Search

Your search keyword '"Prabakaran, N."' showing total 192 results
Start Over Author "Prabakaran, N."
192 results on '"Prabakaran, N."'

6. No Barrier

Author

Guttikonda, Jai, primary, Sanchit, A., additional, Krishnamoorthy, A., additional, Chaudhary, Krish, additional, Likitha, B., additional, and Prabakaran, N., additional

Published
2024
Full Text
View/download PDF

15. Performance Evaluation of Multi DC and Single DC Source on Cascaded Multi-Level Inverter

Author

Gowrishankar, J., Belwin Edward, J., Kaliappan, E., Prabakaran, N., Balasundaram, G., 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, Komanapalli, Venkata Lakshmi Narayana, editor, Sivakumaran, N., editor, and Hampannavar, Santoshkumar, editor

Published
2021
Full Text
View/download PDF

16. Hybrid Model for Stress Detection in Social Media by Using Dynamic Factor Graph Model and Convolutional Neural Networks

Author

Prabakaran, N., Ramanathan, L., Kannadasan, R., 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, Nath, Vijay, editor, and Mandal, J.K., editor

Published
2021
Full Text
View/download PDF

17. Sequential Nonlinear Programming Optimization for Circular Polarization in Jute Substrate-Based Monopole Antenna

Author

Ram Sandeep, D., Prabakaran, N., Madhav, B. T. P., Vinay, D., Sri Hari, A., Jahnavi, L., Salma, S., Inturi, S., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Bhattacharyya, Siddhartha, editor, Nayak, Janmenjoy, editor, Prakash, Kolla Bhanu, editor, Naik, Bighnaraj, editor, and Abraham, Ajith, editor

Published
2021
Full Text
View/download PDF

19. Optimized Adaptive Multi-Scale Dual An for Multi-Objective CHS and Energy-Aware Routing in 6G WC.

Author

Prabakaran, N., Sekar, R., and Vadivel, M.

Subjects
*FEDERATED learning, *OPTIMIZATION algorithms, *MACHINE learning, *WIRELESS communications, *TRAFFIC density, *WIRELESS sensor networks
Abstract

In this research work, an Adaptive Multi-Scale Dual Attention Network with ZOA for Multi-Objective CHS with energy-aware routing in 6G wireless Communication (CHS-EAR-AM-SDAN-6G) is proposed to secure the data transmission by selecting optimum cluster heads in the 6G Wireless Communication network. Initially, the nodes are gathered together to form a cluster using an Adaptive Multi-Scale Dual Attention Network (AM-SDAN). The Zebra Optimization Algorithm (ZOA) strategically selects Cluster Heads (CHs) in wireless networks based on a multi-objective fitness function (MoFF) that minimizes energy consumption while considering factors, like distance, delay, and traffic density. The path and minimum value of fitness are recognized as the routing path and statistics are promoted to the sink node through the cluster head. The proposed scheme has been applied in Python and productivity of the proposed method is predictable with the help of several performances they are energy consumption, detection rate, computational time, packet delivery rate, number of alive nodes, and security. The performance of the proposed CHS-EAR-AMSDAN-6G method attains 25.93%, 24.81%, and 23.38% of alive nodes, 24.45%, 26.71% and 21.32% lower packet delivery rate, 27.56%, 26.43%, and 28.61% low computational period, related with three current methods, such as Generative Adversarial Learning for ITM in 6G Wireless Communication Networks (CHS-EAR-GAL-ITM-6G), ML Algorithms for the Future 6G WCN (CHS-EAR-ML-6G), Energy Efficient Distributed Federated Learning to the 6G Wireless Communication Networks (CHS-EAR-EEDFL-6G), respectively. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

30. Material Replacement and Fatigue Analysis of Nose Landing Gear’s Shock Strut

Author

null Nishanth P, null Prabakaran N, null Akshay R Walikar, null Rakesh Ichangi, null Ravishankar R N, and null Vijay

Abstract

Landing, take off are one of the most maneuvering occurring in aircraft. The landing gear is considered as a non-linear structure due to its complicate behavior during landing period large amount of impact forces transferred into nose gear and main landing gear. Nose landing gear bears approx. 5% of aircraft's weight and is used for operations like landing, takeoff, as well as steering purpose. The cyclic loads acting on the landing gear which cause fatigue failure of landing gear or its parts. The current work includes the design of the nose landing gear which is retractable type of landing gear. During the ground operation of the aircraft, various types of loads will be encountered. Each of these loads will cause axial compression and tension on the wheels and strut of the landing gear. In this work the landing gear part we have chosen is shock strut which is one of the parts in landing gear on which most of the fatigue failures are observed is modeled using CATIA V5 Software. The analysis is carried out in ANSYS 20 R1.

Published
2023

31. Cooperative Sensors for Identifying an Impulsive Events of Asynchronous Environment

Author

Prabakaran, N., Jagadeesh Kannan, R., Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Modi, Nilesh, editor, Verma, Pramode, editor, and Trivedi, Bhushan, editor

Published
2017
Full Text
View/download PDF

36. Robust medical image watermarking technique using integer wavelet transform and shearlet transform with BSVD

Author

Subramani, Sivakannan, Thirugnanam, G., Prabakaran, N., and Fernandes, J. Bennilo

Abstract

There is an increased usage of digital devices in healthcare services for the last few decades because of the advancements made in the medical field. The most appropriate method that is used for the enhancement of security and authentication of medical data is medical image watermarking, which is crucial and used for further diagnosis and reference. This paper focuses on medical image watermarking techniques for the protection and authentication of medical data using hybrid transforms. Several developments on wavelet transform have been proposed in the field of mathematical analysis. One of the recent extensions of wavelet is shearlet transform. A hybrid scheme using integer wavelet transform (IWT) and discrete shearlet transform (DST) is presented in this paper. The images with different textures are examined by this method and resistance is evaluated against various attacks like image processing and geometric attacks. By this proposed method, the results are produced with good transparency and high robustness.

Published
2024
Full Text
View/download PDF

37. AN INTEGRATED DEEP LEARNING APPROACH FOR SENTIMENT ANALYSIS ON TWITTER DATA.

Author

Prabakaran, N. S. and Karthik, S.

Subjects
SOCIAL media, SENTIMENT analysis, DEEP learning, ONLINE social networks, PARTICLE swarm optimization, DATA analysis
Abstract

Analysis of Sentiment (SA) is a computational technique that seeks to extract subjective evaluations, attitudes, and emotional states from online platforms, specifically social media sites like Twitter. The subject has gained significant traction within the research community. The predominant emphasis of traditional sentiment analysis lies in the analysis of textual data. Twitter is widely recognized as a prominent online social networking platform that facilitates microblogging, wherein users share updates pertaining to various subjects through concise messages known as tweets. Twitter is a widely utilized platform that enables individuals to articulate their perspectives and emotions through the medium of tweets. Sentiment analysis refers to the computational methods of classification of given data in text format, categorizing it as either positive, negative, or neutral. The main goal of this study is to use deep learning methods for SA. The purpose is to guess sentiment and then evaluate the results based on accuracy, recall, and f-score. In this paper, a hybrid approach combining Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) techniques, specifically referred to as PSOGA, is proposed to optimize features for a modified neural network (MNN). The final step is to use the K-fold cross-validation method to assess the results. The dataset was obtained through the utilization of the Ruby Twitter API. The ultimate outcome is juxtaposed with the preceding Cuckoo Search (CS) algorithm that had been terminated. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results