Data Mining For Structural Damage Identification Using Hybrid Artificial Neural Network Based Algorithm For Beam And Slab Girder,
2020
Universiti Malaya
Data Mining For Structural Damage Identification Using Hybrid Artificial Neural Network Based Algorithm For Beam And Slab Girder, Gordan Meisam
Student Works (2020-2029)
One of the approaches for structural health monitoring (SHM) consists of two major components, i.e. a network of sensors to collect the response data and an extraction method to obtain information on the structural health condition. Data mining (DM) is a novel data extraction technology which can employ for development of inverse analysis. Implementation of DM techniques in different areas of civil engineering has recently given very good results. However, application of DM in SHM is not used as much as expected, thus, many challenges are still ahead. Therefore, it is necessary to develop the applicability of DM in SHM. …
Statistical Methods For Resolving Intratumor Heterogeneity With Single-Cell Dna Sequencing,
2020
The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences
Statistical Methods For Resolving Intratumor Heterogeneity With Single-Cell Dna Sequencing, Alexander Davis
Dissertations and Theses (Open Access)
Tumor cells have heterogeneous genotypes, which drives progression and treatment resistance. Such genetic intratumor heterogeneity plays a role in the process of clonal evolution that underlies tumor progression and treatment resistance. Single-cell DNA sequencing is a promising experimental method for studying intratumor heterogeneity, but brings unique statistical challenges in interpreting the resulting data. Researchers lack methods to determine whether sufficiently many cells have been sampled from a tumor. In addition, there are no proven computational methods for determining the ploidy of a cell, a necessary step in the determination of copy number. In this work, software for calculating probabilities from …
Learning Transferrable Parameters For Long-Tailed Sequential User Behavior Modeling,
2020
Zhejiang University
Learning Transferrable Parameters For Long-Tailed Sequential User Behavior Modeling, Jianwen Yin, Chenghao Liu, Weiqing Wang, Jianling Sun, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Sequential user behavior modeling plays a crucial role in online user-oriented services, such as product purchasing, news feed consumption, and online advertising. The performance of sequential modeling heavily depends on the scale and quality of historical behaviors. However, the number of user behaviors inherently follows a long-tailed distribution, which has been seldom explored. In this work, we argue that focusing on tail users could bring more benefits and address the long tails issue by learning transferrable parameters from both optimization and feature perspectives. Specifically, we propose a gradient alignment optimizer and adopt an adversarial training scheme to facilitate knowledge transfer …
A Novel Path Loss Forecast Model To Support Digital Twins For High Frequency Communications Networks,
2020
University of Nebraska-Lincoln
A Novel Path Loss Forecast Model To Support Digital Twins For High Frequency Communications Networks, James Marvin Taylor Jr.
Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research
The need for long-distance High Frequency (HF) communications in the 3-30 MHz frequency range seemed to diminish at the end of the 20th century with the advent of space-based communications and the spread of fiber optic-connected digital networks. Renewed interest in HF has emerged as an enabler for operations in austere locations and for its ability to serve as a redundant link when space-based and terrestrial communication channels fail. Communications system designers can create a “digital twin” system to explore the operational advantages and constraints of the new capability. Existing wireless channel models can adequately simulate communication channel conditions with …
Hoop Dreams: An Empirical Analysis Of The Gender Wage Gap In Professional Basketball,
2020
Ursinus College
Hoop Dreams: An Empirical Analysis Of The Gender Wage Gap In Professional Basketball, Hailey Dicicco
Business and Economics Presentations
The gender wage gap is a very prominent point of discussion in the professional world, but in the sports world, it has taken the spotlight in recent years. One sport that has seen discussion and debate over salary differences is the National Basketball Association and Women’s National Basketball Association. In 2018, the average salary in the NBA was 6.4 million dollars, while the average salary in the WNBA was 71,635 dollars. A reason why these salaries are so differently is due to the amount of revenue that each league brings in. The NBA brings in roughly 7.4 billion dollars a …
Data Mining And Image Classification Using Genetic Programming,
2020
Kennesaw State University
Data Mining And Image Classification Using Genetic Programming, Mahsa Shokri Varniab
Master of Science in Computer Science Theses
Genetic programming (GP), a capable machine learning and search method, motivated by Darwinian-evolution, is an evolutionary learning algorithm which automatically evolves computer programs in the form of trees to solve problems. This thesis studies the application of GP for data mining and image processing. Knowledge discovery and data mining have been widely used in business, healthcare, and scientific fields. In data mining, classification is supervised learning that identifies new patterns and maps the data to predefined targets. A GP based classifier is developed in order to perform these mappings. GP has been investigated in a series of studies to classify …
Variability In The Effectiveness Of Psychological Interventions Based On Machine Learning In Stem Education,
2020
University of Nebraska-Lincoln
Variability In The Effectiveness Of Psychological Interventions Based On Machine Learning In Stem Education, Mohammad Hasan, Bilal Khan
School of Computing: Faculty Publications
This manuscript presents a framework to investigate the variability in the effectiveness of psychological interventions supported by Machine Learning (ML) based early-warning systems (EWS) in science, technology, engineering, and mathematics education. It emphasizes the importance of investigating the resulting variability and suggests that effective EWS cannot be designed without a deeper understanding of the variability. The framework uses an ML-based model to predict students’ academic performance early in the semester for a Sophomore-level Computer Science course at a public university in the United States. The students were given psychological interventions by sending their end-of-term performance forecast thrice during the semester. …
Quantitatively Motivated Model Development Framework: Downstream Analysis Effects Of Normalization Strategies,
2020
Kennesaw State University
Quantitatively Motivated Model Development Framework: Downstream Analysis Effects Of Normalization Strategies, Jessica M. Rudd
Doctor of Data Science and Analytics Dissertations
Through a review of epistemological frameworks in social sciences, history of frameworks in statistics, as well as the current state of research, we establish that there appears to be no consistent, quantitatively motivated model development framework in data science, and the downstream analysis effects of various modeling choices are not uniformly documented. Examples are provided which illustrate that analytic choices, even if justifiable and statistically valid, have a downstream analysis effect on model results. This study proposes a unified model development framework that allows researchers to make statistically motivated modeling choices within the development pipeline. Additionally, a simulation study is …
How Intelligent Ci Instruction Gives Law Students A Competitive Edge,
2020
University of Georgia School of Law
How Intelligent Ci Instruction Gives Law Students A Competitive Edge, Heather Simmons, Beau Steenken, Liz Whittington, Joshua Pluta
Presentations
"Competitive intelligence" (CI) is a term that gets bandied about across many sectors, but how exactly do law firms use it to further their business? Academics are aware of CI as a concept, but teaching students how to conduct competitive intelligence requires a more nuanced understanding of how it is actually used. In a discussion moderated by a newer academic librarian who will be teaching competitive intelligence for the first time, a firm librarian will share insights into how competitive intelligence can and should be used, and an academic librarian who regularly teaches competitive intelligence will offer tips on how …
Data, Stats, Go: Navigating The Intersections Of Cataloging, E-Resource, And Web Analytics Reporting,
2020
University of Georgia School of Law
Data, Stats, Go: Navigating The Intersections Of Cataloging, E-Resource, And Web Analytics Reporting, Rachel S. Evans, Wendy Moore, Jessica Pasquale, Andre Davison
Presentations
Do you trudge through gathering statistics at fiscal or calendar year-end? Do you wonder why you track certain things, thinking many seem outdated or irrelevant? Many places seem to keep counting certain statistics because "that's what they've always done." For e-resources, how do you integrate those with physical counts and reconcile the variations (updated e-resources versus re-cataloged physical items)? What about repository downloads and other web traffic? The quantity of stats that libraries track is staggering and keeps growing. This program will encourage attendees to stop and evaluate what and why they're gathering data and help identify possible alternatives to …
Data Science In The Public Interest: Improving Government Performance In The Workforce,
2020
The Ohio State University, Ohio Education Research Center, and Center for Human Resource Research
Data Science In The Public Interest: Improving Government Performance In The Workforce, Joshua D. Hawley
Upjohn Press
This book is about how new and underutilized types of big data sources can inform public policy decisions related to workforce development. Hawley describes how government is currently using data to inform decisions about the workforce at the state and local levels. He then moves beyond standardized performance metrics designed to serve federal agency requirements and discusses how government can improve data gathering and analysis to provide better, up-to-date information for government decision making.
ETH Zurich
Automatic Recognition, Segmentation, And Sex Assignment Of Nocturnal Asthmatic Coughs And Cough Epochs In Smartphone Audio Recordings: Observational Field Study, Filipe Barata, Peter Tinschert, Frank Rassouli, Claudia Steurer-Stey, Elgar Fleisch, Milo Puhan, Martin Brutsche, David Kotz, Tobias Kowatsch
Dartmouth Scholarship
Background: Asthma is one of the most prevalent chronic respiratory diseases. Despite increased investment in treatment, little progress has been made in the early recognition and treatment of asthma exacerbations over the last decade. Nocturnal cough monitoring may provide an opportunity to identify patients at risk for imminent exacerbations. Recently developed approaches enable smartphone-based cough monitoring. These approaches, however, have not undergone longitudinal overnight testing nor have they been specifically evaluated in the context of asthma. Also, the problem of distinguishing partner coughs from patient coughs when two or more people are sleeping in the same room using contact-free audio …
A Data Scientist Looks At Covid19 Part Ii: Mythbusting,
2020
Rowan University
A Data Scientist Looks At Covid19 Part Ii: Mythbusting, Anthony Breitzman
College of Science & Mathematics Departmental Research
No abstract provided.
Novel Technique To Analyze The Effects Of Cognitive And Non-Cognitive Predictors On Students Course Withdrawal In College,
2020
The University of Texas at Tyler
Novel Technique To Analyze The Effects Of Cognitive And Non-Cognitive Predictors On Students Course Withdrawal In College, Mohammed Ali
Technology Faculty Publications and Presentations
A novel technique was applied to a college student database to identify the cognitive and non-cognitive factors that predict college students’ course withdrawal behaviors. Predictors such as high school grade point average (HSGPA), standardized test scores (ACT–American College Test or SAT-Scholastic Aptitude Test), number of credit hours enrolled, and age were analyzed in this study. Data mining software algorithms were used to study information about undergraduate students at a west-south-central state university in the United States. The study results revealed that two factors, number of enrolled credit hours, and a student’s age have the most effect on collegiate course withdrawal …
Identifying Structure Transitions Using Machine Learning Methods,
2020
Louisiana State University
Identifying Structure Transitions Using Machine Learning Methods, Nicholas Walker
LSU Doctoral Dissertations
Methodologies from data science and machine learning, both new and old, provide an exciting opportunity to investigate physical systems using extremely expressive statistical modeling techniques. Physical transitions are of particular interest, as they are accompanied by pattern changes in the configurations of the systems. Detecting and characterizing pattern changes in data happens to be a particular strength of statistical modeling in data science, especially with the highly expressive and flexible neural network models that have become increasingly computationally accessible in recent years through performance improvements in both hardware and algorithmic implementations. Conceptually, the machine learning approach can be regarded as …
Iba Newsletter [July 2020],
2020
Institute of Business Administration
Iba Newsletter [July 2020], Communications Department, Office Of The Registrar
IBA News
No abstract provided.
Covid-19 Is Spatial: Ensuring That Mobile Big Data Is Used For Social Good,
2020
University of Helsinki, Finland
Covid-19 Is Spatial: Ensuring That Mobile Big Data Is Used For Social Good, Age Poom, Olle Järv, Matthew Zook, Tuuli Toivonen
Geography Faculty Publications
The mobility restrictions related to COVID-19 pandemic have resulted in the biggest disruption to individual mobilities in modern times. The crisis is clearly spatial in nature, and examining the geographical aspect is important in understanding the broad implications of the pandemic. The avalanche of mobile Big Data makes it possible to study the spatial effects of the crisis with spatiotemporal detail at the national and global scales. However, the current crisis also highlights serious limitations in the readiness to take the advantage of mobile Big Data for social good, both within and beyond the interests of health sector. We propose …
Combining Machine Learning And Empirical Engineering Methods Towards Improving Oil Production Forecasting,
2020
California Polytechnic State University, San Luis Obispo
Combining Machine Learning And Empirical Engineering Methods Towards Improving Oil Production Forecasting, Andrew J. Allen
Master's Theses
Current methods of production forecasting such as decline curve analysis (DCA) or numerical simulation require years of historical production data, and their accuracy is limited by the choice of model parameters. Unconventional resources have proven challenging to apply traditional methods of production forecasting because they lack long production histories and have extremely variable model parameters. This research proposes a data-driven alternative to reservoir simulation and production forecasting techniques. We create a proxy-well model for predicting cumulative oil production by selecting statistically significant well completion parameters and reservoir information as independent predictor variables in regression-based models. Then, principal component analysis (PCA) …
Query Rewriting With Thesaurus-Based For Handling Semantic Heterogeneity In Database Integration,
2020
Jurusan Teknik Elektro, Politeknik Negeri Bali, Indonesia
Query Rewriting With Thesaurus-Based For Handling Semantic Heterogeneity In Database Integration, I Made Riyan Adi Nugroho, I Wayan Budi Sentana
Knowledge Engineering and Data Science
Nowadays, studies on handling semantic heterogeneity still become a challenge for researcher. Several methods have been used to solve these problems, one of which is query rewriting, implemented by rewriting a query into the latest one by using the selected schema. Semantic query rewriting needs a framework in order to identify the connection through the data schema sources. This line is used as a basis for scheme selection. Also, ontology is a model which often be used in these specific cases. The lack of ontology becomes a significant problem that usually seen. Therefore, this paper will describe an alternative framework …
Flood Prediction Using Artificial Neural Networks: Empirical Evidence From Mauritius As A Case Study,
2020
Cyberange Global Holdings PTE, Singapore
Flood Prediction Using Artificial Neural Networks: Empirical Evidence From Mauritius As A Case Study, A. Z. Dhunny, Reena H. Seebocus, Z. Allam, Mohammad Yasser Chuttur
Knowledge Engineering and Data Science
Artificial Neural Networks (ANN) has been well studied for flood prediction. However, there is not enough empirical evidence to generalize ANN applicability to small countries with microclimates prevailing in a small geographical space. In this paper, we focus on the climatic conditions of Mauritius for which we seek to investigate the accuracy of using ANN to predict flooding using locally collected data from 11 meteorological stations spread across the country. The ANN model for flood prediction presented in this work is trained using 20,000 climate data records, collected over a period of two years for Mauritius. Our input climate features …
