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Annual dilated convolution neural network for newbuilding ship prices forecasting
Anticipating newbuilding ship prices is crucial for participants in the dynamic ship** market. Although the researchers from forecasting and...
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Multivariate Financial Time Series Forecasting with Deep Learning
Today, and aware of how unexpected the events that govern the market trend can be, forecasting financial time series has become a priority for...
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Image denoising in undecimated dual-tree complex wavelet domain using multivariate t-distribution
Denoising of natural images is a basic problem in image processing. The present paper proposes a new algorithm for image denoising based on the...
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Bleeding-Edge Techniques for Multivariate Time Series
In the previous chapter, you learned how to perform univariate time-series forecasting. In this chapter, you will look at single-step and...
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Local differential privacy for unbalanced multivariate nominal attributes
Data with unbalanced multivariate nominal attributes collected from a large number of users provide a wealth of knowledge for our society. However,...
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Data management and selectivity in collaborative pervasive edge computing
Context-aware data management becomes the focus of several research efforts, which can be placed at the intersection between the Internet of Things...
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Time series forecasting of wheat crop productivity in Egypt using deep learning techniques
Egypt’s agricultural sector plays a critical role in the country’s economy, with wheat cultivation being vital for ensuring food security. However,...
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Generalised Diffusion Probabilistic Scale-Spaces
Diffusion probabilistic models excel at sampling new images from learned distributions. Originally motivated by drift-diffusion concepts from...
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Unsupervised Keyphrase Extraction from Scientific Publications
We propose a novel unsupervised keyphrase extraction approach that filters candidate keywords using outlier detection. It starts by training word...
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Dual Non-Local Means: a two-stage information-theoretic filter for image denoising
Image denoise has been explored with the development of various filters used to remove or reduce random disruptions on observed data, but at the same...
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Interpretable Multivariate Time Series Classification Based on Prototype Learning
Recently, the classification of multivariate time series has attracted much attention in the field of machine learning and data mining, due to its...
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MDC: An Interpretable GNNs Method Based on Node Motif Degree and Graph Diffusion Convolution
Graph Neural Networks (GNNs) have achieved significant success in various real-world applications, including social networks, recommendation systems,...
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Efficient Classification of Prostate Cancer Using Artificial Intelligence Techniques
As the primary cause of illness and death for men, Prostate Cancer (PCa) is a serious worldwide health problem. To maximise treatment results and...
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DSEAformer: Forecasting by De-stationary Autocorrelation with Edgebound
Time series analysis is vital for various real-world scenarios. Enhancing multivariate long-sequence time-series forecasting (MLTF) accuracy is...
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Inception inspired CNN-GRU hybrid network for human activity recognition
Human Activity Recognition (HAR) involves the recognition of human activities using sensor data. Most of the techniques for HAR involve hand-crafted...
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Uniform Weighted Approximation by Multivariate Filtered Polynomials
The paper concerns the weighted uniform approximation of a real function on the \(d-\)cube \([-1,1]^d\), with \(d>1\), by means of some multivariate...
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Realization of superhuman intelligence in microstrip filter design based on clustering-reinforcement learning
AbstractMicrostrip filters are widely used in signal processing because of their light weight, compact structure and high reliability. Designing...
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Representation Learning with Deconvolution for Multivariate Time Series Classification and Visualization
We propose a new model based on the convolutional networks and SAX(Symbolic Aggregate Approximation) discretization to learn the representation for...
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Generalised Scale-Space Properties for Probabilistic Diffusion Models
Probabilistic diffusion models enjoy increasing popularity in the deep learning community. They generate convincing samples from a learned...
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Feature selection: a perspective on inter-attribute cooperation
High-dimensional datasets depict a challenge for learning tasks in data mining and machine learning. Feature selection is an effective technique in...
