Artificial intelligence, data science, and automation are just a few of the fields where machine learning has emerged as an influential force. The choice of programming language is crucial for the creation and impleme...
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RGBT tracking is an important research direction in the field of computer vision and has received increasing attention. It aims to exploit the complementary advantages between RGB and TIR modalities to achieve robust ...
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Sonar signal processing simulator is used for simulating ship-radiated noise received by hydrophones in different marine environment. Application of the simulator will greatly reduce the frequency of experiments on la...
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With the rapid development of the production economy, electrified EMU trains have become the main means of transportation for people to travel. But the ensuing power quality problem has also become one of the main con...
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Maximizing influence propagation in modern social networks is essential for applications like viral marketing, information diffusion, and network resilience. Traditional methods, like centrality-based metrics and comm...
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Autonomous Underwater Vehicles (AUVs) encounter various challenges, such as navigation and obstacle avoidance, in complex underwater environments. This paper presents an intelligent path planning algorithm for AUVs th...
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Machine Learning (ML) has revolutionized various sectors including speech recognition, product recommendations, and healthcare. This research focuses on applying ML techniques to address challenges in real estate mark...
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Classifying brain tumors is vital to medical diagnosis since earlv identification and the distinction between benign and malignant tumors can greatly enhance patient outcomes. In this work. we use magnetic resonance i...
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This study aims to address the issues of low efficiency and high manual involvement in existing government affairs processes by proposing an automated system based on large model *** system integrates modules such as ...
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The rapid development of computer networks, such as the internet, has caused a sea change in the ways in which people communicate and share information. The advent of widespread computer networks like the internet has...
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ISBN:
(纸本)9798331505745
The rapid development of computer networks, such as the internet, has caused a sea change in the ways in which people communicate and share information. The advent of widespread computer networks like the internet has caused a sea change in traditional modes of communication and information sharing. On the other hand, this technical progress has also opened the door for malicious actors to take advantage of loopholes and steal sensitive information, disrupt operations, etc. Therefore, intrusion detection systems (IDSs) are essential for screening harmful traffic and warding off frequent attacks. These algorithms used to work by comparing current attacks to those of the past or by following a predetermined set of rules. But now that data and computing power are more accessible than ever before, machine learning seems to be the way to go. In this study, to present DSAM-CGLSTM, a dual-staged attention mechanism based on conversion-gated Long Short Term Memory network, to capture both short-term mutation and long-term dependence info in the study dataset. To enhance the network's capacity to retrieve the short-term mutation information, hyperbolic tangent functions are incorporated into the input-gate and forget-gate of LSTM. Additionally, the network incorporates a dual-staged attention mechanism, which encompasses both input attention and temporal attention. Consequently, a novel algorithm for optimising the parameters of DSAM-CGLSTM, SSA, has been suggested in this research, which is based on metaheuristics. to use a single Jupyter notebook in the Google Colaboratory environment to conduct the entire experiment. to imported and then implemented the necessary software packages, including seaborn, pandas, matplotlib, keras, besides Tensor Flow, and to used the CICIDS 2017 dataset. During the model's training and validation processes, accuracy and loss were taken into account as performance indicators. With a 98.85% accuracy score, the deep learning model performed quit
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