Optimizing skin cancer screening with convolutional neural networks in smart healthcare systems. by Ali Raza, Akhtar Ali, Sami Ullah, Yasir Nadeem Anjum, Basit Rehman

“…A research used an optimized CNN and ResNet152V2 with the HAM10000 dataset to differentiate between the seven forms of skin cancer. Model training involved the use of two optimization functions (RMSprop and Adam) and NGNDG-AF activation functions. …”
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Prediction of shut-off head for centrifugal pumps based on grey theory and GA-BP neural network by Dehui Yu, Jinfeng Zhang, Shouqi Yuan, Wensheng Ma, Xiaobang Bai, Yan Chen, Chao Wang

“…The GA was utilized to optimize the weights and thresholds of the BP model. The training involved 121 samples, while 20 additional samples were used to evaluate the models against three established methods (throne, modified throne, and regression fitting). …”
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Deep learning radiomics based on MRI for differentiating tongue cancer T - staging by Zhaoyi Lu, Bowen Zhu, Hang Ling, Xi Chen

“…Performance was evaluated via AUC, DCA, IDI, and NRI in different sets. …”
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Automated 3D segmentation of the hyoid bone in CBCT using nnU-Net v2: a retrospective study on model performance and potential clinical utility by Ismail Gümüssoy, Emre Haylaz, Suayip Burak Duman, Fahrettin Kalabalik, Seyda Say, Ozer Celik, Ibrahim Sevki Bayrakdar

“…The nnU-Net v2 architecture was utilized to process the training and test datasets, generating the algorithm weight factors. …”
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Clinical application and immune infiltration landscape of stemness‐related genes in heart failure by Wenting Yan, Yanling Li, Gang Wang, Yuan Huang, Ping Xie

“…Feature selection was performed using two machine learning algorithms. Nomogram models were then constructed to predict HF risk based on the selected key genes. …”
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Multimodal Fusion Multi-Task Learning Network Based on Federated Averaging for SDB Severity Diagnosis by Songlu Lin, Renzheng Tang, Yuzhe Wang, Zhihong Wang

“…A shared feature extractor is combined with task-specific heads to enable joint diagnosis, while the FedAvg algorithm is employed to facilitate decentralized training across multiple institutions without sharing raw data, thereby preserving privacy and addressing non-IID challenges. …”
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Latent space autoencoder generative adversarial model for retinal image synthesis and vessel segmentation by K. Radha, Yepuganti Karuna

“…However, it is widely recognized that large datasets are essential for training deep learning models to ensure they can generalize well. …”
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A nomogram to predict the risk of insulin resistance in Chinese women with polycystic ovary syndrome by Benjie Guo, Yuting Shen, Ziying Dai, Kalibinuer Yimamu, Jianhua Sun, Lixia Pei

“…In the training, internal validation, and external validation sets, the AUCs were 0.911 (95% CI 0.878–0.911), 0.842 (95% CI 0.771–0.842), and 0.901 (95% CI 0.856–0.901), respectively. …”
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Implementation Of Deep Learning Using Convolutional Neural Network Method In A Rupiah Banknote Detection System For Those With Low Vision by Dinul Akhiyar, Tukino Tukino, Sarjon Defit

“…The training dataset included 1,260 images, and the model underwent 7,000 iterations during training. …”
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Multimodal nomogram integrating deep learning radiomics and hemodynamic parameters for early prediction of post-craniotomy intracranial hypertension by Zunfeng Fu, Jing Wang, Wenyi Shen, Yanqing Wu, Jiajun Zhang, Yan Liu, Chongqiang Wang, Yanlin Shen, Ye Zhu, Weifu Zhang, Chunju Lv, Lin Peng

“…This study included 238 patients with severe TBI (training cohort: n = 166; testing cohort: n = 72). …”
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Automatic diagnosis of extraocular muscle palsy based on machine learning and diplopia images by Xiao-Lu Jin, Xue-Mei Li, Tie-Juan Liu, Ling-Yun Zhou

“…Diagnostic models were constructed using logistic regression (LR), decision tree (DT), support vector machine (SVM), extreme gradient boosting (XGBoost), and deep learning (DL) algorithms. A total of 2757 diplopia images were randomly selected as training data, while the test dataset contained 487 diplopia images. …”
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Optimization of Rituximab Therapy in Adult Patients With PLA2R1-Associated Membranous Nephropathy With Artificial Intelligence by Alexandre Destere, Maxime Teisseyre, Diane Merino, Marion Cremoni, Alexandre O Gérard, Thomas Crepin, Noémie Jourde-Chiche, Daisy Graça, Kévin Zorzi, Céline Fernandez, Vesna Brglez, Sylvia Benzaken, Vincent L.M. Esnault, Sylvain Benito, Milou-Daniel Drici, Barbara Seitz-Polski

“…The performances were evaluated for accuracy, sensitivity, and specificity in 10-fold cross-validation training and test sets. …”
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Analysing learning behaviour: A data-driven approach to improve time management and active listening skills in students by Vinayak Hegde, Vishrutha M, Pallavi M. Shanthappa, Rekha Bhat, Nisha Raveendran, Roshin C

“…Methodologically, the study began with comprehensive data collection through a survey, data preprocessing tasks and feature selection, followed by training and evaluating predictive models using various ML algorithms. …”
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Adapting Vision Transformer-Based Object Detection Model for Handwritten Text Line Segmentation Task by Osman Furkan KARAKUŞ, Ayla GULCU, Ali Can KARACA

“…Both object detection-based methods involve a learning phase during which the model is trained or fine-tuned on the dataset. For a diverse set of baselines methods, we have also implemented two learning-free algorithms such as A* Search Algorithm and the Genetic Algorithm (GA). …”
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AGW-YOLO-Based UAV Remote Sensing Approach for Monitoring Levee Cracks by HU Weibo, ZHOU Shaoliang, ZHAO Erfeng, ZHAO Xueqiang

“…Comparative experiments indicated that AGW-YOLO outperformed several mainstream object detection algorithms, including Faster R-CNN, YOLOv8n, YOLOv9-tiny, YOLOv10n, RT-DETR-R50, and TPH-YOLO, across most evaluation metrics, offering high recognition accuracy with lower computational complexity. …”
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Relationship between intra-bladder pressure and acute kidney injury in patients with acute pancreatitis: interpretable machine learning approach by Gang Liao, Baning Ye, Jianquan Li, Mingxiang Wen

“…The relationship between IBP and AKI is analyzed using restricted cubic splines. The Boruta algorithm is used to evaluate the prediction ability of IBP and select characteristic variables, and divide the data into a training set and verification set. …”
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Physical Information Neural Network-Based Seepage Behavior Analysis of Earth and Rock Dams by XUE binghan, HUANG zhenhua, LEI Jianwei, FANG Hongyuan

“…As a meshless method, the algorithm operates directly on training set sample points, effectively circumventing the mesh quality sensitivity inherent in traditional numerical approaches. …”
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A scalable framework for soil property mapping tested across a highly diverse tropical data-scarce regionZENODO by Rodrigo de Q. Miranda, Rodolfo L.B. Nóbrega, Anne Verhoef, Estevão L.R. da Silva, Jadson F. da Silva, José C. de Araújo Filho, Magna S.B. de Moura, Alexandre H.C. Barros, Alzira G.S.S. Souza, Wanhong Yang, Hui Shao, Raghavan Srinivasan, Feras Ziadat, Suzana M.G.L. Montenegro, Maria do S.B. Araújo, Josiclêda D. Galvíncio

“…We developed a machine learning framework that integrates the Soil-Landscape Estimation and Evaluation Program (SLEEP) with gradient boosting to predict soil properties at regional scales and multiple depths. …”
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Prediction of EGFR mutations in non-small cell lung cancer: a nomogram based on 18F-FDG PET and thin-section CT radiomics with machine learning by Jianbo Li, Qin Shi, Yi Yang, Jikui Xie, Qiang Xie, Ming Ni, Xuemei Wang, Xuemei Wang

“…Its predictive performance and clinical utility were evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis.ResultsAmong the radiomics models, the RF model showed the best performance with AUCs of 0.785 (95% CI, 0.726-0.844) and 0.776 (95% CI, 0.662-0.889) in the training and validation groups, respectively. …”
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Security decision method for the edge of multi-layer satellite network based on reinforcement learning by Peiliang ZUO, Shaolong HOU, Chao GUO, Hua JIANG, Wenbo WANG

“…This paper uses deep reinforcement learning algorithms to implement edge security decisions for satellite networks. …”
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