Digital Commons Network^™

Towards Personalized Medicine: Computational Approaches For Drug Repurposing And Cell Type Identification, Azam Peyvandipour

Wayne State University Dissertations

The traditional drug discovery process is extremely slow and costly. More than 90% of drugs fail to pass beyond the early stage of development and toxicity tests, and many of the drugs that go through early phases of the clinical trials fail because of adverse reactions, side effects, or lack of efficiency. In spite of unprecedented investments in research and development (R&D), the number of new FDA-approved drugs remains low, reflecting the limitations of the current R&D model.

In this context, finding new disease indications for existing drugs sidesteps these issues and can therefore increase the available therapeutic choices at …

Bioinformatic Extraction Of Functional Genetic Diversity From Heterogeneous Germplasm Collections For Crop Improvement, Patrick A. Reeves, Hannah M. Tetreault, Christopher M. Richards

United States Department of Agriculture-Agricultural Research Service / University of Nebraska-Lincoln: Faculty Publications

Efficient utilization of genetic variation in plant germplasm collections is impeded by large collection size, uneven characterization of traits, and unpredictable apportionment of allelic diversity among heterogeneous accessions. Distributing compact subsets of the complete collection that contain maximum allelic diversity at functional loci of interest could streamline conventional and precision breeding. Using heterogeneous population samples from Arabidopsis, Populus and sorghum, we show that genomewide single nucleotide polymorphism (SNP) data permits the capture of 3–78 fold more haplotypic diversity in subsets than geographic or environmental data, which are commonly used surrogate predictors of genetic diversity. Using a large genomewide SNP data …

Anthrax Event Detection: Analysis Of Public Opinion Using Twitter During Anthrax Scares, The Mueller Investigation, And North Korean Threats, Michele E. Miller

Browse all Theses and Dissertations

When people allow fear to drive their decision making, they often make decisions that do more harm than good. Examples of this include stocking up on ciprofloxacin, flooding doctors’ offices and buying black market antibiotics after the anthrax attacks of 2001. Therefore, it is important to be able to address what people are saying when another anthrax attack occurs. Supervised and unsupervised machine learning methodologies can be utilized to detect an event, classify the tweets by event, and to determine the main topics of discussion. Over the period of data collection, twenty events were detected. Three of these events concerned …

Online Clustering With Bayesian Nonparametrics, Matthew D. Scherreik

Browse all Theses and Dissertations

Clustering algorithms, such as Gaussian mixture models and K-means, often require the number of clusters to be specified a priori. Bayesian nonparametric (BNP) methods avoid this problem by specifying a prior distribution over the cluster assignments that allows the number of clusters to be inferred from the data. This can be especially useful for online clustering tasks, where data arrives in a continuous stream and the number of clusters may dynamically change over time. Classical BNP priors often overestimate the number of clusters, however, leading researchers to develop new priors with more control over this tendency. To date, BNP algorithms …

Extracting Useful Information And Building Predictive Models From Medical And Health-Care Data Using Machine Learning Techniques, Md Faisal Kabir

Computer Science

In healthcare, a large number of medical data has emerged. To effectively use these data to improve healthcare outcomes, clinicians need to identify the relevant measures and apply the correct analysis methods for the type of data at hand. In this dissertation, we present various machine learning (ML) and data mining (DM) methods that could be applied to the type of data sets that are available in the healthcare area. The first part of the dissertation investigates DM methods on healthcare or medical data to find significant information in the form of rules. Class association rule mining, a variant of …

Soybean Leaf Chlorophyll Estimation And Iron Deficiency Field Rating Determination At Plot And Field Scales Through Image Processing And Machine Learning, Oveis Hassanijalilian

Agribusiness and Biosystems Engineering

Iron deficiency chlorosis (IDC) is the most common reason for chlorosis in soybean (Glycine max (L.) Merrill) and causes an average yield loss of 120 million dollars per year across 1.8 million ha in the North Central US alone. As the most effective way to avoid IDC is the use of tolerant cultivars, they are visually rated for IDC by experts; however, this method is subjective and not feasible for a larger scale. An alternate more objective image processing method can be implemented in various platforms and fields. This approach relies on a color vegetation index (CVI) that can quantify …

Ai Report: Humanity Is Doomed. Send Lawyers, Guns, And Money!, Ashley M. London

Law Faculty Publications

AI systems are powerful technologies being built and implemented by private corporations motivated by profit, not altruism. Change makers, such as attorneys and law students, must therefore be educated on the benefits, detriments, and pitfalls of the rapid spread, and often secret implementation of this technology. The implementation is secret because private corporations place proprietary AI systems inside of black boxes to conceal what is inside. If they did not, the popular myth that AI systems are unbiased machines crunching inherently objective data would be revealed as a falsehood. Algorithms created to run AI systems reflect the inherent human categorization …

Coproid Predicts The Source Of Coprolites And Paleofeces Using Microbiome Composition And Host Dna Content, Maxime Borry, Bryan Cordova, Angela Perri, Marsha Wibowo, Tanvi Prasad Honap, Jada Ko, Kate Britton, Linus Girdland-Flink, Robert C. Power, Ingelise Stuijts, Domingo C. Salazar-García, Courtney Hofman, Richard Hagan, Thérèse Samdapawindé Kagoné, Nicolas Meda, Helene Carabin, David Jacobson, Karl Reinhard, Cecil Lewis, Aleksandar Kostic, Choongwon Jeong, Alexander Herbig, Alexander Hübner, Christina Warinner

Karl Reinhard Publications

Shotgun metagenomics applied to archaeological feces (paleofeces) can bring new insights into the composition and functions of human and animal gut microbiota from the past. However, paleofeces often undergo physical distortions in archaeological sediments, making their source species difficult to identify on the basis of fecal morphology or microscopic features alone. Here we present a reproducible and scalable pipeline using both host and microbial DNA to infer the host source of fecal material. We apply this pipeline to newly sequenced archaeological specimens and show that we are able to distinguish morphologically similar human and canine paleofeces, as well as non-fecal …

Molecular Social Reality, The Cultural Helix & Sequencing Social Dna, Aidan Christopher Haughey

University Honors Theses

This thesis provides a theory for measuring Social Reality, along with a framework and model which apply that theory to measure presented social reality in media. The framework and model utilize the double helical structure of DNA to create a baseline unit of social reality that can be extracted from media to help measure and categorize social realities. An experiment is provided utilizing a custom built machine learning algorithm to demonstrate the practical application of the theory, framework, and model.

Machine Learning And The New Civil Procedure, Zoe Niesel

Faculty Articles

There is an increasing emphasis in the legal academy, the media, and the popular consciousness on how artificial intelligence and machine learning will change the foundations of legal practice. In concert with these discussions, a critical question needs to be explored-As computer programming learns to adjust itself without explicit human involvement, does machine learning impact the procedural practice of law? Civil procedure, while sensitive to technology, has been slow to adapt to change. As such, this Article will explore the impact that machine learning will have on procedural jurisprudence in two significant areas-service of process and personal jurisdiction.

The Article …

Sparsity And Weak Supervision In Quantum Machine Learning, Seyran Saeedi

Theses and Dissertations

Quantum computing is an interdisciplinary field at the intersection of computer science, mathematics, and physics that studies information processing tasks on a quantum computer. A quantum computer is a device whose operations are governed by the laws of quantum mechanics. As building quantum computers is nearing the era of commercialization and quantum supremacy, it is essential to think of potential applications that we might benefit from. Among many applications of quantum computation, one of the emerging fields is quantum machine learning. We focus on predictive models for binary classification and variants of Support Vector Machines that we expect to be …

Feature Extraction For Classification Of Auroral Images, Shwetha Herga

Theses

Auroras are a dynamically evolving phenomenon. Different auroral forms are correlated with various physical processes in the magnetosphere and ionosphere system. Millions of auroral images are captured every year by the modern ground-based All-Sky Imager(ASI). In dealing with data from ASI, machine learning techniques play a critical scientific role, facilitating both efficient searches and statistical studies. In this work, we manually label night-side auroral images from various Time History of Events and Macroscale Interactions during Substorms (THEMIS) all-sky imager based on the sky conditions; the labels are clear sky with auroras, cloudy with the moon, cloudy, clear-sky with the moon, …

Sketic: A Machine Learning-Based Digital Circuit Recognition Platform, Mohamamd Abdel Majeed, Tasneem Almousa, Maysaa Alsalman, Abeer Yosef

Turkish Journal of Electrical Engineering and Computer Sciences

In digital system design, digital logic circuit diagrams are built using interconnects and symbolic representations of the basic logic gates. Constructing such diagrams using free sketches is the first step in the design process. After that the circuit schematic or code has to be generated before being able to simulate the design. While most of the mentioned steps are automated using design automation tools, drafting the schematic circuit and then converting it into a valid format that can be simulated are still done manually due to the lack of robust tools that can recognize the free sketches and incorporate them …

Exploring The Parameter Space Of Human Activity Recognition With Mobile Devices, Berrenur Saylam, Muhammad Shoaib, Özlem Durmaz İncel

Turkish Journal of Electrical Engineering and Computer Sciences

Motion sensors available on smart phones make it possible to recognize human activities. Accelerometer, gyroscope, magnetometer, and their various combinations are used to classify, particularly, locomotion activities, ranging from walking to biking. In most of the studies, the focus is on the collection of data and on the analysis of the impact of different parameters on the recognition performance. The parameter space includes the types of sensors used, features, classification algorithms, and position/orientation of the mobile device. In most of the studies, the impact of some of these parameters is partially analyzed; however, in this work, we investigate the parameter …

Computer Vision Gesture Recognition For Rock Paper Scissors, Nicholas Hunter

Senior Independent Study Theses

This project implements a human versus computer game of rock-paper-scissors using machine learning and computer vision. Player’s hand gestures are detected using single images with the YOLOv3 object detection system. This provides a generalized detection method which can recognize player moves without the need for a special background or lighting setup. Additionally, past moves are examined in context to predict the most probable next move of the system’s opponent. In this way, the system achieves higher win rates against human opponents than by using a purely random strategy.

Accounting For Training Data Error In Machine Learning Applied To Earth Observations, Arthur Elmes, Hamed Alemohammad, Ryan Avery, Kelly Caylor, J. Ronald Eastman, Lewis Fishgold, Mark A. Friedl, Meha Jain, Divyani Kohli, Juan Carlos Laso Bayas, Dalton Lunga, Jessica L. Mccarty, Robert Gilmore Pontius, Andrew B. Reinmann, John Rogan, Lei Song, Hristiana Stoynova, Su Ye, Zhuang Fang Yi, Lyndon Estes

Geography

Remote sensing, or Earth Observation (EO), is increasingly used to understand Earth system dynamics and create continuous and categorical maps of biophysical properties and land cover, especially based on recent advances in machine learning (ML). ML models typically require large, spatially explicit training datasets to make accurate predictions. Training data (TD) are typically generated by digitizing polygons on high spatial-resolution imagery, by collecting in situ data, or by using pre-existing datasets. TD are often assumed to accurately represent the truth, but in practice almost always have error, stemming from (1) sample design, and (2) sample collection errors. The latter is …

Experiments On The Neural Network Approach To The Handwritten Digit Classification Problem, William Meissner

College of Graduate Studies: Theses & Dissertations

When the MNIST dataset was introduced in 1998, training a network was a multiple week problem in order to receive results far less accurate than an average CPU can produce within a couple of hours today. While this indicates that training a network on such a dataset is not the complicated problem it may have been twenty years ago, the MNIST dataset makes a good tool for study and testing with beginner and medium complexity neural networks. This paper follows along with the work presented in the online textbook “Neural Networks and Deep Learning” by Michael Nielson and an updated …

Assessing Methods And Tools To Improve Reporting, Increase Transparency, And Reduce Failures In Machine Learning Applications In Healthcare, Christian Garbin

Electronic Theses and Dissertations 2020 - Present

Artificial intelligence (AI) had a few false starts – the AI winters of the 1970s and 1980s. We are now in what looks like an AI summer. There are many useful applications of AI in the field. But there are still unfulfilled promises and outright failures. From self-driving cars that work only in constrained cases, to medical image analysis products that would replace radiologists but never did, we still struggle to translate successful research into successful real-world applications. The software engineering community has accumulated a large body of knowledge over the decades on how to develop, release, and maintain products. …

Combining Traditional And Image Analysis Techniques For Unconsolidated Exposed Terrigenous Beach Sand Characterization, Molly Elizabeth Smith

Electronic Theses and Dissertations 2020 - Present

Traditional sand analysis is labor and cost-intensive, entailing specialized equipment and operators trained in geological analysis. Even a small step to automate part of the traditional geological methods could substantially improve the speed of such research while removing chances of human error. Digital image analysis techniques and computer vision have been well developed and applied in various fields but rarely explored for sand analysis. This research explores capabilities of remote sensing digital image analysis techniques, such as object-based image analysis (OBIA), machine learning, digital image analysis, and photogrammetry to automate or semi-automate the traditional sand analysis procedure. Here presented is …

Modeling Ground Elevation Of Louisiana Coastal Wetlands And Analyzing Relative Sea Level Rise Inundation Using Rset-Mh And Lidar Measurements, Jing Liu

Electronic Theses and Dissertations 2020 - Present

The Louisiana coastal ecosystem is experiencing increasing threats from human flood control construction, sea-level rise (SLR), and subsidence. Louisiana lost about 4,833 km2 of coastal wetlands from 1932 to 2016, and concern exists whether remaining wetlands will persist while facing the highest rate of relative sea-level rise (RSLR) in the world. Restoration aimed at rehabilitating the ongoing and future disturbances is currently underway through the implementation of the Coastal Wetlands Planning Protection and Restoration Act of 1990 (CWPPRA). To effectively monitor the progress of projects in CWPPRA, the Coastwide Reference Monitoring System (CRMS) was established in 2006. To date, more …

Multifaceted Embedding Learning For Networked Data And Systems, Min Shi

Electronic Theses and Dissertations 2020 - Present

Network embedding or representation learning is important for analyzing many real-world applications and systems, i.e., social networks, citation networks and communication networks. It targets at learning low-dimensional vector representations of nodes with preserved graph structure (e.g., link relations) and content (e.g., texts) information. The derived node representations can be directly applied in many downstream applications, including node classification, clustering and visualization. In addition to the complex network structures, nodes may have rich non structure information such as labels and contents. Therefore, structure, label and content constitute different aspects of the entire network system that reflect node similarities from multiple complementary …

A Probabilistic Machine Learning Framework For Cloud Resource Selection On The Cloud, Syeduzzaman Khan

University of the Pacific Theses and Dissertations

The execution of the scientific applications on the Cloud comes with great flexibility, scalability, cost-effectiveness, and substantial computing power. Market-leading Cloud service providers such as Amazon Web service (AWS), Azure, Google Cloud Platform (GCP) offer various general purposes, memory-intensive, and compute-intensive Cloud instances for the execution of scientific applications. The scientific community, especially small research institutions and undergraduate universities, face many hurdles while conducting high-performance computing research in the absence of large dedicated clusters. The Cloud provides a lucrative alternative to dedicated clusters, however a wide range of Cloud computing choices makes the instance selection for the end-users. This thesis …

Can Algorithms Promote Fair Use?, Peter K. Yu

FIU Law Review

No abstract provided.

Introduction: Intelligent Entertainment: Shaping Policies On The Algorithmic Generation And Regulation Of Creative Works, Hannibal Travis

FIU Law Review

No abstract provided.

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 …

Empowering Qualitative Research Methods In Education With Artificial Intelligence, Luca Longo

Conference papers

Artificial Intelligence is one of the fastest growing disciplines, disrupting many sectors. Originally mainly for computer scientists and engineers, it has been expanding its horizons and empowering many other disciplines contributing to the development of many novel applications in many sectors. These include medicine and health care, business and finance, psychology and neuroscience, physics and biology to mention a few. However, one of the disciplines in which artificial intelligence has not been fully explored and exploited yet is education. In this discipline, many research methods are employed by scholars, lecturers and practitioners to investigate the impact of different instructional approaches …

Use Of Machine Learning To Predict Ethical Drift In Law Enforcement, Ryan Mann

Walden Dissertations and Doctoral Studies

U.S. law enforcement agencies are facing a legitimacy crisis. Incidents of police misconduct are the subject of widespread media coverage. Officer conduct continues to be a problem despite effectiveness of candidate screening. Underlying causes of ethical drift must be understood to reduce police misconduct. The purpose of this nonexperimental quantitative study was to examine the relationship between police ethical drift and agency size, officer age, officer gender, and officer education level. Ethical drift was the conceptual framework. Archival secondary data from local law enforcement agencies and the Florida Department of Law Enforcement Criminal Justice Standards and Training Commission were obtained …

Applying Artificial Intelligence To Medical Data, Shaikh Shiam Rahman

College of Graduate Studies: Theses & Dissertations

Machine learning, data mining, and deep learning has become the methodology of choice for analyzing medical data and images. In this study, we implemented three different machine learning techniques to medical data and image analysis. Our first study was to implement different log base entropy for a decision tree algorithm. Our results suggested that using a higher log base for the dataset with mostly categorical attributes with three or more categories for each attribute can obtain a higher accuracy. For the second study, we analyzed mental health data tuning the parameters of the decision tree (splitting method, depth and entropy). …

Analyze Informant-Based Questionnaire For The Early Diagnosis Of Senile Dementia Using Deep Learning, Fubao Zhu, Xiaonan Li, Daniel Mcgonigle, Haipeng Tang, Zhuo He, Chaoyang Zhang, Guang-Uei Hung, Pai-Yi Chiu, Weihua Zhou

Michigan Tech Publications, Part 1

OBJECTIVE: This paper proposes a multiclass deep learning method for the classification of dementia using an informant-based questionnaire.

METHODS: A deep neural network classification model based on Keras framework is proposed in this paper. To evaluate the advantages of our proposed method, we compared the performance of our model with industry-standard machine learning approaches. We enrolled 6,701 individuals, which were randomly divided into training data sets (6030 participants) and test data sets (671 participants). We evaluated each diagnostic model in the test set using accuracy, precision, recall, and F1-Score.

RESULTS: Compared with the seven conventional machine learning algorithms, the DNN …