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RISS 인기검색어
- 검색키워드
- 저자 : Kshirsagar Pravin R. (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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Automation Monitoring With Sensors For Detecting Covid Using Backpropagation Algorithm
( Pravin R. Kshirsagar ),( Hariprasath Manoharan ),( Vineet Tirth ),( Mohd Naved ),( Ahmad Tasnim Siddiqui ),( Arvind K. Sharma ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.7
This article focuses on providing remedial solutions for COVID disease through the data collection process. Recently, In India, sudden human losses are happening due to the spread of infectious viruses. All people are not able to differentiate the number of affected people and their locations. Therefore, the proposed method integrates robotic technology for monitoring the health condition of different people. If any individual is affected by infectious disease, then data will be collected and within a short span of time, it will be reported to the control center. Once, the information is collected, then all individuals can access the same using an application platform. The application platform will be developed based on certain parametric values, where the location of each individual will be retained. For precise application development, the parametric values related to the identification process such as sub-interval points and intensity of detection should be established. Therefore, to check the effectiveness of the proposed robotic technology, an online monitoring system is employed where the output is realized using MATLAB. From simulated values, it is observed that the proposed method outperforms the existing method in terms of data quality with an observed percentage of 82.
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An Explainable Deep Learning Approach for Oral Cancer Detection
Babu P. Ashok,Rai Anjani Kumar,Ramesh Janjhyam Venkata Naga,Nithyasri A.,Sangeetha S.,Kshirsagar Pravin R.,Rajendran A.,Rajaram A.,Dilipkumar S. 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.3
With a high death rate, oral cancer is a major worldwide health problem, particularly in low- and middle-income nations. Timely detection and diagnosis are crucial for efective prevention and treatment. To address this challenge, there is a growing need for automated detection systems to aid healthcare professionals. Regular dental examinations play a vital role in early detection. Transfer learning, which leverages knowledge from related domains, can enhance performance in target categories. This study presents a unique approach to the early detection and diagnosis of oral cancer that makes use of the exceptional sensory capabilities of the mouth. Deep neural networks, particularly those based on automated systems, are employed to identify intricate patterns associated with the disease. By combining various transfer learning approaches and conducting comparative analyses, an optimal learning rate is achieved. The categorization analysis of the reference results is presented in detail. Our preliminary fndings demonstrate that deep learning efectively addresses this challenging problem, with the Inception-V3 algorithm exhibiting superior accuracy compared to other algorithms.
ScienceON
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