What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Database
His primary scientific interests are in Information retrieval, Artificial intelligence, Time complexity, Data mining and Algorithm.
Information retrieval is closely attributed to Image retrieval in his research.
His Artificial intelligence research incorporates elements of Pattern recognition and Natural language processing.
His Data mining study combines topics in areas such as XML Catalog, XML validation, Efficient XML Interchange, Bitmap and Simple API for XML.
His work on Computational complexity theory as part of general Algorithm research is frequently linked to Sequential algorithm, thereby connecting diverse disciplines of science.
In Human–computer information retrieval, Vijay V. Raghavan works on issues like Content-based image retrieval, which are connected to Visual Word and Automatic image annotation.
His most cited work include:
- Content based image retrieval systems (811 citations)
- A linear time algorithm for computing exact Euclidean distance transforms of binary images in arbitrary dimensions (662 citations)
- A critical investigation of recall and precision as measures of retrieval system performance (353 citations)
What are the main themes of his work throughout his whole career to date?
Vijay V. Raghavan mainly investigates Data mining, Information retrieval, Artificial intelligence, Machine learning and Theoretical computer science.
His Data mining research is multidisciplinary, relying on both Metasearch engine and Cluster analysis.
Query expansion, Search engine, Human–computer information retrieval, Document retrieval and Vector space model are the primary areas of interest in his Information retrieval study.
His biological study spans a wide range of topics, including Query language and Ranking.
His Query language study incorporates themes from Web query classification and Query optimization.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Natural language processing and Pattern recognition.
He most often published in these fields:
- Data mining (30.23%)
- Information retrieval (26.05%)
- Artificial intelligence (20.58%)
What were the highlights of his more recent work (between 2012-2021)?
- Data mining (30.23%)
- Artificial intelligence (20.58%)
- Big data (3.86%)
In recent papers he was focusing on the following fields of study:
Vijay V. Raghavan spends much of his time researching Data mining, Artificial intelligence, Big data, Data science and Machine learning.
The concepts of his Data mining study are interwoven with issues in Event, Recommender system and Sequence.
His Recommender system study improves the overall literature in Information retrieval.
Artificial intelligence and Pattern recognition are frequently intertwined in his study.
When carried out as part of a general Data science research project, his work on Analytics is frequently linked to work in Volume, therefore connecting diverse disciplines of study.
His studies deal with areas such as Subspace topology and Gaussian process as well as Machine learning.
Between 2012 and 2021, his most popular works were:
- Big Data and Data Analytics Research: From Metaphors to Value Space for Collective Wisdom in Human Decision Making and Smart Machines (104 citations)
- Big Data: Promises and Problems (87 citations)
- NoSQL Systems for Big Data Management (54 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Database
His primary areas of study are Data science, Big data, Artificial intelligence, Data mining and Stereochemistry.
Vijay V. Raghavan combines subjects such as Social media, Software, Cross-validation and Drug with his study of Data science.
His work deals with themes such as Key and Data management, Database, which intersect with Big data.
His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning and Natural language processing.
Vijay V. Raghavan has included themes like Data modeling and Pattern recognition in his Machine learning study.
His study in Data mining is interdisciplinary in nature, drawing from both Data-driven and Time series.
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