Advancing Geotechnical Evaluation of Wellbores: A Robust and Precise Model for Predicting Uniaxial Compressive Strength (UCS) of Rocks in Oil and Gas Wells by Mohammadali Ahmadi

“…The investigation encompasses Linear Regression, ensemble methods (including Random Forest, Gradient Boosting, XGBoost, and LightGBM), support vector machine-based regression (SVM-SVR), and multilayer perceptron artificial neural network (MLP-ANN) models. …”
Get full text

Accuracy Prediction of Compressive Strength of Concrete Incorporating Recycled Aggregate Using Ensemble Learning Algorithms: Multinational Dataset by Menghay Phoeuk, Minho Kwon

“…Results demonstrate that the proposed models are highly accurate and generalizable, with high coefficients of determination and low error predictions. The CatBoost model performed the best, exhibiting an R2 of 0.938 and low mean absolute error and root mean squared error values of 2.639 and 3.885, respectively, in the blind evaluation process. …”
Get full text

Chemically enhanced water-alternating-gas injection for improved CO2 sweep efficiency and geological sequestration in CO2-EOR: Insights from physical experiments and molecular simu... by Pengwei Fang, Zhengming Yang, Qun Zhang, Hongwei Yu, Fei Feng, Yidi Wan, Jiangfei Wei, Yuhao Mei, Meiwen Cao

Get full text

Laser-induced Breakdown Spectroscopy Based on Pre-classification Strategy for Quantitative Analysis of Rock Samples by Weiheng KONG, Lingwei ZENG, Yu RAO, Sha CHEN, Xu WANG, Yanting YANG, Yixiang DUAN, Qingwen FAN

“…Furthermore, it alleviates the workload of data analysis, simplifying the analytical process and thereby boosting efficiency. However, the current multi-layer quantitative analysis model still exhibits some deviations in regard to different elements. …”
Get full text

Developing an efficient explainable artificial intelligence approach for accurate reverse osmosis desalination plant performance prediction: application of SHAP analysis by Meysam Alizamir, Mo Wang, Rana Muhammad Adnan Ikram, Sungwon Kim, Kaywan Othman Ahmed, Salim Heddam

“…In this study, the predictive accuracy of six different machine learning models, including Natural Gradient-based Boosting (NGBoost), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Support vector regression (SVR), Gaussian Process Regression (GPR), and Extremely Randomized Tree (ERT) was evaluated for modelling the parameter of permeate flow as a key element in system efficiency, energy consumption, and water quality using six various input combinations of feed water salt concentration, condenser inlet temperature, feed flow rate, and evaporator inlet temperature. …”
Get full text

Diabetes Mellitus Disease Prediction and Type Classification Involving Predictive Modeling Using Machine Learning Techniques and Classifiers by B. Shamreen Ahamed, Meenakshi S. Arya, S. K. B. Sangeetha, Nancy V. Auxilia Osvin

“…Various ML algorithms such as Random Forest, Light Gradient Boosting Machine, Gradient Boosting Machine, Support Vector Machine, Decision Tree, and XGBoost are being used. …”
Get full text

RadiomixNet: Integrating Radiomics and Feature Extraction for Advanced Pneumonia Diagnosis by Rahul Gowtham Poola, Siva Sankar Yellampalli

“…Diverse classifiers, including Bernoulli Naïve Bayes, Random Subspace Boost, Quadratic Discriminant, and Gradient Boosting, were employed to classify test X-rays. …”
Get full text

An online learning method for assessing smart grid stability under dynamic perturbations by Alaa Alaerjan, Randa Jabeur

“…Additionally, we perform a comparative analysis between the Bee Algorithm and a benchmark fusion model incorporating Random Forest (RF), Gradient Boosting (GB), and eXtreme Gradient Boosting (XGB) classifiers, under identical conditions, including the presence of dynamic perturbations. …”
Get full text

Machine learning approach for solar irradiance estimation on tilted surfaces in comparison with sky models prediction by Mbah O. M., Madueke C. I., Umunakwe R., Okafor C. O.

“…Compared to the measured data, it was discovered that the Extreme Gradient Boosting (XGBoost) algorithm offered the best performance with the least inaccuracy to sky models.…”
Get full text

Switched Capacitor Based High Step-Up Multilevel Inverter with Self-Balancing Ability and Low Switching Stress by M. Venkatesan, S. Gouse Basha, A. Ramkumar, R. Manikandan, M. Easwaran, Baseem Khan

“…The comparative analysis of the proposed SC inverter is made for the components, peak inverse voltage (PIV), total standing voltage (TSV), boosting ability, and voltage balancing of capacitors. …”
Get full text

Enhanced Diagnosis of Thyroid Diseases Through Advanced Machine Learning Methodologies by Osasere Oture, Muhammad Zahid Iqbal, Xining (Ning) Wang

“…This research focuses on improving the diagnostic process by creating a classification model that utilises various machine learning models and a deeplearning model to categorise three types of thyroid disease conditions. …”
Get full text

Comparative Analysis of Hybrid Attention and Progressive Layering Through a Comprehensive Evaluation of ARU-Net and PLU-Net in Brain Tumour Segmentation by Noman Ahmed Siddiqui, Muhammad Tahir Qadri, Muhammad Ovais Akhter, Tahira Anwar, Fahad Farooq

“…On the other hand, PLU-Net uses a cascaded, multi-stage structure enhanced by attention gates and multi-scale data augmentation where the main emphasis is a progressive boosting of accuracy levels at multiple resolutions to guarantee high boundary accuracy. …”
Get full text

Large Pretrained Foundation Model for Key Performance Indicator Multivariate Time Series Anomaly Detection by Xu Wang, Qisheng Xu, Kele Xu, Ting Yu, Bo Ding, Dawei Feng, Yong Dou

“…Furthermore, to address the multivariate challenge, we introduce a novel feature extraction method based on channel independence to optimize information processing across multidimensional features. Additionally, we leverage frequency domain information to design a feature enhancement method, further boosting the model's detection accuracy. …”
Get full text

Jordanian banknote data recognition: A CNN-based approach with attention mechanism by Ahmad Nasayreh, Ameera S. Jaradat, Hasan Gharaibeh, Waed Dawaghreh, Rabia Mehamad Al Mamlook, Yaqeen Alqudah, Qais Al-Na'amneh, Mohammad Sh. Daoud, Hazem Migdady, Laith Abualigah

“…The study made use of a data set from Kaggle that includes a collection of Jordanian banknotes in five different denominations. Image processing techniques were employed to produce artificial images by boosting the brightness of real ones. …”
Get full text

Sustainable energy: Advancing wind power forecasting with grey wolf optimization and GRU models by Zainab Al-Ibraheemi, Samaher Al-Janabi

“…The original wind dataset (104857613) was reduced to 1093913 after processing missing values, achieving a reduction rate of 0.136. …”
Get full text

Development of Smart Models to Accurately Predict Dynamic Viscosity of CO2-Saturated Polyethylene Glycol by Ayat Hussein Adhab, Morug Salih Mahdi, Bhavesh Kanabar, Anupam Yadav, Ranganathaswamy M K, Rishabh Thakur, Parveen Kumar, Braj Krishna, Aseel Salah Mansoor, Usama Kadem Radi, Nasr Saadoun Abd, Samim Sherzod

“…Precise estimation of viscosity of CO2-saturated PEG is crucial for several scientific and industrial applications, as viscosity directly impacts the material’s performance and feasibility in specific processes. This study, hence, introduces machine learning models utilizing K-nearest neighbors, decision tree, adaptive boosting, multilayer perceptron artificial neural network, convolutional neural network, support vector machine, random forest and ensemble learning algorithms to accurately forecast the dynamic viscosity of CO2-saturated PEG based on PEG molar mass, pressure, and temperature. …”
Get full text

Charge-transfer complexation of coordination cages for enhanced photochromism and photocatalysis by Gen Li, Zelin Du, Chao Wu, Yawei Liu, Yan Xu, Roy Lavendomme, Shihang Liang, En-Qing Gao, Dawei Zhang

Get full text

Research on machine vision online monitoring system for egg production and quality in cage environment by Zhenlong Wu, Hengyuan Zhang, Cheng Fang

“…In the domain of egg production, the application of automation technologies is essential for boosting productivity and quality. This study introduces an online monitoring system designed for egg quality assessment within caged environments, incorporating a robotic patrol system for egg localization and a fixed video stream for quality analysis. …”
Get full text

Electrocardiographic, echocardiographic and lipid parameters in predicting obstructive coronary artery disease in patients with non-ST elevation acute coronary syndrome by M. M. Tsivanyuk, B. I. Geltser, K. I. Shakhgeldyan, E. D. Emtseva, G. S. Zavalin, O. I. Shekunova

“…Mann-Whitney, Fisher, chi-squared, univariate logistic regression (LR) methods were used for data processing and analysis, while miltivariate LR (MLR), gradient boosting (XGBoost) and artificial neural networks (ANN) were used to develop predictive models. …”
Get full text

Machine learning algorithms to predict feeding practices during diarrheal disease and its determinants among under-five children in East Africa by Tirualem Zeleke Yehuala, Nebebe Demis Baykemagn, Bewuketu Terefe

“…Python software was utilized for data processing and machine learning model building. We employed four ML algorithms, such as Random Forest (RF), Decision Tree (DT), XGB (Extreme Gradient Boosting), and Logistic Regression (LR). …”
Get full text