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This textbook summarizes the different statistical, scientific, and financial data analysis methods for users ranging from a high school level to a professional level. It aims to combine the data analysis methods using three different programs¿Microsoft Excel, SPSS, and MATLAB. The book combining the different data analysis tools is a unique approach. The book presents a variety of real-life problems in data analysis and machine learning, delivering the best solution. Analysis methods presented in this book include but are not limited to, performing various algebraic and trigonometric operations, regression modeling, and correlation, as well as plotting graphs and charts to represent the results. Fundamental concepts of applied statistics are also explained here, with illustrative examples. Thus, this book presents a pioneering solution to help a wide range of students, researchers, and professionals learn data processing, interpret different findings derived from the analyses,and apply them to their research or professional fields. The book also includes worked examples of practical problems. The primary focus behind designing these examples is understanding the concepts of data analysis and how it can solve problems. The chapters include practice exercises to assist users in enhancing their skills to execute statistical analysis calculations using software instead of relying on tables for probabilities and percentiles in the present world.

This book deals with analysis techniques for TCP, UDP, Web, and SOAP data transmission performance on the IoT cloud side, CSMA-type, and multi-hop data transmission performance on the IoT edge side, as well as gateway receives buffer and transmit packet assembly data transmission performance.As mathematical tools for the analysis of communication performance, universal analysis methods such as stochastic processes, queues, Markov processes, and communication traffic simulators are applied to actual examples, and their usage is described in detail. This book aims to provide lifelong skills for analyzing communication performance that will serve as the basis for the future evolution of the IoT.

The book explains the important concepts and principles of image processing to implement the algorithms and techniques to discover new problems and applications. It contains numerous fundamental and advanced image processing algorithms and pattern recognition techniques to illustrate the framework. It presents essential background theory, shape methods, texture about new methods, and techniques for image processing and pattern recognition. It maintains a good balance between a mathematical background and practical implementation. This book also contains the comparison table and images that are used to show the results of enhanced techniques. This book consists of novel concepts and hybrid methods for providing effective solutions for society. It also includes a detailed explanation of algorithms in various programming languages like MATLAB, Python, etc. The security features of image processing like image watermarking and image encryption etc. are also discussed in this book. This book will be useful for those who are working in the field of image processing, pattern recognition, and security for digital images. This book targets researchers, academicians, industry, and professionals from R&D organizations, and students, healthcare professionals working in the field of medical imaging, telemedicine, cybersecurity, data scientist, artificial intelligence, image processing, digital hospital, intelligent medicine.

This book offers a comprehensive first-level introduction to data analytics. The book covers multivariate analysis, AI / ML, and other computational techniques for solving data analytics problems using Python. The topics covered include (a) a working introduction to programming with Python for data analytics, (b) an overview of statistical techniques - probability and statistics,  hypothesis testing, correlation and regression, factor analysis, classification (logistic regression, linear discriminant analysis, decision tree, support vector machines, and other methods), various clustering techniques, and survival analysis, (c) introduction to general computational techniques such as market basket analysis, and social network analysis, and (d) machine learning and deep learning.  Many academic textbooks are available for teaching statistical applications using R, SAS, and SPSS. However, there is a dearth of textbooks that provide a comprehensive introduction to the emerging and powerful Python ecosystem, which is pervasive in data science and machine learning applications.   The book offers a judicious mix of theory and practice, reinforced by over 100 tutorials coded in the Python programming language. The book provides worked-out examples that conceptualize real-world problems using data curated from public domain datasets. It is designed to benefit any data science aspirant, who has a basic (higher secondary school level) understanding of programming and statistics. The book may be used by analytics students for courses on statistics, multivariate analysis, machine learning, deep learning, data mining, and business analytics. It can be also used as a reference book by data analytics professionals.

This book contains peer-reviewed selected papers of the 7th International Conference on Educational Innovation (CIIE 2020). It presents excellent educational practices and technologies complemented by various innovative approaches that enhance educational outcomes. In line with the Sustainable Development Goal 4 of UNESCO in the 2030 agenda, CIIE 2020 has attempted to "e;ensure inclusive and equitable quality education and promote lifelong learning opportunities for all."e; The CIIE 2020 proceeding offers diverse dissemination of innovations, knowledge, and lessons learned to familiarize readership with new pedagogical-oriented, technology-driven educational strategies along with their applications to emphasize their impact on a large spectrum of stakeholders including students, teachers and professors, administrators, policymakers, entrepreneurs, governments, international organizations, and NGOs.

This book presents an overview of artificial intelligence (AI) in the automotive section, especially in the modern era of green energy-based electrification of vehicles and smart transportation systems. The book also discusses different Internet of Things aspects involved in the automotive domain with AI. The book presents autonomous driving systems, advanced driver assistance systems (ADAS), autonomy, AI involvement, and machine learning techniques with challenges in electrification, prognostics, and diagnostics. AI and IoT are two emerging technologies, and their importance in other modern technology electrification on transportation, connected vehicle segment are discussed thoroughly in this book with different topologies. It also presents AI applications in the charging profile prediction, state of charge, state of health, battery lifetime, and battery temperature detection in dynamic conditions. Different algorithms are also given in the book to discuss the nearest point charging station for electric vehicle users. The book also discusses cybersecurity issues and challenges in the real-time environment for AI implementation, IoT in transportation, and autonomous driving. The other aspects of telematics, smart sensors for the implementation of the IoT, and AI are also discussed, especially in guidance and control aspects. The book will be useful for the researchers, practitioners, and industry people working in AI, IoT in the electrification and transportation segment.

This book covers major areas of device and data security and privacy related to the Internet of Things (IoT). It also provides an overview of light-weight protocols and cryptographic mechanisms to achieve security and privacy in IoT applications. Besides, the book also discusses intrusion detection and firewall mechanisms for IoT. The book also covers topics related to embedded security mechanisms and presents suitable malware detection techniques for IoT. The book also contains a unique presentation on heterogeneous device and data management in IoT applications and showcases the major communication-level attacks and defense mechanisms related to IoT.