International Journal of Educational Technology in Higher Education is calling for submissions to our Collection on New advances in artificial intelligence applications in higher education. There has been growing interest in the educational potential of Artificial Intelligence (AI) applications within the field of educational technology for the past decade. Despite the recent peak of excitement towards advanced features and techniques of AI-driven language models and OpenAI's ChatGPT, their actual impact on higher education (HE) institutions and participants have been largely unknown. Thus, the discussions in the field have continuously remained, mainly consisting of overstated hype and untested hypotheses, either optimistic or pessimistic, about the impact of AI applications. About three years ago, the editors of the ETHE Special Issue "Can artificial intelligence transform higher education?" However, a lot has happened since then.

As the world becomes increasingly digital, the need for secure and decentralized communication channels has never been greater. This book examines the ways in which integrating ChatGPT with blockchain technology can provide a powerful solution to this problem. The book begins with an overview of the basics of artificial intelligence and blockchain technology, providing a foundation for readers who may be unfamiliar with these concepts. From there, it delves into the specific ways in which ChatGPT, a state-of-the-art language model, can be integrated with blockchain technology to create a secure and decentralized communication platform. Readers will learn about the technical details of integrating these two technologies, as well as the potential applications for such a platform.

Rapid advances in artificial intelligence (AI) have the potential to significantly increase the productivity, quality, and profitability in future manufacturing systems. Traditional mass-production will give way to personalized production, with each item made to order, at the low cost and high-quality consumers have come to expect. Manufacturing systems will have the intelligence to be resilient to multiple disruptions, from small-scale machine breakdowns, to large-scale natural disasters. Products will be made with higher precision and lower variability. While gains have been made towards the development of these factories of the future, many challenges remain to fully realize this vision. To consider the challenges and opportunities associated with this topic, a panel of experts from Industry, Academia, and Government was invited to participate in an active discussion at the 2022 Modeling, Estimation and Control Conference (MECC) held in Jersey City, New Jersey from October 3- 5, 2022. The panel discussion focused on the challenges and opportunities to more fully integrate AI into manufacturing systems. Three overarching themes emerged from the panel discussion. First, to be successful, AI will need to work seamlessly, and in an integrated manner with humans (and vice versa). Second, significant gaps in the infrastructure needed to enable the full potential of AI into the manufacturing ecosystem, including sufficient data availability, storage, and analysis, must be addressed. And finally, improved coordination between universities, industry, and government agencies can facilitate greater opportunities to push the field forward. This article briefly summarizes these three themes, and concludes with a discussion of promising directions.

Here is the list of the Best Artificial Intelligence Books for Beginners and Advanced in 2022 for Data Science to learn. Read this list of best Artificial Intelligence books and if you found any Best Artificial Intelligence Book is missing please comment on the Best Artificial Intelligence books name so that we can add it and update the list. The long-anticipated revision of ArtificialIntelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up to date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multi-agent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI. If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start.

March 9, 2023 (Source) – Visionstate Corp. (TSXV:VIS) ("Visionstate" or the "Company") a leading provider of smart facility-management technology, today announced that it has signed a Memorandum of Understanding (MOU) with Fluido.ai, a prominent provider of artificial-intelligence solutions. The MOU outlines the intention of both companies to explore mutually-beneficial business opportunities related to artificial intelligence (AI). This may include developing new AI-powered features such as machine learning for Visionstate's flagship WANDA platform, which leverages Internet of Things (IoT) technology to monitor and analyze restroom-usage data in real time. "We are thrilled to be partnering with Fluido.ai to delve into the exciting possibilities of AI technology," said Shannon Moore, President of Visionstate IoT Inc. "As we continue to innovate and expand our smart facility-management solutions, we believe that AI has the potential to greatly enhance our proficiencies and provide even more value to our customers." "We are excited to be working with Visionstate to explore the ways in which AI can help to improve efficiencies and reduce costs," said Wessam Gad El-Rab, CEO of Fluido.ai.

This Special Issue focuses on recent developments in the use of artificial intelligence (AI) for stroke imaging in acute and chronic phases. The use of AI has attracted widespread attention as it relates to the detection of steno-occlusive lesions in the cerebral circulation, tissue level markers of injury in ischemia and hemorrhage and perfusion imaging techniques. Manuscripts should be submitted online at www.mdpi.com Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline.

If human societies are so complex, then how can we hope to understand them? Artificial Life gives us one answer. The field of Artificial Life comprises a diverse set of introspective studies that largely ask the same questions, albeit from many different perspectives: Why are we here? Who are we? Why do we behave as we do? Starting with the origins of life provides us with fascinating answers to some of these questions. However, some researchers choose to bring their studies closer to the present day. We are after all, human. It has been a few billion years since our ancestors were self-replicating molecules. Thus, more direct studies of ourselves and our human societies can reveal truths that may lead to practical knowledge. The papers in this special issue bring together scientists who choose to perform this kind of research.

We invite submissions to a special issue on "Machine Learning and Big Data Applications in Applied Economics", in the journal Applied Economic Perspectives and Policy (AEPP). With this special issue, we aim to extend the evidence based on big data and machine learning (ML) methods across a wide range of academic disciplines and industry sectors, including the agricultural sector, food value chains, and nutrition applications. The editors encourage the use of a diverse range of big data and ML methods for addressing issues like product pricing, trade, food security, forecasting approaches, crop production, and environmental and resource evaluations. We will also consider theoretical studies that provide empirically testable and/or policy-relevant insights. Studies using data from various sources, including household surveys, simulation models, and systematic reviews are welcome.

In this thesis a comprehensive verification framework is proposed to contend with some important issues in composability verification and a verification process is suggested to verify composability of different kinds of systems models, such as reactive, real-time and probabilistic systems. With an assumption that all these systems are concurrent in nature in which different composed components interact with each other simultaneously, the requirements for the extensive techniques for the structural and behavioral analysis becomes increasingly challenging. The proposed verification framework provides methods, techniques and tool support for verifying composability at its different levels. These levels are defined as foundations of consistent model composability. Each level is discussed in detail and an approach is presented to verify composability at that level. In particular we focus on the Dynamic-Semantic Composability level due to its significance in the overall composability correctness and also due to the level of difficulty it poses in the process. In order to verify composability at this level we investigate the application of three different approaches namely (i) Petri Nets based Algebraic Analysis (ii) Colored Petri Nets (CPN) based State-space Analysis and (iii) Communicating Sequential Processes based Model Checking. All three approaches attack the problem of verifying dynamic-semantic composability in different ways however they all share the same aim i.e., to confirm the correctness of a composed model with respect to its requirement specifications.

In order to go from continuous data to a discretized dataset that can be used for State-Transition modeling, I need a mapping function. Here I will use the percentile function. Excel has a percentile function. What I am going to do is create a rolling 8-day window to get the percentile of the current value within the 8-day window. In this case, I am using excel.