Time series data is data that is collected periodically and has certain time intervals. Time series data is widely available in the fields of finance, meteorology, signal processing, health, and economics. Weather dat...
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ISBN:
(数字)9798350375886
ISBN:
(纸本)9798350375893
Time series data is data that is collected periodically and has certain time intervals. Time series data is widely available in the fields of finance, meteorology, signal processing, health, and economics. Weather data, stock prices, and sales data are examples of time series data. Time series data analysis can be used to predict future conditions based on patterns and values from previous data through a forecasting process. These forecasting results are useful for identifying trends and data patterns for decision making. The rapid development of deep learning models can now be used as a method for forecasting time series data. Deep learning models that are suitable for forecasting include sequence-to-sequence models such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Transformer. LSTM and Transformer are sequence-to-sequence models that are widely used for forecasting time series data. Research to compare the accuracy of the two models is still very limited. This article will discuss the use of the LSTM and Transformer models for the time series data forecasting process using Hewlett Packard stock price data from 1962 to 2022. The accuracy results show that the LSTM model outperforms the Transformer model in forecasting Hewlett Packard stock prices.
Industry 4.0 will bring not only transformation to the manufacturing technologies but also to the profile of the workforce. Education system should be revised to prepare the future graduates embracing the knowledge of...
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Our study discusses the application of the perspective grid concept as the basis for the process of transforming the 2D coordinates of the image into 3D coordinates in real space for estimating the location of objects...
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Through this pandemic, the world has experienced two major crises, health crisis, and economic crisis. It would be dangerous for us to continue with our "normal"daily lives. We have been forced to stay at ho...
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We have been developing MEIMAT, meiji micro-processor (MPU) architecture design tools. Our MEIMAT has a feature to implement arbitrary instructions. However, the MEIMAT does not have a function to simulate those instr...
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Using soil as a planting medium (conventional system) raises several problems, such as the need for large agricultural land, but the available land is limited. This problem is triggered by an increase in demand for nu...
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Javanese culture is a culture that the Javanese own, and the Javanese have started to exist with their culture since before the era of the Majapahit kingdom, and the Javanese are famous for their ability to sail the o...
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Medical imaging abnormality detection is challenging, but deep learning approaches have shown promise. This paper reviews the current state of the art in deep learning approaches for detecting abnormalities in chest m...
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When making a new microprocessor (MPU), the instruction set architecture is considered. It is also necessary to design how to work the instructions in hardware circuits of the MPU. As a solution to these, we previousl...
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Preventive strategies should be the utmost priority when dealing with diverse patients suffering from malignant ventricular arrhythmia (MVA) that can lead to sudden cardiac death (SCD). Electrocardiogram (ECG) data is...
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Preventive strategies should be the utmost priority when dealing with diverse patients suffering from malignant ventricular arrhythmia (MVA) that can lead to sudden cardiac death (SCD). Electrocardiogram (ECG) data is commonly used as a predictor for MVA predictive models. In this study, all ECG signals from MIT-BIH databases were fragmented into five-minute durations with a frequency sampling of 128 Hz. To solve the absence of hybrid optimizations in Machine Learning (ML) models, a novel Variational Quantum Neural Network (VQNN) was invented. Empowered by deep learning capabilities and optimized quantum circuits design, VQNN achieved remarkable performances designated by an accuracy of up to 95.1%, a perfect 100% recall, and a 95.2% score of the area under the Receiver Operating Characteristic curve (AUC ROC) with Conjugate Gradient as an optimizer and EfficientSU2 as a quantum ansatz. Despite the susceptibility to quantum noise, this research settles a new trajectory of utilizing quantum variational algorithms to predict and expand its applicability for MVA cases.
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