This book gathers the high-quality papers presented at the 19th International Conference on Computing and Information Technology (IC2IT2023), held on May 18–19, 2023, in Bangkok, Thailand. The book presents an original research work for both academic and industry domains, which is aiming to show valuable knowledge, skills and experiences in the field of computing and information technology. The topics covered in the book include natural language processing, image processing, intelligent systems and algorithms, as well as machine learning. These lead to the major research directions for innovating computational methods and applications of information technology.
This book presents the latest research on computer recognition systems. Over the last few years, computer scientists, engineers and users have been confronted with rapid changes in computer interfaces and in the abilities of the machines and the services available. And this is just the beginning: based on recent research findings, we can expect more significant advances and challenges in the next decade. Achievements in the area of artificial intelligence have made an important major contribution to these developments: Machine learning, natural language processing, speech recognition, image and video processing are just some of the major research and engineering directions that have made autonomous driving, language assistants, automatic translation and answering systems as well as other innovative applications such as more human-oriented interfaces possible. Those developments also reflect economic changes in the world, which are increasingly dominated by the needs of enhanced globalization, international cooperation (including its competitive aspects) and emerging global problems.
This book is intended for academic and industrial developers, exploring and developing applications in the area of big data and machine learning, including those that are solving technology requirements, evaluation of methodology advances and algorithm demonstrations. The intent of this book is to provide awareness of algorithms used for machine learning and big data in the academic and professional community. The 17 chapters are divided into 5 sections: Theoretical Fundamentals; Big Data and Pattern Recognition; Machine Learning: Algorithms & Applications; Machine Learning's Next Frontier and Hands-On and Case Study. While it dwells on the foundations of machine learning and big data as a part of analytics, it also focuses on contemporary topics for research and development. In this regard, the book covers machine learning algorithms and their modern applications in developing automated systems. Subjects covered in detail include: Mathematical foundations of machine learning with various examples. An empirical study of supervised learning algorithms like Naïve Bayes, KNN and semi-supervised learning algorithms viz. S3VM, Graph-Based, Multiview. Precise study on unsupervised learning algorithms like GMM, K-mean clustering, Dritchlet process mixture model, X-means and Reinforcement learning algorithm with Q learning, R learning, TD learning, SARSA Learning, and so forth. Hands-on machine leaning open source tools viz. Apache Mahout, H2O. Case studies for readers to analyze the prescribed cases and present their solutions or interpretations with intrusion detection in MANETS using machine learning. Showcase on novel user-cases: Implications of Electronic Governance as well as Pragmatic Study of BD/ML technologies for agriculture, healthcare, social media, industry, banking, insurance and so on.
Over 75 exceptional electrocardiogram case studies curated from the libraries of 60 internationally recognized master teachers of ECG interpretation are brought together in this one-of-a-kind resource for student and teacher alike. Organized by disease type, ECG case studies are presented in a clinical context followed by questions and discussion. Medical students, residents, fellows, physicians — anyone who is involved in caring for patients with various cardiovascular diseases and other systemic pathologies — will find this unique collection with a global perspective useful and practical in developing the skills necessary to reading ECGs.
This book gathers the proceedings of the 18th International Conference on Computing and Information Technology (IC2IT2022), held on May 19–20, 2022, in Kanchanaburi, Thailand. The topics covered include machine learning, natural language processing, image processing, intelligent systems and algorithms, as well as network and cloud computing. These lead to the major research directions for emerging information technology and innovation, reflecting digital trends.
This proceedings book presents recent research work and results in the area of communication and information technologies. The chapters of this book contain the main, well-selected and reviewed contributions of scientists who met at the 12th International Conference on Computing and Information Technology (IC2IT) held during 7th - 8th July 2016 in Khon Kaen, Thailand The book is divided into three parts: “User Centric Data Mining and Text Processing”, “Data Mining Algoritms and their Applications” and “Optimization of Complex Networks”.
In many applications, e.g., bioinformatics, web access traces, system u- lization logs, etc., the data is naturally in the form of sequences. It has been of great interests to analyze the sequential data to find their inherent char- teristics. The sequential pattern is one of the most widely studied models to capture such characteristics. Examples of sequential patterns include but are not limited to protein sequence motifs and web page navigation traces. In this book, we focus on sequential pattern mining. To meet different needs of various applications, several models of sequential patterns have been proposed. We do not only study the mathematical definitions and application domains of these models, but also the algorithms on how to effectively and efficiently find these patterns. The objective of this book is to provide computer scientists and domain - perts such as life scientists with a set of tools in analyzing and understanding the nature of various sequences by : (1) identifying the specific model(s) of - quential patterns that are most suitable, and (2) providing an efficient algorithm for mining these patterns. Chapter 1 INTRODUCTION Data Mining is the process of extracting implicit knowledge and discovery of interesting characteristics and patterns that are not explicitly represented in the databases. The techniques can play an important role in understanding data and in capturing intrinsic relationships among data instances. Data mining has been an active research area in the past decade and has been proved to be very useful.
This three-volume set of LNCS 14086, LNCS 14087 and LNCS 14088 constitutes - in conjunction with the double-volume set LNAI 14089-14090- the refereed proceedings of the 19th International Conference on Intelligent Computing, ICIC 2023, held in Zhengzhou, China, in August 2023. The 337 full papers of the three proceedings volumes were carefully reviewed and selected from 828 submissions. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was "Advanced Intelligent Computing Technology and Applications". Papers that focused on this theme were solicited, addressing theories, methodologies, and applications in science and technology.
A comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs—-kernels—for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.
