Change in Fractional Vegetation Cover and Its Prediction during the Growing Season Based on Machine Learning in Southwest China by Xiehui Li, Yuting Liu, Lei Wang

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Anomaly Detection for Suspension Systems Based on the Gaussian Distribution of Hyperspheres by Ping WANG, Zi MEI, Zhiqiang LONG

“…Although an empirical threshold based on the suspension gap can be obtained according to the "Technical Conditions for the Suspension Control System of Middle-low Speed Maglev Trains CJ/T458—2014", it is affected by the non-unique rated suspension gap and external disturbances, which will cause false negatives in engineering applications. …”
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Deep Learning-Enhanced Spectrogram Analysis for Anatomical Region Classification in Biomedical Signals by Abdul Karim, Semin Ryu, In cheol Jeong

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Winograd Transform-Based Fast Detection of Heart Disease Using ECG Signals and Chest X-Ray Images by Rishabh Anand, Adyasha Rath, Prabodh Kumar Sahoo, Prince Jain, Ganapati Panda, Xinhong Wang, Haipeng Liu

“…This study investigates a fast, DFT-based, one-dimensional Winograd Transform (WT) to extract convolution-based features from 1-D ECG signals. …”
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Prediction model for assessing HER2 status patient with invasive ductal carcinoma based on clinical parameters and ultrasound features: a dual-center study by Lei Zhou, Yingnan Wu, Xin Wen, Xu Guo, Lei Zhang, Tianzhuo Zhao, Weijian Song, Yue Xin, Zehui Su, Litao Sun, Jiawei Tian

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Construction of a nomogram prediction model for the pathological complete response after neoadjuvant chemotherapy in breast cancer: a study based on ultrasound and clinicopathologi... by Pingjuan Ni, Yuan Li, Yu Wang, Xiuliang Wei, Wenhui Liu, Mei Wu, Lulu Zhang, Feixue Zhang

“…The ultrasound and clinicopathological features of the training set were compared, and a nomogram prediction model was constructed based on these features. …”
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FF-YOLO: An Improved YOLO11-Based Fatigue Detection Algorithm for Air Traffic Controllers by Shijie Tan, Weijun Pan, Leilei Deng, Qinghai Zuo, Yao Zheng

“…This paper proposes FF-YOLO, an improved YOLO11-based deep learning algorithm, to detect ATCO fatigue states through facial feature analysis. …”
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Prediction of Synchronous Serum CEA Expression Status Based on Baseline MRI Features of Primary Rectal Cancer Lesions Pre-treatment: A Retrospective Study by Baohua Lv, Donghai Li, Jizheng Li, Kai Shang, Ke Wu, Erhu Jin, Xiujuan Li

“…A nomogram was constructed based on the training cohort and validated using the external validation cohort to predict high baseline CEA expression in rectal cancer patients. …”
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Construction of an oligometastatic prediction model for nasopharyngeal carcinoma patients based on pathomics features and dynamic multi-swarm particle swarm optimization support ve... by Yunfei Li, Dongni Zhang, Yiren Wang, Yiren Wang, Yiheng Hu, Zhongjian Wen, Zhongjian Wen, Cheng Yang, Ping Zhou, Wen-Hui Cheng

“…Model training and hyperparameter tuning were conducted on the training set (n=369), followed by evaluation on a validation set (n=93).Results6 pathomics features were screened as important features. …”
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Prediction of HER2 expression in breast cancer patients based on multi-parametric MRI intratumoral and peritumoral radiomics features combined with clinical and imaging indicators by Xiaoxiao Li, Xiaoxiao Li, Junfang Fang, Fuqian Wang, Lin Zhang, Xingyue Jiang, Xijin Mao

“…The AUC of the combined clinical-radiomics model in the training set, testing set and external validation set was 0.923, 0.915 and 0.837, respectively, which was higher than the intratumoral and peritumoral radiomics model based on DCE+T2FS+ADC sequences (0.854,0.748 and 0.770) and clinical imaging model (0.820,0.789 and 0.709).ConclusionsThe combined model based on DCE+T2FS+ADC intratumoral and peritumoral radiomics integrating with clinical imaging features can better predict the HER2 expression status of breast cancer.…”
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Prediction of cervical cancer lymph node metastasis based on multisequence magnetic resonance imaging radiomics and deep learning features: a dual-center study by Shigang Luo, Yan Guo, Yongqing Ye, Qinglin Mu, Wenguang Huang, Guangcai Tang

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Establishment of a predictive nomogram for breast cancer lympho-vascular invasion based on radiomics obtained from digital breast tomography and clinical imaging features by Gang Liang, Suxin Zhang, Yiquan Zheng, Wenqing Chen, Yuan Liang, Yumeng Dong, Lizhen Li, Jianding Li, Caixian Yang, Zengyu Jiang, Sheng He

“…Abstract Background To develop a predictive nomogram for breast cancer lympho-vascular invasion (LVI), based on digital breast tomography (DBT) data obtained from intra- and peri-tumoral regions. …”
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CT-based machine learning model integrating intra- and peri-tumoral radiomics features for predicting occult lymph node metastasis in peripheral lung cancer by Xiaoyan Lu, Fan Liu, Jiahui E, Xiaoting Cai, Jingyi Yang, Xueqi Wang, Yuwei Zhang, Bingsheng Sun, Ying Liu

“…The aim of this study was to develop and validate a CT-based machine learning model integrating intra-and peri-tumoral features to predict OLNM in lung cancer patients. …”
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Computed tomography-based radiomic features combined with clinical parameters for predicting post-infectious bronchiolitis obliterans in children with adenovirus pneumonia: a retro... by Li Zhang, Ling He, Guangli Zhang, Xiaoyin Tian, Haoru Wang, Fang Wang, Xin Chen, Yinglan Zheng, Man Li, Yang Li, Zhengxiu Luo

“…Multiple statistical methods were used to determine the best radiomic features. Combined models based on radiomic and clinical features were established via logistic regression (LR), random forest (RF), and support vector machine (SVM) algorithms. …”
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Predicting the Event Types in the Human Brain: A Modeling Study Based on Embedding Vectors and Large-Scale Situation Type Datasets in Mandarin Chinese by Xiaorui Ma, Hongchao Liu

“…The resulting dendrogram indicates that verbs can be categorized into three event types—state, activity and transition—based on semantic distance. Two approaches are employed to construct vector matrices: a supervised method that derives word vectors based on linguistic features, and an unsupervised method that uses four models to extract embedding vectors, including Word2Vec, FastText, BERT and ChatGPT. …”
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Predictive model integrating deep learning and clinical features based on ultrasound imaging data for surgical intervention in intussusception in children younger than 8 months by Wan-liang Guo, Yu-feng Qian, Jin-jin Zhou, San-li Shi

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Evaluating the value of machine learning models for predicting hematoma expansion in acute spontaneous intracerebral hemorrhage based on CT imaging features of hematomas and surrou... by Tianyu Yang, Tianyu Yang, Zhen Zhao, Yan Gu, Shengkai Yang, Yonggang Zhang, Lei Li, Ting Wang, Zhongchang Miao

“…This optimized model achieved an AUC of 0.9524, with a sensitivity of 0.9412 and specificity of 0.9091, surpassing both the comprehensive and simplified models.ConclusionThe optimized model, based on CT imaging features of hematomas and surrounding oedema, offers a practical and reliable tool for predicting hematoma expansion in sICH. …”
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Application of quantitative histomorphometric features in computational pathology by Yujie Shi, Bo Hu, Mingyan Xu, Yunhan Yao, Shuaiqiang Gao, Xiang Xia, Xikai Deng, Jianfeng Liu, Jia Gu, Shifu Chen

“…This review contrasts the performance differences between the two methods and traces the development of QH feature representation. The conclusion is that, with the ongoing progress in QH feature representation and segmentation technology, methods based on QH features will leverage their advantages—such as explainability, reduced reliance on large training datasets, and lower computational resource requirements—to play a more significant role in some clinical tasks. …”
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Research on Partial Discharge Spectrum Recognition Technology Used in Power Cables Based on Convolutional Neural Networks by Zhenqing Zhang, Hao Wu, Weiyin Ren, Jian Yan, Zhefu Sun, Man Ding

“…Firstly, a database of typical partial discharge spectrum was established, including partial amplifiers in the laboratory and at the work site, and then the convolutional neural network was used to train the defect spectral library. This paper proposes a processing technology for the on-site partial discharge spectrum; the unified grayscale image is obtained by grayscale processing, linearized stretching and size unification, and then the shape and color feature parameters are extracted according to the grayscale image, which solves the image distortion and statistical spectrum movement caused by the on-site environment or photographic angle on the user side. …”
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RaDiT: A Differential Transformer-Based Hybrid Deep Learning Model for Radar Echo Extrapolation by Wenda Zhu, Zhenyu Lu, Yuan Zhang, Ziqi Zhao, Bingjian Lu, Ruiyi Li

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