arXiv Machine Learning

Predicting Male Domestic Violence Using Explainable Ensemble Learning and Exploratory Data Analysis

arXiv:2403. 15594v4 Announce Type: replace-cross Abstract: Domestic violence is commonly viewed as a gendered issue that primarily affects women, which tends to leave male victims largely overlooked.

arXiv AI

Jun 24

Ensemble Feature Selection and Harris Hawks Optimization for Explainable Mental Health Risk Prediction in Female Sex Workers

arXiv:2606. 24047v1 Announce Type: new Abstract: One of the significant mental health issues affecting female sex workers (FSWs) is mental disorders, especially depression.

By Ahnaf Atef Choudhury, Md. Parvej Hoque Palash, Shahriar Siddique Ayon, Ramkrishna Saha, Abdullah Al Mamun

arXiv AI

Jul 7

Explainable AI for Screening Abuse-Related Trauma in Bangladeshi Children: A Training-Free Multimodal Framework Evaluated on Noise-Aware Synthetic Data

arXiv:2607. 04010v1 Announce Type: new Abstract: Bangladesh has an estimated 1.

By Salma Hoque Talukdar Koli, Fahima Haque Talukder Jely

multimodal benchmarks

arXiv Machine Learning

Jul 21

Interpretable Machine Learning for Air Pollution and Respiratory Health Prediction: A Socioeconomic Subgroup Analysis

arXiv:2607. 17024v1 Announce Type: new Abstract: Air pollution and climate-related stressors are increasingly important concerns for respiratory health, especially in settings with unequal environmental exposure and healthcare capacity.

By Maede Azani Hassan Abadi, Shouyi Wang

arXiv Machine Learning

Jun 16

Temporal Validation Changes the Apparent Public-Health Utility of Under-Five Mortality Prediction in Bangladesh: A Four-Round DHS Machine-Learning Study

arXiv:2602. 03957v2 Announce Type: replace Abstract: Background: Under-five mortality in Bangladesh remains uneven despite national progress.

By Md Muhtasim Munif Fahim, M. Monimul Huq, M. Sabiruzzaman, Md Rezaul Karim

arXiv Machine Learning

Jul 20

Who Became Financially Vulnerable After COVID-19? A Population-Level Machine Learning Analysis Using MEPS Data

arXiv:2607. 15446v1 Announce Type: new Abstract: The cost of healthcare remains a concern in the United States and may have been influenced by disruptions associated with the COVID-19 pandemic.

By Alexey Kresin, Zien Cheng, Ammar Ahad, Ebiyomare Kelvin, Manish Sivaratri, Prabhjeet Singh, Omar Aljawfi, Olabisi Ojo, Nawar Shara

arXiv AI

Jun 4

AICompanionBench: Benchmarking LLMs-as-Judges for AI Companion Safety

arXiv:2606. 04867v1 Announce Type: new Abstract: As AI companion platforms such as Replika and Character.

By Yanjing Ren, Reza Ebrahimi, TengTeng Ma

llms robotics benchmarks safety

arXiv AI

Jul 2

Predicting Lethal Outcome (Cause) And Understanding Key Biomarkers Linked With Acute Myocardial Infarction Using Deep Artificial Neural Network And Ensemble Of Machine Learning Methodologies

arXiv:2607. 00472v1 Announce Type: cross Abstract: Cardiovascular disease is still one of the main causes of death around the world.

By Sagnik Ghosh

arXiv Machine Learning

Jun 2

Model Multiplicity and Predictive Arbitrariness in Recidivism Risk Assessment

arXiv:2606. 02198v1 Announce Type: new Abstract: Prediction tasks over individual futures, which are inherently noisy, often admit multiple similarly accurate models.

By Ashwin Singh, Carlos Castillo

arXiv Machine Learning

Jul 28

Deep learning approaches show promise for predicting childhood malnutrition: A comparative study with traditional machine learning methods using survey data

arXiv:2602. 10381v2 Announce Type: replace Abstract: Childhood malnutrition remains a major public health concern in Nepal and other low-resource settings, while conventional case-finding approaches are labor-intensive and frequently unavailable in remote areas.

By Deepak Bastola, Yang Li

arXiv AI

Jun 10

Conformal Risk Prediction for Non-Alcoholic Fatty Liver Disease Using Gradient Boosting with Distribution-Free Coverages

arXiv:2606. 09860v1 Announce Type: cross Abstract: Non-alcoholic fatty liver disease (NAFLD) affects roughly 25% of global adults, posing substantial hepatic and cardiovascular risks.

By Xinze Zhang

arXiv AI

Jul 15

So Many Opinions, So Many LLMs: Comparing Large Language Models to Traditional Machine Learning for Open- Ended Survey Analysis

arXiv:2607. 11890v1 Announce Type: cross Abstract: Open-ended surveys offer valuable insights, but they are notoriously difficult to analyze at scale.

By Abdullah Akinde, Mariam Akinde, Rasheedat Emiola, Ahmed Akinsola

llms nlp safety

arXiv Machine Learning

1d ago

Detecting Money Laundering in Rwandan Mobile Money: A Machine Learning Framework

arXiv:2608. 15447v1 Announce Type: new Abstract: Mobile money has widened financial access across Sub-Saharan Africa and enlarged the surface for money-laundering and terrorism-financing (ML/TF) activity in ecosystems dominated by high-volume, low-value transactions.

By Emmanuel Nahimana, Ya\'e Ulrich Gaba

benchmarks

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