About This Special Issue
Data science enables businesses to process huge amounts of structured and unstructured big data to detect patterns. This in turn allows companies to increase efficiencies, manage costs, identify new market opportunities, and boost their market advantage. Data science is a field that studies data and how to extract meaning from it, whereas machine learning is a field devoted to understanding and building methods that utilize data to improve performance or inform predictions. Machine learning is a branch of artificial intelligence. The primary goal of this special issue is to explore the need of highly open field of AI&ML with vast advancement from time to time and practical acumen ship towards computer science, statistics and optimization. Provide opportunities to integrate fields to enable researchers and scientists adept latest algorithms. We invite contributions that explore real world problems using modern computing methods, with particular interest in Data science using statistics to draw insights from data to drive action and improve system performance. To guide us through the world of data science and machine learning, using applied examples to demonstrate real-world applications. Bringing up aspiring data scientists to explore everything under data science and machine learning.
Through this special issue, we aim to emphasize original results relating to the theory and/or applications of Data Science and Machine Learning. Review articles, focusing on multidisciplinary views of communication, are also welcome. It will highlight the continued growth and new challenges in Data Science and Machine Learning, for both basic research and application development. We welcome researchers from various disciplines to provide interdisciplinary perspectives on data science and machine learning for advanced computing. Your contributions will play a crucial role in advancing knowledge in this field.
Potential topics include, but are not limited to:
- Statistical and mathematical foundations for data science and analytics
- Creation and extraction, processing, representation and modelling, learning and discovery, fusion and integration, presentation and visualization of complex data, behavior, knowledge and intelligence
- Data analytics, pattern recognition, knowledge discovery, machine learning, deep analytics and deep learning, and intelligent processing of various data (including transaction, text, image, video, graph and network), behaviors and systems
- Active, real-time, personalized, actionable and automated analytics, learning, computation, optimization, presentation and recommendation
- Big data architecture, infrastructure, computing, matching, indexing, query processing, mapping, search, retrieval, interoperability, exchange, and recommendation
- In-memory, distributed, parallel, scalable and high-performance computing, analytics and optimization for big data
- Review, surveys, trends, prospects and opportunities of data science research, innovation and applications
- Data science applications, intelligent devices and services in scientific, business, governmental, cultural, behavioral, social and economic, health and medical, human, natural and artificial (including online/Web, cloud, IoT, mobile and social media) domains
- Ethics, quality, privacy, safety and security, trust, and risk of data science and analytics
