Artificial Intelligence (AI) is an interdisciplinary science with multiple approaches to solve a problem. Advancements in machine learning (ML) and deep learning are creating a paradigm shift in virtually every tech industry sector. This handbook provides a quick introduction to concepts in AI and ML. The sequence of the book contents has been set in a way to make it easy for students and teachers to understand relevant concepts with a practical orientation. This book starts with an introduction to AI/ML and its applications. Subsequent chapters cover predictions using ML, and focused information about AI/ML algorithms for different industries (health care, agriculture, autonomous driving, image classification and segmentation, SEO, smart gadgets and security). Each industry use-case demonstrates a specific aspect of AI/ML techniques that can be used to create pipelines for technical solutions such as data processing, object detection, classification and more. Additional features of the book include a summary and references in every chapter, and several full-color images to visualize concepts for easy understanding. It is an ideal handbook for both students and instructors in undergraduate level courses in artificial intelligence, data science, engineering and computer science who are required to understand AI/ML in a practical context.

Basics of Python Programming: A Quick Guide for Beginners is an essential companion to mastering the Python programming language. The book presents information about Python in 12 structured chapters with a strong emphasis on fundamentals and practical information. Starting with basic operators, functions and expressions, contents explain file handling, exception handling and modules. The book concludes with advanced topics such as object oriented programming and machine learning. Key Features: Fundamental Focus: Covers the core concepts of Python programming to build a strong foundation in python programming in an easy-to-understand format. Practical Demonstrations: Learn by doing. This textbook includes hands-on practical demonstrations that reinforce your understanding of Python concepts. IDE Guidance: Includes programming and installation guidance for Python-supporting Integrated Development Environments (IDEs). Explores Python Frameworks: Introduces Python frameworks such as Matplotlib, TensorFlow, PyTorch, Scikit-Learn, and NLTK for complex projects. Python for Machine Learning: Gives a preliminary understanding of Python for machine learning tasks for data science and AI applications. Basics of Python Programming: A Quick Guide for Beginners is the perfect starting point for aspiring students, programmers and tech enthusiasts. Whether you're a student looking to build a solid foundation in Python or an industry professional venturing into machine learning and artificial intelligence, this textbook has you covered.

This book explains different applications of supervised and unsupervised data engineering for working with multimedia objects. Throughout this book, the contributors highlight the use of Artificial Intelligence-based soft computing and machine techniques in the field of medical diagnosis, biometrics, networking, automation in vehicle manufacturing, data science and automation in electronics industries. The book presents seven chapters which present use-cases for AI engineering that can be applied in many fields. The book concludes with a final chapter that summarizes emerging AI trends in intelligent and interactive multimedia systems. Key features: - A concise yet diverse range of AI applications for multimedia data engineering - Covers both supervised and unsupervised machine learning techniques - Summarizes emerging AI trends in data engineering - Simple structured chapters for quick reference and easy understanding - References for advanced readers This book is a primary reference for data science and engineering students, researchers and academicians who need a quick and practical understanding of AI supplications in multimedia analysis for undertaking or designing courses. It also serves as a secondary reference for IT and AI engineers and enthusiasts who want to grasp advanced applications of the basic machine learning techniques in everyday applications.

Stay informed about recent trends and groundbreaking research driving innovation in the AI-IoT landscape. AI, a simulated form of natural intelligence within machines, has revolutionized various industries, simplifying daily tasks for end-users. This book serves as a handy reference, offering insights into the latest research and applications where AI and IoT intersect. The book includes 12 edited chapters that provide a comprehensive exploration of the synergies between AI and IoT. The contributors attempt to address engineering opportunities and challenges in different fields. Key Topics: AI and IoT in Smart Farming: Explore how these technologies enhance crop yield and sustainability, revolutionizing agricultural practices. AIoT (Artificial Intelligence of Things): Understand the amalgamation of AI and IoT and its applications, particularly focusing on smart cities and agriculture. Smart Healthcare and Predictive Disease Analysis: Uncover the crucial role of AI and IoT in early disease prediction and improving healthcare outcomes. Applications of AI in Various Sectors: Explore how AI contributes to sustainable development, sentiment analysis, education, autonomous vehicles, fashion, virtual trial rooms, and more. Each chapter has structured sections with summaries and reference lists, making it an invaluable resource for researchers, professionals, and enthusiasts keen on understanding the potential and impact of these technologies in today's rapidly evolving world.

This volume explores IoT architectures, their configuration, and operability in wireless sensor networks. The topics are spread across nine structured chapters covering fundamental and applied knowledge about wireless sensor networks using IoT devices. The book starts with an introduction to the subject, giving readers a quick overview of IoT enabled networks and bio-inspired approaches towards network design. This is followed by chapters explaining optimized routing protocols for accident detection, efficiency and performance analysis. The book concludes with four chapters dedicated to security applications of wireless networks, for homes, urban areas and businesses. Overall, the volume gives a balance of theoretical and practical information for readers. The book is intended as a resource for graduate and postgraduate students for understanding network design for home and embedded applications, specifically using single board computing devices. It also serves as a guide for networking courses and assessments.

Geosimulation has recently emerged at the intersection of Geographic Information Science, Complex Systems Theory and Computer Science. Geosimulation aims at understanding the dynamics of complex human-driven spatial systems through the use of spatially explicit computer simulation. The approaches and tools for validating Geosimulation models are especially important for understanding their complex and spatially heterogeneous outcomes. The Ebook presents the recent conceptual and methodological advances achieved in the field. It should be very useful for scientists and graduate students working in the fields of Complex Systems Modelling, Geocomputation, Science, Geography, Regional Science, Computer Science, Artificial Intelligence, Environment Simulation and Modeling, and Environmental Engineering.

The book aims to provide a deeper understanding of the synergistic impact of Artificial intelligence (AI) and the Internet of Things (IoT) for disease detection. It presents a collection of topics designed to explain methods to detect different diseases in humans and plants. Chapters are edited by experts in IT and machine learning, and are structured to make the volume accessible to a wide range of readers. Key Features: - 17 Chapters present information about the applications of AI and IoT in clinical medicine and plant biology - Provides examples of algorithms for heart diseases, Alzheimer’s disease, cancer, pneumonia and more - Includes techniques to detect plant disease - Includes information about the application of machine learning in specific imaging modalities - Highlights the use of a variety of advanced Deep learning techniques like Mask R-CNN - Each chapter provides an introduction and literature review and the relevant protocols to follow The book is an informative guide for data and computer scientists working to improve disease detection techniques in medical and life sciences research. It also serves as a reference for engineers working in the healthcare delivery sector.

AIoT (Artificial Intelligence of Things) and Big Data Analytics are catalyzing a healthcare revolution. This book is an accessible volume that summarizes the information available. In this book, researchers explore how AIoT and Big Data can seamlessly integrate into healthcare, enhancing medical services and devices while adhering to established protocols. The book demonstrates the crucial role of these technologies during healthcare crises like the COVID-19 pandemic. It presents novel solutions and computational techniques powered by AIoT, Machine Learning, and Deep Learning, providing a new frontier in healthcare problem-solving. Key Features: Real-Life Illustrations: Real-world examples showcase AIoT and Big Data in action, highlighting their impact in healthcare. Comprehensive Exploration: The book offers a thorough examination of AIoT, Big Data, and their harmonious synergy within the healthcare landscape. Visual Aids: Complex concepts become approachable through diagrams, flowcharts, and infographics, making technical processes and system designs more digestible. Ethical Insights: Delving into the ethical dimensions of AIoT and Big Data, it addresses concerns like data bias, patient consent, and transparency in healthcare. Forward-Looking Discourse: The book engages with emerging trends, potential innovations, and the future direction of AIoT and Big Data, making it a compass for healthcare transformation. Researchers, whether from academia, industry, or research and development organizations, interested in AIoT, Big Data, artificial intelligence, and healthcare optimization, will find this book informative. It also serves as an update for tech enthusiasts who want to explore the future of healthcare powered by AI and data.

This book explores the dynamic intersection of three cutting-edge technologies—Artificial Intelligence (AI), Internet of Things (IoT), and Cloud Computing—and their profound impact on diverse domains. Beginning with an introduction to AI and its challenges, it delves into IoT applications in fields like transportation, industry 4.0, healthcare, and agriculture. The subsequent chapter explores AI in the cloud, covering areas such as banking, e-commerce, smart cities, healthcare, and robotics. Another section investigates the integration of AI and IoT-Cloud, discussing applications like smart meters, smart cities, smart agriculture, smart healthcare, and smart industry. Challenges like data privacy and security are examined, and the future direction of these technologies, including fog computing and quantum computing, is explored. The book concludes with use cases that highlight the real-world applications of these transformative technologies across various sectors. Each chapter is also supplemented with a list of scholarly references for advanced readers. The book is intended primarily as a resource for students in information technology and technology courses. And as a secondary resource for industry professionals who want to learn about these technologies in the context of digital transformation.

Text Analysis with Python: A Research-Oriented Guide is a quick and comprehensive reference on text mining using python code. The main objective of the book is to equip the reader with the knowledge to apply various machine learning and deep learning techniques to text data. The book is organized into eight chapters which present the topic in a structured and progressive way. Key Features - Introduces the reader to Python programming and data processing - Introduces the reader to the preliminaries of natural language processing (NLP) - Covers data analysis and visualization using predefined python libraries and datasets - Teaches how to write text mining programs in Python - Includes text classification and clustering techniques - Informs the reader about different types of neural networks for text analysis - Includes advanced analytical techniques such as fuzzy logic and deep learning techniques - Explains concepts in a simplified and structured way that is ideal for learners - Includes References for further reading Text Analysis with Python: A Research-Oriented Guide is an ideal guide for students in data science and computer science courses, and for researchers and analysts who want to work on artificial intelligence projects that require the application of text mining and NLP techniques.