:: Library Catalog

Buchumschlag

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Lee, Jay, Ji, Dai-Yan, Hsu, Yuan-Ming
Format:	Preprint
Veröffentlicht:	2024
Schlagworte:	Machine Learning Computers and Society
Online-Zugang:	https://arxiv.org/abs/2404.14728
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Using Machine Learning in Analyzing Air Quality Discrepancies of Environmental Impact
von: Wang, Shuangbao Paul, et al.
Veröffentlicht: (2025)

Maturity Framework for Enhancing Machine Learning Quality
von: Castelli, Angelantonio, et al.
Veröffentlicht: (2025)

Beyond Quantities: Machine Learning-based Characterization of Inequality in Infrastructure Quality Provision in Cities
von: Li, Bo, et al.
Veröffentlicht: (2024)

A Multi-Plant Machine Learning Framework for Emission Prediction, Forecasting, and Control in Cement Manufacturing
von: Fayaz, Sheikh Junaid, et al.
Veröffentlicht: (2026)

Enhancing Manufacturing Quality Prediction Models through the Integration of Explainability Methods
von: Gross, Dennis, et al.
Veröffentlicht: (2024)

ORACLE: Anticipating Scams from Partial Trajectories in Streaming App Usage
von: Gao, Wenbo, et al.
Veröffentlicht: (2026)

Exploring Non-Linear Effects of Built Environment on Travel Using an Integrated Machine Learning and Inferential Modeling Approach: A Three-Wave Repeated Cross-Sectional Study
von: Zafri, Niaz Mahmud, et al.
Veröffentlicht: (2024)

An Ensemble Framework for Explainable Geospatial Machine Learning Models
von: Liu, Lingbo
Veröffentlicht: (2024)

Predictive Modelling of Air Quality Index (AQI) Across Diverse Cities and States of India using Machine Learning: Investigating the Influence of Punjab's Stubble Burning on AQI Variability
von: Sidhu, Kamaljeet Kaur, et al.
Veröffentlicht: (2024)

Counterfactual Fairness Evaluation of Machine Learning Models on Educational Datasets
von: Kim, Woojin, et al.
Veröffentlicht: (2025)

The Effect of Enforcing Fairness on Reshaping Explanations in Machine Learning Models
von: Anderson, Joshua Wolff, et al.
Veröffentlicht: (2025)

MachineLearnAthon: An Action-Oriented Machine Learning Didactic Concept
von: Tkáč, Michal, et al.
Veröffentlicht: (2024)

Machine Learning in Epidemiology
von: Wright, Marvin N., et al.
Veröffentlicht: (2026)

Context-Aware Quantitative Risk Assessment Machine Learning Model for Drivers Distraction
von: Fasanmade, Adebamigbe, et al.
Veröffentlicht: (2024)

Transfer Learning and Machine Learning for Training Five Year Survival Prognostic Models in Early Breast Cancer
von: Pilgram, Lisa, et al.
Veröffentlicht: (2025)

Developing a Novel Holistic, Personalized Dementia Risk Prediction Model via Integration of Machine Learning and Network Systems Biology Approaches
von: Mamidala, Srilekha
Veröffentlicht: (2023)

Intelligent Energy Management with IoT Framework in Smart Cities Using Intelligent Analysis: An Application of Machine Learning Methods for Complex Networks and Systems
von: Nikpour, Maryam, et al.
Veröffentlicht: (2023)

Quality-Diversity Generative Sampling for Learning with Synthetic Data
von: Chang, Allen, et al.
Veröffentlicht: (2023)

SmartPathfinder: Pushing the Limits of Heuristic Solutions for Vehicle Routing Problem with Drones Using Reinforcement Learning
von: Imran, Navid Mohammad, et al.
Veröffentlicht: (2024)

Divide-Conquer Transformer Learning for Predicting Electric Vehicle Charging Events Using Smart Meter Data
von: Ke, Fucai, et al.
Veröffentlicht: (2024)

Stronger Baseline Models -- A Key Requirement for Aligning Machine Learning Research with Clinical Utility
von: Wolfrath, Nathan, et al.
Veröffentlicht: (2024)

Debiasing Machine Learning Models by Using Weakly Supervised Learning
von: Brotto, Renan D. B., et al.
Veröffentlicht: (2024)

Learning With Multi-Group Guarantees For Clusterable Subpopulations
von: Dai, Jessica, et al.
Veröffentlicht: (2024)

Non-Determinism and the Lawlessness of Machine Learning Code
von: Cooper, A. Feder, et al.
Veröffentlicht: (2022)

Insights From Insurance for Fair Machine Learning
von: Fröhlich, Christian, et al.
Veröffentlicht: (2023)

Enhancing Fairness and Performance in Machine Learning Models: A Multi-Task Learning Approach with Monte-Carlo Dropout and Pareto Optimality
von: Zanna, Khadija, et al.
Veröffentlicht: (2024)

Bushfire Severity Modelling and Future Trend Prediction Across Australia: Integrating Remote Sensing and Machine Learning
von: Partheepan, Shouthiri, et al.
Veröffentlicht: (2024)

Diagnosing Psychiatric Patients: Can Large Language and Machine Learning Models Perform Effectively in Emergency Cases?
von: Ahammed, Abu Shad, et al.
Veröffentlicht: (2025)

Macroscopic Emission Modeling of Urban Traffic Using Probe Vehicle Data: A Machine Learning Approach
von: Adlouni, Mohammed Ali El, et al.
Veröffentlicht: (2025)

Evaluating Fair Feature Selection in Machine Learning for Healthcare
von: Zawad, Md Rahat Shahriar, et al.
Veröffentlicht: (2024)

Understanding Disparities in Post Hoc Machine Learning Explanation
von: Mhasawade, Vishwali, et al.
Veröffentlicht: (2024)

Best Practices for Responsible Machine Learning in Credit Scoring
von: Valdrighi, Giovani, et al.
Veröffentlicht: (2024)

What is Fair? Defining Fairness in Machine Learning for Health
von: Gao, Jianhui, et al.
Veröffentlicht: (2024)

Identities are not Interchangeable: The Problem of Overgeneralization in Fair Machine Learning
von: Wang, Angelina
Veröffentlicht: (2025)

Carbon Emission Prediction in China Considering New Quality Productive Forces Using a Deep & Corss Learning Modeling Framework
von: Xie, Haijin, et al.
Veröffentlicht: (2025)

AI in Manufacturing: Market Analysis and Opportunities
von: Abdelaal, Mohamed
Veröffentlicht: (2024)

Enhancing Air Quality Monitoring: A Brief Review of Federated Learning Advances
von: Yarham, Sara, et al.
Veröffentlicht: (2025)

Evaluating Privacy-Utility Tradeoffs in Synthetic Smart Grid Data
von: Catarino, Andre, et al.
Veröffentlicht: (2025)

Machine Learning Algorithms for Detecting Mental Stress in College Students
von: Singh, Ashutosh, et al.
Veröffentlicht: (2024)

AdapFair: Ensuring Adaptive Fairness for Machine Learning Operations
von: Huang, Yinghui, et al.
Veröffentlicht: (2024)