[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] Anomaly detection in financial transactions is a critical aspect of fraud detection and prevention in the financial industry. With the increasing digitization of financial transactions, the need for effective anomaly detection techniques has become…
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[ad_1] Introduction: Imbalanced data refers to a situation where the distribution of classes within a dataset is skewed, with one class significantly outnumbering the other(s). This imbalance can pose a challenge for machine learning algorithms,…
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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: Spatio-temporal data analysis plays a crucial role in urban planning by providing valuable insights into how cities evolve over time. By analyzing data on the spatial and temporal dimensions of urban areas, planners…
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[ad_1] Introduction: Meta-Learning for Neural Architecture Search is an emerging field in machine learning that aims to automate the process of designing neural network architectures. This thesis will explore various meta-learning techniques for neural architecture…
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[ad_1] Introduction: Tensor factorization is a powerful tool used in signal processing to extract relevant information from high-dimensional data. By decomposing a tensor into a set of lower-dimensional factors, tensor factorization allows for efficient representation,…
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[ad_1] Introduction: Federated learning is a decentralized machine learning approach that enables training models across multiple edge devices while keeping the data localized. This allows for improved privacy and reduced latency, making it ideal for…
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[ad_1] Introduction: Multi-Task Learning for Multi-Label Classification is a popular research area in machine learning where multiple related tasks are learned simultaneously to improve the overall performance of the model. This approach is particularly useful…
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[ad_1] Introduction: Topological Data Analysis (TDA) has emerged as a powerful tool in the field of scientific visualization, allowing researchers to analyze complex datasets and extract meaningful patterns and structures. By studying the topological properties…
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