Prediction of EGFR mutation status in non-small cell lung cancer based on CT radiomic features combined with clinical characteristics by YANG Taotao, WANG Xianqi, CHEN Cancan

“…Conclusion‍　Our comprehensive model constructed based on chest CT radiomic features and clinical characteristics shows superior predictive performance for EGFR gene mutations in NSCLC across multiple center datasets, which may be helpful for clinical decision-making for treatment strategies. …”
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Machine Learning‐Based Identification of Children With Intermittent Exotropia Using Multiple Resting‐State Functional Magnetic Resonance Imaging Features by Mengdi Zhou, Huixin Li, Xiaoxia Qu, Lirong Zhang, Xueying He, Xiwen Wang, Jie Hong, Jing Fu, Zhaohui Liu

“…The linear regression (LR) classifier with analysis of variance (ANOVA) feature selection achieved the highest area under the receiver operator characteristic curve values (0.957, 0.804, and 0.818 for the training, validation, and test datasets, respectively) using five features, including the slow‐5 fALFF values of the right inferior parietal gyrus (IPG), right supplementary motor area (SMA), left primary somatosensory complex, right frontal opercula, and left dorsolateral prefrontal cortex (DLPFC), and the accuracy, sensitivity, and specificity values were 0.759, 0.759, and 0.760, respectively. …”
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Integrating ultrasound radiomics and clinicopathological features for machine learning-based survival prediction in patients with nonmetastatic triple-negative breast cancer by Wenwen, Zekun Jiang, Jingyan Liu, Dingbang Liu, Yiyue Li, Yushuang He, Haina Zhao, Lin Ma, Yixin Zhu, Qiongxian Long, Jun Gao, Honghao Luo, Heng Jiang, Kang Li, Xiaorong Zhong, Yulan Peng

“…Abstract Objective This study aimed to evaluate the predictive value of implementing machine learning models based on ultrasound radiomics and clinicopathological features in the survival analysis of triple-negative breast cancer (TNBC) patients. …”
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Towards an intelligent integrated methodology for accurate determination of volume percentages in three-phase flow systems by Abdullah M. Iliyasu, Mohammad Sh. Daoud, Ahmed Sayed Salama, John William Grimaldo Guerrero, Kaoru Hirota

“…Our methodology integrates the time, frequency, and wavelet transform features extracted from X-ray-based measurement systems whose structure consists of an X-ray tube source, two sodium iodide detectors, and a test pipe, all of which were simulated using the Monte Carlo N Particle code. …”
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Individual Identification of Holstein Cows from Top-View RGB and Depth Images Based on Improved PointNet++ and ConvNeXt by Kaixuan Zhao, Jinjin Wang, Yinan Chen, Junrui Sun, Ruihong Zhang

“…To identify cows more accurately and efficiently, a novel individual recognition method based on the using anchor point detection and body pattern features from top-view depth images of cows was proposed. …”
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FEATURES OF DISTANCE LEARNING IN MEDICINE by M.Н. Skikevych, L.І. Voloshyna, K.Р. Lokes, V.М. Havryliev

“…Considering a competency-based approach to professional training, the new role of the higher education instructor in the educational process is determined by us. …”
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Cross-domain autonomous driving visual segmentation based on enhanced target data learning by Chaoyu Rao, Xiaoyong Fang, Yunzhe Zhang, Wanshu Fan, Dongsheng Zhou

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LINGUISTIC AND ACOUSTIC RESOURCES OF THE COMPUTER-BASED SYSTEM FOR ANALYSIS AND INTERPRETATION OF SPEECH INTONATION by Yu. A. Zdaranok

“…This article describes a novel approach to discriminating native and nonnative utterances based on suprasegmental features that constitute the intonation of the syntagma. …”
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Visual Positioning Detection of EMU Brake Pad Based on Deep Learning by GUAN Chunling, XU Yingjie, SONG Yuechao

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Multi-Level Feature Fusion in CNN-Based Human Action Recognition: A Case Study on EfficientNet-B7 by Pitiwat Lueangwitchajaroen, Sitapa Watcharapinchai, Worawit Tepsan, Sorn Sooksatra

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Normalized Intrinsic Deep Features Based Zero-Watermarking Scheme for Remote Sensing Images Using U-Net and K-Means by Jie Zhang, Xu Xi, Jinglong Du, Xin He, Mingkang Wu, Yi Wei

“…During feature extraction, a specially trained U-Net network is employed to extract robust features for generalized recognition across multitype images. …”
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Integrated feature selection-based stacking ensemble model using optimized hyperparameters to predict breast cancer with smart web application by Rajib Kumar Halder, Marzana Akter Lima, Mohammed Nasir Uddin, Md.Aminul Islam, Adri Saha

“…These features are then used to train the model, ensuring that our approach focuses on the most relevant data points for breast cancer classification. …”
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Identifying the risk of Kawasaki disease based solely on routine blood test features through novel construction of machine learning models by Tzu-Hsien Yang, Ying-Hsien Huang, Yuan-Han Lee, Jie-Nan Lai, Kuang-Den Chen, Mindy Ming-Huey Guo, Yan Pan, Chun-Yu Chen, Wei-Sheng Wu, Ho-Chang Kuo

“…To support frontline pediatricians with a more objective diagnostic tool, we developed and implemented KDpredictor, a machine learning-based model for KD risk identification. KDpredictor leverages only the routine blood test features, including complete blood count with differential count, C-reactive protein, and alanine aminotransferase. …”
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Classification and Recognition of Soybean Quality Based on Hyperspectral Imaging and Random Forest Methods by Man Chen, Zhichang Chang, Chengqian Jin, Gong Cheng, Shiguo Wang, Youliang Ni

“…Experimental results showed that the RF classification model based on SPA-BC preprocessed spectra and DE-tuned parameters achieved an optimal prediction accuracy of 1.0000 during training. …”
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Transformer-based multiple instance learning network with 2D positional encoding for histopathology image classification by Bin Yang, Lei Ding, Jianqiang Li, Yong Li, Guangzhi Qu, Jingyi Wang, Qiang Wang, Bo Liu

“…Furthermore, TMIL divides histopathological images into pseudo-bags and trains patch-level feature vectors with deep metric learning to enhance classification performance. …”
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Speech-Based Parkinson’s Detection Using Pre-Trained Self-Supervised Automatic Speech Recognition (ASR) Models and Supervised Contrastive Learning by Hadi Sedigh Malekroodi, Nuwan Madusanka, Byeong-il Lee, Myunggi Yi

“…The experiments, conducted using the NeuroVoz dataset, demonstrated that features extracted from the pre-trained ASR models exhibited superior performance compared to the baseline features. …”
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Fault Diagnosis of Photovoltaic Array Based on Deep Belief Network by Caixia TAO, Xu WANG, Fengyang GAO

“…According to the characteristics of the DBN, the impacts of training sets, training periods and restricted boltzmann machine (RBM) layers on the model performance are analyzed through recognition experiments. …”
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Small object detection in complex open-pit mine backgrounds based on improved YOLOv11 by ZHU Yongjun, CAI Guangqi, HAN Jin, MIAO Yanzi, MA Xiaoping, JIAO Wenhua

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Encrypted traffic classification method based on convolutional neural network by Rongna XIE, Zhuhong MA, Zongyu LI, Ye TIAN

“…Aiming at the problems of low accuracy, weak generality, and easy privacy violation of traditional encrypted network traffic classification methods, an encrypted traffic classification method based on convolutional neural network was proposed, which avoided relying on original traffic data and prevented overfitting of specific byte structure of the application.According to the data packet size and arrival time information of network traffic, a method to convert the original traffic into a two-dimensional picture was designed.Each cell in the histogram represented the number of packets with corresponding size that arrive at the corresponding time interval, avoiding reliance on packet payloads and privacy violations.The LeNet-5 convolutional neural network model was optimized to improve the classification accuracy.The inception module was embedded for multi-dimensional feature extraction and feature fusion.And the 1*1 convolution was used to control the feature dimension of the output.Besides, the average pooling layer and the convolutional layer were used to replace the fully connected layer to increase the calculation speed and avoid overfitting.The sliding window method was used in the object detection task, and each network unidirectional flow was divided into equal-sized blocks, ensuring that the blocks in the training set and the blocks in the test set in a single session do not overlap and expanding the dataset samples.The classification experiment results on the ISCX dataset show that for the application traffic classification task, the average accuracy rate reaches more than 95%.The comparative experimental results show that the traditional classification method has a significant decrease in accuracy or even fails when the types of training set and test set are different.However, the accuracy rate of the proposed method still reaches 89.2%, which proves that the method is universally suitable for encrypted traffic and non-encrypted traffic.All experiments are based on imbalanced datasets, and the experimental results may be further improved if balanced processing is performed.…”
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Improvement of signal detection based on using machine learning by Bassam Abd

“…The deep learning algorithm has become a very attractive tool for distinguishing between signal and noise. Learning and training are the two important steps in designing any deep learning system. …”
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