The process of text classification for the purpose of spam detection relies on the principles of natural language processing (NLP) with the objective of automating the identification and categorization of messages int...
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A long-standing goal in deep learning has been to characterize the learning behavior of black-box models in a more interpretable manner. For graph neural networks (GNNs), considerable advances have been made in formal...
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A long-standing goal in deep learning has been to characterize the learning behavior of black-box models in a more interpretable manner. For graph neural networks (GNNs), considerable advances have been made in formalizing what functions they can represent, but whether GNNs will learn desired functions during the optimization process remains less clear. To fill this gap, we study their training dynamics in function space. In particular, we find that the gradient descent optimization of GNNs implicitly leverages the graph structure to update the learned function, as can be quantified by a phenomenon which we call kernel-graph alignment. We provide theoretical explanations for the emergence of this phenomenon in the overparameterized regime and empirically validate it on real-world GNNs. This finding offers new interpretable insights into when and why the learned GNN functions generalize, highlighting their limitations in heterophilic graphs. Practically, we propose a parameter-free algorithm that directly uses a sparse matrix (i.e. graph adjacency) to update the learned function. We demonstrate that this embarrassingly simple approach can be as effective as GNNs while being orders-of-magnitude faster. Copyright 2024 by the author(s)
In this day and age, corporations are moving towards data driven decision making. They are utilizing data to make applications that can assist them with business intelligence. data assists them with working on their e...
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Dragon fruit is a popular fruit with a unique appearance and taste. It is an important fruit in export and domestic markets. However, its maturity detection is still a challenging task due to the complexity of its phy...
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Airline delay prediction plays a crucial role in the aviation industry, enabling airlines to optimize operations and improve passenger satisfaction. In this research, we propose a comprehensive framework for airline d...
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The most available natural resource on earth is solar energy, and it is the best source to produce electricity. As for the growing needs of humans, there is a huge need for electricity, so this model is a step towards...
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Innovations in technology from the last one decade have led to the generation of colossal amounts of medical data with comparably low cost. Medical data should be collected with utmost care. Sometimes, the data h...
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Innovations in technology from the last one decade have led to the generation of colossal amounts of medical data with comparably low cost. Medical data should be collected with utmost care. Sometimes, the data have high features but not all the features play an important role in drawing the relations to the mining task. For the training of machine learning algorithms, all the attributes in the data set are not relevant. Some of the characteristics may be negligible and some characteristics may not influence the outcome of the forecast. The pressure on machine learning algorithms can be minimized by ignoring or taking out the irrelevant attributes. Reducing the attributes must be done at the risk of information loss. In this research work, an Enhanced Principal Component Analysis (EPCA) is proposed, which reduces the dimensions of the medical dataset and takes paramount care of not losing important information, thereby achieving good and enhanced outcomes. The prominent dimensionality reduction techniques such as Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Partial Least Squares (PLS), Random Forest, Logistic Regression, Decision Tree and the proposed EPCA are investigated on the following Machine Learning (ML) algorithms: Support Vector Machine (SVM), Artificial Neural Networks (ANN), Naïve Bayes (NB) and Ensemble ANN (EANN) using statistical metrics such as F1 score, precision, accuracy and recall. To optimize the distribution of the data in the low-dimensional representation, EPCA directly mapped the data to a space with fewer dimensions. This is a result of feature correlation, which made it easier to recognize patterns. Additionally, because the dataset under consideration was multicollinear, EPCA aided in speeding computation by lowering the data's dimensionality and thereby enhanced the classification model's accuracy. Due to these reasons, the experimental results showed that the proposed EPCA dimensionality reduction technique per
The lockdown due to Covid-19 has resulted in us relying heavily on technology to communicate and see each other. That, in turn, has led to an explosion of applications that provide audio and video conferencing solutio...
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This study addresses the significant public health concern of glaucoma, a collection of eye conditions resulting in optic nerve damage and progressive vision loss. To conduct our research, we employed a dataset obtain...
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To examine customer churn in the telecom sector, we used four classification algorithms: Decision-Tree(DT), Random-Forest(RF), Logistic-Regression(LR), and a Hybrid-Algorithm(HA). The dataset contained information abo...
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