A
Ahmad Khan
Researcher at COMSATS Institute of Information Technology
Publications - 65
Citations - 901
Ahmad Khan is an academic researcher from COMSATS Institute of Information Technology. The author has contributed to research in topics: Medicine & Encryption. The author has an hindex of 11, co-authored 55 publications receiving 389 citations. Previous affiliations of Ahmad Khan include National University of Computer and Emerging Sciences & University of Electronic Science and Technology of China.
Papers
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A Deep Learning Ensemble Approach for Diabetic Retinopathy Detection
Sehrish Qummar,Fiaz Gul Khan,Sajid Shah,Ahmad Khan,Shahaboddin Shamshirband,Zia ur Rehman,Iftikhar Ahmed Khan,Waqas Jadoon +7 more
TL;DR: The experimental results show that the proposed model detects all the stages of DR unlike the current methods and performs better compared to state-of-the-art methods on the same Kaggle dataset.
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Formulation development and characterization of cefazolin nanoparticles-loaded cross-linked films of sodium alginate and pectin as wound dressings.
TL;DR: Optimized formulation with Cefazolin nanoparticles in the size range of 227 nm and 0.5% CL films showed significantly better results as compared to the films with increased cross-linker concentration and were considered more suitable for the treatment of infections when applied as wound dressing.
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Diabetic Retinopathy Detection Using VGG-NIN a Deep Learning Architecture
Zubair Rashid Khan,Fiaz Gul Khan,Ahmad Khan,Zia ur Rehman,Sajid Shah,Sehrish Qummar,Farman Ali,Sangheon Pack +7 more
TL;DR: In this article, the VGG16, spatial pyramid pooling layer (SPP) and network-in-network (NiN) are stacked to make a highly nonlinear scale-invariant deep model called the vGG-NiN model.
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DualFog-IoT: Additional Fog Layer for Solving Blockchain Integration Problem in Internet of Things
TL;DR: A blockchain-based DualFog-IoT architecture with three configuration filter of incoming requests at access level, namely: Real Time, Non-Real Time, and Delay Tolerant Blockchain applications, compared with existing centralized datacenter based IoT architecture.
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Color image segmentation: a novel spatial fuzzy genetic algorithm
TL;DR: This article has applied spatial fuzzy genetic algorithm (SFGA) for the unsupervised segmentation of color images and shows that the proposed technique outperforms state-of-the-art methods.