Research Library

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Research Papers (23598)

Texas rural land market integration: A causal analysis using machine learning applications

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Tian Su, Senarath Dharmasena, David Leatham et al.

Machine Learning with Applications • 2024

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Autonomous Driving Architectures: Insights of Machine Learning and Deep Learning Algorithms

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Mrinal R. Bachute, Javed M. Subhedar

Machine Learning with Applications • 2021

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Predicting classification errors using NLP-based machine learning algorithms and expert opinions

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Peiheng Gao, Chen Yang, Ning Sun et al.

Machine Learning with Applications • 2025

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Machine Learning-Based Cloud Computing Compliance Process Automation

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Unknown Author

Automation and Machine Learning • 2024

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Review of machine learning applications for defect detection in composite materials

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Vahid Daghigh, Hamid Daghigh, Thomas E. Lacy et al.

Machine Learning with Applications • 2024

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Machine learning based risk analysis and predictive modeling of structure fire related casualties

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Andres Schmidt, Eric Gemmil, Russ Hoskins

Machine Learning with Applications • 2025

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Predictions of adsorption energies of methane-related species on Cu-based alloys through machine learning

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Yun Zhang, Xiaojie Xu

Machine Learning with Applications • 2021

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Machine Learning Based Short Video Comment Count Prediction

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Unknown Author

Automation and Machine Learning • 2023

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Increasing trust and fairness in machine learning applications within the mortgage industry

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W. van Zetten, G.J. Ramackers, H.H. Hoos

Machine Learning with Applications • 2022

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Causal Fairness Analysis: A Causal Toolkit for Fair Machine Learning

Drago Plečko, Elias Bareinboim

Foundations and Trends® in Machine Learning • 2023

Decision-making systems based on AI and machine learning have been used throughout a wide range of real-world scenarios, including healthcare, law enforcement, education, and finance. It is no longer far-fetched to envision a future where autonomous systems will drive entire business decisions and, more broadly, support large-scale decision-making infrastructure to solve society’s most challenging problems. Issues of unfairness and discrimination are pervasive when decisions are being made by humans, and remain (or are potentially amplified) when decisions are made using machines with little transparency, accountability, and fairness. In this monograph, we introduce a framework for causal fairness analysis with the intent of filling in this gap, i.e., understanding, modeling, and possibly solving issues of fairness in decision-making settings. The main insight of our approach will be to link the quantification of the disparities present in the observed data with the underlying, often unobserved, collection of causal mechanisms that generate the disparity in the first place, a challenge we call the Fundamental Problem of Causal Fairness Analysis (FPCFA). In order to solve the FPCFA, we study the problem of decomposing variations and empirical measures of fairness that attribute such variations to structural mechanisms and different units of the population. Our effort culminates in the Fairness Map, the first systematic attempt to organize and explain the relationship between various criteria found in the literature. Finally, we study which causal assumptions are minimally needed for performing causal fairness analysis and propose the Fairness Cookbook, which allows one to assess the existence of disparate impact and disparate treatment.

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Machine-learning models for spatially-explicit forecasting of future racial segregation in US cities

Tomasz F. Stepinski, Anna Dmowska

Machine Learning with Applications • 2022

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Predicting firm performance and size using machine learning with a Bayesian perspective

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Debdatta Saha, Timothy M. Young, Jessica Thacker

Machine Learning with Applications • 2023

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Leveraging the momentum effect in machine learning-based cryptocurrency trading

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Gian Pietro Bellocca, Giuseppe Attanasio, Luca Cagliero et al.

Machine Learning with Applications • 2022

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Predicting customer purpose of travel in a low-cost travel environment—A Machine Learning Approach

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Eyden Samunderu, Michael Farrugia

Machine Learning with Applications • 2022

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SSH Application Classification Based on Machine Learning

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Unknown Author

Machine Learning Theory and Practice • 2021

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Tabular machine learning using conjunctive threshold neural networks

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Weijia Wang, Litao Qiao, Bill Lin

Machine Learning with Applications • 2022

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EmoFusion: An integrated machine learning model leveraging embeddings and lexicons to improve textual emotion classification

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Anjali Bhardwaj, Muhammad Abulaish

Machine Learning with Applications • 2025

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Improving mango ripeness grading accuracy: A comprehensive analysis of deep learning, traditional machine learning, and transfer learning techniques

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Md․ Saon Sikder, Mohammad Shamsul Islam, Momenatul Islam et al.

Machine Learning with Applications • 2025

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Spam detection for Youtube video comments using machine learning approaches

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Andrew S. Xiao, Qilian Liang

Machine Learning with Applications • 2024

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Error analysis of classification learning algorithms based on LUMs loss

Xuqing He, Hongwei Sun

Mathematical Foundations of Computing • 2022

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Predictors of COVID-19 vaccination rate in USA: A machine learning approach

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Syed Muhammad Ishraque Osman, Ahmed Sabit

Machine Learning with Applications • 2022

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Machine learning for sports betting: Should model selection be based on accuracy or calibration?

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Conor Walsh, Alok Joshi

Machine Learning with Applications • 2024

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A knowledge representation learning model based on relation rotation in two-dimensional Minkowski space

Mingjie Wang, Juxiang Zhou, Jun Wang et al.

Mathematical Foundations of Computing • 2023

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A survey of machine learning in kidney disease diagnosis

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Jaber Qezelbash-Chamak, Saeid Badamchizadeh, Kourosh Eshghi et al.

Machine Learning with Applications • 2022

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APL Machine Learning reviewer acknowledgment for 2024

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Adnan Mehonic,

APL Machine Learning • 2025

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Predicting severely imbalanced data disk drive failures with machine learning models

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Jishan Ahmed, Robert C. Green II

Machine Learning with Applications • 2022

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Interpretable trading pattern designed for machine learning applications

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Artur Sokolovsky, Luca Arnaboldi, Jaume Bacardit et al.

Machine Learning with Applications • 2023

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Reinforcement learning for control of valves

Rajesh Siraskar

Machine Learning with Applications • 2021

Score: 50%Verified

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Teachable Machine как стартовая точка в Machine Learning

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С.А. Костырева, И.С. Курьян, Д.В. Негина

Научная матрица • 2022

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Deriving mapping functions to tie anthropometric measurements to body mass index via interpretable machine learning

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M.Z. Naser

Machine Learning with Applications • 2022

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Machine learning-based identification of craniosynostosis in newborns

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Malihe Sabeti, Reza Boostani, Ehsan Moradi et al.

Machine Learning with Applications • 2022

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Erratum to “New interpretation of GNRVⓇ knee arthrometer results for ACL injury diagnosis support using machine learning” [Machine Learning with Applications 13 (2023) 100480]

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Jean Mouchotte, Matthieu LeBerre, Théo Cojean et al.

Machine Learning with Applications • 2024

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Machine learning tensile strength and impact toughness of wheat straw reinforced composites

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Yun Zhang, Xiaojie Xu

Machine Learning with Applications • 2021

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Applications of machine learning in surge prediction for vehicle turbochargers

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Hiroki Saito, Dai Kanzaki, Kazuo Yonekura

Machine Learning with Applications • 2024

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Reducing MEG interference using machine learning

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Sammi Hamdan, Kyle DuBray, Jordan Treutel et al.

Machine Learning with Applications • 2023

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Editorial: Welcome to APL Machine Learning

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Adnan Mehonic

APL Machine Learning • 2023

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Detection of fraud in IoT based credit card collected dataset using machine learning

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Mohammed Naif Alatawi

Machine Learning with Applications • 2025

Score: 50%Verified

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Corrigendum to “Machine learning for sports betting: should model selection be based on accuracy or calibration?” [Machine Learning with Applications Volume 16, June 2024, 100539]

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Conor Walsh, Alok Joshi

Machine Learning with Applications • 2025

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Data-driven market segmentation in hospitality using unsupervised machine learning

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Rik van Leeuwen, Ger Koole

Machine Learning with Applications • 2022

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Using machine learning for detecting liquidity risk in banks

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Rweyemamu Ignatius Barongo, Jimmy Tibangayuka Mbelwa

Machine Learning with Applications • 2024

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