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Machine Intelligence and Data Science Applications: Proceedings of MIDAS 2022 (Algorithms for Intelligent Systems)
by T. P. Singh Jung-Sup Um Tanupriya Choudhury Ravi Tomar Amar Ramdane-CherifThis book is a compilation of peer-reviewed papers presented at the International Conference on Machine Intelligence and Data Science Applications (MIDAS 2022), held on October 28 and 29, 2022, at the University of Versailles—Paris-Saclay, France. The book covers applications in various fields like data science, machine intelligence, image processing, natural language processing, computer vision, sentiment analysis, and speech and gesture analysis. It also includes interdisciplinary applications like legal, healthcare, smart society, cyber-physical system, and smart agriculture. The book is a good reference for computer science engineers, lecturers/researchers in the machine intelligence discipline, and engineering graduates.
Machine Intelligence and Smart Systems: Third International Conference, MISS 2023, Bhopal, India, January 24–25, 2023, Revised Selected Papers, Part II (Communications in Computer and Information Science #1952)
by Jitendra Agrawal Manish Gupta Shikha Agrawal Kamlesh Gupta Korhan CengisThe two-volume set CCIS 1951 and 1952 constitutes the refereed post-conference proceedings of the Third International Conference on Machine Intelligence and Smart Systems, MISS 2023, Bhopal, India, during January 24-25, 2023. The 58 full papers included in this book were carefully reviewed and selected from 203 submissions. They were organized in topical sections as follows: Language processing; Recent trends; AI defensive schemes; Principle components; Deduction and prevention models.
Machine Intelligence for Research and Innovations: Proceedings of MAiTRI 2023, Volume 1 (Lecture Notes in Networks and Systems #832)
by Anupam Yadav Rajesh Kumar Lipo Wang Om Prakash VermaThe book is a collection of high-quality peer-reviewed research papers presented in the First International Conference on MAchine inTelligence for Research & Innovations (MAiTRI 2023 Summit), held at Dr. B. R. Ambedkar National Institute of Technology Jalandhar, Panjab, India during 1 – 3 September 2023. This book focuses on recent advancement in the theory and realization of machine intelligence (MI) and their tools and growing applications such as machine learning, deep learning, quantum machine learning, real-time computer vision, pattern recognition, natural language processing, statistical modelling, autonomous vehicles, human interfaces, computational intelligence, and robotics.
Machine Intelligence for Research and Innovations: Proceedings of MAiTRI 2024, Volume 2 (Lecture Notes in Networks and Systems #1358)
by Anupam Yadav Rajesh Kumar Lipo Wang Om Prakash Verma Ranjeet Kumar RoutThe book is a collection of high-quality peer-reviewed research papers presented in the Second International Conference on MAchine inTelligence for Research & Innovations (MAiTRI 2024 Summit), held at National Institute of Technology Srinagar, India during 21 – 23 June 2024. This book focuses on recent advancement in the theory and realization of machine intelligence (MI) and their tools and growing applications such as machine learning, deep learning, quantum machine learning, real-time computer vision, pattern recognition, natural language processing, statistical modelling, autonomous vehicles, human interfaces, computational intelligence, and robotics.
Machine Intelligence, Tools, and Applications: Proceedings of the International Conference on Machine Intelligence, Tools, and Applications—ICMITA 2024 (Learning and Analytics in Intelligent Systems #40)
by Satchidananda Dehuri Ashish Ghosh Sung-Bae Cho Venkat Prasad Padhy Poonkuntrun ShanmugamThis book presents the recent advances including tools and techniques in the constantly changing landscape of machine learning (ML). This would enable the readers with a strong understanding of critical issues in ML by providing both broad and detailed perspectives on cutting-edge theories, algorithms, and tools. This will become a single source of reference on conceptual, methodological, technical, and managerial issues, as well as provide insight into emerging trends and future opportunities in the discipline of ML. This book contains altogether 36 chapters in the area of ML and its applications.
Machine Learning
by Jason BellDig deep into the data with a hands-on guide to machine learningMachine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. A core tenant of machine learning is a strong focus on data preparation, and a full exploration of the various types of learning algorithms illustrates how the proper tools can help any developer extract information and insights from existing data. The book includes a full complement of Instructor's Materials to facilitate use in the classroom, making this resource useful for students and as a professional reference.At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data. Machine Learning is an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers to:Learn the languages of machine learning including Hadoop, Mahout, and WekaUnderstand decision trees, Bayesian networks, and artificial neural networksImplement Association Rule, Real Time, and Batch learningDevelop a strategic plan for safe, effective, and efficient machine learningBy learning to construct a system that can learn from data, readers can increase their utility across industries. Machine learning sits at the core of deep dive data analysis and visualization, which is increasingly in demand as companies discover the goldmine hiding in their existing data. For the tech professional involved in data science, Machine Learning: Hands-On for Developers and Technical Professionals provides the skills and techniques required to dig deeper.
Machine Learning Algorithms: First International Conference, ICMLA 2024, Himachal Pradesh, India, February 23–24, 2024, Proceedings (Communications in Computer and Information Science #2238)
by Abhishek Thakur Rajesh K. Shukla Chin-Shiuh Shieh Meenu Khurana Praveen KanthaThis book constitutes the refereed proceedings of the First International Conference on Machine Learning Algorithms, ICMLA 2024, held in Himachal Pradesh, India, during February 23–24, 2024. The 23 full papers and 17 short papers included in this book were carefully reviewed and selected from 400 submissions. They were organized in the following topical sections: machine learning; image processing; deep learning.
Machine Learning Applications in Medicine and Biology
by Joseph Picone Ammar AhmedThis book combines selected papers from the 2022 IEEE Signal Processing in Medicine and Biology Symposium (IEEE SPMB) held at Temple University. The symposium presents multidisciplinary research in the life sciences. Topics covered include:Signal and image analysis (EEG, ECG, MRI)Machine learningData mining and classificationBig data resourcesApplications of particular interest at the 2022 symposium included digital pathology, computational biology, and quantum computing. The book features tutorials and examples of successful applications that will appeal to a wide range of professionals and researchers in signal processing, medicine, and biology.
Machine Learning Applications in Renewable Energy (Green Energy and Technology)
by Stefan Mozar Mousmi Ajay Chaurasia Chia-Feng Juang Namrata ManoharThis book presents the need for Renewable Energy Technologies (RET) in the context of providing a solution for the depletion of conventional resources, protecting the environment and enhancing the economic situation of a country by way of providing employment opportunities for many people may be as employees in various roles or initiating their own enterprise. The book includes statistics on energy consumption changes over the past few decades from conventional to renewable energies. The future scenario of energy in view of technological advancements and the employment status past, present and future is indicated. The need and importance of standards for the efficient operation of renewable energy systems are explained. The various modern technologies that are enabling the successful implementation of RET are presented. The role of the public and government and the various financial schemes governments provide is highlighted. A few modern applications and those under development would enhance the standard of living. The statistics and situation of the various aspects in the wake of the COVID-19 pandemic before, during and future effects are discussed, for the overall benefit of one and all. The various methods of a cost analysis of a project are indicated. Solar system components and the cost estimation of the solar power system in the present-day market status are provided. The various grid integration issues have been discussed.
Machine Learning Approaches in Financial Analytics (Intelligent Systems Reference Library #254)
by Srikanta Patnaik Leandros A. Maglaras Sonali Das Naliniprava TripathyThis book addresses the growing need for a comprehensive guide to the application of machine learning in financial analytics. It offers a valuable resource for both beginners and experienced professionals in finance and data science by covering the theoretical foundations, practical implementations, ethical considerations, and future trends in the field. It bridges the gap between theory and practice, providing readers with the tools and knowledge they need to leverage the power of machine learning in the financial sector responsibly.
Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization (Genetic and Evolutionary Computation)
by Kalyanmoy Deb Dhish Kumar Saxena Sukrit Mittal Erik D. GoodmanThis book focuses on machine learning (ML) assisted evolutionary multi- and many-objective optimization (EMâO). EMâO algorithms, namely EMâOAs, iteratively evolve a set of solutions towards a good Pareto Front approximation. The availability of multiple solution sets over successive generations makes EMâOAs amenable to application of ML for different pursuits. Recognizing the immense potential for ML-based enhancements in the EMâO domain, this book intends to serve as an exclusive resource for both domain novices and the experienced researchers and practitioners. To achieve this goal, the book first covers the foundations of optimization, including problem and algorithm types. Then, well-structured chapters present some of the key studies on ML-based enhancements in the EMâO domain, systematically addressing important aspects. These include learning to understand the problem structure, converge better, diversify better, simultaneously converge and diversify better, and analyze the Pareto Front. In doing so, this book broadly summarizes the literature, beginning with foundational work on innovization (2003) and objective reduction (2006), and extending to the most recently proposed innovized progress operators (2021-23). It also highlights the utility of ML interventions in the search, post-optimality, and decision-making phases pertaining to the use of EMâOAs. Finally, this book shares insightful perspectives on the future potential for ML based enhancements in the EMâOA domain.To aid readers, the book includes working codes for the developed algorithms. This book will not only strengthen this emergent theme but also encourage ML researchers to develop more efficient and scalable methods that cater to the requirements of the EMâOA domain. It serves as an inspiration for further research and applications at the synergistic intersection of EMâOA and ML domains.
Machine Learning Based Optimization of Laser-Plasma Accelerators (Springer Theses)
by Sören JalasThis book explores the application of machine learning-based methods, particularly Bayesian optimization, within the realm of laser-plasma accelerators. The book involves the implementation of Bayesian optimization to fine tune the parameters of the lux accelerator, encompassing simulations and real-time experimentation. In combination, the methods presented in this book provide valuable tools for effectively managing the inherent complexity of LPAs, spanning from the design phase in simulations to real-time operation, potentially paving the way for LPAs to cater to a wide array of applications with diverse demands.
Machine Learning Design Patterns
by Michael Munn Valliappa Lakshmanan Sara RobinsonThe design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice.In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation.You'll learn how to:Identify and mitigate common challenges when training, evaluating, and deploying ML modelsRepresent data for different ML model types, including embeddings, feature crosses, and moreChoose the right model type for specific problemsBuild a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuningDeploy scalable ML systems that you can retrain and update to reflect new dataInterpret model predictions for stakeholders and ensure models are treating users fairly
Machine Learning For Network Traffic and Video Quality Analysis: Develop and Deploy Applications Using JavaScript and Node.js
by Tulsi Pawan Fowdur Lavesh BabooramThis book offers both theoretical insights and hands-on experience in understanding and building machine learning-based Network Traffic Monitoring and Analysis (NTMA) and Video Quality Assessment (VQA) applications using JavaScript. JavaScript provides the flexibility to deploy these applications across various devices and web browsers. The book begins by delving into NTMA, explaining fundamental concepts and providing an overview of existing applications and research within this domain. It also goes into the essentials of VQA and offers a survey of the latest developments in VQA algorithms. The book includes a thorough examination of machine learning algorithms that find application in both NTMA and VQA, with a specific emphasis on classification and prediction algorithms such as the Multi-Layer Perceptron and Support Vector Machine. The book also explores the software architecture of the NTMA client-server application. This architecture is meticulously developed using HTML, CSS, Node.js, and JavaScript. Practical aspects of developing the Video Quality Assessment (VQA) model using JavaScript and Java are presented. Lastly, the book provides detailed guidance on implementing a complete system model that seamlessly merges NTMA and VQA into a unified web application, all built upon a client-server paradigm. By the end of the book, you will understand NTMA and VQA concepts and will be able to apply machine learning to both domains and develop and deploy your own NTMA and VQA applications using JavaScript and Node.js. What You Will Learn What are the fundamental concepts, existing applications, and research on NTMA? What are the existing software and current research trends in VQA? Which machine learning algorithms are used in NTMA and VQA? How do you develop NTMA and VQA web-based applications using JavaScript, HTML, and Node.js? Who This Book Is For Software professionals and machine learning engineers involved in the fields of networking and telecommunications
Machine Learning Governance for Managers
by Francesca Lazzeri Alexei RobskyMachine Learning Governance for Managers provides readers with the knowledge to unlock insights from data and leverage AI solutions. In today's business landscape, most organizations face challenges in scaling and maintaining a sustainable machine learning model lifecycle. This book offers a comprehensive framework that covers business requirements, data generation and acquisition, modeling, model deployment, performance measurement, and management, providing a range of methodologies, technologies, and resources to assist data science managers in adopting data and AI-driven practices. Particular emphasis is given to ramping up a solution quickly, detailing skills and techniques to ensure the right things are measured and acted upon for reliable results and high performance. Readers will learn sustainable tools for implementing machine learning with existing IT and privacy policies, including versioning all models, creating documentation, monitoring models and their results, and assessing their causal business impact. By overcoming these challenges, bottom-line gains from AI investments can be realized. Organizations that implement all aspects of AI/ML model governance can achieve a high level of control and visibility over how models perform in production, leading to improved operational efficiency and a higher ROI on AI investments. Machine Learning Governance for Managers helps to effectively control model inputs and understand all the variables that may impact your results. Don't let challenges in machine learning hinder your organization's growth - unlock its potential with this essential guide.
Machine Learning Meets Quantum Physics (Lecture Notes in Physics #968)
by Klaus-Robert Müller Kristof T. Schütt Stefan Chmiela O. Anatole von Lilienfeld Alexandre Tkatchenko Koji TsudaDesigning molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.
Machine Learning Methods
by Hang LiThis book provides a comprehensive and systematic introduction to the principal machine learning methods, covering both supervised and unsupervised learning methods. It discusses essential methods of classification and regression in supervised learning, such as decision trees, perceptrons, support vector machines, maximum entropy models, logistic regression models and multiclass classification, as well as methods applied in supervised learning, like the hidden Markov model and conditional random fields. In the context of unsupervised learning, it examines clustering and other problems as well as methods such as singular value decomposition, principal component analysis and latent semantic analysis. As a fundamental book on machine learning, it addresses the needs of researchers and students who apply machine learning as an important tool in their research, especially those in fields such as information retrieval, natural language processing and text data mining. In order to understand the concepts and methods discussed, readers are expected to have an elementary knowledge of advanced mathematics, linear algebra and probability statistics. The detailed explanations of basic principles, underlying concepts and algorithms enable readers to grasp basic techniques, while the rigorous mathematical derivations and specific examples included offer valuable insights into machine learning.
Machine Learning Perspectives of Agent-Based Models: Practical Applications to Economic Crises and Pandemics with Python, R, Netlogo and Julia
by Pedro Campos Anand Rao Joaquim MargaridoThis book provides an overview of agent-based modeling (ABM) and multi-agent systems (MAS), emphasizing their significance in understanding complex economic systems, with a special focus on the emerging properties of heterogeneous agents that cannot be deduced from the characteristics of individual agents. ABM is highlighted as a powerful tool for studying economics, especially in the context of financial crises and pandemics, where traditional models, such as dynamic stochastic general equilibrium (DSGE) models, have proven inadequate. Containing numerous practical examples and applications with R, Python, Julia and Netlogo, the book explores how learning, particularly machine learning, can be integrated into multi-agent systems to enhance the adaptation and behavior of agents in dynamic environments. It compares different learning approaches, including game theory and artificial intelligence, highlighting the advantages of each in modeling economic phenomena.
Machine Learning Q and AI: 30 Essential Questions and Answers on Machine Learning and AI
by Sebastian RaschkaLearn the answers to 30 cutting-edge questions in machine learning and AI and level up your expertise in the field.If you&’re ready to venture beyond introductory concepts and dig deeper into machine learning, deep learning, and AI, the question-and-answer format of Machine Learning Q and AI will make things fast and easy for you, without a lot of mucking about.Born out of questions often fielded by author Sebastian Raschka, the direct, no-nonsense approach of this book makes advanced topics more accessible and genuinely engaging. Each brief, self-contained chapter journeys through a fundamental question in AI, unraveling it with clear explanations, diagrams, and hands-on exercises.WHAT'S INSIDE:FOCUSED CHAPTERS: Key questions in AI are answered concisely, and complex ideas are broken down into easily digestible parts.WIDE RANGE OF TOPICS: Raschka covers topics ranging from neural network architectures and model evaluation to computer vision and natural language processing.PRACTICAL APPLICATIONS: Learn techniques for enhancing model performance, fine-tuning large models, and more.You&’ll also explore how to:• Manage the various sources of randomness in neural network training• Differentiate between encoder and decoder architectures in large language models• Reduce overfitting through data and model modifications• Construct confidence intervals for classifiers and optimize models with limited labeled data• Choose between different multi-GPU training paradigms and different types of generative AI models• Understand performance metrics for natural language processing• Make sense of the inductive biases in vision transformersIf you&’ve been on the hunt for the perfect resource to elevate your understanding of machine learning, Machine Learning Q and AI will make it easy for you to painlessly advance your knowledge beyond the basics.
Machine Learning Risk Assessments in Criminal Justice Settings
by Richard BerkThis book puts in one place and in accessible form Richard Berk’s most recent work on forecasts of re-offending by individuals already in criminal justice custody. Using machine learning statistical procedures trained on very large datasets, an explicit introduction of the relative costs of forecasting errors as the forecasts are constructed, and an emphasis on maximizing forecasting accuracy, the author shows how his decades of research on the topic improves forecasts of risk. Criminal justice risk forecasts anticipate the future behavior of specified individuals, rather than “predictive policing” for locations in time and space, which is a very different enterprise that uses different data different data analysis tools. The audience for this book includes graduate students and researchers in the social sciences, and data analysts in criminal justice agencies. Formal mathematics is used only as necessary or in concert with more intuitive explanations.
Machine Learning Techniques to Predict Terrorist Attacks: Exemplified by Jama'at Nasr al-Islam wal Muslimin (Terrorism, Security, and Computation)
by Roy Lindelauf V.S. Subrahmanian Chiara Pulice Laura Mostert Marnix Provoost Priyanka AminOne of the most influential actors in spreading Islamist violence across the Sahel is Jama&’at Nasr Al Islam Wal Muslimin (JNIM).This book provides the first systematic quantitative analysis of JNIM&’s behavior by analyzing a 12-year database of JNIM&’s attacks and the environment surrounding JNIM. This book leverages AI/ML predictive models to accurately predict almost 40 types of attacks using over 80 independent variables. This book describes a set of temporal probabilistic rules that state that when the environment in which the group operates satisfies some conditions, then an attack of a certain type will likely occur in the next N months. This provides a deep, easy to comprehend understanding of the conditions under which JNIM carries various kinds of attacks up to 6 months into the future. This book will serve as an invaluable guide to scholars (computer scientists, political scientists, policy makers). Military officers, intelligence personnel, and government employees, who seek to understand, predict, and eventually mitigate attacks by JNIM and bring peace to the nations of Mali, Burkina Faso, and Niger will want to purchase this book as well.
Machine Learning Technologies on Energy Economics and Finance: Energy and Sustainable Analytics, Volume 1 (International Series in Operations Research & Management Science #367)
by Mohammad Zoynul Abedin Wang YongThis book explores the latest innovations in energy economics and finance, with a particular focus on the role of machine learning algorithms in advancing the energy sector. It examines key factors shaping this field, including market structures, regulatory frameworks, environmental impacts, and the dynamics of the global energy market. It discusses the critical application of machine learning (ML) in energy financing, introducing predictive tools for forecasting energy prices across various sectors—such as crude oil, electricity, fuelwood, solar, and natural gas. It also addresses how ML can predict investor behavior and assess the efficiency of energy markets, with a focus on both the opportunities and challenges in renewable energy and energy finance. This book serves as a comprehensive guide for academics, practitioners, financial managers, stakeholders, government officials, and policymakers who seek strategies to enhance energy systems, reduce costs and uncertainties, and optimize revenue for economic growth. This is the first volume of a two-volume set.
Machine Learning Technologies on Energy Economics and Finance: Energy and Sustainable Analytics, Volume 2 (International Series in Operations Research & Management Science #368)
by Mohammad Zoynul Abedin Wang YongThis book explores the latest innovations in energy economics and finance, with a particular focus on the role of machine learning algorithms in advancing the energy sector. It examines key factors shaping this field, including market structures, regulatory frameworks, environmental impacts, and the dynamics of the global energy market. It discusses the critical application of machine learning (ML) in energy financing, introducing predictive tools for forecasting energy prices across various sectors—such as crude oil, electricity, fuelwood, solar, and natural gas. It also addresses how ML can predict investor behavior and assess the efficiency of energy markets, with a focus on both the opportunities and challenges in renewable energy and energy finance. This book serves as a comprehensive guide for academics, practitioners, financial managers, stakeholders, government officials, and policymakers who seek strategies to enhance energy systems, reduce costs and uncertainties, and optimize revenue for economic growth. This is the second volume of a two-volume set.
Machine Learning and Artificial Intelligence for Smart Systems in Engineering and Healthcare: Select Proceedings of FLAME 2024 (Lecture Notes in Electrical Engineering #1433)
by Anurag Singh Bhupendra Prakash Sharma Sofia Singh Bhudev Chandra DasThis book presents the select proceedings of the 4th Biennial International Conference on Future Learning Aspects for Mechanical Engineering (FLAME 2024). It covers the applications of machine learning (ML) and artificial intelligence (AI) in the development of smart systems, with a particular focus on engineering and healthcare applications. It provides a comprehensive overview of state-of-the-art techniques, methodologies, and practical implementations of ML and AI to address complex problems in these domains. The book covers theoretical foundations, recent advancements, and case studies demonstrating the impact of smart systems on improving efficiency, accuracy, and overall performance in engineering and healthcare and many other multidisciplinary fields of mechanical engineering. This book will be useful for scientists, engineers, researchers and professionals working in the allied fields of machine learning and artificial intelligence.
Machine Learning and Big Data Analytics: 2nd International Conference on Machine Learning and Big Data Analytics-ICMLBDA, IIT Patna, India, March 2022 (Springer Proceedings in Mathematics & Statistics #401)
by Bharadwaj Veeravalli Rajiv Misra Muttukrishnan Rajarajan Nishtha Kesswani Rana Omer Priyanka MishraThis edited volume on machine learning and big data analytics (Proceedings of ICMLBDA 2022) is intended to be used as a reference book for researchers and professionals to share their research and reports of new technologies and applications in Machine Learning and Big Data Analytics like biometric Recognition Systems, medical diagnosis, industries, telecommunications, AI Petri Nets Model-Based Diagnosis, gaming, stock trading, Intelligent Aerospace Systems, robot control, law, remote sensing and scientific discovery agents and multiagent systems; and natural language and Web intelligence. The intent of this book is to provide awareness of algorithms used for machine learning and big data in the advanced Scientific Technologies, provide a correlation of multidisciplinary areas and become a point of great interest for Data Scientists, systems architects, developers, new researchers and graduate level students. This volume provides cutting-edge research from around the globe on this field. Current status, trends, future directions, opportunities, etc. are discussed, making it friendly for beginners and young researchers.