Fusion of MHSA and Boruta for key feature selection in power system transient angle stability by WANG Man, ZHOU Xiaoyu, CHEN Fan, LAI Yening, ZHU Ying

“…Moreover, the key features identified exhibit higher evaluation accuracy than traditional methods.…”
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Feature fusion with attributed deepwalk for protein–protein interaction prediction by Mei-Yuan Cao, Suhaila Zainudin, Kauthar Mohd Daud

“…This study proposes FFADW (Feature Fusion Method with Attributed DeepWalk), a novel approach that integrates sequence and network features using a weighted fusion strategy controlled by an adjustable α parameter. …”
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Neutrosophic Logic for Secure Hand-Based Biometrics: Quantifying Privacy-Security Tradeoffs in Remote Authentication Systems by A. A. Salama, Abdelnasser Mohamed, Huda E. Khalid, Ahmed K. Essa, Doaa E. Mossa

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Innovative approaches to English pronunciation instruction in ESL contexts: integration of multi-sensor detection and advanced algorithmic feedback by Li Ping, Ning Tao

“…The approach employs multi-sensor detection methods for precise data collection, preprocessing techniques such as pre-emphasis, normalization, framing, windowing, and endpoint detection to ensure high-quality speech signals. Feature extraction focuses on key attributes of pronunciation, which are then fused through a feedback neural network for comprehensive evaluation. …”
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Groundnut (ARACHIS HYPOGAEA L.) seed defect classification using ensemble deep learning techniques by Gebeyehu Belay Gebremeskel, Dinkie Gashaye Mengistie

“…The image dataset is augmented and balanced using a Generative Adversarial Network (GAN). The model development involves a combination of classical and deep-based features, comparing features extracted with (HOG and GLCM) to those extracted with InceptionV3 and VGG16. …”
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Chronic lymphocytic leukemia (CLL) screening and abnormality detection based on multi-layer fluorescence imaging signal enhancement and compensation by Lemin Shi, Ping Gong, Mingye Li, Dianxin Song, Hao Zhang, Zhe Wang, Xin Feng

“…Methods An automated workflow was developed, integrating a dynamic signal enhancement method that optimizes global and local features. An improved Cycle-GAN network was introduced, incorporating residual connections and layer-wise supervision to accurately model and compensate for complex signal characteristics. …”
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Intelligent segmentation of Chinese address elements combining textual and spatial semantic features by Xuefeng Yan, An Luo, Jiping Liu, Yong Wang, Ya Zhang

“…By combining textual and spatial semantic features, the proposed approach uses a bidirectional gated recurrent unit (BiGRU) neural network to automatically segment Chinese address elements. …”
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Evaluating Grayware Characteristics and Risks by Zhongqiang Chen, Zhanyan Liang, Yuan Zhang, Zhongrong Chen

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Feature-Based Federated Transfer Learning: Communication Efficiency, Robustness and Privacy by Feng Wang, M. Cenk Gursoy, Senem Velipasalar

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Research on new energy station network security assessment method based on improved LSTM network by LIU Shan, LI Rui, WANG Yao

“…Finally, important features were input into the long short-term memory network, and attention mechanisms were used to adaptively allocate data time and features, strengthening the emphasis on important time and features in network traffic, thereby improving the accuracy of the model for network security assessment. …”
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A salient feature establishment tactic for cassava disease recognition by Jiayu Zhang, Baohua Zhang, Zixuan Chen, Innocent Nyalala, Kunjie Chen, Junfeng Gao

“…Accurate classification of cassava disease, particularly in field scenarios, relies on object semantic localization to identify and precisely locate specific objects within an image based on their semantic meaning, thereby enabling targeted classification while suppressing irrelevant noise and focusing on key semantic features. The advancement of deep convolutional neural networks (CNNs) paved the way for identifying cassava diseases by leveraging salient semantic features and promising high returns. …”
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Lightweight pose estimation spatial-temporal enhanced graph convolutional model for miner behavior recognition by WANG Jianfang, DUAN Siyuan, PAN Hongguang, JING Ningbo

“…Lite-HRNet performed human detection using a target detector, extracted image features through a convolutional neural network (CNN), and generated anchor boxes via a region proposal network (RPN). …”
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U-Net-Based Deep Learning Hybrid Model: Research and Evaluation for Precise Prediction of Spinal Bone Density on Abdominal Radiographs by Lixiao Zhou, Thongphi Nguyen, Sunghoon Choi, Jonghun Yoon

“…The U-Net model is employed for image preprocessing to reduce background noise and enhance bone tissue features, followed by analysis with the artificial neural network model to predict bone mineral density through nonlinear regression. …”
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DDA-MSLD: A Multi-Feature Speech Lie Detection Algorithm Based on a Dual-Stream Deep Architecture by Pengfei Guo, Shucheng Huang, Mingxing Li

“…It can perform in-depth sequence pattern analysis on manually extracted static prosodic features and nonlinear dynamic features, obtaining high-order dynamic features related to lies. …”
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Systematic Approach for Malware Detection in IoT Devices: Enhancing Security and Performance by Vasudeva Pai, B. H. Karthik Pai, G. S. Sudhiksha, Vandya Kamath, K. Varsha, S. Manjunatha

“…Using the IoT23 dataset, which contains a wide range of network traffic patterns from various IoT devices and malware families, the research explores and evaluates multiple machine learning techniques. …”
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A Novel Lightweight Framework for Non-Contact Broiler Face Identification in Intensive Farming by Bin Gao, Yongmin Guo, Pengshen Zheng, Kaisi Yang, Changxi Chen

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A Brain Network Analysis-Based Double Way Deep Neural Network for Emotion Recognition by Weixin Niu, Chao Ma, Xinlin Sun, Mengyu Li, Zhongke Gao

“…In the second way of the model, we feed the emotional EEG signals directly into another deep neural network block to extract temporal features. At the end of the two ways, the features are concatenated for classification. …”
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Improved Hierarchical Convolutional Features for Robust Visual Object Tracking by Jinping Sun

“…First, the objective function is designed by lasso regression modeling, and a sparse, time-series low-rank filter is learned to increase the interpretability of the model. Second, the features of the last layer and the second pool layer of the convolutional neural network are extracted to realize the target position prediction from coarse to fine. …”
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Spectrogram Features-Based Automatic Speaker Identification For Smart Services by Rashid Jahangir, Mohammed Alreshoodi, Fawaz Khaled Alarfaj

“…This study investigates ASI based on features derived from spectrogram images through a convolution neural network (CNN) with rectangular-shaped kernels. …”
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Deception detection based on micro-expression and feature selection methods by Shusen Yuan, Zilong Shao, Zhongjun Ma, Ting Cao, Hongbo Xing, Yong Liu, Yewen Cao

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