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Variable Selection And Subsequent Case Prediction In Semi-Parametric Models, Jiasong Duan
Variable Selection And Subsequent Case Prediction In Semi-Parametric Models, Jiasong Duan
Electronic Theses and Dissertations
Prediction of health status is a novel technique of forecasting the future health conditions with existing knowledge and available data. A reliable statistical model can lead to high performance of health status prediction. Built upon a semi-parametric variable selection approach, an algorithm to predict health conditions is developed and assessed. This algorithm is compared with three competing prediction methods based on logistic regressions, random forest, and support vector machines. Four statistics, accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), are used to compare the performance across different approaches. The proposed approach, based on the simulation findings, …
Multi-Level Analysis Of Malware Using Machine Learning, Subash Poudyal
Multi-Level Analysis Of Malware Using Machine Learning, Subash Poudyal
Electronic Theses and Dissertations
Malware analysis and detection is a critical capability every business and organization needs to defend itself against a growing number of cyber threats. For example, ransomware, an advanced form of malware, makes hostage of user's data and asks ransom, usually in crypto-currencies, to remain anonymous. Significant efforts have been undertaken to combat these attacks, but the threat factors are dynamic, and there lacks intelligent approach to defeat them. Thus, my study is focused on designing a defensive solution against this advanced malware, i.e., ransomware. Many tools and techniques exist that claim to detect and respond to malware. However, such methods …
The Design And Application Of Enzyme Inter-Residue Interaction Networks Towards Quantum Mechanical Modeling, Thomas Summers
The Design And Application Of Enzyme Inter-Residue Interaction Networks Towards Quantum Mechanical Modeling, Thomas Summers
Electronic Theses and Dissertations
In order to accurately simulate the inner workings of an enzyme active site with quantum mechanics (QM), not only must the reactive species be included in the model, but also any important surrounding residues, solvent, ions, and coenzymes involved in crafting the microenvironment. The Residue Interaction Network ResidUe Selector (RINRUS) toolkit was designed to utilize interatomic contact network information for automated, rational residue selection and QM-cluster model generation. An X-ray crystal structure of a protein is translated into a two-dimensional network which may be then used to discern residues with significant interactions with the enzyme substrates. The rest of the …
Learning Motion Estimation, Ali Salehi
Learning Motion Estimation, Ali Salehi
Electronic Theses and Dissertations
Transformation of any generic scene including structural deformation and fluid flow can be estimated non-invasively from an image sequence using optical flow estimation techniques. Dense optical flow estimation is more challenging when there are large displacements in a scene with heterogeneous motion dynamics, occlusion, and scene homogeneity. Recently developed deep learning methods show significant promise in estimating optical flow with high accuracy. Majority of these methods, however, have been more commonly derived from architectures of related computer vision tasks such as semantic segmentation and require a large number of training parameters. Accuracy of these methods is also affected by the …
Machine Learning Models For Predicting The Imminent Risk Of Impulsive Behaviors Using Mhealth Sensors, Soujanya Chatterjee
Machine Learning Models For Predicting The Imminent Risk Of Impulsive Behaviors Using Mhealth Sensors, Soujanya Chatterjee
Electronic Theses and Dissertations
Researchers have developed machine learning models for detecting behaviors (e.g., smoking, eating, and drinking) and health states (e.g., stress) from wearable and mobile sensors. However, these models help detect events after they occur. The next frontier is the prediction of imminent risk of adverse health events.For mobile sensor-based prediction of imminent risk of impulsive behaviors, passive and continuous detection of risk factors of such behaviors is necessary. But, for human thoughts, perceptions, and contexts (e.g., suicidal ideation, craving/urge, desire for immediate gratification, and exposure to risky environmental cues), that are potential antecedents and precipitants of impulsive behaviors (e.g., suicide attempt, …
Towards Building Intelligent Collaborative Problem Solving Systems, Dipesh Gautam
Towards Building Intelligent Collaborative Problem Solving Systems, Dipesh Gautam
Electronic Theses and Dissertations
Historically, Collaborative Problem Solving (CPS) systems were more focused on Human Computer Interaction (HCI) issues, such as providing good experience of communication among the participants. Whereas, Intelligent Tutoring Systems (ITS) focus both on HCI issues as well as leveraging Artificial Intelligence (AI) techniques in their intelligent agents. This dissertation seeks to minimize the gap between CPS systems and ITS by adopting the methods used in ITS researches. To move towards this goal, we focus on analyzing interactions with textual inputs in online learning systems such as DeepTutor and Virtual Internships (VI) to understand their semantics and underlying intents. In order …
Applications Of Sparse Representations, Pulin Agrawal
Applications Of Sparse Representations, Pulin Agrawal
Electronic Theses and Dissertations
In this dissertation I explore the properties and uses of sparse representations. Sparse representations use high dimensional binary vectors for representing information. They have many properties which make this representation useful for applications involving pattern recognition in highly noisy and complex environments. Sparse representations have a very high capacity. A typical sparse representation vector has a capacity of 10^84 distinct vectors, which is more than the number of atoms in the universe. Sparse representations are highly noise robust. They can tolerate even up to 50% noise. A very powerful and useful property of sparse representations is that they allow us …
Gaining Scientific And Engineering Insight Into Ground Motion Simulation Through Machine Learning And Approximate Modeling Approaches, Naeem Khoshnevis
Gaining Scientific And Engineering Insight Into Ground Motion Simulation Through Machine Learning And Approximate Modeling Approaches, Naeem Khoshnevis
Electronic Theses and Dissertations
This dissertation presents a series of methods for gaining scientific and engineering insight into earthquake ground motion simulation in three areas: synthetic validation, attenuation modeling, and nonlinear effects estimation. First, I present guidelines to reduce the number of metrics used to evaluate the goodness-of-fit (GOF) between ground motion synthetics and recorded data in an application independent framework. Validation of ground motion simulations is mostly done using metrics that are user- or application-biased. Comparisons between synthetics from regional scale ground motion simulations and recorded data from past earthquakes provide opportunities to approach the problems using data-driven methods. I used a combination …
Automated Artifact Removal And Detection Of Mild Cognitive Impairment From Single Channel Electroencephalography Signals For Real-Time Implementations On Wearables, Saleha Khatun
Electronic Theses and Dissertations
Electroencephalogram (EEG) is a technique for recording asynchronous activation of neuronal firing inside the brain with non-invasive scalp electrodes. EEG signal is well studied to evaluate the cognitive state, detect brain diseases such as epilepsy, dementia, coma, autism spectral disorder (ASD), etc. In this dissertation, the EEG signal is studied for the early detection of the Mild Cognitive Impairment (MCI). MCI is the preliminary stage of Dementia that may ultimately lead to Alzheimers disease (AD) in the elderly people. Our goal is to develop a minimalistic MCI detection system that could be integrated to the wearable sensors. This contribution has …
Measuring Semantic Textual Similarity And Automatic Answer Assessment In Dialogue Based Tutoring Systems, Rajendra Banjade
Measuring Semantic Textual Similarity And Automatic Answer Assessment In Dialogue Based Tutoring Systems, Rajendra Banjade
Electronic Theses and Dissertations
This dissertation presents methods and resources proposed to improve onmeasuring semantic textual similarity and their applications in student responseunderstanding in dialogue based Intelligent Tutoring Systems. In order to predict the extent of similarity between given pair of sentences,we have proposed machine learning models using dozens of features, such as thescores calculated using optimal multi-level alignment, vector based compositionalsemantics, and machine translation evaluation methods. Furthermore, we haveproposed models towards adding an interpretation layer on top of similaritymeasurement systems. Our models on predicting and interpreting the semanticsimilarity have been the top performing systems in SemEval (a premier venue for thesemantic evaluation) for …
Automated Speech Act Classification In Tutorial Dialogue, Borhan Samei
Automated Speech Act Classification In Tutorial Dialogue, Borhan Samei
Electronic Theses and Dissertations
Speech act classification is the task of detecting speakers' intentions in discourse. Speech acts are based on the language as action theory according to which when we say something we do something. Speech act classification has various application in natural language processing and dialogue-based intelligent systems. In this thesis, we propose machine learning models for speech act classification that account for both content of the current utterance and context (previous utterances) of dialogue and we present this work on two domains: human-human tutoring sessions and multi-party chat based intelligent tutoring systems. The proposed speech act classification models were trained and …