FAIRness Along the Machine Learning Lifecycle Using Dataverse in Combination with MLflow by Lincoln Sherpa, Valentin Khaydarov, Ralph Müller-Pfefferkorn

Subjects: “…machine learning (ml)…”
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On the Readiness of Scientific Data Papers for a Fair and Transparent Use in Machine Learning by Joan Giner-Miguelez, Abel Gómez, Jordi Cabot

“…Abstract To ensure the fairness and trustworthiness of machine learning (ML) systems, recent legislative initiatives and relevant research in the ML community have pointed out the need to document the data used to train ML models. …”
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Unveiling farmers’ perceptions: a citizen science and machine learning approach to exploring drivers in the adequacy and fairness of water systems by Vasilios Plakandaras, Fahad Khan Khadim, Vassiliki Kazana, Emmanouil Anagnostou, Amvrossios C Bagtzoglou

Subjects: “…machine learning…”
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Towards fairness-aware multi-objective optimization by Guo Yu, Lianbo Ma, Xilu Wang, Wei Du, Wenli Du, Yaochu Jin

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Assessing machine learning for fair prediction of ADHD in school pupils using a retrospective cohort study of linked education and healthcare data by Johnny Downs, Robert Stewart, Alice Wickersham, Sumithra Velupillai, Lucile Ter-Minassian, Natalia Viani, Lauren Cross

“…Using a unique linked health and education data resource, we examined how machine learning (ML) approaches can predict risk of ADHD.Design Retrospective population cohort study.Setting South London (2007–2013).Participants n=56 258 pupils with linked education and health data.Primary outcome measures Using area under the curve (AUC), we compared the predictive accuracy of four ML models and one neural network for ADHD diagnosis. …”
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Achieving Equity via Transfer Learning With Fairness Optimization by Xiaoyang Wang, Chia-Hsuan Chang, Christopher C. Yang

“…This paper proposes Transfer Learning with Fairness Optimization (TLFO), a novel framework that serially optimizes predictive performance and fairness in machine learning models. …”
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Comprehensive Review of Privacy, Utility, and Fairness Offered by Synthetic Data by A. Kiran, P. Rubini, S. Saravana Kumar

“…First and foremost, how well synthetic data can preserve privacy and control disclosure, second is how good is its utility, and third, are they able to give fair results without any bias when used in machine learning. …”
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MLHOps: Machine Learning Health Operations by Faiza Khan Khattak, Vallijah Subasri, Amrit Krishnan, Chloe Pou-Prom, Sedef Akinli-Kocak, Elham Dolatabadi, Deval Pandya, Laleh Seyyed-Kalantari, Frank Rudzicz

“…Machine Learning Health Operations (MLHOps) is the combination of processes for reliable, efficient, usable, and ethical deployment and maintenance of machine learning models in healthcare settings. …”
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AI Algorithms as (un)virtuous knowers by Hans Van Eyghen

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EMS3D-KITTI: Synthetic 3D dataset in KITTI format with a fair distribution of Emergency Medical Services vehicles for autodrive AI model trainingZenodo by Chandra Jaiswal, Sally Acquaah, Christopher Nenebi, Issa AlHmoud, AKM Kamrul Islam, Balakrishna Gokaraju

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Artificial Intelligence in Educational Data Mining and Human-in-the-Loop Machine Learning and Machine Teaching: Analysis of Scientific Knowledge by Eloy López-Meneses, Luis López-Catalán, Noelia Pelícano-Piris, Pedro C. Mellado-Moreno

“…This study explores the integration of artificial intelligence (AI) into educational data mining (EDM), human-assisted machine learning (HITL-ML), and machine-assisted teaching, with the aim of improving adaptive and personalized learning environments. …”
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Tiny Machine Learning Implementation for Guided Wave-Based Damage Localization by Jannik Henkmann, Vittorio Memmolo, Jochen Moll

“…It investigates the use of machine learning (ML) to train the effects of the damage on UGWs to the model. …”
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Machine learning and public health policy evaluation: research dynamics and prospects for challenges by Zhengyin Li, Hui Zhou, Zhen Xu, Qingyang Ma

“…BackgroundPublic health policy evaluation is crucial for improving health outcomes, optimizing healthcare resource allocation, and ensuring fairness and transparency in decision-making. With the rise of big data, traditional evaluation methods face new challenges, requiring innovative approaches.MethodsThis article reviews the principles, scope, and limitations of traditional public health policy evaluation methods and explores the application of machine learning in evaluating public health policies. …”
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Generating the Flood Susceptibility Map for Istanbul with GIS-Based Machine Learning Algorithms by Zehra Koyuncu, Ömer Ekmekcioğlu

“…The main objective of the current study is to generate a flood hazard map by using the machine learning algorithms hybridized with the geographic information systems (GIS). …”
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COVID-19 World Vaccine Adverse Reactions Based on Machine Learning Clustering Algorithm by Lozan M. Abdulrahman, Adnan Mohsin Abdulazeez, Dathar A. Hasan

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SOFTWARE DEVELOPMENT AND RESEARCH FOR MACHINE LEARNING-BASED STRUCTURAL ERRORS DETECTION IN BPMN MODELS by Andrii Kopp, Dmytro Orlovskyi, Igor Gamayun, Illia Sapozhnykov

“…To detect errors in business process models, one of the Machine Learning methods, K-Nearest Neighbors, is chosen, which is a fairly simple and effective classification method. …”
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Fault Diagnosis Method of Check Valve Based on Multikernel Cost-Sensitive Extreme Learning Machine by Jun Ma, Jiande Wu, Xiaodong Wang

“…Therefore, the multikernel function and cost-sensitive mechanism are introduced to construct the fault diagnosis model of check valve based on the multikernel cost-sensitive extreme learning machine (MKL-CS-ELM) in this paper. The comparative test results of check valve for high pressure diaphragm pump show that MKL-CS-ELM can obtain fairly or slightly better performance than ELM, CS-ELM, MKL-ELM, and multikernel cost-sensitive support vector learning machine (MKL-CS-SVM). …”
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Part B: Innovative Data Augmentation Approach to Boost Machine Learning for Hydrodynamic Purposes—Computational Efficiency by Hamed Majidiyan, Hossein Enshaei, Damon Howe, Eric Gubesch

“…A novel data analysis framework was introduced with two purposes to augment data for machine learning (ML) models: transferring features from high-fidelity to low-fidelity surrogates and enhancing simulation data and increasing computational efficiency. …”
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Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging. by Vasileios Nittas, Paola Daniore, Constantin Landers, Felix Gille, Julia Amann, Shannon Hubbs, Milo Alan Puhan, Effy Vayena, Alessandro Blasimme

“…That comes with a fair share of high hopes and hype. We conducted a scoping review on machine learning in medical imaging, providing a comprehensive outlook of the field's potential, limitations, and future directions. …”
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Machine Learning-Integrated Usability Evaluation and Monitoring of Human Activities for Individuals With Special Needs During Hajj and Umrah by Ghadah Naif Alwakid

“…The proposed study used two machine learning models, i.e., random forest and sequential neural networks, both with 93% accuracy. …”
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