Machine Learning and Data Analytics for Solving Business Problems: Methods, Applications, and Case Studies

ISBN-13: 9783031184826 / Angielski / Twarda / 2022 / 206 str.

Bader Alyoubi; Chiheb-Eddine Ben Ncir; Ibraheem Alharbi

Opis

This book presents advances in business computing and data analytics by discussing recent and innovative machine learning methods that have been designed to support decision-making processes. These methods form the theoretical foundations of intelligent management systems, which allows for companies to understand the market environment, to improve the analysis of customer needs, to propose creative personalization of contents, and to design more effective business strategies, products, and services. This book gives an overview of recent methods – such as blockchain, big data, artificial intelligence, and cloud computing – so readers can rapidly explore them and their applications to solve common business challenges. The book aims to empower readers to leverage and develop creative supervised and unsupervised methods to solve business decision-making problems.

Spis treści

Introduction.- Supervised and unsupervised methods for customer segmentation.- Supervised and unsupervised methods for supply chain management.- Supervised and unsupervised methods for logistics improvement.- Design of recommender systems.- Supervised and unsupervised methods for e-marketing.- Analysis of Blockchain data.- Supervised and unsupervised methods applied in banking.- Cryptocurrency analysis.- Supervised and unsupervised methods to improve operational processes.- Big data analysis and summarization.- Intelligent Financial analysis and sales forecasting.- Financial data modeling and decision making.- Integration of learning methods in Smart ERP systems.- E-commerce recommender systems.- Social media and E-business analysis.- Intelligent Business control and monitoring systems.- Conclusion.

O autorze

Bader Alyoubi is full Professor of Management Information Systems and Dean of the College of Business at the University of Jeddah, Saudi Arabia. His research interests include decision support systems and knowledge management methods and its application in business, government, and health domains. He is the author and co-author of more than 50 publications in the field of specialization. He established the Saudi Center for the Preparation and Empowerment of Entrepreneurs at the University of Jeddah. He has many contributions to the structuring of colleges and scientific disciplines at the University of Jeddah and Chairman of several committees. 

 

Chiheb Ediine ben Ncir received his Ph.D in Computer science & management from Higher Institute of Management, University of Tunis, in 2014 and a HDR degree (Habilitation for the Supervision of Doctoral Research) in 2021. He occupied the position of Assistant professor at the Higher School of Digital Economy (University of Manouba, Tunisia) from 2015 to 2018. Currently, he is an Associate Professor at the University of Jeddah, Saudi Arabia and a member of LARODEC laboratory (University of Tunis). He is also a business intelligence and big data instructor at IBM North Africa and Middle East. His research interests concern machine learning methods and data mining tools with a special emphasis on Big data clustering, disjoint and non-disjoint partitioning, kernel methods, as well as many other related fields. He is the author or co-author of more than 50 publications in several prestigious journals and conferences. He is a regular reviewer for many refereed international journals and co-editor of some Springer books.

Ibraheem Mubarak Alharbi received a BA degree from King Abdul Aziz University, Saudi Arabia, in 2002, a Master’s and PhD degree from La Trobe University, Australia, in 2009. Currently, he serves as Associate Professor in the Department of Management Information Systems, College of Business, University of Jeddah, Jeddah, Saudi Arabia. His research interests include business and information ethics, information privacy and electronic commerce. He has published many research articles in reputed journals and participated in many international conferences.

Anis Jarboui is full Professor in Business Administration at the University of Sfax. He holds a Ph.D. in Finance from University of Nice Sophia Antipolis- France (Université Côte d’Azur 2004) and a HDR degree (Habilitation for Supervising Doctoral Research) in 2008. He was Dean (College of Business Administration) of the Higher Institute of Business Administration of Sfax, from 2011 to 2017. He has previously served as a Researcher and/or Professor in numerous other Universities and Business Schools including IAE Nice (2005), IAE Lille (2006-2007) and EM NormandieFrance (2017-2021). He is co-founder and the vice-president of Latige-Lab. in Technology, Governance, and Entrepreneurship. Prof. Jarboui currently serves as member of editorial and scientific committee of various academic international conferences and has been invited as a speaker or moderator at numerous international conferences. Jarboui’s research interests involve several aspects in finance and accounting and entrepreneurship such as Corporate Governance, Voluntary Disclosure; Earnings Quality; entrepreneurial finance and Behavioral Finance. Professor Jarboui authored numerous papers listed among the top 5 most cited articles in journals such as FRL. He has published more than 100 articles in Peer-reviewed/indexed journals and conferences.  He has written a number of book chapters.

Blurb

This book presents advances in business computing and data analytics by discussing recent and innovative machine learning methods that have been designed to support decision-making processes. These methods form the theoretical foundations of intelligent management systems, which allows for companies to understand the market environment, to improve the analysis of customer needs, to propose creative personalization of contents, and to design more effective business strategies, products, and services. This book gives an overview of recent methods – such as blockchain, big data, artificial intelligence, and cloud computing – so readers can rapidly explore them and their applications to solve common business challenges. The book aims to empower readers to leverage and develop creative supervised and unsupervised methods to solve business decision-making problems.

• Provides design and applications of machine learning and data analytics to solve business problems; 
• Includes applications of supervised and unsupervised learning methods in intelligent management systems; 
• Introduces case studies of business problems solved using innovative learning methods and data analytics techniques.