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  1. 61

    Feature Graph Construction With Static Features for Malware Detection by Binghui Zou, Chunjie Cao, Longjuan Wang, Yinan Cheng, Chenxi Dang, Ying Liu, Jingzhang Sun

    Published 2025-01-01
    “…Malware can greatly compromise the integrity and trustworthiness of information and is in a constant state of evolution. Existing feature fusion-based detection methods generally overlook the correlation between features. …”
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    Article
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    Simulation-Based Electrothermal Feature Extraction and FCN–GBM Hybrid Model for Lithium-ion Battery Temperature Prediction by Luyan WANG, Hongliang HAO, Zhongkang ZHOU, Huimin MA, Jin ZHAO, Zeyang LIU, Qiangqiang LIAO

    Published 2025-08-01
    “…This study proposes a hybrid temperature prediction model that integrates Fully Connected Networks (FCN) and Gradient Boosting Machines (GBM) to capture temperature evolution under varying discharge rates. …”
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  4. 64

    AI-based tool wear prediction with feature selection from sound signal analysis by Viet Q. Vu, Tien-Ninh Bui, Minh-Quang Tran

    Published 2025-08-01
    “…Finally, an artificial neural network (ANN) model is designed to estimate tool wear levels. …”
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    Article
  5. 65

    Financial Evolution and Interdisciplinary Research by Ana Njegovanović

    Published 2023-03-01
    “…This paper (summary of the second chapter of the manuscript "quantum dance") talks about the multidimensionality of finance through evolution, philosophy with interdisciplinary features (interweaving of neuroscience, mathematics, quantum physics, biology and artificial intelligence). …”
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  6. 66

    Spatiotemporal pattern evolution and quantitative prediction of electrical carbon emissions from a demand-side perspective in urban areas by Ying Tian, Hui Cao, Dapeng Yan, Jinmei Chen, Yayan Hua

    Published 2025-07-01
    “…Utilizing high-frequency monitoring data from  3000 distribution network stations (May–Sept 2018), it creates an integrated ’spatiotemporal evolution-data driven prediction’ framework to reveal emission dynamics and enhance forecast accuracy. …”
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    Hybrid Big Bang-Big crunch with cuckoo search for feature selection in credit card fraud detection by Mohd Shukri Ab Yajid, Nilesh Bhosle, Gadug Sudhamsu, Ali Khatibi, Sahil Sharma, Rubal Jeet, R. Sivaranjani, A. Bhowmik, A. Johnson Santhosh

    Published 2025-07-01
    “…Here, the CS algorithm uses the Levy flight attribute to help the BB-BC agents escape from stagnation and premature convergence. After feature selection, classification is performed using Deep Convolutional Neural Networks (DCNN) and Enhanced DCNN (EDCNN) to improve detection accuracy. …”
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    Multiscale Attention Feature Fusion Based on Improved Transformer for Hyperspectral Image and LiDAR Data Classification by Aili Wang, Guilong Lei, Shiyu Dai, Haibin Wu, Yuji Iwahori

    Published 2025-01-01
    “…However, the complexity of urban areas and their surrounding structures makes it extremely difficult to capture correlations between features. This article proposes a novel multiscale attention feature fusion network, composed of hierarchical convolutional neural networks and transformer to enhance joint classification accuracy of hyperspectral image (HSI) and light detection and ranging (LiDAR) data. …”
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  12. 72

    Deriving structure from evolution: metazoan segmentation by Paul François, Vincent Hakim, Eric D Siggia

    Published 2007-12-01
    “…Abstract Segmentation is a common feature of disparate clades of metazoans, and its evolution is a central problem of evolutionary developmental biology. …”
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  13. 73

    DeepAptamer: Advancing high-affinity aptamer discovery with a hybrid deep learning model by Xin Yang, Chi Ho Chan, Shanshan Yao, Hang Yin Chu, Minchuan Lyu, Ziqi Chen, Huan Xiao, Yuan Ma, Sifan Yu, Fangfei Li, Jin Liu, Luyao Wang, Zongkang Zhang, Bao-Ting Zhang, Lu Zhang, Aiping Lu, Yaofeng Wang, Ge Zhang, Yuanyuan Yu

    Published 2025-03-01
    “…To address these challenges, we proposed DeepAptamer for identifying high-affinity sequences from unenriched early SELEX rounds. As a hybrid neural network model combining convolutional neural networks and bidirectional long short-term memory, DeepAptamer integrated sequence composition and structural features to predict aptamer binding affinities and potential binding motifs. …”
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    Article
  14. 74

    Flow Field Reconstruction and Prediction of Powder Fuel Transport Based on Scattering Images and Deep Learning by Hongyuan Du, Zhen Cao, Yingjie Song, Jiangbo Peng, Chaobo Yang, Xin Yu

    Published 2025-07-01
    “…Based on the acquired scattering images, a prediction and reconstruction method was developed using a deep network framework composed of a Stacked Autoencoder (SAE), a Backpropagation Neural Network (BP), and a Long Short-Term Memory (LSTM) model. …”
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  15. 75

    Modeling Upscaled Mass Discharge From Complex DNAPL Source Zones Using a Bayesian Neural Network: Prediction Accuracy, Uncertainty Quantification and Source Zone Feature Importance by Xueyuan Kang, Amalia Kokkinaki, Xiaoqing Shi, Jonghyun Lee, Zhilin Guo, Lingling Ni, Jichun Wu

    Published 2024-07-01
    “…Instead, the BNN model chooses three physically meaningful SZ quantities related to mass discharge as input features. Then, we use the expected gradients method to identify the feature importance for mass‐discharge prediction. …”
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    Artificial Intelligence-Powered Insights into Polyclonality and Tumor Evolution by Hong Zhao, Trey Ideker, Stephen T. C. Wong

    Published 2025-01-01
    “…Recent studies have revealed that polyclonality—where multiple distinct subclones cooperate during early tumor development—is a critical feature of tumor evolution, as demonstrated by Sadien et al. and Lu et al. in Nature (October 2024). …”
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