Evaluation of tissue computed tomography number changes and dosimetric shifts after conventional whole-breast irradiation in patients undergoing breast-conserving surgery by Joo Hwan Lee, Dong Soo Lee, So Hyun Park, Young Kyu Lee, Jeong Soo Kim, Yong Seok Kim

“…All the patients had received 50.4 Gy of conventional whole-breast irradiation (WBI) and underwent re-sim CT scans for tumor bed boost. For evaluation of dosimetric shifts between initial and re-sim CT scans, electron boost plans in the same field size with the same monitor unit with source-to-skin distance of 100 cm were conducted. …”
Get full text

Dry sliding tribological characteristics evaluation and prediction of TiB2-CDA/Al6061 hybrid composites exercising machine learning methods by Amit Aherwar, Anamika Ahirwar, Vimal Kumar Pathak

Get full text

Unleasing the potential of seaweed biostimulants—a comparative evaluation for enhancing saffron (Crocus sativus L.) yield with different corm sizes in the Western Himalayas by Sumedha Thakur, Swati Walia, Babita Thakur, Arup Ghosh, Rakesh Kumar

“…In this context, the present study aimed to evaluate the effects of minimally processed homogenates (MPHs) of two red seaweed-derived species, Solieria chordalis (C. …”
Get full text

Aquatic system assessment of potentially toxic elements in El Manzala Lake, Egypt: A statistical and machine learning approach by Asmaa Nour Aly Al-Falal, Salah Elsayed, Ezzat A. El Fadaly, Aissam Gaagai, Hani Amir Aouissi, Mohamed S. Abd El-baki, Mohamed Hamdy Eid, Abdallah Elshawadfy Elwakeel, Zaher Mundher Yaseen, Osama Elsherbiny, E.I. Eltahir, Mohamed Gad

Get full text

Intelligent identification of the acoustic emission characteristics and crack propagation states during of coal failure based on the NRBO-XGBoost model by Tianjun ZHANG, Xinshuang CAO, Shuang SONG, Suinan HE, Yilun XUE, Guoying LIU, Juntao CHEN

“…Employing the NRBO-XGBoost model, this study performed intelligent identification of the stable and unstable crack propagation stages (stages Ⅲ and Ⅳ, respectively) of coals under varying loading rates, achieving adaptive optimization of XGBoost parameters. Furthermore, this study evaluated the performance of various models using four metrics: accuracy, precision, recall, and F1 score.Results and Conclusions The results indicate that in stage Ⅲ, coals exhibited slightly increased values b and S and a decreased proportion of shear cracks. …”
Get full text

An Innovative Approach for Calibrating Hydrological Surrogate Deep Learning Models by Amir Aieb, Antonio Liotta, Alexander Jacob, Iacopo Federico Ferrario, Muhammad Azfar Yaqub

“…A data transformation technique using Gradient Boosting Regression (GBR) is then applied to each homogeneous subregion identified by the Random Forest classifier (RFC), based on elevation parameters (Wflow_dem). …”
Get full text

Prediction and optimization of surface quality and material removal rate in wire-EDM cutting of tungsten–copper alloy (W70Cu30) by Abdullah Eaysin, Muhammad Ali Zinnah, Md. Nayem, Hosney Ara Begum, Md.Injamamul Haque Protyai, Salahuddin Ashrafi, Adib Bin Rashid

“…This study investigates its machinability using wire electrical discharge machining (EDM). Process parameters, including current, servo voltage, wire feed, and wire tension, were optimized to evaluate their impact on Material Removal Rate (MRR) and Surface Roughness (Ra). …”
Get full text

Aging-associated fermentation of palm oil-mill effluent enhances its organo-fertilizer value and the desired agronomic effects in low-fertility soils by Daniel Onyedikachi Ugwu, Parker Elijah Joshua, Sunday Ewele Obalum, Ndeari King Dedan, Obi Uzoma Njoku

“…Relative increases in both soil and crop parameters in the amended over the control were greater in POMEaged than POMEfresh treatments, and reflected increases in soil pH, P release and exchange of plant-nutrient cations. …”
Get full text

Estimation of Tomato Quality During Storage by Means of Image Analysis, Instrumental Analytical Methods, and Statistical Approaches by Paris Christodoulou, Eftichia Kritsi, Georgia Ladika, Panagiota Tsafou, Kostantinos Tsiantas, Thalia Tsiaka, Panagiotis Zoumpoulakis, Dionisis Cavouras, Vassilia J. Sinanoglou

Get full text

Exploring spatial machine learning techniques for improving land surface temperature prediction by Arunab K.S., Mathew A.

“…The purpose of this study is to investigate the effects of including spatial information into the Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models for forecasting LST. The significance and impact of each input parameter on the models' predictive capabilities are assessed using the SHAP (SHapley Additive exPlanations) approach and the model intercomparisons were done using the error evaluation metrices. …”
Get full text

Computational models based on machine learning and validation for predicting ionic liquids viscosity in mixtures by Bader Huwaimel, Jowaher Alanazi, Muteb Alanazi, Tareq Nafea Alharby, Farhan Alshammari

“…These algorithms include Random Forest (RF), Gradient Boosting (GB), and XGBoost (XGB). Furthermore, the study incorporates the use of Glowworm Swarm Optimization (GSO) for hyper-parameter optimization, thereby further elevating the efficacy of the models. …”
Get full text

Establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome features by You Wu, Ke Tang, Chunzheng Wang, Hao Song, Fanfan Zhou, Ying Guo

“…Cytotoxicity, usually represented by cell viability, is a crucial parameter for evaluating drug safety in vitro. Accurate prediction of cell viability/cytotoxicity could accelerate drug development in the early stage. …”
Get full text

Modeling saturation exponent of underground hydrocarbon reservoirs using robust machine learning methods by Abhinav Kumar, Paul Rodrigues, A. K. Kareem, Tingneyuc Sekac, Sherzod Abdullaev, Jasgurpreet Singh Chohan, R. Manjunatha, Kumar Rethik, Shivakrishna Dasi, Mahmood Kiani

“…In addition, the graphical-based and statistical-based evaluations illustrate that AdaBoost and ensemble learning models outperforms all other developed data-driven intelligent models as these two models are associated with lowest values of mean square error (adaptive boosting: 0.017 and ensemble learning: 0.021 based on unseen test data) and largest values of coefficient of determination (adaptive boosting: 0.986 and ensemble learning: 0.983 based on unseen test data).…”
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

Machine learning framework for oxytetracycline removal using nanostructured cupric oxide supported on magnetic chitosan alginate biocomposite by Hassan Rasoulzadeh, Hossein Azarpira, Mojtaba Pourakbar, Amir Sheikhmohammadi, Alieh Rezagholizade-shirvan

“…Prior to applying machine learning models, preprocessing steps were performed, including normalization using Min–Max Scaling to confine all features within the [0,1] range, outlier detection and removal of anomalous values, correlation analysis to avoid redundancy and multicollinearity, and data splitting into training and testing sets at an 80:20 ratio, along with K-fold cross-validation (k = 5) for robust model evaluation. The study assesses the accurate predictions of specific models, including Tikhonov Regularization, Yandex Boosting, and Particle Swarm Optimization (PSO), to improve removal efficiency and provide analysis of the components influencing the method. …”
Get full text

Machine learning with hyperparameter optimization applied in facies-supported permeability modeling in carbonate oil reservoirs by Watheq J. Al-Mudhafar, Alqassim A. Hasan, Mohammed A. Abbas, David A. Wood

“…ML performance was evaluated with four evaluation metrics (RMSE, MAE, R2, and Adjusted R2). …”
Get full text

Leveraging machine learning to proactively identify phishing campaigns before they strike by Kun Zhang, Haifeng Wang, Meiyi Chen, Xianglin Chen, Long Liu, Qiang Geng, Yu Zhou

“…Four ML classifiers—Categorical Boosting, Random Forest (RF), Decision Tree (DT), and Extreme Gradient Boosting (XGB)—were employed, with cross-validation ensuring robust model evaluation. …”
Get full text

Comparative analysis of machine learning models for predicting river water quality: a case study of the Zayandeh Rood River by Elham Fazel Najafabadi, Paria Shojaei, Mojgan Askarizadeh

“…Given the key role of rivers in supplying drinking water, supporting industry, agriculture, and ecosystems, water quality assessment and pollution quantification are essential for sustainable use. This study evaluated five machine learning models, i.e., Lasso Regression, Random Forest (RF), Gradient Boosting (GB), XGBoost, and Support Vector Machine (SVM) for predicting four water quality parameters—EC (Electrical Conductivity), TDS (Total Dissolved Solids), Sodium Adsorption Ratio (SAR), and TH (Total Hardness)—using data collected over a 31-year period from eight monitoring stations along the Zayandeh Rood River, a vital water source for drinking, agriculture, and industry in the arid region of central Iran. …”
Get full text

AE-XGBoost: A Novel Approach for Machine Tool Machining Size Prediction Combining XGBoost, AE and SHAP by Mu Gu, Shuimiao Kang, Zishuo Xu, Lin Lin, Zhihui Zhang

“…Taking the actual size of the machine tool as the response parameter, based on the size parameters in the milling process of the CNC machine tool, the effectiveness of the model is verified. …”
Get full text

Learning models for predicting pavement friction based on non-contact texture measurements: Comparative assessment by Xiuquan Lin, You Zhan, Zilong Nie, Joshua Qiang Li, Xinyu Zhu, Allen A. Zhang

“…In this research, traditional multiple linear regression and four machine learning methods, support vector machine (SVM), random forest (RF), gradient boosting decision tree (GBDT), and convolutional neural network (CNN), are utilized to evaluate and predict pavement frictional performance. …”
Get full text