[ad_1] Introduction: With the advancements in genomic research, there is a growing need for sharing genomic data among researchers and institutions. However, ensuring the privacy and confidentiality of this sensitive data poses a significant challenge.…
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[ad_1] Introduction: Domain generalization is a critical component of robust machine learning, allowing models to perform well on unseen data from different domains. In this thesis, we will explore the concept of domain generalization and…
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[ad_1] Introduction: Disentangled representation learning has emerged as a powerful tool for domain adaptation, allowing for the extraction of meaningful and interpretable features from data. This thesis explores the use of disentangled representation learning for…
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[ad_1] Introduction: Tensor decomposition is a powerful technique used in high-dimensional data analysis to extract meaningful patterns and relationships from complex datasets. By decomposing a tensor into a set of simpler components, researchers can gain…
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[ad_1] Introduction: In recent years, artificial intelligence (AI) has become increasingly integrated into various aspects of our daily lives, from healthcare and finance to autonomous vehicles and social media. However, as AI systems become more…
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[ad_1] Thesis Title: Impact of Inquiry-Based Learning on Scientific Literacy Introduction Scientific literacy is an essential skill in today’s society, enabling individuals to critically evaluate information, make informed decisions, and participate in scientific discourse. Inquiry-based…
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[ad_1] Introduction: Incremental learning is a technique in machine learning where a model is trained continuously over time as new data becomes available. This allows the model to adapt and improve its performance without having…
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[ad_1] Introduction Educational technology plays a crucial role in personalized professional development by providing educators with innovative tools and resources to enhance their teaching practices. With the advancement of technology, educators can now personalize their…
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[ad_1] Introduction: Anomaly detection in time series data is a critical task in various fields such as finance, healthcare, cybersecurity, and manufacturing. Detecting anomalies in time series data can help identify potential issues, prevent fraud,…
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[ad_1] Introduction: Graph Neural Networks (GNNs) have emerged as a powerful tool for analyzing and making predictions on graph-structured data. Knowledge graphs, which represent structured information about entities and their relationships, are a common form…
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