News

IEEE2d
A Novel Deep CNN Model for Classification of Brain Tumor from MR Images
This work introduces a new deep neural network model Lu-Net with less layers, less complexity and very efficient ... and augmented for accurate and rapid training of deep convolutional neural network ...
IEEE3d
Leveraging Deep CNN and Transfer Learning for Side-Channel Attack
Although deep learning is being widely adopted for computer vision, less research has been prominent in template-based profiling power SCA attacks. In addition, most of the existing works fall into a ...
IEEE4d
A Deep 1-D CNN and Bidirectional LSTM Ensemble Model With Arbitration Mechanism for LDDoS Attack Detection
Next, a competitive network composed of 1-D Convolutional Neural Network (1-D CNN) and a bidirectional Long Short Term Memory (bi-LSTM) network is employed to learn both spatial and temporal ...
IEEE6d
Adaptation of Autoencoder for Sparsity Reduction From Clinical Notes Representation Learning
Abstract: When dealing with clinical text classification on a small dataset, recent studies have confirmed that a well-tuned multilayer perceptron outperforms other generative classifiers, including ...
IEEE7d
Research of 3D face recognition algorithm based on deep learning stacked denoising autoencoder theory
With the significant advantage of the characterization of essential features by learning a deep nonlinear network, this paper presents a stacked denoising autoencoder algorithm model based on deep ...
IEEE12d
Deep Reinforcement Learning Based Double-layer Optimization Method for Energy Management of Microgrid
Therefore, this paper proposes a double-layer optimization method based on deep reinforcement learning (DRL) to solve this problem. The upper DRL agent takes Soft actor-critic algorithm to fully ...
IEEE13d
Benchmarking Class Incremental Learning in Deep Learning Traffic Classification
Abstract: Traffic Classification (TC) is experiencing a renewed interest, fostered by the growing popularity of Deep Learning (DL) approaches. In exchange for their proved effectiveness, DL models are ...
IEEE14d
Layer-Wise Mixed-Modes CNN Processing Architecture With Double-Stationary Dataflow and Dimension-Reshape Strategy
Abstract: With the development of convolutional neural networks (CNN) across various domains ... On the software side, a hessian-guided layer precision mapping is adopted to reduce unnecessary ...
IEEE15d
Enhancing CNN Performance Across Diverse Domains: Exploring the Efficacy of Domain Adversarial Neural Network with the Use of CNN for Improved Generalization in Deep Learning
Abstract: Convolutional neural networks (CNNs) are foundational tools for image classification in deep learning, with models like InceptionNet ... This study explores the efficacy of DANN in boosting ...
IEEE15d
Dual Autoencoder-Based Joint Learning: Enhancing Depth Classification of Hard Inclusions in Soft Tissue for Robotic Palpation
To explore the joint learnability of datasets collected from different tactile sensors, we design a dual autoencoder-based joint learning framework that integrates two recurrent autoencoders to ...
IEEE16d
Learning From Noisy Labels With Deep Neural Networks: A Survey
Abstract: Deep learning has achieved remarkable success in numerous domains with help from large amounts of big data. However, the quality of data labels is a concern because of the lack of ...
IEEE16d
Human Emotion Detection using Deep CNN
This research paper delves into the application of deep CNNs for human emotion detection. Leveraging datasets rich in annotated facial expressions, our study explores the architecture and training ...