Leveraging Topic Features in Prediction of Social Network Community Evolutions by Rahman Nahi Abid, Hassan Naderi

“…Most studies in this field have focused on complex network structural features, overlooking the influence of topics and their features on network complexity and time consumption. …”
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Insights into Galaxy Evolution from Interpretable Sparse Feature Networks by John F. Wu

“…Learning the relationship between pixel-level features and galaxy properties is essential for building a physical understanding of galaxy evolution, but we are still unable to explicate the details of how deep neural networks represent image features. …”
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Community evolution prediction based on feature change patterns in social networks by Jingyi Ding, Guojing Sun, Tiwen Wang, Licheng Jiao, Junzhao Du, Jianshe Wu, Hongfei Wang, Ruohui Cheng

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Sequence and taxonomic feature evaluation facilitated the discovery of alcohol oxidases by Yilei Han, Xuwei Ding, Junjian Tan, Yajuan Sun, Yunjiang Duan, Zheng Liu, Gaowei Zheng, Diannan Lu

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Study of the Faults Network of Kunashir Island (Kuril Islands) in Order to Reconstruct the Features of its Tectonic Evolution by Krikun Nikita Sergeevich, Abdrahmanov Il'nur, Talovina Irina

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Evaluating Packaging Design Relative Feature Importance Using an Artificial Neural Network (ANN) by Juan Gu, Euihark Lee

“…The findings of this study demonstrate the effectiveness of artificial neural network (ANN)-based approaches in evaluating the relative importance of packaging design features. …”
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An Empirical Evaluation of Supervised Learning Methods for Network Malware Identification Based on Feature Selection by C. Manzano, C. Meneses, P. Leger, H. Fukuda

“…In the literature, it is reported that this good performance can depend on a reduced set of network features. This study presents an empirical evaluation of two statistical methods of reduction and selection of features in an Android network traffic dataset using six supervised algorithms: Naïve Bayes, support vector machine, multilayer perceptron neural network, decision tree, random forest, and K-nearest neighbors. …”
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Performance Evaluation of Artificial Neural Network Methods Based on Block Machine Learning Classification by Raya Hamdy, Mohammed Younis

“…Thirty cat photos from the Oxford-IIIT Pet dataset were used for evaluation. Five different Artificial Neural Network (ANN) models, including LM, BGFGS, RP, SCG, and GDX, were trained and assessed for both pixel-based and block-based methods. …”
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Multi-Scale Feature Similarity and Object Detection for Small Printing Defects Detection by Haojie Lou, Yuanlin Zheng, Wenqian Chen, Haiwen Liu

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Evaluating Features and Variations in Deepfake Videos Using the CoAtNet Model by Eman Alattas, John Clark, Arwa Al-Aama, Salma Kammoun Jarraya

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A framework for online social networking features by Mohsen Shafiei Nikabadi, Fatemeh Maleki

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A Multi-Path Feature Extraction and Transformer Feature Enhancement DEM Super-Resolution Reconstruction Network by Mingqiang Guo, Feng Xiong, Ying Huang, Zhizheng Zhang, Jiaming Zhang

“…The network structure has three parts: feature extraction, image reconstruction, and feature enhancement. …”
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DOA Estimation by Feature Extraction Based on Parallel Deep Neural Networks and MRMR Feature Selection Algorithm by Ashwaq Neaman Hassan Al-Tameemi, Mahmood Mohassel Feghhi, Behzad Mozaffari Tazehkand

“…In parallel, the proposed model extracts spatial and temporal features using a convolution neural network (CNN) and long short-term memory (LSTM). …”
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Evaluating feature extraction in ovarian cancer cell line co-cultures using deep neural networks by Osheen Sharma, Greta Gudoityte, Rezan Minozada, Olli P. Kallioniemi, Riku Turkki, Lassi Paavolainen, Brinton Seashore-Ludlow

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A multimodal deep learning model with differential evolution-based optimized features for classification of power quality disturbances by Md Nurul Islam

Subjects: “…Multimodal feature fusion…”
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Feature learning and generalization in deep networks with orthogonal weights by Hannah Day, Yonatan Kahn, Daniel A Roberts

“…We speculate that this structure preserves finite-width feature learning while reducing overall noise, thus improving both generalization and training speed in deep networks with depth comparable to width. …”
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Embedded feature selection using dual-network architecture by Abderrahim Abbassi, Arved Dörpinghaus, Niklas Römgens, Tanja Grießmann, Raimund Rolfes

“…However, existing methods often face challenges due to the complexity of feature interdependencies, uncertainty regarding the exact number of relevant features, and the need for hyperparameter optimization, which increases methodological complexity.This research proposes a novel dual-network architecture for feature selection that addresses these issues. …”
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Effects of feature selection and normalization on network intrusion detection by Mubarak Albarka Umar, Zhanfang Chen, Khaled Shuaib, Yan Liu

“…Furthermore, while feature selection benefits simpler algorithms (such as RF), normalization is more useful for complex algorithms like ANNs and deep neural networks (DNNs), and algorithms such as Naive Bayes are unsuitable for IDS modeling. …”
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Adaptive feature interaction enhancement network for text classification by Rui Su, Shangbing Gao, Kefan Zhao, Junqiang Zhang

“…To address this issue, we propose an Adaptive Feature Interactive Enhancement Network (AFIENet). …”
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Application of deconvolutional networks for feature interpretability in epilepsy detection by Sihao Shao, Yu Zhou, Ruiheng Wu, Aiping Yang, Qiang Li

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