A Comparative Study between Different Machine Learning Algorithms for Estimating the Vehicular Delay at Signalized Intersections by Yazan Alatoom, Abdallah Al-Hamdan

“…The delay at signalized intersections is a crucial parameter that determines the performance and level of service (LOS). …”
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Lightweight detection model for safe wear at worksites using GPD-YOLOv8 algorithm by Jian Xing, Chenglong Zhan, Jiaqiang Ma, Zibo Chao, Ying Liu

“…Furthermore, we adopt an Exponential Moving Average (EMA) SlideLoss function, which not only boosts accuracy but also ensures the stability of our safety wear detection model’s performance. …”
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Experimental Performance Investigation of Low Speed Horizontal Axis Wind Turbine for Direct Driven Generator by Muldi Yuhendri, Juli Sardi, Citra Dewi, Fatimah Hanifah

“…The turbine's characteristics were experimentally investigated using a low-speed permanent magnet generator (PMG), a boost converter, and an Arduino Uno controller. Parameter values were observed by testing, the HAWT under varying wind speeds and rotor rotation speeds. …”
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Acremonium terricola culture supplementation in the diet of pregnant and lactating Ewes can improve the production performance of Ewes and lambs by regulating maternal metabolism a... by Mengen Zhang, Rui Han, Anguo Zhang, Chao Xu, Guohong Zhao, Xunsheng Pang, Xichun Jiang, Shiqin Wang

“…Therefore, this study was designed to address two primary objectives: (1) to evaluate the effects of dietary supplementation with ATC on production performance and hematological parameters in ewes; (2) to determine whether maternally ingested ATC can be transmitted to offspring via lactation and subsequently influence lamb growth performance. …”
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Machine Learning Models for Predicting Seismic Response of a Novel Two-Stage Friction Pendulum Isolated Bridge Structure by Hanzlah Akhlaq, Tianbo Peng, Kawsu Jitteh, Muhammad Salman Khan

“…Utilizing this extensive dataset, seven ML models were trained and evaluated using statistical key performance indicators (KPIs). …”
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Solar Energy Forecasting Using Machine Learning Techniques for Enhanced Grid Stability by Attuluri R. Vijay Babu, N. Bharath Kumar, Rajanand Patnaik Narasipuram, Soundhar Periyannan, Alireza Hosseinpour, Aymen Flah

“…Historical solar power and weather datasets were used to train and evaluate the models across multiple performance metrics. …”
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Compressive strength prediction of fly ash/slag-based geopolymer concrete using EBA-optimised chemistry-informed interpretable deep learning model by Yang Yu, Iman Munadhil Abbas Al-Damad, Stephen Foster, Ali Akbar Nezhad, Ailar Hajimohammadi

“…The model integrates key mix parameters such as material proportions, curing conditions, and the chemical composition of FA/GGBS binders, making it chemistry-informed. …”
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Microplastic Deposit Predictions on Sandy Beaches by Geotechnologies and Machine Learning Models by Anderson Targino da Silva Ferreira, Regina Célia de Oliveira, Maria Carolina Hernandez Ribeiro, Pedro Silva de Freitas Sousa, Lucas de Paula Miranda, Saulo de Oliveira Folharini, Eduardo Siegle

“…Using beach face slope (tanβ) and orientation (Aspect) derived from remote sensing images, calibrated by in situ topographic profiles collected through GNSS positioning, and laboratory analyses, six machine learning models Random Forest, Gradient Boosting, Lasso and Ridge regression, Support Vector Regression, and Partial Least Squares regression were tested and evaluated for performance. …”
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AI-driven wastewater management through comparative analysis of feature selection techniques and predictive models by Faruk Dikmen, Ahmet Demir, Bestami Özkaya, Muhammad Owais Raza, Jawad Rasheed, Tunc Asuroglu, Shtwai Alsubai

“…Abstract The integration of artificial intelligence (AI) in wastewater treatment management offers a promising approach to optimizing effluent quality predictions and enhancing operational efficiency. This study evaluates the performance of machine learning models in predicting key wastewater effluent parameters Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), Total Suspended Solids (TSS), Total Effluent Nitrogen and Total Effluent Phosphorus. …”
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Assessing the impact of pozzolanic materials on the mechanical characteristics of UHPC: analysis, and modeling study by Diar Fatah Abdulrahman Askari, Sardam Salam Shkur Shkur, Azad Abdwlqader Mohammed, Hersh F. Mahmood, Brwa Hamah Saeed Hamah Ali, Soran Abdrahman Ahmad

“…The performance of the proposed models is then evaluated using statistical tools such as the Coefficient of Determination (R2), Scatter Index (SI), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) to identify the most reliable method for predicting the compressive strength of UHPC. …”
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Developing a machine learning model for predicting varicocelectomy outcomes: a pilot study by Coşkun Kaya, Mehmet Erhan Aydın, Özer Çelik, Aykut Aykaç, Mustafa Sungur

“…Demographic, anthropometric variables, physical examination findings, hematological, radiological, and semen analysis parameters were evaluated. The patients were separated into two groups according to the improvement in total motile sperm count postoperatively as improvement (Group 1) and no improvement (Group 2). …”
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Machine learning techniques for predicting the peak response of reinforced concrete beam subjected to impact loading by Ali Husnain, Munir Iqbal, Hafiz Ahmed Waqas, Mohammed El-Meligy, Muhammad Faisal Javed, Rizwan Ullah

“…A set of 145 experimental data points from 12 different sources is used to train and evaluate these machine learning models. Key parameters in the data include beam width and depth, span, reinforcement ratios, concrete strength, steel yield strength, deflection, and impact characteristics. …”
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A Transformer-LSTM-SVR hybrid model for AI-driven emotional optimization in NEV embedded interior systems by Zongming Liu, Xuhui Chen, Xinan Liang, Zhicheng Sun, Fengqi Yang, Wenwen Ou, Linwei Li, Xiayan Qin

“…The proposed method combines a Transformer module, which captures higher-order interactions among multidimensional design parameters (e.g., sentiment evaluation coefficients and task completion time), and a Long Short-Term Memory (LSTM) network, configured to enhance time-series feature capture through adjustments to hidden unit count and sequence length. …”
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Factors influencing the response to periodontal therapy in patients with diabetes: post hoc analysis of a randomized clinical trial using machine learning by Nidia CASTRO DOS SANTOS, Arthur MANGUSSI, Tiago RIBEIRO, Rafael Nascimento de Brito SILVA, Mauro Pedrine SANTAMARIA, Magda FERES, Thomas VAN DYKE, Ana Carolina LORENA

“…Abstract Objective To evaluate factors influencing the response to periodontal therapy in patients with periodontitis and type 2 diabetes mellitus (DM) using machine learning (ML) techniques, considering periodontal parameters, metabolic status, and demographic characteristics. …”
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Preoperative pectoralis muscle index predicts distant metastasis-free survival in non-small cell lung cancer patients: a retrospective study by Zhihui Shi, Lin Wu, Dengke Jiang, Ruiling Yang, Rui Liao, Lizhu Liu, Ruimin You, Yanli Li, Xingxiang Dong, Dafu Zhang, Jing Wang, Xuewen Zhang, Xiaobo Chen, Zhenhui Li

“…Finally, the relative influence of each parameter was compared using a gradient boosting model (GBM). …”
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Inflammatory and demographic determinants of elevated white blood cell counts: insights from predictive modeling and NHANES analysis by Saanie Sulley, Yan Zhou, Memory Ndanga

“…A class-weighted Gradient Boosting model was created to forecast elevated WBC counts (top 25th percentile), with model performance evaluated through area under the receiver operating characteristic curve (AUC-ROC), precision, and recall metrics. …”
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Investigation of Rheological Characteristics and Bleeding Behavior of PPA-Modified Bitumen Emulsion for Microsurfacing by Mohammadjavad AkhavanBahabadi, Mohammad Khabiri

“…The value of Jnr parameter decreased with increasing the PPA dosage, indicating a boost in the resistance of the bitumen emulsion to bleeding.…”
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Deep learning multilayer architecture for analysis of three-dimensional Eyring-Powell nanofluid flow subject to viscous dissipation and joule heating by Zahoor Shah, Muflih Alhazmi, Maryam Jawaid, Nafisa A. Albasheir, Mohammed M.A. Alma Zah, Nashwan Adnan Othman, Waqar Azeem Khan

“…It can capture the complex behavior of a specialized fluid flow system with the changing of different physical parameters like Prandtl number, magnetic field parameter, radiation parameter, and Brownian motion parameter and the controlling parameters of non-Newtonian nature of the fluid, ε, δ1, and δ2, make up the source parameters of Eyring-Powell fluid. …”
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Machine learning for predicting 5-year mortality risks: data from the ESSE-RF study in Primorsky Krai by V. A. Nevzorova, T. A. Brodskaya, K. I. Shakhgeldyan, B. I. Geltser, V. V. Kosterin, L. G. Priseko

“…To build predictive models, we used following machine learning (ML) methods: multivariate LR, Weibull regression, and stochastic gradient boosting.Results. The prognostic models developed on the ML basis, using parameters of age, sex, smoking, systolic blood pressure (SBP) and TC level in their structure, had higher quality metrics than Systematic COronary Risk Evaluation (SCORE) system. …”
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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

“…The kNN algorithm was selected using cross-validation to determine the optimal k value, and the key punishment parameter C and RBF width parameter γ of the SVM algorithm were determined using a grid search method. …”
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