The Fourth Industrial Revolution, also known as Industry 4.0, represents the technological evolution from traditional manufacturing systems to cyber-physical systems, which leads to improvement of overall productivity and reductions of environmental impact, thus promoting sustainable economic development. Industry 4.0 has been driven by emerging technology developments in the field of digital twin, artificial intelligence, robotic and automation, Internet of Things (IoT), cloud computing, and edge/fog computing, and has been a hot topic in both academia and industry. The resulting big data are fed to AI-based mission-critical systems to perform effectively production monitoring, quality inspection, root cause analysis, quality prediction, and process control. The proper adoption of relevant industry 4.0 technologies should lead to significant efficiency improvement and cost reduction in various industrial sectors. The goal of this special issue is to bring together researchers and practitioners from academia and industry to provide a forum for discussing industrial automation research on smart manufacturing and machine learning.

Having played a central role at the inception of artificial intelligence research, machine learning has recently reemerged as a major area of study at the very core of the subject. Solid theoretical foundations are being constructed. Machine learning methods are being integrated with powerful performance systems, and practical applications; based on established techniques are emerging.Machine Learning unifies the field by bringing together and clearly explaining the major successful paradigms for machine learning: inductive approaches, explanation-based learning, genetic algorithms, and connectionist learning methods. Each paradigm is presented in depth, providing historical perspective but focusing on current research and potential applications.

This special issue highlights the applications, practices and theory of artificial intelligence in the domain of cyber security. In the past few decades there has been an exponential rise in the application of artificial intelligence technologies (such as deep learning, machine learning, block-chain, and virtualization etc.) for solving complex and intricate problems arising in the domain of cyber security. The versatility of these techniques have made them a favorite among scientists and researchers working in diverse areas. The primary objective of this topical collection is to bring forward thorough, in-depth, and well-focused developments of artificial intelligence technologies and their applications in cyber security domain, to propose new approaches, and to present applications of innovative approaches in real facilities. AI can be both a blessing and a curse for cybersecurity.

Join the audience for an AI in Medical Physics Week live webinar at 3 p.m. BST on 23 June 2022 based on IOP Publishing's special issue, Focus on Machine Learning Models in Medical Imaging Want to take part in this webinar? An overview will be given of the role of artificial intelligence (AI) in automatic delineation (contouring) of organs in preclinical cancer research models. It will be shown how AI can increase efficiency in preclinical research. Speaker: Frank Verhaegen is head of radiotherapy physics research at Maastro Clinic, and also professor at the University of Maastricht, both located in the Netherlands. He is also a co-founder of the company SmART Scientific Solutions BV, which develops research software for preclinical cancer research.

In 2022, Artificial Intelligence is the hottest and most in-demand field; most engineers want to make their careers in AI, Data Science & Data Analytics. Going through the best and most reliable resources is the best way to learn, so here is the list of the best AI books on the market today. Artificial Intelligence is the field of study that simulates the processes of human intelligence on computer systems. These processes include the acquisition of information, using them, and approximating conclusions. The research topics in AI include problem-solving, reasoning, planning, natural language, programming, and machine learning. Automation, robotics, and sophisticated computer software and programs characterize a career in Artificial Intelligence.

Years ago, I had to choose between a neural network and a decision tree learning algorithm. It was necessary to pick an efficient one, because we planned to apply the algorithm to a very large set of users on a limited compute budget. I went with a neural network. I hadn't used boosted decision trees in a while, and I thought they required more computation than they actually do -- so I made a bad call. Fortunately, my team quickly revised my decision, and the project was successful. This experience was a lesson in the importance of learning, and continually refreshing, foundational knowledge. If I had refreshed my familiarity with boosted trees, I would have made a better decision.

Huanfeng Shen, Wuhan University ([email protected]), Jane Liu, University of Toronto ([email protected]), Wenping Yuan, Sun Yat-Sen University ([email protected]), Yongguang Zhang, Nanjing University ([email protected]), Holly Croft, University of Sheffield ([email protected]), Xiaobin Guan, Wuhan University ([email protected]). The dramatic increase in anthropogenic carbon emissions over the last five decades has already led to substantial damage to our environment, including increases in extreme weatherevents, loss of biodiversity, and a rise in sea level. Carbon neutrality, i.e., net-zero anthropogenic carbon emissions, is necessary to ensure the sustainable future of human beings, and hundreds of countries have pledged to achieve this goal by mid-century. Remote sensing techniques can acquire frequent observations of the Earth with various temporal and spatial resolutions, and provide substantial information for carbon emission monitoring and carbon cycle modeling. Remote sensing observations not only can be directly applied to retrieve the atmospheric concentrations of greenhouse gases (e.g., CO2, CO, CH4, CFCs, O3, et al.), but also can be employed to investigate the carbon budget of natural ecosystems.

The last decade has seen the increasingly important, even dominant, application of deep learning (DL) in the field of various applications. Conventional machine learning methods have been the focus of intense investigations for years; however, they have limited capabilities, are biased to dataset selection, and are faced with an overwhelming challenge to integrate large, heterogeneous data sources. On the other hand, recent advancements in deep learning architectures, coupled with high-performance computing, have demonstrated significant breakthroughs in dealing with complexities by radically changing research methodologies toward a data-oriented approach. This Special Issue encourages authors, from academia and industry, to submit new research results about positioning and navigation models based on machine learning for complex systems. Manuscripts should be submitted online at www.mdpi.com

The Data Science Blogathon by Analytics Vidhya began with a simple mission: To bring together a large community of data science enthusiasts to share their knowledge with the world. With 4000 articles under our belt on various topics such as Data Science, Machine Learning, Deep Learning, Data Lakes, and Data Engineering published by over 700 authors who are avid data science enthusiasts, students, professionals and researchers from across the globe. We bring to you the 20th edition of the Data Science Blogathon. This month's Data Science Blogathon brings you more rewards for you through our special referral programme. Yes, you read that right!

This special issue publishes research studies on the advances in the field of computing and artificial intelligence and collects state-of-the-art contributions on the latest research and development and challenges in the field of Medical Informatics and Biomedical Image Processing for the analysis and exploration of the nervous system. We hope to receive innovative contributions in both theoretical and practical aspects. Strong emphasis is placed on innovative results in theory, methodology and applications of artificial intelligence. Topics may be related to computer vision and image understanding, machine learning, search techniques, medical image or data analysis, and use of relevant specialized hardware/software architectures. Papers must be submitted online to Neuroscience Informatics on the online submission website Editorial Manager by August, 31 2022 to be considered and accepted by October 4, 2022.