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Full-Text Articles in Computer Sciences
Cannabidiol Tweet Miner: A Framework For Identifying Misinformation In Cbd Tweets., Jason Turner
Cannabidiol Tweet Miner: A Framework For Identifying Misinformation In Cbd Tweets., Jason Turner
Electronic Theses and Dissertations
As regulations surrounding cannabis continue to develop, the demand for cannabis-based products is on the rise. Despite not producing the psychoactive effects commonly associated with THC, products containing cannabidiol (CBD) have gained immense popularity in recent years as a potential treatment option for a range of conditions, particularly those associated with pain or sleep disorders. However, due to current federal policies, these products have yet to undergo comprehensive safety and efficacy testing. Fortunately, utilizing advanced natural language processing (NLP) techniques, data harvested from social networks have been employed to investigate various social trends within healthcare, such as disease tracking and …
Campus Safety Data Gathering, Classification, And Ranking Based On Clery-Act Reports, Walaa F. Abo Elenin
Campus Safety Data Gathering, Classification, And Ranking Based On Clery-Act Reports, Walaa F. Abo Elenin
Electronic Theses and Dissertations
Most existing campus safety rankings are based on criminal incident history with minimal or no consideration of campus security conditions and standard safety measures. Campus safety information published by universities/colleges is usually conceptual/qualitative and not quantitative and are based-on criminal records of these campuses. Thus, no explicit and trusted ranking method for these campuses considers the level of compliance with the standard safety measures. A quantitative safety measure is important to compare different campuses easily and to learn about specific campus safety conditions.
In this thesis, we utilize Clery-Act reports of campuses to automatically analyze their safety conditions and generate …
Solving The Challenges Of Concept Drift In Data Stream Classification., Hanqing Hu
Solving The Challenges Of Concept Drift In Data Stream Classification., Hanqing Hu
Electronic Theses and Dissertations
The rise of network connected devices and applications leads to a significant increase in the volume of data that are continuously generated overtime time, called data streams. In real world applications, storing the entirety of a data stream for analyzing later is often not practical, due to the data stream’s potentially infinite volume. Data stream mining techniques and frameworks are therefore created to analyze streaming data as they arrive. However, compared to traditional data mining techniques, challenges unique to data stream mining also emerge, due to the high arrival rate of data streams and their dynamic nature. In this dissertation, …
Messiness: Automating Iot Data Streaming Spatial Analysis, Christopher White, Atilio Barreda Ii
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 …
Data-Driven Operational And Safety Analysis Of Emerging Shared Electric Scooter Systems, Qingyu Ma
Data-Driven Operational And Safety Analysis Of Emerging Shared Electric Scooter Systems, Qingyu Ma
Computational Modeling & Simulation Engineering Theses & Dissertations
The rapid rise of shared electric scooter (E-Scooter) systems offers many urban areas a new micro-mobility solution. The portable and flexible characteristics have made E-Scooters a competitive mode for short-distance trips. Compared to other modes such as bikes, E-Scooters allow riders to freely ride on different facilities such as streets, sidewalks, and bike lanes. However, sharing lanes with vehicles and other users tends to cause safety issues for riding E-Scooters. Conventional methods are often not applicable for analyzing such safety issues because well-archived historical crash records are not commonly available for emerging E-Scooters.
Perceiving the growth of such a micro-mobility …
Exploring The Use Of Social Media To Infer Relationships Between Demographics, Psychographics And Vaccine Hesitancy, Abhimanyu Kapur
Exploring The Use Of Social Media To Infer Relationships Between Demographics, Psychographics And Vaccine Hesitancy, Abhimanyu Kapur
Computer Science Senior Theses
The growing popularity of social media as a platform to obtain information and share one's opinions on various topics makes it a rich source of information for research. In this study, we aimed to develop a framework to infer relationships between demographic and psychographic characteristics of a user and their opinion on a specific narrative - in this case, their stance on taking the COVID-19 vaccine. Twitter was the chosen platform due to the large USA user base and easily available data. Demographic traits included Race, Age, Gender, and Human-vs-Organization Status. Psychographic traits included the Big Five personality traits (Conscientiousness, …
Binary Black Widow Optimization Algorithm For Feature Selection Problems, Ahmed Al-Saedi
Binary Black Widow Optimization Algorithm For Feature Selection Problems, Ahmed Al-Saedi
Theses and Dissertations (Comprehensive)
This thesis addresses feature selection (FS) problems, which is a primary stage in data mining. FS is a significant pre-processing stage to enhance the performance of the process with regards to computation cost and accuracy to offer a better comprehension of stored data by removing the unnecessary and irrelevant features from the basic dataset. However, because of the size of the problem, FS is known to be very challenging and has been classified as an NP-hard problem. Traditional methods can only be used to solve small problems. Therefore, metaheuristic algorithms (MAs) are becoming powerful methods for addressing the FS problems. …
Hierarchical Aggregation Of Multidimensional Data For Efficient Data Mining, Safaa Khalil Alwajidi
Hierarchical Aggregation Of Multidimensional Data For Efficient Data Mining, Safaa Khalil Alwajidi
Dissertations
Big data analysis is essential for many smart applications in areas such as connected healthcare, intelligent transportation, human activity recognition, environment, and climate change monitoring. Traditional data mining algorithms do not scale well to big data due to the enormous number of data points and the velocity of their generation. Mining and learning from big data need time and memory efficiency techniques, albeit the cost of possible loss in accuracy. This research focuses on the mining of big data using aggregated data as input. We developed a data structure that is to be used to aggregate data at multiple resolutions. …
Mobile Location Data Analytics, Privacy, And Security, Yunhe Feng
Mobile Location Data Analytics, Privacy, And Security, Yunhe Feng
Doctoral Dissertations
Mobile location data are ubiquitous in the digital world. People intentionally and unintentionally generate numerous location data when connecting to cellular networks or sharing posts on social networks. As mobile devices normally choose to communicate with nearby cell towers outdoor, it is reasonable to infer human locations based on cell tower coordinates. Many social networking platforms, such as Twitter, allow users to geo-tag their posts optionally, publishing personal locations to friends or everyone. These location data are particularly useful for understanding mobile usage behaviors and human mobility patterns. Meanwhile, the public expresses great concern about the privacy and security of …
Statistical Machine Learning Methods For Mining Spatial And Temporal Data, Fei Tan
Statistical Machine Learning Methods For Mining Spatial And Temporal Data, Fei Tan
Dissertations
Spatial and temporal dependencies are ubiquitous properties of data in numerous domains. The popularity of spatial and temporal data mining has thus grown with the increasing prevalence of massive data. The presence of spatial and temporal attributes not only provides complementary useful perspectives, but also poses new challenges to the representation and integration into the learning procedure. In this dissertation, the involved spatial and temporal dependencies are explored with three genres: sample-wise, feature-wise, and target-wise. A family of novel methodologies is developed accordingly for the dependency representation in respective scenarios.
First, dependencies among discrete, continuous and repeated observations are studied …
So What Are You Going To Do With That? The Promises And Pitfalls Of Massive Data Sets, Sigrid Anderson Cordell, Melissa Gomis
So What Are You Going To Do With That? The Promises And Pitfalls Of Massive Data Sets, Sigrid Anderson Cordell, Melissa Gomis
UNL Libraries: Faculty Publications
This article takes as its case study the challenge of data sets for text mining, sources that offer tremendous promise for digital humanities (DH) methodology but present specific challenges for humanities scholars. These text sets raise a range of issues: What skills do you train humanists to have? What is the library’s role in enabling and supporting use of those materials? How do you allocate staff? Who oversees sustainability and data management? By addressing these questions through a specific use case scenario, this article shows how these questions are central to mapping out future directions for a range of library …
Extreme Data Mining: Inference From Small Datasets, Răzvan Andonie
Extreme Data Mining: Inference From Small Datasets, Răzvan Andonie
All Faculty Scholarship for the College of the Sciences
Neural networks have been applied successfully in many fields. However, satisfactory results can only be found under large sample conditions. When it comes to small training sets, the performance may not be so good, or the learning task can even not be accomplished. This deficiency limits the applications of neural network severely. The main reason why small datasets cannot provide enough information is that there exist gaps between samples, even the domain of samples cannot be ensured. Several computational intelligence techniques have been proposed to overcome the limits of learning from small datasets.
We have the following goals: i. To …
Artificial Intelligence – I: A Two-Step Approach For Improving Efficiency Of Feedforward Multilayer Perceptrons Network, Shoukat Ullah, Zakia Hussain
Artificial Intelligence – I: A Two-Step Approach For Improving Efficiency Of Feedforward Multilayer Perceptrons Network, Shoukat Ullah, Zakia Hussain
International Conference on Information and Communication Technologies
An artificial neural network has got greater importance in the field of data mining. Although it may have complex structure, long training time, and uneasily understandable representation of results, neural network has high accuracy and is preferable in data mining. This research paper is aimed to improve efficiency and to provide accurate results on the basis of same behaviour data. To achieve these objectives, an algorithm is proposed that uses two data mining techniques, that is, attribute selection method and cluster analysis. The algorithm works by applying attribute selection method to eliminate irrelevant attributes, so that input dimensionality is reduced …
Relational Methodology For Data Mining And Knowledge Discovery, Engenii Vityaev, Boris Kovalerchuk
Relational Methodology For Data Mining And Knowledge Discovery, Engenii Vityaev, Boris Kovalerchuk
All Faculty Scholarship for the College of the Sciences
Knowledge discovery and data mining methods have been successful in many domains. However, their abilities to build or discover a domain theory remain unclear. This is largely due to the fact that many fundamental KDD&DM methodological questions are still unexplored such as (1) the nature of the information contained in input data relative to the domain theory, and (2) the nature of the knowledge that these methods discover. The goal of this paper is to clarify methodological questions of KDD&DM methods. This is done by using the concept of Relational Data Mining (RDM), representative measurement theory, an ontology of a …
Symbolic Methodology For Numeric Data Mining, Boris Kovalerchuk, Engenii Vityaev
Symbolic Methodology For Numeric Data Mining, Boris Kovalerchuk, Engenii Vityaev
All Faculty Scholarship for the College of the Sciences
Currently statistical and artificial neural network methods dominate in data mining applications. Alternative relational (symbolic) data mining methods have shown their effectiveness in robotics, drug design, and other areas. Neural networks and decision tree methods have serious limitations in capturing relations that may have a variety of forms. Learning systems based on symbolic first-order logic (FOL) representations capture relations naturally. The learned regularities are understandable directly in domain terms that help to build a domain theory. This paper describes relational data mining methodology and develops it further for numeric data such as financial and spatial data. This includes (1) comparing …