Results 61 to 70 of about 32,654 (268)

Estimating the water quality index based on interpretable machine learning models

Water Science and Technology
The water quality index (WQI) is an important tool for evaluating the water quality status of lakes. In this study, we used the WQI to evaluate the spatial water quality characteristics of Dianchi Lake. However, the WQI calculation is time-consuming, and
Shiwei Yang, Ruifeng Liang, Junguang Chen, Yuanming Wang, Kefeng Li   +4 more
doaj   +1 more source

Short term forecasting of base metals prices using a LightGBM and a LightGBM - ARIMA ensemble

Mineral Economics
Abstract Base metals are key materials for various industrial sectors such as electronics, construction, manufacturing, etc. Their selling price is important both for the profitability of the mining and metallurgical companies that produce and trade them, as well as for the countries whose economies rely on their exports or tax revenues as a ...
Konstantinos Oikonomou, Dimitris Damigos
openaire   +1 more source

An artificial neural network–based deep learning model to predict combined stress impact and interaction in plants

Applications in Plant Sciences, EarlyView.
Abstract Premise Plants are frequently exposed to combinations of abiotic and biotic stresses that pose a greater threat to yield and productivity than individual stresses. However, knowledge of the impact of many stress combinations in numerous plants is limited due to the lack of experimental data, which could take decades to generate.
Piyush Priya, Prachi Pandey, Rubi Jain, Manu Kandpal, Shradha Jain, Rim Chaudhury, Venkategowda Ramegowda, Muthappa Senthil‐Kumar   +7 more
wiley   +1 more source

An interpretable machine learning model for predicting forest fire danger based on Bayesian optimization

Emergency Management Science and Technology
As global warming increases forest fire frequency, early prevention and effective management become crucial. This requires models that are both accurate and easily understood.
Zhiyang Liu, Kuibin Zhou, Qichao Yao, Pedro Reszka   +3 more
doaj   +1 more source

Identifying systemic lupus erythematosus from serum proteomic profiles using machine learning and genetic risk stratification

Arthritis &Rheumatology, Accepted Article.
Objectives Proteome‐wide risk models for lupus remain underexplored. We developed classification models to identify lupus from serum proteomic profiles. Methods Lupus patients and individuals with other autoimmune diseases in the UK Biobank were included.
Mehmet Hocaoǧlu, Jishnu Das, Amr H Sawalha   +2 more
wiley   +1 more source

An Evaluation of Classification and Outlier Detection Algorithms [PDF]

, 2018
This paper evaluates algorithms for classification and outlier detection accuracies in temporal data. We focus on algorithms that train and classify rapidly and can be used for systems that need to incorporate new data regularly.
Austin, Jim, Hodge, Victoria J.
core   +1 more source

Analysis of Ruddlesden‐Popper and Dion‐Jacobson 2D Lead Halide Perovskites Through Integrated Experimental and Computational Analysis

Battery Energy, Volume 4, Issue 2, March 2025.
Optimized ML framework for predicting RP and Dj phases in perovskite solar cells. ABSTRACT Two‐dimensional (2D) lead halide perovskites (LHPs) have captured a range of interest for the advancement of state‐of‐the‐art optoelectronic devices, highly efficient solar cells, next‐generation energy harvesting technologies owing to their hydrophobic nature ...
Basir Akbar, Kil To Chong, Hilal Tayara
wiley   +1 more source

Enhanced Botnet Detection and Neutralization through Machine Learning: A Synergistic Analysis of Host Activity, Network Patterns with Explainable Insights [PDF]

Computer Science Journal of Moldova
Botnets continue to be one of the biggest cybersecurity risks since they provide a platform for a number of unlawful operations. The growing sophistication and stealth of contemporary botnet networks, which frequently elude conventional detection tools ...
B. Gomathy, S. V. Riyaayini, K. R. Krishnaprabha, T. Ritanya, A. Kaviyavalli   +4 more
doaj   +1 more source

Machine Learning‐Driven Prediction and Optimization of Cu‐Based Catalysts for CO2 Hydrogenation to Methanol

Carbon and Hydrogen, EarlyView.
A machine‐learning framework integrating multimodel prediction, feature selection, and SHAP interpretability is developed to uncover structure–performance relationships of Cu‐based CO2‐to‐methanol catalysts. The optimized XGBoost model and an online prediction platform enable accurate selectivity prediction and data‐driven catalyst design.
Conglong Su, Zhangkai Jing, Qiuming Zhou, Yuan Zhu, Sishi Ye, Tianye Wang, Kuo Chen, Xuebin Luan, Zhigang Tang, Run Xu, Chaopeng Hou, Hongwei Li   +11 more
wiley   +1 more source

ПОРІВНЯЛЬНИЙ АНАЛІЗ МЕТОДІВ ГЛИБОКОГО ТА МАШИННОГО НАВЧАННЯ ДЛЯ ВИЯВЛЕННЯ МЕРЕЖЕВИХ ВТОРГНЕНЬ

Кібербезпека: освіта, наука, техніка
У статті представлено результати комплексного порівняльного дослідження шести методів машинного та глибокого навчання для задачі багатокласової класифікації мережевих атак.
Володимир Рихва, Ганна Солодовник   +1 more
doaj   +1 more source