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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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Sublinear Algorithms in T-Interval Dynamic Networks
We consider standard T - interval dynamic networks , under the synchronous timing model and the broadcast CONGEST model. In a T - interval dynamic network ,...
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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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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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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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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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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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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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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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Examining ALS: reformed PCA and random forest for effective detection of ALS
ALS (Amyotrophic Lateral Sclerosis) is a fatal neurodegenerative disease of the human motor system. It is a group of progressive diseases that...
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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...
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Explainable decomposition of nested dense subgraphs
Discovering dense regions in a graph is a popular tool for analyzing graphs. While useful, analyzing such decompositions may be difficult without...
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Data reduction in big data: a survey of methods, challenges and future directions
Data reduction plays a pivotal role in managing and analyzing big data, which is characterized by its volume, velocity, variety, veracity, value,...
