Machine Learning, Artificial Intelligence, and Data Science: Current Trends in Big Data, Deep Learning, Neural Networks, and Predictive Modeling
Keywords:
Machine Learning, Artificial Intelligence, Big Data, Deep Learning, Neural Networks, Blockchain, Supply Chain ManagementSynopsis
Artificial intelligence (AI), Data science (DS), machine learning (ML) as well as deep learning (DL) has emerged as the topmost technology that has become the major part of our day-today lives. The impact is more valuable due to integration of machine and AI technologies for the current information age. State-of-arts technologies and technical methodologies as well as algorithms trying to build more reliable and robust systems that provides uniform and easy access to these innovative technologies all around the world.
Artificial General Intelligence (AGI), which is the stepping stone to future Super AI, may be the fastest growing technologies in our culture today. Therefore, in this books we collect quality research paper and book chapters from lots of researchers and academician working in esteemed colleges ,institutions as well as reputed universities all over the India and aboard in all mentioned area of AI in a single book.
We offer our heartiest thank and gratitude to each and every contributing author for their hard work and innovative research contributions to make our book qualitative one. We also very much thankful to respective institutions, colleges as well as universities of all contributed authors. We are also grateful to Deep Science publication publication staff, and reviewers for helping to make this possible. In the long run, we would like to express our sincere gratitude to our esteemed advisors, Prof. Anjana Kakoti Mahanta and Prof. Utpal Bhattacharjee, for their helpful advice and kind assistance. We hope that this book will accelerated further research and discussion in the state-of-art developing fields of sub-domain of data science, machine learning, and artificial intelligence.
References
Ben-Daya, M., Hassini, E., Bahroun, Z.: Internet of things and supply chain management: a literature review. Int. J. Prod. Res. (2019)
Song, Y., Yu, F.R., Zhou, L., Yang, X., et al.: Applications of the Internet of Things (IoT) in smart logistics: A comprehensive survey. IEEE Internet Things J. (2020)
Chopra, S., Sodhi, M.: Reducing the risk of supply chain disruptions. MIT Sloan Manag. Rev. (2014)
Al-Talib, M., Melhem, W.Y., Anosike, A.I., Reyes, J.A.G., et al.: Achieving resilience in the supply chain by applying IoT technology. Procedia CIRP (2020)
Gunasekaran, A., Papadopoulos, T., Dubey, R., et al.: Big data and predictive analytics for supply chain and organizational performance. J. Bus. Res. (2017)
Omitola, T., Wills, G.: Towards mapping the security challenges of the Internet of Things (IoT) supply chain. Procedia Comput. Sci. (2018)
Taj, S., Imran, A.S., Kastrati, Z., Daudpota, S.M., Memon, R.A., et al.: IoT-based supply chain management: A systematic literature review. Internet Things (2023)
Sodhi, M.M.S., Seyedghorban, Z., et al.: Why emerging supply chain technologies initially disappoint: Blockchain, IoT, and AI. Prod. Oper. Manag. (2022)
Page, M.J., McKenzie, J.E., Bossuyt, P.M., et al.: The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 372, n71 (2021)
Adeusi, K.B., Adegbola, A.E., Amajuoyi, P., et al.: The potential of IoT to transform supply chain management through enhanced connectivity and real-time data. World J. Adv. Eng. Technol. Syst. (2024).








