Search Results for author:
Found 18 papers, 2 papers with code
Diagnosis of Skin Cancer Using VGG16 and VGG19 Based Transfer Learning Models
The proposed model increased classification accuracy by 3% (from 94. 2% to 98. 18%) in comparison with other methods.
A Lightweight Attention-based Deep Network via Multi-Scale Feature Fusion for Multi-View Facial Expression Recognition
On the other hand, the PWFS block employs a feature selection mechanism that discards less meaningful features prior to the fusion process.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Non-Intrusive Load Monitoring (NILM) using Deep Neural Networks: A Review
Various methods, including machine learning and deep learning, have been used to implement and improve NILM algorithms.
Sequence-to-Sequence Model with Transformer-based Attention Mechanism and Temporal Pooling for Non-Intrusive Load Monitoring
This paper presents a novel Sequence-to-Sequence (Seq2Seq) model based on a transformer-based attention mechanism and temporal pooling for Non-Intrusive Load Monitoring (NILM) of smart buildings.
Electricity Price Forecasting Model based on Gated Recurrent Units
The participation of consumers and producers in demand response programs has increased in smart grids, which reduces investment and operation costs of power systems.
Spectral Unmixing of Hyperspectral Images Based on Block Sparse Structure
Spectral unmixing is then used as a technique to extract the spectral characteristics of the different materials within the mixed pixels and to recover the spectrum of each pure spectral signature, called endmember.
Deep Learning Based Framework for Iranian License Plate Detection and Recognition
Also, the Faster R-CNN network trained and tested on the developed dataset and achieved 98. 97% recall, 99. 9% precision, and 98. 8% accuracy.
Short-Term Load Forecasting Using Time Pooling Deep Recurrent Neural Network
In this paper, we review some of the existing Deep Learning-based methods and present our solution using Time Pooling Deep Recurrent Neural Network.
Drone Detection Using Convolutional Neural Networks
In this study, the drone was detected using three methods of classification of convolutional neural network (CNN), support vector machine (SVM), and nearest neighbor.
Clustered Multitask Nonnegative Matrix Factorization for Spectral Unmixing of Hyperspectral Data
In this paper, the new algorithm based on clustered multitask network is proposed to solve spectral unmixing problem in hyperspectral imagery.
Sparsity Constrained Distributed Unmixing of Hyperspectral Data
Each pixel in the hyperspectral images is considered as a node in this network.
Hyperspectral Unmixing Based on Clustered Multitask Networks
In this paper, at first hyperspectral images are clustered by fuzzy c- means method, and then a new algorithm based on sparsity constrained distributed optimization is used for spectral unmixing.
Distributed Unmixing of Hyperspectral Data With Sparsity Constraint
Simulation results based on defined performance metrics, illustrate advantage of the proposed algorithm in spectral unmixing of hyperspectral data compared with other methods.
Multilayer Structured NMF for Spectral Unmixing of Hyperspectral Images
Sparseness constraint on both spectral signatures and abundance fractions matrices are used in this paper.
Sparsity Constrained Graph Regularized NMF for Spectral Unmixing of Hyperspectral Data
The presence percentages of endmembers in mixed pixels are called abundance fractions.
Spectral Unmixing of Hyperspectral Imagery using Multilayer NMF
In this letter we proposed using multilayer NMF (MLNMF) for the purpose of hyperspectral unmixing.
Fusion of Hyperspectral and Panchromatic Images using Spectral Uumixing Results
On the other hand panchromatic image has a better spatial resolution.
Hyperspectral Data Unmixing Using GNMF Method and Sparseness Constraint
In this paper we have used graph regularized (GNMF) method with sparseness constraint to unmix hyperspectral data.
