Machine Learning for the Early Prediction of Delayed Cerebral Ischemia in Patients With Subarachnoid Hemorrhage: Systematic Review and Meta-Analysis by Haofuzi Zhang, Peng Zou, Peng Luo, Xiaofan Jiang

“…Therefore, early determination of the risk of DCI is an urgent need. Machine learning (ML) has received much attention in clinical practice. …”
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Multimodal machine learning enables AI chatbot to diagnose ophthalmic diseases and provide high-quality medical responses by Ruiqi Ma, Qian Cheng, Jing Yao, Zhiyu Peng, Mingxu Yan, Jie Lu, Jingjing Liao, Lejin Tian, Wenjun Shu, Yunqiu Zhang, Jinghan Wang, Pengfei Jiang, Weiyi Xia, Xiaofeng Li, Lu Gan, Yue Zhao, Jiang Zhu, Bing Qin, Qin Jiang, Xiawei Wang, Xintong Lin, Haifeng Chen, Weifang Zhu, Dehui Xiang, Baoqing Nie, Jingtao Wang, Jie Guo, Kang Xue, Hongguang Cui, Jinwei Cheng, Xiangjia Zhu, Jiaxu Hong, Fei Shi, Rui Zhang, Xinjian Chen, Chen Zhao

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Downscaling and Projection of Multi-CMIP5 Precipitation Using Machine Learning Methods in the Upper Han River Basin by Ren Xu, Nengcheng Chen, Yumin Chen, Zeqiang Chen

“…However, little information is available regarding the downscaling using machine learning methods, specifically at hydrological basin scale. …”
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Forecasting of virtual power plant generating and energy arbitrage economics in the electricity market using machine learning approach by Tirunagaru V. Sarathkumar, Arup Kumar Goswami, Baseem Khan, Kamel A. Shoush, Sherif S. M. Ghoneim, Ramy N. R. Ghaly

“…On one front, forecasting VPP generation units, including solar photovoltaic, wind power, and combined heat and power, employs a novel Adam Optimizer Long-Short-Term-Memory (AOLSTM) machine learning technique. Conversely, estimating the revenue’s superior frontier is accomplished by integrating energy storage and Monte-Carlo optimization. …”
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Part A: Innovative Data Augmentation Approach to Enhance Machine Learning Efficiency—Case Study for Hydrodynamic Purposes by Hamed Majidiyan, Hossein Enshaei, Damon Howe, Eric Gubesch

Subjects: “…machine learning…”
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Comparing Statistical and Machine Learning Methods for Time Series Forecasting in Data-Driven Logistics—A Simulation Study by Lena Schmid, Moritz Roidl, Alice Kirchheim, Markus Pauly

Subjects: “…machine learning…”
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Machine Learning Enabled High‐Throughput Screening of 2D Ultrawide Bandgap Semiconductors for Flexible Resistive Materials by Chi Chen, Hao Wang, Houzhao Wan, Dan Sun

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A new distal radius fracture classification depending on the specific fragments through machine learning clustering method by Yuling Gao, Yanrui Zhao, Yang Liu, Shan Lei, Hanzhou Wang, Yuerong Lizhu, Tianchao Lu, Zhexian Cheng, Dong Wang, Binzhi Zhao, Ziyi Li, Junlin Zhou

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Machine learning prediction of obesity-associated gut microbiota: identifying Bifidobacterium pseudocatenulatum as a potential therapeutic target by Hao Wu, Yuan Li, Yuxuan Jiang, Xinran Li, Shenglan Wang, Changle Zhao, Changle Zhao, Ximiao Yang, Baocheng Chang, Juhong Yang, Juhong Yang, Jianjun Qiao, Jianjun Qiao

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Construction of Machine Learning Models to Predict the Maximum Absorption Wavelength Considering the Solute and Solvent and Inverse Analysis of the Models by Haruki Ochiai, Hiromasa Kaneko

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Explainable Machine Learning for Efficient Diabetes Prediction Using Hyperparameter Tuning, SHAP Analysis, Partial Dependency, and LIME by Md. Manowarul Islam, Habibur Rahman Rifat, Md. Shamim Bin Shahid, Arnisha Akhter, Md Ashraf Uddin, Khandaker Mohammad Mohi Uddin

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Development and external validation of machine learning-based models to predict patients with cellulitis developing sepsis during hospitalisation by Li Hu, Xilingyuan Chen, Rentao Yu

“…This study was designed to develop and compare different models for predicting patients with cellulitis developing sepsis during hospitalisation.Design This is a retrospective cohort study.Setting This study included both the development and the external-validation phases from two independent large cohorts internationally.Participants and methods A total of 6695 patients with cellulitis in the Medical Information Mart for Intensive care (MIMIC)-IV database were used to develop models with different machine-learning algorithms. The best models were selected and then externally validated in 2506 patients with cellulitis from the YiduCloud database of our university. …”
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Assessing the diagnostic accuracy of machine learning algorithms for identification of asthma in United States adults based on NHANES dataset by Omid Kohandel Gargari, Mobina Fathi, Shahryar Rajai Firouzabadi, Ida Mohammadi, Mohammad Hossein Mahmoudi, Mehran Sarmadi, Arman Shafiee

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Using a robust model to detect the association between anthropometric factors and T2DM: machine learning approaches by Nafiseh Hosseini, Hamid Tanzadehpanah, Amin Mansoori, Mostafa Sabzekar, Gordon A. Ferns, Habibollah Esmaily, Majid Ghayour-Mobarhan

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Machine learning methods for predicting essential metabolic genes from Plasmodium falciparum genome-scale metabolic network. by Itunuoluwa Isewon, Stephen Binaansim, Faith Adegoke, Jerry Emmanuel, Jelili Oyelade

“…To overcome this, a Network-based Machine Learning framework was proposed. It assessed various network properties in Plasmodium falciparum, using a Genome-Scale Metabolic Model (iAM_Pf480) from the BiGG database and essentiality data from the Ogee database. …”
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Combination of gray level features with deep transfer learning for copra classification using machine learning and neural networks by A. Stephen Sagayaraj, T. Kalavathi Devi

“…These concatenated features were evaluated using various machine learning classifiers and neural networks. Among the classifiers tested, Neural Network-based Pattern Recognition (NNPR) achieved the highest accuracy of 99.6%, sensitivity of 99.64%, specificity of 99.64%, F1-Score of 99.6, and a Kappa score of 0.99, demonstrating its superior performance. …”
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Retracted: Analysis Method of Agricultural Total Factor Productivity Based on Stochastic Block Model (SBM) and Machine Learning by Journal of Food Quality

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Biocomposite’s Multiple Uses for a New Approach in the Diagnosis of Parkinson’s Disease Using a Machine Learning Algorithm by Abdallah Al-Husban, Mustafa Mahdi Abdulridha, A. A. Hamad Mohamad, Abdelrahman Mohamed Ibrahim

“…Nowadays, various novel machine learning-based algorithms for evaluating Parkinson’s disease have been designed. …”
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Computer Aided Diagnostic System for Blood Cells in Smear Images Using Texture Features and Supervised Machine Learning by Shakhawan Hares Wady

Subjects: “…Leukaemia diagnosis; blood smear; feature extraction; machine learning…”
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Explainable quality assessment of effective aligned skeletal representations for martial arts movements by multi-machine learning decisions by Yiqun Pang, Kaiqi Zhang, Fengmei Li

“…This study proposes an intelligent scoring method based on machine learning. Firstly, the key features are extracted by the feature alignment technique, which eliminates the influence of athletes’ movement speed, rhythm and duration on the scoring, thus reflecting the athletes’ skill level more realistically. …”
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