Application of generative adversarial networks in the restoration of blurred optical coherence tomography images caused by optical media opacity in eyes by Shuang Zhou, Tsz Kin Ng, Geng Wang, Jianwei Lin, Ming Zhu, Zhengfang Wang, Yeye Zhang, Jinyan Lin, Weifeng Yang

“…Optical media opacity was simulated with an algorithm for training set (420 normal eyes). Images for three test sets were from the following: 56 normal eyes before and after fitting neutral density filter (NDF), 34 eyes before and after cataract surgeries and 90 eyes processed by algorithm. …”
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Quality Prediction Model Based on Novel Elman Neural Network Ensemble by Lan Xu, Yuting Zhang

“…First, the Elman NN parameters are optimized using the grasshopper optimization (GRO) method, and then the weighted average method is improved to combine the outputs of the individual NNs, where the weights are determined by the training errors. Simulations were conducted to compare the proposed method with other NN methods and evaluate its performance. …”
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Machine learning–guided single-cell multiomics uncovers GDF15-driven immunosuppressive niches in NSCLC: A translational framework for overcoming anti-PD-1 resistance by Xianfei Zhang, Zhengxin Yin, Xueyu Chen, Nengchong Zhang, Shengjia Yu, Congcong Zhu, Lianggang Zhu, Liulan Shao, Bin Li, Runsen Jin, Hecheng Li

“…We integrated multiomics profiling with machine learning to systematically identify determinants of ICB efficacy. Comparative evaluation of 22 survival algorithms across four NSCLC cohorts (n=156) led to the development of an Accelerated Oblique Random Survival Forest model, which outperformed conventional Cox regression and deep learning methods in predictive accuracy (training C-index=0.864; test C-index=0.748). …”
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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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Private Data Incrementalization: Data-Centric Model Development for Clinical Liver Segmentation by Stephanie Batista, Miguel Couceiro, Ricardo Filipe, Paulo Rachinhas, Jorge Isidoro, Inês Domingues

“…Pre-processing and post-processing stages, incremental training, and performance evaluations reveal that structured exposure to diverse datasets improves segmentation performance, with ResUNet++ achieving the highest accuracy (0.9972) and Dice Similarity Coefficient (0.9449), and the best Average Symmetric Surface Distance (0.0053 mm), demonstrating the importance of dataset diversity and volume for segmentation models’ robustness and generalization. …”
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Predictive model for sarcopenia in chronic kidney disease: a nomogram and machine learning approach using CHARLS data by Renjie Lu, Shiyun Wang, Pinghua Chen, Fangfang Li, Fangfang Li, Pan Li, Qian Chen, Xuefei Li, Fangyu Li, Suxia Guo, Jinlin Zhang, Jinlin Zhang, Dan Liu, Zhijun Hu

“…Four machine learning algorithms were utilized, with the optimal model undergoing hyperparameter optimization to evaluate the significance of predictive factors.ResultsA total of 1,092 CKD patients were included, with 231 (21.2%) diagnosed with sarcopenia. …”
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Semisupervised Location Awareness in Wireless Sensor Networks Using Laplacian Support Vector Regression by Jaehyun Yoo, H. Jin Kim

“…The algorithms are evaluated for location awareness of multiple mobile robots in a WSN. …”
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The diagnostic efficacy of seven autoantibodies in early detection of ground-glass nodular lung adenocarcinoma by Hua Guo, Wei Zhao, Chunsun Li, Zhen Wu, Ling Yu, Miaoyu Wang, Yuanhui Wei, Zirui Wang, Shangshu Liu, Yue Yin, Zhen Yang, Liangan Chen

“…This provides a non-invasive and highly discriminative method for the evaluation of ground-glass nodules in high-risk patients.…”
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SorpVision: A Comprehensive Dataset for Cementitious Sorptivity Analysis Powered by Computer Vision by Hossein Kabir, Jordan Wu, Sunav Dahal, Tony Joo, Nishant Garg

“…Abstract As the construction industry advances toward more efficient methods for assessing durability, the need for automated sorptivity evaluation has become increasingly critical. Consequently, this study introduces SorpVision, a dataset of 7,384 images (5,000 real and 2,384 synthetic) designed to support our custom computer vision-based framework for automated sorptivity evaluation in cementitious materials. …”
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A prospective multicenter randomized controlled trial on artificial intelligence assisted colonoscopy for enhanced polyp detection by Dong Kyun Park, Eui Joo Kim, Jong Pil Im, Hyun Lim, Yun Jeong Lim, Jeong-Sik Byeon, Kyoung Oh Kim, Jun-Won Chung, Yoon Jae Kim

“…Each fold consisted of 9,639 training images and 1,070 validation images. Video data from 56 patients were used for model training, and transfer learning was performed using the developed still image-based model. …”
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Sensorless real-time solar irradiance prediction in grid-connected PV systems using PSO-MPPT and IoT-enabled monitoring by Ali Zaki Mohammed Nafa, Adel A. Obed, Ahmed J. Abid, Salam J. Yaqoob, Mohit Bajaj, Mohammad Shabaz

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Recommendations for All-Round Newborns and Infants Hearing Screening in Russian Federation by S. S. Chibisova, G. S. Tufatulin, L. S. Namazova-Baranova, I. V. Koroleva, E. R. Tsygankova, T. G. Markova, N. N. Volodin, G. A. Tavartkiladze

“…The following problems were revealed: purchase and renewal of equipment, its timely adjustment, ensuring the continuity of screening stages, specialists training, compliance with methodology and study conditions, results documentation. …”
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Contrast-enhanced ultrasound radiomics model for predicting axillary lymph node metastasis and prognosis in breast cancer: a multicenter study by Shi Yu Li, Yue Ming Li, Yong Qi Fang, Zhi Ying Jin, Jun Kang Li, Xiao Meng Zou, Si Si Huang, Rui Lan Niu, Nai Qing Fu, Yu Hong Shao, Xuan Tong Gong, Mao Ran Li, Wei Wang, Zhi Li Wang

“…Abstract Objective To construct a multimodal ultrasound (US) radiomics model for predicting axillary lymph node metastasis (ALNM) in breast cancer and evaluated its application value in predicting ALNM and patient prognosis. …”
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Exploring rounD Dataset for Domain Generalization in Autonomous Vehicle Trajectory Prediction by Zikai Zhang

“…Our primary objective is to evaluate the generalizability of the proposed model across diverse training and testing datasets. …”
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A CT-based machine learning model for using clinical-radiomics to predict malignant cerebral edema after stroke: a two-center study by Lingfeng Zhang, Gang Xie, Yue Zhang, Yue Zhang, Junlin Li, Junlin Li, Wuli Tang, Wuli Tang, Ling Yang, Ling Yang, Kang Li

“…Ultimately, the efficacy of these models was evaluated by measuring the operating characteristics of the subjects through their area under the curve (AUCs).ResultsLogistic regression (LR) was found to be the most effective machine learning algorithm, for forecasting the MCE. …”
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Model split-based data privacy protection method for federated learning by CHEN Ka

“…The strategy combines leak loss of local private data and model accuracy loss that enhances privacy while maintaining model accuracy. To evaluate the proposed BLDP algorithm, experiments were conducted on four commonly benchmarked datasets and the leakage loss and model accuracy were analyzed. …”
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Development and validation of a nomogram for predicting refractory peritoneal dialysis related peritonitis by Qiqi Yan, Guiling Liu, Ruifeng Wang, Dandan Li, Xiaoli Chen, Deguang Wang

“…The study dataset was randomly divided into a 70% training set and a 30% validation set. Univariate logistic analysis, LASSO regression analysis, and random forest algorithms were utilized to identify the potential risk factors for refractory peritonitis. …”
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Predicting mortality risk in Alzheimer’s disease using machine learning based on lifestyle and physical activity by Ruihong Tang, Liang Tan, Xia Chen, Zhengwu Shan, Zhao Zhang, Panpan Cheng

“…Random Survival Forest (RSF) and Cox proportional hazards models were developed and validated using a training set (n = 42,585) and test set (n = 10,646). …”
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Duck Egg Crack Detection Using an Adaptive CNN Ensemble with Multi-Light Channels and Image Processing by Vasutorn Chaowalittawin, Woranidtha Krungseanmuang, Posathip Sathaporn, Boonchana Purahong

“…The dataset was then split into 836 images for training, 209 images for validation, and 449 images for testing per lighting condition. …”
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A recurrence model for non-puerperal mastitis patients based on machine learning. by Gaosha Li, Qian Yu, Feng Dong, Zhaoxia Wu, Xijing Fan, Lingling Zhang, Ying Yu

“…Through random allocation, these individuals were split into a training cohort and a testing cohort in a 90%:10% ratio for the purpose of building the model. …”
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