Description
Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed.
The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.
Reviews and Testimonials
This encyclopedia provides crucial insights into how artificial intelligence and data science can drive profound changes in Africa, creating both opportunities and challenges. By exploring innovative financing methods to support entrepreneurs and SMEs, and demonstrating AI applications in areas like agriculture and earth monitoring, this work offers practical solutions for sustainable development across the continent. Our research not only highlights the potential of AI to address critical issues like soil aridity but also emphasizes the importance of equitable and ethical implementation of these technologies in the African context.
– Jean-Eric Pelet
Artificial intelligence and machine learning aren't just tools; they're the catalysts transforming data into insights, challenges into opportunities, and innovation into reality. My publication in the field of AI and ML underscores the critical advancements these technologies bring to data analysis, decision-making, and automation. By exploring innovative algorithms and practical applications, I contribute to the broader understanding and adoption of AI and ML, driving progress and efficiency across various industries.
– Hamed Taherdoost
Finance and insurance in particular are going through a digital transformation with the new capabilities of AI and other technologies but the final destination of this journey is not clear. This research offers some clarity with five models optimized for AI.
– Alex Zarifis
Research work on "Customer Analytics: Deep Dive into Customer Data" is essential for uncovering insights into customer behavior and preferences. This work supports personalized marketing strategies, enhances customer experiences, and guides data-driven business decisions, ultimately driving customer satisfaction and loyalty. Additionally, it helps in identifying emerging trends and potential market opportunities, ensuring businesses stay competitive and innovative.
– Devesh Bathla
This study presents an effective approach for addressing the imbalance issue in bankruptcy prediction models. Our method demonstrates favorable performance compared to existing approaches in the literature.
– Son Nguyen
Recommender systems are becoming essential in many industries and, hence, have received more attention in recent years. Recommender systems are usually used to manage massive amounts of data and knowledge. Recommender Systems explore users’ preferences to supply them with items that best meet their needs.
– Houda El Bouhissi
Maritime trade does not exist in a world in equilibrium. Our work explores how the stochastic stocks of pandemic, tariffs, exchange rates, and unemployment affect the volume, composition, and frequency of trade. We hope that it serves as a platform for future exploration.
– Peter Abraldes
The concept of Machine Learning facilitates in adoption of innovative ways of developing decision-process models in the constantly changing business scenario across the globe. This chapter talks about designing business models with this concept.
– N. Raghavendra Rao
Author's/Editor's Biography
John Wang (Ed.)
John Wang
is a professor in the Department of Information Management and Business Analytics at Montclair State University, USA. Having received a scholarship award, he came to the USA and completed his PhD in operations research from Temple University. Due to his extraordinary contributions beyond a tenured full professor, Dr. Wang has been honored with two special range adjustments in 2006 and 2009, respectively. He has published over 100 refereed papers and seventeen books. He has also developed several computer software programs based on his research findings. He serves as Editor-in-Chief for ten Scopus-indexed journals, such as Int. J. of Business Analytics, Int. J. of Information Systems and Supply Chain Management, Int. J. of Information Systems in the Service Sector, Int. J. of Applied Management, Int. J. of Information and Decision Sciences, Int. J. of Data Mining, Modelling and Management, etc. He is the Editor of Encyclopedia of Business Analytics and Optimization (five volumes), Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications (six-volume) and the Editor of the Encyclopedia of Data Warehousing and Mining, 1st (two-volume) and 2nd (four-volume). His long-term research goal is on the synergy of operations research, data mining and cybernetics.