Overview

Today, the convergence of cutting-edge algorithms and actionable insights in business is paramount for success. Scholars and practitioners grapple with the dilemma of optimizing data to drive efficiency, innovation, and competitiveness. The formidable challenge of effectively harnessing the immense power of intelligent optimization techniques and business analytics only increases as the volume of data grows exponentially, and the complexities of navigating the intricate landscape of business analytics becomes more daunting. This pressing issue underscores the critical need for a comprehensive solution, and Intelligent Optimization Techniques for Business Analytics is poised to provide much-needed answers. This groundbreaking book offers an all-encompassing solution to the challenges that academic scholars encounter in the pursuit of mastering the interplay between learning algorithms and intelligent optimization techniques for business analytics. Through a wealth of diverse perspectives and expert case studies, it illuminates the path to effectively implementing these advanced systems in real-world business scenarios. It caters not only to the scholarly community but also to industry professionals and policymakers, equipping them with the necessary tools and insights to excel in the realm of data-driven decision-making. Encompassing a wide range of topics, from foundational principles to practical applications, this book serves as a transformative solution. It is a guide for those seeking to comprehend and harness the potential of machine learning and intelligent optimization in the field of business analytics. It dives into practical challenges, ethical considerations, and showcases the wide-ranging applicability of these techniques across various domains. Intelligent Optimization Techniques for Business Analytics is the solution that empowers scholars to make informed decisions in complex business scenarios, revolutionizing the way data is leveraged and shaping the future of enterprises.

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|Dr Sanjeev Bansal - Editor Nitendra Kumar received a PhD (Mathematics) from Sharda University, Greater Noida, India and a Master of Science (Mathematics and Statistics) from Dr Ram Manohar Lohia Avadh University, Faizabad, India. Currently, he is working as an Assistant Professor at Amity Business School, Amity University, Noida with interests in Wavelets and its Variants, Data Mining, Inverse Problems, Epidemic Modeling, Fractional Derivatives Business Analytics, and Statistical Methods. He has more than 10 years of experience in his research areas. Dr Kumar has published many research papers in reputed journals and also published 6 books on engineering mathematics. He contributes to the research community by undertaking various volunteer activities in the capacity of editor for two edited books and guest Editor of reputed journals. Priyanka Agarwal , Ph.D. in Management, has more than two decades of experience in research, industry, and academia and is presently associated as Associate Professor with Amity Business School, Amity University, Uttar Pradesh in the area of HR, OB & Psychology. She has been in the reviewer panel for conferences and has contributed to the Research community by collaborating with editorial board of special issues of UGC CARE, SCOPUS and WOS journals. She is a keen researcher and published research papers, case studies, book chapters in journals and books of repute . She has two edited books to her credit. Few of her research interests include Multi-generations in the Workplace, optimization in HR, diversity and inclusion, employee engagement, emerging trends in business analytics . In addition to her expertise in teaching and research, her key role includes mentoring and grooming budding managers, being the Programme leader of MBA Programme over the last few years. She is also associate editor of Amity Journal of Entrepreneurship and Leadership. She brings in a good blend of professional expertise and humane approach that makes her quite accessible.

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