[ad_1] Introduction: Opinion mining and topic modeling are two important techniques in the field of natural language processing and machine learning. Opinion mining, also known as sentiment analysis, involves the extraction and analysis of opinions…
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[ad_1] Introduction: Fraud in insurance claims is a significant issue that can have a detrimental impact on the industry, ultimately driving up premiums for policyholders. Therefore, the ability to effectively detect and prevent fraudulent claims…
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[ad_1] Introduction: Time series forecasting is a common method used in financial markets to predict future stock prices based on historical data. This technique involves analyzing past stock price movements and patterns to make informed…
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[ad_1] Introduction: Image classification using deep learning is a rapidly growing field in the field of computer vision. Deep learning techniques have proven to be highly effective in automatically identifying and categorizing images, making them…
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[ad_1] Introduction: Recommender systems have become an integral part of e-commerce platforms, providing personalized recommendations to users based on their preferences and past interactions. These systems use data mining techniques and algorithms to analyze user…
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[ad_1] Introduction: Social media has become an integral part of our daily lives, with millions of users sharing their thoughts, opinions and emotions on various platforms. Sentiment analysis, also known as opinion mining, is a…
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[ad_1] Graph Neural Networks (GNNs) have gained significant attention in recent years for their ability to effectively model relational data. They are neural networks that operate on graph-structured data, allowing them to capture complex relationships…
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[ad_1] Introduction: Disentangled representation learning has emerged as a powerful tool in machine learning for disentangling underlying factors of variation in data. By learning representations that separate different sources of variation, disentangled representation learning can…
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[ad_1] Introduction: Uncertainty quantification is a crucial aspect of predictive maintenance, as it allows for the assessment of the reliability and accuracy of predictive models. By quantifying uncertainties, maintenance planners can make informed decisions about…
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[ad_1] Introduction: AutoML for Automated Data Preprocessing is an innovative approach that leverages machine learning algorithms to automate the data preprocessing tasks, which are often labor-intensive and time-consuming. By using automated tools and techniques, researchers…
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