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Articles 91 - 101 of 101
Full-Text Articles in Data Science
Open Data, Collaborative Working Platforms, And Interdisciplinary Collaboration: Building An Early Career Scientist Community Of Practice To Leverage Ocean Observatories Initiative Data To Address Critical Questions In Marine Science, Robert M. Levine, Kristen E. Fogaren, Johna E. Rudzin, Christopher J. Russoniello, Dax C. Soule, Justine M. Whitaker
Open Data, Collaborative Working Platforms, And Interdisciplinary Collaboration: Building An Early Career Scientist Community Of Practice To Leverage Ocean Observatories Initiative Data To Address Critical Questions In Marine Science, Robert M. Levine, Kristen E. Fogaren, Johna E. Rudzin, Christopher J. Russoniello, Dax C. Soule, Justine M. Whitaker
Publications and Research
Ocean observing systems are well-recognized as platforms for long-term monitoring of near-shore and remote locations in the global ocean. High-quality observatory data is freely available and accessible to all members of the global oceanographic community—a democratization of data that is particularly useful for early career scientists (ECS), enabling ECS to conduct research independent of traditional funding models or access to laboratory and field equipment. The concurrent collection of distinct data types with relevance for oceanographic disciplines including physics, chemistry, biology, and geology yields a unique incubator for cutting-edge, timely, interdisciplinary research. These data are both an opportunity and an incentive …
Machine Learning Applications For Drug Repurposing, Hansaim Lim
Machine Learning Applications For Drug Repurposing, Hansaim Lim
Dissertations, Theses, and Capstone Projects
The cost of bringing a drug to market is astounding and the failure rate is intimidating. Drug discovery has been of limited success under the conventional reductionist model of one-drug-one-gene-one-disease paradigm, where a single disease-associated gene is identified and a molecular binder to the specific target is subsequently designed. Under the simplistic paradigm of drug discovery, a drug molecule is assumed to interact only with the intended on-target. However, small molecular drugs often interact with multiple targets, and those off-target interactions are not considered under the conventional paradigm. As a result, drug-induced side effects and adverse reactions are often neglected …
A Data Exploration Of Jeopardy! From 1984 To The Present, Brian S. Hamilton
A Data Exploration Of Jeopardy! From 1984 To The Present, Brian S. Hamilton
Dissertations, Theses, and Capstone Projects
The gameshow Jeopardy! has been around in its current iteration—hosted by Alex Trebek—since 1984. During this time, it has accumulated data on clues, contestants, and possible strategies on how to win. Using a crowd-sourced archive called J! Archive, this project seeks to find trends in the topics that the game covers and take a deeper look into the performance of its contestants. It employs topic modeling, a text-analysis method, to organize the hundreds of thousands of archived clues and statistical analysis to rate the performance of contestants by gender. Using web-based visualization tools, the data is shown in an …
Sensor Data Analysis In Smart Buildings, Manuel A. Mane Penton
Sensor Data Analysis In Smart Buildings, Manuel A. Mane Penton
Publications and Research
Data analysis and Machine Learning are destined to evolve the current technology infrastructure by solving technology and economy demands present mainly in developed cities like New York. This research proposes a machine learning (ML) based solution to alleviate one of the main issues that big buildings such as CUNY campuses have, that is the waste of energy resources. The analysis of data coming from the readings of different deployed sensors such as CO2, humidity and temperature can be used to estimate occupancy in a specific room and building in general. The outcome of this research established a relationship between the …
Using Data Mining To Identify The Most Influential Factors In Training Results, Xiaoqing Wu, Daanial Ahmad
Using Data Mining To Identify The Most Influential Factors In Training Results, Xiaoqing Wu, Daanial Ahmad
Publications and Research
Data Science is used as a tool to find hidden facts in the data. We want to find out what factors such as ‘AGE’, ‘TAX’, ‘PUPIL-TEACHER RATIO’, ‘PER-CAPITA INCOME’ contribute the most to housing prices. To answer this question, we studied the dataset of “Boston Houses Prices”. By applying the Lasso Regression (a Data Mining Technique) on the data set of “Boston Houses Prices” we identified the influential factors in the linear model. As a conclusion we found that there were six inputs which contributed the most to the prices of houses and those inputs are as follow: (i) CRIM-per …
Philosophical Perspectives, Jochen Albrecht
Philosophical Perspectives, Jochen Albrecht
Publications and Research
This entry follows in the footsteps of Anselin’s famous 1989 NCGIA working paper entitled “What is special about spatial?” (a report that is very timely again in an age when non-spatial data scientists are ignorant of the special characteristics of spatial data), where he outlines three unrelated but fundamental characteristics of spatial data. In a similar vein, I am going to discuss some philosophical perspectives that are internally unrelated to each other and could warrant individual entries in this Body of Knowledge. The first one is the notions of space and time and how they have evolved in …
Edge Device Speaker Verification, Thomas P. Duffy
Edge Device Speaker Verification, Thomas P. Duffy
Dissertations and Theses
The continued shrinking of processors and other physical hardware in concert with development of embeddable machine learning frameworks has enabled new use cases placing machine learning directly in the “wild”. The problem of speaker verification, for a long time, has been deployed to perform inference on systems with significant computations resources. More recently, these systems have been built for smaller, cheaper devices which can be placed in people's homes or other edge locations. Here, we aim to demonstrate that a reasonably accurate, generalizable, text-independent speaker verification system can be built, trained, and, ultimately, deployed onto a microcontroller with as a …
On Properties Of Distance-Based Entropies On Fullerene Graphs, Modjtaba Ghorbani, Matthias Dehmer, Mina Rajabi-Parsa, Abbe Mowshowitz, Frank Emmert-Streib
On Properties Of Distance-Based Entropies On Fullerene Graphs, Modjtaba Ghorbani, Matthias Dehmer, Mina Rajabi-Parsa, Abbe Mowshowitz, Frank Emmert-Streib
Publications and Research
In this paper, we study several distance-based entropy measures on fullerene graphs. These include the topological information content of a graph Ia(G), a degree-based entropy measure, the eccentric-entropy Ifs(G), the Hosoya entropy H(G) and, finally, the radial centric information entropy Hecc. We compare these measures on two infinite classes of fullerene graphs denoted by A12n+4 and B12n+6. We have chosen these measures as they are easily computable and capture meaningful graph properties. To demonstrate the utility of these measures, we investigate the Pearson correlation between them on the fullerene graphs.
An Algorithm For Calculating Top-Dimensional Bounding Chains, J. Frederico Carvalho, Mikael Vejdemo-Johansson, Danica Kragic, Florian T. Pokorny
An Algorithm For Calculating Top-Dimensional Bounding Chains, J. Frederico Carvalho, Mikael Vejdemo-Johansson, Danica Kragic, Florian T. Pokorny
Publications and Research
We describe the Coefficient-Flow algorithm for calculating the bounding chain of an (n-1)-boundary on an n-manifold-like simplicial complex S. We prove its correctness and show that it has a computational time complexity of O(|S(n−1)|) (where S(n−1) is the set of (n-1)-faces of S). We estimate the big-O coefficient which depends on the dimension of S and the implementation. We present an implementation, experimentally evaluate the complexity of our algorithm, and compare its performance with that of solving the underlying linear system.
Content Analysis Of Data Science Graduate Programs In The U.S., Duo Li, Elizabeth Milonas, Qiping Zhang
Content Analysis Of Data Science Graduate Programs In The U.S., Duo Li, Elizabeth Milonas, Qiping Zhang
Publications and Research
Data science is an emerging academic field (Paul & Aithal, 2018), which has its origins in “Big Data/Cloud Computing” and complexity science domains. Data Science is about managing large and complex data (Big Data management) and analytics technologies (Paul & Aithal, 2018). Data, technology, and people are the three pillars of data science. In addition, Data Science is composed of three key areas: analytics, infrastructure, and data curation (Tang & Sae-Lim, 2016). Stanton (2012) defined data science as “an emerging area of work concerned with the collection, preparation, analysis, visualization, management, and preservation of large collections of information (Song & …
Time Series Analysis For Psychological Research: Examining And Forecasting Change, Andrew T. Jebb, Louis Tay, Wei Wang, Qiming Huang
Time Series Analysis For Psychological Research: Examining And Forecasting Change, Andrew T. Jebb, Louis Tay, Wei Wang, Qiming Huang
Publications and Research
Psychological research has increasingly recognized the importance of integrating temporal dynamics into its theories, and innovations in longitudinal designs and analyses have allowed such theories to be formalized and tested. However, psychological researchers may be relatively unequipped to analyze such data, given its many characteristics and the general complexities involved in longitudinal modeling. The current paper introduces time series analysis to psychological research, an analytic domain that has been essential for understanding and predicting the behavior of variables across many diverse fields. First, the characteristics of time series data are discussed. Second, different time series modeling techniques are surveyed that …