What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- The Internet
His primary areas of investigation include Decision support system, Knowledge management, Artificial neural network, Business intelligence and Artificial intelligence.
His Decision support system study combines topics from a wide range of disciplines, such as User interface, Usability, Process management and Decision analysis.
Ramesh Sharda studied Knowledge management and Data warehouse that intersect with Intelligent agent, Systems management, Intelligent decision support system and Marketing and artificial intelligence.
His Artificial neural network study deals with Data mining intersecting with Bankruptcy, Bankruptcy prediction, Multivariate statistics, Linear discriminant analysis and Empirical research.
His Business intelligence research incorporates themes from Business analytics and Analytics.
As a part of the same scientific study, he usually deals with the Artificial intelligence, concentrating on Machine learning and frequently concerns with Set, Motion, Class and Range.
His most cited work include:
- Past, present, and future of decision support technology (1064 citations)
- Business Intelligence (943 citations)
- Bankruptcy prediction using neural networks (555 citations)
What are the main themes of his work throughout his whole career to date?
Data science, Decision support system, Knowledge management, Analytics and Artificial neural network are his primary areas of study.
The various areas that Ramesh Sharda examines in his Data science study include Social network, Process and Big data.
As part of one scientific family, Ramesh Sharda deals mainly with the area of Decision support system, narrowing it down to issues related to the Decision analysis, and often Decision tree.
His Business intelligence and Organizational learning study in the realm of Knowledge management connects with subjects such as Management information systems.
He interconnects Expert system, Linear discriminant analysis and Data mining in the investigation of issues within Artificial neural network.
His Artificial intelligence study frequently draws connections to other fields, such as Machine learning.
He most often published in these fields:
- Data science (15.47%)
- Decision support system (14.92%)
- Knowledge management (12.71%)
What were the highlights of his more recent work (between 2012-2021)?
- Data science (15.47%)
- Analytics (11.05%)
- Big data (6.08%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Data science, Analytics, Big data, Business analytics and Process.
His biological study spans a wide range of topics, including Decision support system and Business intelligence.
While the research belongs to areas of Decision support system, Ramesh Sharda spends his time largely on the problem of Quality, intersecting his research to questions surrounding Task.
Knowledge management covers Ramesh Sharda research in Business intelligence.
His Business analytics research is multidisciplinary, incorporating elements of Data warehouse, Management science and Prescriptive analytics.
His study in Process is interdisciplinary in nature, drawing from both Sequential Pattern Mining, Data mining, Key, Rounding and Workflow.
Between 2012 and 2021, his most popular works were:
- Business Intelligence and Analytics: Systems for Decision Support (69 citations)
- Should I send this message? Understanding the impact of interruptions, social hierarchy and perceived task complexity on user performance and perceived workload (65 citations)
- RFID-enabled track and traceability in job-shop scheduling environment (56 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- The Internet
The scientist’s investigation covers issues in Big data, Analytics, Knowledge management, Data science and Business analytics.
Ramesh Sharda has included themes like Data modeling, Machine learning and Sequential Pattern Mining in his Big data study.
His Analytics study combines topics in areas such as Class, Instructional design and Business intelligence.
His work deals with themes such as Quality, Decision support system and Supervisor, which intersect with Knowledge management.
His Decision support system study integrates concerns from other disciplines, such as Information technology, Conceptual framework, Space, Stakeholder and Key.
His Data science research integrates issues from Behavioral analytics, Data warehouse and Behavioral pattern.
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