A
Ali Javed
Researcher at University of Engineering and Technology
Publications - 119
Citations - 1687
Ali Javed is an academic researcher from University of Engineering and Technology. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 15, co-authored 78 publications receiving 736 citations. Previous affiliations of Ali Javed include University of Rochester & Oakland University.
Papers
More filters
Journal ArticleDOI
An Efficient Brain Tumor Detection Algorithm Using Watershed & Thresholding Based Segmentation
TL;DR: An efficient algorithm is proposed in this paper for tumor detection based on segmentation and morphological operators based on quality of scanned image is enhanced and then Morphological operators are applied to detect the tumor in the scanned image.
Journal ArticleDOI
Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering.
TL;DR: In contrast with state of the art systems, the RCNN is capable to compute deep features with amen representation of Melanoma, and hence improves the segmentation performance.
Journal ArticleDOI
Robust Human Activity Recognition Using Multimodal Feature-Level Fusion
Muhammad Ehatisham-ul-Haq,Ali Javed,Muhammad Awais Azam,Hafiz Malik,Aun Irtaza,Ik Hyun Lee,Muhammad Tariq Mahmood +6 more
TL;DR: The experimental results indicate that the proposed scheme achieves better recognition results as compared to the state of the art, and the feature-level fusion of RGB and inertial sensors provides the overall best performance for the proposed system.
Journal ArticleDOI
Shot Classification of Field Sports Videos Using AlexNet Convolutional Neural Network
TL;DR: This research work proposes an effective shot classification method based on AlexNet Convolutional Neural Networks (AlexNet CNN) for field sports videos that achieves the maximum accuracy of 94.07%.
Journal ArticleDOI
A novel deep learning method for detection and classification of plant diseases
TL;DR: A robust plant disease classification system is introduced by introducing a Custom CenterNet framework with DenseNet-77 as a base network and is more proficient and reliable to identify and classify plant diseases than other latest approaches.