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Text summarization based on semantic graphs: an abstract meaning representation graph-to-text deep learning approach
Nowadays, due to the constantly growing amount of textual information, automatic text summarization constitutes an important research area in natural...
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How clustering affects the convergence of decentralized optimization over networks: a Monte-Carlo-based approach
Decentralized algorithms have gained substantial interest owing to advancements in cloud computing, Internet of Things (IoT), intelligent...
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DQMMBSC: design of an augmented deep Q-learning model for mining optimisation in IIoT via hybrid-bioinspired blockchain shards and contextual consensus
Single-chained blockchains are highly secure but cannot be scaled to larger IIoT (Internet of Industrial Things) network scenarios due to storage...
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Enhancing OCT patch-based segmentation with improved GAN data augmentation and semi-supervised learning
For optimum performance, deep learning methods, such as those applied for retinal and choroidal layer segmentation in optical coherence tomography...
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The Multi-attribute impact of hyperlinks in blogs: an emotion-centric approach
As a digital social medium, blogs have transformed into arenas where individuals can express their perspectives, concepts, and feelings. This...
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HyperMatch: long-form text matching via hypergraph convolutional networks
Semantic text matching plays a vital role in diverse domains, such as information retrieval, question answering, and recommendation. However, longer...
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L2XGNN: learning to explain graph neural networks
Graph Neural Networks (GNNs) are a popular class of machine learning models. Inspired by the learning to explain (L2X) paradigm, we propose L2xGnn , a...
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Building RadiologyNET: an unsupervised approach to annotating a large-scale multimodal medical database
BackgroundThe use of machine learning in medical diagnosis and treatment has grown significantly in recent years with the development of...
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Anomaly analytics in data-driven machine learning applications
Machine learning is used widely to create a range of prediction or classification models. The quality of the machine learning (ML) models depends not...
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Temporal analysis of computational economics: a topic modeling approach
This study offers a comprehensive investigation into the thematic evolution within computational economics over the past two decades, leveraging...
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De-confounding representation learning for counterfactual inference on continuous treatment via generative adversarial network
Counterfactual inference for continuous rather than binary treatment variables is more common in real-world causal inference tasks. While there are...
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Explainable dating of greek papyri images
Greek literary papyri, which are unique witnesses of antique literature, do not usually bear a date. They are thus currently dated based on...
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Transcriptome- and DNA methylation-based cell-type deconvolutions produce similar estimates of differential gene expression and differential methylation
BackgroundChanging cell-type proportions can confound studies of differential gene expression or DNA methylation (DNAm) from peripheral blood...
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Gradient-based explanation for non-linear non-parametric dimensionality reduction
Dimensionality reduction (DR) is a popular technique that shows great results to analyze high-dimensional data. Generally, DR is used to produce...
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Compressed sensing: a discrete optimization approach
We study the Compressed Sensing (CS) problem, which is the problem of finding the most sparse vector that satisfies a set of linear measurements up...
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Enhancing racism classification: an automatic multilingual data annotation system using self-training and CNN
Accurate racism classification is crucial on social media, where racist and discriminatory content can harm individuals and society. Automated racism...
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TOPCOAT: towards practical two-party Crystals-Dilithium
The development of threshold protocols based on lattice-signature schemes has been of increasing interest in the past several years. The main...
