Despite significant improvements over the last few years, cloud-based healthcare applications continue to suffer from poor adoption due to their limitations in meeting stringent security, privacy, and quality of service requirements (such as low latency). The edge computing trend, along with techniques for distributed machine learning such as federated learning, has gained popularity as a viable solution in such settings. In this paper, we leverage the capabilities of edge computing in medicine by evaluating the potential of intelligent processing of clinical data at the edge. We utilized the emerging concept of clustered federated learning (CFL) for an automatic COVID-19 diagnosis. We evaluate the performance of the proposed framework under different experimental setups on two benchmark datasets. Promising results are obtained on both datasets resulting in comparable results against the central baseline where the specialized models (i.e., each on a specific image modality) are trained with central data, and improvements of 16% and 11% in overall F1-Scores have been achieved over the trained model trained (using multi-modal COVID-19 data) in the CFL setup on X-ray and Ultrasound datasets, respectively. We also discussed the associated challenges, technologies, and techniques available for deploying ML at the edge in such privacy and delay-sensitive applications.

The increasing need for economic, safe, and sustainable smart manufacturing combined with novel technological enablers has paved the way for artificial intelligence (AI) and big data in industries. This implies a substantial integration of AI, Industrial Internet of Things (IIoT), Robotics, big data, Blockchain, and 5G communications in support of smart manufacturing and the dynamical processes in modern industries. In this article, we provide a comprehensive overview of different aspects of AI and big data in Industry 4.0 with a particular focus on key applications, techniques, the concepts involved, key enabling technologies, challenges, and research perspective toward deployment of Industry 5.0. In detail, we highlight and analyze how the duo of AI and big data is helping in different applications of Industry 4.0. We also highlight key challenges in a successful deployment of AI and big data solutions in smart industrial applications with a particular emphasis on data-related issues, such as availability, bias, auditing, management, interpretability, communication, and different adversarial attacks and security issues. Finally, we explore the significance of AI and big data toward Industry 4.0 applications through panoramic reviews and discussions. This work is expected to provide a baseline for future research in the domain.

As education constitutes an essential development standard for individuals and societies, researchers have been exploring the use of artificial intelligence (AI) in this domain and have embedded the technology within it through a myriad of applications. In order to provide a detailed overview of the efforts, this article pays particular attention to these developments by highlighting key application areas of data-driven AI in education; it also analyzes existing tools, research trends, as well as limitations of the role data-driven AI can play in education. In particular, this article reviews various applications of AI in education including student grading and assessments, student retention and drop-out predictions, sentiment analysis, intelligent tutoring, classroom monitoring, and recommender systems. This article also provides a detailed bibliometric analysis to highlight the salient research trends in AI in education over nine years (2014–2022) and further provides a detailed description of the tools and platforms developed as the outcome of research and development efforts in AI in education. For the bibliometric analysis, articles from several top venues are analyzed to explore research trends in the domain. The analysis shows sufficient contribution in the domain from different parts of the world with a clear lead for the United States. Moreover, students' grading and evaluation have been observed as the most widely explored application. Despite the significant success, we observed several aspects of education where AI alone has not contributed much. We believe such detailed analysis is expected to provide a baseline for future research in the domain.

Metaverse has evolved as one of the popular research agendas that let users learn, socialize, and collaborate in a networked three-dimensional (3D) immersive virtual world. Due to the rich multimedia streaming capability and immersive user experience with high-speed communication, the metaverse is an ideal model for education, training, and skill development tasks. To facilitate research in this area, we provide a comprehensive review of the various educational use cases and explore how enabling technologies such as Extended reality (XR) and the Internet of Everything (IoE) will play a major role in educational services in future metaverses. Secondly, we provide an overview of metaverse-based educational applications focusing on education, training, and skill development and analyze the technologies they are built upon. We identify common research problems and future research directions in the domain. The paper also identifies core ethical considerations of metaverse for education and potential pitfalls. We believe this survey can fully demonstrate the versatility of metaverse-driven education, which could serve as a potential guideline for the researchers.

In this article, we address the problem of an image-based automatic classification of waste materials. Given the large number of waste categories and the importance of proper management of waste materials, the problem is known to be critical and of a particular interest. To achieve reliable waste classification capability, we propose a novel approach, that we name double fusion, which optimally combines multiple deep learning models using feature and score-level fusion methods. The double fusion scheme ensures an optimized contribution of the deep models by, firstly, combining their capabilities in an early and late fusion scheme followed by a score-level fusion of the classification results obtained with early and late fusion methods. In total, we employ and compare six different fusion methods including two feature-level fusion schemes, namely (i) Discriminant Correlation Analysis and (ii) simple concatenation of deep features, and four late fusion methods, namely (i) Particle Swarm Optimization, (ii) Genetic modeling of deep features (iii) Induced Ordered Weighted Averaging and (iv) a baseline method where all the deep models are treated equally. Moreover, we also evaluate the performance of the individual deep models, and compare our results against state-of-the-art methods demonstrating a significant improvement of 3.58% over state-of-the-art.