- Browse by Author
Browsing by Author "Ben-Miled, Zina"
Now showing 1 - 10 of 11
Results Per Page
Sort Options
Item Deep Learning of Biomechanical Dynamics With Spatial Variability Mining and Model Sparsifiation(2024-08) Liu, Ming; Zhang, Qingxue; King, Brian S.; Ben-Miled, Zina; Xia, YuniDeep learning of biomechanical dynamics is of great promise in smart health and data-driven precision medicine. Biomechanical dynamics are related to the movement patterns and gait characteristics of human people and may provide important insights if mined by deep learning models. However, efficient deep learning of biomechanical dynamics is still challenging, considering that there is a high diversity in the dynamics from different body locations, and the deep learning model may need to be lightweight enough to be able to be deployed in real-time. Targeting these challenges, we have firstly conducted studies on the spatial variability of biomechanical dynamics, aiming to evaluate and determine the optimal body location that is of great promise in robust physical activity type detection. Further, we have developed a framework for deep learning pruning, aiming to determine the optimal pruning schemes while maintaining acceptable performance. More specifically, the proposed approach first evaluates the layer importance of the deep learning model, and then leverages the probabilistic distribution-enabled threshold determination to optimize the pruning rate. The weighted random thresholding method is first investigated to further the understanding of the behavior of the pruning action for each layer. Afterwards, the Gaussian-based thresholding is designed to more effectively optimize the pruning strategies, which can find out the fine-grained pruning schemes with both emphasis and diversity regulation. Even further, we have enhanced and boosted the efficient deep learning framework, to co-optimize the accuracy and the continuity during the pruning process, with the latter metric – continuity meaning that the pruning locations in the weight matrices are encouraged to not cause too many noncontinuous non-pruned locations thereby achieving friendly model implementation. More specifically, the proposed framework leverages the significance scoring and the continuity scoring to quantize the characteristics of each of pruned convolutional filters, then leverages the clustering technique to group the pruned filters for each convolutional stage. Afterwards, the regularized ranking approach is designed to rank the pruned filters, through putting more emphasis on the continuity scores to encourage friendly implementation. In the end, a dual-thresholding strategy is leveraged to increase the diversity in this framework, during significance & continuity co-optimization. Experimental results have demonstrated promising findings, with enhanced understanding of the spatial variability of the biomechanical dynamics and best performance body location selection, with the effective deep learning model pruning framework that can reduce the model size significantly with performance maintained, and further, with the boosted framework that co-optimizes the accuracy and continuity to all consider the friendly implementation during the pruning process. Overall, this research will greatly advance the deep biomechanical mining towards efficient smart health.Item Development of Data-driven and AI-powered Systems Biology Methods for Understanding Human Disease(2024-08) Dang, Pengtao; Zhang, Chi; Salama, Paul; Cao, Sha; King, Brian; Ben-Miled, ZinaSystems biology dynamic models, which are based on differential equations, offer a flexible and accurate framework to explain physiological properties emerging from complex biochem- ical or biological systems. These models enable explicit quantification and interpretation, allowing for simulation and perturbation analysis to study biological features and their inter- actions, as well as understanding system progression and convergence under various initial conditions. However, their application in human disease systems is limited due to unknown kinetics parameters under disease conditions and a reductionist paradigm that fails to cap- ture the complexity of diseases. Meanwhile, the advent of omics technologies provides high- resolution molecular measurements from single cells and spatially resolved samples, as well as comprehensive disease-specific molecular signatures from large patient cohorts. This wealth of data holds the promise for characterizing complex biological systems, necessitating ad- vanced systems biology models and computational tools that can harness multi-omics data to reliably depict biological processes. However, this endeavor faces the challenge of nonlinear relationships between omics data and the system’s dynamic properties, such as the global or local low-rank gene expression patterns across cell types and the nonlinear complexities within transcriptional regulatory networks revealed by single-cell RNA sequencing. The overall goal of this report is to develop new computational frameworks, AI-empowered methods, and related mathematical theories to explicitly represent and approximate the dy- namics of complex biological systems by using biological omics data. Our aim is to unravel the intricacies of context-specific dynamic systems using multi-Omics data. Specifically, we solved two different but related computational tasks and enabled the first-of-its-kind methods to (1) identify local low-rank matrices from large omics data, and (2) a robust optimization strategy to approximate metabolic flux. Subsequently, we delve into the realm of data-driven and AI-powered systems biology, harnessing the power of statistical learning and artificial intelligence to approximate differential equations or their representations. This research en- deavor not only contributes to the advancement of subspace modeling but also offers insights into a wide array of complex phenomena across diverse domains, with profound implications for computational biology and beyond.Item Does Bad News Spread Faster?(IEEE, 2017-01) Fang, Anna; Ben-Miled, Zina; Electrical and Computer Engineering, School of Engineering and TechnologyBad news travels fast. Although this concept may be intuitively accepted, there has been little evidence to confirm that the propagation of bad news differs from that of good news. In this paper, we examine the effect of user perspective on his or her sharing of a controversial news story. Social media not only offers insight into human behavior but has also developed as a source of news. In this paper, we define the spreading of news by tracking selected tweets in Twitter as they are shared over time to create models of user sharing behavior. Many news events can be viewed as positive or negative. In this paper, we compare and contrast tweets about these news events among general users, while monitoring the tweet frequency for each event over time to ensure that news events are comparable with respect to user interest. In addition, we track the tweets of a controversial event between two different groups of users (i.e., those who view the event as positive and those who view it as negative). As a result, we are able to make assessments based on a single event from two different perspectives.Item Estimation of Defocus Blur in Virtual Environments Comparing Graph Cuts and Convolutional Neural Network(2018-12) Chowdhury, Prodipto; Christopher, Lauren; King, Brian; Ben-Miled, ZinaDepth estimation is one of the most important problems in computer vision. It has attracted a lot of attention because it has applications in many areas, such as robotics, VR and AR, self-driving cars etc. Using the defocus blur of a camera lens is one of the methods of depth estimation. In this thesis, we have researched this technique in virtual environments. Virtual datasets have been created for this purpose. In this research, we have applied graph cuts and convolutional neural network (DfD-net) to estimate depth from defocus blur using a natural (Middlebury) and a virtual (Maya) dataset. Graph Cuts showed similar performance for both natural and virtual datasets in terms of NMAE and NRMSE. However, with regard to SSIM, the performance of graph cuts is 4% better for Middlebury compared to Maya. We have trained the DfD-net using the natural and the virtual dataset and then combining both datasets. The network trained by the virtual dataset performed best for both datasets. The performance of graph-cuts and DfD-net have been compared. Graph-Cuts performance is 7% better than DfD-Net in terms of SSIM for Middlebury images. For Maya images, DfD-Net outperforms Graph-Cuts by 2%. With regard to NRMSE, Graph-Cuts and DfD-net shows similar performance for Maya images. For Middlebury images, Graph-cuts is 1.8% better. The algorithms show no difference in performance in terms of NMAE. The time DfD-net takes to generate depth maps compared to graph cuts is 500 times less for Maya images and 200 times less for Middlebury images.Item Identifying High Acute Care Users Among Bipolar and Schizophrenia Patients(2023-12) Li, Shuo; Ben-Miled, Zina; Fang, Shiaofen; Zheng, Jiang YuThe electronic health record (EHR) documents the patient’s medical history, with information such as demographics, diagnostic history, procedures, laboratory tests, and observations made by healthcare providers. This source of information can help support preventive health care and management. The present thesis explores the potential for EHR-driven models to predict acute care utilization (ACU) which is defined as visits to an emergency department (ED) or inpatient hospitalization (IH). ACU care is often associated with significant costs compared to outpatient visits. Identifying patients at risk can improve the quality of care for patients and can reduce the need for these services making healthcare organizations more cost-effective. This is important for vulnerable patients including those suffering from schizophrenia and bipolar disorders. This study compares the ability of the MedBERT architecture, the MedBERT+ architecture and standard machine learning models to identify at risk patients. MedBERT is a deep learning language model which was trained on diagnosis codes to predict the patient’s at risk for certain disease conditions. MedBERT+, the architecture introduced in this study is also trained on diagnosis codes. However, it adds socio-demographic embeddings and targets a different outcome, namely ACU. MedBERT+ outperformed the original architecture, MedBERT, as well as XGB achieving an AUC of 0.71 for both bipolar and schizophrenia patients when predicting ED visits and an AUC of 0.72 for bipolar patients when predicting IH visits. For schizophrenia patients, the IH predictive model had an AUC of 0.66 requiring further improvements. One potential direction for future improvement is the encoding of the demographic variables. Preliminary results indicate that an appropriate encoding of the age of the patient increased the AUC of Bipolar ED models to up to 0.78.Item Innovative mixed reality advanced manufacturing environment with haptic feedback(2018-07-13) Satterwhite, Jesse C.; Ben-Miled, Zina; El-Mounayri, Hazim; Rogers, Christian; Wasfy, TamerIn immersive eLearning environments, it has been demonstrated that incorporating haptic feedback improves the software's pedagogical effectiveness. Due to this and recent advancements in virtual reality (VR) and mixed reality (MR) environments, more immersive, authentic, and viable pedagogical tools have been created. However, the advanced manufacturing industry has not fully embraced mixed reality training tools. There is currently a need for effective haptic feedback techniques in advanced manufacturing environments. The MR-AVML, a proposed CNC milling machine training tool, is designed to include two forms of haptic feedback, thereby providing users with a natural and intuitive experience. This experience is achieved by tasking users with running a virtual machine seen through the Microsoft HoloLens and interacting with a physical representation of the machine controller. After conducting a pedagogical study on the environment, it was found that the MR-AVML was 6.06% more effective than a version of the environment with no haptic feedback, and only 1.35% less effective than hands-on training led by an instructor. This shows that the inclusion of haptic feedback in an advanced manufacturing training environment can improve pedagogical effectiveness.Item Multi-Class Vocation Identification for Heavy Duty Vehicles(2021-12) Yadav, Varun; Ben-Miled, Zina; Dos Santos, Euzeli; Salama, PaulUnderstanding the operating profile of different heavy-duty vehicles is needed by parts manufacturers for improved configuration and better future design of the parts. This study investigates the use of a tournament classification approach for both vocation and fleet identi- fication. The proposed approach is implemented using four different classification techniques, namely, K-Means, Expectation Maximization, Particle Swarm Optimization, and Support Vector Machines. Vocations classifiers are developed and tested for six different vocations ranging from coach buses to rail inspection vehicles. Operational field data are obtained from a number of vehicles for each vocation and aggregated over a pre-set distance that varies according to the data collection rate. In addition, fleet classifiers are implemented for five fleets from the coach bus vocation using a similar approach. The results indicate that both vocation and fleet identification are possible with a high level of accuracy. The macro average precision and recall of the SVM vocation classifier are approximately 85%. This result was achieved despite the fact that each vocation consisted of multiple fleets. The macro average precision and recall of the coach bus fleet classifier are approximately 77% even though some fleets had similar operating profiles. These results suggest that the proposed classifier can help support vocation and fleet identification in practice.Item P2HR, a personalized condition-driven person health record(2017) King, Zachary; Ben-Miled, ZinaHealth IT has recently seen a significant progress with the nationwide migration of several hospitals from legacy patient records to standardized Electronic Health Record (EHR) and the establishment of various Health Information Exchanges that facilitate access to patient health data across multiple networks. While this progress is a major enabler of improved health care services, it is unable to deliver the continuum of the patient's current and historical health data needed by emerging trends in medicine. Fields such as precision and preventive medicine require longitudinal health data in addition to complementary data such as social, demographic and family history. This thesis introduces a person health record (PHR) which overcomes the above gap through a personalized framework that organizes health data according to the patient’s disease condition. The proposed personalized person health record (P2HR) represents a departure from the standardized one-size-fits-all model of currently available PHRs. It also relies on a hybrid peer-to-peer model to facilitate patient provider communication. One of the core challenges of the proposed framework is the mapping between the event-based data model used by current EHRs and PHRs and the proposed condition-based data model. Effectively mapping symptoms and measurements to disease conditions is challenging given that each symptom or measurement may be associated with multiple disease conditions. To alleviate these problems the proposed framework allows users and their health care providers to establish the relationships between events and disease conditions on a case-by-case basis. This organization provides both the patient and the provider with a better view of each disease condition and its progression.Item A social and news media benchmark dataset for topic modeling(Elsevier, 2022-07-04) Miles, Samuel; Yao, Lixia; Meng, Weilin; Black, Christopher M.; Ben-Miled, Zina; Electrical and Computer Engineering, School of Engineering and TechnologyTopic modeling is an active research area with several unanswered questions. The focus of recent research in this area is on the use of a vector embedding representation of the input text with both generative and evolutionary topic modeling techniques. Unfortunately, it is hard to compare different techniques when the underlying data and preprocessing steps that were used to develop the models are not available. This paper presents two secondary datasets that can help address this gap. These datasets are derived from two primary datasets. The first consists of 8145 posts from the r/Cancer health forum and the second consists of 18,294 messages submitted to 20 different news groups. The same preprocessing procedure is applied to both datasets by removing punctuation, stop words and high frequency words. Each dataset is then clustered using three different topic modeling techniques: pPSO, ETM and NVDM and three topic numbers: 10, 20, 30. In addition, for pPSO two text embeddings representation are considered: sBERT and Skipgram. The secondary datasets were originally developed in support of a comparative analysis of the aforementioned topic modeling techniques in a study titled “Comparing PSO-based Clustering over Contextual Vector Embeddings to Modern Topic Modeling” submitted to the Journal of Information Processing and Management. The present paper provides a detailed description of the two secondary datasets including the unique identifier that can be used to retrieve the original documents, the pre-processing scripts, the topic keywords generated by the three topic modeling techniques with varying topic numbers and embedding representations. As such, the datasets allow direct comparison with other topic modeling techniques. To further facilitate this process, the algorithm underlying the evolutionary topic modeling technique, pPSO, proposed by the authors is also provided.Item Transfer learning for medication adherence prediction from social forums self-reported data(2018-12) Haas, Kyle D.; Ben-Miled, Zina; King, Brian; El-Sharkawy, MohamedMedication non-adherence and non-compliance left unaddressed can compound into severe medical problems for patients. Identifying patients that are likely to become non-adherent can help reduce these problems. Despite these benefits, monitoring adherence at scale is cost-prohibitive. Social forums offer an easily accessible, affordable, and timely alternative to the traditional methods based on claims data. This study investigates the potential of medication adherence prediction based on social forum data for diabetes and fibromyalgia therapies by using transfer learning from the Medical Expenditure Panel Survey (MEPS). Predictive adherence models are developed by using both survey and social forums data and different random forest (RF) techniques. The first of these implementations uses binned inputs from k-means clustering. The second technique is based on ternary trees instead of the widely used binary decision trees. These techniques are able to handle missing data, a prevalent characteristic of social forums data. The results of this study show that transfer learning between survey models and social forum models is possible. Using MEPS survey data and the techniques listed above to derive RF models, less than 5% difference in accuracy was observed between the MEPS test dataset and the social forum test dataset. Along with these RF techniques, another RF implementation with imputed means for the missing values was developed and shown to predict adherence for social forum patients with an accuracy >70%. This thesis shows that a model trained with verified survey data can be used to complement traditional medical adherence models by predicting adherence from unverified, self-reported data in a dynamic and timely manner. Furthermore, this model provides a method for discovering objective insights from subjective social reports. Additional investigation is needed to improve the prediction accuracy of the proposed model and to assess biases that may be inherent to self-reported adherence measures in social health networks.