Showing 541 - 560 results of 2,900 for search '(feature OR features) parameters computational', query time: 0.23s Refine Results
  1. 541

    Heterogeneous appetite patterns in depression: computational modeling of nutritional interoception, reward processing, and decision-making by Yuuki Uchida, Yuuki Uchida, Takatoshi Hikida, Manabu Honda, Yuichi Yamashita

    Published 2024-12-01
    “…Furthermore, effects of interoception manipulation were compared with traditional reinforcement learning parameters (e.g., inverse temperature β and delay discount γ), which represent cognitive-behavioral features of depression. …”
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  2. 542

    Real-Time Object Detection Model for Electric Power Operation Violation Identification by Xiaoliang Qian, Longxiang Luo, Yang Li, Li Zeng, Zhiwu Chen, Wei Wang, Wei Deng

    Published 2025-07-01
    “…To handle the second challenge, an adaptive combination of local and global features module is proposed to enhance the discriminative ability of features while maintaining computational efficiency, where the local and global features are extracted respectively via 1D convolutions and adaptively combined by using learnable weights. …”
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  3. 543

    Research on Lightweight Model of Multi-person Pose Estimation Based on Improved YOLOv8s-Pose by FU Yu, GAO Shuhui

    Published 2025-03-01
    “…Firstly, a lightweight module C2f-GhostNetBottleNeckV2 is introduced into the backbone to replace the original C2f, reducing the number of parameters. This paper also introduces the Non_Local attention mechanism to integrate the position information of human key points in the image into the channel dimension, thereby enhancing the efficiency of feature extraction and mitigating the accuracy degradation issues that often occur after model lightweighting. …”
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  4. 544

    Predicting Wind Turbine Blade Tip Deformation With Long Short‐Term Memory (LSTM) Models by Shubham Baisthakur, Breiffni Fitzgerald

    Published 2025-06-01
    “…ABSTRACT Driven by the challenges in measuring blade deformations, this study presents a novel machine learning methodology to predict blade tip deformation using inflow wind data and operational parameters. Using a long short‐term memory (LSTM) model and a novel feature selection approach based on mutual information and recursive feature addition, this study presents a robust framework for multivariate time series prediction. …”
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  5. 545

    MMEFU-Net: A Mamba-Guided Multi-Encoder Fusion U-Net for Tumor Segmentation in CT Images by Renzheng Xue, Zifeng Zhang, Yaxin Zhao, Qing Zhang, Minghui Liang

    Published 2025-01-01
    “…The model achieves superior Dice Similarity Coefficients (DSC) and Intersection over Union (IoU) scores while significantly reducing computational costs. Notably, MMEFU-Net improves the DSC by 2.16% compared to nnU-Net on the LiTS2017 dataset, with a <inline-formula> <tex-math notation="LaTeX">$35\times $ </tex-math></inline-formula> reduction in parameters and a <inline-formula> <tex-math notation="LaTeX">$25\times $ </tex-math></inline-formula> reduction in computational complexity. …”
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    Advancing Cosmological Parameter Estimation and Hubble Parameter Reconstruction with Long Short-term Memory and Efficient Kolmogorov–Arnold Networks by Jiaxing Cui, Marek Biesiada, Ao Liu, Cuihong Wen, Tonghua Liu, Jieci Wang

    Published 2025-01-01
    “…LSTM networks are employed to extract features from observational data, enabling accurate parameter inference and posterior distribution estimation without relying on solvable likelihood functions. …”
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    Method for calculating the parameters of the rotary-impact mechanism by L. A. Sladkova, D. I. Skripnikov

    Published 2025-02-01
    “…The proposed method for evaluating the resistance features of structural elements allows determining the P value of the non-destruction force to provide a safe impact on the groove base during drilling tool operations.…”
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    A New Efficient Hybrid Technique for Human Action Recognition Using 2D Conv-RBM and LSTM with Optimized Frame Selection by Majid Joudaki, Mehdi Imani, Hamid R. Arabnia

    Published 2025-02-01
    “…Recognizing human actions through video analysis has gained significant attention in applications like surveillance, sports analytics, and human–computer interaction. While deep learning models such as 3D convolutional neural networks (CNNs) and recurrent neural networks (RNNs) deliver promising results, they often struggle with computational inefficiencies and inadequate spatial–temporal feature extraction, hindering scalability to larger datasets or high-resolution videos. …”
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