This paper presents a case study exploring the application of large language models (LLMs) in tracing the origins of software errors. We developed an iterative software error tracking framework that combines the analytical capabilities of LLMs with expert human reasoning. Using this framework, we successfully identified and resolved a software error encountered in VirtualBox. The tracking process involved three key stages: generating outputs with the LLM, conducting human analysis of these outputs, and refining prompts to improve the accuracy of the LLM's responses. This study demonstrates the effectiveness of LLMs in software anomaly detection while emphasizing the critical role of human expertise in guiding the process. The findings offer valuable insights for software testing practitioners on leveraging LLMs to track and resolve runtime anomalies.

The fourth industrial revolution has presented the manufacturing sector with both enormous prospects and difficulties. This comprehensive analysis looks at the advantages and disadvantages of applying Industry 4.0 to manufacturing, giving businesses a better understanding of the roadblocks and possible rewards. The implementation of autonomous mobile robots has been considered over traditional fork lifts with the use of state-of-the-art API communication protocols between master entry servers, manufacturing execution system and warehouse management system. This not only brings smart automation in material handling but also saves significant process time. Through a time study of around 15 workstations, a savings of around 1.14 hours/shift has been recorded. Time as well as a cost study were incorporated for a smooth business decision. Cost analysis was done for the duration of 5 years using the OPEX and CAPEX models. Out of the 3 models studied, the CAPEX model of autonomous mobile robot proved to be 30% more economical.

Effective data collection is crucial for improving the performance and energy use of Wireless Sensor Networks (WSNs) in areas like industrial automation and environmental monitoring. WSNs struggle with balancing important Quality of Service (QoS) factors, such as data transfer speed, energy use, data delivery, and transmission delays. To tackle these problems, this research compares three data aggregation methods, namely 'CH Reputation-Based', 'CH Hybrid (Residual Energy + Distance)', and 'Hybrid (Time + Packet)'. The 'CH Reputation-Based' method uses reputation-based metrics to distribute the workload among cluster heads evenly, ensuring the best network performance. Simulation results show that this method is better, with a 12.28% increase in throughput, a 14.83% improvement in packet delivery, and a 12.54% decrease in energy consumption compared to the 'CH Hybrid (RE + Distance)' method. This analysis shows the effectiveness of integrating a reputation-based DA scheme in WSN. Additionally, comparative analysis against existing approaches shows that although other methods have certain benefits, the 'CH Reputation-Based' approach provides the best mix of performance and resource use. This makes it the most trustworthy and energy-saving choice for data transfer in the current WSN. It meets the growing needs of applications that require high-quality service and network reliability.

In the fast-changing world of Social Cloud where online networks use real-life social connections, the quality of cloud service providers depends on trust and reputation. This study looks at important factors affecting service quality, such as multi-user collaboration, sharing resources, and feedback in the Social Cloud. This article tackles the issue of choosing a strong and trustworthy service provider. Our method involves two steps. First, we use statistical analysis to build trust in cloud services. Second, we apply optimization techniques using the artificial bee colony (ABC) algorithm, which is inspired by the social behavior of honey bees. This proposed approach aims to improve the trustworthiness and reliability of cloud services in the Social Cloud environment. To test our new framework, we run simulations that compare how well it works compared to other methods. The results show that our approach, which uses a technique called ABC as a guide, works well and provides a strong, dependable base for cloud services in the ever-changing Social Cloud environment. This research adds to the discussion about trust in cloud services, presenting a fresh viewpoint and useful information.

In this research, we propose a DDoS attack detection and mitigation framework using machine learning for Software-Defined Networking (SDN). Rule-based or statistical models are commonly employed in existing DDoS detection methods, which fall short in terms of generalization to evolving DDoS attacks. In this paper we propose a deep learning based Dynamic Network Traffic Anomaly Detection model based on Hybrid Convolutional Neural Network and Long Short Memory approach. To ensure that the framework can withstand both known and zero-day attacks, it is trained on three heterogeneous datasets: CICIDS- 2017, UNSW-NB15, and Mininet-generated traffic. The key point is that the SDN controller serves as the central intelligence node in this architecture, receiving network flow statistics and using ML-based threat detection to apply security policies in real time. Experimental results prove that the resulting model achieves an accuracy of 99.82%, outperforms classical machine learning models and possesses strong resistance performance against adversarial attacks. The results underline the promise of using deep learning techniques for SDN environments for upcoming cyber threats.

This research focuses on multi-objective optimization of production lines using advanced techniques to improve decision-making and operational efficiency. As a part of Industry 4.0, our study uses multi-agent systems (MAS) modeling and genetic algorithms (GA) to solve the complexities of this process. A large-scale modeling and simulation framework was developed, demonstrating the effectiveness of these approaches in improving the performance and adaptability of production systems. This approach improves the flexibility and responsiveness of industrial environments while ensuring their compliance with contemporary industry standards.