MOEPO: A novel Multi-objective Emperor Penguin Optimizer for global optimization: Special application in ranking of cloud service providers

Kaur, Harinder; Rai, Anurag; Bhatia, Sarvjit Singh; Dhiman, Gaurav

doi:10.1016/j.engappai.2020.104008

Cited by 52 publications

(20 citation statements)

References 47 publications

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“…The nonattendance of powerful and general remedial specialists and much absence provided by the antibody to create resistance in opposition to coronavirus have been developing various populace defence outbreaks [4]. Because of this quick emergency, monstrous abrasion frisson has influenced different areas, for example, the travel industry, medical and healthcare services, logistics, and transport-based supply chains as well as the retail sector [5][6][7][8][9][10][11][12][13][14][15][16] (https://covid.barabasilab.com). These fields are staggering under financial pressures and risks for employees to open again in the new normal [17,18].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods

Poongodi

Hamdi

Malviya

et al. 2021

Pers Ubiquit Comput

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Since the coronavirus (COVID-19) outbreak keeps on spreading all through the world, scientists have been crafting varied technologies mainly focusing on AI for an approach to acknowledge the difficulties of the epidemic. In this current worldwide emergency, the clinical business is searching for new advancements to screen and combat COVID-19 contamination. Strategies used by artificial intelligence can stretch screen the spread of the infection, distinguish highly infected patients, and be compelling in supervising the illness continuously. The artificial intelligence anticipation can further be used for passing dangers by sufficiently dissecting information from past sufferers. International patient support with recommendations for population testing, medical care, notification, and infection control can help fight this deadly virus. We proposed the hybrid deep learning method to diagnose COVID-19. The layered approach is used here to measure the symptom level of the patients and to analyze the patient image data whether he/she is positive with COVID-19. This work utilizes smart AI techniques to predict and diagnose the coronavirus rapidly by the Oura smart ring within 24 h. In the laboratory, a coronavirus rapid test is prepared with the help of a deep learning model using the RNN and CNN algorithms to diagnose the coronavirus rapidly and accurately. The result shows the value 0 or 1. The result 1 indicates the person is affected with coronavirus and the result 0 indicates the person is not affected with coronavirus. X-Ray and CT image classifications are considered here so that the threshold value is utilized for identifying an individual's health condition from the initial stage to a severe stage. Threshold value 0.5 is used to identify coronavirus initial stage condition and 1 is used to identify the coronavirus severe condition of the patient. The proposed methods are utilized for four weighting parameters to reduce both false positive and false negative image classification results for rapid and accurate diagnosis of COVID-19.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods

Poongodi

Hamdi

Malviya

et al. 2021

Pers Ubiquit Comput

Self Cite

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“…10 The performances of different models for different cases and their extension. Overall, it can be seen from the results that further this approach is extended to solve the other real-life complex problems [61][62][63][64][65][66][67][68][69][70][71][72][73][74]…”

Section: Discussionmentioning

confidence: 89%

A novel approach to optimize the production cost of railway coaches of India using situational-based composite triangular and trapezoidal fuzzy LPP models

Kumar

Dhiman²,

Kumar

et al. 2021

Complex Intell. Syst.

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This article offers a comparative study of maximizing and modelling production costs by means of composite triangular fuzzy and trapezoidal FLPP. It also outlines five different scenarios of instability and has developed realistic models to minimize production costs. Herein, the first attempt is made to examine the credibility of optimized cost via two different composite FLP models, and the results were compared with its extension, i.e., the trapezoidal FLP model. To validate the models with real-time phenomena, the Production cost data of Rail Coach Factory (RCF) Kapurthala has been taken. The lower, static, and upper bounds have been computed for each situation, and then systems of optimized FLP are constructed. The credibility of each model of composite-triangular and trapezoidal FLP concerning all situations has been obtained, and using this membership grade, the minimum and the greatest minimum costs have been illustrated. The performance of each composite-triangular FLP model was compared to trapezoidal FLP models, and the intense effects of trapezoidal on composite fuzzy LPP models are investigated.

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“…Moreover, researchers are trying to solve the COVID-19 detecting problem by using their own artificial-intelligence based approaches (Dhiman, 2019 , 2020 ; Dhiman et al, 2020a , 2020b , 2020c , 2021 ; Dhiman & Kumar, 2018a , 2019 ; Dhiman & Kaur, 2019 ; Kaur et al, 2020a , 2020b –).…”

Section: Resultsmentioning

confidence: 99%

ADOPT: automatic deep learning and optimization-based approach for detection of novel coronavirus COVID-19 disease using X-ray images

Dhiman

Chang

Singh³

et al. 2021

Journal of Biomolecular Structure and Dynamics

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In the hospital, because of the rise in cases daily, there are a small number of COVID-19 test kits available. For this purpose, a rapid alternative diagnostic choice to prevent COVID-19 spread among individuals must be implemented as an automatic detection method. In this article, the multi-objective optimization and deep learning-based technique for identifying infected patients with coronavirus using X-rays is proposed. J48 decision tree approach classifies the deep feature of corona affected Xray images for the efficient detection of infected patients. In this study, 11 different convolutional neural network-based (CNN) models (AlexNet, VGG16, VGG19, GoogleNet, ResNet18, ResNet50, ResNet101, InceptionV3, InceptionResNetV2, DenseNet201 and XceptionNet) are developed for detection of infected patients with coronavirus pneumonia using X-ray images. The efficiency of the proposed model is tested using k-fold cross-validation method. Moreover, the parameters of CNN deep learning model are tuned using multi-objective spotted hyena optimizer (MOSHO). Extensive analysis shows that the proposed model can classify the X-ray images at a good accuracy, precision, recall, specificity and F1-score rates. Extensive experimental results reveal that the proposed model outperforms competitive models in terms of well-known performance metrics. Hence, the proposed model is useful for real-time COVID-19 disease classification from X-ray chest images.

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MOEPO: A novel Multi-objective Emperor Penguin Optimizer for global optimization: Special application in ranking of cloud service providers

Cited by 52 publications

References 47 publications

RETRACTED ARTICLE: Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods

RETRACTED ARTICLE: Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods

A novel approach to optimize the production cost of railway coaches of India using situational-based composite triangular and trapezoidal fuzzy LPP models

ADOPT: automatic deep learning and optimization-based approach for detection of novel coronavirus COVID-19 disease using X-ray images

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