Výsledky vyhľadávania - Optimized deep autoencoder

Action recognition using optimized deep autoencoder and CNN for surveillance data streams of non-stationary environments Autor Ullah, Amin, Muhammad, Khan, Haq, Ijaz Ul, Baik, Sung Wook

“… Firstly, frame level deep features are extracted using a pre-trained CNN model. Next, an optimized deep autoencoder (DAE…”
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Optimized Deep Autoencoder Model for Internet of Things Intruder Detection Autor Lahasan, Badr, Samma, Hussein

“…The development of an optimized deep learning intruder detection model that could be executed on IoT devices with limited hardware support has several advantages, such as the…”
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Optimized deep autoencoder and BiLSTM for intrusion detection in IoTs-Fog computing Autor Alqahtani, Abdullah

“… This work proposes an improved hybrid Deep autoencoder (DAE) and Bidirectional long short-term memory (BiLSTM…”
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OPTIMIZED DEEP AUTOENCODER WITH L1 REGULARIZATION AND DROPOUT FOR ANOMALY DETECTION IN 6G NETWORK SLICING Autor Jennifer Kaunang, Valencia Claudia, Alamsyah, Nur, Parama Yoga, Titan, Hendra, Acep, Budiman, Budiman

“…: a supervised random forest classifier, a basic unsupervised autoencoder, and an optimized deep autoencoder enhanced with L1 regularization and dropout techniques…”
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NSGA-II optimized deep autoencoders for enhanced multi-criteria recommendation system Autor Rajput, Ishwari Singh, Tewari, Anand Shanker, Tiwari, Arvind Kumar

“… This paper introduces a novel methodology leveraging deep autoencoders optimized using the Non-dominated sorting genetic algorithm (NSGA-II…”
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Automated atrial fibrillation classification based on denoising stacked autoencoder and optimized deep network Autor Singh, Prateek, Sharma, Ambalika, Maiya, Shreesha

“…) using end-to-end deep neural networks. This is significant since early identification of AFib can help to prevent mortality…”
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GOOSE-Optimized Deep Autoencoder for Intrusion Detection in IoMT Networks: A Scalable and Efficient Approach for Healthcare Security Autor Priyadarshani, Pooja Puspita, Nayak, Janmenjoy, Dash, Pandit Byomakesha

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Automated damage diagnosis of concrete jack arch beam using optimized deep stacked autoencoders and multi-sensor fusion Autor Yu, Yang, Li, Jiantao, Li, Jianchun, Xia, Yong, Ding, Zhenghao, Samali, Bijan

“…A novel hybrid framework of optimized deep learning models combined with multi-sensor fusion is developed for condition diagnosis of concrete arch beam…”
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An Optimized Deep Wavelet Autoencoder System for Detecting Tumors in Brain Autor Sowjanya, Chundu Lakshmi, Deepika, Gudavarapu Venkata Anu, Sri, Chadalawada Bhavya, Harshitha, Athota, Kranthikumar, Konduru

“…). Central to this approach is the introduction of a novel Deep Wavelet Autoencoder (DWAE), which synergistically combines the multi-resolution analysis strength…”
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An Aquatic Image Compression Scheme based on Optimized Deep Convolutional Autoencoder Autor Paul, Raj Kumar, Vishwakarma, Ankit, Chandran, Saravanan

“…: important information must be recovered in the reconstructed image. An optimized deep convolutional autoencoder model has been implemented using the deep learning approach for solving the challenges…”
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Adaptive VMD based optimized deep learning mixed kernel ELM autoencoder for single and multistep wind power forecasting Autor Krishna Rayi, Vijaya, Mishra, S.P., Naik, Jyotirmayee, Dash, P.K.

“…) and Deep learning mixed Kernel ELM (MKELM) Autoencoder (AE) has been presented for precise prediction of wind power…”
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Bi-level deep feature synthesis through variational autoencoder for predicting peptide antiviral activity using optimized deep ensemble Autor Kumar, Aditya, Singh, Deepak

“…•Bi-level deep feature synthesis through variational autoencoders (VAE) for antiviral peptide prediction…”
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Autoencoders Based Optimized Deep Learning Model for the Detection of Cyber Attack in IoT Environment Autor Gupta, Brij B., Gaurav, Akshat, Chui, Kwok Tai, Arya, Varsha, Choi, Chang

“… In order to identify cyber attacks in an IoT environment, this study introduced a deep learning and autoencoder-based model…”
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Optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in Internet of things Autor Otokwala, Uneneibotejit, Petrovski, Andrei, Kalutarage, Harsha

“… In this study, we propose a highly efficient model called optimized common features selection and deep-autoencoder (OCFSDA…”
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Optimised autoencoder-based ensemble deep learning approaches for cyber-physical event classification utilizing synchrophasor PMU data Autor M, Dhinu Lal, Varadarajan, Ramesh

“… The primary objective is to develop a reliable and optimised Deep Learning (DL) framework capable of classifying Power System (PS…”
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Study on transformer fault diagnosisbased on improved deep residual shrinkage network and optimized residual variational autoencoder Autor Yao, Haiyan, Xu, Yuefei, Guo, Qiang, Chen, Shizhe, Lu, Bin, Huang, Yuanjun

“…) and optimized residual variational autoencoders (ORVAE) is proposed. Firstly, this study improves the DRSN to enhance its feature extraction capability…”
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Deep autoencoder-driven feature learning and meta-heuristic optimized machine learning modelling for crop water stress identification Autor Subeesh, A., Chauhan, Naveen, Chandel, Narendra Singh, Rajwade, Yogesh

“…) and deep learning models (DL) due to their remarkable ability to perform complex data analysis…”
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DRED: Deep REDundancy Coding of Speech Using a Rate-Distortion-Optimized Variational Autoencoder Autor Valin, Jean-Marc, Buthe, Jan, Mustafa, Ahmed, Klingbeil, Michael

“… We propose a new approach based on a rate-distortion-optimized variational autoencoder (RDO-VAE), allowing us to optimize a deep speech compression algorithm for the task of encoding large amounts of redundancy at very low bitrate…”
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An optimized deep learning approach based on autoencoder network for P300 detection in brain computer interface systems Autor Afrah, Ramin, Amini, Zahra, Kafieh, Rahele, Vard, Alireza

“…) and autoencoder networks. Methods. In the pre-processing step, channel selection, data augmentation (by ADASYN method…”
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Adaptive VMD based optimized deep learning mixed kernel ELM autoencoder for single and multistep wind power forecasting

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