Performance evaluation of deep learning models in detection of different types of arrhythmia using photo plethysmography signals

Kulkarni, Tripti; Dushyanth, N. D.

doi:10.1007/s41870-021-00795-8

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…These methods have essential applications in CD [40], RS image classification [8], and visual recognition tasks [40]. In several disciplines, deep learning models have been effectively used, including image classification, natural language processing, and voice recognition [41]. Some innovative deep learning techniques usually exploit certain spatial features as support to increase accuracy [8].…”

Section: Deep Learning-based CD Techniquesmentioning

confidence: 99%

Change detection using multispectral satellite images: a systematic review of literature

Vasantrao,

Gupta,

Mishra

et al. 2024

Bulletin EEI

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Change detection (CD) provides information about the changes on earth’s surface over a period of time. Many algorithms have been proposed over the years for effective CD of satellite imagery. This paper presents the steps to preprocess the captured satellite images, which can be used to perform predictive analysis of earth’s surface by CD techniques. To design a highly effective system for CD, it is recommended that algorithm designers select efficient algorithms from any given application. Thus, this paper presents and investigates the review of most appropriate literature on CD, where CD techniques have been presented into three groups; i) thresholding, ii) clustering, and iii) deep learning. The first two categories mainly rely on the traditional machine learning, whereas the last one exploits the state-of-the-art deep learning models. At the end, the standard methods are summarized based on advantages, limitation, and research gap.

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