| Type of Material: | Thesis |
| Title: | Development of Efficient Data Mining Techniques for Cancer Genomic Patterns Classification and Prediction |
| Researcher: | SUBASREE, S |
| Guide: | GOPALAN, N P |
| Department: | Department of Engineering and Technology(Computer Science and Engineering) |
| Publisher: | Bharath University, Chennai |
| Place: | Chennai |
| Year: | 2019 |
| Language: | English |
| Subject: | Computer Science | Computer Science Theory and Methods | 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; 2019; D14CS540 |
| Fulltext: | Shodhganga |
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| 001 | 454685 | |
| 003 | IN-AhILN | |
| 005 | 2024-09-18 12:30:50 | |
| 008 | __ | 240918t2019||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_454685 |
| 040 | __ | |aBHAU_600073|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aSUBASREE, S|eResearcher |
| 110 | __ | |aDepartment of Engineering and Technology(Computer Science and Engineering)|bBharath University, Chennai|dChennai|ein |
| 245 | __ | |aDevelopment of Efficient Data Mining Techniques for Cancer Genomic Patterns Classification and Prediction |
| 260 | __ | |aChennai|bBharath University, Chennai|c2019 |
| 300 | __ | |dDVD |
| 502 | __ | |bPhD|cDepartment of Engineering and Technology(Computer Science and Engineering), Bharath University, Chennai, Chennai|d2019|oD14CS540 |
| 520 | __ | |aResearch Scholars are concentrating Microarray Technologies and its applications such as various Patterns of Classifications. It is facilitating Research Scholars to focus different Cancers Patterns for analysis. This is one of the major applications of Bioinformatics. Recently proposed Classifiers, that used for predicting various Cancer Patterns were identified for analysis. Those identified classifiers are i. Multi-Objective Particle Swarm Optimization (MPSO), ii. Kernelized Fuzzy Rough Set Based SemiSupervised Support Vector Machine (KFRS-S3VM) and iii. Hybrid Ant Bee Algorithm (HABA). The identified classifiers were implemented and studied thoroughly with various Patterns of Cancers in regard to Accuracy, Execution Time, FScore, Memory Utilization, Sensitivity, and Specificity. The result demonstrated that the performances of the above specified Models were relied on the Gene Patterns. It was also noted that the Multiobjective Particle Swarm Optimization (MPSO) is relatively outperforming other two cla |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Theory and Methods |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |aGOPALAN, N P|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/310488|yShodhganga |
| 905 | __ | |afromsg |
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