As per a 2023 publication, addressing mental health problems in India holds immense significance due to the scale of human value impact involved, particularly given the country's population size. It is crucial to ...
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The pandemic of COVID-19 has affected worldwide population. Diagnosing this highly contagious disease at an initial stage is essential for controlling its spread. In this paper, we propose a novel lightweight hybrid c...
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In modern supply chain management, efficient communication and timely decision-making are essential to ensuring smooth operations and avoiding descriptions. This is ensured by efficient communication and timely decisi...
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Recommender systems are techniques designed to enhance user experience in various domains. They suggest relevant items to users based on their behavior and preferences (Linyuan et al. Feb 2012). These systems are bein...
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Improving the quality and resolution of low- resolution digital images is an important task with far-reaching implications for a variety of applications, including medical imaging, surveillance, and content retrieval....
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Human pose estimation (HPE) from images or video is not only a major issue of computer vision, but also it plays a vital role in many real-world applications. The most challenging problems of human pose estimation are...
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Purpose: Coronavirus disease 2019 (COVID-19) has infected about 418 million people across the globe. So, the analysis of biomedical imaging accompanied with artificial intelligence (AI) approaches has transpired a vit...
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Purpose: Coronavirus disease 2019 (COVID-19) has infected about 418 million people across the globe. So, the analysis of biomedical imaging accompanied with artificial intelligence (AI) approaches has transpired a vital role in diagnosing COVID-19. Until now, numerous classification approaches have been demonstrated for the detection of COVID-19. The assessment of COVID-19 patients according to severity level is not so far investigated. For this motivation, the classification of COVID-19 chest X-ray (CXR) images according to severity of the infection is presented in this work. Methods: Primarily, the 1527 CXR images are pre-processed to reshape images into unique size, denoised, and enhanced images through median filter and histogram equalization (HE) techniques, respectively. Afterward, reshaped, denoised, and enhanced CXR images are augmented using synthetic minority oversampling technique (SMOTE) to achieve the balanced dataset of 1752 CXR images. After augmentation, a pre-trained VGG16 and residual network 50 (Resnet50) deep transfer learning models with random forest (RF) and support vector machine (SVM) classifiers are utilized for feature extraction and classification of 1752 CXR images into diverse class labels such as normal, severe COVID-19, and non-severe COVID-19. Results: Our proposed ResNet50 model with SVM classifier provides the highest accuracy of about 95% for severity assessment and classification of COVID-19 CXR images as compared to other permutations. For the ResNet50 model with SVM classifier model, the average value of precision, recall, and F1-score are 91%, 94%, and 92%, respectively. Conclusion: The multi-class classification deep transfer learning models are presented to determine the severity assessment and classification of COVID-19 by using CXR images. Out of these proposed models, the ResNet50 model with SVM classifier will be highly favorable for doctors to classify patients according to their severity assessment and detection of COV
Cardiovascular diseases (CVDs) are among the leading causes of death globally, causing around 17.9 million deaths every year, as reported by the WHO. Patient outcomes would also be maximally improved if predictions an...
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Current cloud-based systems face the challenge of managing storage and eliminating redundant data associated with the exponentially growing multimedia content, where numerous semantically similar but nonidentical file...
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The heart is indispensable for body. Diagnosis of heart disease must be complete and accurate. Heart disease is a dangerous disease. This disease is caused by many problems such as excessive stress, diabetes, high blo...
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