2022 - Research.com Rising Star of Science Award
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.
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.
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.
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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Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features
Amin Ullah;Jamil Ahmad;Khan Muhammad;Muhammad Sajjad.
IEEE Access (2018)
Multi-grade brain tumor classification using deep CNN with extensive data augmentation
Muhammad Sajjad;Salman Khan;Khan Muhammad;Wanqing Wu.
Journal of Computational Science (2019)
Convolutional Neural Networks Based Fire Detection in Surveillance Videos
Khan Muhammad;Jamil Ahmad;Irfan Mehmood;Seungmin Rho.
IEEE Access (2018)
Early fire detection using convolutional neural networks during surveillance for effective disaster management
Khan Muhammad;Jamil Ahmad;Sung Wook Baik.
Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation
Yu-Dong Zhang;Zhengchao Dong;Xianqing Chen;Wenjuan Jia.
Multimedia Tools and Applications (2019)
A hybrid model of Internet of Things and cloud computing to manage big data in health services applications
Mohamed Elhoseny;Ahmed Abdelaziz;Ahmed S. Salama;Ahmed S. Salama;Alaa Mohamed Riad.
Future Generation Computer Systems (2018)
The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems
Ashraf Darwish;Ashraf Darwish;Aboul Ella Hassanien;Mohamed Elhoseny;Mohamed Elhoseny;Arun Kumar Sangaiah.
Journal of Ambient Intelligence and Humanized Computing (2019)
A novel magic LSB substitution method (M-LSB-SM) using multi-level encryption and achromatic component of an image
Khan Muhammad;Muhammad Sajjad;Irfan Mehmood;Seungmin Rho.
Multimedia Tools and Applications (2016)
Secure Surveillance Framework for IoT Systems Using Probabilistic Image Encryption
Khan Muhammad;Rafik Hamza;Jamil Ahmad;Jaime Lloret.
IEEE Transactions on Industrial Informatics (2018)
Hash Based Encryption for Keyframes of Diagnostic Hysteroscopy
Rafik Hamza;Khan Muhammad;Arunkumar N;Gustavo Ramirez-Gonzalez.
IEEE Access (2018)
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