Statistics and Probability Commons^™

From Data To Insight: A Machine Learning Approach In Classifying Dairy Cow Productivity Level And Identifying Important Influencing Variables, Fatkhurokhman Fauzi, Achmad Fauzan, Rhendy K P Widiyanto, Khairil Anwar Notodiputro, Bagus Sartono

Knowledge Engineering and Data Science

Identifying influential predictor variables is crucial for enhancing model interpretability in supervised classification. This study applies Permutation Variable Importance (PVI), a model-agnostic approach, to evaluate variable relevance after model fitting. Using data from the 2024 Indonesia Dairy Cow Productivity Survey, this research investigates five classification techniques: (1) Support Vector Machine (SVM), (2) Neural Network (NN), (3) k-Nearest Neighbors (kNN), (4) Naïve Bayes Classifier (NB), and (5) Logistic Regression (LR), to identify which method(s) yield the best performance based on evaluation metrics such as accuracy, sensitivity, and specificity. PVI is employed to identify the most influential predictor variables within the best-performing …

A Two-Stage Design For Choosing Among Several Normal Treatments In Comparison With A Control: The Case Of Common, Known Variance, Samarasuriyage Sayura Sankalpa Senaratne

UNF Graduate Theses and Dissertations

A two-stage design is developed for comparing the means of multiple normally distributed treatment groups under a known common variance, with the aim of identifying the treatment with the highest mean while minimizing the expected sample size, a crucial consideration in clinical trials. The proposed methodology integrates elements of both hypothesis testing and selection procedures to achieve greater efficiency and decision-making power. In the initial stage, if no treatment exhibits a mean surpassing a predefined efficacy threshold, the trial is terminated early, conserving resources. If one or more treatments exceed the threshold, the procedure advances to a second stage, where …

Evaluation Of Practical Methods To Determine If A Karst Creek Is Gaining Or Losing: Case Study Of Leith Creek, Elizabeth Jones

Graduate Theses/Dissertations

Karst landscapes are abundant in Missouri, with features such as caves, springs, and sinkholes that form through the dissolution of limestone. Leith Creek is a small stream in Polk County, Missouri fed by two springs and the shallow unconfined Springfield Plateau aquifer, a highly karstified aquifer which is made up of limestone and minor interbedded shale-mudstone units. To determine if Leith Creek is gaining or losing, stream flow, water chemistry and temperature sensors were monitored. Stream flow results required multiple visits to take measurements while temperature sensors required two visits, one to install the dataloggers and another to remove the …

Extraction Of Fine-Grained Research Methods In The Field Of Information Science, Jiayi Hao, Yuzhuo Wang, Chengzhi Zhang

Journal of Scientific Information Research

[Purpose/significance]Research methods in information science are one of the critical research directions in this field. Constructing a fine-grained research method corpus and extracting research method entities can help scholars quickly understand the research methods in this field, explore the evolution of methods and their future development trends, and lay the foundation for the service and application of the research method corpus in the subsequent digital wave. [Method/process]Firstly, based on academic articles published in the Journal of the China Society for Scientific and Technical Information from 2000 to 2023, this study randomly selected 50 articles and manually annotated the research methodology …

Identifying Research Paths Based On Main Path Analysis: A Case Study Of Knowledge Graphs, Ruibin Wei, Yidan Wang, Yan Xu

Journal of Scientific Information Research

[Purpose/significance]The main path analysis of citation networks can be used to identify important literature in specific fields and can achieve the extraction of mainstream research threads. This paper will use the main path analysis method to analyze the research path of knowledge graphs and sort out the context of their research development. [Method/process]This paper firstly obtains research papers in the field of knowledge graphs from the Web of Science platform, then uses the HistCite software to generate a direct citation network of the literature, and then imports the data into Pajek to generate multiple main paths of the dataset, and …

Modeling Neighborhoods As Fuel For Wildfire, Bryce Alan Young

Graduate Student Theses, Dissertations, & Professional Papers

Wildfire models drive billions of dollars in risk mitigation efforts. However, the modeling community currently lacks a representative fuelscape on which to base simulations of fire spread in the built environment and the wildland-urban interface (WUI) where vegetation and structures act together as fuel for wildfire. This thesis advances wildfire risk modeling by addressing the underdeveloped representation of the built environment in existing frameworks. By identifying inconsistencies in how structure and defensible space features are defined and used across empirical studies, predictive indices, and fire spread models, this research lays the groundwork for standardized modeling approaches and feature selection (Chapter …

Linking Empirical Data And Numerical Simulation To Characterize Dynamic Fire Behavior Associated With Interacting Firelines, Marta Sergeevna Jerebets

Graduate Student Theses, Dissertations, & Professional Papers

Understanding fuel pattern-fire process relationships is key for predicting fire behavior and effects with follow-on benefits to proactive fire management and model validation. To characterize dynamic fire behavior, this thesis leverages empirical data and numerical simulation through two complementary studies.

In the first study, longwave thermal sensors aboard unmanned aerial systems (UAS) were used to capture fine-scale fire behavior in two experimental grass burns. A novel paired design was used to quantify the effects of fuel arrangement on fire behavior with 3.66 m diameter treatments cut to a height of 0.15 m. The treatments ephemerally reduced fire rate of spread …

Crime Modeling Using An Integrated Cnn–Lstm Architecture With Embedded Self-Excitation, Pawandeep Kaur

Theses and Dissertations (Comprehensive)

It is often assumed that natural phenomena occur randomly over time. However, careful analysis reveals that these events typically form some series or sequences and exhibit distinctive temporal patterns. These patterns are not exclusive to nature. They also appear in human activities, often studied under the concept of bursty human dynamics. The statistical methods analyzing bursty human dynamics not only capture overall trends or seasonality but also explore how past events influence future ones. It makes the analysis more realistic and the results more closely aligned with reality. Bursty human dynamics can be studied at two levels: the individual level …

Predicting Real Estate Prices Using Deep Learning Regression Models On Socio Spatial Data, Gentle Engworo

Graduate Theses/Dissertations

ABSTRACT

Cities keep their own kind of ledger. Every block, bus stop, corner store, and year that slips by leaves a small entry about what homes are worth. That ledger is what we call socio-spatial data: simple facts about what a home is (its age), where it sits (latitude/longitude), how easy it is to get around (distance to the nearest MRT station), what’s nearby (number of convenience stores), and when it sold (transaction date). This thesis asks a practical question in that everyday language: given these common clues, can we predict home prices more accurately and explain why? Using 414 …

การเปรียบเทียบการทำนายผลการแข่งขันฟุตบอลโดยใช้การเรียนรู้เชิงลึกจากการผสานข้อมูลทางสถิติและแผนที่ความร้อนของผู้เล่น, ภานุพันธุ์ พันธุ์พฤกษ์

Chulalongkorn University Theses and Dissertations (Chula ETD)

งานวิจัยนี้มีวัตถุประสงค์เพื่อเปรียบเทียบประสิทธิภาพของแบบจำลอง Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN) และ Multimodal Neural Network (MMNN) ในการทำนายผลการแข่งขันฟุตบอลพรีเมียร์ลีกอังกฤษ โดยใช้ข้อมูลทางสถิติ ข้อมูลแผนที่ความร้อนของผู้เล่น และการผสานข้อมูลทั้งสองรูปแบบ ผ่านแนวทาง Early Fusion, Late Fusion และการแปลง Heatmap เป็นข้อมูลเชิงตัวเลขแบบแบ่งพื้นที่สนาม ผลการวิจัยพบว่า แบบจำลอง MMNN ที่ใช้แนวทาง Late Fusion ให้ค่าความแม่นยำที่ 55.68% ซึ่งสูงกว่า Early Fusion ที่ได้ความแม่นยำ 53.45% และ CNN ที่ใช้ข้อมูลทางสถิติร่วมกับข้อมูลตำแหน่งเชิงพื้นที่จากแผนที่ความร้อนที่แบ่งสนามออกเป็น 4 ส่วน มีค่าความแม่นยำสูงสุดที่ 63.50% และให้คะแนน Precision, Recall และ F1 Score สูงกว่าแบบจำลองอื่น ๆ นอกจากนี้ ยังได้วิเคราะห์ความสำคัญของฟีเจอร์ด้วยเทคนิค SHAP ซึ่งชี้ให้เห็นว่าฟีเจอร์ที่เกี่ยวข้องกับเกมรับของทีมเหย้า เช่น การบล็อก การเคลียร์บอล และการดวลลูกกลางอากาศ มีผลต่อการทำนายมากที่สุด ขณะที่ตำแหน่งของผู้เล่นทีมเยือนมีอิทธิพลน้อยที่สุด

Modeling Non-Normal Distributions With Mixed Third-Order Polynomials Of Standard Normal And Logistic Variables, Mohan D. Pant, Aditya Chakraborty, Ismail El Moudden

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Continuous data associated with many real-world events often exhibit non-normal characteristics, which contribute to the difficulty of accurately modeling such data with statistical procedures that rely on normality assumptions. Traditional statistical procedures often fail to accurately model non-normal distributions that are often observed in real-world data. This paper introduces a novel modeling approach using mixed third-order polynomials, which significantly enhances accuracy and flexibility in statistical modeling. The main objective of this study is divided into three parts: The first part is to introduce two new non-normal probability distributions by mixing standard normal and logistic variables using a piecewise function of …

A Modern Optimization Approach With Data-Driven Analytical Modeling For The Healthcare Business Segment (Hbs) From The S&P 500, Aditya Chakraborty, Chris Tsokos

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Introduction: The S&P consists of eleven business segments, which are classified according to the type of industry. The current study focuses on developing a non-linear analytical model for the Healthcare Business Segment (HBS) of the S&P 500, as a function of different economic & financial indicators. Materials and Methods: The analytical model used six financial indicators together with four economic indicators to predict the weekly average closing price (WCP) of HBS stocks. Johnson’s SB transformation corrected skewness, while desirability-based optimization identified indicator values maximizing WCP. The model’s performance and generalizability were validated through repeated 10-fold cross-validation. Results: All attributable contributors …

Determination Of Electrochemical Parameters For Predicting Reaction Mechanism And Algorithmic Approaches To Pain Assessment, Huize Xue

Dissertations

This dissertation introduces novel advancements in electrochemical kinetics and pain assessment, structured into two main parts. The first part focuses on the comprehensive analysis of the kinetic and mechanistic aspects of electrochemical reactions, utilizing a combination of experimental techniques and simulation methods. A new software tool, Envismetrics, was developed using Python to facilitate the analysis of complex electrochemical data, including cyclic voltammetry (CV), chronoamperometry (CA), and hydrodynamic voltammetry (HDV). The software was rigorously tested and validated with well-characterized redox systems such as the ferricyanide/ferrocyanide couple, dimethylamine borane (DMAB), and Per- and Polyfluoroalkyl Substances (PFAS). It was successfully used to determine …

Machine Learning Methods For Pattern Recognition Analysis Of Genomic And Molecular Data, Kuang Du

Dissertations

While immune therapies achieve remarkable success in treating various cancers, only a subset of patients achieves a durable clinical response, and many exhibit innate or acquired resistance. Precision medicine aims to tailor treatments to individual patients based on specific biological markers, ensuring that each patient receives the therapy most likely to be effective. Predictive biomarkers and gene signatures offer potential for more personalized treatment strategies by identifying patients likely to benefit. Recent studies suggest that gene signatures, comprising sets of genes, hold predictive value for certain clinical variables. Typically derived from biological expert knowledge, these signatures demonstrate substantial predictive potential, …

Impact Of Urban Development On Uv Exposure: A Clustering And Machine Learning Assessment, Taufik Roni Sahroni Mr., Verdi Yasin, Lulut Alfaris, Reza Ariefka, Ruben Cornelius Siagian, Mohammad Alfin Karim, Nana Rahdiana, Ade Suhara

Journal of Environmental Science and Sustainable Development

The relocation of Indonesia's capital city is anticipated to promote inclusive economic growth while embracing cultural diversity. However, this transition may affect ultraviolet (UV) radiation exposure patterns. The study investigated variations in UV exposure in the IKN region, focusing on urban development factors such as land use and population density that affect public health, sun protection, and skin cancer prevention. The research hypothesized that UV radiation is significantly correlated with these factors. UV Index data from 2010-2023, a hierarchical clustering method, identifies complex data patterns without determining the number of clusters. XGBoost, a machine learning model, was used for handling …

Surgical Delay-Associated Mortality Risk Varies By Subtype In Loco-Regional Breast Cancer Patients In Seer-Medicare, Macall Leslie Salewon, Rashmi Pathak, William Dooley, Ronald Squires, Hallgeir Rui, Inna Chervoneva, Takemi Tanaka

Department of Pharmacology, Physiology, and Cancer Biology Faculty Papers

Substantial evidence supports that delay of surgery after breast cancer diagnosis is associated with increased mortality risk, leading to the introduction of a new Commission on Cancer quality measure for receipt of surgery within 60 days of diagnosis for non-neoadjuvant patients. Breast cancer subtype is a critical prognostic factor and determines treatment options; however, it remains unknown whether surgical delay-associated breast cancer-specific mortality (BCSM) risk differs by subtype. This retrospective cohort study aimed to assess whether the impact of delayed surgery on survival varies by subtype (hormone [HR] + /HER2 -, HR -/HER2 -, and HER2 +) in patients with …

Immune Checkpoint Inhibitor-Associated Cutaneous Adverse Events: Mechanisms Of Occurrence, Abdulaziz M. Eshaq, Thomas W. Flanagan, Abdulqader A. Ba Abbad, Zain Alabden A. Makarem, Mohammed S. Bokir, Ahmed K. Alasheq, Sara A. Al Asheikh, Abdullah M. Almashhor, Faroq Binyamani, Waleed A. Al-Amoudi, Abdulaziz S. Bawzir, Youssef Haikel, Mossad Megahed, Mohamed Hassan

School of Medicine Faculty Publications

Immunotherapy, particularly that based on blocking checkpoint proteins in many tumors, including melanoma, Merkel cell carcinoma, non-small cell lung cancer (NSCLC), triple-negative breast (TNB cancer), renal cancer, and gastrointestinal and endometrial neoplasms, is a therapeutic alternative to chemotherapy. Immune checkpoint inhibitor (ICI)-based therapies have the potential to target different pathways leading to the destruction of cancer cells. Although ICIs are an effective treatment strategy for patients with highly immune-infiltrated cancers, the development of different adverse effects including cutaneous adverse effects during and after the treatment with ICIs is common. ICI-associated cutaneous adverse effects include mostly inflammatory and bullous dermatoses, as …

Optimizing Transport Predictive Modeling With Simulation-Based Statistical Inference Authors, Mamunur Rashid, Quyen Tran

Mathematics Faculty Publications

Simulation-based statistical inference (SBI) leverages computer simulations to help scientists understand and analyze complex data. This paper explores how SBI techniques can be used to analyze transportation data. We use modern computational methods, including machine learning models, to improve the accuracy of predictions and decision-making in transportation planning. Our study focuses on two SBI methods, Approximate Bayesian Computation - Markov Chain Monte Carlo and Synthetic Likelihood, to create synthetic data for training machine learning models. These models show the potential of SBI to handle uncertain transportation data. It also highlights the practical benefits of SBI in making better decisions for …

Measurement Of Breast Artery Calcification Using An Artificial Intelligence Detection Model And Its Association With Major Adverse Cardiovascular Events, Suzanne Rose, Josette Hartnett, Zachary Estep, Daniyal Ameen, Shweta Karki, Edward Schuster, Rebecca Newman, David Hsi

Department of Medicine Faculty Papers

Breast artery calcification (BAC) obtained from standard mammographic images is currently under evaluation to stratify risk of major adverse cardiovascular events in women. Measuring BAC using artificial intelligence (AI) technology, we aimed to determine the relationship between BAC and coronary artery calcification (CAC) severity with Major Adverse Cardiac Events (MACE). This retrospective study included women who underwent chest computed tomography (CT) within one year of mammography. T-test assessed the associations between MACE and variables of interest (BAC versus MACE, CAC versus MACE). Risk differences were calculated to capture the difference in observed risk and reference groups. Chi-square tests and/or Fisher's …

Interactions Of The Sars-Cov-2 Viral Genome 3’-Untranslated Region With Viral And Host Rnas, Caleb Frye, Mihaela Rita Mihailescu

Electronic Theses and Dissertations

This dissertation focuses on the characterization of RNA-RNA interactions within the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome and with host microRNAs. As the causative agent of coronavirus disease 2019 (COVID-19), SARS-CoV-2 has evolved rapidly since its appearance. This has warranted prompt characterization of the virus particularly of its single stranded RNA (ssRNA) genome. By using a combination of bioinformatics, biophysics, and/or biological assays, we analyzed the SARS-CoV-2 viral genomic RNA and uncovered interactions of genomic RNA with host RNAs, highlighting an underutilized method of targeting RNA viruses. We showed here that the conserved elements in the viral genomic …