Optimal Design of Interval Type-2 Fuzzy Heart Rate Level Classification Systems Using the Bird Swarm Algorithm

Miramontes, Ivette; Guzman, Juan Carlos; Melín, Patricia; Prado-Arechiga, German

doi:10.3390/a11120206

Cited by 39 publications

(17 citation statements)

References 36 publications

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“…Li et al (2018) used Den-seNet-121 and DenseNet-RNN were two deep learning models utilized to analyze the infections in ChestX-Ray14, where DenseNet-121 getting a sum of 74.5% and DenseNet-RNN was 75.1% to recognizing Pneumonia. Rajpurkar et al (2017) were introduced by taking 121 layers to identify one of the 14 infections at 76.8% of accuracy of the pneumonic class from the others; likewise this model gives a heatmap for possible localization that depends on the forecast done by the convolutional neural network, and more study can be found by applying machine learning and deep learning algorithm to analyze the X-ray and CT images (Basu et al 2020;Pavithra et al 2015;Ozkaya et al 2020;Santos and Melin 2020;Tolga et al 2020;Ramírez et al 2019;Miramontes et al 2018;Melin et al 2018;Kermany, et al 2018b, a;Ayan, and Ü nver, 2019;Varshni et al 2019;Wang et al 2017;Togaçar et al 2019;Jaiswal, et al 2019;Sirazitdinov, et al 2019;Behzadi-khormouji et al 2020;Stephen et al 2019;Xu et al 2020;Shan et al 2020).…”

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RETRACTED ARTICLE: Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm

et al. 2020

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The novel coronavirus infection (COVID-19) that was first identified in China in December 2019 has spread across the globe rapidly infecting over ten million people. The World Health Organization (WHO) declared it as a pandemic on March 11, 2020. What makes it even more critical is the lack of vaccines available to control the disease, although many pharmaceutical companies and research institutions all over the world are working toward developing effective solutions to battle this life-threatening disease. X-ray and computed tomography (CT) images scanning is one of the most encouraging exploration zones; it can help in finding and providing early diagnosis to diseases and gives both quick and precise outcomes. In this study, convolution neural networks method is used for binary classification pneumonia-based conversion of VGG-19, Inception_V2 and decision tree model on X-ray and CT scan images dataset, which contains 360 images. It can infer that fine-tuned version VGG-19, Inception_V2 and decision tree model show highly satisfactory performance with a rate of increase in training and validation accuracy (91%) other than Inception_V2 (78%) and decision tree (60%) models. Keywords COVID-19 Á X-ray images Á CT scan Á CNN Á VGG-16 Á Inception_V2 Á Decision tree 1 Introduction The first case of the virus became exposed in Wuhan city of China in November 2019, there are 1,100,000 peoples living in this city and it interfaces numerous urban communities of China. The outbreak of atypical and individual-to-individual transmissible pneumonia brought about by the severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) has caused a worldwide. There have been in excess of 26,000,000 confirmed cases of the corona virus disease (COVID-19) on the globe, as of April 23, 2020. As indicated by the WHO, 16-21% of individuals with the infection have gotten seriously sick with a Communicated by Valentina E. Balas.

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RETRACTED ARTICLE: Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm

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“…Firefly and harmony search algorithms are also used for optimal power damping [26]. Genetic algorithm is proposed to optimize granular neural network parameters for pattern recognition [27] such as bird swarm optimization [28] for heart-rate classification, firefly algorithm [29] for optimization of modular granular neural networks and grey wolf optimizer [30] for optimizing granular neural networks for human recognition. Sanchez et al [31] proposed to use particle swarm optimization with its fuzzy dynamic parameter adaptation to design modular granular neural network architectures.…”

Section: Evolutionary Algorithms Based Neural Architecture Searchmentioning

confidence: 99%

Image Classification Based on Automatic Neural Architecture Search Using Binary Crow Search Algorithm

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Neural architectures have accelerated the advancement in various domains by enabling automatic pattern detection, image classification, audio recognition, and face recognition etc. However, they are computationally expensive to design and expert knowledge in various domains is required. In this paper, a swarm intelligence algorithm is proposed to search for novel architectures without human intervention that can achieve comparable performance to those of human-designed architectures. This work is inspired by current neural architecture search approaches based on reinforcement learning and genetic algorithm. However, not much attention is paid towards swarm intelligence metaheuristics-based neural architecture search. A framework is proposed for automatically designing neural architectures based on a swarm intelligence metaheuristic: Crow Search Algorithm. First, Crow Search Algorithm is integrated with binary network representation. To make it compatible for Neural Architecture Search, the original distance metric is replaced with hamming distance-based similarity measure. Second, the tuning parameters of Crow Search Algorithm are reduced by replacing the static flight length parameter with our dynamic flight length distribution algorithm. Third, the target selection method (random selection) is replaced by tournament select method. The proposed framework is used to search for architectures on MNIST, CIFAR10, and CIFAR100 datasets and achieved 0.18%, 3.48%, and 15.64% test error, respectively. Furthermore, smallscale transfer experiments are conducted to search architectures for Tiny ImageNet and achieved 34.43% test error. Nonparametric statistical analysis is performed to validate the impact of each modification in improving the quality of search space exploration. The proposed framework has achieved comparable performance with the state-of-the-art approaches, with a comparatively simpler approach and minimum human intervention. The proposed framework can be used to develop completely automated systems for designing architectures for various data-based classification applications.

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“…Also addition tests were done using crow search algorithm to do a performance comparison. 25,26 A unique mechanism for performing BP level classification using Mamdani type has been discussed. 27 Different type-1 and type-2 fuzzy systems for classification have been used, and it has also been proved that type-2 system has a better classification rate than type 1.…”

Section: Related Workmentioning

confidence: 99%

A proposed health monitoring system using fuzzy inference system

Ghosh

Roy

Merdji

2020

Proc Inst Mech Eng H

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Due to the busy schedule of every human being in today’s world, consciousness towards one’s health has become quite alarming. A person suffering from any chronic disease needs a gradual, regular and close monitoring to recover from the disease or to be under control. Because of heavy work pressure, anxiety, change of weather and location or due to some other causes, the effect of the diseases can turn up into an appalling state. Two vital aspects of human diseases are blood pressure (hypertension) and blood sugar imbalance. Hypertension is one of the complications of prolonged untreated diabetes. Other organs like kidney, eye and peripheral nerves are also involved. The various gradations of hypertension and diabetes are required to understand for the progression of the disease and to make plan for the treatment. So these two aspects are considered in this article. The idea is to develop a logic, which could be incorporated in a pocket friendly device in future that would generate an alarm whenever there is imbalance in blood sugar or blood pressure levels. The concept of the fuzzy inference rule and first-order logic is implemented to develop this study.

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Optimal Design of Interval Type-2 Fuzzy Heart Rate Level Classification Systems Using the Bird Swarm Algorithm

Cited by 39 publications

References 36 publications

RETRACTED ARTICLE: Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm

RETRACTED ARTICLE: Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm

Image Classification Based on Automatic Neural Architecture Search Using Binary Crow Search Algorithm

A proposed health monitoring system using fuzzy inference system

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