Sign language is a vital mode of communication for the deaf and dumb community. This research presents a robust and real-time Gesture-Based Sign Language Detection System that leverages computer vision and deep learni...
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In recent times, daily interactions have been taking place largely on online platforms, with forms of interactions such as ***,an important problem lies in the understanding of these messages. These textual interchang...
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Falling at home is the most common cause of injury among the elderly. Wearable devices are inconvenient and costly, while camera systems pose risks to privacy. A fall detection method employing millimeter-wave radar c...
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This research paper explores the application of deep learning techniques for the detection of AI-generated images within the context of news and journalism. In response to the escalating risk posed by the pervasive us...
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With the increasing popularity of digital video applications, video restoration techniques have become increasingly important. This paper presents a flow-based video restoration method that aims to achieve high-qualit...
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Network traffic classification is a critical concern in network security and management, essential for accurately differentiating among various network applications, optimizing service quality, and improving user expe...
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Network traffic classification is a critical concern in network security and management, essential for accurately differentiating among various network applications, optimizing service quality, and improving user experience. The exponential increase in worldwide Internet users and network traffic is continuously augmenting the diversity and complexity of network applications, rendering the Internet environment increasingly intricate and dynamic. Conventional machine learning techniques possess restricted processing abilities for network traffic attributes and struggle to address the progressively intricate traffic classification tasks in contemporary networks. In recent years, the swift advancement of deep learning technologies, particularly Graph Neural Networks (GNN), has yielded significant improvements in network traffic classification. GNN can capture the structured information among network nodes and extract the latent features of network traffic. Nonetheless, current network traffic classification models continue to exhibit deficiencies in the thoroughness of feature extraction. To tackle the problem, this research proposes a method for constructing traffic graphs utilizing numerical similarity and byte distance proximity by exploring the latent correlations among bytes, and it constructs a model, SDA-GNN, based on Graph Isomorphic Networks (GIN) for the categorization of network traffic. In particular, the Dynamic Time Warping (DTW) distance is employed to evaluate the disparity in byte distributions, a channel attention mechanism is utilized to extract additional features, and a Long Short-Term Memory Network (LSTM) enhances the stability of the training process by extracting sequence characteristics. Experimental findings on two actual datasets indicate that the SDA-GNN model surpasses other baseline techniques across multiple assessment parameters in the network traffic classification task, achieving classification accuracy enhancements of 2.19% and 1.49%
This project introduces an innovative earthquake detection system leveraging accelerometers for seismic sensing. By incorporating accelerometers from widely available devices like smartphones into a distributed sensor...
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In the context of medical research, brain tumors present a challenging problem due to their potential for rapid growth and their impact on overall health. Classifying brain tumors is an essential task for evaluating t...
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In real data, there are dependencies between at-tributes, and most distance metrics make attribute independence assumptions, and although they show superior performance, when there are strong dependencies between attr...
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India's agricultural sector is the backbone of the country, yet there are inefficiencies, a lack of transparency, and unequal farmer prices in its supply chain. Issues including inadequate market accessibility, in...
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ISBN:
(纸本)9798331540364
India's agricultural sector is the backbone of the country, yet there are inefficiencies, a lack of transparency, and unequal farmer prices in its supply chain. Issues including inadequate market accessibility, inadequate infrastructure, and insufficient value addition exacerbate these challenges, especially for small and marginal farmers. Stakeholder collaboration is occasionally lacking in traditional supply chain systems, which leads to uneven farmer compensation and inefficient product delivery to consumers. Information and communication technologies (ICT) have been used to solve these problems in the past, but scalability, data security, and cost-effectiveness have all been shown to be lacking. With its core characteristics of automation, security, and transparency, blockchain technology presents a viable substitute. Blockchain technology can be used in the agricultural supply chain, but there are a number of obstacles to overcome, such as high implementation costs, complicated technology, and a need for major stakeholder engagement. This study investigates how blockchain technology could resolve these problems and transform India's agricultural supply chain. The goal is to create a system that leverages smart contracts to improve product traceability, increase trust, and automate procedures. Additionally, the study looks at certain blockchain applications including payment automation and farm-to-customer monitoring that make use of ***, Ganache, Truffle, and MetaMask technologies. This paper offers a thorough evaluation of earlier studies and offers a method for building an agricultural ecosystem that is more open, effective, and sustainable and that benefits supply chain participants, farmers, and consumers. The development of a blockchain-based software program is the main objective of this project, which attempts to increase the efficiency and transparency of the agricultural supply chain. No direct model comparisons have been done because the effort fo
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