In modern military operations, as camouflage technology advances, soldiers, weaponry, and other military materials have become highly similar to their surroundings, making it difficult for traditional optical target r...
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The aim of this research paper is to investigate how machine learning can be incorporated to improve security around multi-clouds, with specific reference to the challenges. Based on the datasets obtained from Kaggle,...
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The proposed super resolution reconstruction method aims to address the issues of missing detailed information in low light environments and poor recovery of high frequency details in reconstructed images. To fully ex...
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In the past, a small size microcontroller was needed to control and gather data from resource-constrained electronic devices aimed at Internet of Things (IoT) applications. While the server nodes receive the requests ...
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multi-camera depth estimation has gained significant attention in autonomous driving due to its importance in perceiving complex environments. However, extending monocular self-supervised methods to multi-camera setup...
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Facial expression recognition is an important subtask in the field of computer vision. Accurate recognition of facial expressions plays a critical role in applications such as human-computer interaction, emotion analy...
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The importance of establishing a strong and resilient cyber-security threat detection system has become increasingly evident. In recent years, a multitude of methodologies have been developed to identify and mitigate ...
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ISBN:
(纸本)9783031537271;9783031537288
The importance of establishing a strong and resilient cyber-security threat detection system has become increasingly evident. In recent years, a multitude of methodologies have been developed to identify and mitigate security problems within computer networks. This study presents a novel methodology for categorizing security risks and effectively tackling these obstacles. Through the utilization of computer vision, network traffic data is converted into visual depictions, facilitating the discernment between secure traffic and possibly malevolent endeavors aimed at infiltrating a network. Furthermore, the integration of a Generative Adversarial Network (GAN) assumes a crucial function in enhancing data and reducing bias in the classification procedure. The focus of this study is around two critical classification components: binary classification, which involves deciding whether a given traffic instance is classified as safe or malicious, and multi-class classification, which involves identifying the specific sort of attack if the instance is truly classified as an attack. By utilizing advanced deep learning models, this study has produced notable outcomes, attaining a commendable level of precision of around 95% in both binary and multi-classification situations. The aforementioned results highlight the effectiveness and potential of the suggested methodology within the field of cybersecurity.
Airborne radar plays an important role in sea surface search and rescue and national defense, and the analysis of control variables in multiple dimensions of the sea conditions of the sea surface to be detected, the f...
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A picker control device based on a truss manipulator integrates the information of various sensors such as force sense, temperature sense, vision and distance, and timely feedback information to realize the flexible p...
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multilevel threshold image segmentation is one of the most widely used methods. Thus, the determination of the best thresholds is crucial. Swarm intelligence optimization algorithm has been widely used to solve the op...
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