This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems.

The idea behind this book is to simplify the journey of aspiring readers and researchers to understand Big Data, IoT and Machine Learning. It also includes various real-time/offline applications and case studies in the fields of engineering, computer science, information security and cloud computing using modern tools. This book consists of two sections: Section I contains the topics related to Applications of Machine Learning, and Section II addresses issues about Big Data, the Cloud and the Internet of Things. This brings all the related technologies into a single source so that undergraduate and postgraduate students, researchers, academicians and people in industry can easily understand them. Features Addresses the complete data science technologies workflow Explores basic and high-level concepts and services as a manual for those in the industry and at the same time can help beginners to understand both basic and advanced aspects of machine learning Covers data processing and security solutions in IoT and Big Data applications Offers adaptive, robust, scalable and reliable applications to develop solutions for day-to-day problems Presents security issues and data migration techniques of NoSQL databases

This book is a collection of papers in the research area of big data, cloud computing, cybersecurity, machine learning, deep learning, e-learning, Internet of Things, reinforcement learning, information system, social media and natural language processing. This book includes papers presented at the 5th International Conference on Big Data Cloud and Internet of Things, BDIoT 2021 during March 17–18, 2021, at ENSIAS, Mohammed V University in Rabat, Morocco.

This volume constitutes the proceedings of the 7th International Conference on BIGDATA 2018, held as Part of SCF 2018 in Seattle, WA, USA in June 2018. The 22 full papers together with 10 short papers published in this volume were carefully reviewed and selected from 97 submissions. They are organized in topical sections such as Data analysis, data as a service, services computing, data conversion, data storage, data centers, dataflow architectures, data compression, data exchange, data modeling, databases, and data management.

With the proliferation of devices connected to the internet and connected to each other, the volume of data collected, stored, and processed is increasing every day, which brings new challenges in terms of information security. As big data expands with the help of public clouds, traditional security solutions tailored to private computing infrastructures and confined to a well-defined security perimeter, such as firewalls and demilitarized zones (DMZs), are no longer effective. New security functions are required to work over the heterogenous composition of diverse hardware, operating systems, and network domains. Security, Privacy, and Forensics Issues in Big Data is an essential research book that examines recent advancements in big data and the impact that these advancements have on information security and privacy measures needed for these networks. Highlighting a range of topics including cryptography, data analytics, and threat detection, this is an excellent reference source for students, software developers and engineers, security analysts, IT consultants, academicians, researchers, and professionals.

Cancer continues to be a growing problem as it is the foremost cause of death worldwide, killing millions of people each year. The number of people battling cancer continues to increase, owing to different reasons, such as lifestyle choices. Clinically, determining the cause of cancer is very challenging and often inaccurate. Incorporating efficient and accurate algorithms to detect cancer cases is becoming increasingly beneficial for scientists in computer science and healthcare, as well as a long-term benefit for doctors, patients, clinic practitioners, and more. Specifically, an automation of computation in machine learning could be a solution in the next generation of big data science technology. Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis presents algorithms that have been developed to evaluate big data approaches and cancer research. The chapters include artificial intelligence and machine learning approaches, as well as case studies to solve the predictive issues in colon cancer research. This book includes concepts and techniques used to run tasks in an automated manner with the intent to improve better accuracy in comparison with previous studies and methods. This book also covers the processes of research design, development, and outcome analytics in this field. Doctors, IT consultants, IT specialists, medical software professionals, data scientists, researchers, computer scientists, healthcare practitioners, academicians, and students can benefit from this critical resource.

Cognitive Big Data Intelligence with a Metaheuristic Approach presents an exact and compact organization of content relating to the latest metaheuristics methodologies based on new challenging big data application domains and cognitive computing. The combined model of cognitive big data intelligence with metaheuristics methods can be used to analyze emerging patterns, spot business opportunities, and take care of critical process-centric issues in real-time. Various real-time case studies and implemented works are discussed in this book for better understanding and additional clarity. This book presents an essential platform for the use of cognitive technology in the field of Data Science. It covers metaheuristic methodologies that can be successful in a wide variety of problem settings in big data frameworks. - Provides a unique opportunity to present the work on the state-of-the-art of metaheuristics approach in the area of big data processing developing automated and intelligent models - Explains different, feasible applications and case studies where cognitive computing can be successfully implemented in big data analytics using metaheuristics algorithms - Provides a snapshot of the latest advances in the contribution of metaheuristics frameworks in cognitive big data applications to solve optimization problems

This book gathers a collection of high-quality peer-reviewed research papers presented at the International Conference on Big Data, IoT and Machine Learning (BIM 2021), held in Cox’s Bazar, Bangladesh, during 23–25 September 2021. The book covers research papers in the field of big data, IoT and machine learning. The book will be helpful for active researchers and practitioners in the field.

This monograph provides comprehensive coverage of the collection, management, and use of big data obtained from remote sensing. The book begins with an introduction to the basics of big data and remote sensing, laying the groundwork for the more specialized information to follow. The volume then goes on to address a wide variety of topics related to the use and management of remote sensing big data, including hot topics such as analysis through machine learning, cyberinfrastructure, and modeling. Examples on how to use the results of big data analysis of remotely sensed data for concrete decision-making are offered as well. The closing chapters discuss geospatial big data initiatives throughout the world and future challenges and opportunities for remote sensing big data applications. The audience for this book includes researchers at the intersection of geoscience and data science, senior undergraduate and graduate students, and anyone else interested in how large datasets obtained through remote sensing can be best utilized. The book presents a culmination of 30 years of research from renowned spatial scientists Drs. Liping Di and Eugene Yu.

This book focuses on different algorithms and models related to AI, big data and IoT used for various domains. It enables the reader to have a broader and deeper understanding of several perspectives regarding the dynamics, challenges, and opportunities for sustainable development using artificial intelligence, big data and IoT. Applications of Artificial Intelligence, Big Data and Internet of Things (IoT) in Sustainable Development focuses on IT-based advancements in multidisciplinary fields such as healthcare, finance, bioinformatics, industrial automation, and environmental science. The authors discuss the key issues of security, management, and the realization of possible solutions to hurdles in sustainable development. The reader will master basic concepts and deep insights of various algorithms and models for various applications such as healthcare, finance, education, smart cities, smart cars, among others. Finally, the book will also examine the applications and implementation of big data IoT, AI strategies to facilitate the sustainable development goals set by the United Nations by 2030. This book is intended to help researchers, academics, and policymakers to analyze the challenges and future aspects for maintaining sustainable development through IoT, big data, and AI.