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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pACP-HybDeep: predicting anticancer peptides using binary tree growth based transformer and structural feature encoding with deep-hybrid learning by Shahid, Maqsood Hayat, Wajdi Alghamdi, Shahid Akbar, Ali Raza, Rabiah Abdul Kadir, Mahidur R. Sarker

“…The selected feature vector is subsequently trained using a CNN + RNN-based deep learning model. …”
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Multipath and Deep Learning-Based Detection of Ultra-Low Moving Targets Above the Sea by Zhaolong Wang, Xiaokuan Zhang, Weike Feng, Binfeng Zong, Tong Wang, Cheng Qi, Xixi Chen

“…Finally, the RD features of the generalized target are learned by training the DL-based target detector, such as you-only-look-once version 7 (YOLOv7) and Faster R-CNN. …”
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Accurate detection of critical LLFs and LGFs in PV arrays based on deep reinforcement learning using proximal policy optimization (PPO) by Sherko Salehpour, Aref Eskandari, Amir Nedaei, Mohammad Gholami, Mohammadreza Aghaei

“…Additionally, to carry out the dataset dimensionality reduction, thus simplifying the training process, a two-stage feature engineering process has been implemented, including a feature importance finding stage using the permutation technique and a feature selection stage. …”
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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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Research on Sleep Staging Based on Support Vector Machine and Extreme Gradient Boosting Algorithm by Wang Y, Ye S, Xu Z, Chu Y, Zhang J, Yu W

“…Yiwen Wang,1 Shuming Ye,2 Zhi Xu,3 Yonghua Chu,1 Jiarong Zhang,4 Wenke Yu5 1Clinical Medical Engineering Department, The Second Affiliated Hospital, Zhejiang University School of Medicine, HangZhou, ZheJiang, People’s Republic of China; 2Department of Biomedical Engineering, Zhejiang University, HangZhou, ZheJiang, People’s Republic of China; 3China Astronaut Research and Training Center, BeiJing, People’s Republic of China; 4Baidu Inc, BeiJing, People’s Republic of China; 5Radiology Department, ZheJiang Province Qing Chun Hospital, HangZhou, ZheJiang, People’s Republic of ChinaCorrespondence: Yiwen Wang; Shuming Ye, Email karenkaren2010@zju.edu.cn; ysmln@vip.sina.comPurpose: To develop a sleep-staging algorithm based on support vector machine (SVM) and extreme gradient boosting model (XB Boost) and evaluate its performance.Methods: In this study, data features were extracted based on physiological significance, feature dimension reduction was performed through appropriate methods, and XG Boost classifier and SVM were used for classification. …”
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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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Early detection of disease outbreaks and non-outbreaks using incidence data: A framework using feature-based time series classification and machine learning. by Shan Gao, Amit K Chakraborty, Russell Greiner, Mark A Lewis, Hao Wang

“…To address this gap, we propose a novel framework using a feature-based time series classification (TSC) method to forecast outbreaks and non-outbreaks. …”
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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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Construction and validation of a risk stratification model based on Lung-RADS® v2022 and CT features for predicting the invasive pure ground-glass pulmonary nodules in China by Qingcheng Meng, Tong Liu, Hui Peng, Pengrui Gao, Wenda Chen, Mengjia Fang, Wentao Liu, Hong Ge, Renzhi Zhang, Xuejun Chen

“…Abstract Objectives A novel risk stratification model based on Lung-RADS® v2022 and CT features was constructed and validated for predicting invasive pure ground-glass nodules (pGGNs) in China. …”
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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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Automated System Using HMM for Lung Disease Recognition Based on Cough Sounds by Shing-Tai Pan, Bo-Kai Lee, You-Qian Wu

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Assessment model of ozone pollution based on SHAP-IPSO-CNN and its application by Xiaolei Zhou, Xingyue Wang, Ruifeng Guo

“…Finally, the IPSO algorithm is combined with SHAP analysis to dynamically adjust the training features to optimize the performance of the CNN model. …”
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Abnormal Electricity Consumption Behaviors Detection Based on Improved Deep Auto-Encoder by Nvgui LIN, Lanxiu HONG, Daoshan HUANG, Yang YI, Zhixuan LIU, Qifeng XU

“…In order to accurately detect the abnormal electricity consumption behaviors for reducing the operating costs of power companies, a detection method of abnormal electricity consumption behaviors is proposed based on the improved deep auto-encoder (DAE). Firstly, the data of normal electricity users are employed as training samples, and the effective features of the data are automatically extracted by AE; and then the data is reconstructed to calculate the detection threshold. …”
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A BEARING DEEP LEARNING TRANSFER DIAGNOSIS METHOD BASED ON OPTIMIZATION OF SYMMETRIC POLAR COORDINATES by WU DingHai, WANG HuaiGuang, SONG Bin, ZHANG YunQiang

“…Aiming at the problem of graphical feature representation of one-dimensional mechanical vibration signals, a bearing fault diagnosis method based on symmetric polar coordinates and residual network migration learning is proposed, which combines the powerful image classification and recognition ability of convolution neural network. …”
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TDA SegUNet: Topological Data Analysis-Based Shape-Aware Brain Tumor Segmentation by Ansar Rahman, Ayesha Satti, Ahmad Raza Shahid, Qaisar M. Shafi, Khadija Farooq, Asad Ali Safi

“…TDA-SegUNet is a U-Net-based segmentation model that integrates topological data analysis (TDA) to extract shape-based local and global features from MRI scans. …”
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