Reliability analysis aims to assess the dependability and performance of a system over time, considering factors such as device failures, communication issues, and data integrity. By integrating taxonomy and reliability analysis, researchers can systematically analyze and enhance the reliability of IoT systems by addressing specific components and categories identified in the taxonomy. IoT based on its reliable applications is classified among various categories such as smart home devices, smart watches, electric vehicles with IoT capabilities, industrial, healthcare, agriculture, soil monitoring sensors, smart city infrastructure, environmental monitoring devices, and smart energy management devices. The need for a consistent taxonomy was one of the problems recognized and examined in this paper even though IoT management technology is developing. To examine the reliability analysis of IoT systems, we created a brand-new taxonomy for IoT gadgets that places a strong focus on administration. We have released a completely new IoT reliability RBD model analysis at different gateways and Round-Trip Time is to be analyzed. It features an aircraft for dependability. Our goal is to help administrators of networks, as well as designers, put processes in place that will analyze and enhance the reliability of IoT systems. To implement techniques to increase the IoT system's reliability, we may utilize the management capabilities to keep an eye on the devices. ChatGPT can act as the conversational interface for ISABELA. By integrating ChatGPT with ISABELA's functionality, users can have natural language conversations with ISABELA, making the interaction more intuitive and user-friendly. A use case study of ISABELA has enhanced the position of the CHATGPT system in the IoT system which increase the efficiency and reliability of IoT designs and networks.

This chapter enables the targeted troubleshooting and remediation of IoT-based technology with AI. Reliability modeling is another key AI application in IoT. By analyzing historical data, AI algorithms can create probabilistic models that assess the reliability of IoT systems. These models estimate failure rates, identify failure modes, and predict system behavior under different conditions. This helps in understanding the reliability characteristics of IoT deployments and optimizing system designs. The paper also highlights the role of AI in the adaptive control and optimization of IoT systems. AI algorithms continuously analyze sensor data to adapt system parameters and configurations in real-time. This adaptive control ensures that the system operates within safe, reliable limits, minimizing the risk of failures and performance degradation. Furthermore, AI contributes to the cyber-security of IoT networks. Machine learning algorithms detect and mitigate cyber security threats by analyzing network traffic, identifying suspicious patterns, and predicting potential attacks. AI-powered intrusion detection systems help maintain the integrity and availability of IoT systems. Lastly, the paper discusses how AI can be utilized as a decision support system in IoT environments. <!----> <!----> <!----> <!---->

Credit cards, mobile wallets, and other electronic payment methods are gaining popularity. Online transactions are increasingly the norm — global fraud increases as electronic payments increase. As credit cards and online shopping become increasingly popular, fraud has skyrocketed. Fraud detection and prevention are being prioritized due to the global economy.

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.

Intense and widespread usage of software in every field of life has attracted the researchers to focus their attention on developing the methods to improve the efficiency of software testing; which is the most crucial and cost intensive phase of software development. Software testing aims to uncover the potential faults in Application Under Test by running the test cases on software code. Software code keeps on changing as the uncovered faults during testing are fixed by the developers. Regression testing is concerned with verifying the modified software code to ensure that changes in software code does not induce any undesired effect on rest of the code. Test Case Prioritization is a regression testing technique which re-schedule the execution sequence of test cases to improve the fault detection rate and enhance the performance of regression test suite. This paper focuses on proposing a novel method "m-ACO" for test case prioritization and the performance evaluation of the proposed method using Average Percentage of faults Detected.