The present reprint contains all of the articles accepted and published in the Special Issue " Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education" from the MDPI journal Mathematics. This Special Issue aims to develop more efficient and effective approaches to healthcare and education, leveraging the increasing availability of big data and advancements in artificial intelligence. By sharing new methods, applications, and case studies, this reprint is dedicated to the development of innovative solutions that improve healthcare and education for all. The topics addressed in this Special Issue cover a wide range of areas, including data mining, machine learning, learning analytics, prediction methods, pattern recognition, decision analysis, probabilistic reasoning, fuzzy systems, student or patient modelling, adaptive systems, collaborative systems, recommendation systems, experimental design, and empirical study cases. We hope that this reprint will enable the scientific community in both medicine and education to leverage the techniques from statistics and artificial intelligence to drive significant advances in their respective fields. These approaches hold promise for improving patient outcomes and enhancing the quality of education for students around the world.

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This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners.

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This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs.

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The chapters of this reprint cover a wide range of topics related to the theoretical aspects of artificial intelligence (AI) and machine learning (ML), as well as their practical applications. The theoretical aspects presented in this reprint are related to model compression, interpretable AI, effective augmentation strategies, time series analysis, etc., but most of the works are applicative and multidisciplinary. The applications include but are not limited to, medical image processing, natural language processing, time series analysis, diffusion model-based music generation, autoencoder applications, and adversarial learning, to name a few. By compiling such a diverse set of articles, the reprint aims to serve as a resource for researchers and AI enthusiasts and to offer them insights into the latest advancements and diverse applications of AI and ML. This reprint includes 26 articles accepted and published in the Special Issue “Artificial Intelligence and Machine Learning Based Methods and Applications, 2023” of the MDPI “Mathematics” journal.

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Human society's progress has increased the quantity of pollutants emitted into the environment. Water is one of the natural resources most affected by economic activity, with pollutants reaching water bodies without proper treatment. In this context, this book presents the latest advances and developments in statistical approaches for evaluating and forecasting water resources quality. New methodologies for analyzing and predicting the water quality that can be applied in related fields are also presented.

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Bankruptcy prediction is one of the most important research areas in corporate finance. Bankruptcies are an indispensable element of the functioning of the market economy, and at the same time generate significant losses for stakeholders. Hence, this book was established to collect the results of research on the latest trends in predicting the bankruptcy of enterprises. It suggests models developed for different countries using both traditional and more advanced methods. Problems connected with predicting bankruptcy during periods of prosperity and recession, the selection of appropriate explanatory variables, as well as the dynamization of models are presented. The reliability of financial data and the validity of the audit are also referenced. Thus, I hope that this book will inspire you to undertake new research in the field of forecasting the risk of bankruptcy.

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Within the idea and objective of bringing together original studies dealing with the continuum aspects of meat, i.e., from farm to fork, this book grouped papers on the study of the nutritional, sensory, and technological aspects of carcass, muscle, meat, and meat-product qualities. This nook highlights a great part of the research activities in the field of meat science. A total of 14 original studies and one comprehensive review were edited within five main topics: (i) production systems and rearing practices, (ii) prediction of meat quality, (iii) statistical approaches for meat quality prediction/management, (iv) muscle biochemistry and proteomics techniques, and (v) consumer acceptability, development, and characterization of meat products.

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Although the design and management of manufacturing systems have been explored in the literature for many years now, they still remain topical problems in the current scientific research. The changing market trends, globalization, the constant pressure to reduce production costs, and technical and technological progress make it necessary to search for new manufacturing methods and ways of organizing them, and to modify manufacturing system design paradigms. This book presents current research in different areas connected with the design and management of manufacturing systems and covers such subject areas as: methods supporting the design of manufacturing systems, methods of improving maintenance processes in companies, the design and improvement of manufacturing processes, the control of production processes in modern manufacturing systems production methods and techniques used in modern manufacturing systems and environmental aspects of production and their impact on the design and management of manufacturing systems. The wide range of research findings reported in this book confirms that the design of manufacturing systems is a complex problem and that the achievement of goals set for modern manufacturing systems requires interdisciplinary knowledge and the simultaneous design of the product, process and system, as well as the knowledge of modern manufacturing and organizational methods and techniques.

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The topic of the reprint is very important nowadays, because ever-evolving machine learning techniques make it possible to obtain better real-world data. Therefore, this reprint contains information related to real data in fields such as automatic sign language translation, bike-sharing travel characteristics, stock index, sports data, fake news data, and more. However, it should be noted that the reprint also contains a lot of information on new developments in machine learning, new algorithms, algorithm modifications, and a new measure of classification quality assessment that also takes into account the preferences of the decision maker.

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This Special Issue publishes the latest advances and developments concerning the modelling of flooding in urban areas and contributes to our scientific understanding of the flooding processes and the appropriate evaluation of flood impacts. This issue contains contributions of novel methodologies including flood forecasting methods, data acquisition techniques, experimental research in urban drainage systems and/or sustainable drainage systems, and new numerical and simulation approaches in nine papers with contributions from over forty authors.

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This Special Issue explores recent developments and advanced concepts related to the valorisation of organic wastes and other biomass through the application of anaerobic digestion (AD). Biological phenomena, the efficiency of the AD process, the viability of full-scale AD installations, the integration of hydrogen and biomethane concepts, and biorefineries are some of the themes covered. While AD is addressed under different perspectives, the results all strengthen AD to fulfil its important role as a sustainable and highly versatile technology. Further research and development needs are also presented.

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With increasing awareness about environmental sustainability issues, the geotechnical engineering community is in search of sustainable materials, innovative applications and ground improvement technologies which are resilient to climate change, involve low carbon emissions, are ecologically friendly and affordable, yet effective. As such, this reprint brings together 18 novel contributions providing the state-of-the-art in “Sustainable Geotechnics” with special focus on the reuse and recycling of waste materials in geotechnical applications as well as sustainable design, construction, and monitoring of the performance of geostructures. This reprint may be of particular interest to geotechnical professional engineers and researchers who are facing the challenging task of increasing the sustainability and resiliency of geotechnical assets and infrastructures.

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heavily Environmental mathematical models represent one of the key aids for scientists to forecast, create, and evaluate complex scenarios. These models rely on the data collected by direct field observations. However, assembly of a functional and comprehensive dataset for any environmental variable is difficult, mainly because of i) the high cost of the monitoring campaigns and ii) the low reliability of measurements (e.g., due to occurrences of equipment malfunctions and/or issues related to equipment location). The lack of a sufficient amount of Earth science data may induce an inadequate representation of the response’s complexity in any environmental system to any type of input/change, both natural and human-induced. In such a case, before undertaking expensive studies to gather and analyze additional data, it is reasonable to first understand what enhancement in estimates of system performance would result if all the available data could be well exploited. Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. Different approaches are available to deal with missing data. Traditional statistical data completion methods are used in different domains to deal with single and multiple imputation problems. More recently, machine learning techniques, such as clustering and classification, have been proposed to complete missing data. This book showcases the body of knowledge that is aimed at improving the capacity to exploit the available data to better represent, understand, predict, and manage the behavior of environmental systems at all practical scales.

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This reprint addresses healthcare transformation towards personalized, participative, preventive, predictive, and precision medicine (5P Medicine) with the support of new technologies such as micro-, nano-, and bio-techniques, as well as artificial intelligence and learning systems. It focuses, therefore, on the representation and management of knowledge from different domains and their actors, using their methodologies and languages but also individual skills and experiences. The outcome is a system-oriented, architecture-centric, ontology-based, policy-driven approach allowing a formal representation and management of health ecosystems, including their integration and interoperability. Such development is accompanied by security, privacy, and ethical challenges to be resolved. The reprint describes the principles, methodologies, and standards for successfully managing the transformation of health and social care, illustrated by many practical examples and implemented use cases. The reprint is based on papers published in the context of the pHealth 2021 Conference in Genoa, Italy. However, the content goes far beyond the focus and size of the original papers.

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The present reprint contains all of the articles in the second edition of the Special Issue titled “Statistical Data Modeling and Machine Learning with Applications II”. This Special Issue belongs to the “Mathematics and Computer Science” Section and aims to publish research on the theory and application of statistical data modeling and machine learning. New mathematical methods and approaches, new algorithms and research frameworks, and their applications aimed at solving diverse and nontrivial practical problems are proposed and developed in this SI. We believe that the chosen papers are attractive and useful to the international scientific community and will contribute to further research in the field of statistical data modeling and machine learning.

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Achieving economic growth and sustainable development requires that we urgently reduce our ecological footprint by changing the way we produce and consume goods and resources. We consume more resources than the planet can generate, and growing rates of pollution and waste only worsen the problem. In this context, globally, a significant number of frameworks and programs on sustainable consumption and production have been introduced. Most of them concern the environmentally sound management of energy and raw materials and all wastes throughout their life cycle; the substantially reduce of waste generation through prevention, reduction, recycling and reuse; the adoption of sustainable practices and the integration of sustainability information into their reporting cycle; the promotion of procurement practices that are sustainable; the enhancement of scientific and technological capacity intending to more sustainable patterns of consumption and production etc. This reprint“ Sustainable Production and Environmentally Responsible Consumption” takes a broad view of all these issues and aimed to inform about ways contributing to sustainable consumption and production by reducing our environmental impact, promoting the use of renewable sources of energy and encouraging responsible purchasing decisions.

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Quantitative movement analysis is widely used in clinical practice and research to investigate movement disorders objectively and in a complete way. Conventionally, body segment kinematic and kinetic parameters are measured in gait laboratories using marker-based optoelectronic systems, force plates, and electromyographic systems. Although movement analyses are considered accurate, the availability of specific laboratories, high costs, and dependency on trained users sometimes limit its use in clinical practice. A variety of compact wearable sensors are available today and have allowed researchers and clinicians to pursue applications in which individuals are monitored in their homes and in community settings within different fields of study, such movement analysis. Wearable sensors may thus contribute to the implementation of quantitative movement analyses even during out-patient use to reduce evaluation times and to provide objective, quantifiable data on the patients’ capabilities, unobtrusively and continuously, for clinical purposes.

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