Version 1
: Received: 17 April 2024 / Approved: 18 April 2024 / Online: 18 April 2024 (07:51:24 CEST)
How to cite:
Alzakari, S.A.; Sivakumar, N.R.; Ibrahim, A.Z.; Justin, S.; Chelliah, C. MIoT-aware Hessian Linear Feature Embedding Based Tucker Congruence Deep Convolutional Network for Epidemic Disease Control with Cloud. Preprints2024, 2024041217. https://doi.org/10.20944/preprints202404.1217.v1
Alzakari, S.A.; Sivakumar, N.R.; Ibrahim, A.Z.; Justin, S.; Chelliah, C. MIoT-aware Hessian Linear Feature Embedding Based Tucker Congruence Deep Convolutional Network for Epidemic Disease Control with Cloud. Preprints 2024, 2024041217. https://doi.org/10.20944/preprints202404.1217.v1
Alzakari, S.A.; Sivakumar, N.R.; Ibrahim, A.Z.; Justin, S.; Chelliah, C. MIoT-aware Hessian Linear Feature Embedding Based Tucker Congruence Deep Convolutional Network for Epidemic Disease Control with Cloud. Preprints2024, 2024041217. https://doi.org/10.20944/preprints202404.1217.v1
APA Style
Alzakari, S.A., Sivakumar, N.R., Ibrahim, A.Z., Justin, S., & Chelliah, C. (2024). MIoT-aware Hessian Linear Feature Embedding Based Tucker Congruence Deep Convolutional Network for Epidemic Disease Control with Cloud. Preprints. https://doi.org/10.20944/preprints202404.1217.v1
Chicago/Turabian Style
Alzakari, S.A., Shekaina Justin and Chandrasekar Chelliah. 2024 "MIoT-aware Hessian Linear Feature Embedding Based Tucker Congruence Deep Convolutional Network for Epidemic Disease Control with Cloud" Preprints. https://doi.org/10.20944/preprints202404.1217.v1
Abstract
Reliable prediction of infectious disease is an essential need for public health organizations to minimize or prevent the disease's spread. With the increase of data growth in the healthcare sector, accurate analysis of such data helps for early disease detection and better patient care. With the availability of huge data, it is a great deal for predicting and handling an epidemic occurrence. To control the epidemic disease, a new Artificial Intelligence called Manhattan Hessian Linear Feature Embedding Based Tucker Congruence Deep Convolutional Network (MHLFE-TCDCN) is introduced with higher accuracy and minimum time consumption. The MIoT device is used to collect the data from the patient body. The proposed MHLFE-TCDCN includes two major processes namely feature selection and classification with multiple layers such as input, two hidden layers, and one output layer. First, the feature selection process is performed using the Manhattan Hessian Locally Linear Embedding technique in the first hidden layer to minimize the dimensionality and time complexity. After selecting the feature, the classification is performed using the Tucker coefficient of congruence regression. The Tucker coefficient of congruence measures the similarity between the training and testing disease data. A positive similarity level is used to accurately identify the disease at the output layer based on the similarity measure. Experimental assessment is carried out with various parameters such as accuracy, precision, recall, F-measure, and computational time concerning several numbers of patient data. The quantitative results and discussion that the proposed MHLFE-TCDCN technique achieves higher accuracy with minimal computation time as compared to the conventional methods.
Keywords
Cloud; Epidemic Disease Control; feature selection; Deep Convolutional Network; Hessian Locally Linear Embedding technique; Manhattan distance measure; Tucker coefficient of congruence regression
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
