Machine learning

Recently, novel metaheuristic techniques have emerged in response to the limitations of conventional approaches, leading to enhanced outcomes. These new methods introduce interesting mechanisms and innovative collaborative strategies that facilitate the efficient exploration and exploitation of extensive search spaces characterized by numerous dimensions. The objective of this book is to present advancements that discuss novel alternative metaheuristic developments that have demonstrated their effectiveness in tackling various complex problems. This book encompasses a variety of emerging metaheuristic methods and their practical applications. The content is presented from a teaching perspective, making it particularly suitable for undergraduate and postgraduate students in fields such as science, electrical engineering, and computational mathematics. The book aligns well with courses in artificial intelligence, electrical engineering, and evolutionary computation. Furthermore, the material offers valuable insights to researchers within the metaheuristic and engineering communities. Similarly, engineering practitioners unfamiliar with metaheuristic computation concepts will recognize the pragmatic value of the discussed techniques. These methods transcend mere theoretical tools that have been adapted to effectively address the significant real-world problems commonly encountered in engineering domains.

This book presents an exploration of linkages among soil-water, agriculture, and climate change with a special focus on thematic areas for assessment, mitigation, and management of natural resources under climate change conditions. This book covers advances in modelling approaches, including machine learning (ML)/ artificial intelligence (AI) applications; GIS and remote sensing; sensors; impacts of climate change on agriculture; subsurface water; contaminants; and socio-economic impacts, which are lacking in a more comprehensive manner in the previous titles. This book encompasses updated information as well as future directions for researchers working in the field of management of natural resources. The goal of this book is to provide scientific evidence to researchers and policymakers and end-to-end value chain practitioners which may help in reducing the overall adverse impacts of climate change on water resources and the related mitigation strategies. This book focuses on the knowledge, modern tools, and techniques, i.e., machine learning, artificial intelligence, etc. for soil-water, agriculture, and climate change. Further, nature-based solutions for management of natural resources with special targets on contaminants, extreme events, disturbances, etc. will be targeted. The book provides readers with the enhanced knowledge for application of engineering principles and economic and regulatory constraints to determine a soil-water, agriculture production action strategy, and select appropriate technologies to implement the strategy for a given data set at a site. It would also cover the application of laboratory, modeling, numerical methods for determination and forecasting of climate change impacts, agriculture production, pollution, soil health, etc. Overall, it provides hydrologists, environmental engineers, administrators, policy makers, consultants, and industrial experts with essential support in effective management of soils health, agricultural productions, and mitigation of extreme climatic events.

This book provides a coherent and complete overview of various Question Answering (QA) systems. It covers three main categories based on the source of the data that can be unstructured text (TextQA), structured knowledge graphs (KBQA), and the combination of both. Developing a QA system usually requires using a combination of various important techniques, including natural language processing, information retrieval and extraction, knowledge graph processing, and machine learning.After a general introduction and an overview of the book in Chapter 1, the history of QA systems and the architecture of different QA approaches are explained in Chapter 2. It starts with early close domain QA systems and reviews different generations of QA up to state-of-the-art hybrid models. Next, Chapter 3 is devoted to explaining the datasets and the metrics used for evaluating TextQA and KBQA. Chapter 4 introduces the neural and deep learning models used in QA systems. This chapter includes the required knowledge of deep learning and neural text representation models for comprehending the QA models over text and QA models over knowledge base explained in Chapters 5 and 6, respectively. In some of the KBQA models the textual data is also used as another source besides the knowledge base; these hybrid models are studied in Chapter 7. In Chapter 8, a detailed explanation of some well-known real applications of the QA systems is provided. Eventually, open issues and future work on QA are discussed in Chapter 9.This book delivers a comprehensive overview on QA over text, QA over knowledge base, and hybrid QA systems which can be used by researchers starting in this field. It will help its readers to follow the state-of-the-art research in the area by providing essential and basic knowledge.

One of the main benefits of this book is that it presents a comprehensive and innovative eHealth framework that leverages deep learning and IoT wearable devices for the evaluation of Parkinson's disease patients. This framework offers a new way to assess and monitor patients' motor deficits in a personalized and automated way, improving the efficiency and accuracy of diagnosis and treatment.Compared to other books on eHealth and Parkinson's disease, this book offers a unique perspective and solution to the challenges facing patients and healthcare providers. It combines state-of-the-art technology, such as wearable devices and deep learning algorithms, with clinical expertise to develop a personalized and efficient evaluation framework for Parkinson's disease patients.This book provides a roadmap for the integration of cutting-edge technology into clinical practice, paving the way for more effective and patient-centered healthcare. To understand this book, readers should have a basic knowledge of eHealth, IoT, deep learning, and Parkinson's disease. However, the book provides clear explanations and examples to make the content accessible to a wider audience, including researchers, practitioners, and students interested in the intersection of technology and healthcare.

With approximately 2500 problems, this book provides a collection of practical problems on the basic and advanced data structures, design, and analysis of algorithms. To make this book suitable for self-instruction, about one-third of the algorithms are supported by solutions, and some others are supported by hints and comments. This book is intended for students wishing to deepen their knowledge of algorithm design in an undergraduate or beginning graduate class on algorithms, for those teaching courses in this area, for use by practicing programmers who wish to hone and expand their skills, and as a self-study text for graduate students who are preparing for the qualifying examination on algorithms for a Ph.D. program in Computer Science or Computer Engineering. About all, it is a good source for exam problems for those who teach algorithms and data structure. The format of each chapter is just a little bit of instruction followed by lots of problems. This book is intended to augment the problem sets found in any standard algorithms textbook. This book * begins with four chapters on background material that most algorithms instructors would like their students to have mastered before setting foot in an algorithms class. The introductory chapters include mathematical induction, complexity notations, recurrence relations, and basic algorithm analysis methods. * provides many problems on basic and advanced data structures including basic data structures (arrays, stack, queue, and linked list), hash, tree, search, and sorting algorithms. * provides many problems on algorithm design techniques: divide and conquer, dynamic programming, greedy algorithms, graph algorithms, and backtracking algorithms. * is rounded out with a chapter on NP-completeness.

This book explores the cognitive plausibility of computational language models and why it¿s an important factor in their development and evaluation. The authors present the idea that more can be learned about cognitive plausibility of computational language models by linking signals of cognitive processing load in humans to interpretability methods that allow for exploration of the hidden mechanisms of neural models. The book identifies limitations when applying the existing methodology for representational analyses to contextualized settings and critiques the current emphasis on form over more grounded approaches to modeling language. The authors discuss how novel techniques for transfer and curriculum learning could lead to cognitively more plausible generalization capabilities in models. The book also highlights the importance of instance-level evaluation and includes thorough discussion of the ethical considerations that may arise throughout the various stages of cognitive plausibility research.

This volume comprises the proceedings of the International Workshop, ShapeMI 2023, which took place alongside MICCAI 2023 on October 8, 2023, in Vancouver, British Columbia, Canada.The 23 selected full papers deal with all aspects of leading methods and applications for advanced shape analysis and geometric learning in medical imaging.

This book provides a new model to explore discoverability and enhance the meaning of information. The authors have coined the term epidata, which includes items and circumstances that impact the expression of the data in a document, but are not part of the ordinary process of retrieval systems.  Epidata affords pathways and points to details that cast light on proximities that might otherwise go unknown.  In addition, epidata are clues to mis-and dis-information discernment.  There are many ways to find needed information; however, finding the most useable information is not an easy task.  The book explores the uses of proximity and the concept of epidata that increases the probability of  finding functional information.  The authors sketch a constellation of proximities, present examples of attempts to accomplish proximity, and provoke a discussion of the role of proximity in the field. In addition, the authors suggest that proximity is a thread between retrieval constructs based on known topics, predictable relations, and types of information seeking that lie outside constructs such as browsing, stumbling, encountering, detective work, art making, and translation.

In ancient games such as chess or go, the most brilliant players can improve by studying the strategies produced by a machine. Robotic systems practice their own movements. In arcade games, agents capable of learning reach superhuman levels within a few hours. How do these spectacular reinforcement learning algorithms work? With easy-to-understand explanations and clear examples in Java and Greenfoot, you can acquire the principles of reinforcement learning and apply them in your own intelligent agents. Greenfoot (M.Klling, King's College London) and the hamster model (D. Bohles, University of Oldenburg) are simple but also powerful didactic tools that were developed to convey basic programming concepts. The result is an accessible introduction into machine learning that  concentrates on reinforcement learning. Taking the reader through the steps of developing intelligent agents, from the very basics to advanced aspects, touching on a variety of machine learning algorithms along the way, one is allowed to play along, experiment, and add their own ideas and experiments.  

This book highlights selected papers from the 4th ICSA-Canada Chapter Symposium, as well as invited articles from established researchers in the areas of statistics and data science. It covers a variety of topics, including methodology development in data science, such as methodology in the analysis of high dimensional data, feature screening in ultra-high dimensional data and natural language ranking; statistical analysis challenges in sampling, multivariate survival models and contaminated data, as well as applications of statistical methods. With this book, readers can make use of frontier research methods to tackle their problems in research, education, training and consultation.

Deep learning is rapidly gaining momentum in the world of finance and trading. But for many professional traders, this sophisticated field has a reputation for being complex and difficult. This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python, and it also helps you create and backtest trading algorithms based on machine learning and reinforcement learning. Sofien Kaabar--financial author, trading consultant, and institutional market strategist--introduces deep learning strategies that combine technical and quantitative analyses. By fusing deep learning concepts with technical analysis, this unique book presents outside-the-box ideas in the world of financial trading. This A-Z guide also includes a full introduction to technical analysis, evaluating machine learning algorithms, and algorithm optimization. Understand and create machine learning and deep learning models Explore the details behind reinforcement learning and see how it's used in time series Understand how to interpret performance evaluation metrics Examine technical analysis and learn how it works in financial markets Create technical indicators in Python and combine them with ML models for optimization Evaluate the models' profitability and predictability to understand their limitations and potential

This book explores new applications in the field of science and technology for healthcare systems. The main focus of this book is to devise smart, efficient and robust solutions for the health care sector to serve the major population of rural areas. Artificial Intelligence-based Healthcare Systems encourages scientists, engineers, and scholars across the multiple disciplines to design smart intelligent innovations on rural healthcare issues and motivate to collaborate multiple ideas to design best solutions. It also helps the readers at various levels of knowledge to further enhance their understanding for new tools and smart solutions.

This book constitutes the refereed proceedings of the 16th International Conference on Similarity Search and Applications, SISAP 2023, held in A Coruña, Spain, during October 9¿11, 2023.The 16 full papers and 4 short papers included in this book were carefully reviewed and selected from 33 submissions. They were organized in topical sections as follows: similarity queries, similarity measures, indexing and retrieval, data management, feature extraction, intrinsic dimensionality, efficient algorithms, similarity in machine learning and data mining.

This book aims to provide a refreshing, friendly and exciting manner artificial intelligence (AI) theoretical concepts and practical methods applied in obstetrics and gynaecology. The book follows the nine and a half months journey from preconception till birth alongside with AI. It discusses topics such as the poignant role of AI in improving the change of women getting pregnant; AI methods for detecting congenital anomalies in first and second trimester foetal sonography; how AI aids physicians in determining what type of birth should be deployed (vaginal versus caesarean); how AI can predict pre-eclampsia, preterm birth, mortality, birth weight, miscarriage, postpartum depression, etc. Additionally, it provides information on AI used for perinatal depression, for the evaluation of the relationship between pollutants and pregnancy outcome and even how AI tools can improve physician training in labour and delivery. The book is designed for bioinformaticians, obstetric and gynaecology researchers and physicians, and all those who wish to learn how to explore, analyse, find novel potential solutions for the challenging domains: obstetrics and gynaecology. Likewise, this book will be useful for application engineers who wish to use AI paradigms in areas such as engineering and science too.

This book constitutes the refereed proceedings of the ¿Second Workshop on Artificial Intelligence over Infrared Images for Medical Applications, AIIIMA 2023 held in conjunction with MICCAI 2023, held in Vancouver, BC, Canada, on October 2, 2023. The 10 full papers presented in this book were carefully peer reviewed and selected from 15 submissions. The second workshop on AIIIMA, similarily to the first, aimes to create a forum to discuss the specific sub-topic of AI over Infrared Images for Medical Applications at MICCAI and promote this novel area of research, that has the potential to hugely impact our society, among the research community.

This book explores the use of advanced geospatial techniques in geomorphic hazards modelling and risk reduction. It also compares the accuracy of traditional statistical methods and advanced machine learning methods and addresses the different ways to reduce the impact of geomorphic hazards.In recent years with the development of human infrastructures, geomorphic hazards are gradually increasing, which include landslides, flood and soil erosion, among others. They cause huge loss of human property and lives. Especially in mountainous, coastal, arid and semi-arid regions, these natural hazards are the main barriers for economic development. Furthermore, human pressure and specific human actions such as deforestation, inappropriate land use and farming have increased the danger of natural disasters and degraded the natural environment, making it more difficult for environmental planners and policymakers to develop appropriate long-term sustainability plans. The most challenging task is to develop a sophisticated approach for continuous inspection and resolution of environmental problems for researchers and scientists. However, in the past several decades, geospatial technology has undergone dramatic advances, opening up new opportunities for handling environmental challenges in a more comprehensive manner.With the help of geographic information system (GIS) tools, high and moderate resolution remote sensing information, such as visible imaging, synthetic aperture radar, global navigation satellite systems, light detection and ranging, Quickbird, Worldview 3, LiDAR, SPOT 5, Google Earth Engine and others deliver state-of-the-art investigations in the identification of multiple natural hazards. For a thorough examination, advanced computer approaches focusing on cutting-edge data processing, machine learning and deep learning may be employed. To detect and manage various geomorphic hazards and their impact, several models with a specific emphasis on natural resources and the environment may be created.

"An advanced exploration of machine learning and AI, with each chapter asking and answering a question from the field. Divided into five sections: deep learning and neural networks; computer vision; natural language processing; production and deployment; and predictive performance and model evaluation"--

This book includes chapters which discuss effective and efficient approaches in dealing with various aspects of social media analysis by using machine learning techniques from clustering to deep learning. A variety of theoretical aspects, application domains and case studies are covered to highlight how it is affordable to maximize the benefit of various applications from postings on social media platforms. Social media platforms have significantly influenced and reshaped various social aspects. They have set new means of communication and interaction between people, turning the whole world into a small village where people with internet connect can easily communicate without feeling any barriers. This has attracted the attention of researchers who have developed techniques and tools capable of studying various aspects of posts on social media platforms with main concentration on Twitter. This book addresses challenging applications in this dynamic domain where it is not possible to continue applying conventional techniques in studying social media postings. The content of this book helps the reader in developing own perspective about how to benefit from machine learning techniques in dealing with social media postings and how social media postings may directly influence various applications.

This book presents an overview and several applications of explainable artificial intelligence (XAI). It covers different aspects related to explainable artificial intelligence, such as the need to make the AI models interpretable, how black box machine/deep learning models can be understood using various XAI methods, different evaluation metrics for XAI, human-centered explainable AI, and applications of explainable AI in health care, security surveillance, transportation, among other areas.The book is suitable for students and academics aiming to build up their background on explainable AI and can guide them in making machine/deep learning models more transparent. The book can be used as a reference book for teaching a graduate course on artificial intelligence, applied machine learning, or neural networks. Researchers working in the area of AI can use this book to discover the recent developments in XAI. Besides its use in academia, this book could be used by practitioners in AI industries, healthcare industries, medicine, autonomous vehicles, and security surveillance, who would like to develop AI techniques and applications with explanations.

This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time. Its data-driven approach to analyzing and modeling time series data helps new learners to visualize and interpret both the raw data and its computed results. Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equally appeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems.

This book describes new theories and applications of artificial neural networks, with a special focus on answering questions in neuroscience, biology and biophysics and cognitive research. It covers a wide range of methods and technologies, including deep neural networks, large-scale neural models, brain-computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self-organization and many more. The book includes both selected and invited papers presented at the XXIV International Conference on Neuroinformatics, held on October 17-21, 2022, in Moscow, Russia.

This book takes the notions of adaptivity and learning from the realm of engineering into the realm of biology and natural processes. It introduces a Hebbian-LMS algorithm, an integration of unsupervised Hebbian learning and supervised LMS learning in neural networks, as a mathematical representation of a general theory for synaptic learning in the brain, and adaptation and functional control of homeostasis in living systems. Written in a language that is able to address students and scientists with different backgrounds, this book accompanies readers on a unique journey through various homeostatic processes in living organisms, such as body temperature control and synaptic plasticity, explaining how the Hebbian-LMS algorithm can help understand them, and suggesting some open questions for future research. It also analyses cell signalling pathways from an unusual perspective, where hormones and hormone receptors are shown to be regulated via the principles of the Hebbian-LMS algorithm. It further discusses addiction and pain, and various kinds of mood disorders alike, showing how they can be modelled with the Hebbian-LMS algorithm.  For the first time, the Hebbian-LMS algorithm, which has been derived from a combination of Hebbian theory from the neuroscience field and the LMS algorithm from the engineering field of adaptive signal processing, becomes a potent model for understanding how biological regulation works. Thus, this book is breaking new ground in neuroscience by providing scientists with a general theory for how nature does control synaptic learning. It then goes beyond that, showing that the same principles apply to hormone-mediated regulation of physiological processes. In turn, the book tackles in more depth the concept of learning. It covers computer simulations and strategies for training neural networks with the Hebbian-LMS algorithm, demonstrating that the resulting algorithms are able to identify relationships between unknown input patterns. It shows how this can translate in useful ideas to understand human memory and design cognitive structures. All in all, this book offers an absolutely, unique, inspiring reading for biologists, physiologists, and engineers, paving the way for future studies on what we could call the nature's secret learning algorithm.

Dive into the exciting world of cutting-edge technology with our comprehensive guide, "Machine Learning and Artificial Intelligence: A Journey through Data Science." Whether you're a seasoned data scientist or a curious beginner, this book takes you on a captivating journey through the realms of machine learning, artificial intelligence, and the mathematics that underpin them.Discover the FoundationsExplore the fundamental concepts that form the bedrock of machine learning and artificial intelligence, including Information Theory, Computational Science, and the Mathematics of Norms and Regularization.Master Data AnalysisEquip yourself with the tools of data exploration and analytics, uncovering insights in large datasets through techniques such as Data Mining, Euclidean and Hamming Distances, and Loss Functions.Unlock the Secrets of Supervised LearningGain expertise in predictive modeling with topics like Linear Regression, Statistical Learning Theory, and Statistical Hypothesis Testing. Understand how algorithms make decisions and why they work.Delve into ClassificationNavigate the intricate world of statistical classification, Binary Classification, Maximum Likelihood, Logistic Regression, and more. Uncover the nuances of separating data into meaningful categories.Unleash Unsupervised LearningHarness the power of unsupervised learning with Cluster Analysis, Dimensionality Reduction, and Principal Component Analysis. Learn how to find patterns in data when you don't have predefined labels.Embark on Reinforcement Learning AdventuresTake a deep dive into Reinforcement Learning, Decision Tree Learning, and Ensemble Learning. Discover how machines learn through interaction and decision-making.Navigate the Landscape of Deep LearningExplore the revolutionary world of Artificial Neural Networks and Deep Learning, the driving force behind modern AI applications.Master Advanced TechniquesConquer challenging concepts like Semi-Supervised Learning, Support Vector Machines, and the intricacies of Random Forests and Boosting.Achieve Data Science ExcellenceAscend to the pinnacle of data science with practical knowledge and real-world insights.Expertly Crafted for All LevelsWhether you're a beginner eager to dive into data science or an experienced practitioner seeking to expand your skillset, "Machine Learning and Artificial Intelligence" offers something for everyone.This book is your passport to the thrilling world of machine learning, artificial intelligence, and data scien

Artificial Intelligence (AI) revolves around creating and utilizing intelligent machines through science and engineering. This book delves into the theory and practical applications of computer science methods that incorporate AI across many domains. It covers techniques such as Machine Learning (ML), Convolutional Neural Networks (CNN), Deep Learning (DL), and Large Language Models (LLM) to tackle complex issues and overcome various challenges.

This book presents and surveys normalization techniques with a deep analysis in training deep neural networks.  In addition, the author provides technical details in designing new normalization methods and network architectures tailored to specific tasks.  Normalization methods can improve the training stability, optimization efficiency, and generalization ability of deep neural networks (DNNs) and have become basic components in most state-of-the-art DNN architectures.  The author provides guidelines for elaborating, understanding, and applying normalization methods.  This book is ideal for readers working on the development of novel deep learning algorithms and/or their applications to solve practical problems in computer vision and machine learning tasks.  The book also serves as a resource researchers, engineers, and students who are new to the field and need to understand and train DNNs.