Search Results - hybrid deep convolutional autoencoder*

DHCAE: Deep Hybrid Convolutional Autoencoder Approach for Robust Supervised Hyperspectral Unmixing by Hadi, Fazal, Yang, Jingxiang, Ullah, Matee, Ahmad, Irfan, Farooque, Ghulam, Xiao, Liang

“… In this paper, we present a new method for robust supervised HSU based on a deep hybrid (3D and 2D) convolutional autoencoder…”
Get full text

Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network by Wang, Wei, Zhao, Mengxue, Wang, Jigang

“…Android security incidents occurred frequently in recent years. To improve the accuracy and efficiency of large-scale Android malware detection, in this work, we propose a hybrid model based on deep autoencoder (DAE…”
Get full text

Classification of Atypical White Blood Cells in Acute Myeloid Leukemia Using a Two-Stage Hybrid Model Based on Deep Convolutional Autoencoder and Deep Convolutional Neural Network by Elhassan, Tusneem A., Mohd Rahim, Mohd Shafry, Siti Zaiton, Mohd Hashim, Swee, Tan Tian, Alhaj, Taqwa Ahmed, Ali, Abdulalem, Aljurf, Mahmoud

“… Using a hybrid model based on geometric…”
Get full text

Driver distracted detection using a new hybrid deep convolutional neural network based on Autoencoder by Hassam, Dhulfekar Dhurgham, Mohamme, Ayas Talib, Sadiq, Arcelan S, Salman, Baraa Yousif

“… To counter these problems, we propose a novel deep convolutional network with the introduction of an Autoencoder-based feature dimensionality reduction, an Attention mechanism towards better feature…”
Get full text

An automated hybrid attention based deep convolutional capsule with weighted autoencoder approach for skin cancer classification by Desale, R.P., Patil, P.S.

“… It is essential to identify the disease at the initial stage and eliminate it from spreading. Hence, this research introduces an automated hybrid deep learning (DL…”
Get full text

RESEARCH ON IIOT SECURITY: NOVEL MACHINE LEARNING-BASED INTRUSION DETECTION USING TCP/IP PACKETS by Agarwal, Neha, Pandey P., Rajendra, Rajagopal, Smitha

“…). In order to detect intrusions in the IIoT environment utilizing TCP/IP packets, this work introduces a novel Hybrid Deep Convolutional Autoencoder and Splinted Decision Tree (HDCA-SDT) technique…”
Get full text

Breast Cancer Detection using Convolutional Autoencoder with Hybrid Deep Learning Model by S. Ranjana, A. Meenakshi

“…) which is called Convolutional Neural Network (CNN). This research focused to create a hybrid DL model with a single test that subjected at inference…”
Get full text

Detecting Brain Tumor Stages Using Convolutional AutoEncoder (CAE) with Hybrid Deep Learning Method by Agalya, D, Kamalakkannan, S.

“… Models are created using Deep Learning (DL) and Magnetic Resonance Imaging (MRI) to classify and diagnose brain tumours…”
Get full text

Deep ensemble model with blockchain technology for lung cancer detection with secured data sharing by Kalidindi, Hari Krishna, Srinivasu, N.

“…Lung cancer is one of the leading causes of cancer-related mortality globally, primarily due to the high frequency of late-stage diagnoses. While existing…”
Get full text

Hybrid Deep Convolutional Neural Networks Combined with Autoencoders and Augmented Data to Predict the Look-Up Table 2006 by Djeddou, Messaoud, Dallal, Jehad Al, Hellal, Aouatef, Hameed, Ibrahim A., Zhao, Xingang

“… This study presents a novel predictive method integrating auto-encoders with deep convolutional neural networks (DCNN…”
Get full text

A deep autoencoder based approach for the inverse design of an acoustic-absorber by Mahesh, K., Ranjith, S. Kumar, Mini, R. S.

“…This paper proposes an algorithm to perform the inverse design of a low-frequency acoustic absorber using a deep convolutional autoencoder network…”
Get full text

Hybrid Deep Convolutional Neural Networks Combined with Autoencoders And Augmented Data To Predict The Look-Up Table 2006 by Djeddou, Messaoud, Hellal, Aouatef, Hameed, Ibrahim A, Zhao, Xingang, Djehad Al Dallal

“…This study explores the development of a hybrid deep convolutional neural network (DCNN…”
Get full text

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 by Sahani, Mrutyunjaya, Choudhury, Sasmita, Siddique, Marif Daula, Parida, Tanmoy, Dash, Pradipta Kishore, Panda, Sanjib Kumar

“… 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…”
Get full text

Hybrid deep learning for computational precision in cardiac MRI segmentation: Integrating Autoencoders, CNNs, and RNNs for enhanced structural analysis by Sufian, Md Abu, Niu, Mingbo

“… The research paper explores the application of hybrid deep learning methodologies, focusing on the roles of Autoencoders, Convolutional Neural Networks (CNNs…”
Get full text

Detection and classification of electrocardiography using hybrid deep learning models by Selvam, Immaculate Joy, Madhavan, Moorthi, Kumarasamy, Senthil Kumar

“… Precision of ECG classification through a hybrid Deep Learning (DL) approach leverages both Convolutional Neural Network (CNN…”
Get more information

IMDAC: A robust intelligent software defect prediction model via multi‐objective optimization and end‐to‐end hybrid deep learning networks by Zhu, Kun, Zhang, Nana, Jiang, Changjun, Zhu, Dandan

“… In this article, we propose a robust intelligent SDP model called IMDAC based on deep learning and soft computing techniques…”
Get full text

Convolutional autoencoder-based deep learning for intracerebral hemorrhage classification using brain CT images by Nageswara Rao, B., Acharya, U. Rajendra, Tan, Ru-San, Dash, Pratyusa, Mohapatra, Manoranjan, Sabut, Sukanta

“… We proposed a hybrid deep learning model for automated ICH diagnosis using NCCT images, which comprises a convolutional autoencoder (CAE…”
Get full text

High-spatiotemporal-resolution PM2.5 forecasting by hybrid deep learning models with ensembled massive heterogeneous monitoring data by Wu, Kuan-Yen, Hsia, I-Wen, Kow, Pu-Yun, Chang, Li-Chiu, Chang, Fi-John

“… quick responses to air pollution incidents. This study proposed a hybrid deep learning model (AE-CNN-BP…”
Get full text

Revolutionary hybrid ensembled deep learning model for accurate and robust side-channel attack detection in cloud computing by Reddy, C. Lakshminatha, Malathi, K.

“… scalability and accuracy. We propose a hybrid ensembled deep learning (HEDL) model that integrates convolutional neural networks (CNN…”
Get full text

Intelligent framework for unsupervised damage detection in bridges using deep convolutional autoencoder with wavelet transmissibility pattern spectra by Li, Shuai, Cao, Yuxi, Gdoutos, Emmanuel E., Tao, Mei, Faisal Alkayem, Nizar, Avci, Onur, Cao, Maosen

“…Deep Learning has been increasingly utilized in structural damage detection. Existing relevant studies often highlight the benefits of supervised deep learning in the intelligent identification of bridge damage…”
Get full text