News

IEEE1d
Deep Reinforcement Learning With Communication Transformer for Adaptive Live Streaming in Wireless Edge Networks - IEEE Xplore
The emerging mobile edge computing (MEC) technology has been recently applied to improve the Quality of Experience (QoE) of network services, such as live video streaming. In this paper, we study an ...
IEEE1d
Integrated Monitoring of Stress and Damage Using CNN Deep Learning of Electromechanical Impedance Responses of CSA Sensor - IEEE Xplore
A multitask 2D CNN model is designed for integrated monitoring stress and damage in concrete specimens utilizing the raw impedance signatures of capsule-like smart aggregated (CSA). The fundamental ...
IEEE2d
Achieving Multisite Generalization for CNN-Based Disease Diagnosis Models by Mitigating Shortcut Learning - IEEE Xplore
When a learned model has high accuracy under familiar settings (internal testing) and a big drop in accuracy under slightly different circumstances (external testing) we suspect it is using shortcuts ...
IEEE2d
Musical Instrument Classification Using Deep Learning CNN Models
Musical instrument recognition is the act of counting on machine learning or the application of signal processing to isolate and classify various musical instruments in an audio track. Such capability ...
IEEE3d
Comparative Analysis of Pre-Trained CNN Backbones and Machine Learning Techniques for High Similarity in Circular Object Image Classification - IEEE Xplore
This research presents a comprehensive comparative analysis of various pre-trained backbone models and machine learning techniques for output layers in convolutional neural networks (CNNs) applied to ...
IEEE4d
Dynamic Semantic Compression for CNN Inference in Multi-Access Edge Computing: A Graph Reinforcement Learning-Based Autoencoder - IEEE Xplore
This paper studies the computational offloading of CNN inference in dynamic multi-access edge computing (MEC) networks. To address the uncertainties in communication time and edge servers’ available ...
IEEE4d
An Interpretable Brain Tumour Classification Approach using Machine Learning and CNN-based features - IEEE Xplore
Cancer, a disease that does not discriminate; impacting people from all walks of life. Numerous types of cancer affect humans, each with distinct characteristics and treatment approaches. One of the ...
IEEE4d
CNN Based Dense Underwater 3D Scene Reconstruction by Transfer Learning Using Bubble Database - IEEE Xplore
Dense 3D shape acquisition of swimming human or live fish is an important research topic for sports, biological science and so on. For this purpose, active stereo sensor is usually used in the air, ...
IEEE5d
Application of Deep Reinforcement Learning on Automated Stock Trading - IEEE Xplore
How to make right decisions in stock trading is a vital and challenging task for investors. Since deep reinforcement learning (DRL) has outperformed human beings in many fields such as playing Atari ...
IEEE5d
Deep CM-CNN for Spectrum Sensing in Cognitive Radio
One of the key problems in spectrum sensing is to design the test statistic. Existing methods generally exploit the model-based features as the test statistic, such as energies and eigenvalues.
IEEE6d
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning - IEEE Xplore
Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning ...
IEEE6d
Deep Reinforcement Learning on Autonomous Driving Policy With Auxiliary Critic Network - IEEE Xplore
Deep reinforcement learning (DRL) is a machine learning method based on rewards, which can be extended to solve some complex and realistic decision-making problems. Autonomous driving needs to deal ...