This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.

This is the first book on multivariate analysis to look at large data sets which describes the state of the art in analyzing such data. Material such as database management systems is included that has never appeared in statistics books before.

The artificial intelligence-based framework, algorithms, and applications presented in this book take the perspective of Society 5.0 – a social order supported by innovation in data, information, and knowledge. It showcases current case studies of Society 5.0 in diverse areas such as healthcare, smart cities, and infrastructure. Key Features: Elaborates on the use of big data, cyber-physical systems, robotics, augmented-virtual reality, and cybersecurity as pillars for Society 5.0. Showcases the use of artificial intelligence, architecture, frameworks, and distributed and federated learning structures in Society 5.0. Discusses speech recognition, image classification, robotic process automation, natural language generation, and decision support automation. Elucidates the application of machine learning, deep learning, fuzzy-based systems, and natural language processing. Includes case studies on the application of Society 5.0 aspects in educational, medical, infrastructure, and smart cities. The book is intendended especially for graduate and postgraduate students, and academic researchers in the fields of computer science and engineering, electrical engineering, and information technology.

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

In geospatial analysis, navigating the complexities of data interpretation and analysis presents a formidable challenge. Traditional methods often need to efficiently handle vast volumes of geospatial data while providing insightful and actionable results. Scholars and practitioners grapple with manual or rule-based approaches, hindering progress in understanding and addressing pressing issues such as climate change, urbanization, and resource management. Ethics, Machine Learning, and Python in Geospatial Analysis offers a solution to the challenges faced by leveraging the extensive library support and user-friendly interface of Python and machine learning. The book’s meticulously crafted chapters guide readers through the intricacies of Python programming and its application in geospatial analysis, from fundamental concepts to advanced techniques.

Raman spectroscopy has become one of the most important techniques effectively applied to detect specific analytes and microorganisms in food samples. Raman Spectroscopy in the Food Industry provides a comprehensive overview of the current state of the art and future prospects of Raman spectroscopy in food analyses. Through in-depth chapters, the authors discuss the fundamental principles of Raman spectroscopy, its instrumentation, methodologies, and its diverse applications across various food matrices. Real-world case studies and practical examples underscore the transformative potential of Raman spectroscopy in reshaping our understanding, analysis, and innovation within the realm of food science. Featured topics included are as follows: Raman spectroscopy applications in food science and technology The unique property of Raman phenomena, along with the variations in signal modes The different food safety topics, such as toxins, biosafety, foodborne bacteria, and fermentation Machine-learning studies on Raman spectrosopy in food science As the editors, we are committed to providing a valuable resource that inspires further exploration and collaboration in harnessing the power of Raman spectroscopy for the advancement of food analyses. We sincerely hope that this book serves as a catalyst for pushing the boundaries of knowledge and driving innovation in this exciting field.