Rough Sets in COVID-19 to Predict Symptomatic Cases

Bhapkar, H. R.; Mahalle, Parikshit N.; Shinde, Gitanjali Rahul; Mahmud, Mufti

doi:10.1007/978-981-15-9682-7_7

Cited by 56 publications

(16 citation statements)

References 14 publications

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“…In this work, challenges like availability on the Internet and electricity in a rural area, creating softcopy of learning material, digital literacy are discussed. Magomedov et al presented the negative and positive impact of a pandemic on education globally [18]. Study focus that there is a difference between online conduction of theory and practical sessions, conduction online learning for theory lectures may not affect that much than practical assignment implementations.…”

Section: Literature Surveymentioning

confidence: 98%

Impact of COVID-19 Pandemic on Education Sector

Shinde¹,

Majumder

Bhapkar

et al. 2021

Studies in Big Data

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The education sector is highly affected by the COVID-19 pandemic, because the traditional teaching process requires students and teachers to meet at one place. Due to the closure, various teaching modes and methods have been adjusted, and the evaluation method has also changed from the traditional way. The ebb and flow of this uncertainty in the distribution of COVID-19 led to poor planning of the teaching process (students and teachers), test methods and assessment techniques, and subject work quality. Ultimately, this affected the placement of students in companies and universities. This chapter is an attempt to understand the impact of COVID-19 on education sector (for different stakeholders such as teachers, students, employers and book printers) using data analysis and mathematical modeling.

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Section: Literature Surveymentioning

confidence: 98%

Impact of COVID-19 Pandemic on Education Sector

Shinde¹,

Majumder

Bhapkar

et al. 2021

Studies in Big Data

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“…On the other hand, AI and Machine Learning (ML) techniques are notable for their predictive abilities in a number of fields such as anomaly detection [30]- [33], biological data mining [34], [35], cyber security [36], disease detection [37]- [45], earthquake prediction [46], elderly care [47], [48], elderly fall detection [49]- [51], financial prediction [52], safeguarding workers in workplaces [53], text analytics [54], [55], and urban planning [56].…”

Section: Introductionmentioning

confidence: 99%

Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey

et al. 2021

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The Internet of Things (IoT) has emerged as a technology capable of connecting heterogeneous nodes/objects, such as people, devices, infrastructure, and makes our daily lives simpler, safer, and fruitful. Being part of a large network of heterogeneous devices, these nodes are typically resourceconstrained and became the weakest link to the cyber attacker. Classical encryption techniques have been employed to ensure the data security of the IoT network. However, high-level encryption techniques can not be employed in IoT devices due to the limitation of resources. In addition, node security is still a challenge for network engineers. Thus, we need to explore a complete solution for IoT networks that can ensure nodes and data security. The rule-based approaches and shallow and deep machine learning algorithms-branches of Artificial Intelligence (AI)-can be employed as countermeasures along with the existing network security protocols. This paper presented a comprehensive layer-wise survey on IoT security threats, and the AIbased security models to impede security threats. Finally, open challenges and future research directions are addressed for the safeguard of the IoT network.

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“…Along with examinations of symptoms and pathogenic testing, imaging examinations are found indispensable in the diagnosis in the screening, detection, diagnosis and prognosis of COVID-19 ( Aradhya et al, 2021 , Bhapkar et al, 2021 , Dey et al, 2020 , Kaiser et al, 2021 , Murugappan et al, 2021 , Singh et al, 2021 ). In a study on imaging modalities in the diagnosis of COVID-19, Yang et al (2020) have shown that Computed Tomography (CT) images are very effective in capturing Ground Glass Opacity (GGO), consolidations and patchy areas in the peripherals of the lungs, in the early and advanced stages of infections.…”

Section: Introductionmentioning

confidence: 99%

Deep transfer learning for COVID-19 detection and infection localization with superpixel based segmentation

Prakash¹,

Murugappan

Hemalakshmi³

et al. 2021

Sustainable Cities and Society

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The evolution the novel corona virus disease (COVID-19) as a pandemic has inflicted several thousand deaths per day endangering the lives of millions of people across the globe. In addition to thermal scanning mechanisms, chest imaging examinations provide valuable insights to the detection of this virus, diagnosis and prognosis of the infections. Though Chest CT and Chest X-ray imaging are common in the clinical protocols of COVID-19 management, the latter is highly preferred, attributed to its simple image acquisition procedure and mobility of the imaging mechanism. However, Chest X-ray images are found to be less sensitive compared to Chest CT images in detecting infections in the early stages. In this paper, we propose a deep learning based framework to enhance the diagnostic values of these images for improved clinical outcomes. It is realized as a variant of the conventional SqueezeNet classifier with segmentation capabilities, which is trained with deep features extracted from the Chest X-ray images of a standard dataset for binary and multi class classification. The binary classifier achieves an accuracy of 99.53% in the discrimination of COVID-19 and Non COVID-19 images. Similarly, the multi class classifier performs classification of COVID-19, Viral Pneumonia and Normal cases with an accuracy of 99.79%. This model called the COVID-19 Super pixel SqueezNet (COVID-SSNet) performs super pixel segmentation of the activation maps to extract the regions of interest which carry perceptual image features and constructs an overlay of the Chest X-ray images with these regions. The proposed classifier model adds significant value to the Chest X-rays for an integral examination of the image features and the image regions influencing the classifier decisions to expedite the COVID-19 treatment regimen.

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Rough Sets in COVID-19 to Predict Symptomatic Cases

Cited by 56 publications

References 14 publications

Impact of COVID-19 Pandemic on Education Sector

Impact of COVID-19 Pandemic on Education Sector

Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey

Deep transfer learning for COVID-19 detection and infection localization with superpixel based segmentation

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