Data Science Commons^™

Big Data With Cloud Computing: Discussions And Challenges, Amanpreet Kaur Sandhu

Big Data Mining and Analytics

With the recent advancements in computer technologies, the amount of data available is increasing day by day. However, excessive amounts of data create great challenges for users. Meanwhile, cloud computing services provide a powerful environment to store large volumes of data. They eliminate various requirements, such as dedicated space and maintenance of expensive computer hardware and software. Handling big data is a time-consuming task that requires large computational clusters to ensure successful data storage and processing. In this work, the definition, classification, and characteristics of big data are discussed, along with various cloud services, such as Microsoft Azure, Google Cloud, …

Exploiting More Associations Between Slots For Multi-Domain Dialog State Tracking, Hui Bai, Yan Yang, Jie Wang

Big Data Mining and Analytics

Dialog State Tracking (DST) aims to extract the current state from the conversation and plays an important role in dialog systems. Existing methods usually predict the value of each slot independently and do not consider the correlations among slots, which will exacerbate the data sparsity problem because of the increased number of candidate values. In this paper, we propose a multi-domain DST model that integrates slot-relevant information. In particular, certain connections may exist among slots in different domains, and their corresponding values can be obtained through explicit or implicit reasoning. Therefore, we use the graph adjacency matrix to determine the …

Sampling With Prior Knowledge For High-Dimensional Gravitational Wave Data Analysis, He Wang, Zhoujian Cao, Yue Zhou, Zong-Kuan Guo, Zhixiang Ren

Big Data Mining and Analytics

Extracting knowledge from high-dimensional data has been notoriously difficult, primarily due to the so-called "curse of dimensionality" and the complex joint distributions of these dimensions. This is a particularly profound issue for high-dimensional gravitational wave data analysis where one requires to conduct Bayesian inference and estimate joint posterior distributions. In this study, we incorporate prior physical knowledge by sampling from desired interim distributions to develop the training dataset. Accordingly, the more relevant regions of the high-dimensional feature space are covered by additional data points, such that the model can learn the subtle but important details. We adapt the normalizing flow …

Toward Intelligent Financial Advisors For Identifying Potential Clients: A Multitask Perspective, Qixiang Shao, Runlong Yu, Hongke Zhao, Chunli Liu, Mengyi Zhang, Hongmei Song, Qi Liu

Big Data Mining and Analytics

Intelligent Financial Advisors (IFAs) in online financial applications (apps) have brought new life to personal investment by providing appropriate and high-quality portfolios for users. In real-world scenarios, identifying potential clients is a crucial issue for IFAs, i.e., identifying users who are willing to purchase the portfolios. Thus, extracting useful information from various characteristics of users and further predicting their purchase inclination are urgent. However, two critical problems encountered in real practice make this prediction task challenging, i.e., sample selection bias and data sparsity. In this study, we formalize a potential conversion relationship, i.e., user→activated user→client and decompose this relationship into …

A Comparison Of Computational Approaches For Intron Retention Detection, Jiantao Zheng, Cuixiang Lin, Zhenpeng Wu, Hong-Dong Li

Big Data Mining and Analytics

Intron Retention (IR) is an alternative splicing mode through which introns are retained in mature RNAs rather than being spliced in most cases. IR has been gaining increasing attention in recent years because of its recognized association with gene expression regulation and complex diseases. Continuous efforts have been dedicated to the development of IR detection methods. These methods differ in their metrics to quantify retention propensity, performance to detect IR events, functional enrichment of detected IRs, and computational speed. A systematic experimental comparison would be valuable to the selection and use of existing methods. In this work, we conduct an …

Development Of Guidelines For Collecting Transit Ridership Data, Hong Yang, Kun Xie, Sherif Ishak, Qingyu Ma, Yang Liu

Computational Modeling & Simulation Engineering Faculty Publications

Transit ridership is a critical determinant for many transit applications such as operation optimizations and project prioritization under performance-based funding mechanisms. As a result, the quality of ridership data is of utmost importance to both transit administrative agencies and transit operators. Many transit operators in Virginia report their ridership data to the Department of Rail and Public Transportation (DRPT) and the National Transit Database (NTD). However, with no specific guidelines available to transit agencies in Virginia for collecting ridership data, the heterogeneous mixture of diverse data collection methods and technologies has often raised concerns about the consistency and quality of …

Representation Learning For Chemical Activity Predictions, Mohamed S. Ayed

Dissertations, Theses, and Capstone Projects

Computational prediction of a phenotypic response upon the chemical perturbation on a biological system plays an important role in drug discovery and many other applications. Chemical fingerprints derived from chemical structures are a widely used feature to build machine learning models. However, the fingerprints ignore the biological context, thus, they suffer from several problems such as the activity cliff and curse of dimensionality. Fundamentally, the chemical modulation of biological activities is a multi-scale process. It is the genome-wide chemical-target interactions that modulate chemical phenotypic responses. Thus, the genome-scale chemical-target interaction profile will more directly correlate with in vitro and in …

Application Of Gravity Data For Hydrocarbon Exploration Using Machine Learning Assisted Workflow, Oluwafemi Temidayo Alaofin

LSU Master's Theses

Gravity survey has played an essential role in many geoscience fields ever since it was conducted, especially as an early screening tool for subsurface hydrocarbon exploration. With continued improvement in data processing techniques and gravity survey accuracy, in-depth gravity anomaly studies, such as characterization of Bouguer and isostatic residual anomalies, have the potential to delineate prolific regional structures and hydrocarbon basins. In this study, we focus on developing a cost-effective, quick, and computationally efficient screening tool for hydrocarbon exploration using gravity data employing machine learning techniques. Since land-based gravity surveys are often expensive and difficult to obtain in remote places, …

Ooi Biogeochemical Sensor Data: Best Practices And User Guide. Version 1.0.0., Hilary I. Palevsky, Sophie Clayton, Dariia Atamanchuk, Roman Battisti, Jennifer Batryn, Annie Bourbonnais, Ellen M. Briggs, Filipa Carvalho, Alison P. Chase, Rachel Eveleth, Rob Fatland, Kristen E. Fogaren, Jonathan Peter Fram, Susan E. Hartman, Isabela Le Bras, Cara C.M. Manning, Joseph A. Needoba, Merrie Beth Neely, Hilde Oliver, Andrew C. Reed, Jennie E. Rheuban, Christina Schallenberg, Michael F. Vardaro, Ian Walsh, Christopher Wingard

OES Faculty Publications

The OOI Biogeochemical Sensor Data Best Practices and User Guide is intended to provide current and prospective users of data generated by biogeochemical sensors deployed on the Ocean Observatories Initiative (OOI) arrays with the information and guidance needed for them to ensure that the data is science-ready. This guide is aimed at researchers with an interest or some experience in ocean biogeochemical processes. We expect that users of this guide will have some background in oceanography, however we do not assume any prior experience working with biogeochemical sensors or their data. While initially envisioned as a “cookbook” for end users …

Universal Design In Bci: Deep Learning Approaches For Adaptive Speech Brain-Computer Interfaces, Srdjan Lesaja

Theses and Dissertations

In the last two decades, there have been many breakthrough advancements in non-invasive and invasive brain-computer interface (BCI) systems. However, the majority of BCI model designs still follow a paradigm whereby neural signals are preprocessed and task-related features extracted using static, and generally customized, data-independent designs. Such BCI designs commonly optimize narrow task performance over generalizability, adaptability, and robustness, which is not well suited to meeting individual user needs. If one day BCIs are to be capable of decoding our higher-order cognitive commands and conceptual maps, their designs will need to be adaptive architectures that will evolve and grow in …

A Unified Health Information System Framework For Connecting Data, People, Devices, And Systems, Wu He, Justin Zuopeng Zhang, Huanmei Wu, Wenzhuo Li, Sachin Shetty

Information Technology & Decision Sciences Faculty Publications

The COVID-19 pandemic has heightened the necessity for pervasive data and system interoperability to manage healthcare information and knowledge. There is an urgent need to better understand the role of interoperability in improving the societal responses to the pandemic. This paper explores data and system interoperability, a very specific area that could contribute to fighting COVID-19. Specifically, the authors propose a unified health information system framework to connect data, systems, and devices to increase interoperability and manage healthcare information and knowledge. A blockchain-based solution is also provided as a recommendation for improving the data and system interoperability in healthcare.

Image-Data-Driven Deep Learning For Slope Stability Analysis, Behnam Azmoon

Dissertations, Master's Theses and Master's Reports

Landslides cause major infrastructural issues, damage the environment, and cause socio-economic disruptions. Therefore, various slope stability analysis methods have been developed to evaluate the stability of slopes and the probability of their failure. This dissertation attempts to take advantage of the recent advancements in remote sensing and computer technology to implement a deep-learning-based landslide prediction method.

Considering the novelty of this approach, this dissertation leads with proof-of-concept studies to evaluate and establish the suitability of deep learning models for slope stability analysis. To achieve this, a simulated 2D dataset of slope images was created with different geometries and soil properties. …

Exploration Of Lignin-Based Superabsorbent Polymers (Hydrogels) For Soil Water Management And As A Carrier For Delivering Rhizobium Spp., Toby Adjuik

Theses and Dissertations--Biosystems and Agricultural Engineering

Superabsorbent polymers (hydrogels) as soil amendments may improve soil hydraulic properties and act as carrier materials beneficial to soil microorganisms. Researchers have mostly explored synthetic hydrogels which may not be environmentally sustainable. This dissertation focused on the development and application of lignin-based hydrogels as sustainable soil amendments. This dissertation also explores the development of pedotransfer transfer functions (PTFs) for predicting saturated hydraulic conductivity using statistical and machine learning methods with a publicly available large data set. A lignin-based hydrogel was synthesized, and its impact on soil water retention was determined in silt loam and loamy fine sand soils. Hydrogel treatment …

Electrochemical Impedance Spectroscopy Analysis Of Corrosion Of Reinforcing Steel In Fly Ash Mortar By Means Of Transmission Line Modeling, Padraig Stack

Williams Honors College, Honors Research Projects

Electrochemical impedance spectroscopy (EIS) is an important non-destructive tool that allows for a deeper understanding of the electrochemical processes and mechanisms occurring in an electrochemical cell. Equivalent electrical circuits (EECs) are used to model the impedance data into electrical components, such as resistors and capacitors, on a circuit. Current potentiostat offers software packages that can analyze the frequency response, but the software only supports “simple” EEC that can be written as fixed electrical components in some combination of series and parallel. Park and Macdonald propose a transmission line model (TLM) that does not use lumped-element models, instead, the values of …

A Citizen-Science Approach For Urban Flood Risk Analysis Using Data Science And Machine Learning, Candace Agonafir

Dissertations and Theses

Street flooding is problematic in urban areas, where impervious surfaces, such as concrete, brick, and asphalt prevail, impeding the infiltration of water into the ground. During rain events, water ponds and rise to levels that cause considerable economic damage and physical harm. The main goal of this dissertation is to develop novel approaches toward the comprehension of urban flood risk using data science techniques on crowd-sourced data. This is accomplished by developing a series of data-driven models to identify flood factors of significance and localized areas of flood vulnerability in New York City (NYC). First, the infrastructural (catch basin clogs, …

Application Of Artificial Intelligence For Co2 Storage In Saline Aquifer (Smart Proxy For Snap-Shot In Time), Marwan Mohammed Alnuaimi

Graduate Theses, Dissertations, and Problem Reports (ETD)

In recent years, artificial intelligence (AI) and machine learning (ML) technology have grown in popularity. Smart Proxy Models (SPM) are AI/ML based data-driven models which have proven to be quite crucial in petroleum engineering domain with abundant data, or operations in which large surface/ subsurface volume of data is generated. Climate change mitigation is one application of such technology to simulate and monitor CO2 injection into underground formations.

The goal of the SPM developed in this study is to replicate the results (in terms of pressure and saturation outputs) of the numerical reservoir simulation model (CMG) for CO2 injection into …

A Workflow For Unconventional Reservoirs Optimization Using Supervised Machine Learning In Conjunction With Orthorhombic Elasticity Modeling, Aymen Ab Ali Alhemdi

Graduate Theses, Dissertations, and Problem Reports (ETD)

Due to the anisotropy and heterogeneous nature of unconventional reservoirs like shale, a comprehensive parametric study to optimize hydraulic fracture treatment for such reservoirs is a tough challenge, especially when natural fractures are present. Most of the current frac simulators do not consider the anisotropy of rock elasticity in the shales. Besides, using the fracture simulation linked with reservoir simulation for the parametric study to understand the impact of multiple different design parameters on fracture propagation and production is time expensive and low efficient. The study proposes a workflow including a new orthorhombic (OB) rock algorithm to interpret geomechanical properties …

Hydrocarbon Pay Zone Prediction Using Ai Neural Network Modeling., Darren D. Guedon

Graduate Theses, Dissertations, and Problem Reports (ETD)

This paper captures the ability of AI neural network technology to analyze petrophysical datasets for pattern recognition and accurate prediction of the pay zone of a vertical well from the Santa Fe field in Kansas.

During this project, data from 10 completed wells in the Santa Fe field were gathered, resulting in a dataset with 25,580 records, ten predictors (logs data), and a single binary output (Yes or No) to identify the availability of Hydrocarbon over a half feet depth segment in the well. Several models composed of different predictors combinations were also tested to determine how impactful some logs …

Using Skeleton Correction To Improve Flash Lidar-Based Gait Recognition, Nasrin Sadeghzadehyazdi, Tamal Batabyal, Alexander Glandon, Nibir Dhar, Babajide Familoni, Khan Iftekharuddin, Scott T. Acton

Electrical & Computer Engineering Faculty Publications

This paper presents GlidarPoly, an efficacious pipeline of 3D gait recognition for flash lidar data based on pose estimation and robust correction of erroneous and missing joint measurements. A flash lidar can provide new opportunities for gait recognition through a fast acquisition of depth and intensity data over an extended range of distance. However, the flash lidar data are plagued by artifacts, outliers, noise, and sometimes missing measurements, which negatively affects the performance of existing analytics solutions. We present a filtering mechanism that corrects noisy and missing skeleton joint measurements to improve gait recognition. Furthermore, robust statistics are integrated with …

Facial Landmark Feature Fusion In Transfer Learning Of Child Facial Expressions, Megan A. Witherow, Manar D. Samad, Norou Diawara, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Automatic classification of child facial expressions is challenging due to the scarcity of image samples with annotations. Transfer learning of deep convolutional neural networks (CNNs), pretrained on adult facial expressions, can be effectively finetuned for child facial expression classification using limited facial images of children. Recent work inspired by facial age estimation and age-invariant face recognition proposes a fusion of facial landmark features with deep representation learning to augment facial expression classification performance. We hypothesize that deep transfer learning of child facial expressions may also benefit from fusing facial landmark features. Our proposed model architecture integrates two input branches: a …

Particle Identification And Tracking In Real Time Using Machine Learning On Fpga, F. Barbosa, L. Belfore, C. Dickover, C. Fanelli, S. Furletov, Y. Furletova, L. Jokhovets, D. Lawrence, D. Romanov

Electrical & Computer Engineering Faculty Publications

This project is a multi-disciplinary endeavour between Physics, Electrical Engineering, and Computer Engineering. The purpose is to develop and implement an FPGA(*) based Machine Learning algorithm for real-time particle identification, filtering, and data reduction. This is important research that can be applied to streaming readout systems being developed now at JLab and other facilities. Real-time data processing is a frontier field in experimental physics, especially in HEP. The application of FPGAs at the trigger level is used by many current and planned experiments (CMS, LHCb, Belle2, PANDA). Usually they use conventional processing algorithms. LHCb has implemented ML elements for real-time …

Stress Classification Using Deep Learning With 1d Convolutional Neural Networks, Abdulrazak Yahya Saleh, Lau Khai Xian

Knowledge Engineering and Data Science

Stress has been a major problem impacting people in various ways, and it gets serious every day. Identifying whether someone is suffering from stress is crucial before it becomes a severe illness. Artificial Intelligence (AI) interprets external data, learns from such data, and uses the learning to achieve specific goals and tasks. Deep Learning (DL) has created an impact in the field of Artificial Intelligence as it can perform tasks with high accuracy. Therefore, the primary purpose of this paper is to evaluate the performance of 1D Convolutional Neural Networks (1D CNNs) for stress classification. A Psychophysiological stress (PS) dataset …

Application Of Competitive Intelligence For Insular Territories: Automatic Analysis Of Scientific And Technology Trends To Fight The Negative Effects Of Climate Change, Henri Dou, Pierre Fournie

International Journal of Islands Research

Islands are fragile territories because of their geographical position. As a result, climate impacts can have serious consequences, of which some are irreversible. Therefore, it is necessary to allow insular territories to benefit from the latest scientific and technological advances in combating climate effects. The current article shows how to deal with automatic analysis of scientific information on the one hand, but also its applications via patents. We will analyse the latest scientific results as well as their possible applications using patent analysis. We will also focus on experts, laboratories, and leading companies, that are active on the field. The …

Uncertainty-Aware Deep Learning For Prediction Of Remaining Useful Life Of Mechanical Systems, Samuel J. Cornelius

Theses and Dissertations

Remaining useful life (RUL) prediction is a problem that researchers in the prognostics and health management (PHM) community have been studying for decades. Both physics-based and data-driven methods have been investigated, and in recent years, deep learning has gained significant attention. When sufficiently large and diverse datasets are available, deep neural networks can achieve state-of-the-art performance in RUL prediction for a variety of systems. However, for end users to trust the results of these models, especially as they are integrated into safety-critical systems, RUL prediction uncertainty must be captured. This work explores an approach for estimating both epistemic and heteroscedastic …

Messiness: Automating Iot Data Streaming Spatial Analysis, Christopher White, Atilio Barreda Ii

Publications and Research

The spaces we live in go through many transformations over the course of a year, a month, or a day; My room has seen tremendous clutter and pristine order within the span of a few hours. My goal is to discover patterns within my space and formulate an understanding of the changes that occur. This insight will provide actionable direction for maintaining a cleaner environment, as well as provide some information about the optimal times for productivity and energy preservation.

Using a Raspberry Pi, I will set up automated image capture in a room in my home. These images will …

A Comprehensive Analysis Of Reward Function For Adaptive Traffic Signal Control, Abu Rafe Md Jamil, Naushin Nower

Knowledge Engineering and Data Science

Adaptive traffic control systems (ATCS) can play an essential role in reducing traffic congestion in urban areas. The main challenge for ATSC is to determine the proper signal timing. Recently, Deep Reinforcement Learning (DRL) has been used to determine proper signal timing. However, the success of the DRL algorithm depends on the appropriate reward function design. There exist various reward functions for ATSC in the existing research. This research presents a comprehensive analysis of the widely used reward function. The pros and cons of various reward algorithms were discussed, and experimental analysis shows that the multi-objective reward function enhances the …

Similarity Identification Of Large-Scale Biomedical Documents Using Cosine Similarity And Parallel Computing, Merlinda Wibowo, Christoph Quix, Nur Syahela Hussien, Herman Yuliansyah, Faisal Dharma Adhinata

Knowledge Engineering and Data Science

Document similarity computation is an important research topic in information retrieval, and it is a crucial issue for automatic document categorization. The similarity value is between 0 and 1, then the closest value to 1 is represented both documents is considered more relevant, vice versa. However, the large scale of textual information has created the problem of finding the relevance level between documents. Therefore, the relevance between mesh heading text in the PubMed documents is higher than the relevance of the abstract text in the PubMed documents. Furthermore, parallel computing is implemented to speed up the large-scale documents similarity identification …

Recognition Of Handwritten Javanese Script Using Backpropagation With Zoning Feature Extraction, Anik Nur Handayani, Heru Wahyu Herwanto, Katya Lindi Chandrika, Kohei Arai

Knowledge Engineering and Data Science

Backpropagation is part of supervised learning, in which the training process requires a target. The resulting error is transmitted back to the units below in its training process. Backpropagation can solve complicated problems because it consumes less memory than other algorithms. In addition, it also can produce solutions with a low error rate while executing less time. In image pattern recognition, backpropagation can be utilized for cultural preservation in many places worldwide, including Indonesia. It is used to recognize picture patterns in Javanese script writings. This study concluded that feature extraction approaches, zoning, and backpropagation could be utilized to distinguish …

Parallel Approach Of Adaptive Image Thresholding Algorithm On Gpu, Adhi Prahara, Andri Pranolo, Nuril Anwar, Yingchi Mao

Knowledge Engineering and Data Science

Image thresholding is used to segment an image into background and foreground using a given threshold. The threshold can be generated using a specific algorithm instead of a pre-defined value obtained from observation or experiment. However, the algorithm involves per pixel operation, histogram calculation, and iterative procedure to search the optimum threshold that is costly for high-resolution images. In this research, parallel implementations on GPU for three adaptive image thresholding methods, namely Otsu, ISODATA, and minimum cross-entropy, were proposed to optimize their computational times to deal with high-resolution images. The approach involves parallel reduction and parallel prefix sum (scan) techniques …

A Comparative Study Of Machine Learning-Based Approach For Network Traffic Classification, Kien Trang, An Hoang Nguyen

Knowledge Engineering and Data Science

Internet usage has increased rapidly and become an essential part of human life, corresponding to the rapid development of network infrastructure in recent years. Thus, protecting users’ confidential information when joining the global network becomes one of the most significant considerations. Even though multiple encryption algorithms and techniques have been applied in different parties, including internet providers, and web hosting, this situation also allows the hacker to attack the network system anonymously. Therefore, the significance of classifying network data streams to improve network system quality and security is attracting increasing study interests. This work introduces a machine learning-based approach to …