A Case Study Using Zoom Touch Gestures: How Does the Size of a Training Dataset Impact User’s Age Estimation Accuracy in Smartphones? - Hossain, Md Shafaeat (2023 IEEE International Conference on Smart Computing (SMARTCOMP), 2023)
In this paper, we focus on improving the age estimation accuracy on smartphones. Estimating a smartphone user’s age has several applications such as protecting our children online by filtering age-inappropriate contents, providing a customized e-commerce experience, etc. However, accuracy of the the state-of-the-art age estimation techniques that use touch behavior on smartphones is still limited because of the lack of sufficient amount of training data. We perform rigorous experiments using zoom gestures on smartphones and demonstrate that increasing the amount of training data can significantly improve the age estimation accuracy. Based on the findings in this study, we recommend creating a large touch dynamics-based age estimation data set so that more accurate age estimation models can be built and in turn, can be used more confidently.
0 citations (Crossref) [2023-10-31]
Keywords: Behavioral sciences; Data models; Estimation; Filtering; Support vector machines; Training; Training data; age estimation; dept.csc; smart computing; smartphone; touch dynamics; zoom gesture

Towards performance improvement of authorship attribution - Suljic, Amar; Hossain, Md Shafaeat (IEEE Access, 2024)
Keywords: dept.csc; peer-reviewed

Who are My Family Members? A Solution Based on Image Processing and Machine Learning - Kiley, Matthew R.; Hossain, Md Shafaeat (International Journal of Image and Graphics, 2020)
Image creation and retention are growing at an exponential rate. Individuals produce more images today than ever in history and often these images contain family. In this paper, we develop a framework to detect or identify family in a face image dataset. The ability to identify family in a dataset of images could have a critical impact on finding lost and vulnerable children, identifying terror suspects, social media interactions, and other practical applications. We evaluated our framework by performing experiments on two facial image datasets, the Y-Face and KinFaceW, comprising 37 and 920 images, respectively. We tested two feature extraction techniques, namely PCA and HOG, and three machine learning algorithms, namely K-Means, agglomerative hierarchical clustering, and K nearest neighbors. We achieved promising results with a maximum detection rate of 94.59% using K-Means, 89.18% with agglomerative clustering, and 77.42% using K-nearest neighbors. © 2020 World Scientific Publishing Company.
1 citations (Crossref) [2023-10-31] WOS:000589375900007
Keywords: Agglomerative clustering; Agglomerative hierarchical clustering; Exponential rates; Facial images; Family detection; Feature extraction techniques; Hierarchical clustering; Image creation; K -means; K-means clustering; K-nearest neighbors; Learning algorithms; Machine learning; Maximum detection rates; Motion compensation; Nearest neighbor search; agglomerative hierarchical clustering; dept.csc; face image clustering; family identification; peer-reviewed

Indoor positioning system for smart spaces - Hossain, Md Shafaeat; Wandell, Richard J (2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT), 2024)
In this paper, we develop an indoor positioning system using smartphones. An indoor positioning system plays a vital role in indoor spaces such as home, office, university, airport, and hospital buildings by locating and tracking persons, devices, and assets. Our indoor positioning system is applicable in any indoor spaces which has smart devices such as smartphones, tablets, smartwatches, and robots with a Wi-Fi connection. We used Wi-Fi-based fingerprinting technique t o build o ur indoor positioning system because a Wi-Fi-based system can leverage existing Wi-Fi infrastructure and hence, it is cost effective. A major challenge in implementing a Wi-Fi-based fingerprinting technique is the missed access points (APs) problem. In this paper, we address this critical challenge by proposing a localization procedure called ‘cosine similarity + k-means clustering'. In this localization procedure, we leverage k-means clustering algorithm in identifying the wrong location estimates produced by the cosine similarity measure because of missed APs problem. To evaluate the effectiveness of our proposed localization procedure, we collected data from three different scenarios, specifically, home, office, a nd university f or creating signal m ap a nd performing localization tests. Additionally, we tested both stationary and walk data. Our experimental results prove that our ‘cosine similarity + k-means clustering’ localization procedure is effective in mitigating the detrimental impact of missed APs, and consequently, it significantly improves localization accuracy.
Keywords: Clustering algorithms; Fingerprint recognition; Indoor positioning systems; Location awareness; Performance evaluation; Robot kinematics; Smart devices; Wi-Fi; dept.csc; fingerprinting; indoor positioning system; localization; smartphone

Analyzing Capacitive Swipe Gesture towards User Identification - Kc, Kiran; Hossain, Md Shafaeat; Tayeen, Abu Saleh Md (IEEE Int. Jt. Conf. Biom., IJCB, 2025)
Traditional physical biometrics - such as fingerprints, facial recognition, and iris scans - have long been utilized for user identification in areas like border control, military operations, law enforcement, and public safety. However, the rise of smartphone technology has introduced new avenues for security research. One emerging area is behavioral biometrics, particularly the use of touchscreen interaction data as a more accessible and user-friendly identification method. In this context, our study focuses on a novel form of touchscreen input: capacitive swipe gestures for user identification. We compiled a comprehensive dataset of capacitive swipe gestures collected over multiple sessions from 30 participants. To evaluate this modality, we conducted thorough experiments using established machine learning algorithms, including Support Vector Machine, Random Forest, and XGBoost. Additionally, we developed a new preprocessing algorithm tailored for capacitive swipe data. Our findings reveal that this algorithm significantly enhances identification performance compared to existing methods. Overall, our results highlight the strong potential of capacitive swipe gestures as a viable biometric modality for user identification. © 2025 IEEE.
Journal Abbreviation: IEEE Int. Jt. Conf. Biom., IJCB
Keywords: Accelerometer; Biometrics; Capacitive sensing; Gesture; Identification (biology); Keystroke logging; Modality (human–computer interaction); SwIPe; Touchscreen; dept.csc

Swipe gestures for user authentication in smartphones - Chao, Jedrik; Hossain, Md Shafaeat; Lancor, Lisa (Journal of Information Security and Applications, 2023)
As more people rely on smartphones to store sensitive information, the need for robust security measures is all the more pressing. Because traditional one shot authentication methods like PINs and passwords are vulnerable to various attacks, we present a behavioral biometrics based smartphone authentication system using swipes. While previous research focused on a single kind of swipe, our data set features swipes using different fingers and directions collected from 36 users across three sessions. In our system, we experimented with support vector machine (SVM) and random forest (RF) classifiers. We investigated which finger, direction, and classifier provided the best individual swipe authentication results. Then, we analyzed whether fusion of different fingers and directions improved results. The best unimodal result came from a rightward swipe with right thumb using SVM, which resulted in an area under ROC curve (AUC) of 0.936 and an equal error rate (EER) of 0.135. We found that swipes using thumbs offered better performance. Fusion improves results for the most part, and our best result was the combination of a leftward swipe with right thumb and a leftward swipe with left thumb. This combination gave an AUC of 0.969 and EER of 0.081 with the SVM classifier.
0 citations (Crossref) [2023-10-31]
Keywords: Authentication; Biometrics; Smartphone security; Swipe gesture; Touchscreen; dept.csc; peer-reviewed

Incorporating deep learning into capacitive images for smartphone user authentication - Hossain, Md Shafaeat; Islam, Mohammad T.; Akhtar, Zahid (Journal of Information Security and Applications, 2022)
We incorporate deep learning techniques into capacitive images of body parts (ear, four fingers, and thumb) to improve the performance of user authentication in smartphones. Use of a capacitive touchscreen as an image sensor has several advantages, such as it is less sensitive to poor illumination conditions, occlusions, and pose variations. Also, it does not need an additional hardware like iris or fingerprint scanner. Use of capacitive images for user authentication is not new. However, the performance, specially, false reject rates (FRRs) of the state-of-the-art capacitive image-based systems are poor. In this paper, we focus on improving the performance and leverage deep learning. Deep learning techniques demonstrated spectacular performance in previous physical biometrics-based research. However, to our knowledge, effectiveness of deep learning is still unexplored in capacitive touchscreen-based user authentication. In order to bridge this research gap, we devise a multi-modal deep learning model, namely UASNet, and compare its performance with a large set of uni- and multi-modal baselines. Using the UASNet, we achieve an accuracy of 99.77%, an EER of 0.48%, and an FRR of 1.19% at FAR of 0.06%.
0 citations (Crossref) [2023-10-31]
Keywords: Biometrics; CNN; Capacitive images; Deep learning; Fusion; Smartphone; Touchscreen; User authentication; dept.csc; peer-reviewed

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.
0 citations (Crossref) [2023-10-31]
Keywords: Conferences; Correlation; Games; Social networking (online); dept.csc; evolutionary game; follow unfollow mechanism; social network

SAS Mobile Application for Diagnosis of Obstructive Sleep Apnea Utilizing Machine Learning Models - Haberfeld, Carl; Sheta, Alaa; Hossain, Md Shafaeat; Turabieh, Hamza; Surani, Salim (2020 11th IEEE Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON), 2020)
In this paper, we provide a consistent, inexpensive, and easy to use graphical user interface (GUI) smart phone application named Sleep Apnea Screener (SAS) that can diagnosis Obstructive Sleep Apnea (OSA) based on demographic data such as: gender, age, height, BMI, neck circumference, waist, etc., allowing a tentative diagnosis of OSA without the need for overnight tests. The developed smart phone application can diagnosis sleep apnea using a model trained with 620 samples collected from a sleep center in Corpus Christi, TX. Two machine learning classifiers (i.e., Logistic Regression (LR) and Support Vector Machine (SVM)) were used to diagnosis OSA. Our preliminary results show that at-home OSA screening is indeed possible, and that our application is effective method for covering large numbers of undiagnosed cases.
7 citations (Crossref) [2023-10-31]
Keywords: COVID-19; Corpus christi; Demographic data; Graphical user interfaces; Graphical user interfaces (GUI); Learning systems; Logistic regression; Logistics; Machine Learning; Machine learning; Machine learning models; Mathematical model; Medical services; Mobile applications; Mobile telecommunication systems; OSA; Obstructive sleep apnea; SVM; Sleep Apnea; Sleep apnea; Sleep research; Smart phones; Smart-phone applications; Smartphones; Support vector machines; Support vector regression; Two machines; Ubiquitous computing; dept.csc; pretesting

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.
29 citations (Crossref) [2023-10-31] Number: 14
Keywords: classification; deep learning; dept.csc; machine learning; notch filter; obstructive sleep apnea; peer-reviewed

Capacitive swipe gesture based smartphone user authentication and identification - Rilvan, Mohamed Azard; Chao, Jedrik; Hossain, Md Shafaeat (Proceedings - 2020 IEEE International Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2020, 2020)
Weaknesses in smartphone security pose a severe privacy threat to users. Currently, smartphones are secured through methods such as passwords, fingerprint scanners, and facial recognition cameras. To explore new methods and strengthen smartphone security, we developed a capacitive swipe based user authentication and identification technique. Swipe is a gesture that a user performs throughout the usage of a smartphone. Our methodology focuses on using the capacitive touchscreen to capture the user's swipe. While the user swipes, a series of capacitive frames are captured for each swipe. We developed an algorithm to process this series of capacitive frames pertaining to the swipe. While different swipes may contain different numbers of capacitive frames, our algorithm normalizes the frames by constructing the same number of frames for every swipe. After applying the algorithm, we transform the normalized frames into gray scale images. We apply principal component analysis (PCA) to these images to extract principal components, which are then used as features to authenticate/identify the user. We tested random forest (RF) and support vector machine (SVM) algorithms as classifiers. For authentication, the performance of SVM (tested with left swipes) was more promising than RF, yielding a maximum accuracy of 79.88% with an FAR and FRR of 15.84% and 50%, respectively. SVM (tested with right swipes) produced our maximum identification accuracy at 57.81% along with an FAR and FRR of 0.60% and 42.18%, respectively. © 2020 IEEE.
7 citations (Crossref) [2023-10-31] WOS:000628978500009
Keywords: Authentication; Decision trees; Face recognition; Facial recognition; Fingerprint scanners; Identification accuracy; Identification techniques; Mobile security; Principal Components; Privacy by design; Smartphone securities; Smartphone security; Smartphones; Support vector machine algorithm; Support vector machines; User authentication; authentication; capacitive swipe; capacitive touchscreen; dept.csc; identification