Výsledky vyhledávání - "Sabeti, Elyas"

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Data Discovery Using Lossless Compression-Based Sparse Representation

Autor: Sabeti, Elyas, Song, Peter X. K., Hero III, Alfred O.

Sparse representation has been widely used in data compression, signal and image denoising, dimensionality reduction and computer vision. While overcomplete dictionaries are required for sparse representation of multidimensional data, orthogonal base

Externí odkaz: http://arxiv.org/abs/2103.08765

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Pattern-Based Analysis of Time Series: Estimation

Autor: Sabeti, Elyas, Song, Peter X. K., Hero, Alfred O.

While Internet of Things (IoT) devices and sensors create continuous streams of information, Big Data infrastructures are deemed to handle the influx of data in real-time. One type of such a continuous stream of information is time series data. Due t

Externí odkaz: http://arxiv.org/abs/2005.00926

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Multiple Abnormality Detection for Automatic Medical Image Diagnosis Using Bifurcated Convolutional Neural Network

Autor: Hajabdollahi, Mohsen, Esfandiarpoor, Reza, Sabeti, Elyas, Karimi, Nader, Najarian, Kayvan, Soroushmehr, S. M. Reza, Samavi, Shadrokh

Automating classification and segmentation process of abnormal regions in different body organs has a crucial role in most of medical imaging applications such as funduscopy, endoscopy, and dermoscopy. Detecting multiple abnormalities in each type of

Externí odkaz: http://arxiv.org/abs/1809.05831

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Atypicality for Heart Rate Variability Using a Pattern-Tree Weighting Method

Autor: Sabeti, Elyas, Høst-Madsen, Anders

Heart rate variability (HRV) is a vital measure of the autonomic nervous system functionality and a key indicator of cardiovascular condition. This paper proposes a novel method, called pattern tree which is an extension of Willem's context tree to r

Externí odkaz: http://arxiv.org/abs/1710.07319

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Data Discovery and Anomaly Detection Using Atypicality: Theory

Autor: Høst-Madsen, Anders, Sabeti, Elyas, Walton, Chad

A central question in the era of 'big data' is what to do with the enormous amount of information. One possibility is to characterize it through statistics, e.g., averages, or classify it using machine learning, in order to understand the general str

Externí odkaz: http://arxiv.org/abs/1709.03189

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Data Discovery and Anomaly Detection Using Atypicality: Signal Processing Methods

Autor: Sabeti, Elyas, Høst-Madsen, Anders

The aim of atypicality is to extract small, rare, unusual and interesting pieces out of big data. This complements statistics about typical data to give insight into data. In order to find such "interesting" parts of data, universal approaches are re

Externí odkaz: http://arxiv.org/abs/1709.03191

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