House Market Prediction Using Machine Learning by Nicușor-Andrei ANDREI

“…Using a dataset from March 2025, comprehensive preprocessing—including imputation, categorical encoding, and feature engineering (e.g., distance to public transport)—was applied. Models were optimized via grid search with 5-fold cross-validation and evaluated using RMSE, MAE, and R². …”
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The role of oxygenation in kidney and liver machine perfusion by M. S. Novruzbekov, A. G. Balkarov, E. Yu. Anosova, I. V. Dmitriev, Yu. A. Anisimov, N. S. Zhuravel, E. V. Klychnikova, A. S. Bogdanova, B. I. Yaremin

“…The review highlights the results of preclinical (on animal models) and clinical studies, as well as achievements in the field of ex-vivo machine perfusion with an emphasis on machine hypothermic perfusion and modified oxygenated hypothermic machine perfusion, subnormothermic machine perfusion and machine normothermic perfusion.Results. …”
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An optimal weighting-based hybrid classifier for Children's congenital heart diseases signal processing by Morteza Ebrahimpour, Mehdi Khashei

“…The main objective of the proposed optimal weighting-based CNN-LSTM-SVM (OCLS) hybrid classifier is to simultaneously leverage the unique advantages of CNN in feature extraction from input signals, LSTM in modeling the sequential patterns of signals, SVM in classifying regular patterns, and especially the proposed weighting algorithm to optimally integrate the outputs of these components. …”
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Intelligent Health Monitoring in 6G Networks: Machine Learning-Enhanced VLC-Based Medical Body Sensor Networks by Bilal Antaki, Ahmed Hany Dalloul, Farshad Miramirkhani

“…In this paper, we present a novel integration of site-specific ray tracing and machine learning (ML) for VLC-enabled Medical Body Sensor Networks (MBSNs) channel modeling in distinct hospital settings. …”
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New Forecasting Metrics Evaluated in Prophet, Random Forest, and Long Short-Term Memory Models for Load Forecasting by Prajowal Manandhar, Hasan Rafiq, Edwin Rodriguez-Ubinas, Themis Palpanas

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Explainable AutoML models for predicting the strength of high-performance concrete using Optuna, SHAP and ensemble learning by Muhammad Salman Khan, Tianbo Peng, Tianbo Peng, Muhammad Adeel Khan, Asad Khan, Mahmood Ahmad, Mahmood Ahmad, Kamran Aziz, Mohanad Muayad Sabri Sabri, N. S. Abd EL-Gawaad

“…Accurately predicting key engineering properties, such as compressive and tensile strength, remains a significant challenge in high-performance concrete (HPC) due to its complex and heterogeneous composition. Early selection of optimal components and the development of reliable machine learning (ML) models can significantly reduce the time and cost associated with extensive experimentation. …”
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Machine Learning-Based Analysis of Travel Mode Preferences: Neural and Boosting Model Comparison Using Stated Preference Data from Thailand’s Emerging High-Speed Rail Network by Chinnakrit Banyong, Natthaporn Hantanong, Supanida Nanthawong, Chamroeun Se, Panuwat Wisutwattanasak, Thanapong Champahom, Vatanavongs Ratanavaraha, Sajjakaj Jomnonkwao

“…The analysis leverages stated preference (SP) data and employs Bayesian optimization in conjunction with a stratified 10-fold cross-validation scheme to ensure model robustness. …”
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Inversion of Water Quality Parameters from UAV Hyperspectral Data Based on Intelligent Algorithm Optimized Backpropagation Neural Networks of a Small Rural River by Manqi Wang, Caili Zhou, Jiaqi Shi, Fei Lin, Yucheng Li, Yimin Hu, Xuesheng Zhang

“…Again, based on the screened features, a back-propagation neural network (BPNN) model optimized using a mixture of the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm was established as a means of estimating water quality parameter concentrations. …”
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Attention-driven hybrid deep learning and SVM model for early Alzheimer’s diagnosis using neuroimaging fusion by Arjun Kidavunil Paduvilan, Godlin Atlas Lawrence Livingston, Sampath Kumar Kuppuchamy, Rajesh Kumar Dhanaraj, Muthuvel Subramanian, Amal Al-Rasheed, Masresha Getahun, Ben Othman Soufiene

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Assessment and Modeling of Green Roof System Hydrological Effectiveness in Runoff Control: A Case Study in Dublin by Mehdi Gholamnia, Payam Sajadi, Salman Khan, Srikanta Sannigrahi, Saman Ghaffarian, Himan Shahabi, Francesco Pilla

“…The comprehensive dataset enabled detailed modeling of runoff hydrograph parameters using rainfall hyetographs, which were subsequently analyzed through sophisticated machine learning algorithms. …”
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Leveraging large language models for automated detection of velopharyngeal dysfunction in patients with cleft palate by Myranda Uselton Shirk, Catherine Dang, Jaewoo Cho, Hanlin Chen, Lily Hofstetter, Jack Bijur, Claiborne Lucas, Andrew James, Ricardo-Torres Guzman, Andrea Hiller, Noah Alter, Amy Stone, Maria Powell, Matthew E. Pontell, Matthew E. Pontell

“…A dataset of 184 audio recordings, including 96 hypernasal (cases) and 88 non-hypernasal samples (controls), was used for training and evaluation. The Whisper model's performance was compared to traditional machine learning approaches, including support vector machines (SVM) and random forest (RF) classifiers.ResultsThe Whisper-based model effectively detected hypernasality in speech, achieving a test accuracy of 97% and an F1-score of 0.97. …”
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Model-Oriented Training of Coordinators of the Decentralized Control System of Technological Facilities With Resource Interaction by Volodymyr M. Dubovoi, Maria S. Yukhimchuk, Viacheslav V. Kovtun, Krzysztof R. Grochla

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Construction of a Prediction Model for Sleep Quality in Embryo Repeated Implantation Failure Patients Undergoing Assisted Reproductive Technology Based on Machine Learning: A Singl... by Zhao Y, Xu C, Qin N, Bai L, Wang X, Wang K

“…Yanjun Zhao,1,* Chenying Xu,1,* Ningxin Qin,2,* Lina Bai,1 Xuelu Wang,1 Ke Wang2 1Operating Room, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092, People’s Republic of China; 2Reproductive Medicine Center, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xuelu Wang, Email wangxuelu@51mch.com Ke Wang, Email wangkeyfy@126.comObjective: Constructing a predictive model for sleep quality in embryo Repeated Implantation Failure(RIF) patients using multiple machine learning algorithms, verifying its performance, and selecting the optimal model.Methods: Retrospective collection of clinical data from RIF patients who underwent assisted reproductive technology at the Reproductive Medicine Center of Tongji University Affiliated Obstetrics and Gynecology Hospital from January 2022 to June 2022, divided into a training set and a validation set in an 8:2 ratio. …”
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A Coupled Multiphysics Framework for Advanced Characterization of PWM-Driven Induction Motors by Omolbanin Taqavi, Pengzhao Song, Alexandre J. Bourgault, Ze Li, Glenn Byczynski, Narayan C. Kar

“…This paper introduces a semi-analytical multiphysics assessment framework tailored for inverter-fed traction induction machines (IMs). By integrating three core physical domains, the framework leverages an electromagnetic model to evaluate rotor and stator currents, traction characteristics, and radial air-gap flux density. …”
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A Short-Term Carbon Emission Accounting Method for Power Industry Using Electricity Data Based on a Combined Model of CNN and LightGBM by ZENG Jincan, HE Gengsheng, LI Yaowang, DU Ershun, ZHANG Ning, ZHU Haojun

“…Moreover, K-fold cross-validation is used in model training, with parameter optimization using grid search to enhance the generalization capability and robustness of the model. …”
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Robust extreme gradient boosting model for predicting the behavior of RC slabs under impact loading: key influencing factors and performance insights by Ammar Babiker

“… Abstract This study presents an advanced approach to analyzing the impact behavior of reinforced concrete (RC) slabs, utilizing an optimized extreme gradient boosting (XGB) machine learning algorithm. …”
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Prediction of three-year all-cause mortality in patients with heart failure and atrial fibrillation using the CatBoost model by Jiacan Wu, Guanghong Tao, Siyuan Xie, Han Yang, Fenglin Qi, Naiyue Bao, Zhuo Li, Guanglei Chang, Hua Xiao

“…This study aimed to develop and validate a machine learning (ML) model predicting the 3-year all-cause mortality risk in HF-AF patients to support personalized risk stratification and management. …”
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A combined prediction model for middle and long-term water and sediment forecasting based on causal analysis feature selection by XuanZhao Kong, KeBin Shi, HaiKun Yao, SiHai Liu

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ProLinker–Generator: Design of a PROTAC Linker Base on a Generation Model Using Transfer and Reinforcement Learning by Yanlin Luo, Danyang Song, Chengwei Zhang, An Su

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Mechanical Properties of Collagen Implant Used in Neurosurgery Towards Industry 4.0/5.0 Reflected in ML Model by Marek Andryszczyk, Izabela Rojek, Dariusz Mikołajewski

“…Industry 4.0 is contributing to this by automating production, using data analytics and machine learning to optimize implant properties and ensure quality control. …”
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