Using Machine Learning to Assess the Effects of Biochar-Based Fertilizers on Crop Production and N₂O Emissions in China by Yuan Zeng, Sujuan Chen, Yunpeng Li, Li Xiong, Cheng Liu, Muhammad Azeem, Xiaoting Jie, Mei Chen, Longjiang Zhang, Jianfei Sun

Subjects: Get full text

Benchmarking with a Language Model Initial Selection for Text Classification Tasks by Agus Riyadi, Mate Kovacs, Uwe Serdült, Victor Kryssanov

Subjects: Get full text

Efficient Fault Diagnosis of Elevator Cabin Door Drives Using Machine Learning with Data Reduction for Reliable Transmission by Jakub Gęca, Dariusz Czerwiński, Bartosz Drzymała, Krzysztof Kolano

Get full text

Comparison of various machine learning regression models based on Human age prediction by Dr.Manaf K Hussein

“…In this study, five widely used machine learning  regression models (Linear support vector regression (L-SVR), radial basis function support vector regression (RBF-SVR), relevance vector regression (RVR), Elastic Net and Gaussian process regression (GPR)) were trained and evaluated to predict brain age using volumes of brain regions data. …”
Get full text

Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review by Florin-Stefan Zamfir, Madalina Carbureanu, Sanda Florentina Mihalache

“…The treatment processes from a wastewater treatment plant (WWTP) are known for their complexity and highly nonlinear behavior, which makes them challenging to analyze, model, and especially, to control. This research studies how machine learning (ML) with a focus on deep learning (DL) techniques can be applied to optimize the treatment processes of WWTPs, highlighting those case studies that propose ML and DL methods that directly address this issue. …”
Get full text

Elderly travel mode choice in Thailand-evaluating MNL and machine learning models by Anantaya Philuek, Panuwat Wisutwattanasak, Fareeda Watcharamaisakul, Chinnakrit Banyong, Anon Chantaratang, Thanapong Champahom, Vatanavongs Ratanavaraha, Sajjakaj Jomnonkwao

“…This investigation analyzes the determinants of transportation mode selection among elderly populations in Thailand through a comparative approach utilizing both traditional statistical modeling and contemporary machine learning techniques. …”
Get full text

Assessment and prediction models for the quantitative and qualitative reserve of the ovary using machine learning by Hiroshi Koike, Miyuki Harada, Kaname Yoshida, Katsuhiko Noda, Chihiro Tsuchida, Toshihiro Fujiwara, Akari Kusamoto, Zixin Xu, Tsurugi Tanaka, Nanoka Sakaguchi, Chisato Kunitomi, Nozomi Takahashi, Yoko Urata, Kenbun Sone, Osamu Wada-Hiraike, Yasushi Hirota, Yutaka Osuga

“…Binary classification models predicting the ovarian reserve were created using machine learning methods developed with many collected feature values. …”
Get full text

Hybridization of stochastic hydrological models and machine learning methods for improving rainfall-runoff modeling by Sianou Ezéckiel Houénafa, Olatunji Johnson, Erick K. Ronoh, Stephen E. Moore

“…It achieves an overall Nash-Sutcliffe Efficiency (NSE) of 0.896, which is 7.30% higher than the NSE of HyMoLAP, and 29.67% and 259.71% higher than those of the standalone machine learning models. The Combined Accuracy (CA) is 38.11, reflecting reductions of 19.81%, 42.30%, and 62.41% compared to the standalone models. …”
Get full text

Intelligent Feature Selection Ensemble Model for Price Prediction in Real Estate Markets by Daniel Cristóbal Andrade-Girón, William Joel Marin-Rodriguez, Marcelo Gumercindo Zuñiga-Rojas

Subjects: Get full text

Daily runoff forecasting using novel optimized machine learning methods by Peiman Parisouj, Changhyun Jun, Sayed M. Bateni, Essam Heggy, Shahab S. Band

“…This study addresses these challenges by introducing a novel bio-inspired metaheuristic algorithm, Artificial Rabbits Optimization (ARO), integrated with various machine learning (ML) models for runoff forecasting in the Carson and Chehalis River basins. …”
Get full text

Predicting Atmospheric Dispersion of Industrial Chemicals Using Machine Learning Approaches by Maria Valle, Jairo A. Cardona, Cesar Viloria-Nunez, Christian G. Quintero M.

“…These advancements establish a foundation for future studies to incorporate additional chemicals and accident scenarios, improving the flexibility and reliability of atmospheric dispersion modeling. Future work will explore hybrid machine learning models and advanced dimensionality reduction methods to enhance the system’s applicability to complex industrial environments.…”
Get full text

An Explainable Machine Learning Model for Predicting Macroseismic Intensity for Emergency Management by Federico Mori, Giuseppe Naso

“…Predicting macroseismic intensity from instrumental ground motion parameters remains a complex task due to the nonlinear relationship with observed damage patterns. An explainable machine learning model based on the XGBoost algorithm was developed to address the challenge. …”
Get full text

Using optimized dimensionality reduction and machine learning to explain driving processes of phytoplankton community assembly in large mountain rivers by Jingxu Ye, Daikui Li, Qi Liu, Jianying Song, Jiawei Song, Zhigang Zu, Yujun Yi

“…In this study, we employed a methodology combining optimized dimensionality reduction with advanced machine learning to construct a path analysis model for explaining the driving processes underlying phytoplankton community assembly in large mountain rivers. …”
Get full text

Advanced Machine Learning Techniques for Predicting Concrete Compressive Strength by Mohammad Saleh Nikoopayan Tak, Yanxiao Feng, Mohamed Mahgoub

“…Advanced methods such as SHapley Additive exPlanations (SHAP) values and partial dependence plots were used to attain deep insights about feature interaction with a view to enhancing interpretability and fostering trust in models. Results highlight the potential of machine learning models to improve concrete mix design with the aim of sustainable construction through the optimization of material usage and waste reduction. …”
Get full text

Optimizing Methanol Injection Quantity for Gas Hydrate Inhibition Using Machine Learning Models by Mohammed Hilal Mukhsaf, Weiqin Li, Ghassan Husham Jani

“…Machine learning offers a robust solution by leveraging pipeline condition data to effectively forecast methanol needs. …”
Get full text

Cross-Layer Analysis of Machine Learning Models for Secure and Energy-Efficient IoT Networks by Rashid Mustafa, Nurul I. Sarkar, Mahsa Mohaghegh, Shahbaz Pervez, Ovesh Vohra

“…To address these IoT issues, we propose a cross-layer IoT architecture employing machine learning (ML) models and lightweight cryptography. …”
Get full text

Enhancing Machine Learning Models Through PCA, SMOTE-ENN, and Stochastic Weighted Averaging by Youngjin Han, Inwhee Joe

“…An ensemble model combining seven machine learning algorithms—Logistic Regression, Support Vector Machine, KNN, Random Forest, XGBoost, LightGBM, and CatBoost—was applied to predict survival outcomes. …”
Get full text

Diagnostic framework to validate clinical machine learning models locally on temporally stamped data by Maximilian Schuessler, Scott Fleming, Shannon Meyer, Tina Seto, Tina Hernandez-Boussard

“…Results Here, we introduce a model-agnostic diagnostic framework to validate clinical machine learning models on time-stamped data, consisting of four stages. …”
Get full text

Enterprise power emission reduction technology based on the LSTM–SVM model by Li Kun, Su Meng, Liu Qiang, Zhang Bin

“…Simulation experiments showed that after data warning, carbon emissions could be reduced by up to 48.26%, and electricity costs could be reduced by up to 60.48%. The machine learning-based power data warning method proposed in this study has important practical application value and can effectively help enterprises achieve emission reduction and cost control goals.…”
Get full text

Learning model combined with data clustering and dimensionality reduction for short-term electricity load forecasting by Hyun-Jung Bae, Jong-Seong Park, Ji-hyeok Choi, Hyuk-Yoon Kwon

“…It has evolved from statistical methods to artificial intelligence-based techniques that use machine learning models. In this study, we investigate short-term load forecasting (STLF) for large-scale electricity usage datasets. …”
Get full text