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.

Artificial Intelligence and Knowledge Processing: Methods and Applications demonstrates the transformative power of Artificial Intelligence (AI) in our lives. The book is a collection of 14 edited reviews that cover a wide range of topics showcasing the application of AI and machine learning to create knowledge, and facilitate different processes. The book starts by illuminating how AI is employed in robotics, IoT, marketing, and operations. It showcases how AI extracts insights from big data, optimizes museum management, and empowers automated garden path planning using reinforcement learning. The book also explores how AI can be used to predict heart disease using artificial neural networks. Furthermore, the book underscores how AI predicts crop suitability, manages crop systems, and can even help to detect violence in using computer vision. Chapters highlight specific techniques or systems such as recommendation systems and reinforcement learning where appropriate. Key Features: · Showcases a wide range of AI applications · Bridges theory and practice with real-word insights · Uses accessible language to explain complex AI concepts · Includes references for advanced readers This book is intended as a guide for a broad range of readers who want to learn about AI applications and the profound influence it has on our lives.

This book informs the reader about applications of Artificial Intelligence (AI) and nature-inspired algorithms in different situations. Each chapter in this book is written by topic experts on AI, nature-inspired algorithms and data science. The basic concepts relevant to these topics are explained, including evolutionary computing (EC), artificial neural networks (ANN), swarm intelligence (SI), and fuzzy systems (FS). Additionally, the book also covers optimization algorithms for data analysis. The contents include algorithms that can be used in systems designed for plant science research, load balancing, environmental analysis and healthcare. The goal of the book is to equip the reader - students and data analysts - with the information needed to apply basic AI algorithms to resolve actual problems encountered in a professional environment.

Augmented intelligence is an alternate approach of artificial intelligence (AI), which emphasizes AI’s assistive role. Augmented intelligence enhances human skills of reasoning in a robotic system or software by simulating expectancy, educational mining, problem solving, recollection, sequencing, and decision-making capabilities. It is based on a combination of techniques such as machine learning, deep learning and cognitive computing. This book explains artificial intelligence models that support assistive processes in different situations. The contributors aim to provide information to a diverse audience with groundbreaking developments in mathematical computing. The book presents 8 chapters on these topics: - Educational data mining in augmented reality virtual learning environment - Brain and computer interfaces - Tree-based tools for chemometric analysis of infrared spectra - Applications of deep learning in medical engineering - Bankruptcy prediction model using an enhanced boosting classifier - Reputation systems for mobile agent security - The crow search algorithm - COVID-19 diagnosis and treatment The contents attempt to integrate various facets of augmented Intelligence, by describing recent research developments and advanced topics of interest to academicians and researchers working on machine learning problems and AI.

This volume reviews cutting-edge innovations in blockchain technology that are propelling the healthcare industry into a new era of efficiency and security. It brings 14 reviews contributed by experts in blockchain and Web3 technologies into a single volume. Each contribution includes a summary for easy understanding and scientific references for advanced readers. Key Themes: Empowering Decentralized Healthcare: Learn about the future of decentralized healthcare, powered by blockchain, ensuring a seamless and patient-centric experience. Clinical Trials: Discover how blockchain is reshaping clinical trials, offering a glimpse into a future of optimized medical research. Data Security: Uncover the strategic use of blockchain in securing vital clinical trials data, ensuring confidentiality and integrity at every step. Blockchain-based Healthcare delivery: Delve into a comprehensive review of blockchain technology in the health sector, revealing its potential to transform healthcare systems with efficient and precise solutions. Predict, Prevent, and Protect: Understand how blockchain technology serves as a predictive tool, aiding in the prevention and control of the spread of COVID-19. This volume is a must-read for healthcare practitioners and administrators seeking to harness the power of blockchain in medicine and healthcare. It also provides information for researchers and business professionals who want to understand the innovative role of blockchain technology in the healthcare sector.

Computational Intelligence and Machine Learning Approaches in Biomedical Engineering and Health Care Systems explains the emerging technology that currently drives computer-aided diagnosis, medical analysis and other electronic healthcare systems. 11 book chapters cover advances in biomedical engineering fields achieved through deep learning and soft-computing techniques. Readers are given a fresh perspective on the impact on the outcomes for healthcare professionals who are assisted by advanced computing algorithms. Key Features: - Covers emerging technologies in biomedical engineering and healthcare that assist physicians in diagnosis, treatment, and surgical planning in a multidisciplinary context - Provides examples of technical use cases for artificial intelligence, machine learning and deep learning in medicine, with examples of different algorithms - Introduces readers to the concept of telemedicine and electronic healthcare systems - Provides implementations of disease prediction models for different diseases including cardiovascular diseases, diabetes and Alzheimer's disease - Summarizes key information for learners - Includes references for advanced readers The book serves as an essential reference for academic readers, as well as computer science enthusiasts who want to familiarize themselves with the practical computing techniques in the field of biomedical engineering (with a focus on medical imaging) and medical informatics.