Computational intelligence and neuroscience
Jul 31, 2022
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...
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...
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...
Computational intelligence and neuroscience
Jul 15, 2022
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...
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...
Computational intelligence and neuroscience
Jun 25, 2022
People are actively expressing their views and opinions via the use of visual pictures and text captions on social media platforms, rather than just publishing them in plain text as a consequence of technical improvements in this field. With the adve...
This study proposes a simple convolutional neural network (CNN)-based model for vehicle classification in low resolution surveillance images collected by a standard security camera installed distant from a traffic scene. In order to evaluate its effe...
Computational intelligence and neuroscience
Jun 20, 2022
In this paper, a multimodal knowledge mapping approach is used to digitize enterprise carbon assets, and a corresponding neural network model is designed for use in the practical process. Rich textual entity labels associated with images are obtained...
It is a well-established practice to build a robust system for sound event detection by training supervised deep learning models on large datasets, but audio data collection and labeling are often challenging and require large amounts of effort. This...
BACKGROUND: Real-world data (RWD) is increasingly being embraced as an invaluable source of information to address clinical and policy-relevant questions that are unlikely to ever be answered by clinical trials. However, the largely unrealised potent...
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