S
Shafaeat Hossain
Researcher at Southern Connecticut State University
Publications - 36
Citations - 202
Shafaeat Hossain is an academic researcher from Southern Connecticut State University. The author has contributed to research in topics: Computer science & Biometrics. The author has an hindex of 5, co-authored 29 publications receiving 80 citations. Previous affiliations of Shafaeat Hossain include Louisiana Tech University.
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Journal ArticleDOI
Diagnosis of Obstructive Sleep Apnea from ECG Signals Using Machine Learning and Deep Learning Classifiers
Alaa Sheta,Hamza Turabieh,Thaer Thaher,Jingwei Too,Majdi Mafarja,Shafaeat Hossain,Salim Surani +6 more
TL;DR: The experimental results show that the proposed approach offers a good performance of DL classifiers to detect OSA, and the proposed model achieves an accuracy of 86.25% in the validation stage.
Journal ArticleDOI
Analyzing the sentiment correlation between regular tweets and retweets
TL;DR: The sentiment correlation between regular tweets and retweets is investigated and it is found that the users with higher betweenness centrality and higher tweets amount tend to exhibit a higher sentiment correlation.
Journal ArticleDOI
Effectiveness of Deep Learning on Serial Fusion Based Biometric Systems
TL;DR: A framework for multibiometric systems is developed, which combines a deep learning technique with a serial fusion method and improves accuracy by leveraging deep learning technology in feature extraction and score generation.
Proceedings ArticleDOI
New impostor score based rejection methods for continuous keystroke verification with weak templates
TL;DR: A new formulation to incorporate reject option in verification with weak templates is introduced and a new impostor score based rejection method called Order Statistic (OS) rejection method is developed which achieves better error-reject trade-off than Otsu and Gaussian rejection methods.
Proceedings ArticleDOI
Capacitive Swipe Gesture Based Smartphone User Authentication and Identification
TL;DR: A capacitive swipe based user authentication and identification technique that focuses on using the capacitive touchscreen to capture the user's swipe and applies principal component analysis to these images to extract principal components, which are then used as features to authenticate/identify the user.