Data‐Driven Forecasting of Low‐Latitude Ionospheric Total Electron Content Using the Random Forest and LSTM Machine Learning Methods by Gebreab K. Zewdie, Cesar Valladares, Morris B. Cohen, David J. Lary, Dhanya Ramani, Gizaw M. Tsidu

“…The random forest machine learning method was used to perform a regression analysis and estimate the variable importance of the input parameters. …”
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Identification and Validation of a Novel Immune Infiltration-Based Diagnostic Score for Early Detection of Hepatocellular Carcinoma by Machine-Learning Strategies by Xuli Guo, Hailin Xiong, Shaoting Dong, Xiaobing Wei

“…After batch effect removal, differentially expressed genes (DEGs) were conducted between 209 HCC and 146 control tissues and functional correlation analyses were performed. Two machine learning algorithms were used to develop diagnostic signatures. …”
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The Use of Machine Learning to Create a Risk Score to Predict Survival in Patients with Hepatocellular Carcinoma: A TCGA Cohort Analysis by Samer Tohme, Hamza O Yazdani, Amaan Rahman, Sanah Handu, Sidrah Khan, Tanner Wilson, David A Geller, Richard L Simmons, Michele Molinari, Christof Kaltenmeier

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Examination of empirical and Machine Learning methods for regression of missing or invalid solar radiation data using routine meteorological data as predictors by Konstantinos X Soulis, Evangelos E Nikitakis, Aikaterini N Katsogiannou, Dionissios P Kalivas

Subjects: “…machine learning…”
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Machine Learning-Enhanced Model-Based Optical Proximity Correction by Using Convolutional Neural Network-Based Variable Threshold Method by Jinhao Zhu, Zhiwei Ren, Ying Li, Xianhe Liu, Qiang Wu, Yanli Li, Qi Wang

“…In this paper, we propose an approach to enhance MBOPC through the integration of machine learning (ML), utilizing convolutional neural network (CNN)-based variable threshold method. …”
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Efficient Feature Selection and Hyperparameter Tuning for Improved Speech Signal-Based Parkinson’s Disease Diagnosis via Machine Learning Techniques by Deepak Painuli, Suyash Bhardwaj, Utku Kose

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Accurate estimation of Jujube leaf chlorophyll content using optimized spectral indices and machine learning methods integrating geospatial information by Nigela Tuerxun, Sulei Naibi, Jianghua Zheng, Renjun Wang, Lei Wang, Binbin Lu, Danlin Yu

“…Hyperspectral data enable precise LCC monitoring by extracting spectral indices through optimal band combination (OBC) and predicting LCC with machine learning. However, OBC faces dimensionality issues, and machine learning models often overlook geographical influences, potentially reducing prediction accuracy. …”
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Enhanced prediction of energy dissipation rate in hydrofoil-crested stepped spillways using novel advanced hybrid machine learning models by Ehsan Afaridegan, Nosratollah Amanian

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Vis/NIR Spectroscopy and Vis/NIR Hyperspectral Imaging for Non-Destructive Monitoring of Apricot Fruit Internal Quality with Machine Learning by Tiziana Amoriello, Roberto Ciorba, Gaia Ruggiero, Francesca Masciola, Daniela Scutaru, Roberto Ciccoritti

“…In recent years, machine learning techniques, such as artificial neural networks (ANNs), have been successfully applied to more efficiently extract valuable information from spectral data and to accurately predict quality traits. …”
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Enhanced detection of mild cognitive impairment in Alzheimer’s disease: a hybrid model integrating dual biomarkers and advanced machine learning by John Sahaya Rani Alex, R. Roshini, G. Maneesha, Jeetashree Aparajeeta, B. Priyadarshini, Chih-Yang Lin, Chi-Wen Lung

“…In stage 1, the hippocampus volume is passed through thirteen machine learning models and fuzzy clustering for classifying symptomatic AD and healthy brain (Normal Control - NC). …”
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Improving catalysts and operating conditions using machine learning in Fischer-Tropsch synthesis of jet fuels (C8-C16) by Parisa Shafiee, Bogdan Dorneanu, Harvey Arellano-Garcia

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Exploring the role of oxidative stress in carotid atherosclerosis: insights from transcriptomic data and single-cell sequencing combined with machine learning by Yiqin Yang, Mei Dong

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m5C‐TNKmer: Identification of 5‐Methylated Base Cytosine of Ribonucleic Acid Using Supervised Machine Learning Techniques by Shahid Qazi, Dilawar Shah, Mohammad Asmat Ullah Khan, Shujaat Ali, Mohammad Abrar, Asfandyar Khan, Muhammad Tahir

“…Accurate and systematic detection and classification of m5C sites in RNA remain challenging tasks. Machine learning techniques offer an efficient alternative to traditional laboratory methods for identifying m5C sites in Homo sapiens. …”
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Proposal for a Sustainable Model for Integrating Robotic Process Automation and Machine Learning in Failure Prediction and Operational Efficiency in Predictive Maintenance by Leonel Patrício, Leonilde Varela, Zilda Silveira

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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 Mahmood Matin, Mohammad Azadi

Subjects: “…machine learning…”
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Prediction and Classification of Financial Criteria of Management Control System in Manufactories Using Deep Interaction Neural Network (DINN) and Machine Learning by Amir Yousefpour, Hamid Mazidabadi Farahani

“…Moreover, the management control system is classified into two financial and nonfinancial factors based on machine learning methods. Based on the results, the presented factors can accurately estimate the company’s performance based on management control criteria with a 93.48% R-square. …”
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Postoperative fever following surgery for oral cancer: Incidence, risk factors, and the formulation of a machine learning-based predictive model by Yanling Zhang, Kun Long, Zhaojian Gong, Ruping Dai, Shuiting Zhang

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Differentiation between multiple sclerosis and neuromyelitis optic spectrum disorders with multilevel fMRI features: A machine learning analysis by Xiao Liang, Qingwen Zeng, Yanyan Zhu, Yao Wang, Ting He, Lin Wu, Muhua Huang, Fuqing Zhou

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Functional Connectivity Changes in Multiple-Frequency Bands in Acute Basal Ganglia Ischemic Stroke Patients: A Machine Learning Approach by Jie Li, Lulu Cheng, Shijian Chen, Jian Zhang, Dongqiang Liu, Zhijian Liang, Huayun Li

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The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning by Fatemeh Bahrambanan, Meysam Alizamir, Kayhan Moradveisi, Salim Heddam, Sungwon Kim, Seunghyun Kim, Meysam Soleimani, Saeid Afshar, Amir Taherkhani

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