Malware visualization methods based on deep convolution neural networks
Sign Up to like & getrecommendations! Published in 2019 at "Multimedia Tools and Applications"
DOI: 10.1007/s11042-019-08310-9
Abstract: In this paper, we propose two visualization methods for malware analysis based on n-gram features of byte sequences. The space filling curve mapping (SFCM) method uses fractal curves to visualize the one-gram features of byte… read more here.
Keywords: gram features; deep convolution; visualization methods; byte sequences ... See more keywords
Deep Convolution Neural Networks for Twitter Sentiment Analysis
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Access"
DOI: 10.1109/access.2017.2776930
Abstract: Twitter sentiment analysis technology provides the methods to survey public emotion about the events or products related to them. Most of the current researches are focusing on obtaining sentiment features by analyzing lexical and syntactic… read more here.
Keywords: convolution neural; sentiment; twitter sentiment; deep convolution ... See more keywords
Comparative Study on Deep Convolution Neural Networks DCNN-Based Offline Arabic Handwriting Recognition
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.2994290
Abstract: Recently, deep learning techniques demonstrated efficiency in building better performing machine learning models which are required in the field of offline Arabic handwriting recognition. Our ancient civilizations presented valuable handwritten manuscripts that need to be… read more here.
Keywords: deep convolution; arabic handwriting; offline arabic; comparative study ... See more keywords
LieNet: A Deep Convolution Neural Network Framework for Detecting Deception
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Cognitive and Developmental Systems"
DOI: 10.1109/tcds.2021.3086011
Abstract: Nowadays, automatic deception detection has received considerable attention in the machine learning community owing to this research interest to its vast applications in the fields of social media, interviews, law enforcement, and the military. In… read more here.
Keywords: neural network; convolution neural; lienet; deception ... See more keywords
Deep Convolution Generative Adversarial Network-Based Electroencephalogram Data Augmentation for Post-Stroke Rehabilitation with Motor Imagery
Sign Up to like & getrecommendations! Published in 2022 at "International journal of neural systems"
DOI: 10.1142/s0129065722500393
Abstract: The motor imagery brain-computer interface (MI-BCI) system is currently one of the most advanced rehabilitation technologies, and it can be used to restore the motor function of stroke patients. The deep learning algorithms in the… read more here.
Keywords: eeg data; rehabilitation; stroke; deep convolution ... See more keywords
A Torn ACL Mapping in Knee MRI Images Using Deep Convolution Neural Network with Inception-v3
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Healthcare Engineering"
DOI: 10.1155/2022/7872500
Abstract: The anterior cruciate ligaments (ACL) are the fundamental structures in preserving the common biomechanics of the knees and most frequently damaged knee ligaments. An ACL injury is a tear or sprain of the ACL, one… read more here.
Keywords: inception; mri images; mri; model ... See more keywords
Classification of Photoplethysmographic Signal Quality with Deep Convolution Neural Networks for Accurate Measurement of Cardiac Stroke Volume
Sign Up to like & getrecommendations! Published in 2020 at "Applied Sciences"
DOI: 10.3390/app10134612
Abstract: As photoplethysmographic (PPG) signals are comprised of numerous pieces of important physiological information, they have been widely employed to measure many physiological parameters. However, only a high-quality PPG signal can provide a reliable physiological assessment.… read more here.
Keywords: quality; convolution neural; neural networks; ppg signals ... See more keywords