The use of Electroencephalography (EEG) signals for emotion identification tasks is common. However, the domain shift issue may cause EEG-based emotion detection models' performance to decline when used in new dom...
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In this research article, we present an original big data processing model that makes use of the HACE theorem in order to fully realize the big data revolution's potential and enhance agricultural growth. Addition...
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Federated learning is a machine learning technique that allows multiple devices to collaboratively train a machine learning model without sharing their data with a central server. The data is kept on the local device,...
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Emotion detection and sentiment analysis algorithms are used in various circumstances, particularly when employing interactive systems, to comprehend the polarity or emotions displayed by individuals. Understanding us...
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Plant leaf disease detection is a critical task in modern agriculture to ensure better crop yield and quality. This provides a unique strategy for detecting plant leaf disease using machine learning techniques. The pr...
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Sentiment Enhanced Movie Recommendation System is the new age movie recommender that considers the sentiments of the user on a higher level and recommends the perfect movie for every user. The movies being classified ...
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Generally, Data Mining or Knowledge Discovery is the procedure of analyzing information from various viewpoints and summary the data for further information Clustering is an unsupervised learning process where it gene...
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When processes execute through their business logic, their activities generate event logs, which contribute to trace sets. Since its introduction, the field of process mining has evolved, however, accuracy issues pers...
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This paper focuses on developing a real-time Morse code translation system using finger movements captured by a camera and processed with the MediaPipe framework. To enable this, the paper compares the performance of ...
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This paper forecasts the microeconomic level household expenditures using a novel hybrid deep learning approach. In terms of research significance, household finance control has a major influence on the finance system...
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ISBN:
(数字)9798331530983
ISBN:
(纸本)9798331530990
This paper forecasts the microeconomic level household expenditures using a novel hybrid deep learning approach. In terms of research significance, household finance control has a major influence on the finance system within the economy. Accurate forecasting of household finances assists in maintaining positive financial behavior among individuals and the economy. The DeepBoost multi-output regressor proposed in this paper is based on the 1D CNN-ANN and the XGBoost. The proposed model in this paper is compared with the R 2 , MSE, and MAE since it’s a regression problem. The experimental results reveal that the proposed DeepBoost multi-output regressor has the best application in forecasting the multiple expenditures of households by outperforming the ANN, 1D CNN-ANN, and Random Forest Regressor models. The proposed DeepBoost multi-output regressor evaluated the housing, food, transportation, healthcare, other necessities, childcare, and tax expenditures that had 0.94, 0.98, 0.83, 0.94, 0.97, 0.97, and 0.99 values for the R 2 , 9037.71, 2692.12, 9788, 15077.33, 1373.93, 13629.36, and 1904.52 values for the MSE, and 66.07, 34.05, 73.17, 87.05, 26.25, 78.74, and 29.47 MAE values than the ANN, RFR, and 1D CNN-ANN models.
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