Weather Dataset Classification Using Deep Learning Algorithms
Keywords:
CNN, MAS-DSS, deep learningAbstract
The goal of the research project "Weather Dataset Classification Using Deep Learning Algorithms" is to assess how well the Multi-Agent System Decision Support System (MAS-DSS) architecture performs and how well it enhances weather forecasting decision-making. Three main goals are being pursued: first, evaluating the influence of the MAS-DSS framework on organizational outcomes, user satisfaction, and decision-making efficiency in weather-related applications; second, assessing the framework's efficacy in enhancing the precision and dependability of weather dataset classification through the use of cutting-edge deep learning algorithms; and third, investigating the MAS-DSS framework's scalability and adaptability in various domains and decision contexts. Utilizing a blend of convolutional neural networks (CNNs) and further deep learning methodologies, the research applies the MAS-DSS framework to an extensive assortment of meteorological datasets. Metrics for user happiness, recall, accuracy, and precision in categorization are used to gauge the framework's effectiveness. The findings show notable gains in user satisfaction and decision-making effectiveness, with improved organizational outcomes as a result of more precise and trustworthy weather forecasts. The research also investigates the framework's scalability, showing that it may be used to a variety of areas outside of weather forecasting. This study demonstrates how deep learning algorithms may be integrated with the MAS-DSS architecture to transform decision support systems across a range of application domains.
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