‌Rockburst intensity grading prediction based on the LOF-ENN-KNN model‌ by Haoran Ge, Jiyong Zhang, Congbo Ma, Kai Hui, Yicong Li, Ziniu Wu

Get full text

A Prediction Model of Stable Warfarin Doses in Patients After Mechanical Heart Valve Replacement Based on a Machine Learning Algorithm by Bowen Guo, Cong Chen, Junhang Jia, Jubing Zheng, Yue Song, Taoshuai Liu, Kui Zhang, Yang Li, Ran Dong

“…The support vector machine radial basis function (SVM Radial) algorithm showed the best performance of all models, with the highest R2 value of 0.98 and the lowest MAE of 0.14 mg/day (95% confidence interval (CI): 0.11–0.17). …”
Get full text

Predicting Sugar Yield From Sugarcane Using Machine Learning for Jaggery Production by Kathirvel Narayanasamy, Ilayaraja Venkatachalam

“…After rigorous evaluation and hyperparameter optimization, the Random Forest model demonstrated superior performance with a coefficient of determination (R<inline-formula> <tex-math notation="LaTeX">${^{{2}}} = 0.9503$ </tex-math></inline-formula>), Root Mean Squared Error (RMSE = 3.13), and Mean Absolute Error (MAE = 1.63). …”
Get full text

A Novel Approach for Automatic Detection of Concrete Surface Voids Using Image Texture Analysis and History-Based Adaptive Differential Evolution Optimized Support Vector Machine by Nhat-Duc Hoang, Quoc-Lam Nguyen

“…To improve the productivity of the inspection work, this study develops a hybrid intelligence approach that combines image texture analysis, machine learning, and metaheuristic optimization. …”
Get full text

Proximity-based solutions for optimizing autism spectrum disorder treatment: integrating clinical and process data for personalized care by Annarita Vignapiano, Annarita Vignapiano, Francesco Monaco, Francesco Monaco, Stefania Landi, Luca Steardo, Carlo Mancuso, Claudio Pagano, Gianvito Petrillo, Alessandra Marenna, Martina Piacente, Stefano Leo, Carminia Marina Ingenito, Rossella Bonifacio, Benedetta Di Gruttola, Marco Solmi, Marco Solmi, Marco Solmi, Marco Solmi, Maria Pontillo, Giorgio Di Lorenzo, Giorgio Di Lorenzo, Alessio Fasano, Alessio Fasano, Alessio Fasano, Giulio Corrivetti, Giulio Corrivetti

“…This project utilizes the power of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), to improve Autism Spectrum Disorder diagnosis and treatment. …”
Get full text

From Pairwise Comparisons of Complex Behavior to an Overall Performance Rank: A Novel Alloy Design Strategy by Rafael Herschberg, Lisa Rateau, Laure Martinelli, Fanny Balbaud-Célérier, Jean Dhers, Anna Fraczkiewicz, Gérard Ramstein, Franck Tancret

“…This generic method can therefore be applied to model other complex material properties—such as environmental resistance, contact properties, or processability—and to design alloys with improved performance.…”
Get full text

Language Models for Predicting Organic Synthesis Procedures by Mantas Vaškevičius, Jurgita Kapočiūtė-Dzikienė

Get full text

DBO-DELM Method for Predicting Rolling Forces in Cold Rolling by LI Xiaoyang, PIAO Chunhui, WANG Xuelei, ZHANG Mingzhi

Subjects: Get full text

The application of large language models in ophthalmology by ZHANG Wencheng, SHE Lingbing, HE Jingjing

“…The application of Large Language Models (LLMs) in ophthalmology presents tremendous potential for the healthcare field, particularly in enhancing diagnostic efficiency, optimizing doctor-patient communication, and promoting personalized medicine. …”
Get full text

Multi-information fusion welding defect identification combining neighborhood rough set and optimized SVM by Zhiqiang FENG, Xianping ZENG, Naiwen FANG, Daidi ZHAO, Quan LI, Jiutian LUO, Xin LI

Get full text

Enhancing Extreme Learning Machine Robustness via Residual-Variance-Aware Dynamic Weighting and Broyden–Fletcher–Goldfarb–Shanno Optimization: Application to Metro Crowd Flow Predi... by Lihui Wang, Jianguang Xie

“…The experiment is based on the passenger flow data of 80 subway stations and compares traditional machine learning algorithms, ensemble learning methods, and ELM variant models. …”
Get full text

Antagonistic Trends Between Binding Affinity and Drug-Likeness in SARS-CoV-2 Mpro Inhibitors Revealed by Machine Learning by Anacleto Silva de Souza, Vitor Martins de Freitas Amorim, Eduardo Pereira Soares, Robson Francisco de Souza, Cristiane Rodrigues Guzzo

“…Our Support Vector Machine (SVM) model achieved strong performance (training accuracy = 0.84, ROC AUC = 0.91; test accuracy = 0.79, ROC AUC = 0.86), while our Logistic Regression model (training accuracy = 0.78, ROC AUC = 0.85; test accuracy = 0.76, ROC AUC = 0.83). …”
Get full text

A Novel Multi-Objective Hybrid Evolutionary-Based Approach for Tuning Machine Learning Models in Short-Term Power Consumption Forecasting by Aleksei Vakhnin, Ivan Ryzhikov, Harri Niska, Mikko Kolehmainen

“…Accurately forecasting power consumption is crucial important for efficient energy management. Machine learning (ML) models are often employed for this purpose. …”
Get full text

Predictive study of machine learning combined with serum Neuregulin 4 levels for hyperthyroidism in type II diabetes mellitus by Huilan Gu, Ye Lu

“…Pearson correlation was used to identify features correlated with NRG4. A parameter-optimized SVM model (C=1, linear kernel) was constructed for structured data modeling. …”
Get full text

Application of machine learning for the analysis of peripheral blood biomarkers in oral mucosal diseases: a cross-sectional study by Huiyu Yao, Zixin Cao, Liangfu Huang, Haojie Pan, Xiaomin Xu, Fucai Sun, Xi Ding, Wan Wu

“…Additionally, it evaluated a Random Forest machine learning model for classifying various oral mucosal diseases based on peripheral blood biomarkers. …”
Get full text

Parameter Calculation of Steam Pipeline Based on Hybrid Modeling by Hongwei CHEN, Zherui MA, Chunwang LV, Yuqiang LIU, Wei ZHANG, Ruikun WANG

“…Based on the mechanism model, this paper establishes a data-driven error prediction model by virtue of the vector machine algorithm to predict the mechanism error caused by the mechanism model calculation, then uses the error prediction result of the model to correct the mechanism model calculation results. …”
Get full text

Data‐Driven Kinematic Modeling of Physical Origami Robots by Mustafa Mete, Alexander Schüßler, Yves Martin des Taillades, Bruno Trivelli, Jamie Paik

Get full text

Investigation of Mechanical and Corrosion Behavior of ECAP Processed AA7075 Through ML, ANNW, RSM, and SA Methodologies by Majed Alinizzi, W. H. El‐Garaihy, A. I. Alateyah, Samar El‐Sanabary, Fahad Nasser Alsunaydih, Mansour Alturki, H. Abd El‐Hafez, Mohamed S. El‐Asfoury, Eman M. Zayed, Hanan Kouta

“…ABSTRACT This study employs a multi‐perspective modeling approach combining Response Surface Methodology (RSM), Machine Learning (ML), Artificial Neural Networks (ANNW), and Simulated Annealing (SA) to optimize Equal Channel Angular Pressing (ECAP) parameters for improving the mechanical and corrosion properties of AA7075 alloy. …”
Get full text

Detection of Substation Pollution in District Heating and Cooling Systems: A Comprehensive Comparative Analysis of Machine Learning and Artificial Neural Network Models by Emrah ASLAN, Yıldırım ÖZÜPAK

“…In order to improve the performance of the machine learning models, hyperparameter tuning was performed by Grid Search Optimization method. …”
Get full text

A novel hybrid approach for thyroid disease detection: Integrating cuttlefish algorithm and simulated annealing for optimal feature selection by Kapil Shrivastava, Saroj Pandey, Rishav Dubey, Mayank Namdev, Vipin Tiwari, Aditi Sharma

“…This study advances medical diagnostics by combining machine learning algorithms with nature-inspired optimization techniques to detect thyroid illnesses in their early stages. • This article proposes a novel hybrid algorithm that combines the Cuttlefish Optimization Algorithm (CFA) and Simulated Annealing (SA) to find the best features for finding thyroid disease. • The study uses machine-learning models for classification. • The integration of machine learning and nature-inspired optimization significantly enhances the diagnostic capabilities of healthcare systems, enabling prompt diagnosis and treatment planning for thyroid disorders.…”
Get full text