Looking at the issues of low efficiency, poor control performance, and difficult access control of the traditional role-based access control model, an artificial intelligence technique-based power information system access control model has been designed. The detector is designed by artificial intelligence technology, combining artificial neural network, and artificial immune algorithm, which provide the basis for checking the access request module. It has been proved that the design model can effectively support the access and modification of legitimate users and prevent illegal users from accessing, and the control accuracy is high. The use of artificial intelligence (AI) in the power sector is now reaching emerging markets, where it may have a critical impact, as clean, cheap, and reliable energy is essential to development. Artificial intelligence can be proven very efficient for resolving the control and decision-making issues in high complex systems.

The World Health Organization reports that heart disease is the most common cause of death globally, accounting for 17.9 million fatalities annually. The fundamentals of a cure, it is thought, are important symptoms and recognition of the illness. Traditional techniques are facing many challenges, ranging from delayed or unnecessary treatment to incorrect diagnoses, which can affect treatment progress, increase the bill, and give the disease more time to spread and harm the patient’s body. Such errors could be avoided and minimized by employing ML and AI techniques. Many significant efforts have been made in recent years to increase computer-aided diagnosis and detection applications, which is a rapidly growing area of research. Machine learning algorithms are especially important in CAD, which is used to detect patterns in medical data sources and make nontrivial predictions to assist doctors and clinicians in making timely decisions. This study aims to develop multiple methods for machine learning using the UCI set of data based on individuals’ medical attributes to aid in the early detection of cardiovascular disease. Various machine learning techniques are used to evaluate and review the results of the UCI machine learning heart disease dataset. The proposed algorithms had the highest accuracy, with the random forest classifier achieving 96.72% and the extreme gradient boost achieving 95.08%. This will assist the doctor in taking appropriate actions. The proposed technology will only be able to determine whether or not a person has a heart issue. The severity of heart disease cannot be determined using this method.

Machine learning and data analytics are two of the most popular subdisciplines of modern computer science which have a variety of scopes in most of the industries ranging from hospitals to hotels, manufacturing to pharmaceuticals, mining to banking, etc. Additionally, mining and hospitals are two of the most critical industries where applications when deployed security, accuracy, and cost effectiveness are the major concerns, due to the huge involvement of man and machines. In this paper, the problem of finding out the location of man and machines has been focused on in case of an accident during the mining process. The primary scope of the research is to guarantee that the projected position is near to the real place so that the trained model’s performance can be tested. The solution has been implemented by first proposing the MLAELD (Machine Learning Architecture for Excavators’ Location Detection), in which Bluetooth Low Energy (BLE) beacons have been used for tracking the live locations of excavators preceded by collecting the data of the signal strength mapping from multiple beacons at each specific point in a closed area. Second, machine learning techniques are proposed to develop and train multioutput regression models using linear regression, K-nearest neighbor regression, decision tree regression, and random forest regression. These techniques can predict the live locations of the required persons and machines with a high level of precision from the last beacon strengths received.

A peer-to-peer (P2P) decentralized information-sharing network is used to share data and maintain security, privacy, and integrity standards called blockchain. In this case, information sharing and updating require regular simplification. The presented systematic review mainly focuses on the interoperability of electronic health records (EHRs) using blockchain. Correspondingly, 18 blockchain-based solutions were selected to address the interoperability challenges of EHRs. The limitation of solutions includes reliability, privacy, integrity, sharing, and standards. This systematic review contains six phase’s research question, research phase, article selection, abstract-based keyword, data extraction, and progress tracking. Various Web resources such as Google Scholar, Web of Science, and IEEE are used to extract the relevant manuscripts. Primarily, 18 articles were selected to present the interoperable requirements of EHRs using blockchain, standards of blockchain-based EHRs, and solutions for interoperability of EHRs using blockchain. The conducted study explains the best available interoperable blockchain-based EHR standards, implementations, applications, and challenges.

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