Predicting weather-related power outages in large scale distribution grids with deep learning ensembles by L. Prieto-Godino, C. Peláez-Rodríguez, J. Pérez-Aracil, J. Pastor-Soriano, S. Salcedo-Sanz

“…Diversity is ensured by training each model with slightly different randomly sampled data. …”
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Optimising coronary imaging decisions with machine learning: an external validation study by Floor Groepenhoff, Leonard Hofstra, Sophie Heleen Bots, Saskia Haitjema, Imo Hoefer, L. Malin Overmars, Bram van Es, Mark C. H. De Groot, G. Aernout Somsen, I. Igor Tulevski, Hester M. den Ruijter, Wouter W. van Solinge

“…This study aimed to externally validate sex-stratified machine learning algorithms based on EHR data to predict the absence of coronary stenosis, evaluated in diverse clinical settings.Methods Sex-stratified XGBoost algorithms were trained on EHR data from patients who underwent coronary imaging at the University Medical Center Utrecht (n=14 674) and externally tested on EHR data of 13 Cardiology centres in the Netherlands (n=9252). …”
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An Automated Image-Based Dietary Assessment System for Mediterranean Foods by Fotios S. Konstantakopoulos, Eleni I. Georga, Dimitrios I. Fotiadis

“…Based on the EfficientNet family of CNNs, we use the EfficientNetB2 both for the pre-trained model and its weights evaluation, as well as for classifying food images in the MedGRFood dataset. …”
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A BERT-Based Classification Model: The Case of Russian Fairy Tales by Валерий Дмитриевич Соловьев, Марина Ивановна Солнышкина, Andrey Ten, Николай Аркадиевич Прокопьев

“…Focused on the mechanism of tokenization and embeddings design as the key components in BERT’s text processing, the research also evaluates the standard benchmarks used to train classification models and analyze complex cases, possible errors and improvement algorithms thus raising the classification models accuracy. …”
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Risk prediction for acute kidney disease and adverse outcomes in patients with chronic obstructive pulmonary disease: an interpretable machine learning approach by Siqi Jiang, Lingyu Xu, Xinyuan Wang, Chenyu Li, Chen Guan, Lin Che, Yanfei Wang, Xuefei Shen, Yan Xu

“…Data were split into 80% for training and 20% for testing. Eight machine learning algorithms were used, and model performance was evaluated using various metrics. …”
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A Systematic Literature Review of Concept Drift Mitigation in Time-Series Applications by Mujaheed Abdullahi, Hitham Alhussian, Norshakirah Aziz, Said Jadid Abdulkadir, Yahia Baashar, Abdussalam Ahmed Alashhab, Afroza Afrin

“…Based on the identified records, 60 studies published between 2013 and 2024 were thoroughly surveyed and evaluated using PRISMA guidelines. The findings show that Support Vector Machines (SVM) is the most effective learning algorithms for the detection and adaptation of CD in regression and classification tasks using time-series data. …”
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Analysis of shared pathogenic mechanisms and drug targets in myocardial infarction and gastric cancer based on transcriptomics and machine learning by Junyang Ma, Junyang Ma, Shufu Hou, Xinxin Gu, Peng Guo, Jiankang Zhu

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Compare three deep learning-based artificial intelligence models for classification of calcified lumbar disc herniation: a multicenter diagnostic study by Zhiming Liu, Hao Zhang, Min Zhang, Changpeng Qu, Lei Li, Yihao Sun, Xuexiao Ma

“…The participants were then divided into separate sets for training, testing, and external validation. Ultimately, we developed a deep learning model using the ResNet-34 algorithm model and evaluated its diagnostic efficacy.ResultsA total of 1,224 eligible patients were included in this study, consisting of 610 males and 614 females, with an average age of 53.34 ± 10.61 years. …”
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Bridging experiments and models, towards a new paradigm: DROP-PINN, a physics-informed neural network to predict droplet rupture in multiphase systems by Grégory Bana, Fabrice Lamadie, Sophie Charton, Didier Lucor, Nida Sheibat-Othman

“…This paper introduces a novel PINN-based algorithm that is trained to infer the droplet breakage frequencies in a turbulent agitated vessel without prior knowledge of the breakage kernel. …”
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Lossy DICOM conversion may affect AI performance by Robin Sebastian Mayer, Fabian Fliedner, Ingvild Frøberg Mathisen, Anna Laib, Julia Bein, Marco Eichelberg, Peter J. Wild, Nadine Flinner

“…So, if DICOM images are intended for a diagnostic use, all processes and algorithms must be (re-)evaluated with the converted files, as images are not identical. …”
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A hybrid learning network with progressive resizing and PCA for diagnosis of cervical cancer on WSI slides by Nitin Kumar Chauhan, Krishna Singh, Amit Kumar, Ashutosh Mishra, Sachin Kumar Gupta, Shubham Mahajan, Seifedine Kadry, Jungeun Kim

“…Furthermore, two ML methods, Support Vector Machine (SVM) and Random Forest (RF) models, are trained on this reduced feature set, and their predictions are integrated using a majority voting approach for evaluating the final classification results, thereby enhancing overall accuracy and reliability. …”
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An Efficient Method for Counting Large-Scale Plantings of Transplanted Crops in UAV Remote Sensing Images by Huihua Wang, Yuhang Zhang, Zhengfang Li, Mofei Li, Haiwen Wu, Youdong Jia, Jiankun Yang, Shun Bi

“…The existing counting methods primarily rely on manual counting or estimation, which are inefficient, costly, and difficult to evaluate statistically. Additionally, some deep-learning-based algorithms can only crop large-scale remote sensing images obtained by Unmanned Aerial Vehicles (UAVs) into smaller sub-images for counting. …”
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A Multifrequency Brain Network-Based Deep Learning Framework for Motor Imagery Decoding by Juntao Xue, Feiyue Ren, Xinlin Sun, Miaomiao Yin, Jialing Wu, Chao Ma, Zhongke Gao

“…The filter bank common spatial pattern (FBCSP) algorithm filters the MI-based EEG signals in the spatial domain to extract features. …”
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A Temporal Convolutional Network–Bidirectional Long Short-Term Memory (TCN-BiLSTM) Prediction Model for Temporal Faults in Industrial Equipment by Jinyin Bai, Wei Zhu, Shuhong Liu, Chenhao Ye, Peng Zheng, Xiangchen Wang

“…Traditional algorithms and single predictive models often face challenges such as limited prediction accuracy and insufficient modeling capabilities for complex time-series data in fault prediction tasks. …”
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Development and validation of a machine learning model for predicting pulmonary metastasis in hepatocellular carcinoma patients by Gangfeng Zhu, Qiang Yi, Rui Xu, Yi Xie, Siying Chen, Yipeng Song, Yi Xiang, Xiangcai Wang, Li Huang

“…Feature selection was conducted using the Boruta algorithm and multivariate logistic regression. Eight machine learning models were then developed and evaluated using validation cohorts for predictive performance. …”
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A radiomics-clinical predictive model for difficult laparoscopic cholecystectomy based on preoperative CT imaging: a retrospective single center study by Rui-Tao Sun, Chang-Lei Li, Yu-Min Jiang, Ao-Yun Hao, Kui Liu, Kun Li, Bin Tan, Xiao-Nan Yang, Jiu-Fa Cui, Wen-Ye Bai, Wei-Yu Hu, Jing-Yu Cao, Chao Qu

“…A combination of radiomic and clinical features was selected using the Boruta-LASSO algorithm. Predictive models were constructed using six machine learning algorithms and validated, with model performance evaluated based on the AUC, accuracy, Brier score, and DCA to identify the optimal model. …”
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Exploration of shared pathogenic factors and causative genes in early-stage endometrial cancer and osteoarthritis by Yiyun Bai, Sang Luo, Ruzhen Shuai, Xiaomei Zhang, Liwei Yuan, Dan Liu

“…Genes with diagnostic value were identified using multiple machine learning algorithms to construct EC prediction models and evaluate their performance. …”
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Bayesian Model Prediction for Breast Cancer Survival: A Retrospective Analysis by Islam Bani Mohammad, Muayyad M. Ahmad

“…The discriminative ability of models was evaluated by accuracy and the area under the curve (AUC) in terms of superior predictive performance for breast cancer outcomes. …”
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Combining OBIA, CNN, and UAV imagery for automated detection and mapping of individual olive trees by Oumaima Ameslek, Hafida Zahir, Hanane Latifi, El Mostafa Bachaoui

“…Initially, The CNN-based classifier was created, trained, validated, and applied to generate the Olive trees probability maps on the ortho-photo. …”
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Predicting neoadjuvant chemotherapy response in locally advanced gastric cancer using a machine learning model combining radiomics and clinical biomarkers by Tong Ling, Zhichao Zuo, Liucheng Wu, Jie Ma, Tingan Wang, Mingwei Huang

“…The cohort was divided into a training set (n = 178) and a validation set (n = 77) in a 7:3 ratio. …”
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