What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Operating system
Shashi Shekhar mainly focuses on Data mining, Spatial analysis, Data science, Geographic information system and Association rule learning.
His Data mining research is multidisciplinary, relying on both Spatial database, Spatial data mining, Data set and Outlier.
His Spatial analysis study integrates concerns from other disciplines, such as Database, Spatial query, Information retrieval, Spatial dependence and Regression analysis.
His Data science research includes themes of Data modeling, Interpretability, Climate science and Geospatial analysis.
His study in Geographic information system is interdisciplinary in nature, drawing from both Data type, Grid, Similitude and Partition.
His study in the field of Apriori algorithm also crosses realms of Business process discovery.
His most cited work include:
- Multilevel hypergraph partitioning: application in VLSI domain (696 citations)
- Multilevel hypergraph partitioning: applications in VLSI domain (620 citations)
- Spatial Databases: A Tour (554 citations)
What are the main themes of his work throughout his whole career to date?
Shashi Shekhar spends much of his time researching Data mining, Artificial intelligence, Data science, Spatial analysis and Geographic information system.
His biological study spans a wide range of topics, including Spatial data mining, Cluster analysis, Spatial database and Pruning.
Shashi Shekhar has included themes like Natural language processing, Machine learning and Pattern recognition in his Artificial intelligence study.
His Data science study combines topics from a wide range of disciplines, such as Global Positioning System, Geospatial analysis and Big data.
The Geographic information system study combines topics in areas such as Intelligent transportation system and Database.
He most often published in these fields:
- Data mining (32.25%)
- Artificial intelligence (16.45%)
- Data science (12.12%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (16.45%)
- Data mining (32.25%)
- Natural language processing (2.60%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Data mining, Natural language processing, Reinforcement learning and Mathematical optimization are his primary areas of study.
The study incorporates disciplines such as Scan statistic, Machine learning and Pattern recognition in addition to Artificial intelligence.
His research in Pattern recognition intersects with topics in Spurious relationship and Cluster analysis.
His Data mining research is multidisciplinary, incorporating perspectives in Spatial data mining, Spatial network, Baseline and Bounding overwatch.
Shashi Shekhar usually deals with Reinforcement learning and limits it to topics linked to Intelligent transportation system and Range and Adversarial system.
Shashi Shekhar studied Mathematical optimization and Shortest path problem that intersect with Efficient energy use.
Between 2017 and 2021, his most popular works were:
- Big Spatiotemporal Data Analytics: a research and innovation frontier (30 citations)
- Intelligent systems for geosciences: an essential research agenda (28 citations)
- A TIMBER Framework for Mining Urban Tree Inventories Using Remote Sensing Datasets (11 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Operating system
Shashi Shekhar mainly investigates Artificial intelligence, Natural language processing, Reinforcement learning, Spatial analysis and Pattern recognition.
His study in the fields of Deep learning, Random forest, Boosting and Ensemble learning under the domain of Artificial intelligence overlaps with other disciplines such as Ambiguity.
His Reinforcement learning research integrates issues from Automotive engineering, Fuel efficiency and Domain knowledge.
Shashi Shekhar is interested in Spatial data mining, which is a branch of Spatial analysis.
Shashi Shekhar interconnects False positive paradox, Cluster analysis, Spurious relationship, Scan statistic and Computation in the investigation of issues within Pattern recognition.
His Geospatial analysis research incorporates themes from Network architecture and Data mining.
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