Computer Vision-Enabled Character Recognition of Hand Gestures for Patients with Hearing and Speaking Disability

Juneja, Sapna; Juneja, Abhinav; Dhiman, Gaurav; Jain, Sweta; Dhankhar, Anu; Kautish, Sandeep

doi:10.1155/2021/4912486

Cited by 28 publications

(18 citation statements)

References 48 publications

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“…A complete understanding of these factors and their immensity of impact on the spread of COVID-19 would be very fruitful for the decision-makers to administer the spread of COVID-19 [6]. Various approaches such as machine earning, neural network, data science [7], fuzzy AHP, AHPU, and multicriteria decision-making [8] can be used in order to learn the intensity of impact on the spread of COVID-19. is paper presents an approach to recognizing the important factors responsible for the spread of COVID-19 using these above-mentioned techniques.…”

Section: Introductionmentioning

confidence: 99%

IoT-Based Technological Framework for Inhibiting the Spread of COVID-19: A Pandemic Using Machine Learning and Fuzzy-Based Processes

Kaur

Elarabawy

Abd‐Elnaby

et al. 2022

Security and Communication Networks

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The COVID-19 infestation has resulted in the loss of the lives of millions of people and the destruction of commercial, communal, and healthcare systems around the world. To control these kinds of pandemics, complete knowledge of the traits and responses to the disease is required, and this knowledge can be gathered using various computational methods. These computational techniques can be very helpful in information building required for taking appropriate action timely and applying preventive mechanisms. Although there are numerous computational approaches that the researchers are applying over the past few months, there is a need to re-evaluate and compare these available techniques for finding out the best technique that can help reduce the extent of COVID-19. Hence, in this paper, we carried out a literature review to emphasize the assistance of various computational approaches around COVID-19. The paper presents a categorization of approaches such as multicriteria decision-making, fuzzy AHP, machine learning, neural network, and data science that have been used to deal with the epidemic. Furthermore, this study discusses various challenges experienced when accessing the COVID-19 data. The conclusions of the paper propose the best computational approach among all to be appraised for future investigations and implementations.

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Section: Introductionmentioning

confidence: 99%

IoT-Based Technological Framework for Inhibiting the Spread of COVID-19: A Pandemic Using Machine Learning and Fuzzy-Based Processes

Kaur

Elarabawy

Abd‐Elnaby

et al. 2022

Security and Communication Networks

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“…The first is learning from the ground up, and the second is transfer learning. A convolutional layer, activation layer, a batch normalization layer, a pooling layer, and a classification layer are among the network layers that make up the CNN, all of which are described as follows [ 31 , 32 ].…”

Section: Background On Cnnmentioning

confidence: 99%

CNN Based Multiclass Brain Tumor Detection Using Medical Imaging

Tiwari

Pant

Elarabawy

et al. 2022

Computational Intelligence and Neuroscience

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Brain tumors are the 10th leading reason for the death which is common among the adults and children. On the basis of texture, region, and shape there exists various types of tumor, and each one has the chances of survival very low. The wrong classification can lead to the worse consequences. As a result, these had to be properly divided into the many classes or grades, which is where multiclass classification comes into play. Magnetic resonance imaging (MRI) pictures are the most acceptable manner or method for representing the human brain for identifying the various tumors. Recent developments in image classification technology have made great strides, and the most popular and better approach that has been considered best in this area is CNN, and therefore, CNN is used for the brain tumor classification issue in this paper. The proposed model was successfully able to classify the brain image into four different classes, namely, no tumor indicating the given MRI of the brain does not have the tumor, glioma, meningioma, and pituitary tumor. This model produces an accuracy of 99%.

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“…However, these also directly contain a large amount of personal information [26]. Once obtained by an illegal third party, they will now threaten patients' privacy [27].…”

Section: Sources and Characteristics Of Medical Big Datamentioning

confidence: 99%

Federated Learning Approach to Protect Healthcare Data over Big Data Scenario

Juneja¹,

Mohafez

El-Bayoumy³

et al. 2022

Sustainability

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The benefits and drawbacks of various technologies, as well as the scope of their application, are thoroughly discussed. The use of anonymity technology and differential privacy in data collection can aid in the prevention of attacks based on background knowledge gleaned from data integration and fusion. The majority of medical big data are stored on a cloud computing platform during the storage stage. To ensure the confidentiality and integrity of the information stored, encryption and auditing procedures are frequently used. Access control mechanisms are mostly used during the data sharing stage to regulate the objects that have access to the data. The privacy protection of medical and health big data is carried out under the supervision of machine learning during the data analysis stage. Finally, acceptable ideas are put forward from the management level as a result of the general privacy protection concerns that exist throughout the life cycle of medical big data throughout the industry.

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Computer Vision-Enabled Character Recognition of Hand Gestures for Patients with Hearing and Speaking Disability

Cited by 28 publications

References 48 publications

IoT-Based Technological Framework for Inhibiting the Spread of COVID-19: A Pandemic Using Machine Learning and Fuzzy-Based Processes

IoT-Based Technological Framework for Inhibiting the Spread of COVID-19: A Pandemic Using Machine Learning and Fuzzy-Based Processes

CNN Based Multiclass Brain Tumor Detection Using Medical Imaging

Federated Learning Approach to Protect Healthcare Data over Big Data Scenario

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