Artificial Intelligence and Robotics Commons^™

A Physics-Based Machine Learning Study Of The Behavior Of Interstitial Helium In Single Crystal W–Mo Binary Alloys, Adib J. Samin

Faculty Publications

In this work, the behavior of dilute interstitial helium in W–Mo binary alloys was explored through the application of a first principles-informed neural network (NN) in order to study the early stages of helium-induced damage and inform the design of next generation materials for fusion reactors. The neural network (NN) was trained using a database of 120 density functional theory (DFT) calculations on the alloy. The DFT database of computed solution energies showed a linear dependence on the composition of the first nearest neighbor metallic shell. This NN was then employed in a kinetic Monte Carlo simulation, which took into …

Applying Imitation And Reinforcement Learning To Sparse Reward Environments, Haven Brown

Computer Science and Computer Engineering Undergraduate Honors Theses

The focus of this project was to shorten the time it takes to train reinforcement learning agents to perform better than humans in a sparse reward environment. Finding a general purpose solution to this problem is essential to creating agents in the future capable of managing large systems or performing a series of tasks before receiving feedback. The goal of this project was to create a transition function between an imitation learning algorithm (also referred to as a behavioral cloning algorithm) and a reinforcement learning algorithm. The goal of this approach was to allow an agent to first learn to …

A Framework For Vector-Weighted Deep Neural Networks, Carter Chiu

UNLV Theses, Dissertations, Professional Papers, and Capstones

The vast majority of advances in deep neural network research operate on the basis of a real-valued weight space. Recent work in alternative spaces have challenged and complemented this idea; for instance, the use of complex- or binary-valued weights have yielded promising and fascinating results. We propose a framework for a novel weight space consisting of vector values which we christen VectorNet. We first develop the theoretical foundations of our proposed approach, including formalizing the requisite theory for forward and backpropagating values in a vector-weighted layer. We also introduce the concept of expansion and aggregation functions for conversion between real …

Supervised Optimal Decision Machine Learning Approach To Class- And Method-Level Data Preprocessing Towards Effective Software Defect Prediction, Felix Ebubeogu Amarachukwu

Student Works (2020-2029)

Software defect prediction provides actionable outputs to software teams while contributing to industrial success. Therefore, predicting the number of defects in a new version of software at both the class and method levels is an important goal of defect prediction studies to assist software teams in optimizing their test efforts towards improving software quality. However, despite remarkable achievements in defect prediction, the quality of the data applied in defect prediction studies has been a major concern, with related quality issues leading to numerous contradictory findings in machine learning research. In addition, a demonstrated approach for predicting the number of defects …

Classification Of Dividend News Based On The Movement Of The Share Market Prices Of Public Listed Companies In Bursa Malaysia, Vijaya Kumar Shubana

Student Works (2020-2029)

Stock market is naturally complex and plays a major role in towards the nation’s growth. However, the performance of a company in stock market varies due to many influences but not limited to economics, political and financial related news. This study attempts to classify the share market dividend news announcement in Bursa Malaysia based on the pattern of share market price. Samples including five hundred (500) observations of dividend news from forty-seven (47) listed companies in Bursa Malaysia during the period of 2000 to 2018 are used in this study. There are three (3) main objectives in this study which …

Development Of Fully Balanced Ssfp And Computer Vision Applications For Mri-Assisted Radiosurgery (Mars), Jeremiah Sanders

Dissertations and Theses (Open Access)

Prostate cancer is the second most common cancer in men and the second-leading cause of cancer death in men. Brachytherapy is a highly effective treatment option for prostate cancer, and is the most cost-effective initial treatment among all other therapeutic options for low to intermediate risk patients of prostate cancer. In low-dose-rate (LDR) brachytherapy, verifying the location of the radioactive seeds within the prostate and in relation to critical normal structures after seed implantation is essential to ensuring positive treatment outcomes.

One current gap in knowledge is how to simultaneously image the prostate, surrounding anatomy, and radioactive seeds within the …

Classification Of Labour Pain Using Electroencephalogram Signal Based On Wavelet Method, Chong Yeh Sai

Student Works (2020-2029)

Electroencephalogram (EEG) is the recording of electrical activity of the cerebral cortex through electrodes placed on the scalp. EEG is used to acquire neurophysiological signals for application in clinical diagnosis and brain computer interface (BCI). However, in practical settings the EEG signals are often contaminated by signal artifacts known as the biological and environmental artifacts. These artifacts degrade EEG signals, thereby obstructing clinical diagnosis or BCI applications by distorting the observed power spectrum. Procedures for automated removal of EEG artifacts are frequently sought after in pre-processing and filtering of the EEG signals. In recent years, a combination of independent component …

Does Applying Deep Learning In Financial Sentiment Analysis Lead To Better Classification Performance?, Tao Wang, Changhe Yuan, Cuiyuan Wang

Publications and Research

Using a unique data set from Seeking Alpha, we compare the deep learning approach with traditional machine learning approaches in classifying financial text. We apply the long short-term memory (LSTM) as the deep learning method and Naive Bayes, SVM, Logistic Regression, XGBoost as the traditional machine learning approaches. The results suggest that the LSTM model outperforms the conventional machine learning methods on all metrics. Based on the tSNE graph, the success of the LSTM model is partially explained as the high-accuracy LSTM model distinguishes between positive and negative important sentiment words while those words are chosen based on SHAP values …

Applications Of Machine Learning To Threat Intelligence, Intrusion Detection And Malware, Charity Barker

Senior Honors Theses

Artificial Intelligence (AI) and Machine Learning (ML) are emerging technologies with applications to many fields. This paper is a survey of use cases of ML for threat intelligence, intrusion detection, and malware analysis and detection. Threat intelligence, especially attack attribution, can benefit from the use of ML classification. False positives from rule-based intrusion detection systems can be reduced with the use of ML models. Malware analysis and classification can be made easier by developing ML frameworks to distill similarities between the malicious programs. Adversarial machine learning will also be discussed, because while ML can be used to solve problems or …

Truck Trailer Classification Using Side-Fire Light Detection And Ranging (Lidar) Data, Olcay Sahin

Civil & Environmental Engineering Theses & Dissertations

Classification of vehicles into distinct groups is critical for many applications, including freight and commodity flow modeling, pavement management and design, tolling, air quality monitoring, and intelligent transportation systems. The Federal Highway Administration (FHWA) developed a standardized 13-category vehicle classification ruleset, which meets the needs of many traffic data user applications. However, some applications need high-resolution data for modeling and analysis. For example, the type of commodity being carried must be known in the freight modeling framework. Unfortunately, this information is not available at the state or metropolitan level, or it is expensive to obtain from current resources.

Nevertheless, using …

Treatment Effects Of Modafinil For Cocaine Use Disorders: A Retrospective Analysis Of Aggregated Clinical Trial Data From Three Cocaine Treatment Studies, Daniel Ruskin

Honors Scholar Theses

Approximately 913,000 individuals in the United States meet the diagnostic criteria for cocaine use disorder (CUD). The widespread usage of cocaine, along with the negative cardiac and neurological effects associated with the drug, has made cocaine one of the top three drugs associated with overdose deaths in the United States. This epidemic has brought cocaine dependency into the public spotlight and has prompted extensive research into treatment strategies. However, at the time of writing, no drugs have been approved by the United States Food and Drug Administration (FDA) for use in treating CUD. The purpose of this study is to …

Algorithm Selection Framework: A Holistic Approach To The Algorithm Selection Problem, Marc W. Chalé

Theses and Dissertations

A holistic approach to the algorithm selection problem is presented. The “algorithm selection framework" uses a combination of user input and meta-data to streamline the algorithm selection for any data analysis task. The framework removes the conjecture of the common trial and error strategy and generates a preference ranked list of recommended analysis techniques. The framework is performed on nine analysis problems. Each of the recommended analysis techniques are implemented on the corresponding data sets. Algorithm performance is assessed using the primary metric of recall and the secondary metric of run time. In six of the problems, the recall of …

Artificial Intelligence: A New Paradigm In Obstetrics And Gynecology Research And Clinical Practice, Pulwasha Iftikhar, Marcela V. Kuijpers, Azadeh Khayyat, Aqsa Iftikhar, Maribel Degouvia De Sa

Publications and Research

Artificial intelligence (AI) is growing exponentially in various fields, including medicine. This paper reviews the pertinent aspects of AI in obstetrics and gynecology (OB/GYN) and how these can be applied to improve patient outcomes and reduce the healthcare costs and workload for clinicians.

Herein, we will address current AI uses in OB/GYN, and the use of AI as a tool to interpret fetal heart rate (FHR) and cardiotocography (CTG) to aid in the detection of preterm labor, pregnancy complications, and review discrepancies in its interpretation between clinicians to reduce maternal and infant morbidity and mortality. AI systems can be used …

Cyber-Physical Security With Rf Fingerprint Classification Through Distance Measure Extensions Of Generalized Relevance Learning Vector Quantization, Trevor J. Bihl, Todd J. Paciencia, Kenneth W. Bauer Jr., Michael A. Temple

Faculty Publications

Radio frequency (RF) fingerprinting extracts fingerprint features from RF signals to protect against masquerade attacks by enabling reliable authentication of communication devices at the “serial number” level. Facilitating the reliable authentication of communication devices are machine learning (ML) algorithms which find meaningful statistical differences between measured data. The Generalized Relevance Learning Vector Quantization-Improved (GRLVQI) classifier is one ML algorithm which has shown efficacy for RF fingerprinting device discrimination. GRLVQI extends the Learning Vector Quantization (LVQ) family of “winner take all” classifiers that develop prototype vectors (PVs) which represent data. In LVQ algorithms, distances are computed between exemplars and PVs, and …

Monocular Depth Image Mark-Less Pose Estimation Based On Feature Regression, Chen Ying, Shen Li

Journal of System Simulation

Abstract: Monocular camera mark-less pose estimation system suffers low accuracy, robustness and efficiency due to variety of action, self-occlusion of human body. A method of feature exaction from point clouds was proposed, in which a single-to-multiple (S2M) feature regressor and a joint position regressor were designed to quickly and accurately predict the 3D positions of body joints from a single depth image without any temporal information. Experiment result shows that the estimation accuracy is superior to that of state-of-the-arts and multi-camera based methods.

Harnessing Artificial Intelligence Capabilities To Improve Cybersecurity, Sherali Zeadally, Erwin Adi, Zubair Baig, Imran A. Khan

Information Science Faculty Publications

Cybersecurity is a fast-evolving discipline that is always in the news over the last decade, as the number of threats rises and cybercriminals constantly endeavor to stay a step ahead of law enforcement. Over the years, although the original motives for carrying out cyberattacks largely remain unchanged, cybercriminals have become increasingly sophisticated with their techniques. Traditional cybersecurity solutions are becoming inadequate at detecting and mitigating emerging cyberattacks. Advances in cryptographic and Artificial Intelligence (AI) techniques (in particular, machine learning and deep learning) show promise in enabling cybersecurity experts to counter the ever-evolving threat posed by adversaries. Here, we explore AI's …

Final Presentation To The Library Of Congress On Digital Libraries, Intelligent Data Analytics, And Augmented Description, Elizabeth Lorang, Leen-Kiat Soh, Yi Liu, Chulwoo Pack

University of Nebraska-Lincoln Libraries: Presentations

This presentation to Library of Congress staff, delivered onsite on January 10, 2020, presents a tour through the demonstration project pursued by the Aida digital libraries research team with the Library of Congress in 2019-2020. In addition to providing an overview and analysis of the specific machine learning projects scoped and explored, this presentation includes a number of high-level take-aways and recommendations designed to influence and inform the Library of Congress's machine learning efforts going forward.

Digital Libraries, Intelligent Data Analytics, And Augmented Description: A Demonstration Project, Elizabeth Lorang, Leen-Kiat Soh, Yi Liu, Chulwoo Pack

University of Nebraska-Lincoln Libraries: Faculty Publications

From July 16-to November 8, 2019, the Aida digital libraries research team at the University of Nebraska-Lincoln collaborated with the Library of Congress on “Digital Libraries, Intelligent Data Analytics, and Augmented Description: A Demonstration Project.“ This demonstration project sought to (1) develop and investigate the viability and feasibility of textual and image-based data analytics approaches to support and facilitate discovery; (2) understand technical tools and requirements for the Library of Congress to improve access and discovery of its digital collections; and (3) enable the Library of Congress to plan for future possibilities. In pursuit of these goals, we focused our …

Machine Learning In Manufacturing: Review, Synthesis, And Theoretical Framework, Ajit Sharma, Zhibo Zhang, Rahul Rai

Business Administration Faculty Research Publications

There has been a paradigmatic shift in manufacturing as computing has transitioned from the programmable to the cognitive computing era. In this paper we present a theoretical framework for understanding this paradigmatic shift in manufacturing and the fast evolving role of artificial intelligence. Policy, Strategic and Operational implications are discussed. Implications for the future of strategy and operations in manufacturing are also discussed. Future research directions are presented.

Facial Action Unit Detection With Deep Convolutional Neural Networks, Siddhesh Padwal

Electronic Theses and Dissertations

The facial features are the most important tool to understand an individual's state of mind. Automated recognition of facial expressions and particularly Facial Action Units defined by Facial Action Coding System (FACS) is challenging research problem in the field of computer vision and machine learning. Researchers are working on deep learning algorithms to improve state of the art in the area. Automated recognition of facial action units has man applications ranging from developmental psychology to human robot interface design where companies are using this technology to improve their consumer devices (like unlocking phone) and for entertainment like FaceApp. Recent studies …

Renewable Energy Integration In Distribution System With Artificial Intelligence, Yi Gu

Electronic Theses and Dissertations

With the increasing attention of renewable energy development in distribution power system, artificial intelligence (AI) can play an indispensiable role. In this thesis, a series of artificial intelligence based methods are studied and implemented to further enhance the performance of power system operation and control.

Due to the large volume of heterogeneous data provided by both the customer and the grid side, a big data visualization platform is built to feature out the hidden useful knowledge for smart grid (SG) operation, control and situation awareness. An open source cluster calculation framework with Apache Spark is used to discover big data …

Automated Recognition Of Facial Affect Using Deep Neural Networks, Behzad Hasani

Electronic Theses and Dissertations

Automated Facial Expression Recognition (FER) has been a topic of study in the field of computer vision and machine learning for decades. In spite of efforts made to improve the accuracy of FER systems, existing methods still are not generalizable and accurate enough for use in real-world applications. Many of the traditional methods use hand-crafted (a.k.a. engineered) features for representation of facial images. However, these methods often require rigorous hyper-parameter tuning to achieve favorable results.

Recently, Deep Neural Networks (DNNs) have shown to outperform traditional methods in visual object recognition. DNNs require huge data as well as powerful computing units …

Machine Learning Prediction Of Glioblastoma Patient One-Year Survival, Andrew Du '20, Warren Mcgee, Jane Y. Wu

Student Publications & Research

Glioblastoma (GBM) is a grade IV astrocytoma formed primarily from cancerous astrocytes and sustained by intense angiogenesis. GBM often causes non-specific symptoms, creating difficulty for diagnosis. This study aimed to utilize machine learning techniques to provide an accurate one-year survival prognosis for GBM patients using clinical and genomic data from the Chinese Glioma Genome Atlas. Logistic regression (LR), support vector machines (SVM), random forest (RF), and ensemble models were used to identify and select predictors for GBM survival and to classify patients into those with an overall survival (OS) of less than one year and one year or greater. With …

A Machine Learning System For Glaucoma Detection Using Inexpensive Machine Learning, Jon Kilgannon

West Chester University Master’s Theses

This thesis presents a neural network system which segments images of the retina to calculate the cup-to-disc ratio, one of the diagnostic indicators of the presence or continuing development of glaucoma, a disease of the eye which causes blindness. The neural network is designed to run on commodity hardware and to be run with minimal skill required from the user by packaging the software required to run the network into a Singularity image. The RIGA dataset used to train the network provides images of the retina which have been annotated with the location of the optic cup and disc by …

A Deep Learning Approach To Mapping Irrigation: U-Net Irrmapper, Thomas Henry Colligan Iv

Graduate Student Theses, Dissertations, & Professional Papers

Accurate maps of irrigation are essential for understanding and managing water resources in light of a warming climate. We present a new method for mapping irrigation and apply it to the state of Montana over the years 2000-2019. The method is based on an ensemble of convolutional neural networks that only rely on raw Landsat surface reflectance data. The ensemble of networks method learns to mask clouds and ignore Landsat 7 scan-line failures without supervision, reducing the need for preprocessing data or feature engineering. Unlike other approaches to mapping irrigation, the method doesn't use other mapping products like the Cropland …

Language Model Co-Occurrence Linking For Interleaved Activity Discovery, Eoin Rogers, Robert J. Ross, John D. Kelleher

Conference papers

As ubiquitous computer and sensor systems become abundant, the potential for automatic identification and tracking of human behaviours becomes all the more evident. Annotating complex human behaviour datasets to achieve ground truth for supervised training can however be extremely labour-intensive, and error prone. One possible solution to this problem is activity discovery: the identification of activities in an unlabelled dataset by means of an unsupervised algorithm. This paper presents a novel approach to activity discovery that utilises deep learning based language production models to construct a hierarchical, tree-like structure over a sequential vector of sensor events. Our approach differs from …

Modelling Interleaved Activities Using Language Models, Eoin Rogers, Robert J. Ross, John D. Kelleher

Conference papers

We propose a new approach to activity discovery, based on the neural language modelling of streaming sensor events. Our approach proceeds in multiple stages: we build binary links between activities using probability distributions generated by a neural language model trained on the dataset, and combine the binary links to produce complex activities. We then use the activities as sensor events, allowing us to build complex hierarchies of activities. We put an emphasis on dealing with interleaving, which represents a major challenge for many existing activity discovery systems. The system is tested on a realistic dataset, demonstrating it as a promising …

Special Section Guest Editorial: Machine Learning In Optics, Jonathan Howe, Travis Axtell, Khan Iftekharuddin

Electrical & Computer Engineering Faculty Publications

This guest editorial summarizes the Special Section on Machine Learning in Optics.

Toward Efficient Automation Of Interpretable Machine Learning Boosting, Nathan Neuhaus

All Master's Theses

Developing efficient automated methods for Interpretable Machine Learning (IML) is an important and long-term goal in the field of Artificial Intelligence. Currently the Machine Learning landscape is dominated by Neural Networks (NNs) and Support Vector Machines (SVMs), models which are often highly accurate. Despite high accuracy, such models are essentially “black boxes” and therefore are too risky for situations like healthcare where real lives are at stake. In such situations, so called “glass-box” models, such as Decision Trees (DTs), Bayesian Networks (BNs), and Logic Relational (LR) models are often preferred, however can succumb to accuracy limitations. Unfortunately, having to choose …

Comparing Predictive Performance Of Statistical Learning Models On Medical Data, Francis Biney

Open Access Theses & Dissertations

This work investigates the predictive performance of 10 Machine learning models on three medical data including Breast cancer, Heart disease and Prostate cancer. Furthermore, we use the models to identify risk factors that contribute significantly to these diseases.

The models considered include; Logistic regression with L1 and L_2 penalties, Principal component logistic regression(PCR-LR), Partial least squares logistic regression(PLS-LR), Multivariate adaptive regression splines(MARS), Support vector machine with Radial Basis Kernel (SVM-RBK), Random Forest(RF), Gradient Boosting Machines(GBM), Elastic Net (Enet) and Feedforward Neural Network(FFNN). The models were grouped according to their similarities and learning style; i) Linear regularized models: LR-Lasso, LR-Ridge and …