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Physical Sciences and Mathematics Commons™
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Articles 1 - 8 of 8
Full-Text Articles in Physical Sciences and Mathematics
Unlocking User Identity: A Study On Mouse Dynamics In Dual Gaming Environments For Continuous Authentication, Marcho Setiawan Handoko
Unlocking User Identity: A Study On Mouse Dynamics In Dual Gaming Environments For Continuous Authentication, Marcho Setiawan Handoko
All Graduate Theses, Dissertations, and Other Capstone Projects
With the surge in information management technology reliance and the looming presence of cyber threats, user authentication has become paramount in computer security. Traditional static or one-time authentication has its limitations, prompting the emergence of continuous authentication as a frontline approach for enhanced security. Continuous authentication taps into behavior-based metrics for ongoing user identity validation, predominantly utilizing machine learning techniques to continually model user behaviors. This study elucidates the potential of mouse movement dynamics as a key metric for continuous authentication. By examining mouse movement patterns across two contrasting gaming scenarios - the high-intensity "Team Fortress" and the low-intensity strategic …
Detecting Overlapping Gene Regions Using The U-Net Attention Mechanism, Samuel Lemma
Detecting Overlapping Gene Regions Using The U-Net Attention Mechanism, Samuel Lemma
All Graduate Theses, Dissertations, and Other Capstone Projects
The current issue of locating, diagnosing, and treating cancer and other diseases linked to specific target genes necessitates the creation of a reliable system for precisely identifying target genes that are initially extracted from a human chromosome. Current methodologies often suffer from overlapping gene regions in the target gene that occurs during the analysis process, which can have a substantial impact on the accuracy of the results. Our recommended approach, which was the appropriate model to apply for this particular problem, is set to enhance the analytical process by utilizing neural networks' U-Net with an attention mechanism. We were able …
Xtreme-Noc: Extreme Gradient Boosting Based Latency Model For Network-On-Chip Architectures, Ilma Sheriff
Xtreme-Noc: Extreme Gradient Boosting Based Latency Model For Network-On-Chip Architectures, Ilma Sheriff
All Graduate Theses, Dissertations, and Other Capstone Projects
Multiprocessor System-on-Chip (MPSoC) integrating heterogeneous processing elements (CPU, GPU, Accelerators, memory, I/O modules ,etc.) are the de-facto design choice to meet the ever-increasing performance/Watt requirements from modern computing machines. Although at consumer level the number of processing elements (PE) are limited to 8-16, for high end servers, the number of PEs can scale up to hundreds. A Network-on-Chip (NoC) is a microscale network that facilitates the packetized communication among the PEs in such complex computational systems. Due to the heterogeneous integration of the cores, execution of diverse (serial and parallel) applications on the PEs, application mapping strategies, and many other …
A Methodology For Detecting Credit Card Fraud, Kayode Ayorinde
A Methodology For Detecting Credit Card Fraud, Kayode Ayorinde
All Graduate Theses, Dissertations, and Other Capstone Projects
Fraud detection has appertained to many industries such as banking, retails, financial services, healthcare, etc. As we know, fraud detection is a set of campaigns undertaken to avert the acquisition of illegal means to obtain money or property under false pretense. With an unlimited and growing number of ways fraudsters commit fraud crimes, detecting online fraud was so tricky to achieve. This research work aims to examine feasible ways to identify credit card fraudulent activities that negatively impact financial institutes. In the United States, an average of U.S consumers lost a median of $429 from credit card fraud in 2017, …
Assessing And Forecasting Chlorophyll Abundances In Minnesota Lake Using Remote Sensing And Statistical Approaches, Ben Von Korff
Assessing And Forecasting Chlorophyll Abundances In Minnesota Lake Using Remote Sensing And Statistical Approaches, Ben Von Korff
All Graduate Theses, Dissertations, and Other Capstone Projects
Harmful algae blooms (HABs) can negatively impact water quality, lake aesthetics, and can harm human and animal health. However, monitoring for HABs is rare in Minnesota. Detecting blooms which can vary spatially and may only be present briefly is challenging, so expanding monitoring in Minnesota would require the use of new and cost efficient technologies. Unmanned aerial vehicles (UAVs) were used for bloom mapping using RGB and near-infrared imagery. Real time monitoring was conducted in Bass Lake, in Faribault County, MN using trail cameras. Time series forecasting was conducted with high frequency chlorophyll-a data from a water quality sonde. Normalized …
Classification Of Chess Games: An Exploration Of Classifiers For Anomaly Detection In Chess, Masudul Hoque
Classification Of Chess Games: An Exploration Of Classifiers For Anomaly Detection In Chess, Masudul Hoque
All Graduate Theses, Dissertations, and Other Capstone Projects
Chess is a strategy board game with its inception dating back to the 15th century. The Covid-19 pandemic has led to a chess boom online with 95,853,038 chess games being played during January, 2021 on lichess.com. Along with the chess boom, instances of cheating have also become more rampant. Classifications have been used for anomaly detection in different fields and thus it is a natural idea to develop classifiers to detect cheating in chess. However, there are no specific examples of this, and it is difficult to obtain data where cheating has occurred. So, in this paper, we develop 4 …
A Statistical Analysis And Machine Learning Of Genomic Data, Jongyun Jung
A Statistical Analysis And Machine Learning Of Genomic Data, Jongyun Jung
All Graduate Theses, Dissertations, and Other Capstone Projects
Machine learning enables a computer to learn a relationship between two assumingly related types of information. One type of information could thus be used to predict any lack of informaion in the other using the learned relationship. During the last decades, it has become cheaper to collect biological information, which has resulted in increasingly large amounts of data. Biological information such as DNA is currently analyzed by a variety of tools. Although machine learning has already been used in various projects, a flexible tool for analyzing generic biological challenges has not yet been made. The recent advancements in the DNA …
An Exploration Of Multi-Agent Learning Within The Game Of Sheephead, Brady Brau
An Exploration Of Multi-Agent Learning Within The Game Of Sheephead, Brady Brau
All Graduate Theses, Dissertations, and Other Capstone Projects
In this paper, we examine a machine learning technique presented by Ishii et al. used to allow for learning in a multi-agent environment and apply an adaptation of this learning technique to the card game Sheephead. We then evaluate the effectiveness of our adaptation by running simulations against rule-based opponents. Multi-agent learning presents several layers of complexity on top of a single-agent learning in a stationary environment. This added complexity and increased state space is just beginning to be addressed by researchers. We utilize techniques used by Ishii et al. to facilitate this multi-agent learning. We model the environment of …