Suchergebnisse - hybrid deep conventional autoencoder~

Copula entropy regularization transformer with C2 variational autoencoder and fine-tuned hybrid DL model for network intrusion detection von Akkepalli, Srinivas, K, Sagar

“… In this paper, a novel Copula Entropy Regularization Transformer withC2 variational autoencoder and Fine-tuned Hybrid Deep Learning (DL …”
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A hybrid Intrusion Detection System based on Sparse autoencoder and Deep Neural Network von Narayana Rao, K., Venkata Rao, K., P.V.G.D., Prasad Reddy

“… Machine learning methodologies show effective results in intrusion detection system. We proposed a two-stage hybrid methodology for intrusion detection …”
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An advanced hybrid deep adversarial autoencoder for parameterized nonlinear fluid flow modelling von Cheng, M., Fang, F., Pain, C.C., Navon, I.M.

“… In this work, we propose a hybrid deep adversarial autoencoder (VAE-GAN) to integrate generative adversarial network (GAN …”
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A hybrid autoencoder and index modulation framework for OTFS modulation von Tek, Yusuf İslam, Doğukan, Ali Tuğberk, Gevez, Yarkın, Pıhtılı, Mehmet Ertuğ, Başar, Ertuğrul

“… This paper presents an innovative approach to orthogonal time frequency space (OTFS) modulation by integrating autoencoder-based enhanced …”
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Hybrid twin attention based convolutional stacked sparse autoencoder for classification of defected weld images von Srikanth, T., Mani, M. Radhika

“… Welding is an essential joining process in industrial manufacturing. Many deep learning models are introduced to detect welding errors …”
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A Hybrid Quantum-Classical Autoencoder Framework for End-to-End Communication Systems von Zhang, Bolun, Zheng, Gan, Huynh, Nguyen Van

“… ) communication systems in wireless fading scenarios. We introduce a novel hybrid quantum-classical autoencoder architecture that combines parameterized quantum circuits with classical deep neural networks (DNNs …”
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QCAE: A quadruple branch CNN autoencoder for real-time electricity price forecasting von Yang, Haolin, Schell, Kristen R.

“… by the “curse of dimensionality” for such data. However, autoencoders, or neural networks specifically designed to reduce the noise and dimensions of input data, may prove useful to advance the accuracy of real-time price forecasting models …”
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Real-Time Outlier Detection Applied to a Doppler Velocity Log Sensor Based on Hybrid Autoencoder and Recurrent Neural Network von Davari, Narjes, Aguiar, A. Pedro

“… This article presents a real-time outlier detection deep-learning (OD-DL)-based method using a hybridized artificial neural network (ANN) approach …”
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A hybrid Convolutional Autoencoder training algorithm for unsupervised bearing health indicator construction von Milani, Ali Eftekhari, Zappalá, Donatella, Watson, Simon J.

“… Conventional Deep Learning (DL) methods for bearing health indicator (HI) adopt supervised approaches, requiring expert knowledge of the component degradation trend …”
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Hybrid Encryption Scheme for Medical Imaging Using AutoEncoder and Advanced Encryption Standard von Alslman, Yasmeen, Alnagi, Eman, Ahmad, Ashraf, AbuHour, Yousef, Younisse, Remah, Abu Al-haija, Qasem

“… The majority of these studies rely on conventional encryption techniques. However, such improvements have come with increased computational complexity and slower processing for encryption and decryption processes …”
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Deep autoencoder based image enhancement approach with hybrid feature extraction for plant disease detection using supervised classification von Huddar, Suma, Prabhushetty, Kopparagaon, Jakati, Jagadish, Havaldar, Raviraj, Sirdeshpande, Nandakishor

“… This study presents an innovative approach for plant leaf disease detection by integrating wavelet analysis, color, and texture features, coupled with autoencoder denoising and support vector machine (SVM) classification …”
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Enhanced Early Detection of Diabetic Nephropathy Using a Hybrid Autoencoder-LSTM Model for Clinical Prediction von Rani, U. Sudha, Subhas, C.

“… This research delves into the application of a hybrid Autoencoder-LSTM model to improve DN detection …”
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A Novel Hybrid Attentive Convolutional Autoencoder (HACA) Framework for Enhanced Epileptic Seizure Detection von Vaddi, Venkata Narayana, Sikha, Madhu Babu, Kodali, Prakash

“… This study presents a Hybrid Attentive Convolutional Autoen-coder (HACA) framework designed to address challenges in EEG signal processing for seizure detection …”
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A Hybrid Heuristic‐Aided Algorithm of Serial Cascaded Autoencoder and ALSTM for Channel Estimation in Millimeter‐Wave Massive MIMO Communication System von Suneetha, Nallamothu, Satyanarayana, Penke

“… To overcome the particular issue, this paper proposes a new method as a serial cascaded autoencoder with attention‐based long short‐term memory (SCA‐ALSTM …”
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Stacked LSTM Sequence-to-Sequence Autoencoder with Feature Selection for Daily Solar Radiation Prediction: A Review and New Modeling Results von Ghimire, Sujan, Deo, Ravinesh C., Wang, Hua, Al-Musaylh, Mohanad S., Casillas-Pérez, David, Salcedo-Sanz, Sancho

“… We review the latest modeling techniques and propose new hybrid SAELSTM framework based on Deep Learning (DL …”
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Enhanced Emotion Recognition Using a Hybrid Autoencoder-LSTM Model Optimized with a Hybrid ACO-WOA Algorithm for Hyperparameter Tuning von Waiker, Vinod, Ramesh, Janjhyam Venkata Naga, Bala, Kiran, Krishnaiah, V. V. Jaya Rama, Jackulin, T., Muniyandy, Elangovan, Shahin, Osama R.

“… Therefore, this paper proposes an improved method for emotion recognition regarding the Hybrid Autoencoder-Long Short-Term Memory (LSTM …”
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A weighted deep autoencoder (WDAE) based hybrid feature extraction model for the detection of communities in social networks von Suganthi, R., Prabha, K.

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A Scalable Hybrid Autoencoder–Extreme Learning Machine Framework for Adaptive Intrusion Detection in High-Dimensional Networks von Kumar, Anubhav, Radhakrishnan, Rajamani, Sumithra, Mani, Kaliyaperumal, Prabu, Balusamy, Balamurugan, Benedetto, Francesco

“… This study presents a novel, scalable Hybrid Autoencoder–Extreme Learning Machine (AE–ELM) framework for Intrusion Detection Systems (IDS …”
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Secure MRI Brain Image Transmission Using IOT Devices Based on Hybrid Autoencoder and Restricted Boltzmann Approach von Deepthi, S. Aruna, Rao, E. Sreenivasa, Giriprasad, M. N.

“… Hence, the machine learning methods are effective for medical image analysis, where the deep learning models are used in the machine …”
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Data-driven nonlinear parametric model order reduction framework using deep hierarchical variational autoencoder von Lee, SiHun, Lee, Sangmin, Jang, Kijoo, Cho, Haeseong, Shin, SangJoon

“… LSH-VAE differs from existing networks in two major respects: a deep hierarchical structure and a hybrid weighted, probabilistic loss function …”
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