Showing 241 - 260 results of 2,900 for search '(feature OR features) parameters (computation OR computational)', query time: 0.31s Refine Results
  1. 241

    Lightweight Band-Adaptive Hyperspectral Image Compression With Feature Decouple and Recurrent Model by Jiahui Liu, Lili Zhang, Jingang Wang, Lele Qu

    Published 2025-01-01
    “…An adaptive weighted fusion mechanism is also employed to optimize the integration of multilevel features. Evaluation across six hyperspectral datasets indicates that the LBA-HIM model significantly enhances both compression efficiency and image quality while simultaneously lowering computational costs. …”
    Get full text
    Article
  2. 242

    Partial feature reparameterization and shallow-level interaction for remote sensing object detection by Minh Tai Pham Nguyen, Quoc Duy Nam Nguyen, Hoang Viet Anh Le, Minh Khue Phan Tran, Tadashi Nakano, Thi Hong Tran

    Published 2025-08-01
    “…Firstly, an extraction block is proposed called PRepConvBlock that leverages reparameterization convolution and partial feature utilization to effectively reduce the complexity in convolution operations, allowing for the utilization of larger kernel sizes in order to form the longer interactions between features and significantly expand receptive fields. …”
    Get full text
    Article
  3. 243

    Exploring unsupervised feature extraction algorithms: tackling high dimensionality in small datasets by Hongqi Niu, Gabrielle B. McCallum, Anne B. Chang, Khalid Khan, Sami Azam

    Published 2025-07-01
    “…Furthermore, we compared these algorithms in terms of transformation approach, goals, parameters, and computational complexity. Finally, we evaluated each algorithm against state-of-the-art research using various datasets to assess their accuracy, highlighting which algorithm is most appropriate for specific scenarios. …”
    Get full text
    Article
  4. 244

    A Reparameterization Feature Redundancy Extract Network for Unmanned Aerial Vehicles Detection by Shijie Zhang, Xu Yang, Chao Geng, Xinyang Li

    Published 2024-11-01
    “…Additionally, it has the lowest parameter count and FLOPs, making it highly efficient in terms of computational resources.…”
    Get full text
    Article
  5. 245

    FGBNet: A Bio-Subspecies Classification Network with Multi-Level Feature Interaction by Yang Yuan, Danping Huang, Bingbin Cai, Yang Shen, Jingdan Wang, Jiale Xv, Siyu Chen

    Published 2025-03-01
    “…Through experimentation and optimization, the ConvNeXt is selected as the backbone network for FGBNet feature extraction, and the effectiveness of the multi-level feature interaction method is verified. …”
    Get full text
    Article
  6. 246

    Breast Cancer Diagnosis Using Bagging Decision Trees with Improved Feature Selection by Deepak Dudeja, Ajit Noonia, S. Lavanya, Vandana Sharma, Varun Kumar, Sumaiya Rehan, R. Ramkumar

    Published 2023-12-01
    “…The random forest method used bagging techniques for selecting data points, and feature optimization was also carried out. Through our experiments, it has been found that the results obtained with the bagging trees algorithm outperform the result obtained with the best decision tree parameters. …”
    Get full text
    Article
  7. 247

    Visual Object Tracking in RGB-D Data via Genetic Feature Learning by Ming-xin Jiang, Xian-xian Luo, Tao Hai, Hai-yan Wang, Song Yang, Ahmed N. Abdalla

    Published 2019-01-01
    “…Visual object tracking is a fundamental component in many computer vision applications. Extracting robust features of object is one of the most important steps in tracking. …”
    Get full text
    Article
  8. 248
  9. 249

    Soybean Weed Detection Based on RT-DETR with Enhanced Multiscale Channel Features by Hua Yang, Yanjie Lyu, Yunpeng Jiang, Feng Jiang, Taiyong Deng, Lihao Yu, Yuanhao Qiu, Hao Xue, Junying Guo, Zhaoqi Meng

    Published 2025-04-01
    “…To solve the missed and wrong detection problems of the object detection model in identifying soybean companion weeds, this paper proposes an enhanced multi-scale channel feature model based on RT-DETR (EMCF-RTDETR). First, we designed a lightweight hybrid-channel feature extraction backbone network, which consists of a CGF-Block module and a FasterNet-Block module working together, aiming to reduce the amount of computation and the number of parameters while improving the efficiency of feature extraction. …”
    Get full text
    Article
  10. 250

    Machine Learning-Driven Acoustic Feature Classification and Pronunciation Assessment for Mandarin Learners by Gulnur Arkin, Tangnur Abdukelim, Hankiz Yilahun, Askar Hamdulla

    Published 2025-06-01
    “…Based on acoustic feature analysis, this study systematically examines the differences in vowel pronunciation characteristics among Mandarin learners at various proficiency levels. …”
    Get full text
    Article
  11. 251

    Balancing Precision and Speed: Introducing the Performance Efficiency Evaluation Ratio (PEER) in Visual Odometry by Cem Atilgan, Muharrem Mercimek

    Published 2025-01-01
    “…To address this limitation, we propose the Performance Efficiency Evaluation Ratio (PEER), a novel, adaptive, and lightweight metric that jointly evaluates algorithm performance based on both fidelity and computation time. PEER incorporates a tunable weighting parameter to prioritize performance, speed, or a balanced trade-off, and employs normalization techniques to ensure comparability across different algorithms and systems. …”
    Get full text
    Article
  12. 252

    Optimal Statistical Feature Subset Selection for Bearing Fault Detection and Severity Estimation by Chhaya Grover, Neelam Turk

    Published 2020-01-01
    “…The performance of bearing fault detection systems based on machine learning techniques largely depends on the selected features. Hence, selection of an ideal number of dominant features from a comprehensive list of features is needed to decrease the number of computations involved in fault detection. …”
    Get full text
    Article
  13. 253
  14. 254

    Multimodal Feature-Driven Deep Learning for the Prediction of Duck Body Dimensions and Weight by Wenbo Xiao, Qiannan Han, Gang Shu, Guiping Liang, Hongyan Zhang, Song Wang, Zhihao Xu, Weican Wan, Chuang Li, Guitao Jiang, Yi Xiao

    Published 2025-05-01
    “…The proposed method innovatively employs PointNet++ to extract key feature points from point clouds, extracts and computes corresponding 3D geometric features, and fuses them with multi-view convolutional 2D features. …”
    Get full text
    Article
  15. 255
  16. 256

    Optical wave features and sensitivity analysis of a coupled fractional integrable system by Jan Muhammad, Usman Younas, D.K. Almutairi, Aziz Khan, Thabet Abdeljawad

    Published 2025-01-01
    “…In addition, a variety of plots demonstrating the effect of fractional derivatives are provided to observe the physical behavior of the derived solutions by the assistance of the parameters. The outcomes obtained indicate that the implemented computational strategies are proficient, succinct, effective and they can be combined with representative computations to tackle more intricate phenomena.…”
    Get full text
    Article
  17. 257

    Implementation features of the third-party DLL connection mechanism in the information system «Channel» by Nadezhda S. Mogilevskaya, Konstantin A. Chugunniy

    Published 2015-09-01
    “…The system “Channel” allows simulating the noise-immune digital communication channels and solving the problem of matching the communication channel and the algebraic method of the jamproof protection of this channel. The main feature of the system is that it can be used not only by the researchers who do not have programming skills, but also by experts in the computing aids programming. …”
    Get full text
    Article
  18. 258

    Reminder and Online Booking Features at Android-Based Motorcycle Repair Shop Marketplace by Wayan Dony Mahendra, I Made Sukarsa, AA.Kt. Agung Cahyawan

    Published 2020-06-01
    “…The results of the UAT test (user acceptance testing) from 20 users show 55,8% answered agree to the display, features and flow of the system, 39,5% answered strongly agree to the three question parameters, and 4,7% answered disagree with the flow and display of the system.…”
    Get full text
    Article
  19. 259
  20. 260