Deep Learning-Based Seedling Row Detection and Localization Using High-Resolution UAV Imagery for Rice Transplanter Operation Quality Evaluation by Yangfan Luo, Jiuxiang Dai, Shenye Shi, Yuanjun Xu, Wenqi Zou, Haojia Zhang, Xiaonan Yang, Zuoxi Zhao, Yuanhong Li

“…We have introduced convolutional block attention module (CBAM) and attention gate (AG) modules on the basis of the original UNet network, which can merge multiple feature maps or information flows together, helping the model better select key areas or features of seedling rows in the image, thereby improving the understanding of image content and task execution performance. …”
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Integrating particle swarm optimization with backtracking search optimization feature extraction with two-dimensional convolutional neural network and attention-based stacked bidir... by Jyotirmayee Rautaray, Sangram Panigrahi, Ajit Kumar Nayak

“…This study introduces an improvised Particle Swarm Optimization with Backtracking Search Optimization (PSOBSA) designed for feature extraction. For classification purpose, it recommends two-dimensional convolutional neural network (2D CNN) along with an attention-based stacked bidirectional long short-term memory (ABS-BiLSTM) model to generate new summarized sentences by analyzing entire sentences. …”
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Hybrid Approach for WDM Network Restoration: Deep Reinforcement Learning and Graph Neural Networks by Isaac Ampratwum, Amiya Nayak

“…The proposed method leverages the decision-making capabilities of DRL and the graph-structured learning potential of GNN to dynamically adapt to network disruptions. By modeling network topology as a graph, the GNN extracts structural features, while the DRL agent intelligently selects restoration paths, balancing network load and minimizing restoration time. …”
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Deep Complex Gated Recurrent Networks-Based IoT Network Intrusion Detection Systems by Engy El-Shafeiy, Walaa M. Elsayed, Haitham Elwahsh, Maazen Alsabaan, Mohamed I. Ibrahem, Gamal Farouk Elhady

“…Furthermore, DCGR_IoT harnesses complex gated recurrent networks (CGRNs) to construct multidimensional feature subsets, enabling a more detailed spatial representation of network traffic and facilitating the extraction of critical features that are essential for intrusion detection. …”
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Semantic Segmentation of Archaeological Features on Public Lands: Case Study of Historical Cotton Terraces within the Piedmont National Wildlife Refuge, Georgia, USA by Claudine Gravel-Miguel, Grant Snitker, Jayde N. Hirniak, Katherine Peck, Alex Fetterhoff

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Learning Feature Fusion in Deep Learning-Based Object Detector by Ehtesham Hassan, Yasser Khalil, Imtiaz Ahmad

“…Our hypothesis is to reinforce these features with handcrafted features by learning the optimal fusion during network training. …”
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ACFM: Adaptive Channel Feature Matching for Pedestrian Re-Identification by Zhengcai Lu, Zhengwei Tian

“…The multi-branch structure is designed to handle both global and local features, enabling the network to comprehensively capture and integrate image information. …”
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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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Multiple-Feature Construction for Image Segmentation Based on Genetic Programming by David Herrera-Sánchez, José-Antonio Fuentes-Tomás, Héctor-Gabriel Acosta-Mesa, Efrén Mezura-Montes, José-Luis Morales-Reyes

“…Genetic programming is used to automatically create and construct pipelines to extract meaningful features for segmentation tasks. Additionally, a co-evolution strategy is proposed within the evolution process to increase diversity without affecting segmentation performance. …”
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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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