Most of the times we have to test out the entire application functionality, for any code modification done to cater the need of the larger audiences or any bug fixes. This results to consumption of time and the effort...
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Deep learning for defect detection has become a critical imperative in contemporary electronics manufacturing. We propose an inpainting-based anomaly detection system to identify defects without labeled defects. An im...
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Distribution grids in individual countries inside the European Union have different properties, while the grid operators use different approaches to integrate photovoltaic (PV) systems into the grid, which influences ...
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Extreme events jeopardize power network operations, causing beyond-design failures and massive supply interruptions. Existing market designs fail to internalize and systematically assess the risk of extreme and rare e...
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Internet of Medical Things (IoMT) is a technology that encompasses medical devices, wearable sensors, and applications connected to the Internet. In road accidents, it plays a crucial role in enhancing emergency respo...
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Discontinuity in long Deoxyribonucleic Acid (DNA) sequences creates harmful diseases. Changes in the DNA structure refers to changes in the human immunity system. Tuberculosis is a critical disease that causes coughin...
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This Landslide susceptibility prediction is a critical task for Alleviating the risks associated with landslide hazards, which have significant socio-economic and environmental impacts. Existing techniques face challe...
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The Internet of Vehicles (IoV) has progressed remarkably, leading to a heightened dependence on applications that require low latency and high bandwidth. In light of the growing computational demands, fog computing ha...
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Secure file encryption has become increasingly important in various disciplines, including finance, healthcare, and government, where protecting sensitive data is paramount. The proposed framework aims to meet this re...
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Accurate forecasting of traffic flow provides a powerful traffic decision-making basis for an intelligent transportation system. However, the traffic data's complexity and fluctuation, as well as the noise produce...
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Accurate forecasting of traffic flow provides a powerful traffic decision-making basis for an intelligent transportation system. However, the traffic data's complexity and fluctuation, as well as the noise produced during collecting information and summarizing original data of traffic flow, cause large errors in the traffic flow forecasting results. This article suggests a solution to the above mentioned issues and proposes a fully connected time-gated neural network based on wavelet reconstruction(WT-FCTGN). To eliminate the potential noise and strengthen the potential traffic trend in the data, we adopt the methods of wavelet reconstruction and periodic data introduction to preprocess the data. The model introduces fully connected time-series blocks to model all the information including time sequence information and fluctuation information in the flow of traffic, and establishes the time gate block to comprehend the periodic characteristics of the flow of traffic and predict its flow. The performance of the WT-FCTGN model is validated on the public Pe MS data set. The experimental results show that the WT-FCTGN model has higher accuracy, and its mean absolute error(MAE), mean absolute percentage error(MAPE) and root mean square error(RMSE) are obviously lower than those of the other algorithms. The robust experimental results prove that the WT-FCTGN model has good anti-noise ability.
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