Data Science Commons^™

Enhancing Password Security And Memorability Using Machine Learning And Linguistic Patterns, Jared Wise

LSU New Orleans Theses and Dissertations

In the digital age, text-based passwords remain a primary method for securing online accounts. Yet, users frequently face a dilemma between creating passwords that are easy to remember and sufficiently secure against cyberattacks. This research introduces an approach to password generation that bridges this gap by utilizing linguistic patterns, particularly song lyrics, to develop highly secure and naturally memorable passwords. Using large lyric datasets gained from web scrapes from popular song lyric websites (AZ Lyrics, Genius), features are extracted from a corpus of over 5 million lyrics using sentence structure and natural language processing in a novel way. In using …

Chatgpt Vs Expert-Guided Care Pathways For Postesophagectomy Symptom Management, Mohamad K Abou Chaar, Giovanna Grigsby-Rocca, Ming Huang, Shanda H Blackmon

Faculty, Staff and Student Publications

BACKGROUND: The objective of this study was to compare generative artificial intelligence-initiated care pathways, using ChatGPT, with expert-guided consensus-initiated care pathways from AskMayoExpert (AME) for symptom management of esophageal cancer patients after esophagectomy.

METHODS: A formal protocol for development of 9 AME care pathways was followed for specific patient-identified domains after esophagectomy for esophageal cancer. Domain scores were measured and assessed through the Upper Digestive Disease tool. These care pathways were developed by experts validated by a consensus-driven methodology. ChatGPT was used to answer specific questions similar to the AME care pathway on April 9, 2023, and March 28, 2024. …

Addressing Ethical Issues In Healthcare Artificial Intelligence Using A Lifecycle-Informed Process, Benjamin X Collins, Jean-Christophe Bélisle-Pipon, Barbara J Evans, Kadija Ferryman, Xiaoqian Jiang, Camille Nebeker, Laurie Novak, Kirk Roberts, Martin Were, Zhijun Yin, Vardit Ravitsky, Joseph Coco, Rachele Hendricks-Sturrup, Ishan Williams, Ellen W Clayton, Bradley A Malin, Bridge2ai Ethics And Trustworthy Ai Working Group

Faculty, Staff and Student Publications

OBJECTIVES: Artificial intelligence (AI) proceeds through an iterative and evaluative process of development, use, and refinement which may be characterized as a lifecycle. Within this context, stakeholders can vary in their interests and perceptions of the ethical issues associated with this rapidly evolving technology in ways that can fail to identify and avert adverse outcomes. Identifying issues throughout the AI lifecycle in a systematic manner can facilitate better-informed ethical deliberation.

MATERIALS AND METHODS: We analyzed existing lifecycles from within the current literature for ethical issues of AI in healthcare to identify themes, which we relied upon to create a lifecycle …

Manifold Learning And Undersampling Approaches For Imbalanced Class Sentiment Classification, L.M. Risman Dwi Jumansyah, Agus Mohamad Soleh, Utami Dyah Syafitri

Knowledge Engineering and Data Science

Movie reviews are crucial in determining a film's success by influencing audience decisions. Automating sentiment classification is essential for efficient public opinion analysis. However, it faces challenges such as high-dimensional data and imbalanced class distributions. This study addresses these issues by applying manifold learning techniques, Principal Component Analysis (PCA) and Laplacian Eigenmaps (LE) to reduce data complexity and undersampling strategies (Random Undersampling (RUS) and EasyEnsemble) to balance data and improve predictions for both sentiment classes. On reviews of The Raid 2: Berandal, EasyEnsemble achieved the highest average G-Mean of 0.694 using Term Frequency-Inverse Document Frequency (TF IDF) features with a …

Constructing Qur’An Recitation Classification Using Alexnet Algorithm, Harits Ar Rosyid, Dzulkifli Abdullah, Mohammed S. Alqahtani

Knowledge Engineering and Data Science

The growing demands for accurate and efficient methods in the Qur'an recitation classification highlight the limitations of existing models, particularly in assisting the memorization process. This study aims to address these challenges by implementing the AlexNet Convolutional Neural Network architecture, widely recognized for its effectiveness in image classification, to classify the Qur'an recitations using the Mel Frequency Cepstral Coefficient (MFCC) as the feature extraction method. The research involves several stages, including data collection, preprocessing (audio segmentation by verse), data augmentation, feature extraction, and classification using the AlexNet architecture, followed by performance evaluation. Key results demonstrate that the combination of MFCC …

Deep Learning Approach For Dental Anomalies X-Ray Imaging Using Yolov8, Amelia Ritahani Ismail, Md Salim Sadman Taseen

Knowledge Engineering and Data Science

Dental X-ray imaging is a critical diagnostic tool for identifying various dental anomalies. However, manual interpretation is time-consuming, prone to human error, and requires specialized expertise. Deep learning models, particularly object detection frameworks like YOLO, have demonstrated promising results in automating medical image analysis. This study aims to develop and evaluate a YOLOv8-based deep learning model for automated detection and classification of 14 dental anomaly categories, including Caries, Crowns, Fillings, Implants, and Periapical lesions. The proposed approach addresses limitations in previous YOLO versions by leveraging anchor-free detection and enhanced feature extraction for improved accuracy. The model was trained on a …

Classification Of Anxiety Levels Entering The World Of Work In Final Year Students Using The Neighbor Weighted K-Nearest Neighbor Method, Awang Hendrianto Pratomo, Muhammad Fahmi Adam, Dessyanto Boedi Prasetyo

Knowledge Engineering and Data Science

This study evaluates the accuracy of the Neighbor Weighted K-Nearest Neighbor (NWKNN) method in classifying the anxiety levels of final-year students as they prepare to enter the workforce, particularly in cases of unbalanced data distribution. The system was developed using the prototype method, and NWKNN was applied to classify anxiety levels into low, medium, and high categories. Testing using the Confusion Matrix demonstrated strong performance, achieving an accuracy of 94% based on a dataset of 1009 students, with a 90:10 ratio of training to test data. The results indicate that NWKNN effectively provides classification input values, making it a reliable …

Comparative Analysis Of Bpnn And Lvq For Sundanese Character Recognition, Haviluddin Haviluddin, Herman Santoso Pakpahan, Dinda Izmya Nurpadillah, Hario Jati Setyadi, Medi Taruk, Rayner Alfred

Knowledge Engineering and Data Science

The Sundanese script (Aksara Sunda), an essential part of Sundanese cultural heritage, has been used since the 14th century AD. However, recognizing handwritten Sundanese characters remains challenging due to variations in individual writing styles. This study compares the performance of Backpropagation Neural Network (BPNN) and Learning Vector Quantization (LVQ) for recognizing handwritten Sundanese vowel (Swara) characters. A dataset was collected from 15 individuals, each writing seven Sundanese vowel characters, which were then used for training and testing the recognition models. Experimental results show that BPNN outperforms LVQ, achieving a higher classification accuracy (95.23%), lower Mean Squared Error (MSE), and faster …

Computational Representation, Analysis And Verification Of Requirements In Engineering Design And Systems Engineering, Chandan Kumar Sahu

All Dissertations

Systems are developed to satisfy a set of requirements derived from stakeholders’ needs, defining the problem space for which the system is created as a feasible solution. The system design process begins with eliciting these requirements and concludes with validating whether the created system meets them. Requirements engineering (RE) encompasses elicitation, representation, analysis, documentation, verification, and validation. However, challenges in RE, such as imprecision in natural language (NL), proprietary restrictions, and a lack of standardized quality metrics, hinder the creation of well-formed and comprehensive requirements. These challenges complicate formalization and analysis of requirements.

This dissertation addresses these challenges by proposing …

Leveraging High-Frequency Water Quality Data And Machine Learning For Monitoring Harmful Algal Blooms, Ibrahim Busari

All Dissertations

Freshwater management is one of the most critical resources on the earth due to the plethora of water use and its limited availability. Increased algae proliferation is one of the significant problems of freshwater bodies that is triggered by nutrient enrichment and enabling conditions such as light and warm temperatures. This algal proliferation is toxic to the ecosystem through their biomass and potential toxin production that can cause hypoxic conditions and is often referred to as Harmful Algal Blooms (HABs). Current monitoring approaches include laboratory analysis of water samples to observe algal cells, monitoring of water quality parameters using water …

Decoding Neural Networks: An Information-Theoretic Guide To Interpretability, Error Analysis And Efficiency, Mackenzie J. Meni

Theses and Dissertations

This dissertation addresses critical challenges in neural network design by leveraging entropy-based techniques to improve model efficiency, interpretability, and bias reduction. Focusing on the unique demands of computer vision applications, particularly object detection and classification for real-time systems, this work introduces a series of innovative methods centered on information theory. At the core of these methods is the Probabilistic Explanations of Entropic Knowledge (PEEK) framework, a tool developed to analyze and visualize entropy distributions across feature maps. PEEK offers insights into information flow within neural networks, making it possible to pinpoint layers that contribute meaningfully to decision-making or identify those …

A Machine Learning Approach For Survival Analysis Of Transplanted Kidneys Based On Donors’ And Recipients’ Factors., Alain Edward Despeignes

Theses and Dissertations

Over seven thousand people on average die each year in the United States waiting for an organ transplant due to the shortage of donated organs. With this alarming concern, efforts from the health organizations like the United Network Organ Sharing (UNOS) and government officials have considered avenues to remedy this distress, one of which is to investigate the characteristics among donors and recipients that affects the longevity of donated organs. The goal of this project is to investigate the survival time of transplanted kidneys from 1987 to 2018 with regards to the donors’ and the recipients’ characteristics including gender, ethnicity, …

Anomaly Detection Using Unsupervised Machine Learning Algorithms: A Simulation Study, Edmund F. Agyemang

School of Mathematical & Statistical Sciences Faculty Publications

This study presents a comprehensive evaluation of five prominent unsupervised machine learning anomaly detection algorithms: One-Class Support Vector Machine (One-Class SVM), One-Class SVM with Stochastic Gradient Descent (SGD), Isolation Forest (iForest), Local Outlier Factor (LOF), and Robust Covariance (Elliptic Envelope). Through systematic analysis on a synthetically simulated dataset, the study assessed each algorithm’s predictive performance using accuracy, precision, recall, and F1 score specifically for outlier detection. The evaluation reveals that One-Class SVM, Isolation Forest, and Robust Covariance are more effective in identifying outliers in the synthetic simulated dataset, with Isolation Forest slightly outperforming the other algorithms in terms of balancing …

De-Identification Is Not Enough: A Comparison Between De-Identified And Synthetic Clinical Notes, Atiquer Rahman Sarkar, Yao-Shun Chuang, Noman Mohammed, Xiaoqian Jiang

Faculty, Staff and Student Publications

For sharing privacy-sensitive data, de-identification is commonly regarded as adequate for safeguarding privacy. Synthetic data is also being considered as a privacy-preserving alternative. Recent successes with numerical and tabular data generative models and the breakthroughs in large generative language models raise the question of whether synthetically generated clinical notes could be a viable alternative to real notes for research purposes. In this work, we demonstrated that (i) de-identification of real clinical notes does not protect records against a membership inference attack, (ii) proposed a novel approach to generate synthetic clinical notes using the current state-of-the-art large language models, (iii) evaluated …

Digital Humanities: Using Computational Methods On Literature To Understand Human-Water Relations, Ariel Yang

Cybersecurity Undergraduate Research Showcase

By using computational techniques to analyze literature, deeper insights can be gained into human-water relationships across different historical and cultural contexts. Natural Language Processing (NLP) and other data science methods can explore applications of traditional ecological knowledge (TEK) and underlying emotions or beliefs in literature to help understand sustainability. Protecting this sensitive cultural data through ethical applications can further secure future implementations of policies, urban planning, and environmental relationships.

Holistic Correlation Measure For Enhanced Encapsulation Of Trait Heterogeneity And Discovery Of Co-Expression, Zachary Valleroy

Theses

Large-scale, high-dimensional data analyses can be computationally prohibitive due to combinatorial explosion of the search space for finding complex patterns; a viable alternative is network modeling for abstraction and quantifying intrinsic data associations. Prominent network analysis methods furnish frameworks for model synthesis and validation but rely on standard correlation measures impaired by semi-supervised biases, latent heterogeneity, and uneven discretization techniques. Here we investigate a holistic measure for encapsulating data heterogeneity for enhanced efficacy of revealing complex patterns through network analysis. Our unique correlation metric, K-medoids Utility for Duo Original Similarities (Kudos), exhaustively factors real-valued analyte data to compute …

Dynamic Knowledge Elicitation: Leveraging Student Feedback For Improved Language Model Distillation, Reuven Muller

Master's Theses

Large Language Models (LLMs) have significantly advanced the field of natural language processing but remain resource-intensive and impractical for many organizations. Specialist models offer a viable alternative, often developed through Knowledge Distillation (KD) techniques. However, traditional KD methods rely on predefined static datasets to elicit knowledge from the teacher model, failing to dynamically address the weaknesses of the student model during training. This research introduces two novel methods for adaptive knowledge elicitation: Feedback-Driven Question Generation and Agent-Based Targeted Question Generation. These methods iteratively expand the training dataset based on the student model’s performance, leveraging a teacher model to generate targeted …

Benefits And Challenges Of Constructing Low-Altitude Air Route Network Infrastructure For Developing Low-Altitude Economy, Xiaohan Liao, Chenchen Xu, Huping Ye

Bulletin of Chinese Academy of Sciences (Chinese Version)

Low-altitude airspace is a resource that needs to be fully explored, and the low-altitude economy is a new type of economic activity resulted from low-altitude airspace exploration and utilization. As the primary players of low-altitude flight activities, the commercialization and wide applications of unmanned aerial vehicles (UAVs) are promoting the prosperity of the low-altitude economy. A low-altitude air route network is an effective means to ensure the safe and efficient operations of a large number of UAVs, and it is also a new key infrastructure hosting low-altitude traffic. The history shows that the investment on public transport infrastructure as a …

Prospect And Problem Analysis Of Industry Data Application In Livestock And Poultry Breeding, Yiran Chen, Zhuqing Xiong, Jiaogen Zhou, Quan Wang, Jiancheng Shu, Yinfa Yan, Lanlin Yang, Zemeng Feng, Benhai Xiong, Yulong Yin

Bulletin of Chinese Academy of Sciences (Chinese Version)

Livestock and poultry breeding is a pillar industry in China. The massive data in livestock and poultry breeding is a valuable resource. The market-oriented utilization of livestock and poultry breeding data plays an important role in improving industry standards, increasing industry profits, and driving the development of the entire industry chain. Currently, based on the demand for marketization of livestock and poultry breeding data, the application of new generation information technologies such as artificial intelligence and the Internet of Things in the process of livestock and poultry breeding to collect breeding process data, after de-sensitization and de-classification, through cloud computing, …

Promote Deep Integration Of Real Economy And Digital Economy, Qinmin Wang

Bulletin of Chinese Academy of Sciences (Chinese Version)

Deep integration of digital technology, industrial development, and data resources empowers development of the digital economy, constantly opens up new tracks, creates new momentum, and establishes new advantages, becoming a new powerful engine for global economic and social development. China attaches great importance to the high-quality development of the digital economy. This study discusses the practical experience of vigorously promoting digital industrialization and industrial digitization, promoting the deep integration of digital technology and the real economy, and accelerating the construction of a network power and digital China. It also proposes countermeasures and suggestions to further promote the efficient empowerment of …

Reflections On Strengthening Digital Public Goods Institutions To Promote Construction Of Open Source Innovation Ecosystem, Chao Zhang, Ze Feng, Kaihua Chen, Qigang Zhu

Bulletin of Chinese Academy of Sciences (Chinese Version)

The digital public goods institutions are important foundations for promoting the development of an open source innovation ecosystem, which helps to break the key limitations faced by China’s open source innovation ecosystem, such as the lack of unity in technology routes and concentration of R&D power. Based on discussing the significance of improving digital public goods institutions, this study reviews the experience of major countries in the world in building digital public goods institutions and analyzes the prominent problems faced by the construction of China’s digital public goods institutions. This study points out that China’s open source innovation ecosystem faces …

Status Quo Of Large-Scale Models, Risks And Challenges, And Recommended Countermeasures, Le Cheng, Yang Xiao

Bulletin of Chinese Academy of Sciences (Chinese Version)

Large-scale models (large models) are not only central to technological innovation, but also deeply entwined with national security, economic transformation, and social governance. This study examines the status quo of large-model development, identifies the key risks and challenges, and proposes response strategies, aiming to provide theoretical and policy insights for China’s navigations in global artificial intelligence (AI) competition and advances technological innovation. The research indicates that competition in the large-model market is fierce, while the industry is gradually consolidating. Competition in large models between China and the United States has escalated into a form of geopolitical contest. From a technical …

Construction Of Data Factor Circulation Law System In The United States And Its Reflection For China, Zihan Lin, Youmei Ma, Feng Guo

Bulletin of Chinese Academy of Sciences (Chinese Version)

As a pioneer country in the global digital economy, the United States has established a relatively complete system for the circulation of data elements. The focus is on government data openness, with the United States forming a legislative system for data openness at both federal and state levels to ensure data freedom and openness. In the field of personal data trading, the United States has established a relatively relaxed legal environment and data brokers to promote data circulation and trading. At present, China is actively promoting the market-oriented allocation reform of data elements, building a multi-level data element market, and …

Video Label Refinement And Temporal Localization Using Motion Signal Patterns, Jennifer Piane

College of Computing and Digital Media Dissertations

Performing video analysis for activity recognition presents challenges beyond classification, including obtaining class labels and performing temporal localization. One such challenge is precisely labeling a video with class labels having the exact start and end frames of an activity - a difficult task for a human to perform. Moreover, the task of annotating a video at any level of precision can quickly become tedious, impacting the attentiveness of the annotator and resulting in class label errors. Temporally localizing an activity within a video presents a second challenge. This dissertation investigates novel signal and image processing methods for motion features extracted …

Assessing Economic Losses With Covid-19 Integrated Models: A Retrospective Analysis, Timothy Robin Teng, Elvira De Lara-Tuprio, Joselito T. Sescon, Cymon Kayle Lubangco, Rolly Czar Joseph T. Castillo, Mark Anthony C. Tolentino, Maria Regina Justina E. Estuar, Lenard Paulo V. Tamayo, Christian E. Pulmano

Mathematics Faculty Publications

The COVID-19 pandemic led to a global crisis that forced governments to implement restrictive measures to control the spread of the disease. Although these restrictions, such as community quarantines, played a pivotal role in stabilizing healthcare systems, they also caused forced closures of various economic sectors that resulted in huge societal costs and severely impacted the marginalized. In order to understand and quantify the economic impact of the COVID-19 pandemic in the Philippines, an integrated modeling approach, which combined an epidemiological compartmental model and an economic model, was utilized. The evolving nature of COVID-19 required continuous updating of the integrated …

Seshaiyer: Data-Driven Machine Learning Framework To Predict Dynamics Of Infectious Diseases Incorporating Human Behavior, Alonso Gabriel Ogueda Oliva, Dr. Padmanabhan Seshaiyer

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Competitive Conquest: Charting The Climb To Pokémon Supremacy, Robert Dilworth

BCoE Publications

This manuscript presents a comprehensive exploration of optimizing Pokémon gameplay through data-driven methodologies, aimed at enhancing competitive performance in high-stakes environments. In the first section, we introduce a robust Pokémon teambuilding algorithm that leverages statistical analysis of championship-winning compositions. By employing multiple linear regression techniques, we predict team performance based on critical factors such as Base Stat Totals (BSTs) and various coverage types. This integration of data science principles into Pokémon strategy underscores the importance of offensive capabilities over defensive considerations, ultimately contributing to advancements in teambuilding strategies. Our proficiency in R programming facilitated the development of an efficient codebase …

Enhancing Data Standards To Advance Translation In Spinal Cord Injury, Vanessa K. Noonan, Suzanne Humphreys, Fin Biering-Sørensen, Susan Charlifue, Yuying Chen, James D. Guest, Linda A. T. Jones, Jennifer French, Eva Widerström-Noga, Vance P. Lemmon, Allen W. Heinemann, Jan M. Schwab, Aaron A. Phillips, Marzieh M. Rizi, John L. K. Kramer, Catherine R. Jutzeler, Abel Torres-Espin

Department of Physical Therapy Faculty Papers

Data standards are available for spinal cord injury (SCI). The International SCI Data Sets were created in 2002 and there are currently 27 freely available. In 2014 the National Institute of Neurological Disorders and Stroke developed clinical common data elements to promote clinical data sharing in SCI. The objective of this paper is to provide an overview of SCI data standards, describe learnings from the traumatic brain injury (TBI) field using data to enhance research and care, and discuss future opportunities in SCI. Given the complexity of SCI, frameworks such as a systems medicine approach and Big Data perspective have …

The Wallet And The Gut: Forecasting The 2024 Presidential Election With A State-By-State Adaptation Of The Time-For-Change Model, Simeon A. Betapudi, Hadassah Betapudi

Science University Research Symposium (SURS)

This study adapts Abramowitz's Time-for-Change model to a state-level framework to forecast the 2024 U.S. presidential election. The Time-for-Change model’s focus on the popular vote has become less relevant in recent years, given the growing divergence between popular vote outcomes and electoral college results. Our model addresses these issues by adapting the original Time-for-Change predictors (presidential approval rating, GDP, and time in office) to the state level. Using data from five election cycles (2004–2020), we employ an Ordinary Least Squares (OLS) regression to predict incumbent two-party vote share. Unlike the original model, state-level GDP and incumbency duration were found to …

Ipydisp V2 Alias Dudutracker: A Web-Based Version, Komi Mensah Agboka, Elfatih M. Abdel-Rahman, Samira A. Mohamed, Sunday Ekesi

All Peer-Reviewed Publications

This study presents the updated version v2 of IpyDisp named DuduTracker which improves on the window-only-requirement of IpyDisp. The updated version is web-based that can be used in alternative operating systems like Ubuntu, Mac, Linux, and others. The update's effectiveness was also evaluated using a survey involving a diverse range of users including students, data analysts, and academic researchers from different age groups, geographical locations, and computer literacy levels. Areas for future enhancement were identified, primarily focused on making the software responsive to various screen types and improving certain interface aspects.