Comparative Micro Blogging News Analysis on the COVID-19 Pandemic Scenario

Kaushal, Chetna; Refat, Abu Rumman; Amin, Al; Islam, Md Khairul

doi:10.1007/978-981-16-5348-3_30

Cited by 8 publications

(2 citation statements)

References 18 publications

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“…Keras is extensively used for neural network training with TensorFlow as a backend. The parameters and hyperparameters used in the training process are represented in Table 1 [ 105 ].…”

Section: Experiments and Resultsmentioning

confidence: 99%

A Secure Framework toward IoMT‐Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services

Islam

Kaushal

Amin

et al. 2022

Contrast Media & Molecular Imaging

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The abnormal growth of the skin cells is known as skin cancer. It is one of the main problems in the dermatology area. Skin lesions or malignancies have been a source of worry for many individuals in recent years. Irrespective of the skin tone, there exist three major classes of skin lesions, i.e., basal cell carcinoma, squamous cell carcinoma, and melanoma. The early diagnosis of these lesions is equally important for human life. In the proposed work, a secure IoMT-Assisted framework is introduced that can help the patients to do the initial screening of skin lesions remotely. The initially proposed approach uses an IoMT-based data collection device which is accessible by patients to capture skin lesions images. Next, the captured skin sample is encrypted and sent to the collected image toward cloud storage. Later, the received sample image is classified into appropriate class labels using an ensemble classifier. In the proposed framework, four CNN models were ensemble i.e., VGG-16, DenseNet-201, Inception-V3, and Efficient-B7. The framework has experimented with the “HAM10000” dataset having 7 different kinds of skin lesions data. Although DenseNet-201 performed well, the ensemble model provides the highest accuracy with 87.22 percent as well as its test loss/error is lower than others with 0.4131. Moreover, the ensemble model’s classification ability is much higher with an AUC score of 0.9745. Moreover, A recommendation team has been assigned to assess the sample of the patient as well as suggest the patient according to classified results by the CAD.

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“…Keras is extensively used for neural network training with TensorFlow as a backend. The parameters and hyperparameters used in the training process are represented in Table 1 [ 105 ].…”

Section: Experiments and Resultsmentioning

confidence: 99%

A Secure Framework toward IoMT‐Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services

Islam

Kaushal

Amin

et al. 2022

Contrast Media & Molecular Imaging

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“…We see a clear upward trend beginning in 2019, and continuous growth afterwards. Additionally, following COVID-19 [43][44][45][46], the overall tendency grew. As a result, we may infer from worldwide publishing patterns that the industry is currently going through a phase of steady expansion.…”

Section: Overview Of the Data Collected And Annual Scientific Productionmentioning

confidence: 98%

Healthcare Trust Evolution with Explainable Artificial Intelligence: Bibliometric Analysis

Dhiman,

Bonkra,

Kaur

et al. 2023

Information

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Recent developments in IoT, big data, fog and edge networks, and AI technologies have had a profound impact on a number of industries, including medical. The use of AI for therapeutic purposes has been hampered by its inexplicability. Explainable Artificial Intelligence (XAI), a revolutionary movement, has arisen to solve this constraint. By using decision-making and prediction outputs, XAI seeks to improve the explicability of standard AI models. In this study, we examined global developments in empirical XAI research in the medical field. The bibliometric analysis tools VOSviewer and Biblioshiny were used to examine 171 open access publications from the Scopus database (2019–2022). Our findings point to several prospects for growth in this area, notably in areas of medicine like diagnostic imaging. With 109 research articles using XAI for healthcare classification, prediction, and diagnosis, the USA leads the world in research output. With 88 citations, IEEE Access has the greatest number of publications of all the journals. Our extensive survey covers a range of XAI applications in healthcare, such as diagnosis, therapy, prevention, and palliation, and offers helpful insights for researchers who are interested in this field. This report provides a direction for future healthcare industry research endeavors.

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WhyMyFace: A Novel Approach to Recognize Facial Expressions Using CNN and Data Augmentations

Refat

Sarker

Kaushal

et al. 2022

Advances in Intelligent Systems and Computing

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Comparative Micro Blogging News Analysis on the COVID-19 Pandemic Scenario

Cited by 8 publications

References 18 publications

A Secure Framework toward IoMT‐Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services

A Secure Framework toward IoMT‐Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services

Healthcare Trust Evolution with Explainable Artificial Intelligence: Bibliometric Analysis

WhyMyFace: A Novel Approach to Recognize Facial Expressions Using CNN and Data Augmentations

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