Results 1 to 10 of about 6,908 (119)

Deep Learning-Enhanced Autoencoder for Multi-Carrier Wireless Systems

Mathematics
In a multi-carrier (MC) system, the transmitted data are split across several sub-carriers as a crucial approach for achieving high data rates, reliability, and spectral efficiency.
Md Abdul Aziz, Md Habibur Rahman, Rana Tabassum, Mohammad Abrar Shakil Sejan, Myung-Sun Baek, Hyoung-Kyu Song   +5 more
doaj   +1 more source

Detection of Bacterial Leaf Spot Disease in Sesame (Sesamum indicum L.) Using a U-Net Autoencoder

Remote Sensing
Hyperspectral imaging (HSI) integrates spectroscopy and imaging, providing detailed spectral–spatial information, and the selection of task-relevant wavelengths can streamline data acquisition and processing for field deployment.
Minju Lee, Jeseok Lee, Amit Ghimire, Yegyeong Bae, Tae-An Kang, Youngnam Yoon, In-Jung Lee, Choon-Wook Park, Byungwon Kim, Yoonha Kim   +9 more
doaj   +1 more source

Denoising Autoencoder and Contrast Enhancement for RGB and GS Images with Gaussian Noise

Mathematics
Robust image processing systems require input images that closely resemble real-world scenes. However, external factors, such as adverse environmental conditions or errors in data transmission, can alter the captured image, leading to information loss ...
Armando Adrián Miranda-González, Alberto Jorge Rosales-Silva, Dante Mújica-Vargas, Edwards Ernesto Sánchez-Ramírez, Juan Pablo Francisco Posadas-Durán, Dilan Uriostegui-Hernandez, Erick Velázquez-Lozada, Francisco Javier Gallegos-Funes   +7 more
doaj   +1 more source

An Optimized Transformer–GAN–AE for Intrusion Detection in Edge and IIoT Systems: Experimental Insights from WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT Datasets

Future Internet
The rapid expansion of Edge and Industrial Internet of Things (IIoT) systems has intensified the risk and complexity of cyberattacks. Detecting advanced intrusions in these heterogeneous and high-dimensional environments remains challenging.
Ahmad Salehiyan, Pardis Sadatian Moghaddam, Masoud Kaveh   +2 more
doaj   +1 more source

Probabilistic Principal Component Analysis and Channel Attention for End-to-End Image Compression Optimization

IEEE Access
In recent years, deep learning has shown significant progress for image compression compared to traditional image compression methods. Although conventional standard-based methods are still used, they are limited in handling repetitive patterns and ...
Andri Agustav Wirabudi, Sung-Chang Lim, Woong Lim, Jeongil Seo, Haechul Choi   +4 more
doaj   +1 more source

AutoEffFusionNet: A New Approach for Cervical Cancer Diagnosis Using ResNet-Based Autoencoder With Attention Mechanism and Genetic Feature Selection

IEEE Access
Cervical cancer poses a significant global health challenge, necessitating accurate and efficient diagnostic solutions. This study introduces a novel hybrid framework, AutoEffFusionNet, that integrates unsupervised feature learning through ResNet-based ...
Yahya Dogan
doaj   +1 more source

Ensemble-Based Model-Agnostic Meta-Learning with Operational Grouping for Intelligent Sensory Systems

Sensors
Model-agnostic meta-learning (MAML), coupled with digital twins, is transformative for predictive maintenance (PdM), especially in robotic arms in assembly lines, where rapid and accurate fault classification of arms is essential.
Mainak Mallick, Young-Dae Shim, Hong-In Won, Seung-Kyum Choi   +3 more
doaj   +1 more source

High-Capacity Coherent WDM Networks Empowered by Probabilistic Shaping and End-to-End Deep Learning

Journal of Telecommunications and Information Technology
To optimize the functionality of coherent optical fiber communication (OFC) systems and enhance their capacity related to long-haul transmissions, wavelength-division multiplexing (WDM) and probabilistic constellation shaping (PCS) techniques have been ...
Ayam M. Abbass, Raad Fyath
doaj   +1 more source

Deep Neural Network with Anomaly Detection for Single-Cycle Battery Lifetime Prediction

Batteries
Large-scale battery datasets often contain anomalous data due to sensor noise, communication errors, and operational inconsistencies, which degrade the accuracy of data-driven prognostics.
Junghwan Lee, Longda Wang, Hoseok Jung, Bukyu Lim, Dael Kim, Jiaxin Liu, Jong Lim   +6 more
doaj   +1 more source

A comprehensive survey on design and application of autoencoder in deep learning

Applied Soft Computing Journal, 2023
Yan Pei, Jianqiang Li
exaly