A novel algorithm for global optimization: Rat Swarm Optimizer

Dhiman, Gaurav; Garg, Meenakshi; Nagar, Atulya K.; Kumar, Vijay; Dehghani, Mohammad

doi:10.1007/s12652-020-02580-0

Cited by 308 publications

(159 citation statements)

References 99 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RETRACTED ARTICLE: Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods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

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“…The momentous standards of VANET are the dedicated short-range communication (DSRC) protocol, IEEE 802.11 [2], and wireless access in vehicular environment (WAVE) [3,4]. Delays due to traffic, traffic that leads to congestion, consumption of energy, and the emission of pollution are the disputable in traffic management for smart cities [5][6][7][8][9]. The traffic management must effectuate the smart system for parking, an intelligent system for vehicles in routing management, and an intelligent system that predicts the traffic [10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

Kothai¹,

Poovammal²,

Dhiman³

et al. 2021

Wireless Communications and Mobile Computing

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The vehicular adhoc network (VANET) is an emerging research topic in the intelligent transportation system that furnishes essential information to the vehicles in the network. Nearly 150 thousand people are affected by the road accidents that must be minimized, and improving safety is required in VANET. The prediction of traffic congestions plays a momentous role in minimizing accidents in roads and improving traffic management for people. However, the dynamic behavior of the vehicles in the network degrades the rendition of deep learning models in predicting the traffic congestion on roads. To overcome the congestion problem, this paper proposes a new hybrid boosted long short-term memory ensemble (BLSTME) and convolutional neural network (CNN) model that ensemble the powerful features of CNN with BLSTME to negotiate the dynamic behavior of the vehicle and to predict the congestion in traffic effectively on roads. The CNN extracts the features from traffic images, and the proposed BLSTME trains and strengthens the weak classifiers for the prediction of congestion. The proposed model is developed using Tensor flow python libraries and are tested in real traffic scenario simulated using SUMO and OMNeT++. The extensive experimentations are carried out, and the model is measured with the performance metrics likely prediction accuracy, precision, and recall. Thus, the experimental result shows 98% of accuracy, 96% of precision, and 94% of recall. The results complies that the proposed model clobbers the other existing algorithms by furnishing 10% higher than deep learning models in terms of stability and performance.

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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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A novel algorithm for global optimization: Rat Swarm Optimizer

Cited by 308 publications

References 99 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 New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

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

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