EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation by Suwicha Jirayucharoensak, Setha Pan-Ngum, Pasin Israsena

“…To alleviate overfitting problem, principal component analysis (PCA) is applied to extract the most important components of initial input features. …”
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Water‐to‐Air Imaging: A Recovery Method for the Instantaneous Distorted Image Based on Structured Light and Local Approximate Registration by Bijian Jian, Ting Peng, Xuebo Zhang, Changyong Lin

“…Then, the displacement information of the feature points on the distorted structured light image and the standard structured light image is obtained through the feature extraction algorithm and is used to estimate the distortion vector field of the corresponding sampling points in the distorted scene image. …”
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PNN: A novel progressive neural network for fault classification in rotating machinery under small dataset constraint by Praveen Chopra, Himanshu Kumar, Sandeep Yadav

“…The PNN sequentially estimates the fixed-size refined features of the higher order with the help of all previously estimated features and appends them to the feature set. …”
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A Novel Hybrid Methodology Based on Transfer Learning, Machine Learning, and ReliefF for Chickpea Seed Variety Classification by İbrahim Kılıç, Nesibe Yalçın

“…In this study, a novel hybrid methodology is proposed for the chickpea classification problem. This methodology combines three well-suited and robust components: feature extraction using three pre-trained models, feature selection with the ReliefF algorithm, and classification employing classical machine learning methods to enhance classification accuracy and efficiency. …”
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Research on scene matching positioning technology for large-scale indoor scenes by Chenchen Zhou, Yujin Kuang, Tongfei Hu, Xiaoguo Zhang

“…Firstly, a prior database via the Oriented FAST and Rotated BRIEF feature extraction algorithm, employing a bag-of-words model for efficient feature matching, is proposed. …”
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Research on bearing fault diagnosis based on improved northern goshawk algorithm optimizing SVM by WU Xiaojun, LI Quwei

“…The energy of each frequency band in the 3rd layer was used as the feature vector and input into the fault diagnosis model. …”
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MVHGCN: Predicting circRNA-disease associations with multi-view heterogeneous graph convolutional neural networks. by Yan Miao, Xuan Tang, Chunyu Wang, Zhenyuan Sun, Guohua Wang, Shan Huang

“…MVHGCN first constructs a heterogeneous graph and generates feature descriptors by integrating multiple databases. …”
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Author name disambiguation based on heterogeneous graph neural network. by Ge Wang, Zikai Sun, Weiyang Hu, MengHuan Cai

“…The current mainstream methods for author disambiguation are mainly divided into two methods: feature-based clustering and connection-based clustering, but none of the current mainstream methods can efficiently deal with the author name disambiguation problem, For this reason, this paper proposes the author name ablation method based on the relational graph heterogeneous attention neural network, first extract the semantic and relational information of the paper, use the constructed graph convolutional embedding module to train the splicing to get a better feature representation, and input the constructed network to get the vector representation. …”
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CATransU-Net: Cross-attention TransU-Net for field rice pest detection. by Xuwei Lu, Yunlong Zhang, Congqi Zhang

“…Accurate detection of rice pests in field is a key problem in field pest control. U-Net can effectively extract local image features, and Transformer is good at dealing with long-distance dependencies. …”
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Unsupervised SAR Fine-Grained Ship Classification via Spherical Metric Refinement With Deep Subdomain Adaptation by Zhichao Han, Haitao Lang

“…Unsupervised domain adaptation (UDA) is a promising method for addressing the problem of SAR fine-grained ship classification in target domain with no labeled data available by leveraging a large number of labeled samples from source domains. …”
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Anomaly Detection for Suspension Systems Based on the Gaussian Distribution of Hyperspheres by Ping WANG, Zi MEI, Zhiqiang LONG

“…Firstly, FWHT(Fast Walsh-Hadamard Transform) technology was used to extract sample features, and the feature distribution space was made approxim ate to a hypersphere. …”
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An efficient fusion detector for road defect detection by Li Yang, Jingwei Deng, Hailong Duan, Chenchen Yang

“…To address this problem, an SCB-AF-Detector is proposed, which combines space-to-depth convolution with bottleneck transformer and employs enhanced asymptotic feature pyramid network to fuse features. …”
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Cross-dataset person re-identification method based on multi-pool fusion and background elimination network by Yanfeng LI, Bin ZHANG, Jia SUN, Houjin CHEN, Jinlei ZHU

“…The existing cross-dataset person re-identification methods were generally aimed at reducing the difference of data distribution between two datasets,which ignored the influence of background information on recognition performance.In order to solve this problem,a cross-dataset person re-ID method based on multi-pool fusion and background elimination network was proposed.To describe both global and local features and implement multiple fine-grained representations,a multi-pool fusion network was constructed.To supervise the network to extract useful foreground features,a feature-level supervised background elimination network was constructed.The final network loss function was defined as a multi-task loss,which combined both person classification loss and feature activation loss.Three person re-ID benchmarks were employed to evaluate the proposed method.Using MSMT17 as the training set,the cross-dataset mAP for Market-1501 was 35.53%,which was 9.24% higher than ResNet50.Using MSMT17 as the training set,the cross-dataset mAP for DukeMTMC-reID was 41.45%,which was 10.72% higher than ResNet50.Compared with existing methods,the proposed method shows better cross-dataset person re-ID performance.…”
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Evaluating the Predictive Power of Software Metrics for Fault Localization by Issar Arab, Kenneth Magel, Mohammed Akour

“…This study investigates the predictive power of various software metrics for fault localization by framing the task as a multi-class classification problem and evaluating it using the Defects4J dataset. …”
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IMP‐DETR: Optimization model for defect detection of injection‐moulded products by Anzhan Liu, Lei Han

“…Additionally, the Conv3XC‐Fusion module is designed to resolve the problem of integrating multi‐scale features, improving the stability of detection. …”
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Research on the role of multi-sensor system information fusion in improving hardware control accuracy of intelligent system by Li Xin, Li Yuesong

“…In the process of multi-sensor information fusion, there is not only the problem of information redundancy but also obvious time delay. …”
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Fault diagnosis method of telecom cloud platform based on deep CNN model by Qingpu Hu, Jian Hu

“…In response to the problem of fault location caused by massive alarm logs in the complex architecture of telecom cloud platforms, this study proposes a time-frequency image recognition model (WCNN) based on depthwise separable small convolution kernels, which replaces traditional pooling layers to achieve efficient feature extraction. …”
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DEVELOPMENT STRATEGY ELITE AND ELITE EDUCATION by N. L. Podvoiskaya

“…The review concluded that the given monograph is a quite successful attempt to contribute to solving the problems of modern elitology education at a theoretical level. …”
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Anomaly-Based Intrusion Detection System in Wireless Sensor Networks Using Machine Learning Algorithms by Belal Al-Fuhaidi, Zainab Farae, Farouk Al-Fahaidy, Gawed Nagi, Abdullatif Ghallab, Abdu Alameri

“…This model applied mutual information (MI) for feature selection and the synthetic minority oversampling technique (SMOTE) for solving the imbalanced dataset problem. …”
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Business Valuation with Machine learning by P. S. Koklev

“…The study also addresses the problem of the interpretability of the trained models. …”
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