CORTEXA
← Browse
crossrefElectronics2024-01-30Cited by 1

Guest Editorial: Foreword to the Special Issue on Advanced Research and Applications of Deep Learning and Neural Network in Image Recognition

Ganggang Dong, Yuanxin Ye, Zhongling Huang

Over the last two decades, the realm of image recognition has undergone a remarkable transformation, characterized by an astonishing pace of advancement [...]

View free PDFSource page

Related papers

crossrefElectronics2025-01-19Cited by 17

AI on Wheels: Bibliometric Approach to Mapping of Research on Machine Learning and Deep Learning in Electric Vehicles

Adrian Domenteanu, Liviu-Adrian Cotfas, Paul Diaconu, George-Aurelian Tudor, Camelia Delcea

The global transition to sustainable energy systems has placed the use of electric vehicles (EVs) among the areas that might contribute to reducing carbon emissions and optimizing energy usage. This paper presents a bibliometric analysis of the interconnected domains of EVs, arti…

View free PDFSource page
crossrefElectronics2025-04-16Cited by 2

Batchnorm-Free Binarized Deep Spiking Neural Network for a Lightweight Machine Learning Model

Hasna Nur Karimah, Chankyu Lee, Yeongkyo Seo

The development of deep neural networks, although demonstrating astounding capabilities, leads to more complex models, high energy consumption, and expensive hardware costs. While network quantization is a widely used method to address this problem, the typical binary neural netw…

View free PDFSource page
crossrefElectronics2025-03-28Cited by 5

Novel Learning Framework with Generative AI X-Ray Images for Deep Neural Network-Based X-Ray Security Inspection of Prohibited Items Detection with You Only Look Once

Dongsik Kim, Jinho Kang

As the rapid expansion of future mobility systems increases, along with the demand for fast and accurate X-ray security inspections, deep neural network (DNN)-based systems have gained significant attention for detecting prohibited items by constructing high-quality datasets and…

View free PDFSource page
crossrefElectronics2025-09-22Cited by 9

Machine Learning and Neural Networks for Phishing Detection: A Systematic Review (2017–2024)

Jacek Lukasz Wilk-Jakubowski, Lukasz Pawlik, Grzegorz Wilk-Jakubowski, Aleksandra Sikora

Phishing remains a persistent and evolving cyber threat, constantly adapting its tactics to bypass traditional security measures. The advent of Machine Learning (ML) and Neural Networks (NN) has significantly enhanced the capabilities of automated phishing detection systems. This…

View free PDFSource page
crossrefElectronics2024-12-27Cited by 4

Detection of Domain Name Server Amplification Distributed Reflection Denial of Service Attacks Using Convolutional Neural Network-Based Image Deep Learning

Hoon Shin, Jaeyeong Jeong, Kyumin Cho, Jaeil Lee, Ohjin Kwon, Dongkyoo Shin

Domain Name Server (DNS) amplification Distributed Reflection Denial of Service (DRDoS) attacks are a Distributed Denial of Service (DDoS) attack technique in which multiple IT systems forge the original IP of the target system, send a request to the DNS server, and then send a l…

View free PDFSource page