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Posted: 29 Dec 2021 05:00

“GRU” December 2021 — summary from Crossref and DOAJ

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“GRU” December 2021 — summary from Crossref and DOAJ main image

The content below is machine-generated by Brevi Technologies’ NLG model, and the source content was collected from open-source databases/integrate APIs.

Crossref - summary generated by Brevi Assistant

With the advancement of smart grid, the secure operation of grid has placed onward higher needs for system dispatch. In Particularly, short-term load projecting of power systems is a crucial factor of power grid administration systems, which relates to the security, economy, and secure operation of the smart grid. Due to the boost in the global aging population and its associated age-related obstacles, numerous cognitive, physical, and social problems can emerge in older grownups, such as reduced walking rate, flexibility, falls, tiredness, problems in carrying out day-to-day activities, memory-related and social isolation issues. Experiments were performed on two datasets with bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques and compared to other state of art techniques. Structural damage identification has been the focus of engineering fields, while the existing damage recognition techniques greatly depend upon removed handmade attributes. Experiments on a range model of the three-span continual stiff frame bridge revealed that the CNN-GRU model does significantly much better than CNN, LSTM, and GRU models for structural damage recognition.

Binary code homology analysis describes identifying whether two items of binary code are put together from the same piece of source code, which is a basic method for many protection applications, such as vulnerability search, plagiarism detection, and malware detection. If they were natural language, existing methods for cross-platform binary code homology detection usually transform binary code into direction sequences and do semantic embedding of the series.

Chinese word division is the basis of the Chinese all-natural language processing. With the development of deep learning, numerous neural network models are applied to the Chinese word division.

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DOAJ - summary generated by Brevi Assistant

The Cryptocurrency is a new kind of possession that has emerged as an outcome of the development of financial innovation and it has created a big opportunity for researches. Outcomes gotten from these models show that gated recurrent unit performed better in prediction for all kinds of cryptocurrency than the long short-term memory and bidirectional LSTM models. Conventional forecast approach normally faced the unpredictability quantification issues triggered by streamlined failure settings and indirect measures. The experiment result using the inconsistency of frequency-domain signal outcome from circuit shows that the method below can effectively use real-time information, continually change the prediction precision, update and optimize the time-varying specifications of integrity performance, forecast the dependability chance circulation of the circuit in real time.

The demand forecast of common bicycles straight figures out the application rate of vehicles and jobs operation benefits. The speculative outcomes reveal that the forecast performance of the recommended model is better than other forecast models, suggesting the significance of the social benefits.

Binary code homology evaluation describes discovering whether 2 items of binary code are assembled from the very same piece of resource code, which is a fundamental technique for many protection applications, such as vulnerability search, plagiarism detection, and malware discovery. Nevertheless, the void in between all-natural language and binary code is large, and the spatial functions of the binary code are quickly shed by straight comparing the semiotics.

The international exchange market is among the most significant financial markets in the globe. In addition, we have compared the efficiency of our model versus a standalone LSTM model, a standalone GRU model and straightforward moving ordinary based analytical model where the proposed hybrid GRU-LSTM model surpasses all models for 10-mins duration and for a 30-mins timeframe gives the ideal result for GBP/USD and USD/CAD money sets in terms of MSE, RMSE, and MAE efficiency metrics.

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