M
Muhammad Sajjad
Researcher at Islamia College University
Publications - Â 83
Citations - Â 4034
Muhammad Sajjad is an academic researcher from Islamia College University. The author has contributed to research in topics: Automatic summarization & Computer science. The author has an hindex of 28, co-authored 73 publications receiving 2346 citations. Previous affiliations of Muhammad Sajjad include Norwegian University of Science and Technology & Sejong University.
Papers
More filters
Journal ArticleDOI
Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features
TL;DR: A novel action recognition method by processing the video data using convolutional neural network (CNN) and deep bidirectional LSTM (DB-LSTM) network that is capable of learning long term sequences and can process lengthy videos by analyzing features for a certain time interval.
Journal ArticleDOI
Multi-grade brain tumor classification using deep CNN with extensive data augmentation
TL;DR: A novel convolutional neural network (CNN) based multi-grade brain tumor classification system that is experimentally evaluated on both augmented and original data and results show its convincing performance compared to existing methods.
Journal ArticleDOI
A Novel CNN-GRU-Based Hybrid Approach for Short-Term Residential Load Forecasting
Muhammad Sajjad,Zulfiqar Ahmad Khan,Amin Ullah,Tanveer Hussain,Waseem Ullah,Mi Young Lee,Sung Wook Baik +6 more
TL;DR: The proposed model is an effective alternative to the previous hybrid models in terms of computational complexity as well prediction accuracy, due to the representative features’ extraction potentials of CNNs and effectual gated structure of multi-layered GRU.
Journal ArticleDOI
Clustering-Based Speech Emotion Recognition by Incorporating Learned Features and Deep BiLSTM
TL;DR: A novel framework for SER is introduced using a key sequence segment selection based on redial based function network (RBFN) similarity measurement in clusters to reduce the computational complexity of the overall model and normalize the CNN features before their actual processing, so that it can easily recognize the Spatio-temporal information.
Journal ArticleDOI
A novel magic LSB substitution method (M-LSB-SM) using multi-level encryption and achromatic component of an image
TL;DR: Experimental results validate that the proposed method not only enhances the visual quality of stego images but also provides good imperceptibility and multiple security levels as compared to several existing prominent methods.