Comparison of Various Machine Learning Models for Estimating Construction Projects Sales Valuation Using Economic Variables and Indices by Yazan Alzubi

“…This research will undertake a comparative analysis to investigate the efficiency of the different machine learning models, identifying the most effective approach for estimating the sales valuation of construction projects. …”
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Source Analysis of Ozone Pollution in Liaoyuan City’s Atmosphere Based on Machine Learning Models and HYSPLIT Clustering Method by Xinyu Zou, Xinlong Li, Dali Wang, Ju Wang

“…Firstly, this study investigates the spatiotemporal distribution characteristics of the ozone (O₃) pollution in Liaoyuan City using monitoring data from 2015 to 2024. Then, three machine learning models (ML)—random forest (RF), support vector machine (SVM), and artificial neural network (ANN)—are employed to quantify the influence of meteorological and non-meteorological factors on O₃ concentrations. …”
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Understanding household VMT generation: A comparative analysis with traditional statistical models and a machine-learning approach by Guang Tian, Bob Danton, Bin Li, Vijaya Gopu, Julius A. Codjoe

“…Key thresholds and nonlinear effects of land-use variables on household VMT generation are identified from the BRT model. Both models indicate that land-use patterns that are denser, more diverse, and have increased access to transit result in reductions of vehicular trips and overall VMT, while the BRT model provides effective thresholds for these variables useful for developing planning solutions. …”
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Bridging the Gap: A Review of Machine Learning in Water Quality Control by Herlina Abdul Rahim, Nur Athirah Syafiqah Noramli, Indrabayu

“…This review critically examines the integration of machine learning (ML) with conventional water quality monitoring and treatment methods, presenting a systematic comparison of their capabilities, limitations, and synergies. …”
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Deep Learning Model-Based Detection of Anemia from Conjunctiva Images by Najmus Sehar, Nirmala Krishnamoorthi, C. Vinoth Kumar

“…These processed and augmented images were then utilized to train and test multiple models, including statistical regression, machine learning algorithms, and deep learning frameworks. …”
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A Retrospective Machine Learning Analysis to Predict 3-Month Nonunion of Unstable Distal Clavicle Fracture Patients Treated with Open Reduction and Internal Fixation by Ma C, Lu W, Liang L, Huang K, Zou J

“…Our results suggest that ML, particularly the CatBoost model, can be integrated into clinical workflows to aid surgeons in optimizing intraoperative techniques and postoperative management to reduce nonunion rates.Keywords: distal clavicle fracture, machine learning, prediction, nonunion…”
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A Data-Driven Framework for Accelerated Modeling of Stacking Fault Energy from Density of States Spectra by Md Tohidul Islam, Scott R. Broderick

“…By integrating density of states (DOS) spectral data, dimensionality reduction techniques, and machine learning models, it was found that the SFE behavior is indeed represented within the electronic structure and that this information can be used to accelerate the prediction of SFE. …”
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Enhancing Route Optimization in Road Transport Systems Through Machine Learning: A Case Study of the Dakhla-Paris Corridor by Najib El Karkouri, Lahcen Hassine, Younes Ledmaoui, Hasna Chaibi, Rachid Saadane, Nourddine Enneya, Mohamed El Aroussi

“…The study relies on applying advanced mathematical modeling techniques and analyzing several datasets to train various machine learning algorithms. …”
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Hyperspectral Remote Sensing Estimation of Rice Canopy LAI and LCC by UAV Coupled RTM and Machine Learning by Zhongyu Jin, Hongze Liu, Huini Cao, Shilong Li, Fenghua Yu, Tongyu Xu

“…Using these wavelengths, rice phenotype estimation models were constructed with back propagation neural network (BPNN), extreme learning machine (ELM), and broad learning system (BLS) methods. …”
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Machine learning assisted CFD optimization of fuel-staging natural gas burners for enhanced combustion efficiency and reduced NOx emissions by Muhammad Mubashir, Dekui Shen, Habib Kraiem, Aymen Flah, Nahar F. Alshammari, Muhammad Mubashar Hanif

“…Using Computational Fluid Dynamics (CFD) simulations combined with Machine Learning (ML)-assisted predictive modeling, the burner geometry, fuel–air mixing behavior, and heat transfer dynamics were systematically optimized. …”
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Hybrid Machine Learning Model for Electricity Consumption Prediction Using Random Forest and Artificial Neural Networks by Witwisit Kesornsit, Yaowarat Sirisathitkul

“…This study presents a hybrid machine learning model by integrating dimensionality reduction and feature selection algorithms with a backpropagation neural network (BPNN) to predict electricity consumption in Thailand. …”
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Machine Learning-Based Lithium Battery State of Health Prediction Research by Kun Li, Xinling Chen

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Joint Tomek Links (JTL): An Innovative Approach to Noise Reduction for Enhanced Classification Performance by Goksu Tuysuzoglu, Yunus Dogan, Elife Ozturk Kiyak, Mustafa Ersahin, Bita Ghasemkhani, Kokten Ulas Birant, Derya Birant

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Large Language Model Enhanced Particle Swarm Optimization for Hyperparameter Tuning for Deep Learning Models by Saad Hameed, Basheer Qolomany, Samir Brahim Belhaouari, Mohamed Abdallah, Junaid Qadir, Ala Al-Fuqaha

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A machine learning model for predicting anatomical response to Anti-VEGF therapy in diabetic macular edema by Wenrui Lu, Kunhong Xiao, Xuemei Zhang, Yuqing Wang, Wenbin Chen, Xierong Wang, Yunxi Ye, Yan Lou, Li Li

“…PurposeTo develop a machine learning model to predict anatomical response to anti-VEGF therapy in patients with diabetic macular edema (DME).MethodsThis retrospective study included patients with DME who underwent intravitreal anti-VEGF treatment between January 2023 and February 2025. …”
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Dynamic Dual-Phase Forecasting Model for New Product Demand Using Machine Learning and Statistical Control by Chien-Chih Wang

“…This research proposes the Dynamic Dual-Phase Forecasting Framework (DDPFF) that amalgamates machine learning-based classification, similarity-driven analogous forecasting, ARMA-based residual compensation, and statistical process control for adaptive model refinement. …”
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Synergising Machine Learning and Remote Sensing for Urban Heat Island Dynamics: A Comprehensive Modelling Approach by Guglielmina Mutani, Alessandro Scalise, Xhoana Sufa, Stefania Grasso

“…Geographic Information Systems (GIS) and satellite imagery were integrated with machine learning (ML) models to analyse the urban environment, human activities, and climate data in Turin, Italy. …”
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Development and Implementation of a Machine Learning Model to Identify Emotions in Children with Severe Motor and Communication Impairments by Caryn Vowles, Kate Patterson, T. Claire Davies

“…The models were not reliable for the effective identification of emotions; however, these findings highlight the feasibility of using machine learning to bridge communication gaps for children with SMCIs, enabling better emotional understanding. …”
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Investigating the Capabilities of Ensemble Machine Learning Model in Identifying Near-Fault Pulse-Like Ground Motions by Jafar Al Thawabteh, Jamal Al Adwan, Yazan Alzubi, Ahmad Al-Elwan

“…This study applies various ensemble machine learning models, such as random forests, gradient boosting machines, and extreme gradient boosting, for the identification and characterization of pulse-like ground motions. …”
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Enhanced intrusion detection model based on principal component analysis and variable ensemble machine learning algorithm by Ayuba John, Ismail Fauzi Bin Isnin, Syed Hamid Hussain Madni, Farkhana Binti Muchtar

“…This paper proposes a variable ensemble machine learning method to solve the problem and achieve a low variance model with high accuracy and low false alarm. …”
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