التفاصيل البيبلوغرافية
| العنوان: |
Hybrid Ant Lion Mutated Ant Colony Optimizer Technique With Particle Swarm Optimization for Leukemia Prediction Using Microarray Gene Data |
| المؤلفون: |
T. R. Mahesh, D. Santhakumar, A. Balajee, H. S. Shreenidhi, V. Vinoth Kumar, Jonnakuti Rajkumar Annand |
| المصدر: |
IEEE Access, Vol 12, Pp 10910-10919 (2024) |
| بيانات النشر: |
IEEE, 2024. |
| سنة النشر: |
2024 |
| المجموعة: |
LCC:Electrical engineering. Electronics. Nuclear engineering |
| مصطلحات موضوعية: |
Leukemia, gene expression data, feature selection, ant lion optimization (ALO) algorithm, evolutionary computation, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| الوصف: |
Leukemia refers to a type of blood malignancy that develops due to certain hematological disorders. Identifying leukemia at its earlier stages through clinical operations are highly complicated task with invasive methods. Gene expression data could be collected and computational methods could be adopted which could lead to better prediction of leukemia that leads to prevention at its earlier stages. Today, feature selection has become an important step in pre-processing that helps bring improvement to the classification system and its performance that is done by choosing optimal feature subsets by means of reducing or eliminating redundant or irrelevant features. Particle Swarm Optimization (PSO) is a popular algorithm wherein certain solutions that are generated randomly move within the search space to obtain optimal solutions. Another relatively new and evolutionary method computation is the Ant Lion Optimization (ALO) algorithm that has lower computation cost compared to the other techniques. In this work, a new technique known as the Hybrid Ant Lion Mutated Ant Colony Optimize along with Particle Swarm Optimization (PSO) was proposed for the prediction of leukaemia with the microarray gene data. The proposed model that is used for identifying the optimal set of features from which the classification has been done using the Support Vector Machine (SVM) has produced a significant prediction accuracy of 87.88%. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2169-3536 |
| Relation: |
https://ieeexplore.ieee.org/document/10385074/; https://doaj.org/toc/2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3351871 |
| URL الوصول: |
https://doaj.org/article/554eeb3f0ccc45f2bed44cc610411d7d |
| رقم الانضمام: |
edsdoj.554eeb3f0ccc45f2bed44cc610411d7d |
| قاعدة البيانات: |
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