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.
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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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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In this research work, the authors present an improved secure search algorithm that will allow for optimum multi-keyword ranked search matching in public cloud storage that uses encrypted data. The goal of the plan is...
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In the era of digital world data flows are saturated with development of universal multifunctional system to solve problems and to optimise the computing resources. The information system is highly loaded with modern ...
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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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Learning modality-fused representations and processing unaligned multimodal sequences are meaningful and challenging in multimodal emotion *** approaches use directional pairwise attention or a message hub to fuse lan...
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Learning modality-fused representations and processing unaligned multimodal sequences are meaningful and challenging in multimodal emotion *** approaches use directional pairwise attention or a message hub to fuse language,visual,and audio ***,these fusion methods are often quadratic in complexity with respect to the modal sequence length,bring redundant information and are not *** this paper,we propose an efficient neural network to learn modality-fused representations with CB-Transformer(LMR-CBT)for multimodal emotion recognition from unaligned multi-modal ***,we first perform feature extraction for the three modalities respectively to obtain the local structure of the ***,we design an innovative asymmetric transformer with cross-modal blocks(CB-Transformer)that enables complementary learning of different modalities,mainly divided into local temporal learning,cross-modal feature fusion and global self-attention *** addition,we splice the fused features with the original features to classify the emotions of the ***,we conduct word-aligned and unaligned experiments on three challenging datasets,IEMOCAP,CMU-MOSI,and *** experimental results show the superiority and efficiency of our proposed method in both *** with the mainstream methods,our approach reaches the state-of-the-art with a minimum number of parameters.
A significant factor in data transfer is computerscience. Security considerations make it difficult to transmit sensitive data or have conversations via the internet. Typically, cryptography is used to transmit text-...
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This paper presents a novel system for dynamic playlist recommendations by integrating multi-modal mood detection with contextual learning. The system utilizes three primary data sources: text-based sentiment analysis...
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Intrusion Detection Systems (IDS) rely heavily on accurate classification of network traffic to identify malicious activity. This paper explores an ensemble learning approach for IDS that prioritizes high recall to mi...
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