The burgeoning application of artificial intelligence (AI) technologies for the diagnosis and detection of defects has marked a significant area of interest among researchers in recent years. This study presents a fuzzy logic-based approach to identify failures within industrial systems, with a focus on operational anomalies in a real-world context, particularly within the competitive landscape of Omar Benamour, in Al-Fajjouj region, Guelma, Algeria. The analysis has been started with the employment of the Activity-Based Costing (ABC) method to identify the critical machinery within the K-short dough production line. Subsequently, an elaborate failure tree analysis has been conducted on the pressing machine, enabling the deployment of a fuzzy logic approach for the detection of failures in the dough cutter of AMOR BENAMOR's K production line press. The effectiveness of the proposed method has been validated through an evaluation conducted with an authentic and real-time data from the facility, where the study took place. The results underscore the efficacy of the fuzzy logic approach in enhancing fault detection within industrial systems, offering substantial implications for the advancement of defect diagnosis methodologies. The study advocates for the integration of fuzzy logic principles in the operational oversight of industrial machinery, aiming to mitigate potential failures and optimize production efficiency.
The rapid advancement of technology has correspondingly escalated the sophistication of cyber threats. In response, the integration of artificial intelligence (AI) into cybersecurity (CS) frameworks has been recognized as a crucial strategy to bolster defenses against these evolving challenges. This analysis scrutinizes the effects of AI implementation on CS effectiveness, focusing on a case study involving company XYZ's adoption of an AI-driven threat detection system. The evaluation centers on several pivotal metrics, including False Positive Rate (FPR), Detection Accuracy (DA), Mean Time to Detect (MTTD), and Operational Efficiency (OE). Findings from this study illustrate a marked reduction in false positives, enhanced DA, and more streamlined security operations. The integration of AI has demonstrably fortified CS resilience and expedited incident response capabilities. Such improvements not only underscore the potential of AI-driven solutions to significantly enhance CS measures but also highlight their necessity in safeguarding digital assets within a continuously evolving threat landscape. The implications of these findings are profound, suggesting that leveraging AI technologies is imperative for effectively mitigating cyber threats and ensuring robust digital security in contemporary settings.
Open Access
Journal Metrics
Journal Overview
Associated Journals