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195 results on '"Manikandan, T."'

5. Autonomous Devices in Smart Farming

Author

Manikandan, T., primary, Duraiarasu, E., additional, Kumar, C. Ganesh, additional, Jeevitha, S., additional, Harihara Sudhan, S., additional, and Stephen Olatunde, Olabiyisi, additional

Published
2023
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11. Secured Communication Using Hash Function and Steganography

Author

Priya, P. Shanmuga, Manikandan, T., Sathya, R., Vidhya, T. Helan, Sasirekha, D., Tamilarasi, M., 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, Li, Yong, 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, Oneto, Luca, 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, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Kumar, Amit, editor, Senatore, Sabrina, editor, and Gunjan, Vinit Kumar, editor

Published
2023
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18. Typical MANET Design for 5G Communication Network

Author

Bharathy, R., Manikandan, T., Keerthivasan, P., Fayaz, P. Mohamed, Zafer, A. Mohammed Shahid, Krishnan, K. Mohana, 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, Hu, Yu-Chen, editor, Tiwari, Shailesh, editor, Trivedi, Munesh C., editor, and Mishra, K. K., editor

Published
2022
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19. Monitoring and controlling of paddy fields using IoT applications.

Author

Manikandan, T., Ramesh, S., Selvakumar, A., Kanakala, Aishwarya, and Yong, Lau Chee

Subjects
*PADDY fields, *ARCHITECTURAL design, *INSECT diseases, *RURAL geography, *AGRICULTURE
Abstract

Agriculture plays a vital role in everyone's life. Several problems occur in the agriculture farm due to reduced production. This work has proposed an IoT-based smart monitoring system to increase production sustainably. The main research is to find diseases and insects in an early stage and send messages to the remote user. The rural area farms are focused since the farmers get more demand gap about the technology development. Our proposed IoT-based architecture is designed for rural areas with limited connectivity. The architecture is designed to send messages to remote users, save the crop and increase productivity. [ABSTRACT FROM AUTHOR]

Published
2024
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20. Monitoring and controlling of bacterial blight disease using K-means clustering algorithm.

Author

Ramesh, S., Manikandan, T., Kumar, M. Vinoth, Kanakala, Aishwarya, and Yong, Lau Chee

Subjects
*K-means clustering, *IMAGE segmentation, *CROP losses, *EARLY diagnosis, *AGRICULTURE
Abstract

India is an agricultural country. The GDP of the country will depend upon agriculture. The production of agriculture is reduced nowadays due to several reasons. By the vision of 2050, the need for agriculture will be increased. However, the disease can affect the crop in several aspects due to environmental conditions. This research paper focuses on disease detection in the early stages to avoid crop losses. The proposed methodology will focus on disease detection using an image segmentation algorithm. The disease taken is a bacterial blight to cluster the disease portion and non-disease portion using k- means clustering techniques. Using the proposed method, we got the disease detection accurate. [ABSTRACT FROM AUTHOR]

Published
2024
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23. Design and Development of a New Microstrip Patch Antenna for ISM Band Applications

Author

Venkataramanan, V., Manikandan, T., Sathish, M., Srilakshmi, R., Kalaiarasan, K., 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, Suresh, P., editor, Saravanakumar, U., editor, and Hussein Al Salameh, Mohammed Saleh, editor

Published
2021
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24. Automatic Speed Control System in Vehicles Using VANET

Author

Sathyapriya, A., Sathiya, K., Sneha, T. M., Rohit Raja, D., Manikandan, T., 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, Suresh, P., editor, Saravanakumar, U., editor, and Hussein Al Salameh, Mohammed Saleh, editor

Published
2021
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25. FPGA-Based Reconfigurable Architectures for DSP Computations

Author

Mazher Iqbal, J. L., Manikandan, T., 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, Suresh, P., editor, Saravanakumar, U., editor, and Hussein Al Salameh, Mohammed Saleh, editor

Published
2021
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26. An analysis of video-based human action recognition using residual neural network in comparison with Lasso Regression.

Author

Narmatha, V., Ramesh, S., and Manikandan, T.

Subjects
HUMAN behavior, HUMAN activity recognition, STATISTICAL significance, CONFIDENCE intervals, RESEARCH personnel, MACHINE learning
Abstract

The proposed work is to analyze and compare the effectiveness of two machine learning techniques-Residual Neural Networks and Lasso Regression, for recognizing human actions in videos. By conducting this analysis, the researchers aim to identify which technique is more suitable for this application and to provide insights into how these techniques can be further improved for better accuracy and performance. The 80% of the UCF101 dataset is used for training and 20% is used for testing in the suggested machine learning classifier model. The output from two classifiers is grouped in SPSS for analysis, with 20 samples in each group. G-power pretest, with a sample size of 80% and 95% confidence interval and a p value of 0.001 (p<0.05). The results show at the selected novel Residual Neural network (ResNet), effectively classified human action with a rate of 95.63%, while the Lasso Regression (LR) achieved a rate of 93.23% accuracy. The statistical significance value between ResNet and LR is p = 0.001, which indicates that there is a statistically significant difference between these two machine learning structures. The proposed research investigates a range of activities that can be completed in a constrained amount of time. Additionally, the classifier that was constructed using the Novel Residual Neural Network functioned efficiently and achieved an accuracy of 95.63%, whereas the Lasso Regression classifier achieved an accuracy of 93.23%. [ABSTRACT FROM AUTHOR]

Published
2024
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38. IoT Based Shrewd Monitoring Framework for Children Safety

Author

Revathi K P and Manikandan T

Abstract

In the system, we have developed a smart watch that can be used to locate missing or lost children and also track the child movements outside from the home as well as for facilitating women safety. Here the user itself can create his own circle in a mobile app with some radius of distance according to their comfort. When the person is out of the location, which means out of the radius, immediately the message has been sent to the emergency contacts which are already selected before by the user in the mobile app. This process can be controlled by the end user. If the user hurts in any case, it will send the alert messages to the pre-elite contacts. GPS (Global Positioning System) is employed to urge the position of a widget in terms of latitude and meridian. Latitude and meridian values are extracted from NMEA sentences. In our system, GPS helps to send the latitude and meridian values to the list of contacts elite by the user, once the user is not within the range of the circle. This can also be used for children as well, but when it comes to children, the complete process will be done by their parents. The app will be under parental control and they create the radius of their children to know his presence or location. This device gives the solution for knowing their location faster and facilitates to take the necessary action immediately.

Published
2022

48. VANET Security Enhancement in Cloud Navigation with Internet of Things Based Trust Model in Deep Learning Architecture

Author

Gnanajeyaraman Gnanajeyaraman, Arul U, Miichael G, Selvakumar A, Ramesh S, and Manikandan T

Abstract

Many intelligent services are available for developing sensor-based vehicle-to-vehicle communication systems through vehicular ad hoc network (VANET). Although neighbour locating and interconnected vehicle sensor processes have been improved by geographic routing methods. However, reliability and data continuity among data routing are crucial for developing transportation systems due to the high level of mobility and realistic environment.The vehicles' wireless communication is also unrestricted and open, making them more vulnerable to security threats and compromising data for improper uses.This research propose novel technique in security improvement in VANET with vehicle cloud based navigation and trust model using deep learning techniques. the vehicle network navigation is carried out using cloud network integrated with IoT and its data transmission to the base station is analysed. then the navigated vehicle security is enhanced using trust based federated transfer quadratic authentication system. the experimental analysis is carried out based on number of vehicles in network as well as its security enhancement. the parameters analysed are throughput, data transmission rate, latency, network traffic analysis, scalability. the proposed technique attained throughput of 95%, data transmission rate of 67%, latency of 56%, network traffic analysis of 76%, scalability of 75%

Published
2023

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