Cutting-Edge AI Approaches with MAS for PdM in Industry 4.0: Challenges and Future Directions

This research explores the optimization of firewall systems within private cloud environments, specifically focusing on a 30-day evaluation of the Omni-Secure Firewall. Employing a multi-metric approach, the study introduces an innovative effectiveness metric (E) that amalgamates precision, recall, and redundancy considerations. The evaluation spans various machine learning models, including random forest, support vector machines, neural networks, k-nearest neighbors, decision tree, stochastic gradient descent, naive Bayes, logistic regression, gradient boosting, and AdaBoost. Benchmarking against service level agreement (SLA) metrics showcases the Omni-Secure Firewall’s commendable performance in meeting predefined targets. Noteworthy metrics include acceptable availability, target response time, efficient incident resolution, robust event detection, a low false-positive rate, and zero data-loss incidents, enhancing the system’s reliability and security, as well as user satisfaction. Performance metrics such as prediction latency, CPU usage, and memory consumption further highlight the system’s functionality, efficiency, and scalability within private cloud environments. The introduction of the effectiveness metric (E) provides a holistic assessment based on organizational priorities, considering precision, recall, F1 score, throughput, mitigation time, rule latency, and redundancy. Evaluation across machine learning models reveals variations, with random forest and support vector machines exhibiting notably high accuracy and balanced precision and recall. In conclusion, while the Omni-Secure Firewall System demonstrates potential, inconsistencies across machine learning models underscore the need for optimization. The dynamic nature of private cloud environments necessitates continuous monitoring and adjustment of security systems to fully realize benefits while safeguarding sensitive data and applications. The significance of this study lies in providing insights into optimizing firewall systems for private cloud environments, offering a framework for holistic security assessment and emphasizing the need for robust, reliable firewall systems in the dynamic landscape of private clouds. Study limitations, including the need for real-world validation and exploration of advanced machine learning models, set the stage for future research directions.

This paper discusses the application of Web wrapping technology in extracting metadata from Web sources. This capability has been incorporated into a software tool known as dynamic Dublin core/resource description framework metadata editor (DDC/RDF-Editor) which supports metadata development and management for resources in the World Wide Web. One key feature of the editor is the ability to automatically extract relevant values of metadata elements from the Web sources in question according to the Dublin core (DC) metadata standard and represent it in resource description framework (RDF) language

Cameleon# is a web data extraction and management tool that provides information aggregation with advanced capabilities that are useful for developing value-added applications and services for electronic business and electronic commerce. To illustrate its features, we use an airfare aggregation example that collects data from eight online sites, including Travelocity, Orbitz, and Expedia. This paper covers the integration of Cameleon# with commercial database management systems, such as MS SQL Server, and XML query languages, such as XQuery.

Developing the Readiness and Success Model of Information System Implementation in the Indonesian Equestrian Industry