Suchergebnisse - hybrid deep convolutional autoencoder~

DHCAE: Deep Hybrid Convolutional Autoencoder Approach for Robust Supervised Hyperspectral Unmixing von 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 …”
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Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network von 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 …”
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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 von 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 …”
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Driver distracted detection using a new hybrid deep convolutional neural network based on Autoencoder von 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 …”
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An automated hybrid attention based deep convolutional capsule with weighted autoencoder approach for skin cancer classification von 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 …”
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Breast Cancer Detection using Convolutional Autoencoder with Hybrid Deep Learning Model von 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 …”
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RESEARCH ON IIOT SECURITY: NOVEL MACHINE LEARNING-BASED INTRUSION DETECTION USING TCP/IP PACKETS von 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 …”
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Detecting Brain Tumor Stages Using Convolutional AutoEncoder (CAE) with Hybrid Deep Learning Method von Agalya, D, Kamalakkannan, S.

“… Models are created using Deep Learning (DL) and Magnetic Resonance Imaging (MRI) to classify and diagnose brain tumours …”
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Deep ensemble model with blockchain technology for lung cancer detection with secured data sharing von 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 …”
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A deep autoencoder based approach for the inverse design of an acoustic-absorber von 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 …”
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Hybrid Deep Convolutional Neural Networks Combined with Autoencoders and Augmented Data to Predict the Look-Up Table 2006 von 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 …”
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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 von 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 …”
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Hybrid deep learning for computational precision in cardiac MRI segmentation: Integrating Autoencoders, CNNs, and RNNs for enhanced structural analysis von 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 …”
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Convolutional autoencoder-based deep learning for intracerebral hemorrhage classification using brain CT images von 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 …”
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Hybrid Deep Convolutional Neural Networks Combined with Autoencoders And Augmented Data To Predict The Look-Up Table 2006 von 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 …”
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IMDAC: A robust intelligent software defect prediction model via multi‐objective optimization and end‐to‐end hybrid deep learning networks von 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 …”
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Detection and classification of electrocardiography using hybrid deep learning models von 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 …”
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Intelligent framework for unsupervised damage detection in bridges using deep convolutional autoencoder with wavelet transmissibility pattern spectra von 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 …”
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A hybrid deep learning-based fruit classification using attention model and convolution autoencoder von Xue, Gang, Liu, Shifeng, Ma, Yicao

“… chain, factories, supermarkets, and other fields. In this paper, we develop a hybrid deep learning-based fruit image classification framework, named attention-based densely connected convolutional networks with convolution autoencoder (CAE-ADN …”
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A novel hybrid framework for wind speed forecasting using autoencoder‐based convolutional long short‐term memory network von Kosana, Vishalteja, Madasthu, Santhosh, Teeparthi, Kiran

“… ) autoencoder, convolutional neural network (CNN), and LSTM model for enhanced WSF. The proposed hybrid approach is divided into two main components …”
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