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This book discusses the dew computing paradigm with the evolution of future-generation technologies through the cloud and the Internet of Things in the scope of machine intelligence. Dew computing is an emerging paradigm that inherits a flexible and super-hybrid methodology to afford personal information to users with self-regulating internetwork connectivity. The contents conceptualize how the end-users can benefit from data analytics through intelligent data sensing, computing, analytics, and distributed scenarios using a dew-cloud computational framework over the Internet of Things environment. The main focus of this book is to bring all the related technologies into a single platform so that undergraduate and postgraduate students, researchers, academicians, and the industry can easily understand dew computing, future generations of cloud computing, machine intelligence, and representation learning in IoT-enabled technologies.
Machine learning is able to solve real-time problems. It has several algorithms such as classification, clustering, and more. To learn these essential algorithms, we require tools like Scikit and TensorFlow. Machine Learning Algorithms Using Scikit and TensorFlow Environments assists researchers in learning and implementing these critical algorithms. Covering key topics such as classification, artificial neural networks, prediction, random forest, and regression analysis, this premier reference source is ideal for industry professionals, computer scientists, researchers, academicians, scholars, practitioners, instructors, and students.
The text presents aspects of virtual worlds and highlights the emerging trends in simulation and modeling, comprising machine learning, artificial intelligence, deep learning, robotics, cloud computing, and data mining algorithms. It further discusses concepts including multimedia for the Internet of Things, graphical modeling using emerging technologies, and securing communication with secure data transmission in the modeling of virtual worlds. This book: Discusses secure data transmission in the modeling of virtual worlds in the Internet of Things environment. Covers the integration of concepts and technical know-how about multiple technologies in visual world modeling, system configurations, and hardware issues. Explores the use of next-generation technologies such as deep learning, blockchain, and artificial intelligence in visual world modeling scenarios. Presents architectures and system models for the Internet of Things based visual world modeling systems. Provides real-time case scenarios, highlighting emerging challenges and issues. The text is primarily written for senior undergraduate students, graduate students, and academic researchers in the fields of electrical engineering, electronics, communications engineering, computer engineering, and information technology.
In this book, contributors provide insights into the latest developments of Edge Computing/Mobile Edge Computing, specifically in terms of communication protocols and related applications and architectures. The book provides help to Edge service providers, Edge service consumers, and Edge service developers interested in getting the latest knowledge in the area. The book includes relevant Edge Computing topics such as applications; architecture; services; inter-operability; data analytics; deployment and service; resource management; simulation and modeling; and security and privacy. Targeted readers include those from varying disciplines who are interested in designing and deploying Edge Computing. Features the latest research related to Edge Computing, from a variety of perspectives; Tackles Edge Computing in academia and industry, featuring a variety of new and innovative operational ideas; Provides a strong foundation for researchers to advance further in the Edge Computing domain.
Artificial Intelligence and Machine Learning for Predictive and Analytical Rendering in Edge Computing focuses on the role of AI and machine learning as it impacts and works alongside Edge Computing. Sections cover the growing number of devices and applications in diversified domains of industry, including gaming, speech recognition, medical diagnostics, robotics and computer vision and how they are being driven by Big Data, Artificial Intelligence, Machine Learning and distributed computing, may it be Cloud Computing or the evolving Fog and Edge Computing paradigms. Challenges covered include remote storage and computing, bandwidth overload due to transportation of data from End nodes to Cloud leading in latency issues, security issues in transporting sensitive medical and financial information across larger gaps in points of data generation and computing, as well as design features of Edge nodes to store and run AI/ML algorithms for effective rendering. - Provides a reference handbook on the evolution of distributed systems, including Cloud, Fog and Edge Computing - Integrates the various Artificial Intelligence and Machine Learning techniques for effective predictions at Edge rather than Cloud or remote Data Centers - Provides insight into the features and constraints in Edge Computing and storage, including hardware constraints and the technological/architectural developments that shall overcome those constraints
The book includes insights that reflect the advances in the field of Internet of Things from upcoming researchers and leading academicians across the globe. It contains the high-quality peer-reviewed papers of ‘International Conference on Internet of Things for Technological Development (IoT4TD 2017)’, held at Kadi Sarva Vishvavidyalaya, Gandhinagar, Gujarat, India during April 1-2, 2017. The book covers variety of topics such as Internet of things, Intelligent Image Processing, Networks and Mobile Communications, Big Data and Cloud. The book is helpful for the perspective readers’ from computer industry and academia to derive the advances of next generation communication and computational technology and shape them into real life applications.
This book provides an overview of the Internet of Things (IoT) – covering new ideas, concepts, research and innovation to enable the development of IoT technologies in a global context. The work is intended as a standalone book in a series covering the activities of the Internet of Things European Research Cluster (IERC) – including research, technological innovation, validation, and deployment. The book chapters build on the developments and innovative ideas put forward by the IERC, the IoT European Large-Scale Pilots Programme and the IoT European Security and Privacy Projects – presenting new concepts, ideas and future IoT trends and ways of integrating open data frameworks and IoT marketplaces into larger deployment ecosystems. The IoT and Industrial Internet of Things technologies are moving towards hyperautomated solutions – combining hyperconnectivity, artificial intelligence (AI), distributed ledger technologies and virtual/augmented extended reality, with edge computing and deep edge processing becoming an assertive factor across industries for implementing intelligent distributed computing resources and data to keep the efficient data exchange and processing local to reduce latency, exploit the sensing/actuating capabilities and enable greater autonomy. Expanding the adoption of consumer, business, industrial and tactile IoT requires further development of hyperautomated IoT concepts for collaborative solutions involving machines and humans to expand augmented creativity at the application level using AI to optimise the industrial processes and progress towards a symbiotic economy based on distributed federated cloud/edge infrastructure allowing resource sharing in the form of computing, memory and analytics capabilities. The advances of autonomous IoT applications delivering services in real-time encompasses development in servitisation, robotisation, automation and hyperconnectivity, which are essential for the rapid evolution of industrial enterprises in the new digital era. The rise of digital twins integrated into IoT platforms as fully interactive elements embedded into the simulation and optimisation environment, as well as the embedment of AI techniques and methods, enhances the accuracy and performance of models in the various IoT and Industrial Internet of Things applications. The convergence of technologies to provide scalable, interoperable IoT-enabled applications pushed the requirements for high bandwidth, low latency and robust and dependable connectivity to support the industry’s demand for deeper integration and improved analytics to deliver sustainable competitive advantage products and services, enabling digital transformation with a focus on new business models.Safety and security are interlinked for the next wave of IoT technologies and applications and combined, prove a greater value for rapid adoption. The new IoT technologies are essential for facilitating sustainable development, reducing energy consumption and, by supporting the optimisation of products and processes, mitigating unnecessary carbon emissions – thereby reducing the environmental impact through real-time data collection, analysis, exchange, and processing.
This book constitutes seven refereed workshops and symposiums, SpaCCS Workshops 2020, which are held jointly with the 13th International Conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage, SpaCCS 2020, in Nanjing, China, in December 2020. The 48 papers were carefully reviewed and selected from 131 submissions and cover a broad range of topics on security, privacy and anonymity in computation communication, and storage, including the 11th International Workshop on Trust, Security and Privacy for Big Data (TrustData 2020), the 10th International Symposium on Trust, Security and Privacy for Emerging Applications (TSP 2020), the 9th International Symposium on Security and Privacy on Internet of Things (SPIoT 2020), the 6th International Symposium on Sensor-Cloud Systems (SCS 2020), the Second International Workshop on Communication, Computing, Informatics and Security (CCIS 2020), the First International Workshop on Intelligence and Security in Next Generation Networks (ISNGN 2020), the First International Symposium on Emerging Information Security and Applications (EISA 2020).
DESCRIPTION Our book is divided into several useful concepts and techniques of machine learning. This book serves as a valuable resource for individuals seeking to deepen their understanding of advanced topics in this field. Learn about various learning algorithms, including supervised, unsupervised, and reinforcement learning, and their mathematical foundations. Discover the significance of feature engineering and selection for enhancing model performance. Understand model evaluation metrics like accuracy, precision, recall, and F1-score, along with techniques like cross-validation and grid search for model selection. Explore ensemble learning methods along with deep learning, unsupervised learning, time series analysis, and reinforcement learning techniques. Lastly, uncover real-world applications of the machine and deep learning algorithms. After reading this book, readers will gain a comprehensive understanding of machine learning fundamentals and advanced techniques. With this knowledge, readers will be equipped to tackle real-world problems, make informed decisions, and develop innovative solutions using machine and deep learning algorithms. KEY FEATURES ● Basic understanding of machine learning algorithms via MATLAB, R, and Python. ● Inclusion of examples related to real-world problems, case studies, and questions related to futuristic technologies. ● Adding futuristic technologies related to machine learning and deep learning. WHAT YOU WILL LEARN ● Ability to tackle complex machine learning problems. ● Understanding of foundations, algorithms, ethical issues, and how to implement each learning algorithm for their own use/ with their data. ● Efficient data analysis for real-time data will be understood by researchers/ students. ● Using data analysis in near future topics and cutting-edge technologies. WHO THIS BOOK IS FOR This book is ideal for students, professors, and researchers. It equips industry experts and academics with the technical know-how and practical implementations of machine learning algorithms. TABLE OF CONTENTS 1. Introduction to Machine Learning 2. Statistical Analysis 3. Linear Regression 4. Logistic Regression 5. Decision Trees 6. Random Forest 7. Rule-Based Classifiers 8. Naïve Bayesian Classifier 9. K-Nearest Neighbors Classifiers 10. Support Vector Machine 11. K-Means Clustering 12. Dimensionality Reduction 13. Association Rules Mining and FP Growth 14. Reinforcement Learning 15. Applications of ML Algorithms 16. Applications of Deep Learning 17. Advance Topics and Future Directions