In this paper, we provide an extensive evaluation of machine learning (ML) and deep learning (DL) methods for automatic sleep stage classification using a single-channel electrocardiogram (ECG) signal. To explore ML m...
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IEC 61499 is an emerging standard for distributed automation which requires well-defined design practises to improve development efficiency. In this paper, we extend the one-line engineering design pattern and provide...
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An automated material handling system (AMHS) is part of a production system that transports products from one machine to another for manufacturing processes. While conveyor belts have been commonly employed as AMHS, t...
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Detecting synthetically generated text in the wild has become increasingly difficult with advances in Natural Language Generation techniques and the proliferation of freely available Large Language Models (LLMs). Soci...
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Providing Quality of Service (QoS) under a variety of network conditions and security threats has become more difficult as the demand for large-scale wireless networks based on blockchain technology has increased. We ...
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ISBN:
(纸本)9798350381931
Providing Quality of Service (QoS) under a variety of network conditions and security threats has become more difficult as the demand for large-scale wireless networks based on blockchain technology has increased. We address these issues and significantly raise the overall QoS of blockchain-based networks in this paper by presenting an effective incremental learning bioinspired model This work is necessary because large-scale wireless networks must significantly improve their energy efficiency, throughput, delay reduction, and packet delivery ratio. Existing models frequently have difficulty meeting these demands and reducing the effects of different attacks, including Finney, Distributed Denial of Service (DDoS), Man-in-the-Middle (MITM), Sybil, and Masquerading scenarios. We suggest a novel strategy that combines two optimization techniques to get around these restrictions. In order to create sidechains, we first use Grey Wolf Optimization (GWO), which improves network partitioning and scalability in blockchain-based networks. Our model efficiently distributes the computational load and boosts system performance by dynamically adjusting the sidechain formation. In order to choose the best miner nodes for data mining between network nodes, we integrate Q Learning in the second step. The Q Learning algorithm makes intelligent decisions about the best miner nodes by taking into consideration parameters like throughput, latency, and energy efficiency. This deft choice of miner nodes improves network performance and QoS overall while optimizing data mining process. Through a thorough examination of spatial and temporal parameters, consensus is attained, allowing the system to assess the accuracy and dependability of mined blocks. This guarantees the blockchain network's integrity and security. Results from experiments show how effective our suggested model is. Our model improves energy efficiency by 8.5 percent, delays are cut by 10.4 percent, throughput is increased b
Velocity and position control for metro trains is typically achieved by classical control methods (PID, etc). Challenges in this control problem include imprecise position sensing, time delay, and external disturbance...
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Waste management has become a new challenge for the construction industries since rapid urbanization is taking place worldwide. Ceramic waste is one such material which is being originated from construction sites and ...
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Communication is one of the fields that adopts quantum characteristics to improve its potential. In particular, quantum teleportation (QT) is considered a fundamental tool for enabling quantum networks. This paper foc...
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Paper and board for printing commonly contains optical brightening agents that fluoresce in the presence of UV radiation, making the prints appear brighter and bluer. If the relative amount of optical brightening agen...
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Numerous studies have demonstrated significant correlations between segmented pathological objects in various medical imaging modalities and disease-related pathology. While previous investigations have employed handc...
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