL), supervised learning (SL), and semi-supervised learning (SSL), are extensively analyzed in relation to their specific applications. The study explores AI-based hierarchical controls at primary, secondary, and tertiary levels. Furthermore, AI methods like deep learning for load forecasting and reinforcement learning for optimal control are emphasized for their substantial contributions to enhancing microgrid reliability and efficiency. The research concludes that integrating distributed energy resources (DER) and using advanced optimization algorithms can lead to significant financial benefits and improved sustainability in microgrid operations. Over 200 research papers were referenced in this study.

Energy storage systems play a crucial role in the overall performance of hybrid electric vehicles. Therefore, the state of the art in energy storage systems for hybrid electric vehicles is discussed in this paper along with appropriate background information for facilitating future research in this domain. Specifically, we compare key parameters such as cost, power density, energy density, cycle life, and response time for various energy storage systems. For energy storage systems employing ultra capacitors, we present characteristics such as cell voltage, cycle life, power density, and energy density. Furthermore, we discuss and evaluate the interconnection topologies for existing energy storage systems. We also discuss the hybrid battery–flywheel energy storage system as well as the mathematical modeling of the battery–ultracapacitor energy storage system. Toward the end, we discuss energy efficient powertrain for hybrid electric vehicles.

The Industrial Internet of Things (IIoT) has led to the growth and expansion of various new opportunities in the new Industrial Transformation. There have been notable challenges regarding the security of data and challenges related to privacy when collecting real-time and automatic data while observing applications in the industry. This paper proposes an Federated Transfer Learning for Authentication and Privacy Preservation Using Novel Supportive Twin Delayed DDPG (S-TD3) Algorithm for IIoT. In FT-Block (Federated transfer learning blockchain), several blockchains are applied to preserve privacy and security for all types of industrial applications. Additionally, by introducing the authentication mechanism based on transfer learning, blockchains can enhance the preservation and security standards for industrial applications. Specifically, Novel Supportive Twin Delayed DDPG trains the user model to authenticate specific regions. As it is considered one of the most open and scalable interacting platforms of information, it successfully helps in the positive transfer of different kinds of data between devices in more significant and local operations of the industry. It is mainly due to a single authentication factor, and the poor adaptation to regular increases in the number of users and different requirements that make the current authentication mechanism suffer a lot in IIoT. As a result, it has been very clearly observed that the given solutions are very useful.

Voltage source converter based multi – terminal DC grids (VSC – MTDC) are widely used for integration of renewable resources. Control of these grids requires vector control method, which includes inner and outer current controllers, tuning of these controllers is very important. Conventionally used tuning methods consider approximated linear model to tune the proportional – integral (PI) parameters of voltage source converter (VSC) which fails to produce optimum disturbance rejection results. In this research an Ant Colony Optimization (ACO) technique is used to get the optimum parameters of inner and outer power control layers for multi – terminal DC system. ACO is applied simultaneously on inner and outer control layers and results are compared with classical tuning method and well established meta - herustic technique, Particle Swarm Optimization (PSO) using MATLAB software. Dynamic models of VSC – MTDC are developed in PSCAD/EMTDC to exalt the qualities of the presented tuning method. In this paper, a four terminal VSC – MTDC has been used under different sequence of events of disturbances e.g. load change on AC side, active power reference change for inverter and rectifier mode and disconnection of one terminal to evaluate the robustness of ACO algorithm with respect to classical method. The proposed tuning method gives superior results under different disturbance compared to conventional method.

Fault-tolerant control schemes are essential for affirming the safe and dependable operation of industrial robots. In this detailed review, we discuss the current developments in fault-tolerant control strategies for industrial robots. The main focus is given to highlight some major contributions in fault-tolerant control systems used in robotic manipulators with single or multiple joints, incorporating any linear or non-linear robust approach for industrial robots, design, and implementation. The paper also discusses adaptive fault-tolerant control of robots with sensor and/or actuator faults and unknown parameters, and fault-tolerant cooperative control of multiple robot teams for collaborative tasks. The present work provides a comprehensive overview of the recent advancements in fault-tolerant control strategies for industrial robots using both classical nonlinear methods as well as intelligent approaches using AI and machine learning, which will be useful for researchers and engineers working in this field.