Digital Biomarkers for Diagnosis of Muscle Disorders Using Stimulated Muscle Contraction Signal by Kwangsub Song, Sangui Choi, Jin-Hong Shin, Woo-Hyeon Son, Hyuntae Park, Hooman Lee, Nam-Jong Paik

“…Finally, to estimate the strength and endurance of the muscle, the feature vector was passed through the DB estimation model learned through the MLP. To evaluate the performance of the DB measurement algorithm, we collected the MFES-based IRS database for 50 subjects and tested the model with quantitative evaluation methods using the reference for the DB. …”
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Federated learning for digital twin applications: a privacy-preserving and low-latency approach by Jie Li, Dong Wang

“…To evaluate the efficiency of our proposed scheme, we conducted extensive experiments, with results validating that our approach achieves training accuracy and security on par with baseline methods, while substantially reducing FL iteration time. …”
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A lightweight knowledge graph-driven question answering system for field-based mineral resource survey by Mingguo Wang, Chengbin Wang, Jianguo Chen, Bo Wang, Wei Wang, Xiaogang Ma, Jiangtao Ren, Zichen Li, Yicai Ye, Jiakai Zhang, Yue Wang

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Predicting outcomes following open abdominal aortic aneurysm repair using machine learning by Ben Li, Badr Aljabri, Derek Beaton, Leen Al-Omran, Mohamad A. Hussain, Douglas S. Lee, Duminda N. Wijeysundera, Ori D. Rotstein, Charles de Mestral, Muhammad Mamdani, Mohammed Al-Omran

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Efficient diagnosis of diabetes mellitus using an improved ensemble method by Blessing Oluwatobi Olorunfemi, Adewale Opeoluwa Ogunde, Ahmad Almogren, Abidemi Emmanuel Adeniyi, Sunday Adeola Ajagbe, Salil Bharany, Ayman Altameem, Ateeq Ur Rehman, Asif Mehmood, Habib Hamam

“…The second phase employed the same algorithms alongside sequential ensemble methods—XG Boost, AdaBoostM1, and Gradient Boosting—using an average voting algorithm for binary classification. …”
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Prognostic risk modeling of endometrial cancer using programmed cell death-related genes: a comprehensive machine learning approach by Tianshu Chen, Yuhan Yang, Zhizhong Huang, Feng Pan, Zhendi Xiao, Kunxue Gong, Wenguang Huang, Liu Xu, Xueqin Liu, Caiyun Fang

“…Methods Utilizing transcriptomic data from TCGA-UCEC and GSE119041 datasets, we employed a comprehensive approach involving 117 machine learning algorithms. Key methodologies included differential gene expression analysis, weighted gene co-expression network analysis, functional enrichment studies, immune landscape evaluation, and multi-dimensional risk stratification. …”
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Predicting Livestock Farmers’ Attitudes towards Improved Sheep Breeds in Ahar City through Data Mining Methods by Jabraeil Vahedi, Masoumeh Niazifar, Mohammad Ghahremanzadeh, Akbar Taghizadeh, Soheila Abachi, Valiollah Palangi, Maximilian Lackner

“…Comparing statistical evaluation criteria, we found that the random tree algorithm outperformed other methods in predicting and classifying livestock farmers, achieving a prediction accuracy rate of 86% for a sample of 100 farmers. …”
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An Indoor Scene Classification Method for Service Robot Based on CNN Feature by Shaopeng Liu, Guohui Tian

“…In addition, overfitting is eliminated by our method even though the training data is limited. The presented method was evaluated on two benchmark scene datasets, Scene 15 dataset and MIT 67 dataset, acquiring 96.49% and 81.69% accuracy, respectively. …”
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Computational Molecular Modeling of Pin1 Inhibition Activity of Quinazoline, Benzophenone, and Pyrimidine Derivatives by Nicolás Cabrera, Jose R. Mora, Edgar A. Marquez

“…In this sense, a modeling evaluation of the inhibition of Pin1 using quinazoline, benzophenone, and pyrimidine derivatives was performed by using multilinear, random forest, SMOreg, and IBK regression algorithms on a dataset of 51 molecules, which was divided randomly in 78% for the training and 22% for the test set. …”
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Privacy-Preserving Diabetes and Heart Disease Prediction via Federated Learning and WCO by Sachikanta Dash, Sasmita Padhy, Preetam Suman, Sandip Mal, Lokesh Malviya, Amrit Suman, Jaydeep Kishore

“…Following extensive training and evaluation, the AdaBoost classifier delivered superior outcomes, achieving a 94.02% accuracy rate, an F1 score of 93.32%, and an AUC of 0.95. …”
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Deep learning framework based on ITOC optimization for coal spontaneous combustion temperature prediction: a coupled CNN-BiGRU-CBAM model by Xuming Shao, Wenhao Liu, Gang Bai, Yan Chen, Yu Liu, Jiahe Guang

“…The dataset was split into training, validation, and test sets at an 8:2:1 ratio. …”
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LSTM and TCN application for airport surface distress detection by Ludek Cicmanec, Adam Kubica, Jiri Maslan

“…Both algorithms demonstrated high accuracy on training and validation data and performed well on other independent test samples.…”
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On the Minimum Dataset Requirements for Fine-Tuning an Object Detector for Arable Crop Plant Counting: A Case Study on Maize Seedlings by Samuele Bumbaca, Enrico Borgogno-Mondino

“…We also implemented a handcrafted computer graphics algorithm as baseline. Models were tested with varying training sources (in-domain vs. out-of-distribution data), training dataset sizes (10–150 images), and annotation quality levels (10–100%). …”
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ADF-SL: An Adaptive and Fair Scheme for Smart Learning Task Distribution by Ahmed A. Al-Saedi, Veselka Boeva

“…Extensive experiments performed on time series electrocardiogram (ECG) databases (MITDB, SVDB, and INCARTDB) indicate that ADF-SL significantly outperforms the three existing algorithms that served as baselines. Compared to these baseline methods, ADF-SL accelerates model training on clients by up to 22.7%, 10.4%, and 5.8% compared to Vanilla SL, SplitFed, and FairFed, respectively, while maintaining model convergence and accuracy. …”
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A Deep Learning Method for Photovoltaic Power Generation Forecasting Based on a Time-Series Dense Encoder by Xingfa Zi, Feiyi Liu, Mingyang Liu, Yang Wang

“…In this study, we apply a deep learning algorithm, known as the time-series dense encoder (TiDE), which is an MLP-based encoder–decoder model, to forecast PV power generation. …”
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A data-centric and interpretable EEG framework for depression severity grading using SHAP-based insights by Anruo Shen, Jingnan Sun, Xiaogang Chen, Xiaorong Gao

“…A hybrid feature selection method was used, combining p-value and SHapley Additive exPlanations (SHAP) methods to select features that are both independently significant and jointly informative. The system was trained and evaluated on a large-scale, multi-site resting-state EEG dataset, using random forest for both classification and regression tasks. …”
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A machine learning-based model to predict intravenous immunoglobulin resistance in Kawasaki disease by Yuhan Xia, Yuezhong Huang, Min Gong, Weirong Liu, Yuanhui Meng, Huiyang Wu, Hui Zhang, Hao Zhang, Luyi Weng, Xiao-Li Chen, Huixian Qiu, Xing Rong, Rongzhou Wu, Maoping Chu, Xiu-Feng Huang

“…This study aimed to develop a predictive model for IVIG resistance in patients with Kawasaki disease and to identify the key predictors. The training set underwent cross-validation, and models were constructed using six machine learning algorithms. …”
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Integrated artificial intelligence approach for well-log fluid identification in dual-medium tight sandstone gas reservoirs by Wurong Wang, Wurong Wang, Linbo Qu, Linbo Qu, Dali Yue, Dali Yue, Wei Li, Wei Li, Junlong Liu, Wujun Jin, Jialin Fu, Jialin Fu, Jiarui Zhang, Jiarui Zhang, Dongxia Chen, Dongxia Chen, Qiaochu Wang, Qiaochu Wang, Sha Li, Sha Li

“…To address the limitations of conventional machine learning algorithms, which have low accuracy due to data inhomogeneity and weak fluid logging responses, this study introduces a novel method for fluid logging evaluation in dual-medium tight sandstone gas reservoirs.MethodsThe method integrates core, thin section, and scanning electron microscope observations, taking into account the effect of fractures.ResultsReservoirs are divided into three types: fractured reservoirs (FR), porous reservoirs (PR), and microfracture-pore composite reservoirs (MPCR), highlighting the distinct fluid logging responses of each type. …”
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Two-layer multi-objective optimal sizing of electric-hydrogen energy storage with the integration of extreme scenario generation and preference-information decision-making by Zihan Sun, Jian Chen, Yang Chen, Wen Zhang, Tingting Zhang, Yicheng Zhang, Guangsheng Pan

“…The evaluation results demonstrate that the proposed integrated GAN scenario generation method reduced the training losses by 60.97% in comparison with other traditional methods. …”
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AN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK by LAWRENCE OMOTOSHO, IBRAHIM OGUNDOYIN, OLAJIDE ADEBAYO, JOSHUA OYENIYI

“…The developed multimodal biometrics system was evaluated on a dataset of 700 iris and facial images, the training database contain 600 iris and face images, 100 iris and face images were used for testing. …”
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