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International Journal of Social Impact, Volume 1, Issue 1
International Journal of Social Impact is the official journal of the RED'SHINE Publication. The principal purpose of the journal is to publish scholarly work in the social sciences defined in the classical sense that is in the social sciences, the humanities, and the natural sciences. The research that is published may take a theoretical or speculative model as well as statistical and mathematical. Contributions are welcome from all fields which have relevant and insightful comments to make about the social sciences. While International Journal of Social Impact (IJSI) is the publication of a regional association, it attracts submissions from a wide range of countries.
International Journal of Social Impact, Volume 1, Issue 2
International Journal of Social Impact is the official journal of the RED'SHINE Publication. The principal purpose of the journal is to publish scholarly work in the social sciences defined in the classical sense that is in the social sciences, the humanities, and the natural sciences. The research that is published may take a theoretical or speculative model as well as statistical and mathematical. Contributions are welcome from all fields which have relevant and insightful comments to make about the social sciences. While International Journal of Social Impact (IJSI) is the publication of a regional association, it attracts submissions from a wide range of countries.
Combinatorial Optimization and Applications
The two-volume set LNCS 14461 and LNCS 14462 constitutes the refereed proceedings of the 17th International Conference on Combinatorial Optimization and Applications, COCOA 2023, held in Hawaii, HI, USA, during December 15–17, 2023. The 73 full papers included in the proceedings were carefully reviewed and selected from 117 submissions. They were organized in topical sections as follows: Part I: Optimization in graphs; scheduling; set-related optimization; applied optimization and algorithm; Graph planer and others; Part II: Modeling and algorithms; complexity and approximation; combinatorics and computing; optimization and algorithms; extreme graph and others; machine learning, blockchain and others.
Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms
Based on current literature and cutting-edge advances in the machine learning field, there are four algorithms whose usage in new application domains must be explored: neural networks, rule induction algorithms, tree-based algorithms, and density-based algorithms. A number of machine learning related algorithms have been derived from these four algorithms. Consequently, they represent excellent underlying methods for extracting hidden knowledge from unstructured data, as essential data mining tasks. Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms presents widely used data-mining algorithms and explains their advantages and disadvantages, their mathematical treatment, applications, energy efficient implementations, and more. It presents research of energy efficient accelerators for machine learning algorithms. Covering topics such as control-flow implementation, approximate computing, and decision tree algorithms, this book is an essential resource for computer scientists, engineers, students and educators of higher education, researchers, and academicians.
