Testing and implementation of integrated and intelligent transport systems (IITS) of an electrical vehicle need many high‐performance and high‐precision subsystems. The existing systems confine themselves with limited features and have driving range anxiety, charging and discharging time issues, and inter‐ and intravehicle communication problems. The above issues are the critical barriers to the penetration of EVs with a smart grid. This paper proposes the concepts which consist of connected vehicles that exploit vehicular ad hoc network (VANET) communication, embedded system integrated with sensors which acquire the static and dynamic parameter of the electrical vehicle, and cloud integration and dig data analytics tools. Vehicle control information is generated based on machine learning‐based control systems. This paper also focuses on improving the overall performance (discharge time and cycle life) of a lithium ion battery, increasing the range of the electric vehicle, enhancing the safety of the battery that acquires the static and dynamic parameter and driving pattern of the electrical vehicle, establishing vehicular ad hoc network (VANET) communication, and handling and analyzing the acquired data with the help of various artificial big data analytics techniques.

The mouse is one of the wonderful inventions of Human-Computer Interaction (HCI) technology. Currently, wireless mouse or a Bluetooth mouse still uses devices and is not free of devices completely since it uses a battery for power and a dongle to connect it to the PC. In the proposed AI virtual mouse system, this limitation can be overcome by employing webcam or a built-in camera for capturing of hand gestures and hand tip detection using computer vision. The algorithm used in the system makes use of the machine learning algorithm. Based on the hand gestures, the computer can be controlled virtually and can perform left click, right click, scrolling functions, and computer cursor function without the use of the physical mouse. The algorithm is based on deep learning for detecting the hands. Hence, the proposed system will avoid COVID-19 spread by eliminating the human intervention and dependency of devices to control the computer.

Rapid Internet use growth and applications of diverse military have managed researchers to develop smart systems to help applications and users achieve the facilities through the provision of required service quality in networks. Any smart technologies offer protection in interactions in dispersed locations such as, e-commerce, mobile networking, telecommunications and management of network. Furthermore, this article proposed on intelligent feature selection methods and intrusion detection (ISTID) organization in webs based on neuron-genetic algorithms, intelligent software agents, genetic algorithms, particulate swarm intelligence and neural networks, rough-set. These techniques were useful to identify and prevent network intrusion to provide Internet safety and improve service value and accuracy, performance and efficiency. Furthermore, new algorithms of intelligent rules-based attributes collection algorithm for efficient function and rules-based improved vector support computer, were proposed in this article, along with a survey into the current smart techniques for intrusion detection systems.

Recent years have seen a surge in curiosity in machine-to-machine (M2M) collaborations between academics and industry. Machine-to-machine communication devices (MTCDs) are able to communicate automatically and with minimum human intervention in an M2M communications infrastructure. While MTCDs are anticipated to deliver a range of services, resource allocation and clustering approaches in M2M transceivers face issues and limits due to the diverse quality of service (QoS) needs in various network conditions. A major issue in M2M communication systems is how to distribute and cluster resources. This article presents a clustering technique and collaborative resource allocation for MTCD resource management. The clustering and integrated resource allocation challenge is characteristic as a maximization of energy efficiency problem. As a consequence of the original optimization model’s inability to tackle nonlinear fractional utilizations, we separate the issue into two subproblems: power redistribution and cluster. We begin by obtaining the optimal power distribution plan through an iterative energy efficiency maximization algorithm and then offer a modified -means technique for clustering. The effectiveness of the proposed approach is shown by the numerical solution.