Polyomaviruses and the risk of breast cancer: a systematic review and meta-analysis by Tahoora Mousavi, Fatemeh Shokoohy, Mahmood Moosazadeh

“…Data analysis was performed using STATA software, and standard errors of prevalence were calculated using the binomial distribution formula. …”
Get full text

Bridging the Gap: A Review of Machine Learning in Water Quality Control by Herlina Abdul Rahim, Nur Athirah Syafiqah Noramli, Indrabayu

“…Hybrid frameworks such as sensor fusion systems (reducing measurement errors by 83%) and digital twins (preventing 60% of public health risks during contamination events) demonstrate transformative potential by bridging these approaches. …”
Get full text

NIRS identification of cashmere and wool fibers based on spare representation and improved AdaBoost algorithm by Zhu Yaolin, Li Zheng, Chen Xin, Chen Jinni, Zhang Hongsong

“…This has increased misclassification errors. This article uses AdaBoost to assign weights to samples of different species. …”
Get full text

Recognition of field-grown tobacco plant type characteristics based on three-dimensional point cloud and ensemble learning by JIA Aobo, DONG Tianhao, ZHANG Yan, ZHU Binglin, SUN Yanguo, WU Yuanhua, SHI Yi, MA Yuntao, GUO Yan

“…The results indicated the coefficients of determination (R²) of the plant height and maximum width of leaf layer extracted from the 3D point cloud were all greater than 0.97, and the root mean square errors were 3.0, 3.1 cm, respectively. Meanwhile, the extracted phenotypic parameters of tobacco plants were analyzed by different methods. …”
Get full text

PM2.5 concentration prediction using machine learning algorithms: an approach to virtual monitoring stations by Ahmad Makhdoomi, Maryam Sarkhosh, Somayyeh Ziaei

“…This suggests that GBR is the best model for reducing large errors, making it more robust in capturing variations in PM2.5 levels. …”
Get full text

Study on the evolution patterns and predictive modeling of ambient air quality in oasis cities of arid regions: a case study of Urumqi by Bian Jing, Ren Xiaofeng

Get full text

Accurate estimation of permeability reduction resulted from low salinity water flooding in clay-rich sandstones by Xiaojuan Zhang, Muntadher Abed Hussein, Tarak Vora, Anupam Yadav, Asha Rajiv, Aman Shankhyan, Sachin Jaidka, Mehul Manu, Farzona Alimova, Issa Mohammed Kadhim, Zainab Jamal Hamoodah, Fadhil Faez, Ahmad Khalid

“…The results show that random forest and ensemble learning algorithms delivered the highest predictive accuracy, evidenced by the most substantial coefficient of determination (R2) and minimal error metrics. …”
Get full text

The effects of rhythm control strategies versus rate control strategies for atrial fibrillation and atrial flutter: A systematic review with meta-analysis and Trial Sequential Anal... by Naqash J Sethi, Joshua Feinberg, Emil E Nielsen, Sanam Safi, Christian Gluud, Janus C Jakobsen

“…We used Trial Sequential Analysis (TSA) to control for random errors. Statistical heterogeneity was assessed by visual inspection of forest plots and by calculating inconsistency (I2) for traditional meta-analyses and diversity (D2) for TSA. …”
Get full text

Investigating Spatial Effects through Machine Learning and Leveraging Explainable AI for Child Malnutrition in Pakistan by Xiaoyi Zhang, Muhammad Usman, Ateeq ur Rehman Irshad, Mudassar Rashid, Amira Khattak

“…Secondly, Spatial Durbin Error Model (SDEM) was used to detect and capture the impact of spatial spillover on childhood stunting. …”
Get full text

Evaluation of Soil Moisture Retrievals from a Portable L-Band Microwave Radiometer by Runze Zhang, Abhi Nayak, Derek Houtz, Adam Watts, Elahe Soltanaghai, Mohamad Alipour

Get full text

Intelligent Data Processing Methods for the Atypical Values Correction of Stock Quotes by T. V. Zolotova, D. A. Volkova

“…The multiple imputation method creates for each missing value not one, but many imputations, which avoids a systematic error, but at the expense of high computational costs. …”
Get full text

SS-OPDet: A Semi-Supervised Open-Set Detection Framework for Dead Pine Wood Detection by Xiaojian Lu, Shiguo Huang, Songqing Wu, Feiping Zhang, Mingqing Weng, Jianlong Luo, Xiaolin Li

“…Pine wilt disease poses a significant threat to pine forests worldwide, necessitating efficient and accurate detection of dead pine wood for effective disease control and forest management. …”
Get full text

Do Machine Learning and Business Analytics Approaches Answer the Question of ‘Will Your Kickstarter Project be Successful? by Murat Kılınç, Can Aydın, Çiğdem Tarhan

“…F1-Score, Recall, Precision, Mean Squared Error (MSE), Kappa and AUC values were analyzed to determine the most successful models. …”
Get full text

Optimizing Renewable Energy Integration Using IoT and Machine Learning Algorithms by Orken Mamyrbayev, Ainur Akhmediyarova, Dina Oralbekova, Janna Alimkulova, Zhibek Alibiyeva

“…Results showed significant improvements in forecasting accuracy, with the LSTM model achieving a 59.1% reduction in Mean Absolute Percentage Error compared to the persistence model. Grid stability improved substantially, with a 64.2% reduction in supply-demand mismatches. …”
Get full text

Metal loss defect detection and depth estimation using multi-spectral image analysis of cooling excited steel specimen with corrosion by Shamendra Egodawela, Amirali K. Gostar, H. A. D. Samith Buddika, W. A. N. I. Harischandra, A. J. Dhammika, Mojtaba Mahmoodian

“…The FNN showed the best results in solving the regression problem with a least Root Mean Square Error of 0.2829 and an R2 score 0.976. The 700 nm–900 nm range was identified as the optimal wavelength span for spectral imaging.…”
Get full text

Adaptive Smart System for Energy-Saving Campus by Ziling Chen, Ray-I Chang, Quincy Wu

“…In total, 59 out of 62 participants responded with a sampling error of ±2.8% at a 95% confidence level. The average system usability scale (SUS) score reached 84, surpassing the cross-industry average standard, indicating that the system is user-friendly. …”
Get full text

Synergistic application of artificial intelligence and response surface methodology for predicting and enhancing in vitro tuber production of potato (Solanum tuberosum). by Rajermani Thinakaran, Ecenur Korkmaz, Başak Ünver, Seyid Amjad Ali, Zeshan Iqbal, Muhammad Aasim

“…Results analyzed by Machine learning (ML) models revealed maximum predictive accuracy for tuberization by Random Forest (RF) model with an R2 of 0.379. However, all other models also faced challenges with high error rates, indicating the need for improved feature engineering. …”
Get full text

Computational fluid dynamics analysis and machine learning study of heat transfer in solar air heaters with distinct ribs configuration by Eid S. Alatawi

“…Critically, the developed Convolutional Neural Network (CNN) model significantly outperformed Random Forest Regression and Support Vector Regression, achieving a Mean Square Error (MSE) of 0.004 and an R2 value of 0.96 in predicting heat transfer coefficients. …”
Get full text

A Method to Obtain Remotely Sensed Grain Size Distributions From Granular Deposits With Complex Surfaces by H. L. Jacobson, G. Walton, K. R. Barnhart, F. K. Rengers

“…This approach combines an existing random forest machine learning method with a novel iterative clustering algorithm. …”
Get full text

Batch evaluation of collective owned commercialised construction land using machine learning by Wenzhu Zhang, Licheng Huang, Shengquan Lu, Shiyu Deng, Bin Wu, Yanfei Wei

“…The results demonstrate that the RF model exhibits superior performance, achieving a mean absolute error of 17.50 yuan and a prediction accuracy of 94.77%, compared with 91.21% for BPNN and 91.94% for SVM. …”
Get full text