What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Segmentation, Computer vision and Field.
His study in Image segmentation, Support vector machine, Cursive, Preprocessor and Feature detection falls within the category of Artificial intelligence.
Amjad Rehman interconnects Feature and Deep learning in the investigation of issues within Pattern recognition.
His Segmentation research incorporates elements of Silhouette, Pectoral muscle and Medical imaging.
Much of his study explores Computer vision relationship to Robustness.
The concepts of his Field study are interwoven with issues in Image and Data mining.
His most cited work include:
- Medical Image Segmentation Methods, Algorithms, and Applications (138 citations)
- Neural networks for document image preprocessing: state of the art (65 citations)
- A framework of human detection and action recognition based on uniform segmentation and combination of Euclidean distance and joint entropy-based features selection (64 citations)
What are the main themes of his work throughout his whole career to date?
Amjad Rehman mainly focuses on Artificial intelligence, Pattern recognition, Segmentation, Computer vision and Feature extraction.
Artificial intelligence is closely attributed to Machine learning in his study.
His Pattern recognition research focuses on subjects like Feature, which are linked to Cluster analysis.
Amjad Rehman works mostly in the field of Segmentation, limiting it down to topics relating to Natural language processing and, in certain cases, Field, as a part of the same area of interest.
His work carried out in the field of Computer vision brings together such families of science as Facial expression, Computer graphics and Emotional expression.
His research integrates issues of Image processing and Histogram in his study of Support vector machine.
He most often published in these fields:
- Artificial intelligence (67.30%)
- Pattern recognition (36.49%)
- Segmentation (20.38%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (67.30%)
- Pattern recognition (36.49%)
- Deep learning (8.53%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Pattern recognition, Deep learning, Machine learning and Feature extraction.
His study in Segmentation, Convolutional neural network, Feature selection, Support vector machine and Image retrieval are all subfields of Artificial intelligence.
His biological study spans a wide range of topics, including Cursive, Natural language processing and White matter.
His studies deal with areas such as Region of interest, Image, Feature and Cluster analysis as well as Pattern recognition.
He combines subjects such as Coherence and Multiclass classification with his study of Deep learning.
His Feature extraction study integrates concerns from other disciplines, such as Confusion matrix, Image segmentation and Histogram equalization.
Between 2019 and 2021, his most popular works were:
- Hand-crafted and deep convolutional neural network features fusion and selection strategy: An application to intelligent human action recognition (32 citations)
- Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists. (31 citations)
- Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection (31 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Amjad Rehman focuses on Artificial intelligence, Deep learning, Pattern recognition, Convolutional neural network and Machine learning.
His Artificial intelligence study frequently draws connections between adjacent fields such as Fitness function.
His Deep learning research includes themes of Contextual image classification, Image, Routing and X ray image.
His study looks at the relationship between Pattern recognition and fields such as Entropy, as well as how they intersect with chemical problems.
The Convolutional neural network study combines topics in areas such as Classifier, Feature selection and Discrete cosine transform.
His Segmentation research is multidisciplinary, relying on both White matter and Pattern recognition.
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