Digital Commons Network^™

Estimation Of Soil Moisture At Different Soil Levels Using Machine Learning Techniques And Unmanned Aerial Vehicle (Uav) Multispectral Imagery, Mahyar Aboutalebi, L. Niel Allen, Alfonso F. Torres-Rua, Mac Mckee, Calvin Coopmans

AggieAir Publications

Soil moisture is a key component of water balance models. Physically, it is a nonlinear function of parameters that are not easily measured spatially, such as soil texture and soil type. Thus, several studies have been conducted on the estimation of soil moisture using remotely sensed data and data mining techniques such as artificial neural networks (ANNs) and support vector machines (SVMs). However, all models developed based on these techniques are limited to site-specific applications where they are trained and their parameters are tuned. Moreover, since the system of non-linear equations produced by and conducted in the machine learning process …

Big Data: Learning, Analytics, And Applications, Nasir Safdari, Hussein Alrubaye, Wajdi Aljedaani, Bladimir Baez Baez, Andrew Distasi, Mohamed Wiem Mkaouer

Presentations and other scholarship

With the rise of autonomous systems, the automation of faults detection and localization becomes critical to their reliability. An automated strategy that can provide a ranked list of faulty modules or files with respect to how likely they contain the root cause of the problem would help in the automation bug localization. Learning from the history if previously located bugs in general, and extracting the dependencies between these bugs in particular, helps in building models to accurately localize any potentially detected bugs. In this study, we propose a novel fault localization solution based on a learning-to-rank strategy, using the history …

Identification And Analysis Of Behavioral Phenotypes In Autism Spectrum Disorder Via Unsupervised Machine Learning, Elizabeth Stevens, Dennis R. Dixon, Marlena N. Novack, Doreen Granpeesheh, Tristram Smith, Erik Linstead

Engineering Faculty Articles and Research

Background and objective: Autism spectrum disorder (ASD) is a heterogeneous disorder. Research has explored potential ASD subgroups with preliminary evidence supporting the existence of behaviorally and genetically distinct subgroups; however, research has yet to leverage machine learning to identify phenotypes on a scale large enough to robustly examine treatment response across such subgroups. The purpose of the present study was to apply Gaussian Mixture Models and Hierarchical Clustering to identify behavioral phenotypes of ASD and examine treatment response across the learned phenotypes.

Materials and methods: The present study included a sample of children with ASD (N = 2400), …

Depressiongnn: Depression Prediction Using Graph Neural Network On Smartphone And Wearable Sensors, Param Bidja

Honors Scholar Theses

Depression prediction is a complicated classification problem because depression diagnosis involves many different social, physical, and mental signals. Traditional classification algorithms can only reach an accuracy of no more than 70% given the complexities of depression. However, a novel approach using Graph Neural Networks (GNN) can be used to reach over 80% accuracy, if a graph can represent the depression data set to capture differentiating features. Building such a graph requires 1) the definition of node features, which must be highly correlated with depression, and 2) the definition for edge metrics, which must also be highly correlated with depression. In …

Explanation Methods For Neural Networks, Jack H. Chen, Christof Teuscher

Student Research Symposium

Neural Networks (NNs) have become a basis of almost all state-of-the-art machine learning algorithms and classifiers. While NNs have been shown to generalize well to real-world examples, researchers have struggled to show why they work on an intuitive level. We designed several methods to explain the decisions of two state-of-the-art NN classifiers, ResNet and an All-CNN, in the context of the Japanese Society of Radiological Technology (JSRT) lung nodule dataset and the CIFAR-10 image dataset. Leading explanation methods LIME and Grad-CAM generate variations of heat maps which represent the regions of the input determined salient by the NN. We analyze …

Reliable Explanations Via Adversarial Examples On Robust Networks, Walt Woods, Jack H. Chen, Christof Teuscher

Student Research Symposium

Neural Networks (NNs) are increasingly used as the basis of advanced machine learning techniques in sensitive fields such as autonomous vehicles and medical imaging. However, NNs have been found vulnerable to a class of imperceptible attacks, called adversarial examples, which arbitrarily alter the output of the network. To close the schism between needing reliability in real-world applications and the fragility of NNs, we propose a new method for stabilizing networks, and show that as an added bonus, our technique results in reliable, high-fidelity explanations for the NN's decision. Compared to the state-of-the-art, this technique increased the area under the curve …

Exploring And Expanding The One-Pixel Attack, Umairullah Khan, Walt Woods, Christof Teuscher

Student Research Symposium

In machine learning research, adversarial examples are normal inputs to a classifier that have been specifically perturbed to cause the model to misclassify the input. These perturbations rarely affect the human readability of an input, even though the model’s output is drastically different. Recent work has demonstrated that image-classifying deep neural networks (DNNs) can be reliably fooled with the modification of a single pixel in the input image, without knowledge of a DNN’s internal parameters. This “one-pixel attack” utilizes an iterative evolutionary optimizer known as differential evolution (DE) to find the most effective pixel to perturb, via the evaluation of …

Diagnostic Imaging Of Structural Concrete Using Ground Penetrating Radar And Ultrasonic Array, Sina Mehdinia, Thomas Schumacher, Eric Wan, Xubo Song

Student Research Symposium

Structural concrete is the most widely used construction material in the world. Many structures critical to a society such as bridges, hospitals, and airports are built with concrete. While this material is well understood from a mechanical design point of view, still no accurate quantitative tools exist to assess it for damage and deterioration. This is of particular concern for an urban area like Portland with a mega-thrust earthquake waiting to occur. Non-destructive evaluation tools that can quickly and accurately give a full picture of the integrity of structural concrete elements will be key to help plan effective and safe …

Machine Learning Predictions Of No-Show Appointments In A Primary Care Setting, Joseph Denney, Samuel Coyne, Sohail Rafiqi

SMU Data Science Review

No-show appointments are a significant financial and operational burden for the entire healthcare system. In primary care, the rate of no-show appointments ranges from 19% [1] to 42% [2]. The data set contains just over 988,000 unique encounters spanning 7 years’ worth of appointment, clinical, demographic, and financial data from a large rural Federally Qualified Health Center. Prediction of the probability of a patient missing a scheduled appointment in a primary health care center can be a significant advantage to operational and financial success for a primary care practice. Using the predictive data generated, in combination with targeted interventions, can …

Identifying Fake News Using Emotion Analysis, Brady Gilleran

Computer Science and Computer Engineering Undergraduate Honors Theses

This paper presents research applying Emotional Analysis to “Fake News” and “Real News” articles to investigate whether or not there is a difference in the emotion used in these two types of news articles. The paper reports on a dataset for Fake and Real News that we created, and the natural language processing techniques employed to process the collected text. We use a lexicon that includes predefined words for eight emotions (anger, anticipation, disgust, fear, surprise, sadness, joy, trust) to measure the emotional impact in each of these eight dimensions. The results of the emotion analysis are used as features …

The P-Art Framework For Placement Of Virtual Network Services In A Multi-Cloud Environment, Lav Gupta, Raj Jain, Aiman Erbad, Deval Bhamare

Computer Science Faculty Works

Carriers’ network services are distributed, dynamic, and investment intensive. Deploying them as virtual network services (VNS) brings the promise of low-cost agile deployments, which reduce time to market new services. If these virtual services are hosted dynamically over multiple clouds, greater flexibility in optimizing performance and cost can be achieved. On the flip side, when orchestrated over multiple clouds, the stringent performance norms for carrier services become difficult to meet, necessitating novel and innovative placement strategies. In selecting the appropriate combination of clouds for placement, it is important to look ahead and visualize the environment that will exist at the …

Deep Reinforcement Learning-Based Portfolio Management, Nitin Kanwar

Computer Science and Engineering Theses - Archive

Machine Learning is at the forefront of every field today. The subfields of Machine Learning called Reinforcement Learning and Deep Learning, when combined have given rise to advanced algorithms which have been successful at reaching or surpassing the human-level performance at playing Atari games to defeating multiple times champion at Go. These successes of Machine Learning have attracted the interest of the financial community and have raised the question if these techniques could also be applied in detecting patterns in the financial markets. Until recently, mathematical formulations of dynamical systems in the context of Signal Processing and Control Theory have …

From Body To Brain: Using Artificial Intelligence To Identify User Skills & Intentions In Interactive Scenarios, Michalis Papakostas

Computer Science and Engineering Dissertations - Archive

Artificial Intelligence has probably been the most rapidly evolving field of science during the last decade. Its numerous real-life applications have radically altered the way we experience daily-living with great impact in some of the most basic aspects of human lives including but not limited to health and well-being, communication and interaction, education, driving, daily, and entertainment. Human-Computer Interaction (HCI) is the field of Computer Science lying in the epicenter of this evolution and is responsible for transforming rudimentary research findings and theoretical principles into intuitive tools, responsible for enhancing human performance, increasing productivity and ensuring safety. Two of the …

Clustering Of Multiple Instance Data., Andrew D. Karem

Electronic Theses and Dissertations

An emergent area of research in machine learning that aims to develop tools to analyze data where objects have multiple representations is Multiple Instance Learning (MIL). In MIL, each object is represented by a bag that includes a collection of feature vectors called instances. A bag is positive if it contains at least one positive instance, and negative if no instances are positive. One of the main objectives in MIL is to identify a region in the instance feature space with high correlation to instances from positive bags and low correlation to instances from negative bags -- this region is …

Studying And Handling Iterated Algorithmic Biases In Human And Machine Learning Interaction., Wenlong Sun

Electronic Theses and Dissertations

Algorithmic bias consists of biased predictions born from ingesting unchecked information, such as biased samples and biased labels. Furthermore, the interaction between people and algorithms can exacerbate bias such that neither the human nor the algorithms receive unbiased data. Thus, algorithmic bias can be introduced not only before and after the machine learning process but sometimes also in the middle of the learning process. With a handful of exceptions, only a few categories of bias have been studied in Machine Learning, and there are few, if any, studies of the impact of bias on both human behavior and algorithm performance. …

Learning Representations Using Reinforcement Learning, Sourabh Bose

Computer Science and Engineering Dissertations - Archive

The framework of reinforcement learning is a powerful suite of algorithms that can learn generalized solutions to complex decision making problems. However, the applications of reinforcement learning algorithms to traditional machine learning problems such as clustering, classification and representation learning, have rarely been explored. With the advent of large amounts of data, robust models are required which can extract meaningful representations from the data that can potentially be applied to new unseen tasks. The presented work investigates the applications of reinforcement learning algorithms in the perspective of transfer learning by applying algorithms in the framework of reinforcement learning to address …

Seeing Eye To Eye: A Machine Learning Approach To Automated Saccade Analysis, Maigh Attre

Honors Scholar Theses

Abnormal ocular motility is a common manifestation of many underlying pathologies particularly those that are neurological. Dynamics of saccades, when the eye rapidly changes its point of fixation, have been characterized for many neurological disorders including concussions, traumatic brain injuries (TBI), and Parkinson’s disease. However, widespread saccade analysis for diagnostic and research purposes requires the recognition of certain eye movement parameters. Key information such as velocity and duration must be determined from data based on a wide set of patients’ characteristics that may range in eye shapes and iris, hair and skin pigmentation [36]. Previous work on saccade analysis has …

Watersheds For Semi-Supervised Classification, Aditya Challa, Sravan Danda, B. S.Daya Sagar, Laurent Najman

Journal Articles

Watershed technique from mathematical morphology (MM) is one of the most widely used operators for image segmentation. Recently watersheds are adapted to edge weighted graphs, allowing for wider applicability. However, a few questions remain to be answered - How do the boundaries of the watershed operator behave? Which loss function does the watershed operator optimize? How does watershed operator relate with existing ideas from machine learning. In this letter, a framework is developed, which allows one to answer these questions. This is achieved by generalizing the maximum margin principle to maximum margin partition and proposing a generic solution, morphMedian, resulting …

Distilling Managerial Insights And Lessons From Ai Projects At Singapore's Changi Airport (Part 2), Steve Lee, Steven M. Miller

Asian Management Insights

Since 2017, Changi Airport group (CAG) has initiated a host of pilot projects that use connective and intelligent technologies to enable its move towards digital transformation and SMART Airport Vision. This has resulted in a first wave of deployment of AI and Machine Learning-enabled applications across various functions that can better sense, analyse, predict, and interact with people.

Ai Gets Real At Singapore's Changi Airport (Part 1), Steve Lee, Steven M. Miller

Asian Management Insights

Ranked as the best airport for seven consecutive years, Singapore’s Changi Airport is lauded the world over for the efficient, safe, pleasurable and seamless service it offers the millions of passengers that pass through its facilities annually. Much of Changi Airport’s success can be attributed to the organisation’s customer-oriented business focus and deeply embedded culture of service excellence, combined with a host of advanced technologies operating invisibly in the background. The framework for this technology enablement is Changi Airport Group’s (CAG’s) SMART Airport Vision—an enterprise-wide approach to connective technologies that leverages sensors, data fusion, data analytics, and artificial intelligence (AI), …

Modeling And Counteracting Exposure Bias In Recommender Systems., Sami Khenissi

Electronic Theses and Dissertations

Recommender systems are becoming widely used in everyday life. They use machine learning algorithms which learn to predict our preferences and thus influence our choices among a staggering array of options online, such as movies, books, products, and even news articles. Thus what we discover and see online, and consequently our opinions and decisions, are becoming increasingly affected by automated predictions made by learning machines. Similarly, the predictive accuracy of these learning machines heavily depends on the feedback data, such as ratings and clicks, that we provide them. This mutual influence can lead to closed-loop interactions that may cause unknown …

Machine Learning Classification Of Primary Tissue Origin Of Cancer From Dna Methylation Markers, Sravani Gannavarapu Surya Naga

UNLV Theses, Dissertations, Professional Papers, and Capstones

Cancer is one of the leading causes of death globally and was responsible for approximately 9.6 million deaths in 2018. One of the main reason for deaths from cancer is late-stage presentation and inaccessible diagnosis and treatment. Cancer often spreads from the part of the body where it started (primary site) to a different part of the body (metastatic site). Identifying the primary site of cancer plays a key role as it directs the appropriate treatment. Cancer which spreads needs the same treatment as its origin. Having this knowledge can help doctors to decide the type of treatment.

All cancers …

Clustering Heterogeneous Autism Spectrum Disorder Data., Mariem Boujelbene

Electronic Theses and Dissertations

Autism spectrum disorder (ASD) is a developmental disorder that affects communication and behavior. Several studies have been conducted in the past years to develop a better understanding of the disease and therefore a better diagnosis and a better treatment by analyzing diverse data sets consisting of behavioral surveys and tests, phenotype description, and brain imagery. However, data analysis is challenged by the diversity, complexity and heterogeneity of patient cases and by the need for integrating diverse data sets to reach a better understanding of ASD. The aim of our study is to mine homogeneous groups of patients from a heterogeneous …

Detection And Classification Of Vibrating Objects In Sar Images, Francisco German Perez Venegas

Electrical and Computer Engineering ETDs

The vibratory response of buildings and machines contains key information that can be exploited to infer their operating conditions and to diagnose failures. Furthermore, since vibration signatures observed from the exterior surfaces of structures are intrinsically linked to the type of machinery operating inside of them, the ability to monitor vibrations remotely can enable the detection and identification of the machinery.

This dissertation focuses on developing novel techniques for the detection and M-ary classification of vibrating objects in SAR images. The work performed in this dissertation is conducted around three central claims. First, the non-linear transformation that the micro-Doppler return …

Improved Evolutionary Support Vector Machine Classifier For Coronary Artery Heart Disease Prediction Among Diabetic Patients, Narasimhan B, Malathi A Dr

Library Philosophy and Practice (e-journal)

Soft computing paves way many applications including medical informatics. Decision support system has gained a major attention that will aid medical practitioners to diagnose diseases. Diabetes mellitus is hereditary disease that might result in major heart disease. This research work aims to propose a soft computing mechanism named Improved Evolutionary Support Vector Machine classifier for CAHD risk prediction among diabetes patients. The attribute selection mechanism is attempted to build with the classifier in order to reduce the misclassification error rate of the conventional support vector machine classifier. Radial basis kernel function is employed in IESVM. IESVM classifier is evaluated through …

Deepreview: Automatic Code Review Using Deep Multi-Instance Learning, Hengyi Li, Shuting Shi, Ferdian Thung, Xuan Huo, Bowen Xu, Ming Li, David Lo

Research Collection School Of Computing and Information Systems

Code review, an inspection of code changes in order to identify and fix defects before integration, is essential in Software Quality Assurance (SQA). Code review is a time-consuming task since the reviewers need to understand, analysis and provide comments manually. To alleviate the burden of reviewers, automatic code review is needed. However, this task has not been well studied before. To bridge this research gap, in this paper, we formalize automatic code review as a multi-instance learning task that each change consisting of multiple hunks is regarded as a bag, and each hunk is described as an instance. We propose …

An Intelligent Smart City, Erik Sanchez

Architecture Senior Theses

How intelligent is the typical smart-city design approach? In an era when artificial intelligence and big data promise to improve urban life in unprecedented ways, are smart cities being imagined and designed in ways that are actually inspiring and truly innovative? This project examines the proposals and approaches of Sidewalk Labs’ designs for Quayside, located in Toronto Canada, and asks how intelligent, really, is the city the propose. Sidewalk Labs (the city-building subsidiary of Alphabet, Google’s parent company), in partnership with Waterfront Toronto (a government-appointed nonprofit development corporation), claims to be reimagining cities from the internet up.” But is their …

Synthesizing Cyber Intrusion Alerts Using Generative Adversarial Networks, Christopher R. Sweet

Theses

Cyber attacks infiltrating enterprise computer networks continue to grow in number, severity, and complexity as our reliance on such networks grows. Despite this, proactive cyber security remains an open challenge as cyber alert data is often not available for study.

Furthermore, the data that is available is stochastically distributed, imbalanced, lacks homogeneity, and relies on complex interactions with latent aspects of the network structure. Currently, there is no commonly accepted way to model and generate synthetic alert data for further study; there are also no metrics to quantify the fidelity of synthetically generated alerts or identify critical attributes within the …

Machine Learning Methods For Nanophotonic Design, Simulation, And Operation, Alec Michael Hammond

Theses and Dissertations

Interest in nanophotonics continues to grow as integrated optics provides an affordable platform for areas like telecommunications, quantum information processing, and biosensing. Designing and characterizing integrated photonics components and circuits, however, remains a major bottleneck. This is especially true when complex circuits or devices are required to study a particular phenomenon.To address this challenge, this work develops and experimentally validates a novel machine learning design framework for nanophotonic devices that is both practical and intuitive. As case studies, artificial neural networks are trained to model strip waveguides, integrated chirped Bragg gratings, and microring resonators using a small number of simple …

Training Set Density Estimation For Trajectory Predictions Using Artificial Neural Networks, Zachary Reinke

Masters Theses

Demand on earth orbiting surveillance systems in increasing as more equipment is put into orbit. These systems rely on predictive techniques to periodically track objects. The demand on these systems may be reduced if object trajectory data to develop scalable training sets used for training artificial neural networks (ANNs) to predict trajectories of a dynamic system. These methods use multi-variable statistics to analyze data energy content to provide the ANN with low density, feature-rich, training data. The developed techniques have been shown to increase ANN prediction accuracy while reducing the size of the training set when applied to a linear …