PRAVIN BALASAHEB KHATKALE
@sanjivani.edu.in
Controller of Examination
Sanjivani University
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Vision and Pattern Recognition, Artificial Intelligence, Multidisciplinary, Engineering
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
P. M. Yawalkar, , P. William, V. M. Tidake, P. M. Patare, P. B. Khatkale, A. A. Khatri, S. S. Ingle, , ,et al.
Sumy State University
S. R. Thorat, , V. M. Tidake, P. M. Patare, P. B. Khatkale, A. A. Khatri, P. M. Yawalkar, S. S. Ingle, , ,et al.
Sumy State University
P. B. Khatkale, , A. A. Khatri, P. M. Yawalkar, V. Verma, V. M. Tidake, P. M. Patare, S. Kulkarni, , ,et al.
Sumy State University
P. William, Pravin B. Khatkale, and N Yogeesh
CRC Press
P. William, Pravin B. Khatkale, and N Yogeesh
CRC Press
Pravin B. Khatkale, P. William, Oluwadare Joshua Oyebode, Aman Sharma, Vandana Kumari, and Vikram Singh
Springer Nature Singapore
Praveen B. Khatkale, Anupama Deshpande, and Anil B. Pawar
IEEE
The module responsible for user safety is one of the most vital components of computer systems. It has been shown that simple passwords and logins cannot ensure great efficiency and are simple for hackers to get. The well-known alternative is biometric identity recognition. In recent years, iris as a biometrics attribute has garnered more attention. This was owing to the great efficiency and precision assured by this quantifiable characteristic. In the literature, the effects of this curiosity may be found. Several diverse ways have been offered by various writers. Neither employs discrete fast Fourier transform (DFFT) components to characterise the iris sample. In this paper, the authors offer their unique method for iris-based human identification recognition using DFFT components determined via principal component analysis. Three techniques were utilised for classification: k-nearest neighbours, support vector machines, and artificial neural networks. Tests conducted have shown that the suggested procedure may provide good results.
Harshal P. Varade, Sonal C. Bhangale, Sandip R. Thorat, Pravin B. Khatkale, Santosh Kumar Sharma, and P. William
IEEE
Exhale and inhale filthy air has major health consequences. Air pollution's influence may be mitigated by conducting regular monitoring and keeping a record of it. Government organizations may also take proactive measures to protect the environment by accurately anticipating pollution levels in real time. In future smart cities, we propose using the Internet of Things and machine learning to track pollution levels in the air we breathe. The Pearson correlation test is performed to see whether pollutants and meteorological indicators have a high link. A cloud-centric IoT middleware architecture is used in this research instead of a standard sensor network to gather data from both air pollution and current weather sensors. This means that both reliability and cost have been greatly improved. Sulphur Dioxide (SO2) and Particulate Matter levels were predicted using an Artificial Neural Network (ANN) (PM2.5). The positive results show that ANNs may be used to monitor and forecast air pollution. RMSE values of 0.0128 and 0.0001 for SO2 and PM2.5 were found using our models.