MOSOA: A new multi-objective seagull optimization algorithm

Dhiman, Gaurav; Singh, Krishna Kant; Soni, Mukesh; Nagar, Atulya K.; Dehghani, Mohammad; Słowik, Adam; Kaur, Amandeep; Sharma, Ashutosh; Houssein, Essam H.; Cengiz, Korhan

doi:10.1016/j.eswa.2020.114150

Cited by 168 publications

(57 citation statements)

References 62 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RETRACTED ARTICLE: Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods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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“…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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“…e results of the proposed technique are 1% to 8% better than the existing technique that is proposed by Smith et al. It can be seen that the researchers will apply this approach for solving the real-life complex problems also [39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58].…”

Section: Comparison With the State-of-the Art Techniquesmentioning

confidence: 99%

Optimization of Ultrasound Information Imaging Algorithm in Cardiovascular Disease Based on Image Enhancement

Shao

et al. 2021

Mathematical Problems in Engineering

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To improve the interpretability or perception of information in images for human viewers is the main goal of image enhancement. Aiming at the problem that image edges are difficult to determine due to artefacts, plaques, and vascular branches in cardiovascular ultrasound, an edge ultrasound imaging detection algorithm based on spatial-frequency-domain image enhancement is proposed to improve the clarity of ultrasound images. Firstly, this paper uses the space-frequency-domain enhancement algorithm to enhance the image. This algorithm overcomes the problem of low contrast of conventional algorithms. The enhanced data matrix is used as the cost matrix, and then, the heuristic image search method is used to search the image of the cost matrix. The results show that the use of spatial-frequency-domain image ultrasound imaging algorithm can improve the contrast and sharpness of ultrasound images of cardiovascular disease, which can make the middle edge of the image clearer, the detection accuracy rate is increased to 92.76%, and the ultrasound of cardiovascular disease is improved. The edge of the image gets accuracy. The paper confirms that the ultrasound imaging algorithm based on spatial-frequency-domain image enhancement is worthy of application in clinical ultrasound image processing. The performance of the proposed technique is 32.54%, 75.30%, 21.19%, 21.26%, and 11.10% better than the existing technique in terms of edge energy, detail energy, sharpness, contrast, and information entropy, respectively.

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MOSOA: A new multi-objective seagull optimization algorithm

Cited by 168 publications

References 62 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

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

Optimization of Ultrasound Information Imaging Algorithm in Cardiovascular Disease Based on Image Enhancement

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