Výsledky vyhledávání - Reduced deep convolutional stack autoencoder

  • Zobrazuji výsledky 1 - 8 z 8
Upřesnit hledání
  1. 1

    Epileptic Seizure Recognition Using Reduced Deep Convolutional Stack Autoencoder and Improved Kernel RVFLN From EEG Signals Autor Sahani, Mrutyunjaya, Rout, Susanta Kumar, Dash, Pradipta Kishor

    ISSN: 1932-4545, 1940-9990, 1940-9990
    Vydáno: New York IEEE 01.06.2021
    “…In this paper, reduced deep convolutional stack autoencoder (RDCSAE) and improved kernel random vector functional link network (IKRVFLN…”
    Získat plný text
    Journal Article
  2. 2

    Precise single step and multistep short-term photovoltaic parameters forecasting based on reduced deep convolutional stack autoencoder and minimum variance multikernel random vector functional network Autor Sahani, Mrutyunjaya, Choudhury, Sasmita, Siddique, Marif Daula, Parida, Tanmoy, Dash, Pradipta Kishore, Panda, Sanjib Kumar

    ISSN: 0952-1976
    Vydáno: Elsevier Ltd 01.10.2024
    “… To address this, we have developed a novel hybrid model: a reduced deep convolutional stack autoencoder with a minimum variance multikernel random vector functional link network (RDCSAE-MVMRVFLN…”
    Získat plný text
    Journal Article
  3. 3

    RDCSAE-RKRVFLN: A unified deep learning framework for robust and accurate DOA estimation Autor Raiguru, Priyadarshini, Swain, Bhanja Kishor, Rout, Susanta Kumar, Sahani, Mrutyunjaya, Mishra, Rabindra Kishore

    ISSN: 1568-4946, 1872-9681
    Vydáno: Elsevier B.V 01.09.2024
    Vydáno v Applied soft computing (01.09.2024)
    “…This paper introduces an innovative unified deep learning (DL) model, “reduced deep convolutional stack autoencoder (RDCSAE…”
    Získat plný text
    Journal Article
  4. 4

    Deep Convolutional Stack Autoencoder of Process Adaptive VMD Data With Robust Multikernel RVFLN for Power Quality Events Recognition Autor Sahani, Mrutyunjaya, Dash, Pradipta Kishore

    ISSN: 0018-9456, 1557-9662
    Vydáno: New York IEEE 2021
    “…). A novel reduced deep convolutional neural network (RDCNN) embedded with stack autoencoder, that is, RDCSAE structure is introduced to extract the most discriminative…”
    Získat plný text
    Journal Article
  5. 5

    Coupling of a lightweight model of reduced convolutional autoencoder with linear SVM classifier to detect brain tumours on FPGA Autor Chatterjee, Soumita, Pandit, Soumya, Das, Arpita

    ISSN: 0957-4174
    Vydáno: Elsevier Ltd 25.09.2025
    Vydáno v Expert systems with applications (25.09.2025)
    “… Following this preprocessing step, a dual-stack Reduced Convolutional Autoencoder (RCA) unit is coupled with a linear Support Vector Machine (SVM) classifier…”
    Získat plný text
    Journal Article
  6. 6

    Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 Imagery Autor Shahabi, Hejar, Rahimzad, Maryam, Tavakkoli Piralilou, Sepideh, Ghorbanzadeh, Omid, Homayouni, Saied, Blaschke, Thomas, Lim, Samsung, Ghamisi, Pedram

    ISSN: 2072-4292, 2072-4292
    Vydáno: Basel MDPI AG 20.11.2021
    Vydáno v Remote sensing (Basel, Switzerland) (20.11.2021)
    “…This paper proposes a new approach based on an unsupervised deep learning (DL) model for landslide detection…”
    Získat plný text
    Journal Article
  7. 7

    Impact of Patch-Size on Classification Accuracy of Latent Fingerprint Image in Stacked Convolutional Auto-encoder based Segmentation and Detection Autor Chhabra, Megha, Shukla, Manoj Kumar, Ravulakollu, Kiran Kumar

    Vydáno: IEEE 07.10.2020
    “…) using a stack of convolutional auto-encoders. The idea is to early detect the structure of interest from the image using a color-based mask…”
    Získat plný text
    Konferenční příspěvek
  8. 8

    Light Field Intrinsics with a Deep Encoder-Decoder Network Autor Alperovich, Anna, Johannsen, Ole, Strecke, Michael, Goldluecke, Bastian

    ISSN: 1063-6919
    Vydáno: IEEE 01.06.2018
    “…We present a fully convolutional autoencoder for light fields, which jointly encodes stacks of horizontal and vertical epipolar plane images through a deep network of residual layers…”
    Získat plný text
    Konferenční příspěvek