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53 results on '"S. K. Lakshmanaprabu"'

51. Multi-loop decentralized controller for two interacting conical tank process

Author

U. Sabura Banu and S. K. Lakshmanaprabu

Subjects
Loop (topology), Nonlinear system, Matrix (mathematics), Engineering, Control theory, business.industry, Singular value decomposition, Open-loop controller, PID controller, business, Condition number
Abstract

In proposed method Multiloop decentralized PI controller with decoupler is designed for highly nonlinear interacting process. Relative Gain Array is computed for the interaction analysis and variable pairing. Singular value decomposition matrix technique used and condition number is calculated to determine the illness of the plant. Multi loop PI is designed and decoupler controller is implemented to reduce the loop interaction effect. Two input two output processes are decomposed and controlled by two decentralized SISO controllers and controller performances were shown. Simulation results are given for the proposed method.

Published
2014
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52. Optimal feature level fusion based ANFIS classifier for brain MRI image classification.

Author

K, Shankar, Elhoseny, Mohamed, S K, Lakshmanaprabu, M, Ilayaraja, RM, Vidhyavathi, A. Elsoud, Mohamed, and Alkhambashi, Majid

Subjects
BRAIN imaging, BRAIN tumors, IMAGE processing, MAGNETIC resonance imaging, FEATURE selection
Abstract

Summary: The cases identified with Brain tumor have increased with respect to time owing to various reasons. One of the major challenging issues can be defined by incorporating image processing along with data mining models as classification approach. There are various procedures as of now exhibited for segmentation of brain tumor effectively. In any case, it is as yet unequivocal to distinguish the brain tumor from MR images. In this new tumor classifying, considering two significant models, such as Feature Selection (FS) and Machine Learning classification techniques, are extremely valuable for distinguishing and visualizing the tumor in the MRI brain images; it is classified using Adaptive Neuro‐Fuzzy Interface System (ANFIS). For better classification of image, Optimal Feature Level Fusion (OFLF) is considered to fuse low and high‐level feature of brain image; from this analysis, the images are classifying as Benign or Malignant. From this implementation of medical images, the experiment results are evaluating performance metrics are compared existing classifiers. From the proposed MRI image classification process the accuracy as 96.23%, sensitivity as 92.3%, and specificity as 94.52%, compared to existing classifier. It is in the working platform of MATLAB that this proposed methodology is implemented. [ABSTRACT FROM AUTHOR]

Published
2020
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53. Active database system approach and rule based in the development of academic information system

Author

K. Shankar, S. K. Lakshmanaprabu, Eswaran Perumal, Andino Maseleno, R. M. Vidhyavathi, and M. Miftakul Amin

Subjects
Environmental Engineering, Information retrieval, Computer science, 020209 energy, General Chemical Engineering, General Engineering, Rule-based system, 02 engineering and technology, Active database, Development (topology), Hardware and Architecture, 0202 electrical engineering, electronic engineering, information engineering, Computer Science (miscellaneous), Information system, Biotechnology
Abstract

Active database system is a database system which is capable of generating a certain action automatically if it detects an event that meets certain conditions. The existence of Event Condition Action (ECA) rules and functional components such as triggers, stored procedures, and stored functions that are owned by active database system make the database system has the ability to automatically monitor input and output data. Separation of ECA rules components in the database with the application program will also facilitate the development of information systems. This research applies active database system in academic information system, so that academic business rules can be planted in database software and be able to produce the right solution automatically. The addition of active database system components to the database software makes procedures such as subject distribution, study plans, academic leave, values, thesis defense and other processes can be monitored by the system automatically. This can be done because between applications and databases use the model driven approach parameters to communicate with each other. The results of this research prove that a database is not only has function as a container of data, but can control the information system actively, this is caused by the logic of programs that are generally planted in the application layer that can be moved and planted in the database software.

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