Optimizing XGBoost Hyperparameters for Credit Scoring Classification Using Weighted Cognitive Avoidance Particle Swarm by Atul Vikas Lakra, Sudarson Jena, Kaushik Mishra

“…The optimal hyperparameter values for the XGBoost model can vary significantly depending on the specific problem at hand. …”
Get full text

Experimental validation of machine learning for contamination classification of polluted high voltage insulators using leakage current by Umer Amir Khan, Mansoor Asif, Muhammad Hamza Zafar, Luai Alhems

“…The Bayesian optimization technique was used to optimize the parameters of Machine Learning Models. …”
Get full text

Machine Learning for Early Detection of Phishing URLs in Parked Domains: An Approach Applied to a Financial Institution by Jaqueline D. Duarte, Pedro Chagas Junior, Joao Paulo Javidi da Costa, Elena J. da Costa, Laerte Peotta de Melo, Rafael Rabelo Nunes, Carlos V. N. Gabriel Soares, Thiago Erivan da Cunha Silva

“…Using a curated dataset comprising 211,659 URLs obtained from real-time SSL (Secure Sockets Layer) certificate monitoring, popular domain listings, and phishing incident reports, the methods encompass data pre-processing, feature engineering, and model optimization. A Light Gradient Boosting Machine classifier achieved recall of 96.02% and accuracy of 97.28% on a balanced dataset, validated through 10-fold cross-validation. …”
Get full text

Enhancing prediction of wildfire occurrence and behavior in Alaska using spatio-temporal clustering and ensemble machine learning by A. Ahajjam, M. Allgaier, R. Chance, E. Chukwuemeka, J. Putkonen, T. Pasch

“…This study addresses the challenge of wildfire occurrence and behavior prediction in Alaska by developing a comprehensive framework that leverages satellite-based data, geospatial features, advanced optimization, and machine learning (ML). First, NASA’s Fire Information for Resource Management System (FIRMS) dataset spanning +20 years is processed using a spatio-temporal clustering algorithm to create refined wildfire datasets. …”
Get full text

Optimized Application of CGA-SVM in Tight Reservoir Horizontal Well Production Prediction by Chao Wang, Ruogu Wang, Yuhan Lin, Jiafei Zhang, Xiaofei Xie, Zidan Zhao, Yunlin Xu

“…In this paper, chaotic genetic algorithm is used to optimize the traditional support vector machine, and the problems of slow convergence and local convergence are solved by chaotic genetic algorithm, and an improved support vector machine horizontal well production prediction method is established. …”
Get full text

Predicting Three-Dimensional (3D) Printing Product Quality with Machine Learning-Based Regression Methods by Ahmet Burak Tatar

“…This study examines how printing parameters affect the roughness, tensile strength, and elongation of 3D-printed parts used in various applications. Machine learning-based regression models were employed to optimize product quality. …”
Get full text

Enhancing Fault Diagnosis: A Hybrid Framework Integrating Improved SABO with VMD and Transformer–TELM by Jingzong Yang, Xuefeng Li, Min Mao

“…Subsequently, the optimized parameters are used to model and decompose the signal through VMD, and the optimal signal components are selected through a constructed two-dimensional evaluation system. …”
Get full text

Active Learning Query Strategies for Linear Regression Based on Efficient Global Optimization by Tianxin Zong, Na Li, Zhigang Zhang

“…Active learning, a subfield of machine learning, can train a good model by selecting a minimum number of labeled samples. …”
Get full text

Learning Optimal Dynamic Treatment Regime from Observational Clinical Data through Reinforcement Learning by Seyum Abebe, Irene Poli, Roger D. Jones, Debora Slanzi

Get full text

A systematic review on machine learning-aided design of engineered biochar for soil and water contaminant removal by Yunpeng Ge, Kaiyang Ying, Guo Yu, Muhammad Ubaid Ali, Abubakr M. Idris, Abubakr M. Idris, Asfandyar Shahab, Habib Ullah, Habib Ullah

“…This work fills that gap by analyzing ML’s role in optimizing biochar properties using pilot and industrial-scale datal. …”
Get full text

AOAFS: A Malware Detection System Using an Improved Arithmetic Optimization Algorithm by Rafat Alshorman, Bilal H. Abed-alguni, Yaqeen E. Alqudah

“…This issue diminishes the efficacy of Machine Learning models used for malware detection. …”
Get full text

Design and fabrication study of a small remote-controlled bionic butterfly flapping-wing flying machine by Yaozeng Mao, Fan Wu, Junjie Lao, Minglei Li

“…Then, through 3D modeling and finite element analysis, an innovative design scheme of small bionic butterfly flight vehicle was proposed and verified, and its lift force was analyzed after assembly. …”
Get full text

Development of Decline Curve Analysis Parameters for Tight Oil Wells Using a Machine Learning Algorithm by Weirong Li, Zhenzhen Dong, John W. Lee, Xianlin Ma, Shihao Qian

“…The validation result shows that the production rate and cumulative production predicted by the proposed machine learning–decline curve analysis (ML-DCA) model agreed well with those simulated by reservoir simulation. …”
Get full text

Utility-driven virtual machine allocation in edge cloud environments using a partheno-genetic algorithm by Jie Cao, Cuicui Zhang, Ping Qi, Kekun Hu

“…Next, the framework dynamically reallocates virtual machines across sub-service centers, based on task arrival rates and varying QoS requirements, to optimize overall service utility. …”
Get full text

Research on Prediction of Mudstone Breakthrough Pressure Based on Support Vector Machine in CO2 Geological Storage by Lin Jianhong, Ma Yongjie, Zhang Yu

“…This study aims to use the Support Vector Machine (SVM) model to predict the breakthrough pressure of mudstone. …”
Get full text

Socially Responsible Investment Portfolio Construction with a Double-Screening Mechanism considering Machine Learning Prediction by Jun Zhang, Xuedong Chen

“…First, this paper develops a novel double-screening mechanism incorporating environmental, social, and corporate governance (ESG) and return potential criteria to ensure that high-quality stocks with good ESG performance and high-return potential are input into the optimal portfolio. Specifically, to obtain accurate stock return predictions, an extreme learning machine model optimized by the genetic algorithm is employed to predict stock prices. …”
Get full text

Anomaly Detection Using Machine Learning in Hydrochemical Data From Hot Springs: Implications for Earthquake Prediction by Ruijie Zhu, Fengtian Yang, Xiaocheng Zhou, Jiao Tian, Yongxian Zhang, Miao He, Jingchao Li, Jinyuan Dong, Ying Li

“…Therefore, adjustments are needed to optimize the model's performance for practical use. …”
Get full text

Efficient and accurate determination of the degree of substitution of cellulose acetate using ATR-FTIR spectroscopy and machine learning by Frank Rhein, Timo Sehn, Michael A. R. Meier

“…A repeated k-fold cross validation ensured unbiased assessment of model accuracy. Using the DS obtained from 1H NMR data as reference, the machine learning model achieved a mean absolute error (MAE) of 0.069 in DS on test data, demonstrating higher accuracy compared to the manual evaluation based on peak integration. …”
Get full text

State-of-the-Art Fault Detection and Diagnosis in Power Transformers: A Review of Machine Learning and Hybrid Methods by Lebo Dina Mashifane, Bongumsa Mendu, Bessie Baakanyang Monchusi

“…Hybrid models combining machine learning with optimization have made detection more accurate. …”
Get full text

Use of Machine Learning Algorithms to Predict Almen (Shot Peening) Intensity Values of Various Steel Materials by Murat İnce, Hatice Varol Özkavak

“…With an RMSE of 0.0731, R2 of 0.9665, and MAE of 0.0613, the deep neural network (DNN) surpassed the other models in terms of prediction accuracy. The results indicate that artificial intelligence technology could be utilized to accurately evaluate Almen intensity.…”
Get full text