[HTML][HTML] Machine learning in concrete science: applications, challenges, and best practices
Concrete, as the most widely used construction material, is inextricably connected with
human development. Despite conceptual and methodological progress in concrete science …
human development. Despite conceptual and methodological progress in concrete science …
[HTML][HTML] On the importance of interpretable machine learning predictions to inform clinical decision making in oncology
Machine learning-based tools are capable of guiding individualized clinical management
and decision-making by providing predictions of a patient's future health state. Through their …
and decision-making by providing predictions of a patient's future health state. Through their …
[HTML][HTML] Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence
Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …
applications, but the outcomes of many AI models are challenging to comprehend and trust …
[HTML][HTML] Short-term electricity load forecasting—A systematic approach from system level to secondary substations
MG Pinheiro, SC Madeira, AP Francisco - Applied Energy, 2023 - Elsevier
Energy forecasting covers a wide range of prediction problems in the utility industry, such as
forecasting demand, generation, price, and power load over time horizons and different …
forecasting demand, generation, price, and power load over time horizons and different …
Additive mil: Intrinsically interpretable multiple instance learning for pathology
Abstract Multiple Instance Learning (MIL) has been widely applied in pathology towards
solving critical problems such as automating cancer diagnosis and grading, predicting …
solving critical problems such as automating cancer diagnosis and grading, predicting …
Counterfactual explanations for models of code
Machine learning (ML) models play an increasingly prevalent role in many software
engineering tasks. However, because most models are now powered by opaque deep …
engineering tasks. However, because most models are now powered by opaque deep …
[HTML][HTML] Relating the partial dependence plot and permutation feature importance to the data generating process
Scientists and practitioners increasingly rely on machine learning to model data and draw
conclusions. Compared to statistical modeling approaches, machine learning makes fewer …
conclusions. Compared to statistical modeling approaches, machine learning makes fewer …
[HTML][HTML] Machine learning for an explainable cost prediction of medical insurance
Predictive modeling in healthcare continues to be an active actuarial research topic as more
insurance companies aim to maximize the potential of Machine Learning (ML) approaches …
insurance companies aim to maximize the potential of Machine Learning (ML) approaches …
[HTML][HTML] An empirical survey on explainable ai technologies: Recent trends, use-cases, and categories from technical and application perspectives
M Nagahisarchoghaei, N Nur, L Cummins, N Nur… - Electronics, 2023 - mdpi.com
In a wide range of industries and academic fields, artificial intelligence is becoming
increasingly prevalent. AI models are taking on more crucial decision-making tasks as they …
increasingly prevalent. AI models are taking on more crucial decision-making tasks as they …
[HTML][HTML] Interpretable dropout prediction: towards XAI-based personalized intervention
M Nagy, R Molontay - International Journal of Artificial Intelligence in …, 2024 - Springer
Student drop-out is one of the most burning issues in STEM higher education, which induces
considerable social and economic costs. Using machine learning tools for the early …
considerable social and economic costs. Using machine learning tools for the early …