[HTML][HTML] Building performance optimization through sensitivity Analysis, and economic insights using AI
Optimizing building designs for energy efficiency and occupant comfort presents significant
challenges due to the complex and often conflicting requirements of various stakeholders …
challenges due to the complex and often conflicting requirements of various stakeholders …
Using XGBoost Regression to Analyze the Importance of Input Features Applied to an Artificial Intelligence Model for the Biomass Gasification System
Recently, artificial intelligence models have been developed to simulate the biomass
gasification systems. The extant research models use different input features, such as …
gasification systems. The extant research models use different input features, such as …
[HTML][HTML] Unveiling causal connections: Long-term particulate matter exposure and type 2 diabetes mellitus mortality in Southern China
Evidence of the potential causal links between long-term exposure to particulate matters
(PM, ie, PM 1, PM 2.5, and PM 1–2.5) and T2DM mortality based on large cohorts is limited …
(PM, ie, PM 1, PM 2.5, and PM 1–2.5) and T2DM mortality based on large cohorts is limited …
Proposal of a machine learning approach for traffic flow prediction
M Berlotti, S Di Grande, S Cavalieri - Sensors, 2024 - mdpi.com
Rapid global urbanization has led to a growing urban population, posing challenges in
transportation management. Persistent issues such as traffic congestion, environmental …
transportation management. Persistent issues such as traffic congestion, environmental …
Application of machine learning for mass spectrometry-based multi-omics in thyroid diseases
Y Che, M Zhao, Y Gao, Z Zhang… - Frontiers in Molecular …, 2024 - frontiersin.org
Thyroid diseases, including functional and neoplastic diseases, bring a huge burden to
people's health. Therefore, a timely and accurate diagnosis is necessary. Mass spectrometry …
people's health. Therefore, a timely and accurate diagnosis is necessary. Mass spectrometry …
[HTML][HTML] Long-term AI prediction of ammonium levels in rivers using transformer and ensemble models
This study provides a cutting-edge machine learning approach to forecast ammonium (NH
4+) levels in River Lee London. Ammonium concentrations were predicted over several time …
4+) levels in River Lee London. Ammonium concentrations were predicted over several time …
Crop Yield Estimation Using Sentinel-3 SLSTR, Soil Data, and Topographic Features Combined with Machine Learning Modeling: A Case Study of Nepal
Effective crop monitoring and accurate yield estimation are fundamental for informed
decision-making in agricultural management. In this context, the present research focuses …
decision-making in agricultural management. In this context, the present research focuses …
Developing machine learning models with metaheuristic algorithms for droplet size prediction in a microfluidic microchannel
Droplet-based microfluidics holds promise for advanced biomedical applications due to its
enhanced accuracy, throughput, rapid reaction time, and minimal reagent consumption …
enhanced accuracy, throughput, rapid reaction time, and minimal reagent consumption …
Machine Learning-Assisted Cervical Cancer Prediction Using Particle Swarm Optimization for Improved Feature Selection and Prediction
Cervical cancer is a common and deadly disease that affects women worldwide. Early
diagnosis and treatment can improve the survival and quality of life of patients. Machine …
diagnosis and treatment can improve the survival and quality of life of patients. Machine …
Predicting Heat Capacity of Molecular Fluids Using Interpretable Machine Learning Model
Heat capacity at constant pressure (C p) of a molecular liquid medium is not only a basic
physical property applicable in the calculation of microscopic characteristics but also a …
physical property applicable in the calculation of microscopic characteristics but also a …