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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Automated tools for systematic review screening methods: an application of machine learning for sexual orientation and gender identity measurement in health research by Ashleigh Rich, Emma McGorray, Carrie Baldwin-SoRelle, Michelle Cawley, Karen Grigg, Lauren B. Beach, Gregory Phillips, Tonia Poteat

“…In Phase 3, supervised machine learning using DoCTER was used to further identify which studies deemed low relevance in Phase 2 should be prioritized for manual screening. …”
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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 interpretation of non-contrast feature tracking strain analysis and T1/T2 mapping for assessing myocardial viability by Amir GhaffariJolfayi, Alireza Salmanipour, Kiyan Heshmat-Ghahdarijani, MohammadHossein MozafaryBazargany, Amir Azimi, Pirouz Pirouzi, Ali Mohammadzadeh

“…This study investigates the potential of non-contrast CMR techniques—feature tracking strain analysis and T1/T2 mapping—combined with machine learning (ML) models, as an alternative to LGE-CMR for myocardial viability assessment. …”
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Machine learning-based novel-shaped THz MIMO antenna with a slotted ground plane for future 6G applications by Md Ashraful Haque, Kamal Hossain Nahin, Jamal Hossain Nirob, Redwan A. Ananta, Narinderjit Singh Sawaran Singh, Liton Chandra Paul, Abeer D. Algarni, Mohammed ElAffendi, Abdelhamied A. Ateya

“…Abstract This study discusses the results of using a regression machine learning technique to improve the performance of 6G applications that use multiple-input multiple-output (MIMO) antennas operating at the terahertz (THz) frequency band. …”
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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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Through the Citizen Scientists’ Eyes: Insights into Using Citizen Science with Machine Learning for Effective Identification of Unknown-Unknowns in Big Data by Kameswara Bharadwaj Mantha, Hayley Roberts, Lucy Fortson, Chris Lintott, Hugh Dickinson, William Keel, Ramanakumar Sankar, Coleman Krawczyk, Brooke Simmons, Mike Walmsley, Izzy Garland, Jason Shingirai Makechemu, Laura Trouille, Clifford Johnson

“…In the era of rapidly growing astronomical data, the gap between data collection and analysis is a significant barrier, especially for teams searching for rare scientific objects. Although machine learning (ML) can quickly parse large data sets, it struggles to robustly identify scientifically interesting objects, a task at which humans excel. …”
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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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Enhancing stroke disease classification through machine learning models via a novel voting system by feature selection techniques. by Mahade Hasan, Farhana Yasmin, Md Mehedi Hassan, Xue Yu, Soniya Yeasmin, Herat Joshi, Sheikh Mohammed Shariful Islam

“…While state of the art work has focused on various machine learning approaches for predicting heart disease, but they could not able to achieve remarkable accuracy. …”
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A machine learning-based model to predict POD24 in follicular lymphoma: a study by the Chinese workshop on follicular lymphoma by Jie Zha, Qinwei Chen, Wei Zhang, Hongmei Jing, Jingjing Ye, Huanhuan Liu, Haifeng Yu, Shuhua Yi, Caixia Li, Zhong Zheng, Wei Xu, Zhifeng Li, Zhijuan Lin, Lingyan Ping, Xiaohua He, Liling Zhang, Ying Xie, Feili Chen, Xiuhua Sun, Liping Su, Huilai Zhang, Haiyan Yang, Weili Zhao, Lugui Qiu, Zhiming Li, Yuqin Song, Bing Xu

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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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