AI Medical Compendium Topic

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Data Augmentation for Deep-Learning-Based Multiclass Structural Damage Detection Using Limited Information.

Sensors (Basel, Switzerland)
The deterioration of infrastructure's health has become more predominant on a global scale during the 21st century. Aging infrastructure as well as those structures damaged by natural disasters have prompted the research community to improve state-of...

Convolution neural network with batch normalization and inception-residual modules for Android malware classification.

Scientific reports
Deep learning technology is changing the landscape of cybersecurity research, especially the study of large amounts of data. With the rapid growth in the number of malware, developing of an efficient and reliable method for classifying malware has be...

GLD-Net: Deep Learning to Detect DDoS Attack via Topological and Traffic Feature Fusion.

Computational intelligence and neuroscience
Distributed denial of service (DDoS) attacks are the most common means of cyberattacks against infrastructure, and detection is the first step in combating them. The current DDoS detection mainly uses the improvement or fusion of machine learning and...

Analysis of e-Mail Spam Detection Using a Novel Machine Learning-Based Hybrid Bagging Technique.

Computational intelligence and neuroscience
e-mail service providers and consumers find it challenging to distinguish between spam and nonspam e-mails. The purpose of spammers is to spread false information by sending annoying messages that catch the attention of the public. Various spam ident...

Risky-Driving-Image Recognition Based on Visual Attention Mechanism and Deep Learning.

Sensors (Basel, Switzerland)
Risky driving behavior seriously affects the driver's ability to react, execute and judge, which is one of the major causes of traffic accidents. The timely and accurate identification of the driving status of drivers is particularly important, since...

A Lightweight CNN Model Based on GhostNet.

Computational intelligence and neuroscience
The existing deep learning models have problems such as large weight parameters and slow inference speed of equipment. In practical applications such as fire detection, they often cannot be deployed on equipment with limited resources due to the huge...

A dynamic AES cryptosystem based on memristive neural network.

Scientific reports
This paper proposes an advanced encryption standard (AES) cryptosystem based on memristive neural network. A memristive chaotic neural network is constructed by using the nonlinear characteristics of a memristor. A chaotic sequence, which is sensitiv...

In-depth evaluation of machine learning methods for semi-automating article screening in a systematic review of mechanistic literature.

Research synthesis methods
We aimed to evaluate the performance of supervised machine learning algorithms in predicting articles relevant for full-text review in a systematic review. Overall, 16,430 manually screened titles/abstracts, including 861 references identified releva...

Explainable Artificial Intelligence-Based IoT Device Malware Detection Mechanism Using Image Visualization and Fine-Tuned CNN-Based Transfer Learning Model.

Computational intelligence and neuroscience
Automated malware detection is a prominent issue in the world of network security because of the rising number and complexity of malware threats. It is time-consuming and resource intensive to manually analyze all malware files in an application usin...

A Novel Method for Improved Network Traffic Prediction Using Enhanced Deep Reinforcement Learning Algorithm.

Sensors (Basel, Switzerland)
Network data traffic is increasing with expanded networks for various applications, with text, image, audio, and video for inevitable needs. Network traffic pattern identification and analysis of traffic of data content are essential for different ne...