Evaluating machine learning algorithms for predicting HIV status among young Thai men who have sex with men by Krittaka Soha, Sadiporn Phuthomdee, Thanapat Srichai, Lanchakorn Kittiratanawasin, Win Min Han, Sirinya Teeraananchai

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Predicting Weaning Weight of Romanov Lambs From Biometric Measurements Before Weaning Age Using Machine Learning Algorithms by Mehmet Eroğlu, Ali Osman Turgut, Mürsel Küçük, Muhammed Furkan Önen

“…Objective This study aimed to investigate the performance of different machine learning algorithms in predicting weaning weight based on biometric measurements of Romanov lambs at 30 days of age. …”
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Predicting response to anti-VEGF therapy in neovascular age-related macular degeneration using random forest and SHAP algorithms by Peng Zhang, Jialiang Duan, Caixia Wang, Xuejing Li, Jing Su, Qingli Shang

“…Finally, the SHAP algorithm was applied to explain the model's predictions. …”
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An Overview of Remaining Useful Life Prediction of Battery Using Deep Learning and Ensemble Learning Algorithms on Data-Dependent Models by Sravanthi C. L., Chandra Sekhar J. N., N. Chinna Alluraiah, Dhanamjayulu C., Harish Kumar Pujari, Baseem Khan

“…The goal of this work in this context is to present an overview of all recent advancements in RUL prediction utilising all three data-driven models. This article is also followed by a categorisation of several types of ML, DL and EL algorithms for RUL prediction. …”
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Use of machine learning algorithms to predict outcomes among frail patients undergoing percutaneous coronary intervention: Are we there yet? by Josip Andelo Borovac, Mihajlo Kovacic, Stefan Harb

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Predicting COVID-19 severity in pediatric patients using machine learning: a comparative analysis of algorithms and ensemble methods by Babak Pourakbari, Setareh Mamishi, Sepideh Keshavarz Valian, Shima Mahmoudi, Reihaneh Hosseinpour Sadeghi, Mohammad Reza Abdolsalehi, Mahmoud Khodabandeh, Mohammad Farahmand

“…This study evaluates the performance of machine learning algorithms in predicting disease severity among pediatrics. …”
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Conversion Prediction in Google Search Ads Keyword Selection Using the K-Nearest Neighbor and C4.5 Algorithms by Muhammad Sya'ban Harahap, Alva Hendi Muhammad

“…This study was conducted to analyze and compare the effectiveness of two algorithms—K-Nearest Neighbor (K-NN) and C4.5—in predicting keyword conversion on the Google Ads platform. …”
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Predicting indoor temperature of solar green house by machine learning algorithms: A comparative analysis and a practical approach by Wenhe Liu, Tao Han, Cong Wang, Feng Zhang, Zhanyang Xu

“…This research not only compared the performance of different machine learning algorithms in solar greenhouse temperature prediction but also explored the applicability of each algorithm across various prediction horizons. …”
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Machine learning algorithms for diabetic kidney disease risk predictive model of Chinese patients with type 2 diabetes mellitus by Lu-Xi Zou, Xue Wang, Zhi-Li Hou, Ling Sun, Jiang-Tao Lu

“…More sensitive methods for early DKD prediction are urgently needed. This study aimed to set up DKD risk prediction models based on machine learning algorithms (MLAs) in patients with type 2 DM (T2DM).Methods The electronic health records of 12,190 T2DM patients with 3-year follow-ups were extracted, and the dataset was divided into a training and testing dataset in a 4:1 ratio. …”
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Chemometric and meta-heuristic algorithms to find optimal wavelengths and predict ‘Red Delicious’ apples traits using Vis-NIR by Mahsa S. Razavi, Vali Rasouli Sharabiani, Mohammad Tahmasebi, Silvia Grassi, Mariusz Szymanek

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Study on Short Term Temperature Forecast Model in Jiangxi Province based on LightGBM Machine Learning Algorithm by Kanghui SUN, An XIAO, Houjie XIA

“…In order to achieve further improvement in the forecast accuracy of station temperatures and enhance the forecast capability for extreme temperatures， this study establishes a 24-hour national station daily maximum （minimum） temperature forecast model for Jiangxi Province based on the LightGBM machine-learning algorithm and the MOS forecast framework by using the surface observation data of 91 national stations in Jiangxi Province and the upper-air and surface forecast data of the ECMWF model from 2017 to 2019.The results of the 2020 evaluation show that the LightGBM model daily maximum （minimum） temperature forecast is consistent with the observed trend， and the annual average forecast is better than that of three numerical models， ECMWF， CMA-SH9 and CMA-GFS， two machine learning products， RF and SVM， and subjective revision products.In terms of the spatial and temporal distribution of forecast errors， the model's daily maximum （minimum） temperature forecast errors in winter and spring are slightly larger than those in summer and autumn； the daily maximum temperature forecast errors show the spatial distribution characteristics of "larger in the south and smaller in the north， and larger in the periphery than in the centre"， while the opposite is true for the daily minimum temperatures.In terms of important weather processes， the LightGBM model has the best prediction effect among the seven products in the high temperature process； in the strong cold air process， the LightGBM model is still better than the three numerical model products and the other two machine-learning models， but the prediction effect of the daily minimum temperature is not as good as that of the subjective revision products.After a simple empirical correction for the low-temperature forecast error in the strong cold air process， the model low-temperature forecast effect is close to that of the subjective revision product.The model significance analysis shows that the recent surface observation features also contribute to the model construction， and the results can be used as a reference for model improvement and temperature forecast product development.At present， the LightGBM model temperature forecast products have been applied to meteorological operations in Jiangxi Province.…”
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An emotional neural network based approach for wind power prediction by Guoling ZHANG

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Evaluation of Thermal Damage Effect of Forest Fire Based on Multispectral Camera Combined with Dual Annealing Algorithm by Pan Pei, Xiaojian Hao, Ziqi Wu, Rui Jia, Shenxiang Feng, Tong Wei, Wenxiang You, Chenyang Xu, Xining Wang, Yuqian Dong

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Fast adaptive mode decision algorithm for H.264 based on spatial correlation by FENG Bin, LIU Wen-yu, ZHU Guang-xi

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Tropical Cyclones Across Global Basins: Dynamics, Tracking Algorithms, Forecasting, and Emerging Scientometric Research Trends by Vivek Singh, Gaurav Tiwari, Amarendra Singh, Rajeeb Samanta, Atul Kumar Srivastava, Deewan Singh Bisht, Ashish Routray, Sushil Kumar, Shivaji Singh Patel, Abhishek Lodh

“…Notably, research since 2019 highlights a shift toward machine‐based algorithms aimed at improving intensity predictions. …”
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Research Progress on Machine Learning Prediction of Compressive Strength of Nano-Modified Concrete by Ruyan Fan, Ankang Tian, Yikun Li, Yue Gu, Zhenhua Wei

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Ensemble Learning-Based Metamodel for Enhanced Surface Roughness Prediction in Polymeric Machining by Elango Natarajan, Manickam Ramasamy, Sangeetha Elango, Karthikeyan Mohanraj, Chun Kit Ang, Ali Khalfallah

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Development of a MVI associated HCC prognostic model through single cell transcriptomic analysis and 101 machine learning algorithms by Jiayi Zhang, Zheng Zhang, Chenqing Yang, Qingguang Liu, Tao Song

“…Additionally, we affirmed the predictive precision and superiority of our model through a meta-analysis against existing HCC models. …”
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Development and Validation of a Machine Learning Model for Early Prediction of Sepsis Onset in Hospital Inpatients from All Departments by Pierre-Elliott Thiboud, Quentin François, Cécile Faure, Gilles Chaufferin, Barthélémy Arribe, Nicolas Ettahar

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Technology of Post-Institutional Support to Exiters of State-Run Institutions for Orphans in the Process of Obtaining Professional Education and Primary Employment by Oslon V.N., Celenina E.V.

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