Detecting Overlapping Gene Regions Using The U-Net Attention Mechanism,
2023
Minnesota State University, Mankato
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 …
Unlocking User Identity: A Study On Mouse Dynamics In Dual Gaming Environments For Continuous Authentication,
2023
Minnesota State University, Mankato
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 …
Generalized Sparse Bayesian Learning And Application To Image Reconstruction,
2023
Old Dominion University
Generalized Sparse Bayesian Learning And Application To Image Reconstruction, Jan Glaubitz, Anne Gelb, Guohui Song
Mathematics & Statistics Faculty Publications
Image reconstruction based on indirect, noisy, or incomplete data remains an important yet challenging task. While methods such as compressive sensing have demonstrated high-resolution image recovery in various settings, there remain issues of robustness due to parameter tuning. Moreover, since the recovery is limited to a point estimate, it is impossible to quantify the uncertainty, which is often desirable. Due to these inherent limitations, a sparse Bayesian learning approach is sometimes adopted to recover a posterior distribution of the unknown. Sparse Bayesian learning assumes that some linear transformation of the unknown is sparse. However, most of the methods developed are …
Aircraft Damage Classification By Using Machine Learning Methods,
2023
Bahcesehir University
Aircraft Damage Classification By Using Machine Learning Methods, Tüzün Tolga İnan
International Journal of Aviation, Aeronautics, and Aerospace
Safety is the most significant factor that affected incidents (non-fatal) and accidents (fatal) in civil aviation history related to scheduled flights. In the history of scheduled flights, the total incident and accident number until 2022 is 1988. In this study, 677 of them are taken into consideration since 11 September 2001. The purpose of this study is to reveal the factors that can classify type of aircraft damages such as none, minor and substantial in all-time incidents and accidents. ML algorithms with different configurations are applied for the classification process. The RFE and PCA are used to find the most …
Making Data-Driven Decisions For Investing In Restaurant Business: A Case Study Based On Zomato Dataset,
2023
Minnesota State University, Mankato
Making Data-Driven Decisions For Investing In Restaurant Business: A Case Study Based On Zomato Dataset, Rachna Shah
All Graduate Theses, Dissertations, and Other Capstone Projects
In today’s fast-paced world, where time is a precious commodity, the ability to order a wide array of cuisines from the comfort of your home or office impacts your quality of life. With an increasing number of food delivery services, with just a few taps on the smartphone or clicks on the computer, we can enjoy the food we want. The importance of this convenience cannot be overstated, as it allows people to save time and effort that would otherwise be spent on cooking, grocery shopping, or dining out. As the food delivery system grows and develops, its economic framework …
Dynamic Predictions Of Thermal Heating And Cooling Of Silicon Wafer,
2023
San Jose State University
Dynamic Predictions Of Thermal Heating And Cooling Of Silicon Wafer, Hitesh Kumar
Master's Projects
Neural Networks are now emerging in every industry. All the industries are trying their best to exploit the benefits of neural networks and deep learning to make predictions or simulate their ongoing process with the use of their generated data. The purpose of this report is to study the heating pattern of a silicon wafer and make predictions using various machine learning techniques. The heating of the silicon wafer involves various factors ranging from number of lamps, wafer properties and points taken in consideration to capture the heating temperature. This process involves dynamic inputs which facilitates the heating of the …
Application Of Knowledge Graph Techniques On Textbooks,
2023
San Jose State University
Application Of Knowledge Graph Techniques On Textbooks, Yutong Yao
Master's Projects
Textbooks are written and organized in a way that facilitates learning and understanding. Sections like glossary terms at the end of a textbook provide guidance on the topic of interest. However, it takes manual effort to create the index terms in the glossary that highlight the key referenced terminologies and related terms. Knowledge graphs, which have been used to represent and even reason over data and knowledge, can potentially capture textbook’s important terms, concepts, and their relations. Popular since the initial introduction by Google Knowledge Graphs (KGs), they combine graph and data to capture and model enormous amounts of relational …
Classification Of Darknet Traffic By Application Type,
2023
San Jose State University
Classification Of Darknet Traffic By Application Type, Shruti Sharma
Master's Projects
The darknet is frequently exploited for illegal purposes and activities, which makes darknet traffic detection an important security topic. Previous research has focused on various classification techniques for darknet traffic using machine learning and deep learning. We extend previous work by considering the effectiveness of a wide range of machine learning and deep learning technique for the classification of darknet traffic by application type. We consider the CICDarknet2020 dataset, which has been used in many previous studies, thus enabling a direct comparison of our results to previous work. We find that XGBoost performs the best among the classifiers that we …
An Incentivization Mechanism Towards Network-Slice As A Service,
2023
San Jose State University
An Incentivization Mechanism Towards Network-Slice As A Service, Yosha Mundhra
Master's Projects
Next-generation communication networks are expected to support many different types of services. Network slicing virtually divides one physical network into several virtual networks in order to accommodate diverse demands for network connections. Assuming multiple network domains are stitched together to provide end-to-end network slices for such diverse services, it is critical to consider the economic behaviors and revenue models of the stakeholders. This project analyzes the economic behaviors from the perspective of a Global Slice Coordinator (GSC) that receives a slice request and coordinates the end-to-end resource allocations across multiple network domains. The primary role of the GSC is to …
On Measuring The Degree Of Resource Isolation In Elastic Network Slicing,
2023
San Jose State University
On Measuring The Degree Of Resource Isolation In Elastic Network Slicing, Nitin Datta Movva
Master's Projects
With the growing popularity of 5G networks and their vast use cases, users must be provided with the services they request. Network slicing is a technique for establishing numerous distinct logical and virtualized networks over a shared multi-domain infrastructure. In particular, the emerging elastic slicing, which allows users to scale up or down the reserved amount of resources during the slice life cycle, is a promising technique to accommodate diverse network services more affordably. However, users may sometimes need more resources, which is impossible to provide due to the abundance of users and lack of additional resources. To our knowledge, …
Machine Learning Prediction Of Dod Personal Property Shipment Costs,
2023
Air Force Institute of Technology
Machine Learning Prediction Of Dod Personal Property Shipment Costs, Tiffany Tucker, Torrey J. Wagner, Paul Auclair, Brent T. Langhals
Faculty Publications
U.S. Department of Defense (DoD) personal property moves account for 15% of all domestic and international moves - accurate prediction of their cost could draw attention to outlier shipments and improve budget planning. In this work 136,140 shipments between 13 personal property shipment hubs from April 2022 through March 2023 with a total cost of $1.6B were analyzed. Shipment cost was predicted using recursive feature elimination on linear regression and XGBoost algorithms, as well as through neural network hyperparameter sweeps. Modeling was repeated after removing 28 features related to shipment hub location and branch of service to examine their influence …
Tenvr: Matlab-Based Toolbox For Environmental Research,
2023
Old Dominion University
Tenvr: Matlab-Based Toolbox For Environmental Research, Aleksandar I. Goranov, Rachel L. Sleighter, Dobromir A. Yordanov, Patrick G. Hatcher
Chemistry & Biochemistry Faculty Publications
With the advancements in science and technology, datasets become larger and more multivariate, which warrants the need for programming tools for fast data processing and multivariate statistical analysis. Here, the MATLAB-based Toolbox for Environmental Research "TEnvR" (pronounced "ten-ver") is introduced. This novel toolbox includes 44 open-source codes for automated data analysis from a multitude of techniques, such as ultraviolet-visible, fluorescence, and nuclear magnetic resonance spectroscopies, as well as from ultrahigh resolution mass spectrometry. Provided are codes for processing data (e.g., spectral corrections, formula assignment), visualization of figures, calculation of metrics, multivariate statistics, and automated work-up of large datasets. TEnvR allows …
Utilizing Machine Learning In Healthcare In An Ethical Fashion,
2023
Claremont Colleges
Utilizing Machine Learning In Healthcare In An Ethical Fashion, Nishka Ayyar
CMC Senior Theses
This thesis paper explores the ethical considerations surrounding the use of machine learning (ML) solutions in healthcare. The background section discusses the basics of machine learning techniques and algorithms, and the increasing interest in their utilization in the healthcare sector. The paper then reviews and critically analyzes four studies that highlight concerns related to using ML in healthcare, including issues of bias, privacy, accountability, and transparency. Based on the analysis of these studies, the paper presents several recommendations for addressing these concerns. The paper concludes with a discussion on the potential benefits of using machine learning technology in healthcare. Ultimately, …
Predicting Heart Disease Using Tree-Based Model,
2023
University of Central Florida
Predicting Heart Disease Using Tree-Based Model, Emil Agbemade
Data Science and Data Mining
The paper presents a study on the use of machine learning algorithms for the prediction of heart disease, which is the leading cause of death worldwide. The study focuses on the use of decision tree algorithms, which have the advantage of considering a large number of risk factors. The heart disease data set was obtained from the UCI Machine Learning Repository and was analyzed using a decision tree classifier. The data set had 6 missing data points, which were deleted, leaving 279 instances for analysis. One-hot-encoding was performed on categorical variables with more than two responses. The decision tree classifier …
Developing A Data-Driven Statistical Model For Accurately Predicting The Superconducting Critical Temperature Of Materials Using Multiple Regression And Gradient-Boosted Methods,
2023
University of Central Florida
Developing A Data-Driven Statistical Model For Accurately Predicting The Superconducting Critical Temperature Of Materials Using Multiple Regression And Gradient-Boosted Methods, Emil Agbemade
Data Science and Data Mining
This study focuses on developing a statistical model for estimating the superconducting critical temperature (Tc) of materials using a data-driven strategy. The study analyzed 21,263 superconductors and used a combination of multiple regression and gradient-boosted models to make predictions. The analysis included a descriptive analysis of the distribution of Tc, feature selection using the Backwards selection method, and model diagnostics. The results showed that the gradient-boosted method outperformed the multiple linear regression method with an RMSE of 12.01 and an R2 value of 88.23 after fine-tuning its hyperparameters. The study concludes that the gradient-boosted method is an effective approach …
Classification Of Adult Income Using Decision Tree,
2023
University of Central Florida
Classification Of Adult Income Using Decision Tree, Roland Fiagbe
Data Science and Data Mining
Decision tree is a commonly used data mining methodology for performing classification tasks. It is a tree-based supervised machine learning algorithm that is used to classify or make predictions in a path of how previous questions are answered. Generally, the decision tree algorithm categorizes data into branch-like segments that develop into a tree that contains a root, nodes, and leaves. This project seeks to explore the decision tree methodology and apply it to the Adult Income dataset from the UCI Machine Learning Repository, to determine whether a person makes over 50K per year and determine the necessary factors that improve …
Variable Selection Using Lasso And Elastic Net Regression On High Dimensional Genetic Architecture Data Of Maize Flowering Time,
2023
University of Central Florida
Variable Selection Using Lasso And Elastic Net Regression On High Dimensional Genetic Architecture Data Of Maize Flowering Time, Pradip Dhakal
Data Science and Data Mining
Variable selection is one of the key components in the machine learning area. This method reduces the unwanted and redundant predictors in the model, which prevents the overfitting situation. Since the model contains few significant predictors, the model is less likely to learn the trend from the noise. Further, the time to train the model reduces when we have only a few valuable variables.
A Linear Regression Model To Predict The Critical Temperature Of A Superconductor,
2023
University of Central Florida
A Linear Regression Model To Predict The Critical Temperature Of A Superconductor, Amir Alipour Yengejeh
Data Science and Data Mining
Since the superconductivity has been introduced, almost all studies in this area have been striving to predict the critical temperature ($T_{c}$) through the features extracted from the superconductor's chemical formula. In this study, thus, we are interested in exploring the linear association between $T_{c}$ and the related features.
Variable Selection And Regression Analysis,
2023
University of Central Florida
Variable Selection And Regression Analysis, Emil Agbemade
Data Science and Data Mining
One of the most valuable crop species, maize, has been the subject of genetic study and experimentation for more than a century. However, species that share similarities and differences across a wide spectrum have developed astonishing adaptations as a result of small changes throughout time. Because it is usual practice to determine the genotypes of thousands of single nucleotide polymorphism (SNP) markers for thousands of patients, the data set we are dealing with has an issue with small n and large p. The result of this is that there are noticeably more predictor factors than responder variables. The original data …
Silent Agony: Automated Detection Of Ethnic And Religious Cyberbullying Using Machine Learning,
2023
University of Central Florida
Silent Agony: Automated Detection Of Ethnic And Religious Cyberbullying Using Machine Learning, Emil Agbemade
Data Science and Data Mining
The use of electronic mobile devices, social media, and networking websites has increased tremendously in recent years. Despite the advantages of these systems, such as exchanging ideas and information, being sociable, and providing entertainment, users may encounter adverse behaviors like toxicity, bullying, extremism, and cruelty. The prevalence of such behaviors has grown significantly in cyberspace, posing a threat to individuals and communities. To address this issue, there is a high demand for automated cyberbullying detection systems. Machine learning algorithms have been widely used to build such systems by classifying and detecting cyberbullying. In this study, we employed popular machine learning …
