What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- Algorithm
His primary areas of study are Parallel computing, Algorithm, Data mining, Scheduling and Artificial intelligence.
He works mostly in the field of Parallel computing, limiting it down to topics relating to Graph and, in certain cases, Linear programming, Graph, Polygon mesh and Embedding, as a part of the same area of interest.
The Algorithm study combines topics in areas such as Distribution, k-means clustering, Monitor unit and Multileaf collimator.
Sanjay Ranka interconnects Tree, Database transaction, Set and Cluster analysis in the investigation of issues within Data mining.
The concepts of his Scheduling study are interwoven with issues in Bounded function and Distributed computing.
The Artificial neural network research he does as part of his general Artificial intelligence study is frequently linked to other disciplines of science, such as Set, therefore creating a link between diverse domains of science.
His most cited work include:
- Elements of artificial neural networks (701 citations)
- Original Contribution: Forecasting the behavior of multivariate time series using neural networks (403 citations)
- An efficient k-means clustering algorithm (369 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Parallel computing, Artificial intelligence, Algorithm, Distributed computing and Parallel algorithm.
His study looks at the intersection of Parallel computing and topics like Fortran with Compiler.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning, Computer vision and Pattern recognition.
Sanjay Ranka has included themes like Scheduling and Fair-share scheduling in his Distributed computing study.
Scheduling is a component of his Dynamic priority scheduling and Processor scheduling studies.
His Multi-core processor study frequently draws connections between related disciplines such as Embedded system.
He most often published in these fields:
- Parallel computing (28.79%)
- Artificial intelligence (16.29%)
- Algorithm (14.51%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (16.29%)
- Parallel computing (28.79%)
- Machine learning (4.69%)
In recent papers he was focusing on the following fields of study:
Sanjay Ranka mostly deals with Artificial intelligence, Parallel computing, Machine learning, Real-time computing and Speedup.
The study incorporates disciplines such as Computer vision and Pattern recognition in addition to Artificial intelligence.
Specifically, his work in Parallel computing is concerned with the study of Multi-core processor.
His biological study spans a wide range of topics, including Assignment problem and Association.
His Real-time computing study incorporates themes from Control system, Traffic analysis, Intersection, Intersection and Control theory.
In his study, Job shop scheduling, Turbulence and Dynamic data is strongly linked to Load balancing, which falls under the umbrella field of Speedup.
Between 2017 and 2021, his most popular works were:
- Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification. (33 citations)
- A smartwatch-based framework for real-time and online assessment and mobility monitoring. (19 citations)
- A smartwatch-based framework for real-time and online assessment and mobility monitoring. (19 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Statistics
Sanjay Ranka focuses on Artificial intelligence, Real-time computing, Pattern recognition, Multi-core processor and Load balancing.
His research in Artificial intelligence intersects with topics in Machine learning and Preventive care.
Many of his research projects under Pattern recognition are closely connected to Body movement with Body movement, tying the diverse disciplines of science together.
His Multi-core processor study results in a more complete grasp of Parallel computing.
His studies examine the connections between Load balancing and genetics, as well as such issues in Speedup, with regards to Image resolution, Job shop scheduling, Dynamic load balancing and Turbulence.
His research in Convolutional neural network tackles topics such as Cerebral cortex which are related to areas like Artificial neural network.
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