Hybrid Machine Learning Model for Predicting Shear Strength of Rock Joints by Daxing Lei, Yaoping Zhang, Zhigang Lu, Hang Lin, Yifan Chen

“…However, traditional machine learning (ML) models often suffer from local optima and limited generalization ability when dealing with complex nonlinear problems, thereby compromising prediction accuracy and stability. …”
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Defect modeling in semiconductors: the role of first principles simulations and machine learning by Md Habibur Rahman, Arun Mannodi-Kanakkithodi

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Machine Learning Models for Predicting Thermal Properties of Radiative Cooling Aerogels by Chengce Yuan, Yimin Shi, Zhichen Ba, Daxin Liang, Jing Wang, Xiaorui Liu, Yabei Xu, Junreng Liu, Hongbo Xu

“…A comparative analysis of various machine learning algorithms revealed that an optimized XGBoost model demonstrated superior predictive performance, achieving an R² value of 0.943 and an RMSE of 1.423 for the test dataset. …”
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Computational fluid dynamic and machine learning modeling of nanofluid flow for determination of temperature distribution and models comparison by Farag M. A. Altalbawy, Ahmad Alkhayyat, Ramdevsinh Jhala, Anupam Yadav, T. Ramachandran, Aman Shankhyan, A. Karthikeyan, Dhirendra Nath Thatoi, Vladimir Vladimirovich Sinitsin

“…Overall, the combination of the PO algorithm and the machine learning models showcases promising results in temperature prediction. …”
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Advanced Hybrid Machine Learning Model for Accurate Detection of Cardiovascular Disease by Navita, Pooja Mittal, Yogesh Kumar Sharma, Umesh Kumar Lilhore, Sarita Simaiya, Kashif Saleem, Ehab Seif Ghith

“…Thus, there is an urgent need for a detection model comprising intelligent technologies, including Machine Learning (ML) and deep learning, to predict the future state of an individual suffering from cardiovascular disease by effectively analyzing patient data. …”
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Stunting Prediction Modeling in Toddlers Using a Machine Learning Approach and Model Implementation for Mobile Application by Eko Abdul Goffar, Rosa Eliviani, Lili Ayu Wulandhari

“…The results indicate that the XGBoost model outperforms the other models with an accuracy of 85%, making it the optimal choice for implementation in a mobile application. …”
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Machine learning model for random forest acute oral toxicity prediction by A.M. Elsayad, M.M. Zeghid, K.A. Elsayad, A.N. Khan, ِA.K.M. Baareh, A. Sadiq, S.A. Mukhtar, H.F. Ali, S. Abd El-kader

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Enhancing Supply Chain Agility and Sustainability through Machine Learning: Optimization Techniques for Logistics and Inventory Management by Vikram Pasupuleti, Bharadwaj Thuraka, Chandra Shikhi Kodete, Saiteja Malisetty

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Coronary Heart Disease Risk Prediction Model Based on Machine Learning by YUE Haitao, HE Chanchan, CHENG Yuyou, ZHANG Sencheng, WU You, MA Jing

“…Among them, the XGBoost model exhibited superior performance and can be referenced for future optimization of CHD prediction models. …”
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Alleviating small sample problem in continuous forest monitoring with remote sensing-assisted Copulas by Xinjie Cheng, Zhengyang Hou, Annika Kangas, Jean-Pierre Renaud, Hao Tang, Weisheng Zeng, Qing Xu

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Understanding the flowering process of litchi through machine learning predictive models by SU Zuanxian, NING Zhenchen, WANG Qing, CHEN Houbin

“…The algorithms (RF and STR) with the smallest Mean Absolute Error (MAE) and the highest residual error (RMSE) and the highest correlation coefficient (RP2) were selected for further parameter optimization and evaluation. A 5-fold cross-validation with 999 repetitions was performed on all trained machine learning models. …”
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Prognostic predictions in psychosis: exploring the complementary role of machine learning models by Metten Somers, Hugo G Schnack, Rene S Kahn, Diane F van Rappard, Frank L Gerritse, Edwin van Dellen, Violet van Dee, Seyed M Kia, Caterina Fregosi, Wilma E Swildens, Anne Alkema, Albert Batalla, Coen van den Berg, Danko Coric, Lotte G Dijkstra, Arthur van den Doel, Livia S Dominicus, John Enterman, Marte Z van der Horst, Fedor van Houwelingen, Charlotte S Koch, Lisanne E M Koomen, Marjan Kromkamp, Michelle Lancee, Brian E Mouthaan, Eline J Regeer, Raymond W J Salet, Jorgen Straalman, Marjolein H T de Vette, Judith Voogt, Inge Winter-van Rossum, Wiepke Cahn

“…Understanding how ML models (MLMs) can complement psychiatrists’ predictions and bridge the gap between MLM capabilities and practical use is key.Objective This vignette study aims to compare the performance of psychiatrists and MLMs in predicting short-term symptomatic and functional remission in patients with first-episode psychosis and explore whether MLMs can improve psychiatrists’ prognostic accuracy.Method 24 psychiatrists predicted symptomatic and functional remission probabilities at 10 weeks based on written baseline information from 66 patients in the OPtimization of Treatment and Management of Schizophrenia in Europe (OPTiMiSE) trial. …”
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The hybrid algorithms in constrained optimization of the permanent magnet motors by Łukasz Knypiński, Ramesh Devarapalli, Frederic Gillon

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Using Machine Learning for Recognition of Alzheimer’s Disease Based on Transcription Information by U. A. Vishniakou, Chu Yue Yu

“…This was followed by training the model based on vectorized text features using a random forest classifier, in which the authors used the GridSearchCV method to optimize hyperparameters. …”
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Machine Learning in Information and Communications Technology: A Survey by Elias Dritsas, Maria Trigka

“…Specifically, we review the effectiveness of different ML models across ICT subdomains and assess how ML integration enhances crucial performance metrics, including operational efficiency, scalability, and security. …”
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Tri-objective parallel machine with job splitting and sequence dependent setup times using differential evolution and particle swarm optimization by Warisa Wisittipanich, Nuttachat Wisittipanit

“…Some incoming jobs have different sizes and due dates; plus, the production capacity, setup time, job processing time and energy requirement of each machine can be different, possibly due to distinct models and brands. …”
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Implementation of the Models of Operational Research for the Solution of the CNC Machines Life Cycle by Šaffová Damiána, Petráš Dávid, Mykhei Maksym

“…We used quantitative mathematical models of operational research. By calculation, we found that the optimal – operational lifetime of the CNC machine is 6 years. …”
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Research on rock strength prediction model based on machine learning algorithm by Xiang Ding, Mengyun Dong, Wanqing Shen

“…By selecting different features, the optimal feature combination for predicting rock compressive strength was obtained, and the optimal parameters for different models were obtained through the Sparrow Search Algorithm (SSA). …”
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Predictive modeling of ICU-AW inflammatory factors based on machine learning by Yuanyaun Guo, Wenpeng Shan, Jie Xiang

“…The optimal model was visualized for prediction using nomograms. …”
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Predicting clinical trial duration via statistical and machine learning models by Joonhyuk Cho, Qingyang Xu, Chi Heem Wong, Andrew W. Lo

“…We apply survival analysis as well as machine learning models to predict the duration of clinical trials using the largest dataset so far constructed in this domain. …”
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