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Articles 1 - 14 of 14
Full-Text Articles in Engineering
Modeling And Simulation Methodologies For Spinal Cord Stimulation., Saliya Kumara Kirigeeganage
Modeling And Simulation Methodologies For Spinal Cord Stimulation., Saliya Kumara Kirigeeganage
Electronic Theses and Dissertations
The use of neural prostheses to improve health of paraplegics has been a prime interest of neuroscientists over the last few decades. Scientists have performed experiments with spinal cord stimulation (SCS) to enable voluntary motor function of paralyzed patients. However, the experimentation on the human spinal cord is not a trivial task. Therefore, modeling and simulation techniques play a significant role in understanding the underlying concepts and mechanics of the spinal cord stimulation. In this work, simulation and modeling techniques related to spinal cord stimulation were investigated. The initial work was intended to visualize the electric field distribution patterns in …
A Transfer Learning Approach For Sentiment Classification., Omar Abdelwahab
A Transfer Learning Approach For Sentiment Classification., Omar Abdelwahab
Electronic Theses and Dissertations
The idea of developing machine learning systems or Artificial Intelligence agents that would learn from different tasks and be able to accumulate that knowledge with time so that it functions successfully on a new task that it has not seen before is an idea and a research area that is still being explored. In this work, we will lay out an algorithm that allows a machine learning system or an AI agent to learn from k different domains then uses some or no data from the new task for the system to perform strongly on that new task. In order …
Why We Do Not Evolve Software? Analysis Of Evolutionary Algorithms, Roman V. Yampolskiy
Why We Do Not Evolve Software? Analysis Of Evolutionary Algorithms, Roman V. Yampolskiy
Faculty Scholarship
In this article, we review the state-of-the-art results in evolutionary computation and observe that we do not evolve nontrivial software from scratch and with no human intervention. A number of possible explanations are considered, but we conclude that computational complexity of the problem prevents it from being solved as currently attempted. A detailed analysis of necessary and available computational resources is provided to support our findings.
A Psychopathological Approach To Safety Engineering In Ai And Agi, Vahid Behzadan, Arslan Munir, Roman V. Yampolskiy
A Psychopathological Approach To Safety Engineering In Ai And Agi, Vahid Behzadan, Arslan Munir, Roman V. Yampolskiy
Faculty Scholarship
The complexity of dynamics in AI techniques is already approaching that of complex adaptive systems, thus curtailing the feasibility of formal controllability and reachability analysis in the context of AI safety. It follows that the envisioned instances of Artificial General Intelligence (AGI) will also suffer from challenges of complexity. To tackle such issues, we propose the modeling of deleterious behaviors in AI and AGI as psychological disorders, thereby enabling the employment of psychopathological approaches to analysis and control of misbehaviors. Accordingly, we present a discussion on the feasibility of the psychopathological approaches to AI safety, and propose general directions for …
Spam Elimination And Bias Correction : Ensuring Label Quality In Crowdsourced Tasks., Lingyu Lyu
Spam Elimination And Bias Correction : Ensuring Label Quality In Crowdsourced Tasks., Lingyu Lyu
Electronic Theses and Dissertations
Crowdsourcing is proposed as a powerful mechanism for accomplishing large scale tasks via anonymous workers online. It has been demonstrated as an effective and important approach for collecting labeled data in application domains which require human intelligence, such as image labeling, video annotation, natural language processing, etc. Despite the promises, one big challenge still exists in crowdsourcing systems: the difficulty of controlling the quality of crowds. The workers usually have diverse education levels, personal preferences, and motivations, leading to unknown work performance while completing a crowdsourced task. Among them, some are reliable, and some might provide noisy feedback. It is …
The Singularity May Be Near, Roman V. Yampolskiy
The Singularity May Be Near, Roman V. Yampolskiy
Faculty Scholarship
Toby Walsh in "The Singularity May Never Be Near" gives six arguments to support his point of view that technological singularity may happen, but that it is unlikely. In this paper, we provide analysis of each one of his arguments and arrive at similar conclusions, but with more weight given to the "likely to happen" prediction.
Wisdom Of Artificial Crowds Feature Selection In Untargeted Metabolomics: An Application To The Development Of A Blood-Based Diagnostic Test For Thrombotic Myocardial Infarction, Patrick J. Trainor, Roman V. Yampolskiy, Andrew P. Defilippis
Wisdom Of Artificial Crowds Feature Selection In Untargeted Metabolomics: An Application To The Development Of A Blood-Based Diagnostic Test For Thrombotic Myocardial Infarction, Patrick J. Trainor, Roman V. Yampolskiy, Andrew P. Defilippis
Faculty Scholarship
Introduction: Heart disease remains a leading cause of global mortality. While acute myocardial infarction (colloquially: heart attack), has multiple proximate causes, proximate etiology cannot be determined by a blood-based diagnostic test. We enrolled a suitable patient cohort and conducted a non-targeted quantification of plasma metabolites by mass spectrometry for developing a test that can differentiate between thrombotic MI, non-thrombotic MI, and stable disease. A significant challenge in developing such a diagnostic test is solving the NP-hard problem of feature selection for constructing an optimal statistical classifier. Objective: We employed a Wisdom of Artificial Crowds (WoAC) strategy for solving the feature …
Horse Racing Prediction Using Graph-Based Features., Mehmet Akif Gulum
Horse Racing Prediction Using Graph-Based Features., Mehmet Akif Gulum
Electronic Theses and Dissertations
This thesis presents an applied horse racing prediction using graph based features on a set of horse races data. We used artificial neural network and logistic regression models to train then test to prediction without graph based features and with graph based features. This thesis can be explained in 4 main parts. Collect data from a horse racing website held from 2015 to 2017. Train data to using predictive models and make a prediction. Create a global directed graph of horses and extract graph-based features (Core Part) . Add graph based features to basic features and train to using same …
Machine Learning For Omics Data Analysis., Ameni Trabelsi
Machine Learning For Omics Data Analysis., Ameni Trabelsi
Electronic Theses and Dissertations
In proteomics and metabolomics, to quantify the changes of abundance levels of biomolecules in a biological system, multiple sample analysis steps are involved. The steps include mass spectrum deconvolution and peak list alignment. Each analysis step introduces a certain degree of technical variation in the abundance levels (i.e. peak areas) of those molecules. Some analysis steps introduce technical variations that affect the peak areas of all molecules equally while others affect the peak areas of a subset of molecules with varying degrees. To correct these technical variations, some existing normalization methods simply scale the peak areas of all molecules detected …
Maintainability Analysis Of Mining Trucks With Data Analytics., Abdulgani Kahraman
Maintainability Analysis Of Mining Trucks With Data Analytics., Abdulgani Kahraman
Electronic Theses and Dissertations
The mining industry is one of the biggest industries in need of a large budget, and current changes in global economic challenges force the industry to reduce its production expenses. One of the biggest expenditures is maintenance. Thanks to the data mining techniques, available historical records of machines’ alarms and signals might be used to predict machine failures. This is crucial because repairing machines after failures is not as efficient as utilizing predictive maintenance. In this case study, the reasons for failures seem to be related to the order of signals or alarms, called events, which come from trucks. The …
End-To-End Learning Framework For Circular Rna Classification From Other Long Non-Coding Rnas Using Multi-Modal Deep Learning., Mohamed Chaabane
End-To-End Learning Framework For Circular Rna Classification From Other Long Non-Coding Rnas Using Multi-Modal Deep Learning., Mohamed Chaabane
Electronic Theses and Dissertations
Over the past two decades, a circular form of RNA (circular RNA) produced from splicing mechanism has become the focus of scientific studies due to its major role as a microRNA (miR) ac tivity modulator and its association with various diseases including cancer. Therefore, the detection of circular RNAs is a vital operation for continued comprehension of their biogenesis and purpose. Prediction of circular RNA can be achieved by first distinguishing non-coding RNAs from protein coding gene transcripts, separating short and long non-coding RNAs (lncRNAs), and finally pre dicting circular RNAs from other lncRNAs. However, available tools to distinguish circular …
Robust Fuzzy Clustering For Multiple Instance Regression., Mohamed Trabelsi
Robust Fuzzy Clustering For Multiple Instance Regression., Mohamed Trabelsi
Electronic Theses and Dissertations
Multiple instance regression (MIR) operates on a collection of bags, where each bag contains multiple instances sharing an identical real-valued label. Only few instances, called primary instances, contribute to the bag labels. The remaining instances are noise and outliers observations. The goal in MIR is to identify the primary instances within each bag and learn a regression model that can predict the label of a previously unseen bag. In this thesis, we introduce an algorithm that uses robust fuzzy clustering with an appropriate distance to learn multiple linear models from a noisy feature space simultaneously. We show that fuzzy memberships …
A Framework For Cardio-Pulmonary Resuscitation (Cpr) Scene Retrieval From Medical Simulation Videos Based On Object And Activity Detection., Anju Panicker Madhusoodhanan Sathik
A Framework For Cardio-Pulmonary Resuscitation (Cpr) Scene Retrieval From Medical Simulation Videos Based On Object And Activity Detection., Anju Panicker Madhusoodhanan Sathik
Electronic Theses and Dissertations
In this thesis, we propose a framework to detect and retrieve CPR activity scenes from medical simulation videos. Medical simulation is a modern training method for medical students, where an emergency patient condition is simulated on human-like mannequins and the students act upon. These simulation sessions are recorded by the physician, for later debriefing. With the increasing number of simulation videos, automatic detection and retrieval of specific scenes became necessary. The proposed framework for CPR scene retrieval, would eliminate the conventional approach of using shot detection and frame segmentation techniques. Firstly, our work explores the application of Histogram of Oriented …
Network Science Algorithms For Mobile Networks., Heba Mohamed Elgazzar
Network Science Algorithms For Mobile Networks., Heba Mohamed Elgazzar
Electronic Theses and Dissertations
Network Science is one of the important and emerging fields in computer science and engineering that focuses on the study and analysis of different types of networks. The goal of this dissertation is to design and develop network science algorithms that can be used to study and analyze mobile networks. This can provide essential information and knowledge that can help mobile networks service providers to enhance the quality of the mobile services. We focus in this dissertation on the design and analysis of different network science techniques that can be used to analyze the dynamics of mobile networks. These techniques …