Chinese Science Bulletin

This research introduces the Multi-objective Exponential Distribution Optimizer (MOEDO), a novel algorithm tailored to solve complex optimization challenges in power grid management, focusing on Optimal Power Flow (OPF) problems. MOEDO enhances the basic Exponential Distribution Optimizer by incorporating advanced techniques such as non-dominated sorting and crowding distance, alongside an Information Feedback Mechanism (IFM). This integration aims to optimize the efficiency of power systems by reducing fuel consumption and better integrating renewable energy sources within an IEEE-30 bus fram

Internet is not just about connecting computers or browsing. Now internet took a step ahead and it is evolving into Internet of Things. Infrastructure, devices, smart objects and even physical environment are getting connected. If we combine Internet of Things with Artificial Internet then it will maximize the potential of both technologies. The combination of Internet of Things and Cyber physical system with data science could bring about the next “smart digital revolution“. Internet of Things allows business to gather continuous data on various physical activities offering valuable insig

For the manufacturing sector to become economically viable, innovation and adaptation with new technologies are essential. Sustainable manufacturing is already being achieved through the application of machine learning and other AI techniques. To assist with this analysis, we used UCINET and NVivo 12 software along with databases such as Web of Science and SCOPUS. One attractive discovery was that after Industry 4.0 started, the United States published a greater number of articles and there was an upsurge in interest in this subject. Current developments in advanced robotics have been greatly

The outbreak of the COVID-19 pandemic has presented significant challenges to global healthcare systems, necessitating a timely and accurate detection of COVID-19 cases. In this research paper, the authors have proposed an adapted Detrac model (a novel deep learning-based approach) designed to classify input images into three distinct categories: pneumonia, COVID, normal) called DeepEnTraCT for COVID-19 detection using chest X-ray images. This innovation aims to improve the precision and efficiency of COVID-19 detection models by incorporating advanced techniques in feature extraction, selecti

In modern computing environments characterized by high variability and complex workloads, traditional load balancing algorithms such as Round Robin and Least Connections are often lower in effectively distributing tasks and maintaining optimal performance. This paper presents a Hybrid- Machine Learning (Hybrid-ML) load balancing algorithm that combines the strengths of Fastest Response Load Balancing (FRLB) and Priority-based Load Balancing (PBLB) with advanced machine learning (ML) techniques. The Hybrid-ML algorithm leverages real-time data to predict optimal server allocations, dynamically