ICSET is “International Conference on Science, Engineering and Technology”. ICSET on 2019 was held on November 23, 2019 in Grand Tjokro Hotel - Jakarta Barat - Indonesia. The conference was hosted by IDRI Province DKI Jakarta and collaborated with other universities in Indonesia . The ICSET-2019 focus on “Enhance knowledge and innovation for sustainable society in Industry 4.0 ”. The conference aims to provide opportunities to exchange research ideas and produce new insights. This opportunity also could be used as a way to broaden international network.
This book features selected papers presented at International Conference on Data Analytics and Computing (ICDAC 2022), organized by Department of Mathematics, College of Science and Technology, Wenzhou-Kean University, Wenzhou, China, held during May 28–29, 2022. This book includes state-of-the-art current trends in data science, data analytics optimization, soft computing and related areas. Its primary readers are postgraduate students, researchers and academic professionals.
This book presents the peer-reviewed proceedings of the Sixth International Conference on Intelligent Computing and Applications (ICICA 2020), held at Government College of Engineering, Keonjhar, Odisha, India, during December 22–24, 2020. The book includes the latest research on advanced computational methodologies such as neural networks, fuzzy systems, evolutionary algorithms, hybrid intelligent systems, uncertain reasoning techniques, and other machine learning methods and their applications to decision-making and problem-solving in mobile and wireless communication networks.
This book contains original, peer-reviewed research articles from the Second International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications, held in March 28-29th 2021 at CMR Institute of Technology, Hyderabad, Telangana India. It covers the latest research trends and developments in areas of machine learning, artificial intelligence, neural networks, cyber-physical systems, cybernetics, with emphasis on applications in smart cities, Internet of Things, practical data science and cognition. The book focuses on the comprehensive tenets of artificial intelligence, machine learning and deep learning to emphasize its use in modelling, identification, optimization, prediction, forecasting and control of future intelligent systems. Submissions were solicited of unpublished material, and present in-depth fundamental research contributions from a methodological/application perspective in understanding artificial intelligence and machine learning approaches and their capabilities in solving a diverse range of problems in industries and its real-world applications.
As technology continues to advance, it is critical for businesses to implement systems that can support the transformation of data into information that is crucial for the success of the company. Without the integration of data (both structured and unstructured) mining in business intelligence systems, invaluable knowledge is lost. However, there are currently many different models and approaches that must be explored to determine the best method of integration. Integration Challenges for Analytics, Business Intelligence, and Data Mining is a relevant academic book that provides empirical research findings on increasing the understanding of using data mining in the context of business intelligence and analytics systems. Covering topics that include big data, artificial intelligence, and decision making, this book is an ideal reference source for professionals working in the areas of data mining, business intelligence, and analytics; data scientists; IT specialists; managers; researchers; academicians; practitioners; and graduate students.
This book presents high-quality peer-reviewed papers from the International Conference on Electronics, Biomedical Engineering, and Health Informatics (ICEBEHI) 2022 held at Surabaya, Indonesia, virtually. The contents are broadly divided into three parts: (a) Electronics, (b) Biomedical Engineering, and (c) Health Informatics. The major focus is on emerging technologies and their applications in the domain of biomedical engineering. It includes papers based on original theoretical, practical, and experimental simulations, development, applications, measurements, and testing. Featuring the latest advances in the field of biomedical engineering applications, this book serves as a definitive reference resource for researchers, professors, and practitioners interested in exploring advanced techniques in the fields of electronics, biomedical engineering, and health informatics. The applications and solutions discussed here provide excellent reference material for future product development.
Steganography is the art of secret writing. The purpose of steganography is to hide the presence of a message from the intruder by using state-of-the-art methods, algorithms, architectures, models, and methodologies in the domains of cloud, internet of things (IoT), and the Android platform. Though security controls in cloud computing, IoT, and Android platforms are not much different than security controls in an IT environment, they might still present different types of risks to an organization than the classic IT solutions. Therefore, a detailed discussion is needed in case there is a breach in security. It is important to review the security aspects of cloud, IoT, and Android platforms related to steganography to determine how this new technology is being utilized and improved continuously to protect information digitally. The benefits and challenges, along with the current and potential developments for the future, are important keystones in this critical area of security research. Multidisciplinary Approach to Modern Digital Steganography reviews the security aspects of cloud, IoT, and Android platforms related to steganography and addresses emerging security concerns, new algorithms, and case studies in the field. Furthermore, the book presents a new approach to secure data storage on cloud infrastructure and IoT along with including discussions on optimization models and security controls that could be implemented. Other important topics include data transmission, deep learning techniques, machine learning, and both image and text stenography. This book is essential for forensic engineers, forensic analysts, cybersecurity analysts, cyber forensic examiners, security engineers, cybersecurity network analysts, cyber network defense analysts, and digital forensic examiners along with practitioners, researchers, academicians, and students interested in the latest techniques and state-of-the-art methods in digital steganography.
Artificial Intelligence-Empowered Modern Electric Vehicles in Smart Grid Systems: Fundamentals, Technologies, and Solutions is an essential reference for energy researchers, graduate students and engineers who aim to understand the opportunities offered by artificial intelligence for the integration of electric vehicles into smart grids. This book begins by building foundational knowledge for the reader, covering the essentials of artificial intelligence and its applications for electric vehicles in a clear and holistic manner. Next, it breaks down two essential areas of application in more detail: energy management (from to energy harvesting to demand response and complex forecasting), and market strategies (including peer-to-peer, vehicle-to-vehicle, and vehicle-to-everything trading, plus the cyber-security implications). A final part provides detailed case studies and close consideration of challenges, including code and data sets for replication of techniques. Providing a clear pathway from fundamentals to practical implementation, Artificial Intelligence-Empowered Modern Electric Vehicles in Smart Grid Systems will provide multidisciplinary guidance for implementing this cutting-edge technology in the energy systems of the future. - Supports fundamental understanding of artificial intelligence and its opportunities for energy system specialists - Collects the real-world experiences of global experts - Enables practical implementation of artificial intelligence strategies that support renewable energy integration across energy systems, markets, and grids
