| Type of Material: | Thesis |
| Title: | LEVEL FLOW AND TEMPERATURE PREDICTIONS USING RAGSyS MACHINE LEARNING FRAMEWORK |
| Researcher: | Kalaiselvi, B |
| Guide: | Karthik, B |
| Department: | Department of Electronics and Communication Engineering |
| Publisher: | Bharath University, Chennai |
| Place: | Chennai |
| Year: | 2022 |
| Language: | English |
| Subject: | Computer Science | Computer Science Artificial Intelligence | Engineering and Technology | Computer Science and Information Technology | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Electronics and Communication Engineering, Bharath University, Chennai, Chennai; 2022; D17EC506 |
| Fulltext: | Shodhganga |
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| 001 | 455158 | |
| 003 | IN-AhILN | |
| 005 | 2024-09-23 16:14:00 | |
| 008 | __ | 240923t2022||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_455158 |
| 040 | __ | |aBHAU_600073|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aKalaiselvi, B|eResearcher |
| 110 | __ | |aDepartment of Electronics and Communication Engineering|bBharath University, Chennai|dChennai|ein|0U-0446 |
| 245 | __ | |aLEVEL FLOW AND TEMPERATURE PREDICTIONS USING RAGSyS MACHINE LEARNING FRAMEWORK |
| 260 | __ | |aChennai|bBharath University, Chennai|c2022 |
| 300 | __ | |dDVD |
| 502 | __ | |cDepartment of Electronics and Communication Engineering, Bharath University, Chennai, Chennai|d2022|oD17EC506|bPhD |
| 518 | __ | |d2022|oDate of Award |
| 518 | __ | |oDate of Registration|d2017 |
| 520 | __ | |aAn innovative approach for designing control system instrumentation to meet the new situation will examine the reasons using Artificial Intelligence is a major part of the process industry. Having such a significant thrust area attracted the research studies here and triggered to address the problem of identifying the parameter’s measurement. As more research work surfaced recently with encouraging results a novel structure ‘RAGSyS’ is applied and verified with a unique combination of image processing algorithms of the first of its kind and the four components are designed as follows. Firstly, the R – Component denotes the models in ‘Regression’ based on an architecture with few layers in a neural network or few levels in decision trees/Rules and probabilistic networks. Secondly, the A – Component denotes the attribute selection done for all measurement techniques in this research article to enhance the accuracy level with minimal feature and maximum information gain in the selected attributes |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |2AIU|aEngineering and Technology |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Artificial Intelligence |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |eGuide|aKarthik, B |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/456440|yShodhganga |
| 905 | __ | |afromsg |
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