| Type of Material: | Thesis |
| Title: | Construction and Verification of Arts Data Preprocessing Framework For E Learning |
| Researcher: | SasiKumar, C S |
| Guide: | Kumaravel, A |
| Department: | Department of Engineering and Technology(Computer Science and Engineering) |
| Publisher: | Bharath University, Chennai |
| Place: | Chennai |
| Year: | 2021 |
| 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 Engineering and Technology(Computer Science and Engineering), Bharath University, Chennai, Chennai; 2021; D14CS530 |
| Fulltext: | Shodhganga |
| 000 | 00000ntm a2200000ua 4500 | |
| 001 | 454984 | |
| 003 | IN-AhILN | |
| 005 | 2024-09-20 14:44:47 | |
| 008 | __ | 240920t2021||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_454984 |
| 040 | __ | |aBHAU_600073|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aSasiKumar, C S|eResearcher |
| 110 | __ | |aDepartment of Engineering and Technology(Computer Science and Engineering)|bBharath University, Chennai|dChennai|ein|0U-0446 |
| 245 | __ | |aConstruction and Verification of Arts Data Preprocessing Framework For E Learning |
| 260 | __ | |aChennai|bBharath University, Chennai|c2021 |
| 300 | __ | |dDVD |
| 502 | __ | |bPhD|cDepartment of Engineering and Technology(Computer Science and Engineering), Bharath University, Chennai, Chennai|d2021|oD14CS530 |
| 520 | __ | |aResearch studies on data preprocessing is crucial as it determines and highly influences the quality of prediction in the later phases. Hence the proposal for recommending a unique and practical combination in the spectrum of data preprocessing task types framework and it contains the following four technical aspects briefly justified. Firstly the A-aspect denotes the attribute selection based on modern research which makes comprehensive efforts on attribute selection processes to achieve effective preprocessing by reducing the data search space considerably. The attribute selection extends out both vertically and horizontally, because of increasing demands for dimensionality reduction and regulate on space complexity. The exploration of space as been reduced very well by trimming the insignificant attributes. Secondly in the R-aspect randomized subset of big datasets are applied for the purpose namely the improved efficiency. Different approaches for tackling the same problem yields more powerful validatio |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Artificial Intelligence |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |aKumaravel, A|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/354282|yShodhganga |
| 905 | __ | |afromsg |
User Feedback Comes Under This section.