Artificial Intelligence (AI) has made tremendous progress in anomaly detection. However, AI models work as a black-box, making it challenging to provide reasoning behind their judgments in a Log Anomaly Detection (LAD). To the rescue, Explainable Artificial Intelligence (XAI) improves system log analysis. It follows a white-box model for transparency, understandability, trustworthiness, and dependability of Machine Learning (ML) and Deep Learning (DL) Models. In addition, Shapely Additive Explanation (SHAP), added to system dynamics, makes informed judgments and adoptable proactive methods to optimize system functionality and reliability. Therefore, this paper proposed the Shapely eXplainable Anomaly Detection (SXAD) framework to identify different events (features) that impact the models’ interpretability, trustworthiness, and explainability. The framework utilizes the Kernel SHAP approach, which is based on Shapley values principle, providing an innovative approach to event selection and identifying specific events causing abnormal behavior. This study addresses the LAD by transforming it from a black-box model into a white-box one, leveraging XAI to make it transparent, interpretable, explainable, and dependable. It utilizes benchmark data from the Hadoop Distributed File System (HDFS), organized using a Drain parser, and employs several ML models, such as Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB). These models achieve impressive accuracy rates of 99.99%, 99.85%, and 99.99%, respectively. Our contribution are novel because no earlier work has been done in the area of Log Anomaly Detection (LAD) with integration of XAI-SHAP.

Autonomous Vehicles (AVs) can significantly contribute to environmental benefits by optimizing driving patterns and traffic flow. The process of training AVs in real-world conditions can be risky as it involves real traffic, pedestrians, and unpredictable scenarios. This makes accidents and safety concerns a genuine issue during the learning phase of autonomous systems. However, simulated environments offer a safer way to test autonomous systems in diverse scenarios, including rare and dangerous situations, without risking real-world accidents or injuries. We used Proximal Policy Optimization (PPO), a type of deep reinforcement learning algorithm, to train our driving agent by using Light Detection and Ranging (Lidar), and a camera sensor. We used Lidar for path tracking and a combination of Lidar and a camera sensor for obstacle avoidance. Results show that using the PPO is an effective technique to train AVs for path tracking and obstacle avoidance in a simulated environment. Results also show that the improved PPO is performing way better than the default PPO.

Applications well-defined with high Fault Tolerance index are considered reliable and trustworthy to drive quality. Fault Tolerance metrics for web mobile applications are neither defined formally nor are they organized at a single place for measurement to researchers and quality engineers. Fault Tolerance metrics determines the quality of web mobile applications provided they are measured accurately. There is a phenomenal gap between Fault Tolerance (FT) metrics development and measurement for web based mobile applications. Unfortunately, existing research has failed in providing a precise and well-organized definition for Fault Tolerance metrics in the domain of web mobile applications. This has led to inadequate Fault Tolerance metrics to perfectly measure and benchmark modern day web mobile applications. This research work has developed a comprehensive Fault Tolerance Metrics Framework, targeting Web and Mobile Applications to measure associated quality attributes by applying GQM technique. Dispersed FT attributes are identified and organized from existing Quality Models and standards. Sub factors have been well defined using standard units in line with web architectures. Particularly, this research applies Systematic Mathematical formulation to quantify Fault Tolerance to target web mobile applications. Further Fault Tolerance is measured and evaluated through an experimental study designed on target web mobile applications.

Hybrid Reality (HyR) is the place where human beings and artificial entities interact. HyR modelling relies simultaneously on the cognitive power of humans and artificial entities. In addition, HyR is an evolving paradigm where natural and artificial intelligence can intervene in processes that demand proper control. This work aims to lay the foundation for a systematic approach to understanding and modeling present and future human–machine symbiosis under a systems engineering perspective. It introduces a novel cyber-systemic methodology for managing the engineering of purposeful regulation for HyR phenomena by integrating the Blockchain technology framework and principled methods of cybernetics. This formalized interdisciplinary methodology integrates system dynamics, agent-based computation, artificial intelligence, and Blockchain-powered security and safety layers. The Blockchain framework, seen under a new cyber-systemic perspective, provides new opportunities and tools for the organization and control of HyR. A Cybersystemic Security Kit is here defined as a major component of the methodology, representing a candidate to offer viable breakthroughs in the field with respect to the best practices of Industry 5.0 when a systemically augmented perspective is adopted. Ongoing research and experimentation in the real field of sustainable supply chains is used as a motivating use case to support the proposed position. The industrial target is the primary one in its multi-dimensional and multi-faceted sustainability impacts, but this study will also reveal other potential societal areas of intervention.