The emergence of Industry 5.0 marks a transformative era in manufacturing industry that is driven by the integration of advanced technologies. It is a renewed focus on human–robot collaboration. At the forefront of this industrial revolution stands Cobotics, a field that emphasizes the cooperative interaction between humans and robots in shared workspaces. This research paper delves into the critical role and implications of Cobotics in the context of Industry 5.0. The study addresses the research problem of effectively integrating Cobots into industrial processes, considering technical, economic, and social challenges. The paper’s objectives include surveying existing literature, analyzing developments and trends, showcasing real‐world examples, and proposing future research directions. A comprehensive literature review explores Cobotics’ foundations and its alignment with Industry 5.0 principles. The research presents real‐world examples of successful Cobot integration across various industries, demonstrating benefits such as increased productivity, improved quality, and enhanced worker safety. The paper identifies gaps in existing knowledge and contributions to the field, emphasizing the need for research in simplified programming, cost–benefit analysis, and ethical frameworks. This research paper illuminates the central role of Cobotics in Industry 5.0, where human–robot collaboration, innovation, and inclusivity are paramount. It anticipates a future where Cobotics will continue to evolve, shaping industries, empowering the workforce, and driving sustainable, data‐driven industrial transformations.

Almost all of the existing approaches to determining online local community are typically deliberated like-minded users who have similar topical interests. However, such methodologies overlook the prospective temporality of users’ interests as well as users’ degree of topical activeness. As a result, the consequential communities might have extremely lower active users. This research investigates how online social users’ behaviors and topical activeness vary over time and how these parameters can be employed in order to improve the quality of the detected local community. For a given input query, consisting a query node (user) and a set of attributes, this research intends to find densely-connected community in which community members are temporally similar in terms of their activities related to the query attributes. To address the proposed problem, we develop a temporal activity biased weight model which gives higher weight to users’ recent activities and develop an algorithm to search an effective community. The effectiveness of the proposed methodology is justified using four benchmark datasets and compared with four other baseline methods. Experimental results demonstrate that our proposed framework yields better outcomes than the baseline methods for all four benchmark datasets.

COVID-19 is the worst pandemic that has hit the globe in recent history, causing an increase in deaths. As a result of this pandemic, a number of research interests emerged in several fields such as medicine, health informatics, medical imaging, artificial intelligence and social sciences. Lung infection or pneumonia is the regular complication of COVID-19, and Reverse Transcription Polymerase Chain Reaction (RT-PCR) and computed tomography (CT) have played important roles to diagnose the disease. This research proposes an image enhancement method employing fuzzy expected value to improve the quality of the image for the detection of COVID-19 pneumonia. The principal objective of this research is to detect COVID-19 in patients using CT scan images collected from different sources, which include patients suffering from pneumonia and healthy people. The method is based on fuzzy histogram equalization and is organized with the improvement of the image contrast using fuzzy normalized histogram of the image. The effectiveness of the algorithm has been justified over several experiments on different features of CT images of lung for COVID-19 patients, like Ground-Glass Opacity (GGO), crazy paving, and consolidation. Experimental investigations indicate that among the 254 patients, 81.89 had features on both lungs; 9.5 on the left lung; and 10.24 on the right lung. The predominantly affected lobe was the right lower lobe (79.53).

Image enhancement is a technique to improve the quality of an image. The aim of image enhancement technique is to improve the interpretability or perception of information in images for human viewers, or to provide better input for other automated image processing techniques. This paper presents a new approach for image enhancement with fuzzy rule-based filtering. Compared to other non-linear techniques, fuzzy filter gives the better performance and is able to represent knowledge in a comprehensible way.