Modeling Follow-Unfollow Mechanism in Social Networks with Evolutionary Game - Chen, Jundong; Hossain, Md Shafaeat; Serwadda, Abdul; Han, Fei (2021 IEEE International Conference on Agents (ICA), 2021)
In online social networks (OSN), followers count is a sign of the social influence of an account. Some users expect to increase the followers count by following more accounts. However, in reality more followings do not generate more followers. In this paper, we propose a two player follow-unfollow game model and then introduce a factor for promoting cooperation. Based on the two player follow-unfollow game, we create an evolutionary follow-unfollow game with more players to simulate a miniature social network. We design an algorithm and conduct the simulation. From the simulation, we find that our algorithm for the evolutionary follow-unfollow game is able to converge and produce a stable network. Results obtained with different values of the cooperation promotion factor show that the promotion factor increases the total connections in the network especially through increasing the number of the follow follow connections.
Keywords: Conferences; Correlation; Games; Social networking (online); dept.csc; evolutionary game; follow unfollow mechanism; social network

Diagnosis of Obstructive Sleep Apnea from ECG Signals Using Machine Learning and Deep Learning Classifiers - Sheta, Alaa; Turabieh, Hamza; Thaher, Thaer; Too, Jingwei; Mafarja, Majdi; Hossain, Md Shafaeat; Surani, Salim R. (Applied Sciences, 2021)
Obstructive sleep apnea (OSA) is a well-known sleep ailment. OSA mostly occurs due to the shortage of oxygen for the human body, which causes several symptoms (i.e., low concentration, daytime sleepiness, and irritability). Discovering the existence of OSA at an early stage can save lives and reduce the cost of treatment. The computer-aided diagnosis (CAD) system can quickly detect OSA by examining the electrocardiogram (ECG) signals. Over-serving ECG using a visual procedure is challenging for physicians, time-consuming, expensive, and subjective. In general, automated detection of the ECG signal’s arrhythmia is a complex task due to the complexity of the data quantity and clinical content. Moreover, ECG signals are usually affected by noise (i.e., patient movement and disturbances generated by electric devices or infrastructure), which reduces the quality of the collected data. Machine learning (ML) and Deep Learning (DL) gain a higher interest in health care systems due to its ability of achieving an excellent performance compared to traditional classifiers. We propose a CAD system to diagnose apnea events based on ECG in an automated way in this work. The proposed system follows the following steps: (1) remove noise from the ECG signal using a Notch filter. (2) extract nine features from the ECG signal (3) use thirteen ML and four types of DL models for the diagnosis of sleep apnea. The experimental results show that our proposed approach offers a good performance of DL classifiers to detect OSA. The proposed model achieves an accuracy of 86.25% in the validation stage.
Keywords: classification; deep learning; dept.csc; machine learning; notch filter; obstructive sleep apnea; peer-reviewed