An enhanced substation equipment detection method based on distributed federated learning by Zhuyun Li, Qiutong Qin, Yingyi Yang, Xiaoming Mai, Yuya Ieiri, Osamu Yoshie

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Classification algorithm for imbalance data of ECG based on PSOFS and TSK fuzzy system by Xinhui LI, Qing SHEN, Xiongtao ZHANG

“…A new classification model of electrocardiogram (ECG) signal based on particle swarm optimization feature selection (PSOFS) and TSK (Takagi-Sugeno-Kang) fuzzy system was proposed, i.e., parallel ensemble fuzzy neural network based on PSOFS and TSK (PE-PT-FN), which was used for ECG prediction.Each class sample in the training set was randomly sampled, and the samples obtained by randomly sampled were added.Then, the feature selection method PSOFS was carried out independently and parallelly.In PSOFS, particles that were random initial positions represent different feature subsets and converge to the optimal positions after many iterations.Each subset had a corresponding feature subset.Several groups of TSK fuzzy neural network (TSK-FNN) were trained by each feature subset in parallel.Medical researchers could effectively find the correlation between ECG signal data and different types of disease through the interpretability of the fuzzy system and the feature subsets by the PSOFS algorithm.Experiments prove that PE-PT-FN greatly improves the macro-R to 92.35% while retaining interpretability.…”
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Honeycomb lung segmentation network based on P2T with CNN two-branch parallelism by Zhichao Li, Gang Li, Ling Zhang, Guijuan Cheng, Shan Wu

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A Novel Fault Diagnosis Model for Bearing of Railway Vehicles Using Vibration Signals Based on Symmetric Alpha-Stable Distribution Feature Extraction by Yongjian Li, Weihua Zhang, Qing Xiong, Tianwei Lu, Guiming Mei

“…Condition monitoring is of great benefit to ensure the healthy status of bearings in the railway train. In this paper, a novel fault diagnosis model for axle box bearing based on symmetric alpha-stable distribution feature extraction and least squares support vector machines (LS-SVM) using vibration signals is proposed which is conducted in three main steps. …”
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Clinical, radiological, and radiomics feature-based explainable machine learning models for prediction of neurological deterioration and 90-day outcomes in mild intracerebral hemor... by Weixiong Zeng, Jiaying Chen, Linling Shen, Genghong Xia, Jiahui Xie, Shuqiong Zheng, Zilong He, Limei Deng, Yaya Guo, Jingjing Yang, Yijun Lv, Genggeng Qin, Weiguo Chen, Jia Yin, Qiheng Wu

“…After exclusions, 148 patients were included in the ND study and 144 patients in the 90-day prognosis study. We trained five ML models using filtered data, including clinical, traditional imaging, and radiomics indicators based on non-contrast computed tomography (NCCT). …”
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Machine Learning Enabled Prediction of Biologically Relevant Gene Expression Using CT‐Based Radiomic Features in Non‐Small Cell Lung Cancer by Shrey S. Sukhadia, Christoph Sadée, Olivier Gevaert, Shivashankar H. Nagaraj

“…Combining the data from two cohorts post binarization (of gene expression) or batch normalization (of radiomic features) in each cohort proved to be a better approach as compared to training the model on one cohort and validating on the other. …”
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Preoperative prediction of lymph node metastasis in patients with ovarian cancer using contrast-enhanced computed tomography-based intratumoral and peritumoral radiomics features by Jing Zhang, Qiyuan Li, Haoyu Liang, Yao Wang, Li Sun, Qingyuan Zhang, Chuanping Gao

“…The DCA results showed that the combined radiomics signature had better clinical application than the clinical model and the radiomics nomogram.ConclusionsA CT-based combined radiomics signature incorporating intratumoral and peritumoral radiomics features can predict LNM in patients with OC before surgery.…”
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Interpretable machine learning models for predicting skip metastasis in cN0 papillary thyroid cancer based on clinicopathological and elastography radiomics features by Xiaohua Yao, Mingming Tang, Min Lu, Jie Zhou, Debin Yang

“…Our study aims to develop and validate a machine learning (ML) model that combines elastography radiomics with clinicopathological data to predict pre-surgical SLNM risk in cN0 PTC patients with increased risk of lymph node metastasis (LNM), improving their treatment strategies.MethodsOur study conducted a retrospective analysis of 485 newly diagnosed primary PTC patients, divided into training and external validation cohorts. Patients were categorized into SLNM and non-SLNM groups based on follow-up outcomes and postoperative pathology. …”
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Probabilistic Forecasting of Offshore Wind Power Based on Dual-stage Attentional LSTM and Joint Quantile Loss Function by Xiangjing SU, Haibo YU, Yang FU, Shuxin TIAN, Haiyu LI, Fuhai GENG

“…Secondly, during model training, the multi-task joint quantile loss based on task uncertainty is used to improve the final prediction results by dynamically adjusting the proportion of each loss weight. …”
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Research on Multi-Step Prediction of Pipeline Corrosion Rate Based on Adaptive MTGNN Spatio-Temporal Correlation Analysis by Mingyang Sun, Shiwei Qin

“…In order to comprehensively investigate the spatio-temporal dynamics of corrosion evolution under complex pipeline environments and improve the corrosion rate prediction accuracy, a novel framework for corrosion rate prediction based on adaptive multivariate time series graph neural network (MTGNN) multi-feature spatio-temporal correlation analysis is proposed. …”
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MED-AGNeT: An attention-guided network of customized augmentation of samples based on conditional diffusion for textile defect detection by Jun Liu, Haolin Li, Hao Liu, Jiuzhen Liang

“…Therefore, this paper proposes a textile defect detection method called MED-AGNet. Firstly, based on the diffusion model, a mask-embedding data augmentation method, MEDiffusion, is proposed. …”
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Semi-supervised multi-task learning based framework for power system security assessment by Muhy Eddin Za’ter, Amir Sajadi, Bri-Mathias Hodge

“…Various experiments on the IEEE 68-bus and 118-bus systems were conducted to validate the proposed method, employing two distinct database generation techniques to generate the required data to train the machine learning algorithm. The results demonstrate that our algorithm outperforms existing state-of-the-art machine learning based techniques for security assessment in terms of accuracy and robustness. …”
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Laser weld spot detection based on YOLO-weld by Jianxin Feng, Jiahao Wang, Xinyu Zhao, Zhiguo Liu, Yuanming Ding

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Online monitoring method for longitudinal displacement of multiple rails based on rotary laser sensing by YAN Rong, WU Jianbo, WANG Jie, JIN Xuexi, DING Yuzhu, KANG Xuan

“…Rail breakage and expansion resulting from changes in longitudinal displacement of the steel rail pose a significant threat to train safety. To address the need for high-precision, non-contact and multi-rail displacement detection, an online monitoring method for multi-rail longitudinal displacement based on rotary laser sensing was proposed in this paper. …”
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Pedestrian trajectory prediction model based on self-supervised spatiotemporal graph network by Shiji Yang, Xuezhong Xiao

“…Thus, a pedestrian trajectory prediction model based on a self - supervised spatiotemporal graph network is proposed. …”
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Leveraging BiLSTM-CRF and adversarial training for sentiment analysis in nature-based digital interventions: Enhancing mental well-being through MOOC platforms by Juanjuan Zang

“…This involves incorporating perturbations in the embedding space, generating adversarial samples at the embedding layer and semantic feature fusion layer, and combining these with the original samples for model training. …”
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Analysis of Gearbox Bearing Fault Diagnosis Method Based on 2D Image Transformation and 2D-RoPE Encoding by Xudong Luo, Minghui Wang, Zhijie Zhang

“…Traditional models face difficulties in handling complex industrial time-series data due to insufficient feature extraction capabilities and poor training stability. …”
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Diagnosis of Custard Apple Disease Based on Adaptive Information Entropy Data Augmentation and Multiscale Region Aggregation Interactive Visual Transformers by Kunpeng Cui, Jianbo Huang, Guowei Dai, Jingchao Fan, Christine Dewi

“…EDA–ViT overcomes these by using a multi-scale weighted feature aggregation and a feature interaction module, enhancing both local and global feature extraction. …”
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Non-invasive prediction of cholesterol levels from photoplethysmogram (PPG)-based features using machine learning techniques: a proof-of-concept study by Erick Javier Argüello-Prada, Angie Vanessa Villota Ojeda, María Yoselin Villota Ojeda

“…This study explores the usefulness of fiducial-based features extracted from the photoplethysmogram (PPG) in estimating blood cholesterol by combining feature selection methods and machine learning techniques. …”
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Comparative analysis of fitness coach training systems by Ilya Krugovykh, Vitaliy Avsiyevich, Zhanna Sabyrbek, Toktassyn Bekbolatov, Sharkul Taubayeva

“…It was determined that in the American fitness coach training system, a distinctive feature is a high diversity of educational programmes, which is supported by the standardisation of qualifications. …”
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