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2,137 results on '"Saravanan M"'

108. A New Technique for Wind Turbine Power Curve Incomplete Bin Evaluation

Author

Ramdas, Bhukya, Saravanan, M., Solomon, J. C. David, Balaraman, K., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Dewan, Lillie, editor, C. Bansal, Ramesh, editor, and Kumar Kalla, Ujjwal, editor

Published
2021
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109. A Compact Vertex Fed Heptagon Monopole Antenna in a Wide Diamond Slot for UWB Applications

Author

Priya, A., Saravanan, M., Balasubramaniam, D., Subahar, A., Purushothaman, V., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Ranganathan, G., editor, Chen, Joy, editor, and Rocha, Álvaro, editor

Published
2021
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110. Probabilistic Optimization of Incorporating Security Ciphers and Encryption of Data Storage in Cloud

Author

Kaduvu, Haripriya, Lavanya, V., Saravanan, M., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Hemanth, D. Jude, editor, Vadivu, G., editor, Sangeetha, M., editor, and Balas, Valentina Emilia, editor

Published
2021
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111. Unusual Behavior Analysis of Virtual Machine in Cloud Environment

Author

Nivetha, S., Saravanan, M., Lavanya, V., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Hemanth, D. Jude, editor, Vadivu, G., editor, Sangeetha, M., editor, and Balas, Valentina Emilia, editor

Published
2021
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119. An efficient fingerprint detection system using SVM and neural network (ANN) classifications algorithm.

Author

Sravanthi, R. and Saravanan, M. S.

Subjects
*ARTIFICIAL neural networks, *SUPPORT vector machines, *CLASSIFICATION algorithms, *STANDARD deviations, *ALGORITHMS
Abstract

To be more specific, we are interested in comparing the accuracy of Support Vector Machine with a novel artificial neural network classifier when it comes to fingerprint verification. We compare the new method of artificial neural networks to an SVM. Following evaluating the suggested procedure with a combined total of fifteen samples in both groups, the G power value stands at eighty percent. When it comes to identifying biometric identity, the Support Vector Machine technique only manages 83% accuracy; in contrast, the Novel Artificial Neural Network technique approach achieves an impressive 87% accuracy. With a protest power analysis completed at 85% and test sizes of 15 for one group and 30 for the overall set, the Novel Artificial Neural Network approach outperformed SVM marginally in terms of mean accuracy and standard deviation (1.25369 vs. 1.30700). The Novel Artificial Neural Network Method (1.25369) outperforms the SVM method (1.30700) with a significant value of 0.01 (p<0.05) in terms of mean exactness and standard deviation, although the SVM algorithm (83.10%) ranks somewhat lower. The results demonstrated that when it came to biometric expectancies, the Novel Artificial Neural Network Method Algorithm outperformed the SVM algorithm. [ABSTRACT FROM AUTHOR]

Published
2024
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120. Prediction of liver tumor segmentation using novel logistic regression and comparing with XG boosting algorithm for better accuracy.

Author

Kumar, I. Punidha and Saravanan, M. S.

Subjects
*MACHINE learning, *LIVER tumors, *LIVER cancer, *CONFIDENCE intervals, *ALGORITHMS
Abstract

The primary goal of this research is to create a new machine learning method based on Logistic Regression that has the ability to significantly enhance the accuracy of liver tumor prediction. Actions to take and Items to Bring: This research used the following statistical parameters: a 95% confidence interval, a 0.05 significance level, and an 80% g-power. Two ML algorithms are used in this investigation, which uses a total of 52 data points. The present accuracy of the XG boosting algorithm in predicting liver tumors is 69.21%; however, this work improves upon that by using the innovative Logistic Regression approach. The findings showed that a unique Logistic Regression method could accurately predict 71.11% of liver cancers, with a significance level of 0.026 (p<0.05) and a 95% confidence interval. When compared to the DT method, the innovative Logistic Regression algorithm was more effective in predicting the presence of liver cancers. [ABSTRACT FROM AUTHOR]

Published
2024
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121. Real time spam detection system using LGBM classifier over the countvectorizer machine learning algorithms.

Author

Sekhar, B. and Saravanan, M. S.

Subjects
*MACHINE learning, *SYSTEM identification, *FRAUD, *BLOGS, *ACQUISITION of data
Abstract

This study's main aim is to recognize the Spam messages that are prevalent on the majority of competing websites and blog sites. The suggested predictions algorithms' trained as well as assessment sets were constructed using a minimum of two characteristics and a sampling level of 150. Entries from well-known blogs made up the data collection. Low Gradients Booster, an approach to machine-learning that is similar to SVM using a count-vectorizer as an assisting approach, was used to create the architecture. Countvectorizer, an artificial training approach, achieves an extraction precision rate of 90.00 percentages, while the LGBM method achieves a rate of 90.00 percentages (96.30 percentages). A significant difference between the two approaches is shown by the p-value of 0.03 (p0.05). Results from the Innovative Fraud Identification Systems utilizing the CV technique outperform those from the LGBM method developed in Python, according to this research's result. [ABSTRACT FROM AUTHOR]

Published
2024
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131. Variety ICAR-CICR GJHV 374 Bt

Author

Singh, Suman Bala, Kranthi, K. R., Kranthi, Sandhya, Santhy, V., Raghavendra, K. P., Fande, B., Bhave, S., Satija, U., Prakash, A. H., Manickam, S., Desouza, Blaise, Saravanan, M., Santosh, H.B., and Khanpara, M. D.

Published
2023

135. Advanced Materials for Strategic and Societal Applications

Author

Dhakate, Sanjay R., Singh, Bhanu P., Gupta, Bipin Kumar, Subhedar, Kiran M., Srivastava, Sanjay Kumar, Saravanan, M., Saini, Parveen, Kumar, Sushil, Prathap, P., Kumari, Saroj, Singh, Rajeev Kumar, Patra, Asit, Vandana, Kumar, Pankaj, Kumar, Rachana, Maheshwari, Priyanka H., Gahtori, Bhasker, Tiwari, Jai Prakash, Tyagi, Kriti, Ramanujam, Jeyakumar, and Aswal, Dinesh K., editor

Published
2020
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149. DIRECT METAL LASER SINTERING TO DEVELOP COMPLEX CONTOURS OF AlSi10Mg ALUMINIUM ALLOY — BUILD PARAMETERS OPTIMIZATION.

Author

JASWIN, M. AROCKIA, GEETHA, R., SARAVANAN, M., and ELVIN, R. P.

Subjects
DIRECT metal laser sintering, ALUMINUM alloys, SURFACE roughness, MANUFACTURING industries, HARDNESS
Abstract

In the recent years, Additive Manufacturing (AM) has become a very demanding process utilized by majority of the manufacturing industries due to its complex geometrical capabilities, high accuracy, minimal material usage and better build quality. Among the various types of AM methods, Direct Metal Laser Sintering (DMLS) is a feasible method with many applications and a lot of scope for research. This paper focuses on optimizing the important parameters of DMLS process for better build quality of AlSi10Mg Aluminium alloy. The parameters identified are laser power, hatch distance, scanning speed, and layer thickness. Grey–Taguchi multi response optimization method has been used. Experimental specimens were fabricated and tested. Micro-Vickers hardness and average surface roughness were studied. Experiments were analyzed to determine the best combination of parameters of the DMLS process. A detailed micro-structural study was conducted on the specimens. Also, crystallographic study was made. The resultant specimen thus produced had a better build quality along with satisfying micro-structural characteristics. [ABSTRACT FROM AUTHOR]

Published
2024
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