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All Journal Bulletin of Electrical Engineering and Informatics
Karthikram Anbalagan
Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology
Electrical & Electronics Engineering

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Automatic wireless health instructor for schools and colleges Jeyalakshmi Chelliah; Manjunathan Alagarsamy; Karthikram Anbalagan; Dineshkumar Thangaraju; Edwin Santhkumar Wesley; Kannadhasan Suriyan
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3330

Abstract

The suggested work demonstrates the preventive measures that can be used in schools and colleges during the present pandemic, which are the most important considerations once all of the institutions have reopened. Right now, sanitizers are the most important goods. According to WHO's new standards and regulations, a high level of sanitization is required to live. Despite the fact that all guidelines have been implemented, the majority of students are irresponsible, exacerbating the current scenario. To keep a student's hand sterilised, the proposed design incorporates an automatic hand sanitizer and a temperature detection system based on their ID card. The specific status of the student will be delivered to the class coordinator's mobile phone via genitourinary syndrome of menopause GSM whenever a person wishes to do it, even if there is no contact with the sanitising machine. Our method also uses a camera to snap a picture of the student, which can be viewed on a computer if any of the students do not answer. This also verifies the student's attendance, and the temperature of a specific student will be checked without the student's contact or touch in order to ensure safety and security. This allows everyone to keep an eye on the students while adhering to regulatory regulations.
Classification of covid patient image dataset using modified deep convolutional neural network system Manjunathan Alagarsamy; Karthikram Anbalagan; Yuvaraja Thangavel; Jeevitha Sakkarai; Jenopaul Pauliah; Kannadhasan Suriyan
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3290

Abstract

The number of people infected with the corona virus is steadily rising. Even after being treated and returned to normality, many who were impacted are still suffering from a variety of health problems. We suggest a new, more effective approach to dealing with this issue, as well as putting in place preventative measures to prevent the spread of disease. The modified convolutional neural networks (M-CNN) architecture is modified deepCNN architecture. Using existingcorona virus disease 2019(COVID-19) computerizedtomographyscan (CT scan) images, this suggested approach intends to develop a deep model for screening and forecasting the risk of disease propagation. The suggested model was trained using 1000 scan pictures from various sources, yielding a prediction accuracy of 93 percent, which is much greater than previous methods.
Co-Authors Dineshkumar Thangaraju Edwin Santhkumar Wesley Jeevitha Sakkarai Jenopaul Pauliah Jeyalakshmi Chelliah Kannadhasan Suriyan Kannadhasan Suriyan Manjunathan Alagarsamy Manjunathan Alagarsamy Yuvaraja Thangavel