Modeling strength characteristics of basalt fiber reinforced concrete using multiple explainable machine learning with a graphical user interface

W Kulasooriya, RSS Ranasinghe, US Perera… - Scientific Reports, 2023 - nature.com
This study investigated the importance of applying explainable artificial intelligence (XAI) on
different machine learning (ML) models developed to predict the strength characteristics of …

[HTML][HTML] Eco-friendly mix design of slag-ash-based geopolymer concrete using explainable deep learning

RSS Ranasinghe, W Kulasooriya, US Perera… - Results in …, 2024 - Elsevier
Geopolymer concrete is a sustainable and eco-friendly substitute for traditional OPC
(Ordinary Portland Cement) based concrete, as it reduces greenhouse gas emissions. With …

[HTML][HTML] A novel explainable AI-based approach to estimate the natural period of vibration of masonry infill reinforced concrete frame structures using different machine …

P Thisovithan, H Aththanayake, DPP Meddage… - Results in …, 2023 - Elsevier
In this study, we used four different machine learning models-artificial neural network (ANN),
support vector regression (SVR), k-nearest neighbor (KNN), and random forest (RF)-to …

Ensemble machine learning approach for quantitative structure activity relationship based drug discovery: A Review

TR Noviandy, A Maulana, GM Idroes… - Infolitika Journal of …, 2023 - heca-analitika.com
This comprehensive review explores the pivotal role of ensemble machine learning
techniques in Quantitative Structure-Activity Relationship (QSAR) modeling for drug …

[HTML][HTML] Evaluating expressway traffic crash severity by using logistic regression and explainable & supervised machine learning classifiers

JPSS Madushani, RMK Sandamal… - Transportation …, 2023 - Elsevier
The number of expressway road accidents in Sri Lanka has significantly increased (by 20%)
due to the expansion of the transport network and high traffic volume. It is crucial to identify …

Modeling streamflow in non-gauged watersheds with sparse data considering physiographic, dynamic climate, and anthropogenic factors using explainable soft …

C Madhushani, K Dananjaya, IU Ekanayake… - Journal of …, 2024 - Elsevier
Streamflow forecasting is essential for effective water resource planning and early warning
systems. Streamflow and related parameters are often characterized by uncertainties and …

[HTML][HTML] A novel machine learning approach for diagnosing diabetes with a self-explainable interface

G Dharmarathne, TN Jayasinghe, M Bogahawaththa… - Healthcare …, 2024 - Elsevier
This study introduces the first-ever self-explanatory interface for diagnosing diabetes
patients using machine learning. We propose four classification models (Decision Tree (DT) …

[HTML][HTML] A new frontier in streamflow modeling in ungauged basins with sparse data: A modified generative adversarial network with explainable AI

U Perera, DTS Coralage, IU Ekanayake… - Results in …, 2024 - Elsevier
Streamflow forecasting is crucial for effective water resource planning and early warning
systems, especially in regions with complex hydrological behaviors and uncertainties. While …

[HTML][HTML] Predicting transient wind loads on tall buildings in three-dimensional spatial coordinates using machine learning

DPP Meddage, D Mohotti, K Wijesooriya - Journal of Building Engineering, 2024 - Elsevier
Abstract Machine learning (ML) as a subset of artificial intelligence (AI), has gained
significant attention in wind engineering applications over the past decade. Wind load …

[HTML][HTML] Recent Applications of Explainable AI (XAI): A Systematic Literature Review

M Saarela, V Podgorelec - Applied Sciences, 2024 - mdpi.com
This systematic literature review employs the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of …