Deconvolution Tools For Extracting Insight From Challenging Two-Flavin Systems,
2020
University of Kentucky
Deconvolution Tools For Extracting Insight From Challenging Two-Flavin Systems, Dallas Michael Bell
Theses and Dissertations--Chemistry
Flavoproteins have long been explored for their ubiquity among a number of metabolic and energetic reactions. The flavin cofactor has the inherent benefit of distinct spectral changes associated with redox transitions; however, the double-edged sword is incurred as these distinct signatures overlap and take up much of the UV-vis spectral range. Therefore, it is crucial to create a method to demarcate the expressed redox transitions for studying these systems. The first portion of these studies discusses the creation of a program that deduces spectra for redox transitions in a single-flavin containing model protein: flavodoxin. The latter portions discuss the application …
A Study On Real-Time Database Technology And Its Applications,
2020
Eastern Illinois University
A Study On Real-Time Database Technology And Its Applications, Geethmi Nimantha Dissanayake
Masters Theses
No abstract provided.
Fusion-Net: Integration Of Dimension Reduction And Deep Learning Neural Network For Image Classification,
2020
Kennesaw State University
Fusion-Net: Integration Of Dimension Reduction And Deep Learning Neural Network For Image Classification, Mohammad Masum, Philippe Laval
Published and Grey Literature from PhD Candidates
Building a deep network using original digital images requires learning many parameters which may reduce the accuracy rates. The images can be compressed by using dimension reduction methods and extracted reduced features can be feeding into a deep network for classification. Hence, in the training phase of the network, the number of parameters will be decreased. Principal Component Analysis is a well-known dimension reduction technique that leverage orthogonal linear transformation of the original data. In this paper, we propose a neural network-based framework, named Fusion-Net, which implements PCA on an image dataset (CIFAR-10) and then a neural network applies on …
Invariance And Invertibility In Deep Neural Networks,
2020
Virginia Commonwealth University
Invariance And Invertibility In Deep Neural Networks, Han Zhang
Theses and Dissertations
Machine learning is concerned with computer systems that learn from data instead of being explicitly programmed to solve a particular task. One of the main approaches behind recent advances in machine learning involves neural networks with a large number of layers, often referred to as deep learning. In this dissertation, we study how to equip deep neural networks with two useful properties: invariance and invertibility. The first part of our work is focused on constructing neural networks that are invariant to certain transformations in the input, that is, some outputs of the network stay the same even if the input …
Experiments On The Neural Network Approach To The Handwritten Digit Classification Problem,
2020
Georgia Southern University
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 …
Mutual Information Decay Curves And Hyper-Parameter Grid Search Design For Recurrent Neural Architectures,
2020
Technological University Dublin
Mutual Information Decay Curves And Hyper-Parameter Grid Search Design For Recurrent Neural Architectures, Abhijit Mahalunkar, John Kelleher
Conference papers
We present an approach to design the grid searches for hyper-parameter optimization for recurrent neural architectures. The basis for this approach is the use of mutual information to analyze long distance dependencies (LDDs) within a dataset. We also report a set of experiments that demonstrate how using this approach, we obtain state-of-the-art results for DilatedRNNs across a range of benchmark datasets.
Multimodal Fusion Strategies For Outcome Prediction In Stroke,
2020
Technological University Dublin
Multimodal Fusion Strategies For Outcome Prediction In Stroke, Esra Zihni, John D. Kelleher, Vince I. Madai, Ahmed Khalil, Ivana Galinovic, Jochen Fiebach, Michelle Livne, Dietmar Frey
Conference papers
Data driven methods are increasingly being adopted in the medical domain for clinical predictive modeling. Prediction of stroke outcome using machine learning could provide a decision support system for physicians to assist them in patient-oriented diagnosis and treatment. While patient-specific clinical parameters play an important role in outcome prediction, a multimodal fusion approach that integrates neuroimaging with clinical data has the potential to improve accuracy. This paper addresses two research questions: (a) does multimodal fusion aid in the prediction of stroke outcome, and (b) what fusion strategy is more suitable for the task at hand. The baselines for our experimental …
Modelling Interleaved Activities Using Language Models,
2020
Technological University Dublin
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 …
Health Risks Of E-Cigarettes: Analysis Of Twitter Data Using Topic Mining,
2020
Slippery Rock University of Pennsylvania
Health Risks Of E-Cigarettes: Analysis Of Twitter Data Using Topic Mining, Abdullah Wahbeh, Mohammad A. Al-Ramahi, Omar El-Gayar, Tareq Nasralah
Computer Information Systems Faculty Publications (Archived)
The recent rise of e-cigarettes and vaping products has increased concerns that another young generation may become addicted to nicotine. Recently, it becomes evident that several health issues are related to the use of e-cigarettes and vaping products. The objective of this paper is to understand and identify such health issues by collecting and analyzing social media data. The analysis reflects the most important themes and topics discussed by online user’s about e-cigarettes, vaping, and associated health issues. Using topic modeling techniques, we were able to identify several health issues related to the use of e-cigarettes and vaping products. These …
Causal Discovery In Radiographic Markers Of Knee Osteoarthritis And Prediction For Knee Osteoarthritis Severity With Attention-Long Short-Term Memory,
2020
University of Texas Health Science Center at Houston, School of Health Information Sciences, Houston TX, USA
Causal Discovery In Radiographic Markers Of Knee Osteoarthritis And Prediction For Knee Osteoarthritis Severity With Attention-Long Short-Term Memory, Yanfei Wang, Lei You, Jacqueline Chyr, Lan Lan, Weiling Zhao, Yujia Zhou, Hua Xu, Philip Noble, Xiaobo Zhou
Faculty, Staff and Student Publications
The goal of this study is to build a prognostic model to predict the severity of radiographic knee osteoarthritis (KOA) and to identify long-term disease progression risk factors for early intervention and treatment. We designed a long short-term memory (LSTM) model with an attention mechanism to predict Kellgren/Lawrence (KL) grade for knee osteoarthritis patients. The attention scores reveal a time-associated impact of different variables on KL grades. We also employed a fast causal inference (FCI) algorithm to estimate the causal relation of key variables, which will aid in clinical interpretability. Based on the clinical information of current visits, we accurately …
Continuous Deployment Transitions At Scale,
2020
North Carolina State University
Continuous Deployment Transitions At Scale, Laurie Williams, Kent Beck, Jeffrey Creasey, Andrew Glover, James Holman, Jez Humble, David Mclaughlin, John Thomas Micco, Brendan Murphy, Jason A. Cox, Vishnu Pendyala, Steven Place, Zachary T. Pritchard, Chuck Rossi, Tony Savor, Michael Stumm, Chris Parnin
Faculty Research, Scholarly, and Creative Activity
Predictable, rapid, and data-driven feature rollout; lightning-fast; and automated fix deployment are some of the benefits most large software organizations worldwide are striving for. In the process, they are transitioning toward the use of continuous deployment practices. Continuous deployment enables companies to make hundreds or thousands of software changes to live computing infrastructure every day while maintaining service to millions of customers. Such ultra-fast changes create a new reality in software development. Over the past four years, the Continuous Deployment Summit, hosted at Facebook, Netflix, Google, and Twitter has been held. Representatives from companies like Cisco, Facebook, Google, IBM, Microsoft, …
Evolution Of Integration, Build, Test, And Release Engineering Into Devops And To Devsecops,
2020
San Jose State University
Evolution Of Integration, Build, Test, And Release Engineering Into Devops And To Devsecops, Vishnu Pendyala
Faculty Research, Scholarly, and Creative Activity
Software engineering operations in large organizations are primarily comprised of integrating code from multiple branches, building, testing the build, and releasing it. Agile and related methodologies accelerated the software development activities. Realizing the importance of the development and operations teams working closely with each other, the set of practices that automated the engineering processes of software development evolved into DevOps, signifying the close collaboration of both development and operations teams. With the advent of cloud computing and the opening up of firewalls, the security aspects of software started moving into the applications leading to DevSecOps. This chapter traces the journey …
Sediment Dynamics In The Magdalena River Basin, Colombia: Implications For Understanding Tropical River Processes And Hydropower Development,
2020
The University Of Montana
Sediment Dynamics In The Magdalena River Basin, Colombia: Implications For Understanding Tropical River Processes And Hydropower Development, Luke H. Fisher
Graduate Student Theses, Dissertations, & Professional Papers
The Magdalena River Basin of Colombia has a globally relevant sediment flux, however, studies of the sediment regime in the basin are limited in scope. This knowledge gap limits application of understanding of sediment dynamics to hydropower decision making. To close this gap, we implemented a sediment budget framework to quantify the impacts of hydropower development in a 118,000 km2 portion of the Magdalena River basin. We informed this framework with analysis of background erosion rates derived from 10Be cosmogenic nuclides and modern sediment fluxes derived from monitoring and optical remote sensing. We standardized these data to spatially …
Modeling Twitter Sentiment As A Function Of Particulate Matter 2.5 For Communities Impacted By Wildfire Across Montana And Idaho,
2020
University of Montana
Modeling Twitter Sentiment As A Function Of Particulate Matter 2.5 For Communities Impacted By Wildfire Across Montana And Idaho, Matthew Kelly
Graduate Student Theses, Dissertations, & Professional Papers
Fine particulate matter (PM2.5) is a known pollutant with clinically detrimental physiological and behavioral effects. We consider Twitter sentiment as a potential indicator for well-being in communities impacted by wildfire-associated PM2.5 across Montana and Idaho spanning 5 years (2014-2018). From these geospatial air quality data and geo-tagged tweets, we trained county level models to examine the power of Twitter sentiment as a function of PM2.5. For all 24 counties sampled, we found between 1 and 8 affective dimensions where a positive �� 2 was detected with a significant F-statistic (�� < 0.05). Specifically, we show that sentiment for anticipation in the wildfire-prone county of Missoula, MT yielded respective training/test set �� 2 of 0.0958 and 0.0686 with a p-value for the F-statistic of 3.09E-07. These analyses support social media sentiment as a potential public health metric by showing one of the first observations of a relationship between PM2.5 and Twitter sentiment.
Health-Aware Food Planner: A Personalized Recipe Generation Approach Based On Gpt-2,
2020
Wilfrid Laurier University
Health-Aware Food Planner: A Personalized Recipe Generation Approach Based On Gpt-2, Bushra Aljbawi
Theses and Dissertations (Comprehensive)
"What to eat today?" With the flourish of Internet, more and more people nowadays are inclined to find an answer to this most problematic question online. The recent explosion of food networks; however, produces large volumes of recipes, making it even harder to make an informed decision. This yields the need for advanced decision-making algorithms and efficient recommendation systems. Conventional recommender systems are not feasible anymore as food is a complicated feature that presents unique challenges and is less studied. For example, it can be one of the main reasons for obesity and many other chronic diseases. Food recommender system …
Digilego For Peripartum Depression: A Novel Patient-Facing Digital Health Instantiation,
2020
University of Texas Health Science Center at Houston, School of Health Information Sciences, Houston TX, USA
Digilego For Peripartum Depression: A Novel Patient-Facing Digital Health Instantiation, J Rodin, C Timko, S Harris
Faculty, Staff and Student Publications
Digital health technologies offer unique opportunities to improve health outcomes for mental health conditions such as peripartum depression (PPD), a disorder that affects approximately 10-15% of women in the U.S. every year. In this paper, we present the adaption of a digital technology development framework, Digilego, in the context of PPD. Methods include mapping of the Behavior Intervention Technology (BIT) model and the Patient Engagement Framework (PEF) to translate patient needs captured through focus groups. This informs formative development and implementation of digital health features for optimal patient engagement in PPD screening and management. Results show an array ofPPD-specific Digilego …
Interpreting Health Events In Big Data Using Qualitative Traditions,
2020
Edith Cowan University
Interpreting Health Events In Big Data Using Qualitative Traditions, Roschelle L. Fritz, Gordana Dermody
Research outputs 2014 to 2021
© The Author(s) 2020. The training of artificial intelligence requires integrating real-world context and mathematical computations. To achieve efficacious smart health artificial intelligence, contextual clinical knowledge serving as ground truth is required. Qualitative methods are well-suited to lend consistent and valid ground truth. In this methods article, we illustrate the use of qualitative descriptive methods for providing ground truth when training an intelligent agent to detect Restless Leg Syndrome. We show how one interdisciplinary, inter-methodological research team used both sensor-based data and the participant’s description of their experience with an episode of Restless Leg Syndrome for training the intelligent agent. …
Cooperative Co-Evolution For Feature Selection In Big Data With Random Feature Grouping,
2020
Edith Cowan University
Cooperative Co-Evolution For Feature Selection In Big Data With Random Feature Grouping, A.N.M. Bazlur Rashid, Mohiuddin Ahmed, Leslie F. Sikos, Paul Haskell-Dowland
Research outputs 2014 to 2021
© 2020, The Author(s). A massive amount of data is generated with the evolution of modern technologies. This high-throughput data generation results in Big Data, which consist of many features (attributes). However, irrelevant features may degrade the classification performance of machine learning (ML) algorithms. Feature selection (FS) is a technique used to select a subset of relevant features that represent the dataset. Evolutionary algorithms (EAs) are widely used search strategies in this domain. A variant of EAs, called cooperative co-evolution (CC), which uses a divide-and-conquer approach, is a good choice for optimization problems. The existing solutions have poor performance because …
Correction To: Cooperative Co‑Evolution For Feature Selection In Big Data With Random Feature Grouping (Journal Of Big Data, (2020), 7, 1, (107), 10.1186/S40537-020-00381-Y),
2020
Edith Cowan University
Correction To: Cooperative Co‑Evolution For Feature Selection In Big Data With Random Feature Grouping (Journal Of Big Data, (2020), 7, 1, (107), 10.1186/S40537-020-00381-Y), A. N.M.Bazlur Rashid, Mohiuddin Ahmed, Leslie F. Sikos, Paul Haskell‑Dowland
Research outputs 2014 to 2021
© 2020, The Author(s). Following publication of the original article [1], the author reported that the 2nd author affiliation was incorrect. It should only be “School of Science, Edith Cowan University, Joondalup, WA, Australia”. The affiliation is presented correctly in this correction article. The original article [1] has been corrected.
Topik Modeling Penelitian Dosen Jptei Uny Pada Google Scholar Menggunakan Latent Dirichlet Allocation,
2019
Universitas Negeri Yogyakarta, Indonesia
Topik Modeling Penelitian Dosen Jptei Uny Pada Google Scholar Menggunakan Latent Dirichlet Allocation, Akhsin Nurlayli, Moch. Ari Nasichuddin
Elinvo (Electronics, Informatics, and Vocational Education)
The mapping of research topics for lecturers is necessary to determine the research tendencies in a department or study program. This study aims to implement topic modeling in the publication titles of the Department of Electronics and Informatics Education Engineering of Universitas Negeri Yogyakarta (JPTEI UNY) lecturers taken from Google Scholar. The method used for topic modeling is the Latent Dirichlet Allocation (LDA). LDA is a generative probabilistic model for finding the semantic structure of a corpus collection based on the hierarchical bayesian analysis. After the topic modeling process, the results showed that JPTEI UNY lecturers tend to have …
