Implementing an Outgoing Longwave Radiation Climate Dataset from Fengyun 3E Satellite Data with a Machine-Learning Algorithm by Yanjiao Wang, Feng Yan

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Prediction of case types from non-searchable pdf documents in arabic: Comparison of machine learning and deep learning with image processing by El Arrasse Mouad, Khourdifi Youness, Mounir Soufyane, El Alami Alae

Subjects: “…machine learning…”
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Lessons Learned Developing and Using a Machine Learning Model to Automatically Transcribe 2.3 Million Handwritten Occupation Codes by Bjørn-Richard Pedersen, Einar Holsbø, Trygve Andersen, Nikita Shvetsov, Johan Ravn, Hilde Leikny Sommerseth, Lars Ailo Bongo

Subjects: “…Machine learning…”
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Rehabilitation monitoring and assessment: a comparative analysis of feature engineering and machine learning algorithms on the UI-PRMD and KIMORE benchmark datasets by Moamen Zaher, Amr S. Ghoneim, Laila Abdelhamid, Ayman Atia

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Integrating machine learning and structure-based approaches for repurposing potent tyrosine protein kinase Src inhibitors to treat inflammatory disorders by Muhammad Waleed Iqbal, Muhammad Shahab, Zakir ullah, Guojun Zheng, Irfan Anjum, Gamal A. Shazly, Atrsaw Asrat Mengistie, Xinxiao Sun, Qipeng Yuan

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Experimental Setup and Machine Learning-Based Prediction Model for Electro-Cyclone Filter Efficiency: Filtering of Ship Particulate Matter Emission by Aleksandr Šabanovič, Jonas Matijošius, Dragan Marinković, Aleksandras Chlebnikovas, Donatas Gurauskis, Johannes H. Gutheil, Artūras Kilikevičius

“…In this paper, a random forest machine learning model developed to predict particulate concentrations post-cleaning demonstrated robust performance (MAE = 0.49 P/cm³, <i>R</i>² = 0.97). …”
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Predicting bullying victimization among adolescents using the risk and protective factor framework: a large-scale machine learning approach by Ethan Low, Joshua Monsen, Lindsay Schow, Rachel Roberts, Lucy Collins, Hayden Johnson, Carl L. Hanson, Quinn Snell, E. Shannon Tass

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Shapley additive explanation on machine learning predictions of fatigue lifetimes in piston aluminum alloys under different manufacturing and loading conditions by Mohammad Azadi, Mahmood Matin

Subjects: “…machine learning…”
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Machine learning based QSAR and Molecular Dynamics simulations in the structural design and mechanism of action of imidazole derivatives with anti-melanoma activity by Rosalvo Ferreira de Oliveira Neto, Sérgio Ruschi Bergamachi Silva, Cintia Emi Yanaguibashi Leal, Edilson Beserra de Alencar Filho

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Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer by Pu Zhou, Pu Zhou, Hongyan Qian, Pengfei Zhu, Jiangyuan Ben, Jiangyuan Ben, Guifang Chen, Qiuyi Chen, Lingli Chen, Jia Chen, Ying He, Ying He

“…BackgroundThis study explores the clinical value of a machine learning (ML) model based on ultrasound radiomics features of primary foci, combined with clinicopathologic factors to predict the pathological complete response (pCR) of neoadjuvant chemotherapy (NAC) for patients with breast cancer (BC).MethodWe retrospectively analyzed ultrasound images and clinical information from 231 participants with BC who received NAC. …”
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Total Organic Carbon Content Prediction in Lacustrine Shale Using Extreme Gradient Boosting Machine Learning Based on Bayesian Optimization by Xingzhou Liu, Zhi Tian, Chang Chen

“…Based on the degree of correlation, six logging curves reflecting TOC content were selected to construct training dataset for machine learning. Then, the performance of the XGBoost model was tested using K-fold cross-validation, and the hyperparameters of the model were determined using a Bayesian optimization method to improve the search efficiency and reduce the uncertainty caused by the rule of thumb. …”
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Enhanced thyroid disease prediction using ensemble machine learning: a high-accuracy approach with feature selection and class balancing by Md. Rezaul Islam, Aniruddha Islam Chowdhury, Sharmin Shama, Md. Masudul Hasan Lamyea

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Predicting hydropower generation: A comparative analysis of Machine learning models and optimization algorithms for enhanced forecasting accuracy and operational efficiency by Chunyang Wang, Chao Li, Yudong Feng, Shoufeng Wang

“…Optimizing hydropower generation is crucial for addressing economic and environmental concerns, though it requires comprehensive monitoring and understanding of energy conversion processes. Machine Learning techniques such as integrated Gradient Boosting and Categorical Gradient Boosting, optimized with Hunger Games search, Chaos game optimization, and Archimedes Optimization Algorithm algorithms, are used to forecast and optimize hydropower generation. …”
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Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates by Siham Acharki, Ali Raza, Dinesh Kumar Vishwakarma, Mina Amharref, Abdes Samed Bernoussi, Sudhir Kumar Singh, Nadhir Al-Ansari, Ahmed Z. Dewidar, Ahmed A. Al-Othman, Mohamed A. Mattar

“…This study evaluates the performance of eight empirical models and four machine learning (ML) models, along with their hybrid counterparts, in estimating daily RET within the Gharb and Loukkos irrigated perimeters in Morocco. …”
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Analyzing the relationship between gene expression and phenotype in space-flown mice using a causal inference machine learning ensemble by James A. Casaletto, Ryan T. Scott, Makenna Myrick, Graham Mackintosh, Hamed Chok, Amanda Saravia-Butler, Adrienne Hoarfrost, Jonathan M. Galazka, Lauren M. Sanders, Sylvain V. Costes

“…In this work, we use a machine learning ensemble of causal inference methods called the Causal Research and Inference Search Platform (CRISP) which was developed to predict causal features of a binary response variable from high-dimensional input. …”
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Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease by Xunliang Li, Zhijuan Wang, Wenman Zhao, Rui Shi, Yuyu Zhu, Haifeng Pan, Deguang Wang

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Estimating the Pile Settlement Using a Machine Learning Technique Optimized by Henry's Gas Solubility Optimization and Particle Swarm Optimization by Saravana Kumar, Savarimuthu Robinson

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Remote sensing assessment of dam impact on arid basins in Southern Saudi Arabia: A machine learning and space-for-time approach by Raid Almalki, Mehdi Khaki, Patricia M. Saco, Jose F. Rodriguez

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A machine learning framework for exploring the relationship between supply chain management best practices and agility, risk management, and performance by Tyler Ward, Sam Khoury, Selva Staub, Kouroush Jenab

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Evaluation of Machine Learning Algorithms for Classification of Visual Stimulation-Induced EEG Signals in 2D and 3D VR Videos by Mingliang Zuo, Xiaoyu Chen, Li Sui

Subjects: “…machine learning…”
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