Classification of Structural and Functional Development Stage of Cardiomyocytes Using Machine Learning Techniques by V. R. Bondarev, K. O. Ivanko, N. G. Ivanushkina

“…A pre-processed and augmented dataset is used for training of the convolutional neural network having an architecture with hierarchical structure and residual block usage. The model is evaluated based on the confusion matrix and the heat maps of different convolutional layers are analyzed. …”
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Specific features of pregnancy in patients with type 1 diabetes in the prepubertal period. Results of a 15-year long clinical observation by Lyubov' Leonidovna Bolotskaya, Natalya Vladimirovna Efremova, Yury Ivanovich Suntsov

“…Aim. To evaluate effect of different factors on pregnancy course in patients with type 1 diabetes mellitus developing in the prepubertal period. …”
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Machine Learning Approach to Model Soil Resistivity Using Field Instrumentation Data by Md Jobair Bin Alam, Ashish Gunda, Asif Ahmed

“…Different types of sensors were installed at multiple depths in the ET covers, and resistivity tests were conducted periodically at the same location. …”
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Analysis of potential molecular targets and mechanisms of brominated flame retardants in causing osteoarthritis using network toxicology, machine learning, SHAP analysis, and molec... by Yu Liu, Guohang Shen, Zidong Xia, Ruoyan Wang, Yupei Dai

“…Subsequently, a comprehensive machine learning workflow involving 113 different algorithms was used to identify 10 core target genes as potential mediators of BDE-47-induced osteoarthritis. …”
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MRI quantified enlarged perivascular space volumes as imaging biomarkers correlating with severity of anxiety depression in young adults with long-time mobile phone use by Li Li, Yalan Wu, Jiaojiao Wu, Bin Li, Rui Hua, Feng Shi, Lizhou Chen, Yeke Wu

“…For classifying depression status, eight features were selected to construct a K nearest neighbors model with an AUC value of 0.931 (95%CI 0.814-1.000) in the testing dataset.DiscussionThe utilization of MRI-quantified EPVS metrics combined with machine-learning algorithms presents a promising method for evaluating severity of anxiety and depression symptoms in patients with LTMPU, which might introduce a non-invasive, objective, and quantitative approach to enhance diagnostic efficiency and guide personalized treatment strategies.…”
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Galectin-9 as a biomarker for disease activity in systemic lupus erythematosus. by Naoki Matsuoka, Yuya Fujita, Jumpei Temmoku, Makiko Yashiro Furuya, Tomoyuki Asano, Shuzo Sato, Haruki Matsumoto, Hiroko Kobayashi, Hiroshi Watanabe, Eiji Suzuki, Hideko Kozuru, Hiroshi Yastuhashi, Kiyoshi Migita

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Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post stereotactic radiosurgery by Hemalatha Kanakarajan, Wouter De Baene, Patrick Hanssens, Margriet Sitskoorn

“…Though the models performed less effectively for follow-ups, the addition of public data enhanced their performance, providing a viable solution to improve their efficacy for the follow-ups. These algorithms hold promise as a valuable tool for clinicians for automated segmentation of planning and follow-up MRI scans during stereotactic radiosurgery treatment planning and response evaluations, respectively. …”
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Optimizing solar maximum power point tracking with adaptive PSO: A comparative analysis of inertia weight and acceleration coefficient strategies by Denesh Sooriamoorthy, Aaruththiran Manoharan, Siva Kumar Sivanesan, Soon Kian Lun, Alexander Chee Hon Cheong, Sathish Kumar Selva Perumal

“…Using a 4 × 5 PV array model simulated in MATLAB/Simulink, different adaptive PSO variants i.e. linear, exponential, and performance-based adaptive w along with linear and trigonometric-based adaptive c1 and c2 are evaluated across diverse irradiance scenarios. …”
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Development and validation of a machine learning model for online predicting the risk of in heart failure: based on the routine blood test and their derived parameters by Jianchen Pu, Yimin Yao, Xiaochun Wang

“…Variables associated with HF were selected using LASSO regression analysis. In addition, eight different machine learning algorithms were applied for prediction, and the prediction performances of these algorithms were comprehensively evaluated using the receiver operating characteristic curve, area under the curve (AUC), calibration curve analysis, and decision curve analysis and confusion matrix.ConclusionsUsing LASSO regression analysis, leukocyte, neutrophil, red blood cell, hemoglobin, platelet, and monocyte-to-lymphocyte ratios were identified as risk factors for HF. …”
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Machine learning insights on activities of daily living disorders in Chinese older adults by Huanting Zhang, Wenhao Zhou, Jianan He, Xingyou Liu, Jie Shen

“…Nine machine learning algorithms, including neural networks and an ensemble model, were employed with a 2/3 training and 1/3 testing split. …”
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Predicting Oncological and Functional Outcomes by Nephrectomy Type for T1 Renal Tumors Using Machine Learning Models by Dongrul Shin, Maisy Song, Jungyo Suh, Cheryn Song

“…Materials and Methods Using demographic and preoperative variables of 823 patients with clinical T1N0M0 renal tumors who underwent PN or RN between 2007 and 2019, we employed 5 different machine learning algorithms—general linear model (GLM), extreme gradient boosting (XgBoost), gradient boosting machine, distributed random forest, deep learning—and compared to predict recurrence probability and estimated glomerular filtration rate (eGFR) at 5-year after surgery. …”
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A peer-support lifestyle intervention for preventing type 2 diabetes in India: A cluster-randomized controlled trial of the Kerala Diabetes Prevention Program. by Kavumpurathu R Thankappan, Thirunavukkarasu Sathish, Robyn J Tapp, Jonathan E Shaw, Mojtaba Lotfaliany, Rory Wolfe, Pilvikki Absetz, Elezebeth Mathews, Zahra Aziz, Emily D Williams, Edwin B Fisher, Paul Z Zimmet, Ajay Mahal, Sajitha Balachandran, Fabrizio D'Esposito, Priyanka Sajeev, Emma Thomas, Brian Oldenburg

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Rural Renewable Energy Resources Assessment and Electricity Development Scenario Simulation Based on the LEAP Model by Hai Jiang, Haoshuai Jia, Yong Qiao, Wenzhi Liu, Yijun Miao, Wuhao Wen, Ruonan Li, Chang Wen

“…This study combines convolutional neural network (CNN) recognition technology, Greenwich engineering software, and statistical yearbook methods to evaluate rural solar, wind, and biomass energy resources in pilot cities in China, respectively. …”
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Predicting postoperative neurological outcomes of degenerative cervical myelopathy based on machine learning by Shuai Zhou, Shuai Zhou, Shuai Zhou, Shuai Zhou, Zexiang Liu, Zexiang Liu, Zexiang Liu, Haoge Huang, Haoge Huang, Haoge Huang, Hanxu Xi, Xiao Fan, Xiao Fan, Xiao Fan, Yanbin Zhao, Yanbin Zhao, Yanbin Zhao, Xin Chen, Xin Chen, Xin Chen, Yinze Diao, Yinze Diao, Yinze Diao, Yu Sun, Yu Sun, Yu Sun, Hong Ji, Feifei Zhou, Feifei Zhou, Feifei Zhou

“…We evaluated the importance of the features and created a feature-reduced model. …”
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Improving TMJ Diagnosis: A Deep Learning Approach for Detecting Mandibular Condyle Bone Changes by Kader Azlağ Pekince, Adem Pekince, Buse Yaren Kazangirler

“…Model performance was evaluated using datasets with different distributions, specifically 70:30 and 80:20 training-test splits. …”
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PREDICTION INTERVALS IN MACHINE LEARNING: RESIDUAL BOOTSTRAP AND QUANTILE REGRESSION FOR CASH FLOW ANALYSIS by Wa Ode Rahmalia Safitri, Farit Mochamad Afendi, Budi Susetyo

“…Model performance was evaluated using RMSLE, MAE, and MAPE metrics, with RMSLE chosen as primary due to its ability to handle skewed distributions. …”
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A machine learning-based model for predicting survival in patients with Rectosigmoid Cancer. by Yifei Wang, Bingbing Chen, Jinhai Yu

“…Subsequently, models were constructed using six different machine learning algorithms. Finally, the discrimination, calibration, and clinical applicability of each model were evaluated to determine the optimal model.…”
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The role of advanced machine learning in COVID-19 medical imaging: A technical review by Abdul Muiz Fayyaz, Said Jadid Abdulkadir, Shahab Ul Hassan, Safwan Mahmood Al-Selwi, Ebrahim Hamid Sumiea, Lareib Fatima Talib

“…It focuses on approaches such as Deep Learning (DL) algorithms and Transfer Learning, which have demonstrated significant potential in developing automated, accurate COVID-19 detection systems. …”
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Expanded Design by Roberto Bottazzi

“…Their ability to relate parts to parts not only resonates with the technical operations of ml models, but they also provide a conceptual space for designers to speculate different spatial organisation aided by algorithmic processes.  …”
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Robust ground control point localization from multi-aspect stereo SAR observations using Chinese commercial satellite SVN-2 by Minzheng Mu, Zhiwei Li, Heng Zhang, Xin He, Yan Zhu, Yifan Zhang, Chenglong Cao, Xun Du, Kun Han

“…Simulation experiments are conducted to evaluate the effectiveness of the positioning model, the reliability of the proposed method in assigning appropriate weights to different types of observations, and its robustness in handling gross errors. …”
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