Showing 501 - 520 results of 2,360 for search 'convolutional framework', query time: 0.10s Refine Results
  1. 501

    Hybrid CNN-Ensemble Framework for Intelligent Optical Fiber Fault Detection and Diagnosis by Salem Titouni, Idris Messaoudene, Yassine Himeur, Omar Alnaseri, Farouk Chetouah, Boualem Hammache, Massinissa Belazzoug, Shadi Atalla, Wathiq Mansoor

    Published 2025-01-01
    “…This paper introduces a novel Hybrid CNN-Ensemble framework, combining convolutional neural networks (CNNs) for deep feature extraction with ensemble learning techniques including XGBoost, Support Vector Machines (SVM), and Random Forest (RF). …”
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    Article
  2. 502

    FRORS: An Effective Fine-Grained Retrieval Framework for Optical Remote Sensing Images by Yong-Qiang Mao, Zhizhuo Jiang, Yu Liu, Yiming Zhang, Kehan Qi, Hanbo Bi, You He

    Published 2025-01-01
    “…With the rapid development of convolutional neural networks (CNN) in the field of remote sensing, it has become possible for remote sensing image retrieval tasks to move toward more fine-grained classes. …”
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  3. 503
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  5. 505

    Deep Neural Framework With Visual Attention and Global Context for Predicting Image Aesthetics by Yifei Xu, Nuo Zhang, Pingping Wei, Genan Sang, Li Li, Feng Yuan

    Published 2025-01-01
    “…Specifically, the framework can be any state-of-the-art convolution classification network compatible with visual attention. …”
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    Article
  6. 506
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    The Reliability of Diagnosing Schizophrenia Using the GRU Layer in Conjunction with EEG Rhythms by Pankaj Kumar Sahu, Karan Jain

    Published 2025-07-01
    “…Results: The RDCGRU framework performs efficiently with alpha-EEG rhythm (88.06%) and harshly with delta-EEG rhythm (60.05%). …”
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    Article
  8. 508

    Improved crop row detection by employing attention-based vision transformers and convolutional neural networks with integrated depth modeling for precise spatial accuracy by Hassan Afzaal, Derek Rude, Aitazaz A. Farooque, Gurjit S. Randhawa, Arnold W. Schumann, Nicholas Krouglicof

    Published 2025-08-01
    “…The proposed framework employs the latest attention and convolution-based encoders, such as ConvFormer, CAFormer, Swin Transformer, and ConvNextV2, in precisely identifying crop rows across varied and challenging agricultural environments. …”
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    Article
  9. 509

    Research on an Intelligent Sedimentary Microfacies Recognition Method Based on Convolutional Neural Networks Within the Sequence Stratigraphy of Well Logging Curve Image Groups by Xinyi Yuan, Xidong Wang, Shutian Wang, Feng Tian, Zichun Yang

    Published 2025-06-01
    “…This study focuses on the Lower Cretaceous Yaojia Formation Member 1 (K2y1) in the satellite oilfield of the Songliao Basin, integrating sequence stratigraphy into a machine learning framework to propose an innovative convolutional neural network (CNN)-based facies recognition method using log-curve image groups by graphically transforming five log curves and establishing a CNN model that correlates log responses with microfacies. …”
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  10. 510

    Radar-Based Damage Detection in a Wind Turbine Blade Using Convolutional Neural Networks: A Proof-of-Concept Under Fatigue Loading by Erik Streser, Sercan Alipek, Manuel Rao, Jonas Simon, Jochen Moll, Peter Kraemer, Viktor Krozer

    Published 2025-05-01
    “…This paper reports a convolutional neural network (CNN)-based damage detection approach for radar-based structural health monitoring of wind turbine blades. …”
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    Article
  11. 511

    Melt Density Monitoring of Extruder Extrusion Process Based on Multi-source Data Fusion and Convolutional Long Short-term Memory Neural Network by Binbin ZHANG, Zhuyun CHEN, Fei ZHANG, Gang JIN

    Published 2024-11-01
    “…Ensuring precise control over melt density is imperative for achieving desired product characteristics and maintaining process stability in polymer blending operations.Methods The research proposes a novel methodological framework that integrates multi-source data fusion with a convolutional long short-term memory (LSTM) neural network architecture to address this challenge. …”
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  12. 512

    A Temporal Convolutional Network–Bidirectional Long Short-Term Memory (TCN-BiLSTM) Prediction Model for Temporal Faults in Industrial Equipment by Jinyin Bai, Wei Zhu, Shuhong Liu, Chenhao Ye, Peng Zheng, Xiangchen Wang

    Published 2025-02-01
    “…First, preprocessed industrial operation data are fed into the model, and hyperparameter optimization is conducted using the Optuna framework to improve training efficiency and generalization capability. …”
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    Article
  13. 513

    A swin transformer and CNN fusion framework for accurate Parkinson disease classification in MRI by Sayyed Shahid Hussain, Pir Masoom Shah, Hussain Dawood, Xu Degang, Ahmad Alshamayleh, Muhammad Adnan Khan, Taher M. Ghazal

    Published 2025-04-01
    “…The proposed model comprises three primary blocks: Transformer-block, convolutional block, and dense-block. The input image is processed concurrently by the cosine transformer and convolutional block. …”
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    Personalized trajectory inference framework integrating driving behavior recognition and temporal dependency learning. by Jinhao Yang, Junwen Cao, Mingyu Fang

    Published 2025-01-01
    “…Comparative analysis with baseline models (LSTM, Social-LSTM, Social-Velocity-LSTM, Convolutional-Social-LSTM) reveals particularly enhanced accuracy in long-term predictions. …”
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  17. 517

    A Framework for Optimizing Deep Learning-Based Lane Detection and Steering for Autonomous Driving by Daniel Yordanov, Ashim Chakraborty, Md Mahmudul Hasan, Silvia Cirstea

    Published 2024-12-01
    “…This project aims to provide a novel framework for optimizing a self-driving vehicle that can detect lanes and steer accordingly. …”
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    Revolutionizing Alzheimer’s disease detection with a cutting-edge CAPCBAM deep learning framework by Houmem Slimi, Sabeur Abid, Mounir Sayadi

    Published 2025-04-01
    “…While the integration of deep learning techniques for AD classification is not entirely new, this study introduces CAPCBAM—a framework that extends prior approaches by combining Capsule Networks with a Convolutional Block Attention Module (CBAM). …”
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  20. 520

    Enhancing urban air quality prediction using time-based-spatial forecasting framework by Shrikar Jayaraman, Nathezhtha T, Abirami S, Sakthivel G

    Published 2025-02-01
    “…The proposed Time-Based-Spatial (TBS) forecasting framework is integrated with spatial and temporal information using machine learning techniques on data collected from a wide range of cities. …”
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