[ad_1] Introduction Biometric identification has become increasingly important in various fields such as security, banking, and healthcare. Among the various biometric modalities, face recognition has gained widespread attention due to its non-intrusive nature and ease…
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[ad_1] Introduction: Visual question answering (VQA) is an emerging research area that aims to enable machines to understand and answer questions about visual content. With the increasing availability of multimedia data, including images and videos,…
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[ad_1] Introduction 1.1 Introduction 1.2 Background of study 1.3 Problem Statement 1.4 Objective of study 1.5 Limitation of study 1.6 Scope of study 1.7 Significance of study 1.8 Structure of the Thesis 1.9 Definition of…
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[ad_1] Introduction Recurrent neural networks (RNNs) have gained significant attention in recent years for their ability to model sequential data and make predictions based on this data. In particular, recurrent neural network language models have…
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[ad_1] Introduction In recent years, deep belief networks (DBNs) have gained significant attention in the field of artificial intelligence and machine learning due to their ability to learn hierarchical representations of data. These networks are…
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[ad_1] Introduction Variational autoencoders (VAEs) have gained significant attention in the field of generative modeling due to their ability to learn complex distributions and generate realistic samples. This thesis aims to explore the use of…
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[ad_1] Introduction Convolutional neural networks (CNNs) have gained significant attention in recent years due to their exceptional performance in various domains, including computer vision, natural language processing, and speech recognition. CNNs are particularly well-suited for…
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[ad_1] Introduction Random forests are a powerful ensemble learning method that has gained popularity in various fields of study, including machine learning, data mining, and bioinformatics. This thesis focuses on understanding the principles behind random…
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[ad_1] Introduction: Decision trees are a popular and widely used machine learning technique that provides an interpretable model for making decisions. This thesis explores the use of decision trees for developing interpretable models, focusing on…
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[ad_1] Introduction: Particle filters are a powerful tool in the field of sequential Monte Carlo methods for estimating the state of a dynamic system based on noisy observations. These filters are widely used in a…
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