What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer network
- Machine learning
His primary scientific interests are in Artificial intelligence, Convolutional neural network, Computer vision, Key and Encryption.
His Artificial intelligence study incorporates themes from Machine learning and Pattern recognition.
His Convolutional neural network research includes themes of Real-time computing, Feature extraction, Deep learning and Speech recognition.
His Key research integrates issues from Field, Computational intelligence and Control.
The study incorporates disciplines such as Data mining and Steganography in addition to Encryption.
His Benchmark study deals with Discriminative model intersecting with Robustness.
His most cited work include:
- Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features (215 citations)
- Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation (131 citations)
- Multi-grade brain tumor classification using deep CNN with extensive data augmentation (126 citations)
What are the main themes of his work throughout his whole career to date?
Khan Muhammad mostly deals with Artificial intelligence, Convolutional neural network, Pattern recognition, Computer vision and Feature extraction.
His studies in Artificial intelligence integrate themes in fields like Machine learning and Encryption.
His Encryption research is multidisciplinary, incorporating elements of Key, Information security and Robustness.
Khan Muhammad interconnects Real-time computing, Search engine indexing, Image retrieval and Benchmark in the investigation of issues within Convolutional neural network.
His work carried out in the field of Pattern recognition brings together such families of science as Hash function, Feature and Biometrics.
His Feature extraction research incorporates elements of Visualization and Data mining.
He most often published in these fields:
- Artificial intelligence (50.00%)
- Convolutional neural network (20.93%)
- Pattern recognition (17.44%)
What were the highlights of his more recent work (between 2020-2021)?
- Artificial intelligence (50.00%)
- Deep learning (12.21%)
- Machine learning (10.47%)
In recent papers he was focusing on the following fields of study:
Khan Muhammad mainly investigates Artificial intelligence, Deep learning, Machine learning, Convolutional neural network and Pattern recognition.
He works mostly in the field of Artificial intelligence, limiting it down to topics relating to Computer vision and, in certain cases, Robustness.
His Deep learning research focuses on subjects like Reliability, which are linked to Telecommunications network and Mobile edge computing.
His Machine learning research is multidisciplinary, incorporating perspectives in Exploit and Semantics.
Khan Muhammad has researched Convolutional neural network in several fields, including Sequence, Segmentation, Classifier and Benchmark.
His Pattern recognition research incorporates themes from Global optimization, Hybrid algorithm, Memetic algorithm, Local search and Discrete optimization.
Between 2020 and 2021, his most popular works were:
- Chaotic random spare ant colony optimization for multi-threshold image segmentation of 2D Kapur entropy (23 citations)
- Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey (19 citations)
- Fuzzy-aided solution for out-of-view challenge in visual tracking under IoT-assisted complex environment (16 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer network
- Machine learning
Khan Muhammad focuses on Artificial intelligence, Deep learning, Feature extraction, Image segmentation and Data mining.
A large part of his Artificial intelligence studies is devoted to Robustness.
The various areas that Khan Muhammad examines in his Deep learning study include Transfer of learning, Convolutional neural network and Reliability.
His work deals with themes such as Classifier and Softmax function, which intersect with Feature extraction.
His Image segmentation research includes elements of Local optimum, Algorithm and Ant colony optimization algorithms.
His studies deal with areas such as Automatic summarization, Sequence, Redundancy and Benchmark as well as Data mining.
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