| Type of Material: | Thesis |
| Title: | An Multi Objective Optimization Algorithm for The Design of Sentiment Analysis In Cloud |
| Researcher: | Vasudevan, P |
| Guide: | Kaliyamurthie, K P |
| Department: | Department of Engineering and Technology(Computer Science and Engineering) |
| Publisher: | Bharath University, Chennai |
| Place: | Chennai |
| Year: | 2022 |
| Language: | English |
| Subject: | Computer Science | Computer Science Interdisciplinary Applications | 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; 2022; D14CS037 |
| Fulltext: | Shodhganga |
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| 035 | __ | |a(IN-AhILN)th_455120 |
| 040 | __ | |aBHAU_600073|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aVasudevan, P|eResearcher |
| 110 | __ | |aDepartment of Engineering and Technology(Computer Science and Engineering)|bBharath University, Chennai|dChennai|ein|0U-0446 |
| 245 | __ | |aAn Multi Objective Optimization Algorithm for The Design of Sentiment Analysis In Cloud |
| 260 | __ | |bBharath University, Chennai|aChennai|c2022 |
| 300 | __ | |dDVD |
| 502 | __ | |bPhD|cDepartment of Engineering and Technology(Computer Science and Engineering), Bharath University, Chennai, Chennai|d2022|oD14CS037 |
| 520 | __ | |aToday, most enterprises and organizations have stored large amounts of data in a systematic way, but without a clear idea of its potential usefulness. Cloud computing has given an adaptable environment online that encourages the capacity to be able to handle the work of an expanded volume without it affecting the execution of the framework. Several disciplines For involving features in large numbers, you'll need to deal with some large datasets. The methods of feature selection have been aimed at removing noisy, irrelevant, or redundant features that can degrade classification performance. However, most traditional methods lack the scalability needed to deal with the results in a timely manner. If the number of variables isn't too huge, an exhaustive search could be possible. However, since the feature selection issue is NP-hard, the search becomes computationally intractable rapidly.. This research work has been executed in three phases, namely, feature selection using metaheuristics like Genetic Algorithm |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Interdisciplinary Applications |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |aKaliyamurthie, K P|eGuide |
| 856 | __ | |yShodhganga|uhttp://shodhganga.inflibnet.ac.in/handle/10603/397756 |
| 905 | __ | |afromsg |
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