This paper presents a comprehensive review of machine learning (ML) based approaches, especially artificial neural networks (ANNs) in time series data prediction problems. According to literature, around 80% of the world’s total energy demand is supplied either through fuel-based sources such as oil, gas, and coal or through nuclear-based sources. Literature also shows that a shortage of fossil fuels is inevitable and the world will face this problem sooner or later. Moreover, the remote and rural areas that suffer from not being able to reach traditional grid power electricity need alternative sources of energy. A “hybrid-renewable-energy system” (HRES) involving different renewable resources can be used to supply sustainable power in these areas. The uncertain nature of renewable energy resources and the intelligent ability of the neural network approach to process complex time series inputs have inspired the use of ANN methods in renewable energy forecasting. Thus, this study aims to study the different data driven models of ANN approaches that can provide accurate predictions of renewable energy, like solar, wind, or hydro-power generation. Various refinement architectures of neural networks, such as “multi-layer perception” (MLP), “recurrent-neural network” (RNN), and “convolutional-neural network” (CNN), as well as “long-short-term memory” (LSTM) models, have been offered in the applications of renewable energy forecasting. These models are able to perform short-term time-series prediction in renewable energy sources and to use prior information that influences its value in future prediction.

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.

The latest 5G technology is being introduced the Internet of Things (IoT) Era. The study aims to focus the 5G technology and the current healthcare challenges as well as to highlight 5G based solutions that can handle the COVID-19 issues in different arenas. This paper provides a comprehensive review of 5G technology with the integration of other digital technologies (like AI and machine learning, IoT objects, big data analytics, cloud computing, robotic technology, and other digital platforms) in emerging healthcare applications. From the literature, it is clear that the promising aspects of 5G (such as super-high speed, high throughput, low latency) have a prospect in healthcare advancement. Now healthcare is being adopted 5G-based technologies to aid improved health services, more effective medical research, enhanced quality of life, better experiences of medical professionals and patients in anywhere–anytime. This paper emphasizes the evolving roles of 5G technology for handling the epidemiological challenges. The study also discusses various technological challenges and prospective for developing 5G powered healthcare solutions. Further works will incorporate more studies on how to expand 5G-based digital society as well as to resolve the issues of safety–security–privacy and availability–accessibility–integrity in future health crises.

The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic’s dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.