Adaptive weights learning in CNN feature fusion for crime scene investigation image classification by Liu Ying, Zhang Qian Nan, Wang Fu Ping, Chiew Tuan Kiang, Lim Keng Pang, Zhang Heng Chang, Chao Lu, Lu Guo Jun, Ling Nam

“…The combination of features from the convolutional layer and the fully connected layer of a convolutional neural network (CNN) provides an effective way to improve the performance of crime scene investigation (CSI) image classification. …”
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An effective dual encoder network with a feature attention large kernel for building extraction by Shaobo Qiu, Jingchun Zhou, Yuan Liu, Xiangrui Meng

“…However, convolutional networks’ potential in local feature extraction remains underutilized in CNN + Transformer models, limiting performance. …”
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Fish Detection in Fishways for Hydropower Stations Using Bidirectional Cross-Scale Feature Fusion by Junming Wang, Yuanfeng Gong, Wupeng Deng, Enshun Lu, Xinyu Hu, Daode Zhang

“…Finally, the performance of the fish detection model is demonstrated based on the Fish26 dataset, in which the detection accuracy, computational cost, and parameter count are significantly optimized by 1.7%, 23.4%, and 24%, respectively, compared to the state-of-the-art model. …”
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Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition by Pawel Powroznik, Maria Skublewska-Paszkowska, Krzysztof Dziedzic, Marcin Barszcz

“…Thus, this study proposes a new model, the Feature Fusion Graph Consecutive-Attention Network (FFGCAN), in order to enhance performance in the classification of the main tennis strokes: forehand, backhand, volley forehand, and volley backhand. …”
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A Parallel Image Denoising Network Based on Nonparametric Attention and Multiscale Feature Fusion by Jing Mao, Lianming Sun, Jie Chen, Shunyuan Yu

“…Convolutional neural networks have achieved excellent results in image denoising; however, there are still some problems: (1) The majority of single-branch models cannot fully exploit the image features and often suffer from the loss of information. (2) Most of the deep CNNs have inadequate edge feature extraction and saturated performance problems. …”
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Optimizing multimodal scene recognition through relevant feature selection approach for scene classification by Sumathi K, Pramod Kumar S, H R Mahadevaswamy, Ujwala B S

“…We leverage widely used convolutional neural networks (CNN) for feature extraction, followed by relevant feature selection techniques to enhance the performance of the model and increase computational efficiency. …”
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Looming Detection in Complex Dynamic Visual Scenes by Interneuronal Coordination of Motion and Feature Pathways by Bo Gu, Jianfeng Feng, Zhuoyi Song

“…Existing insect‐inspired looming detection models typically rely on either motion‐pathway or feature‐pathway signals, yet both are susceptible to dynamic visual scene interference. …”
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Vision Transformers (ViTs) for Feature Extraction and Classification of AI-Generated Visual Designs by Qing Yun

“…ViT achieves 97% accuracy shows that superior performance validates its ability, using its transformer structure, to analyze and learn about the complex features of images which disclose their origin as compared to HRNet model of 95%. …”
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A Unified Approach to Voice Classification: Leveraging Spectrograms, Mel Spectrograms, and Statistical Features by Muhammad Talha, Huma Ghafoor, Seung Yeob Nam

“…Extensive experiments evaluated the performance of individual and combined inputs. Results demonstrate that the multi-input model, particularly when using spectrograms, Mel spectrograms, and statistical features together, achieves the highest accuracy. …”
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Pattern-Based Feature Extraction for Improved Deep Learning in Financial Time Series Classification by Seyed Ali Hosseini, Francesco Grimaccia, Alessandro Niccolai, Silvia Trimarchi

“…In this paper, the authors introduce a novel feature extraction method based on pattern detection in financial data to enhance the performance of deep learning models for financial time series classification. …”
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Deep Learning Technology for Weld Defects Classification Based on Transfer Learning and Activation Features by Chiraz Ajmi, Juan Zapata, Sabra Elferchichi, Abderrahmen Zaafouri, Kaouther Laabidi

“…The results obtained also in the validation test set are compared to the others offered by DCNN models, which show a best performance in less time. …”
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Research on multi-label recognition of tongue features in stroke patients based on deep learning by Honghua Liu, Peiqin Zhang, Yini Huang, Shanshan Zuo, Lu Li, Chang She, Mailan Liu

“…Then, considering that tongue color, coating color, and coating texture are interrelated in TCM theory and jointly reflect the body’s physiological and pathological state, a label-guided multi-label recognition model for tongue images is designed. This model extracts features from the tongue images of stroke patients, learns the correlations among the features, and performs classification to automatically identify key characteristics such as tongue shape, color, and coating. …”
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Selective Feature Sets Based Fake News Detection for COVID-19 to Manage Infodemic by Manideep Narra, Muhammad Umer, Saima Sadiq, Ala' Abdulmajid Eshmawi, Hanen Karamti, Abdullah Mohamed, Imran Ashraf

“…Therefore, the primary objective of this study is to investigate the impact of features to obtain high performance. For this purpose, this study analyzes the impact of different subset feature selection techniques on the performance of models for fake news detection. …”
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Segurança e produção: um modelo para o planejamento e controle integrado Safety and production: an integrated planning and control model by Tarcisio A. Saurin, Carlos T. Formoso, Lia B. M. Guimarães

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In Situ Active Contour-Based Segmentation and Dimensional Analysis of Part Features in Additive Manufacturing by Tushar Saini, Panos S. Shiakolas

“…The framework employs a composite approach to segment features by combining simple thresholding for external features with the Chan–Vese (C–V) active contour model to identify low-contrast internal features. …”
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Multi-View Stereo Using Perspective-Aware Features and Metadata to Improve Cost Volume by Zongcheng Zuo, Yuanxiang Li, Yu Zhou, Fan Mo

“…Feature matching is pivotal when using multi-view stereo (MVS) to reconstruct dense 3D models from calibrated images. …”
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Advancements in energetic metal-organic frameworks, alkali and alkaline earth metal salts, and transition metal complexes: Predictive models for detonation velocity, heat, and pres... by Mohammad Hossein Keshavarz, Nasser Hassanzadeh, Mohammad Jafari

“…Recent advancements have led to the synthesis of various new metal-containing explosives, particularly energetic metal-organic frameworks (EMOFs), which feature high-energy ligands within well-ordered crystalline structures. …”
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Deep learning radiomics of left atrial appendage features for predicting atrial fibrillation recurrence by Yanping Yin, Sixiang Jia, Jing Zheng, Wei Wang, Ziwen Wang, Jiangbo Lin, Wenting Lin, Chao Feng, Shudong Xia, Weili Ge

“…LAA segmentation was performed using an nnUNet-based model, followed by radiomic feature extraction. …”
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No-Reference Stereoscopic Image Quality Assessment Based on Binocular Statistical Features and Machine Learning by Peng Xu, Man Guo, Lei Chen, Weifeng Hu, Qingshan Chen, Yujun Li

“…After feature extraction, these features of distorted stereoscopic image and its human perceptual score are used to construct a statistical regression model with the machine learning technique. …”
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Cross-domain topic transfer learning method based on multiple balance and feature fusion by Zhenshun Xu, Zhenbiao Wang, Wenhao Zhang, Zengjin Tang

“…In transfer learning, traditional homogeneous transfer learning assumes similar data and feature distributions between the source and target domains, focusing primarily on parameter sharing to enhance model performance. …”
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