Credit card fraud detection is an increasingly critical issue due to the growth of digital transactions and the sophistication of fraudulent activities. This study proposes a hybrid framework combining Graph neural Ne...
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Malicious code detection is one of the important research directions in the field of cybersecurity. Converting code into image information using convolutional neuralnetworks (CNN) for malicious code detection has bee...
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An overview of the use of Generative Adversarial networks (GANs) in computer vision-image synthesis and manipulation-is given in this study. A generator and a discriminator are two complex neuralnetworks that are tra...
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The proceedings contain 172 papers. The topics discussed include: augmented reality in education: enhancing learning experiences;vision-based hand gesture recognition system for assistive communication using neural ne...
ISBN:
(纸本)9798331530389
The proceedings contain 172 papers. The topics discussed include: augmented reality in education: enhancing learning experiences;vision-based hand gesture recognition system for assistive communication using neuralnetworks and GSM integration;advancing techniques for deepfake detection and evaluation: challenges and innovations;continual learning techniques to reduce forgetting: a comparative study;adaptive fuzzy logic framework for threat detection in distributed honeypot systems leveraging blockchain technology;decentralizing cybersecurity: a dynamic distributed honeypot system leveraging blockchain technology;skin lesion prediction and classification using deep learning methods;statistical analysis of social media and e-commerce data to get insights on consumer behavior;and analyzing the historical data and trends in stock market using machine learning.
In view of the sudden and unpredictable nature of stroke, with patients often having no obvious symptoms or risk factors, a stroke risk prediction model based on deep neural network (DNN) and CatBoost was proposed, co...
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Object detection is the backbone of many modern real-world applications, including autonomous driving, surveillance, and robotics. As the object detection technology improves, particularly convolutional neural network...
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An autonomous driving system requires efficient image recognition to interpret the environment, detect obstacles, and make real-time decisions. This study compares Convolutional neuralnetworks (CNNs) and Vision Trans...
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Data Augmentation is a prevalent practice within computer vision, which uses transformations like random flipping, rotation, jittered colors and advanced techniques like Mixup and CutMix to artificially increase a tra...
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The integration of deep learning into visual communication design offers transformative possibilities for style transfer and automation. This paper proposes a framework that combines neural style transfer (NST) techni...
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Image Super-Resolution (SR) is a key challenge in computer vision, focusing on enhancing low-resolution images while preserving details. This paper presents a novel method using Generative Adversarial networks (GANs) ...
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