Facial expressions are a challenging area in image recognition, and ensuring the universality of the model in the presence of a large sample size is a difficult point in such research. This article conducts repeatabil...
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With the popularity of blockchain concepts and the appreciation of cryptocurrencies, an increasing number of hackers and illegal elements have started to focus on stealing users’ hardware resources for mining activit...
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Multivariate time series anomaly detection has been extensively studied in diverse domains. Within the horizon of unsupervised methods, density estimation is regarded as a promising direction. However, the anomalies t...
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This paper proposes a high-precision filtering scheme for standard bottle calibration pressure based on Transformer model feature extraction and Kalman filtering methods, which is used to analyze the data of standard ...
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Additive manufacturing (AM) is currently considered one of the most promising technologies for fostering innovation in the industrial sector, specifically in product design. Considering the non-subtractive nature of A...
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This book includes peer reviewed articles from the 4th internationalconference on data Science, machinelearning and Applications, 2022, held at the Hyderabad Institute of Technology & Management...
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ISBN:
(数字)9789819920587
ISBN:
(纸本)9789819920570;9789819920600
This book includes peer reviewed articles from the 4th internationalconference on data Science, machinelearning and Applications, 2022, held at the Hyderabad Institute of Technology & Management on 26-27th December, India. ICDSMLA is one of the most prestigious conferences conceptualized in the field of data Science & machinelearning offering in-depth information on the latest developments in Artificial Intelligence, machinelearning, Soft Computing, Human Computer Interaction, and various data science & machinelearning applications. It provides a platform for academicians, scientists, researchers and professionals around the world to showcase broad range of perspectives, practices, and technical expertise in these fields. It offers participants the opportunity to stay informed about the latest developments in data science and machinelearning.
data-driven methods for air quality forecasting are beneficial to early-warning of air pollution, especially in small and medium-sized cities. In this study, the Prophet model is used for extracting time-related featu...
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data-driven methods for air quality forecasting are beneficial to early-warning of air pollution, especially in small and medium-sized cities. In this study, the Prophet model is used for extracting time-related features, such as trend, annual cycles, and weekly cycles, from the PM2.5 series, and the gradient boosting regression trees (GBM) and long short-term memory networks (LSTM) are used to build forecasting models utilizing predictors including time-related features. All predictors are grouped into time-related features, antecedent air pollutant concentration, and meteorological parameters, and permutation feature importance (PFI) is used for measuring the importance of these feature groups. A series of forecasting experiments on a middle city in China are carried out, and the results are analyzed for a better understanding of the mechanisms of the forecasting skill. The result shows that time-related features extracted by Prophet can enhance the forecasting skill significantly, and this advantage is more apparent when the forecasting horizon is large. PM2.5 time series has short memory and a rapid response to changes in meteorological conditions, and only meteorological conditions in the previous 2 days have useful signals for forecasting. The rapid response and short memory of PM2.5 make LSTM show no advantage over GBM in this case study. We show that feature engineering is important for forecasting PM2.5, and the time-related features and antecedent pollutant concentrations can be seen as proxy variables for some variables that are difficult to obtain, such as pollutant emission and missing meteorological conditions.
In this paper, a pair of regularized and scalable very large scale integration (VLSI) architectures are presented for matching patterns in a serial data stream. One is grounded on a shift register (SR), whereas the ot...
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Understanding users’ travel behaviors is an important subject in traffic science, which helps traffic management departments to formulate appropriate traffic control strategies. Travel mode identification is a critic...
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This paper introduces the notion of jumping graph grammars. In particular it focuses on the non-confluent version. As a graphical counterpart of general jumping grammars, jumping graph grammars have a greater generati...
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