Digital Commons Network^™

Bringing Defensive Artificial Intelligence Capabilities To Mobile Devices, Kevin Chong, Ahmed Ibrahim

Australian Information Security Management Conference

Traditional firewalls are losing their effectiveness against new and evolving threats today. Artificial intelligence (AI) driven firewalls are gaining popularity due to their ability to defend against threats that are not fully known. However, a firewall can only protect devices in the same network it is deployed in, leaving mobile devices unprotected once they leave the network. To comprehensively protect a mobile device, capabilities of an AI-driven firewall can enhance the defensive capabilities of the device. This paper proposes porting AI technologies to mobile devices for defence against today’s ever-evolving threats. A defensive AI technique providing firewall-like capability is being …

Estimating The Optimal Cutoff Point For Logistic Regression, Zheng Zhang

Open Access Theses & Dissertations

Binary classification is one of the main themes of supervised learning. This research is concerned about determining the optimal cutoff point for the continuous-scaled outcomes (e.g., predicted probabilities) resulting from a classifier such as logistic regression. We make note of the fact that the cutoff point obtained from various methods is a statistic, which can be unstable with substantial variation. Nevertheless, due partly to complexity involved in estimating the cutpoint, there has been no formal study on the variance or standard error of the estimated cutoff point.

In this Thesis, a bootstrap aggregation method is put forward to estimate the …

A Study Into The Feasibility Of Using Natural Language Processing And Machine Learning For The Identification Of Alcohol Misuse In Trauma Patients, Andrew Phillips

Master's Theses

Alcohol misuse is a leading cause of premature death in the United States, with nearly a third of trauma patients found to have elevated blood alcohol levels upon admission. However, timely intervention has been shown to reduce this. It is thus important to be able to quickly screen patients to identify alcohol misuse. Many medical centers use standardized questionnaires to identify alcohol misuse, but since these instruments are not usually a part of routine care, there are many cases where it is not done.

In this study, large quantities of notes were processed with natural language processing and machine learning …

Estimating Meteorological Visibility Range Under Foggy Weather Conditions: A Deep Learning Approach, Hazar Chaabani, Naoufel Werghi, Faouzi Kamoun, Bilal Taha, Fatma Outay, Ansar Ul Haque Yasar

All Works

© 2018 The Authors. Published by Elsevier Ltd. Systems capable of estimating visibility distances under foggy weather conditions are extremely useful for next-generation cooperative situational awareness and collision avoidance systems. In this paper, we present a brief review of noticeable approaches for determining visibility distance under foggy weather conditions. We then propose a novel approach based on the combination of a deep learning method for feature extraction and an SVM classifier. We present a quantitative evaluation of the proposed solution and show that our approach provides better performance results compared to an earlier approach that was based on the combination …

Motion-Induced Artifact Mitigation And Image Enhancement Strategies For Four-Dimensional Fan-Beam And Cone-Beam Computed Tomography, Matthew J. Riblett

Theses and Dissertations

Four dimensional imaging has become part of the standard of care for diagnosing and treating non-small cell lung cancer. In radiotherapy applications 4D fan-beam computed tomography (4D-CT) and 4D cone-beam computed tomography (4D-CBCT) are two advanced imaging modalities that afford clinical practitioners knowledge of the underlying kinematics and structural dynamics of diseased tissues and provide insight into the effects of regular organ motion and the nature of tissue deformation over time. While these imaging techniques can facilitate the use of more targeted radiotherapies, issues surrounding image quality and accuracy currently limit the utility of these images clinically.

The purpose of …

Visualizing The Opioid Overdose With A Dynamic Heat Map To Identify And Predict Vulnerable Communities, Justin R. Chang, Garrett K. Wong, Chase Greco, Yadunandan Pillai, Mohammad A. Shahrezaei, Melissa H. Burton, Rob Lawrence, Alan Dow

Graduate Research Posters

Background: Opioid and heroin overdose epidemic is a public health emergency in the state of Virginia. In order to prevent overdose deaths, we need the target expertise in substance use disorders to areas with high rates of overdose. In particular, an area with an acute spike in overdoses might represent an urgent need for intervention.

Aims: The CDC urges the use of near real-time surveillance to effectively identify overdose incidence, and to coordinate community responses in the states affected by the epidemic, including Virginia. However, current opioid overdose datasets for Virginia lack adequate consistency, granularity, and temporality for …

Quantitative Forecasting Of Risk For Ptsd Using Ecological Factors: A Deep Learning Application, Nuriel S. Mor, Kathryn L. Dardeck

Journal of Social, Behavioral, and Health Sciences

Forecasting the risk for mental disorders from early ecological information holds benefits for the individual and society. Computational models used in psychological research, however, are barriers to making such predictions at the individual level. Preexposure identification of future soldiers at risk for posttraumatic stress disorder (PTSD) and other individuals, such as humanitarian aid workers and journalists intending to be potentially exposed to traumatic events, is important for guiding decisions about exposure. The purpose of the present study was to evaluate a machine learning approach to identify individuals at risk for PTSD using readily collected ecological risk factors, which makes scanning …

A Comprehensive Review Of Fruit And Vegetable Classification Techniques, Khurram Hameed, Douglas Chai, Alexander Rassau

Research outputs 2014 to 2021

Recent advancements in computer vision have enabled wide-ranging applications in every field of life. One such application area is fresh produce classification, but the classification of fruit and vegetable has proven to be a complex problem and needs to be further developed. Fruit and vegetable classification presents significant challenges due to interclass similarities and irregular intraclass characteristics. Selection of appropriate data acquisition sensors and feature representation approach is also crucial due to the huge diversity of the field. Fruit and vegetable classification methods have been developed for quality assessment and robotic harvesting but the current state-of-the-art has been developed for …

Old English Character Recognition Using Neural Networks, Sattajit Sutradhar

College of Graduate Studies: Theses & Dissertations

Character recognition has been capturing the interest of researchers since the beginning of the twentieth century. While the Optical Character Recognition for printed material is very robust and widespread nowadays, the recognition of handwritten materials lags behind. In our digital era more and more historical, handwritten documents are digitized and made available to the general public. However, these digital copies of handwritten materials lack the automatic content recognition feature of their printed materials counterparts. We are proposing a practical, accurate, and computationally efficient method for Old English character recognition from manuscript images. Our method relies on a modern machine learning …

On The Spatial Modelling Of Mixed And Constrained Geospatial Data, Hassan Talebi

Theses: Doctorates and Masters

Spatial uncertainty modelling and prediction of a set of regionalized dependent variables from various sample spaces (e.g. continuous and categorical) is a common challenge for geoscience modellers and many geoscience applications such as evaluation of mineral resources, characterization of oil reservoirs or hydrology of groundwater. To consider the complex statistical and spatial relationships, categorical data such as rock types, soil types, alteration units, and continental crustal blocks should be modelled jointly with other continuous attributes (e.g. porosity, permeability, seismic velocity, mineral and geochemical compositions or pollutant concentration). These multivariate geospatial data normally have complex statistical and spatial relationships which should …

Anatomy Of Online Hate: Developing A Taxonomy And Machine Learning Models For Identifying And Classifying Hate In Online News Media, Joni Salminen, Hind Almerekhi, Milica Milenkovic, Soon-Gyu Jung, Haewoon Kwak, Haewoon Kwak, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

Online social media platforms generally attempt to mitigate hateful expressions, as these comments can be detrimental to the health of the community. However, automatically identifying hateful comments can be challenging. We manually label 5,143 hateful expressions posted to YouTube and Facebook videos among a dataset of 137,098 comments from an online news media. We then create a granular taxonomy of different types and targets of online hate and train machine learning models to automatically detect and classify the hateful comments in the full dataset. Our contribution is twofold: 1) creating a granular taxonomy for hateful online comments that includes both …

Early Emerging Pathogen Detection, Mackenzie Wangenstein

Maseeh Summer Undergraduate Research Experience

A supervised learning technique was employed to identify emerging pathogen species. Portland State University has partnered with the University of New Mexico to take encodings of unknown pathogen molecular structures to determine emerging species.

Smart Augmented Reality Instructional System For Mechanical Assembly, Ze-Hao Lai

Masters Theses

"Quality and efficiency are pivotal indicators of a manufacturing company. Many companies are suffering from shortage of experienced workers across the production line to perform complex assembly tasks such as assembly of an aircraft engine. This could lead to a significant financial loss. In order to further reduce time and error in an assembly, a smart system consisting of multi-modal Augmented Reality (AR) instructions with the support of a deep learning network for tool detection is introduced. The multi-modal smart AR is designed to provide on-site information including various visual renderings with a fine-tuned Region-based Convolutional Neural Network, which is …

Deep Recurrent Learning For Efficient Image Recognition Using Small Data, Mahbubul Alam

Electrical & Computer Engineering Theses & Dissertations

Recognition is fundamental yet open and challenging problem in computer vision. Recognition involves the detection and interpretation of complex shapes of objects or persons from previous encounters or knowledge. Biological systems are considered as the most powerful, robust and generalized recognition models. The recent success of learning based mathematical models known as artificial neural networks, especially deep neural networks, have propelled researchers to utilize such architectures for developing bio-inspired computational recognition models. However, the computational complexity of these models increases proportionally to the challenges posed by the recognition problem, and more importantly, these models require a large amount of data …

Using Artificial Intelligence And Machine Learning To Develop Synthetic Well Logs, Marwan Mohammed Alnuaimi

Graduate Theses, Dissertations, and Problem Reports (ETD)

There has been an increase in the need for energy in the recent past. Oil and gas stand as the source of energy that are widely used. The oil and gas reservoirs are targeted for the purposes of field development. The conventional methods of reservoir characteristics require computing techniques that are unique and complex, some of which are labor and time intensive. Mohaghegh argues that all efforts must be tried and made possible to apply Petroleum Data analytics in production and management of reservoir so as to earn a maximum return (Mohaghegh, Shale Analytics, 2017). Different methodologies have been applied …

Looping Predictive Method To Improve Accuracy Of A Machine Learning Model, Subramanyam Reddy Pogili

Theses

The topic of this project is an analysis of drug-related tweets. The goal is to build a Machine Learning Model that can distinguish between tweets that indicate drug abuse and other tweets that also contain the name of a drug but do not describe abuse. Drugs can be illegal, such as heroin, or legal drugs with a potential of abuse, such as painkillers. However, building a good Machine Learning Model requires a large amount of training data. For each training tweet, a human expert has determined whether it indicates drug abuse or not. This is difficult work for humans. …

Visual Odometry Using Convolutional Neural Networks, Alec Graves, Steffen Lim, Thomas Fagan, Kevin Mcfall Phd.

The Kennesaw Journal of Undergraduate Research

Visual odometry is the process of tracking an agent's motion over time using a visual sensor. The visual odometry problem has only been recently solved using traditional, non-machine learning techniques. Despite the success of neural networks at many related problems such as object recognition, feature detection, and optical flow, visual odometry still has not been solved with a deep learning technique. This paper attempts to implement several Convolutional Neural Networks to solve the visual odometry problem and compare slight variations in data preprocessing. The work presented is a step toward reaching a legitimate neural network solution.

A Test Driven Approach To Develop Web-Based Machine Learning Applications, Armin Esmaeilzadeh

UNLV Theses, Dissertations, Professional Papers, and Capstones

The purpose of this thesis is to propose the design and architecture of a testable, scalable, and ef-cient web-based application that models and implements machine learning applications in cancer prediction. There are various components that form the architecture of our web-based application including server, database, programming language, web framework, and front-end design. There are also other factors associated with our application such as testability, scalability, performance, and design pattern. Our main focus in this thesis is on the testability of the system while consid- ering the importance of other factors as well.

The data set for our application is a …

Uncovering New Links Through Interaction Duration, Laxmi Amulya Gundala

Boise State University Theses and Dissertations

Link Prediction is the problem of inferring new relationships among nodes in a network that can occur in the near future. Classical approaches mainly consider neighborhood structure similarity when linking nodes. However, we may also want to take into account whether the two nodes we are going to link will benefit from that by having an active interaction over time. For instance, it is better to link two nodes � and � if we know that these two nodes will interact in the social network in the future, rather than suggesting �, who may never interact with �. Thus, the …

Machine Learning To Discover And Optimize Materials, Conrad Waldhar Rosenbrock

Theses and Dissertations

For centuries, scientists have dreamed of creating materials by design. Rather than discovery by accident, bespoke materials could be tailored to fulfill specific technological needs. Quantum theory and computational methods are essentially equal to the task, and computational power is the new bottleneck. Machine learning has the potential to solve that problem by approximating material behavior at multiple length scales. A full end-to-end solution must allow us to approximate the quantum mechanics, microstructure and engineering tasks well enough to be predictive in the real world. In this dissertation, I present algorithms and methodology to address some of these problems at …

Ethics And Bias In Machine Learning: A Technical Study Of What Makes Us “Good”, Ashley Nicole Shadowen

Student Theses

The topic of machine ethics is growing in recognition and energy, but bias in machine learning algorithms outpaces it to date. Bias is a complicated term with good and bad connotations in the field of algorithmic prediction making. Especially in circumstances with legal and ethical consequences, we must study the results of these machines to ensure fairness. This paper attempts to address ethics at the algorithmic level of autonomous machines. There is no one solution to solving machine bias, it depends on the context of the given system and the most reasonable way to avoid biased decisions while maintaining the …

A Machine Learning Approach To The Classification Of Phishing Bot Accounts Within Twitter, Christopher James Brake

The Plymouth Student Scientist

Social network bots are becoming an ever-greater threat to online users. Most studies carried out have looked at bots which generate a lot of tweets known as spam, as these are very common. In recent years research into the area of bots within Twitter has been carried out using machine learning to attempt to find patterns in these ac-counts to aide with detection. However, limited research has been carried out that focuses on a sub set of Twitter bots which are involved in phishing campaigns which tweet very little to avoid detection. In this project an application was developed that …

A Decision Tree Approach For The Musical Genres Classification, Glaucia M. Bressan, Beatriz C. F. De Azevedo, Elisangela Ap. S. Lizzi

Applied Mathematics & Information Sciences

The interest in the music classification has increased due to its wide applicability and discoveries obtained from researches. However, efficient methods for systemic organization of digital libraries are required, since users need to classify the available music files. When an automatic classification is desired, the extraction of input attributes and an efficient system, able to process them, are needed. In this context, the use of decision trees as a tool to predict musical genres classes allows the monitoring of the ramification, since nodes and branches of the tree can be accessed in this process. Decision tree is a technique very …

An Integrated Framework For Modeling And Predicting Spatiotemporal Phenomena In Urban Environments, Tuc Viet Le

Dissertations and Theses Collection (Open Access)

This thesis proposes a general solution framework that integrates methods in machine learning in creative ways to solve a diverse set of problems arising in urban environments. It particularly focuses on modeling spatiotemporal data for the purpose of predicting urban phenomena. Concretely, the framework is applied to solve three specific real-world problems: human mobility prediction, trac speed prediction and incident prediction. For human mobility prediction, I use visitor trajectories collected a large theme park in Singapore as a simplified microcosm of an urban area. A trajectory is an ordered sequence of attraction visits and corresponding timestamps produced by a visitor. …

Scalable Online Kernel Learning, Jing Lu

Dissertations and Theses Collection (Open Access)

One critical deficiency of traditional online kernel learning methods is their increasing and unbounded number of support vectors (SV’s), making them inefficient and non-scalable for large-scale applications. Recent studies on budget online learning have attempted to overcome this shortcoming by bounding the number of SV’s. Despite being extensively studied, budget algorithms usually suffer from several drawbacks.
First of all, although existing algorithms attempt to bound the number of SV’s at each iteration, most of them fail to bound the number of SV’s for the final averaged classifier, which is commonly used for online-to-batch conversion. To solve this problem, we propose …

Anomaly Detection For A Water Treatment System Using Unsupervised Machine Learning, Jun Inoue, Yoriyuki Yamagata, Yuqi Chen, Christopher M. Poskitt, Jun Sun

Research Collection School Of Computing and Information Systems

In this paper, we propose and evaluate the application of unsupervised machine learning to anomaly detection for a Cyber-Physical System (CPS). We compare two methods: Deep Neural Networks (DNN) adapted to time series data generated by a CPS, and one-class Support Vector Machines (SVM). These methods are evaluated against data from the Secure Water Treatment (SWaT) testbed, a scaled-down but fully operational raw water purification plant. For both methods, we first train detectors using a log generated by SWaT operating under normal conditions. Then, we evaluate the performance of both methods using a log generated by SWaT operating under 36 …

Fast On-Line Kernel Density Estimation For Active Object Localization, Anthony D. Rhodes, Max H. Quinn, Melanie Mitchell

Computer Science Faculty Publications and Presentations

A major goal of computer vision is to enable computers to interpret visual situations—abstract concepts (e.g., “a person walking a dog,” “a crowd waiting for a bus,” “a picnic”) whose image instantiations are linked more by their common spatial and semantic structure than by low-level visual similarity. In this paper, we propose a novel method for prior learning and active object localization for this kind of knowledge-driven search in static images. In our system, prior situation knowledge is captured by a set of flexible, kernel-based density estimations— a situation model—that represent the expected spatial structure of the given situation. These …

Predicting Adverse Outcomes In Chronic Kidney Disease Using Machine Learning Methods: Data From The Modification Of Diet In Renal Disease, Zeid Khitan, Anna P. Shapiro, Preeya T. Shah, Juan R. Sanabria, Prasanna Santhanam, Komal Sodhi, Nader G. Abraham, Joseph I. Shapiro

Marshall Journal of Medicine

Background: Understanding factors which predict progression of renal failure is of great interest to clinicians.

Objectives: We examined machine learning methods to predict the composite outcome of death, dialysis or doubling of serum creatinine using the modification of diet in renal disease (MDRD) data set.

Methods: We specifically evaluated a generalized linear model, a support vector machine, a decision tree, a feed-forward neural network and a random forest evaluated within the context of 10 fold validation using the CARET package available within the open source architecture R program.

Results: We found that using clinical parameters available at entry into the …

Evolution Of Bias In Human And Machine Learning Algorithm Interaction, Wenlong Sun, Olfa Nasraoui, Patrick Shafto

Commonwealth Computational Summit

Human algorithm interaction:

• people are now affected by the output of all types of machine learning algorithms.
• social media, blogs, social networks, and other services and applications.

Motivation:

• ML algorithm relied on reliable labels from experts to build prediction.
• However, ML algorithm started to receive data from the more general population.
• The interaction leads to biased result which is caused by ingesting unchecked information from general population, such as biased samples and biased labels.

Identification Of Reverse Engineering Candidates Utilizing Machine Learning And Aircraft Cannibalization Data, Marc Banghart

International Journal of Aviation, Aeronautics, and Aerospace

As military aircraft continue to remain in service and age, cannibalization of parts is increasing. Proactive identification of parts that are at high risk for cannibalization will inform engineering processes such as reverse engineering, thus allowing potentially reducing lead time to develop new parts. The research objective was to develop a causal structure that can be used for prediction of when cannibalization actions may occur. Bayesian networks allow encoding of causality between various descriptive features given a data set. The method utilized a tabu search algorithm, identified the underlying causal structure and the associated node probabilities. The method is then …