The innovative method was developed by bringing federated learning methods to the medical field. In this study, the author refined federated learning models with data sets containing photographs of brain tumors. The c...
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Partial discharge is a common harmful phenomenon in industry, it is necessary to establish a distributed low-cost online monitoring system. In this essay, we use ACP theory of parallel systems [1] to design the measur...
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systems' performance evaluation and improvement are endless as long as a system runs. This requires several records of data containing the chronological order of executed process. In this case, logging comes into ...
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Social sensing is emerging as an effective and pervasive sensing paradigm to collect timely data and observations from human sensors. This paper focuses on the problem of COVID-19 misinformation detection on social me...
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ISBN:
(纸本)9781665439299
Social sensing is emerging as an effective and pervasive sensing paradigm to collect timely data and observations from human sensors. This paper focuses on the problem of COVID-19 misinformation detection on social media. Our work is motivated by the lack of COVID-specific knowledge in current misinformation detection solutions, which is critical to assess the truthfulness of social media claims about the emerging COVID19 disease. In this paper, we leverage human intelligence on a crowdsourcing platform to obtain essential knowledge facts for detecting the COVID-19 misinformation on social media. Two critical challenges exist in solving our problem: i) how to efficiently acquire accurate and timely knowledge that is both inclusive and specific to COVID-19? ii) How to effectively coordinate the efforts from both expert and non-expert workers to detect COVID-19 misinformation? To address these challenges, we develop FakeSens, a social sensing based crowd knowledge graph approach that explicitly explores the knowledge facts specific to COVID-19 and models the reliability of different types of crowd workers to capture the misleading COVID-19 claims Evaluation results on a real-world dataset show that FakeSens significantly outperforms state-of-the-art baselines in accurately detecting misleading claims of COVID-19 on social media.
The goal of this study is to develop an effective strategy for mitigating the effects of distributed generator (DG) integration on protective device coordination. The recloser-fuse coordination is greatly influenced b...
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The Industrial Internet of Things (IIoT) uses reliable wireless network solutions such as Time-Slotted Channel Hopping (TSCH) to integrate cyber-physical systems with industrial environments via the IPv6 over TSCH (6T...
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The adoption of Blockchain technology in the medical sector has started gaining popularity because it mitigates crucial security, privacy, and interoperability challenges. Moreover, the massive volume of data produced...
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Accurate sensor node localization in wireless sensor networks is essential for many uses, such as smart cities, military operations, and environmental monitoring. In order to achieve precise and economical sensor node...
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ISBN:
(数字)9798350355093
ISBN:
(纸本)9798350355109
Accurate sensor node localization in wireless sensor networks is essential for many uses, such as smart cities, military operations, and environmental monitoring. In order to achieve precise and economical sensor node localization, this paper introduces a hybrid optimization approach that combines Particle Swarm Optimization (PSO) with Grey Wolf Optimizer (GWO). While GWO has better local search capabilities, PSO is renowned for its strong global search power. The suggested approach combines these two algorithms, utilizing their respective advantages to improve performance overall. To establish a good approximation of sensor node placements, PSO is first used. This provides a refined baseline for the GWO. GWO then refines these places even further in order to get greater accuracy. Simulation is used to validate the hybrid technique and show how well it works to precisely estimate sensor node locations within a constrained search space. When compared to PSO and GWO, the hybrid PSO-GWO algorithm enhances coverage accuracy by about 9.41% and 5.68%, respectively and positioning accuracy by about 9.76% compared to PSO and 5.88% compared to GWO.
Wireless sensor networks are built from nodes called mod - small autonomous devices powered by batteries and microchips with radio communication at a frequency - for example 2.4 GHz. A special software allows mods to ...
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Vocally impaired individuals find it extremely challenging to communicate with general populace. Since the general populaces are not trained in hand sign language, so interaction is quite challenging. Here, we suggest...
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