@article{soleimani_guo_haley_jacks_lobaton_2024, title={The Impact of Pause and Filler Word Encoding on Dementia Detection with Contrastive Learning}, volume={14}, ISSN={["2076-3417"]}, url={https://www.mdpi.com/2076-3417/14/19/8879}, DOI={10.3390/app14198879}, abstractNote={Dementia is primarily caused by neurodegenerative diseases like Alzheimer’s disease (AD). It affects millions worldwide, making detection and monitoring crucial. This study focuses on the detection of dementia from speech transcripts of controls and dementia groups. We propose encoding in-text pauses and filler words (e.g., “uh” and “um”) in text-based language models and thoroughly evaluating their impact on performance (e.g., accuracy). Additionally, we suggest using contrastive learning to improve performance in a multi-task framework. Our results demonstrate the effectiveness of our approaches in enhancing the model’s performance, achieving 87% accuracy and an 86% f1-score. Compared to the state of the art, our approach has similar performance despite having significantly fewer parameters. This highlights the importance of pause and filler word encoding on the detection of dementia.}, number={19}, journal={APPLIED SCIENCES-BASEL}, author={Soleimani, Reza and Guo, Shengjie and Haley, Katarina L. and Jacks, Adam and Lobaton, Edgar}, year={2024}, month={Oct} }
 @article{soleimani_lobaton_2022, title={Enhancing Inference on Physiological and Kinematic Periodic Signals via Phase-Based Interpretability and Multi-Task Learning}, volume={13}, ISSN={["2078-2489"]}, url={https://www.mdpi.com/2078-2489/13/7/326}, DOI={10.3390/info13070326}, abstractNote={Physiological and kinematic signals from humans are often used for monitoring health. Several processes of interest (e.g., cardiac and respiratory processes, and locomotion) demonstrate periodicity. Training models for inference on these signals (e.g., detection of anomalies, and extraction of biomarkers) require large amounts of data to capture their variability, which are not readily available. This hinders the performance of complex inference models. In this work, we introduce a methodology for improving inference on such signals by incorporating phase-based interpretability and other inference tasks into a multi-task framework applied to a generative model. For this purpose, we utilize phase information as a regularization term and as an input to the model and introduce an interpretable unit in a neural network, which imposes an interpretable structure on the model. This imposition helps us in the smooth generation of periodic signals that can aid in data augmentation tasks. We demonstrate the impact of our framework on improving the overall inference performance on ECG signals and inertial signals from gait locomotion.}, number={7}, journal={INFORMATION}, author={Soleimani, Reza and Lobaton, Edgar}, year={2022}, month={Jul} }
 @article{azizi_soleimani_ahmadi_malekan_abualigah_dashtiahangar_2022, title={Performance enhancement of an uncertain nonlinear medical robot with optimal nonlinear robust controller}, volume={146}, ISSN={["1879-0534"]}, DOI={10.1016/j.compbiomed.2022.105567}, abstractNote={Cardiopulmonary resuscitation refers to the process of sending oxygen and blood to the body's vital organs during cardiac arrest. For this reason, designing and controlling an accurate robot is crucial to saving the lives of patients. This study aims to optimize two nonlinear robust controllers for the first time for the parallel manipulator for cardiopulmonary resuscitation to reduce overshoot, increase accuracy, increase convergence speed, and increase robustness to destructive factors affecting the precision of the robots. The paper first presents the kinematics and dynamics of a translational parallel manipulator robot. Then, to reduce the difference between the practical and simulation results, the paper presents a nonlinear model under uncertainties, disturbances, and noise. Then, the ONSTSMC awaiting the uncertainty band is designed to eliminate the singularity problem and increase the accuracy and robustness to destructive factors, as well as improve stability using the Lyapunov principle. Furthermore, the results of applying this robust controller to the robot are compared with the results of a non-singular terminal sliding mode controller without considering the uncertainty band, a conventional sliding mode controller, and a PID controller for the same model. The developed controller exhibits better performance in terms of accuracy and convergence time even when external and internal destructive factors are present. The accuracy is 0.21 mm and the convergence time is 0.7 seconds when compared with PID. Furthermore, it is approximately 0.17 mm and 0.4 seconds faster compared with conventional sliding mode controllers.}, journal={COMPUTERS IN BIOLOGY AND MEDICINE}, author={Azizi, SeyedArmin and Soleimani, Reza and Ahmadi, Mohsen and Malekan, Ali and Abualigah, Laith and Dashtiahangar, Fatemeh}, year={2022}, month={Jul} }