Scopus Research — Ruaa Satar Jabar Alatapee

This research explores the intersection of sustainability, load balancing, request management, resource diversity, system capability, and cost-effectiveness in the design of resilient and environmentally responsible engineering systems. Sustainability, a cornerstone of societal progress, encompasses the pursuit of enduring harmony on Earth, encompassing environmental preservation, economic prosperity, and social welfare. Technological advancement is intricately tied to the efficient and responsible utilization of resources. Load balancing, a critical component of computing communication, seeks to optimize workload distribution across computing resources to mitigate redundancies and bottlenecks, ensuring smooth network operation. In this paradigm, the amount and type of requests becomes a critical component, which determines the extent to which resources can be adequately distributed to process the incoming tasks. Every system has different system parameters, such as CPU counts and memory, which determine the capability of a system to process different types of tasks. Load balancing, which balances the variables, including requests, ensures that imbalances do not occur and that this approach strengthens system performance as a demonstration of a close connection between sustainability and load balancing. However, the proposed algorithm makes a futuristic approach by suggesting that a resource allocation matrix, considering the number of requests, types of resources, CPU counts, cost, and memory. Moreover, the algorithm also enhances durability goals as it helps in the improvement of resource utilization and thus helping in load distribution. Lastly, the dynamic resource allocation is made achievable by the algorithm can also enhance higher resource efficiency, and reduced system power consumption. In conclusion, some of the traditional algorithms was compared with the proposed algorithm which is reflect better or high performance noted on the proposed algorithm. © (2024), (Intelligent Network and Systems Society). All rights reserved.

Modern world technologies such as the integration of technologies such as the Internet of Things (IoT), cloud computing, and machine learning (ML) enhance the challenges of smart industrial management. Detecting anomalies in predictive maintenance within smart factories, and monitoring machine health to prevent unexpected breakdowns. This research presents an advanced model for designing automatic encoders capable of distinguishing between sounds emitted by machines in industrial environments and identifying faults. The MIMII dataset and advanced feature extraction techniques, such as MFCCs, are adopted as key factors in making the proposed model. The four evaluation measures: accuracy, recall, recall, and F1 score, in addition to the confusion matrix, were also adopted. To evaluate the model's performance. The results confirm the effectiveness and robustness of the proposed deep neural network model designed for autoencoders in the field of artificial audio classification. With a commendable accuracy rate of 93.95% and F1 score of 95.31%. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).