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The book presents the proceedings of two conferences: the 16th International Conference on Data Science (ICDATA 2020) and the 19th International Conference on Information & Knowledge Engineering (IKE 2020), which took place in Las Vegas, NV, USA, July 27-30, 2020. The conferences are part of the larger 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'20), which features 20 major tracks. Papers cover all aspects of Data Science, Data Mining, Machine Learning, Artificial and Computational Intelligence (ICDATA) and Information Retrieval Systems, Information & Knowledge Engineering, Management and Cyber-Learning (IKE). Authors include academics, researchers, professionals, and students. Presents the proceedings of the 16th International Conference on Data Science (ICDATA 2020) and the 19th International Conference on Information & Knowledge Engineering (IKE 2020); Includes papers on topics from data mining to machine learning to informational retrieval systems; Authors include academics, researchers, professionals and students.
CSIE 2011 is an international scientific Congress for distinguished scholars engaged in scientific, engineering and technological research, dedicated to build a platform for exploring and discussing the future of Computer Science and Information Engineering with existing and potential application scenarios. The congress has been held twice, in Los Angeles, USA for the first and in Changchun, China for the second time, each of which attracted a large number of researchers from all over the world. The congress turns out to develop a spirit of cooperation that leads to new friendship for addressing a wide variety of ongoing problems in this vibrant area of technology and fostering more collaboration over the world. The congress, CSIE 2011, received 2483 full paper and abstract submissions from 27 countries and regions over the world. Through a rigorous peer review process, all submissions were refereed based on their quality of content, level of innovation, significance, originality and legibility. 688 papers have been accepted for the international congress proceedings ultimately.
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Advances in Computer and Information Sciences and Engineering includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art research projects in the areas of Computer Science, Software Engineering, Computer Engineering, and Systems Engineering and Sciences. Advances in Computer and Information Sciences and Engineering includes selected papers from the conference proceedings of the International Conference on Systems, Computing Sciences and Software Engineering (SCSS 2007) which was part of the International Joint Conferences on Computer, Information and Systems Sciences and Engineering (CISSE 2007).
This book brings insight into data science and offers applications and implementation strategies. It includes current developments and future directions and covers the concept of data science along with its origins. It focuses on the mechanisms of extracting data along with classifications, architectural concepts, and business intelligence with predictive analysis. Data Science in Engineering and Management: Applications, New Developments, and Future Trends introduces the concept of data science, its use, and its origins, as well as presenting recent trends, highlighting future developments; discussing problems and offering solutions. It provides an overview of applications on data linked to engineering and management perspectives and also covers how data scientists, analysts, and program managers who are interested in productivity and improving their business can do so by incorporating a data science workflow effectively. This book is useful to researchers involved in data science and can be a reference for future research. It is also suitable as supporting material for undergraduate and graduate-level courses in related engineering disciplines.
"This book provides analysis, characterization and refinement of software engineering data in terms of machine learning methods. It depicts applications of several machine learning approaches in software systems development and deployment, and the use of machine learning methods to establish predictive models for software quality while offering readers suggestions by proposing future work in this emerging research field"--Provided by publisher.
Handbook of Data Science Approaches for Biomedical Engineering covers the research issues and concepts of biomedical engineering progress and the ways they are aligning with the latest technologies in IoT and big data. In addition, the book includes various real-time/offline medical applications that directly or indirectly rely on medical and information technology. Case studies in the field of medical science, i.e., biomedical engineering, computer science, information security, and interdisciplinary tools, along with modern tools and the technologies used are also included to enhance understanding. Today, the role of Big Data and IoT proves that ninety percent of data currently available has been generated in the last couple of years, with rapid increases happening every day. The reason for this growth is increasing in communication through electronic devices, sensors, web logs, global positioning system (GPS) data, mobile data, IoT, etc. - Provides in-depth information about Biomedical Engineering with Big Data and Internet of Things - Includes technical approaches for solving real-time healthcare problems and practical solutions through case studies in Big Data and Internet of Things - Discusses big data applications for healthcare management, such as predictive analytics and forecasting, big data integration for medical data, algorithms and techniques to speed up the analysis of big medical data, and more
This book contains high-quality refereed research papers presented at the Fifth International Conference on Computer Science, Engineering, and Education Applications (ICCSEEA2022), which took place in Kyiv, Ukraine, on February 21–22, 2022, and was organized by the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute," National Aviation University, and the International Research Association of Modern Education and Computer Science. State-of-the-art studies in computer science, artificial intelligence, engineering methodologies, genetic coding systems, deep learning with medical applications, and knowledge representation with educational applications are among the topics covered in the book. For academics, graduate students, engineers, management practitioners, and undergraduate students interested in computer science and its applications in engineering and education, this book is a valuable resource.
This book reviews and summarizes the development and achievement in cartography and geographic information engineering in China over the past 60 years after the founding of the People's Republic of China. It comprehensively reflects cartography, as a traditional discipline, has almost the same long history with the world's first culture and has experienced extraordinary and great changes. The book consists of nineteen thematic chapters. Each chapter is in accordance with the unified directory structure, introduction, development process, major study achievements, problem and prospect, representative works, as well as a lot of references. It is useful as a reference both for scientists and technicians who are engaged in teaching, researching and engineering of cartography and geographic information engineering.
This book presents selected peer-reviewed papers from the International Conference on Artificial Intelligence and Data Engineering (AIDE 2019). The topics covered are broadly divided into four groups: artificial intelligence, machine vision and robotics, ambient intelligence, and data engineering. The book discusses recent technological advances in the emerging fields of artificial intelligence, machine learning, robotics, virtual reality, augmented reality, bioinformatics, intelligent systems, cognitive systems, computational intelligence, neural networks, evolutionary computation, speech processing, Internet of Things, big data challenges, data mining, information retrieval, and natural language processing. Given its scope, this book can be useful for students, researchers, and professionals interested in the growing applications of artificial intelligence and data engineering.