Application Of Tda Mapper To Water Data And Bird Data,
2020
University of Wisconsin - La Crosse
Application Of Tda Mapper To Water Data And Bird Data, Wako Bungula
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
A Study Of Sentiment Of Covid-19 Related Tweets In The Usa,
2020
Illinois State University
A Study Of Sentiment Of Covid-19 Related Tweets In The Usa, Jack Luu, Rosangela Follmann
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Stochastic Modeling Of Ovarian Follicle Growth In Adult Female Rats,
2020
Illinois State University
Stochastic Modeling Of Ovarian Follicle Growth In Adult Female Rats, Zhaozhi Li
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Viral Data,
2020
Western University, Canada
Viral Data, Agnieszka Leszczynski, Matthew Zook
Geography Faculty Publications
We are experiencing a historical moment characterized by unprecedented conditions of virality: a viral pandemic, the viral diffusion of misinformation and conspiracy theories, the viral momentum of ongoing Hong Kong protests, and the viral spread of #BlackLivesMatter demonstrations and related efforts to defund policing. These co-articulations of crises, traumas, and virality both implicate and are implicated by big data practices occurring in a present that is pervasively mediated by data materialities, deeply rooted dataist ideologies that entrench processes of datafication as granting objective access to truth and attendant practices of tracking, data analytics, algorithmic prediction, and data-driven targeting of individuals …
Ensemble Labeling Towards Scientific Information Extraction (Elsie),
2020
DePaul University
Ensemble Labeling Towards Scientific Information Extraction (Elsie), Erin Murphy
College of Computing and Digital Media Dissertations
Extracting scientific facts from unstructured text is difficult due to challenges specific to the ambiguity of the language, the complexity of the scientific named entities and relations to be extracted. This problem is well illustrated through the extraction of polymer names and their properties. Even in the cases where the property is a temperature, identifying the polymer name associated with the temperature may require expertise due to the use of acronyms, synonyms, complicated naming conventions and by the fact that new polymer names are being “introduced” to the vernacular as polymer science advances. While there exist domain-specific machine learning toolkits …
An Analysis Of Technological Components In Relation To Privacy In A Smart City,
2020
James Madison University
An Analysis Of Technological Components In Relation To Privacy In A Smart City, Kayla Rutherford, Ben Lands, A. J. Stiles
James Madison Undergraduate Research Journal (JMURJ)
A smart city is an interconnection of technological components that store, process, and wirelessly transmit information to enhance the efficiency of applications and the individuals who use those applications. Over the course of the 21st century, it is expected that an overwhelming majority of the world’s population will live in urban areas and that the number of wireless devices will increase. The resulting increase in wireless data transmission means that the privacy of data will be increasingly at risk. This paper uses a holistic problem-solving approach to evaluate the security challenges posed by the technological components that make up a …
Cash Flow Forecasting Using Probabilistic Neural Networks,
2020
University of Baghdad - Iraq
Cash Flow Forecasting Using Probabilistic Neural Networks, Marwan Ashour
Journal of the Arab American University مجلة الجامعة العربية الامريكية للبحوث
This paper aimed to compare the modern methods of cash flow forecasting with the traditional ones. In other words, the researcher compared between the Probabilistic Neural Networks and Transfer Function. It is worth mentioning that cash flow forecasting , nowadays, is very important and helps the upper management plan, control, assess the performance and make decisions. More specifically, in this paper, the Artificial Neural networks were used to diagnose the nature of the cash flow for the next period of time and then forecast the cash flow. The experiment was conducted in The General company for Electricity Distribution in Baghdad. …
Lifespan Analysis Of Earth Satellites,
2020
University of Arkansas Little Rock
Lifespan Analysis Of Earth Satellites, Venkata Jaipal Reddy Batthula
Theses and Dissertations
Different countries have their own satellites for their various needs like communication, weather forecast, and security. The first satellite was launched in 1957 into space. Thousands of satellite lifetimes have already ended but they are still in orbit. The present world has more advanced technology when compared with previous technology. So, the technology for satellites is improving compared with the past. We need to understand trends in improvements to satellites related to lifespans better, using a new dataset that has not been available before, as well as datasets that we have worked with before, and that is the purpose of …
Lis Online Graduate Certificate In Data Science,
2020
University of Rhode Island
Lis Online Graduate Certificate In Data Science, Joanna Burkhardt
Library Impact Statements
No abstract provided.
Using Data Analytics To Predict Students Score,
2020
Singapore University of Social Sciences
Using Data Analytics To Predict Students Score, Nang Laik Ma, Gim Hong Chua
Research Collection School Of Computing and Information Systems
Education is very important to Singapore, and the government has continued to invest heavily in our education system to become one of the world-class systems today. A strong foundation of Science, Technology, Engineering, and Mathematics (STEM) was what underpinned Singapore's development over the past 50 years. PISA is a triennial international survey that evaluates education systems worldwide by testing the skills and knowledge of 15-year-old students who are nearing the end of compulsory education. In this paper, the authors used the PISA data from 2012 and 2015 and developed machine learning techniques to predictive the students' scores and understand the …
Efficient And Fair Data Valuation For Horizontal Federated Learning,
2020
Singapore Management University
Efficient And Fair Data Valuation For Horizontal Federated Learning, Shuyue Wei, Yongxin Tong, Zimu Zhou, Tianshu Song
Research Collection School Of Computing and Information Systems
Availability of big data is crucial for modern machine learning applications and services. Federated learning is an emerging paradigm to unite different data owners for machine learning on massive data sets without worrying about data privacy. Yet data owners may still be reluctant to contribute unless their data sets are fairly valuated and paid. In this work, we adapt Shapley value, a widely used data valuation metric to valuating data providers in federated learning. Prior data valuation schemes for machine learning incur high computation cost because they require training of extra models on all data set combinations. For efficient data …
A New Efficient Method To Detect Genetic Interactions For Lung Cancer Gwas,
2020
Dartmouth College
A New Efficient Method To Detect Genetic Interactions For Lung Cancer Gwas, Jennifer Luyapan, Xuemei Ji, Siting Li, Xiangjun Xiao, Dakai Zhu, Eric J. Duell, David C. Christiani, Matthew B. Schabath, Susanne M. Arnold, Shanbeh Zienolddiny, Hans Brunnström, Olle Melander, Mark D. Thornquist, Todd A. Mackenzie, Christopher I. Amos, Jiang Gui
Markey Cancer Center Faculty Publications
BACKGROUND: Genome-wide association studies (GWAS) have proven successful in predicting genetic risk of disease using single-locus models; however, identifying single nucleotide polymorphism (SNP) interactions at the genome-wide scale is limited due to computational and statistical challenges. We addressed the computational burden encountered when detecting SNP interactions for survival analysis, such as age of disease-onset. To confront this problem, we developed a novel algorithm, called the Efficient Survival Multifactor Dimensionality Reduction (ES-MDR) method, which used Martingale Residuals as the outcome parameter to estimate survival outcomes, and implemented the Quantitative Multifactor Dimensionality Reduction method to identify significant interactions associated with age of …
Comparing Variable Importance In Prediction Of Silence Behaviours Between Random Forest And Conditional Inference Forest Models.,
2020
Technological University Dublin
Comparing Variable Importance In Prediction Of Silence Behaviours Between Random Forest And Conditional Inference Forest Models., Stephen Barrett Dr, Geraldine Gray Dr, Colm Mcguinness Dr, Michael Knoll Dr.
Articles
This paper explores variable importance metrics of Conditional Inference Trees (CIT) and classical Classification And Regression Trees (CART) based Random Forests. The paper compares both algorithms variable importance rankings and highlights why CIT should be used when dealing with data with different levels of aggregation. The models analysed explored the role of cultural factors at individual and societal level when predicting Organisational Silence behaviours.
Towards High Performance Stock Market Prediction Methods,
2020
Harrisburg University of Science and Technology
Towards High Performance Stock Market Prediction Methods, Warren M. Landis, Sangwhan Cha
Other Student Works
Stock markets of today, and will continue to in the future, rely on the metrics of timeliness and efficiency to reach optimal profits. A way stock investors have continued to strive for the best of these two factors of the business is through the use of predictive machine learning systems to help aid in their decision making. However, among the many systems currently in use, it could be said that the myriad of data that they are based on may not be sufficient. In an effort to devise an ensemble learning predictive system that will utilize an array of big …
Tapping Twitter Data For Analyzing And Visualizing Public Sentiments On Censorship,
2020
CFEES, Defence Research Development Organisation, New Delhi, India.
Tapping Twitter Data For Analyzing And Visualizing Public Sentiments On Censorship, Naveen Kumar Yadav, Akhilesh K.S. Yadav
Library Philosophy and Practice (e-journal)
The main objective of this research study is to analyse and visualize Twitter data with tags “#Censorship”. A connection was established with twitter using Twitter API, and receiving the tweets on Google Spreadsheets. Data visualization was performed using various tools such as Voyant Tools, Tableau, Google Spreadsheet and Orange in order to generate different visualizations based upon, language, geographical areas, retweets etc. The sentiment analysis was performed for the sentiments that were attached to the given set of data by the public in their respective tweets. The 23680 tweets were retrieved during the data collection time and there were 13,771 …
Espade: An Efficient And Semantically Secure Shortest Path Discovery For Outsourced Location-Based Services,
2020
Montclair State University
Espade: An Efficient And Semantically Secure Shortest Path Discovery For Outsourced Location-Based Services, Bharath K. Samanthula, Divyadharshini Karthikeyan, Boxiang Dong, K. Anitha Kumari
Department of Computer Science Faculty Scholarship and Creative Works
With the rapid growth of smart devices and technological advancements in tracking geospatial data, the demand for Location-Based Services (LBS) is facing a constant rise in several domains, including military, healthcare and transportation. It is a natural step to migrate LBS to a cloud environment to achieve on-demand scalability and increased resiliency. Nonetheless, outsourcing sensitive location data to a third-party cloud provider raises a host of privacy concerns as the data owners have reduced visibility and control over the outsourced data. In this paper, we consider outsourced LBS where users want to retrieve map directions without disclosing their location information. …
Enhancing A Cluster-Based Tdma Mac Protocol For Vehicle-To-Vehicle Communications,
2020
Universiti Malaya
Enhancing A Cluster-Based Tdma Mac Protocol For Vehicle-To-Vehicle Communications, Abubakar Bello Tambawal
Student Works (2020-2029)
Vehicular Ad hoc Network technology (VANET) is one of the emerging and promising wireless technology, providing support for vehicles to communicate and share resources, (such as safety messages) through vehicle-to-vehicle (V2V) communications. Sequel to that the Time Division Multiple Access (TDMA) MAC protocol using a cluster-based topology has been proposed by the research community. Most of the existing research works focused on the cluster head (CH) election with very few addressing other critical issues, including cluster formation, efficient time slot allocation, and cluster maintenance. These challenges result in an unstable cluster, which could affect the timely delivery of safety applications. …
Fall 2020,
2020
DePaul University
Fall 2020
In The Loop
Studio CDM Documents Remote Initiatives; "Tom of Your Life" Film Release; Animation Jam Goes Virtual; DePaul Experimental Film Showcase 2020; Trackmania Soundtrack; Alumni Games at Pixel Pop; Alumnus Commemorates St. Vincent de Paul; Cybersecurity Champion Alina Kuzmenkova; Walking the Walk: Youth programs at CDM express DePaul’s Vincentian values; Fair Treatment: Three initiatives address racial inequity in health care; They've Got You Covered: A School of Design instructor leads a cottage industry of makers protecting essential workers from the novel coronavirus; Meet Would-Be Hot Topic Influencer Vera Drew; Data Detectives: CDM helps Chicago track the racial proportions of its COVID-19 cases
Using Spatial Analysis And Machine Learning Techniques To Develop A Comprehensive Highway-Rail Grade Crossing Consolidation Model,
2020
Louisiana State University and Agricultural and Mechanical College
Using Spatial Analysis And Machine Learning Techniques To Develop A Comprehensive Highway-Rail Grade Crossing Consolidation Model, Samira Soleimani
LSU Doctoral Dissertations
The safety of highway-railroad grade crossings (HRGC) is still an issue in the United States of America (USA). The grade crossing is where a railroad crosses a road at the same level without any over or underpass. To improve the safety of crossings, the crossings’ condition should be explored from several aspects such as engineering design (speed limit, warning signs, etc.), road condition (number of lanes, surface markings, etc.), rail design (the type of track, ballast, etc.), temporal variables (weather, visibility, time of day, lightning, etc.), social variables (population, race, etc.), and last but not least, spatial variables (the type …
Research In Data Science Dsp 599,
2020
University of Rhode Island
Research In Data Science Dsp 599, Harrison Dekker
Library Impact Statements
No abstract provided.
