What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His primary areas of study are Artificial intelligence, Information retrieval, Pattern recognition, Computer vision and Search engine indexing.
His Artificial intelligence study incorporates themes from Matching and Natural language processing.
His study in the field of Semantic similarity, View and Database tuning also crosses realms of Tokenization.
His Pattern recognition study combines topics in areas such as Orientation, Multiple target and Optimal matching.
In the subject of general Computer vision, his work in Color quantization, Color normalization and Object detection is often linked to Viewpoints and Application software, thereby combining diverse domains of study.
His work carried out in the field of Search engine indexing brings together such families of science as Table and Index.
His most cited work include:
- Multimedia database for use over networks (231 citations)
- System for selecting multimedia databases over networks (196 citations)
- View-invariant Alignment and Matching of Video Sequences (120 citations)
What are the main themes of his work throughout his whole career to date?
Tanveer Syeda-Mahmood spends much of his time researching Artificial intelligence, Pattern recognition, Computer vision, Segmentation and Information retrieval.
His studies in Artificial intelligence integrate themes in fields like Machine learning and Natural language processing.
His Pattern recognition research integrates issues from Artificial neural network and Matching.
Tanveer Syeda-Mahmood interconnects Similarity and Cardiac echo in the investigation of issues within Computer vision.
The study incorporates disciplines such as Annotation and Medical imaging in addition to Segmentation.
His study connects Database and Information retrieval.
He most often published in these fields:
- Artificial intelligence (63.30%)
- Pattern recognition (32.21%)
- Computer vision (25.09%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (63.30%)
- Pattern recognition (32.21%)
- Deep learning (11.24%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Deep learning, Machine learning and Artificial neural network.
He combines subjects such as Emergency rooms, Radiography and Natural language processing with his study of Artificial intelligence.
His Pattern recognition research includes themes of Image and Catheter.
His Deep learning research is multidisciplinary, relying on both Recurrent neural network, Classifier, Inference, Transfer of learning and Anomaly.
His research in the fields of Boosting overlaps with other disciplines such as Association.
His Medical imaging research incorporates themes from Computer vision and Neural network classifier.
Between 2018 and 2021, his most popular works were:
- Identifying disease-free chest x-ray images with deep transfer learning (6 citations)
- Age prediction using a large chest x-ray dataset (5 citations)
- Automatic Bounding Box Annotation of Chest X-Ray Data for Localization of Abnormalities (4 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Artificial intelligence, Deep learning, Pattern recognition, Machine learning and Medical imaging are his primary areas of study.
Tanveer Syeda-Mahmood incorporates Artificial intelligence and Workload in his research.
His Deep learning study combines topics from a wide range of disciplines, such as Artificial neural network, Recurrent neural network, Isolation and Inference.
The various areas that Tanveer Syeda-Mahmood examines in his Pattern recognition study include Image, Minimum bounding box and Catheter.
The concepts of his Machine learning study are interwoven with issues in State, Anatomical location, Radiography and Radiology report.
His research in Medical imaging tackles topics such as Receiver operating characteristic which are related to areas like Computer vision and Statistic.
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