Author
Rohit Sharma
Other affiliations: University of Utah, Jaipuria Institute of Management
Bio: Rohit Sharma is an academic researcher from National Institute of Industrial Engineering. The author has contributed to research in topics: Supply chain & Big data. The author has an hindex of 11, co-authored 15 publications receiving 885 citations. Previous affiliations of Rohit Sharma include University of Utah & Jaipuria Institute of Management.
Topics: Supply chain, Big data, Microelectrode, Industry 4.0, Population
Papers
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TL;DR: The findings from the study suggest that traceability was the most significant reason for BT implementation in ASC followed by auditability, immutability, and provenance, which will help the practitioners to design the strategies forBT implementation in agriculture, creating a real-time data-driven ASC.
471 citations
TL;DR: This study is first of its kind to identify industry 4.0 adoption barriers and develop hierarchical relationships between them using interpretive structural modeling (ISM) and fuzzy MICMAC methodology in the Indian manufacturing context.
373 citations
TL;DR: An ML applications framework for sustainable ASC is proposed and identifies the role of ML algorithms in providing real-time analytic insights for pro-active data-driven decision-making in the ASCs and provides the researchers, practitioners, and policymakers with guidelines on the successful management of ASCs for improved agricultural productivity and sustainability.
284 citations
TL;DR: The agricultural supply chains (ASCs) are exposed to unprecedented risks following COVID-19 and it is necessary to investigate the impact of risks and to create resilient ASC organizations as discussed by the authors.
Abstract: The agricultural supply chains (ASCs) are exposed to unprecedented risks following COVID-19. It is necessary to investigate the impact of risks and to create resilient ASC organisations. In this st...
181 citations
28 Jan 2021
TL;DR: A comprehensive review of AI in marketing is offered using bibliometric, conceptual and intellectual network analysis of extant literature published between 1982 and 2020 to identify the scientific actors' performance like most relevant authors and most relevant sources.
Abstract: Disruptive technologies such as the internet of things, big data analytics, blockchain, and artificial intelligence have changed the ways businesses operate. Of all the disruptive technologies, artificial intelligence (AI) is the latest technological disruptor and holds immense marketing transformation potential. Practitioners worldwide are trying to figure out the best fit AI solutions for their marketing functions. However, a systematic literature review can highlight the importance of artificial intelligence (AI) in marketing and chart future research directions. The present study aims to offer a comprehensive review of AI in marketing using bibliometric, conceptual and intellectual network analysis of extant literature published between 1982 and 2020. A comprehensive review of one thousand five hundred and eighty papers helped to identify the scientific actors' performance like most relevant authors and most relevant sources. Furthermore, co-citation and co-occurrence analysis offered the conceptual and intellectual network. Data clustering using the Louvain algorithm helped identify research sub-themes and future research directions to expand AI in marketing.
143 citations
Cited by
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01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
10,141 citations
22 Mar 2021
TL;DR: In this paper, the authors present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application and highlight the challenges and potential research directions based on their study.
Abstract: In the current age of the Fourth Industrial Revolution (4IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study’s key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view.
659 citations
TL;DR: In this article, the authors introduce a measures framework for sustainability based on the United Nations Sustainable Development Goals (SDGs) incorporating various economic, environmental and social attributes, and develop a hybrid multi-situation decision method integrating hesitant fuzzy set, cumulative prospect theory and VIKOR.
485 citations
TL;DR: In this paper, a comprehensive overview of barriers for adopting blockchain technology to manage sustainable supply chains is provided using technology, organizational, and environmental framework followed by inputs from academics and industry experts and then analyzed using the Decision-Making Trial and Evaluation Laboratory (DEMATEL).
472 citations
TL;DR: The findings from the study suggest that traceability was the most significant reason for BT implementation in ASC followed by auditability, immutability, and provenance, which will help the practitioners to design the strategies forBT implementation in agriculture, creating a real-time data-driven ASC.
471 citations