Machine learning based predictive insights into compressive strength and strength retention of magnesium oxychloride cement by Ahmad Nawaz, M. Zohaib Nawaz, Leyang Lv, Yingwu Zhou

“…So, this study employed four machine learning (ML) models to predict the CS and SR of MOC using two literature-derived databases. …”
Get full text

Estimating shear strength of dredged soils for marine engineering: experimental investigation and machine learning modeling by Zheng Yao, Kaiwei Xu, Zejin Wang, Haodong Sun, Peng Cui, Peng Cui

“…For performance verification, four alternative predictive models were established, including LDA–ANN, support vector machines (SVM), Particle Swarm Optimization (PSO), and a GA-tuned BA–ANN. …”
Get full text

The impact of cultural factors on digital marketing strategies with Machine learning and honey bee Algorithm (HBA) by Muhammad Khan, Masood Ahmad, Rakhmonov Dilshodjon Alidjonovich, Kalonov Mukhiddin Bakhritdinovich, Kurbanbekova Mohichehra Turobjonovna, Imomov Jamshidxon Odilovich

“…This paper analyses the impact of cultural factors on digital marketing strategies in Pakistan. Improvement of machine learning (ML) techniques combined with the Honey Bee Algorithm (HBA) has been incorporated for better solutions. …”
Get full text

An Elderly Fall Detection Method Based on Federated Learning and Extreme Learning Machine (Fed-ELM) by Zhigang Yu, Jiahui Liu, Mingchuan Yang, Yanmin Cheng, Jie Hu, Xinchi Li

“…To solve the above issue, this paper proposes a fall detection algorithm combining Federated Learning and Extreme Learning Machine (Fed-ELM). First, the online extreme learning machine can use a small amount of misclassified user data to update the parameters so that its performance is improved for individual users. …”
Get full text

Machine Learning-Powered Segmentation of Forage Crops in RGB Imagery Through Artificial Sward Images by Hugo Moreno, Christian Rueda-Ayala, Victor Rueda-Ayala, Angela Ribeiro, Carlos Ranz, Dionisio Andújar

“…Accurate assessment of forage quality is essential for ensuring optimal animal nutrition. Key parameters, such as Leaf Area Index (LAI) and grass coverage, are indicators that provide valuable insights into forage health and productivity. …”
Get full text

Interpretable Prediction of a Decentralized Smart Grid Based on Machine Learning and Explainable Artificial Intelligence by Ahmet Cifci

“…This study addresses this challenge by leveraging machine learning (ML) models and explainable artificial intelligence (XAI) techniques to predict the stability of a decentralized smart grid. …”
Get full text

High throughput computational screening and interpretable machine learning for iodine capture of metal-organic frameworks by Haoyi Tan, Yukun Teng, Guangcun Shan

“…Initially, the relationship between the structural characteristics of MOF materials (including density, surface area and pore features) and their adsorption properties was explored, with the aim of identifying the optimal structural parameters for iodine capture. Subsequently, two machine learning regression algorithms—Random Forest and CatBoost, were employed to predict the iodine adsorption capabilities of MOF materials. …”
Get full text

Etiology of Late-Onset Alzheimer’s Disease, Biomarker Efficacy, and the Role of Machine Learning in Stage Diagnosis by Manash Sarma, Subarna Chatterjee

“…The other biomarker datasets associated with the brain structure, functionality, connectivity, and related parameters of an individual are broadly categorized as clinical-stage biomarkers. …”
Get full text

A Comparative Study of Ensemble Machine Learning and Explainable AI for Predicting Harmful Algal Blooms by Omer Mermer, Eddie Zhang, Ibrahim Demir

“…This study enhances the prediction of HABs in Lake Erie, part of the Great Lakes system, by utilizing ensemble machine learning (ML) models coupled with explainable artificial intelligence (XAI) for interpretability. …”
Get full text

Integrating machine learning and symbolic regression for predicting damage initiation in hybrid FRP bolted connections by Sherif Samy Sorour, Chahinaz Abdelrahman Saleh, Mostafa Shazly

“…Abstract The increasing adoption of machine learning (ML) in fiber-reinforced polymer (FRP) composite design has led to a reliance on black-box models, which achieve high predictive accuracy but lack interpretability. …”
Get full text

Predicting CO2 adsorption in KOH-activated biochar using advanced machine learning techniques by Raouf Hassan, Alireza Baghban

“…We employed a comprehensive suite of machine learning methods, like convolutional neural networks, random forests, artificial neural networks, linear regression, ridge and lasso regressions, elastic net, support vector machines, decision trees, gradient boosting machines, k-nearest neighbors, light gradient boosting machines, extreme gradient boosting, CatBoost, and Gaussian process, to build predictive models. …”
Get full text

Utilization of Ensemble Techniques in Machine Learning to Predict the Porosity and Hardness of Plasma-Sprayed Ceramic Coating by N. Radhika, M. Sabarinathan, S. Sivaraman

“…To address this challenge, the present study employs advanced machine learning ensemble techniques, including bagging, boosting, stacking, weighted averaging, voting, and hybrid methods, to accurately predict the porosity and hardness of plasma-sprayed ceramic coatings based on key process parameters. …”
Get full text

A Comprehensive Investigation of Anomaly Detection Methods in Deep Learning and Machine Learning: 2019–2023 by Shalini Kumari, Chander Prabha, Asif Karim, Md. Mehedi Hassan, Sami Azam

“…These techniques are categorized further into machine learning (ML), deep learning (DL), and federated learning (FL). …”
Get full text

Explainable Machine Learning and Predictive Statistics for Sustainable Photovoltaic Power Prediction on Areal Meteorological Variables by Sajjad Nematzadeh, Vedat Esen

“…This study proposes an explainable machine-learning framework that simultaneously ranks the most informative weather parameters and reveals their physical relevance to PV generation. …”
Get full text

Machine learning approach for prediction of safe mud window based on geochemical drilling log data by Hongchen Cai, Yunliang Yu, Yingchun Liu, Xiangwei Gao

“…Traditional geomechanical methods for SMW determination face challenges in handling complex, nonlinear relationships within drilling datasets.PurposeThis study aims to develop robust machine learning (ML) models to predict two key SMW parameters—Mud Pressure below shear failure (MWsf) and tensile failure (MWtf)—using geochemical drilling log data from Middle Eastern carbonate reservoirs.MethodsHybrid ML models combining Least Squares Support Vector Machine (LSSVM) and Multilayer Perceptron (MLP) with optimization algorithms (Gray Wolf Optimization, GWO; Grasshopper Optimization Algorithm, GOA) were trained on 2,820 data points from three wells. …”
Get full text

Forecasting pandemic-induced changes in real estate market values through machine learning approaches by Adile Gülsüm Ulucan, Aslı Bozdağ, Murat Karakoyun, Tansu Alkan

Get full text

Advanced machine learning techniques for predicting mechanical properties of eco-friendly self-compacting concrete by Arslan Qayyum Khan, Syed Ghulam Muhammad, Ali Raza, Amorn Pimanmas

“…This study evaluates the performance of advanced machine learning (ML) models in predicting the mechanical properties of eco-friendly self-compacting concrete (SCC), with a focus on compressive strength, V-funnel time, L-box ratio, and slump flow. …”
Get full text

Lost circulation intensity characterization in drilling operations: Leveraging machine learning and well log data by Ahmad Azadivash

“…The prime causes of lost circulation are related to several geological parameters, especially in problem-prone formations. …”
Get full text

Machine Learning Analysis to Identify Predictive Factors of Caudal Epidural Pulse Radiofrequency in the Treatment of Coccygodynia by Sir E, Aydogan S, Batur Sir GD, Celenlioglu AE

“…Ender Sir,1 Sena Aydogan,2 Gul Didem Batur Sir,2 Alp Eren Celenlioglu1 1Department of Algology and Pain Medicine, University of Health Sciences Gulhane School of Medicine, Ankara, Turkey; 2Department of Industrial Engineering, Gazi University, Ankara, TurkeyCorrespondence: Ender Sir, Email endersir@gmail.comBackground: This study aims to use machine learning (ML) to explore predictive parameters related to the efficacy of caudal epidural pulsed radiofrequency (CEPRF) treatment for coccygodynia.Methods: Five different ML methods were used to predict treatment success at 6 months after CEPRF. …”
Get full text

Enhancing Multi-Disease Prediction with Machine Learning: A Comparative Analysis and Hyperparameter Optimization Approach by Mariam Kili Bechir, Ferhat Atasoy

“…Although traditional methods based on statistical parameters are still important in healthcare, Machine learning (ML) algorithms offer promising results for analyzing health data. …”
Get full text