What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Artificial intelligence, Data mining, Machine learning, Artificial neural network and Pattern recognition are his primary areas of study.
His work carried out in the field of Data mining brings together such families of science as Feature, Support vector machine and Fitness function.
He interconnects Expert system and FSA-Red Algorithm in the investigation of issues within Machine learning.
His Artificial neural network research is multidisciplinary, relying on both Group method of data handling, Principal component analysis and Rough set.
The concepts of his Rough set study are interwoven with issues in Evolutionary algorithm, Bankruptcy and Case-based reasoning.
His Pattern recognition study combines topics in areas such as Fuzzy rule, Bankruptcy prediction, Cross-validation and Receiver operating characteristic.
His most cited work include:
- Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review (717 citations)
- A survey on opinion mining and sentiment analysis (580 citations)
- Detection of financial statement fraud and feature selection using data mining techniques (248 citations)
What are the main themes of his work throughout his whole career to date?
Vadlamani Ravi mainly focuses on Artificial intelligence, Data mining, Machine learning, Artificial neural network and Support vector machine.
His Artificial intelligence study incorporates themes from Bankruptcy prediction and Pattern recognition.
Vadlamani Ravi studies Decision tree, a branch of Data mining.
Within one scientific family, Vadlamani Ravi focuses on topics pertaining to Benchmark under Machine learning, and may sometimes address concerns connected to Mathematical optimization.
His Artificial neural network research incorporates themes from Group method of data handling, Multivariate adaptive regression splines and Big data.
His study explores the link between Support vector machine and topics such as Sentiment analysis that cross with problems in Data science and Deep learning.
He most often published in these fields:
- Artificial intelligence (55.45%)
- Data mining (44.08%)
- Machine learning (33.18%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (55.45%)
- Data mining (44.08%)
- Deep learning (5.69%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Data mining, Deep learning, Artificial neural network and Sentiment analysis.
His research in Artificial intelligence intersects with topics in Data modeling, Machine learning and Pattern recognition.
His Machine learning study integrates concerns from other disciplines, such as Statistic and Emphasis.
His study in the field of Association rule learning is also linked to topics like Value.
His Artificial neural network research integrates issues from Group method of data handling and Cluster analysis.
The study incorporates disciplines such as Embedding, Customer relationship management and Support vector machine in addition to Sentiment analysis.
Between 2018 and 2021, his most popular works were:
- A review of deep learning with special emphasis on architectures, applications and recent trends (55 citations)
- A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends (10 citations)
- A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends (8 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
The scientist’s investigation covers issues in Artificial intelligence, Deep learning, Data science, Data mining and Evolutionary computation.
His research integrates issues of Credit card, Credit card fraud and Reduction in his study of Artificial intelligence.
His study in Data science is interdisciplinary in nature, drawing from both Field, Body of knowledge, CUDA, Customer relationship management and Social media.
His Data mining research includes themes of Radial basis function, Group method of data handling, k-means clustering, Cluster analysis and Mean squared error.
His Evolutionary computation research is within the category of Machine learning.
In his papers, Vadlamani Ravi integrates diverse fields, such as Fault detection and isolation and Artificial neural network.
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