Intelligent Predicting Learning Disabilities in School Going Children Using Fuzzy Logic K Mean Clustering in Machine Learning
Margaret Mary. T1, Hanumanthappa. M2, Sangamithra A3
1Margaret Mary. T, Assistant Professor in Computer Dept., Kristu Jayanti College (Autonomous) Bangalore.
2Hanumanthappa. M, Professor, Department of Computer Science & Applications, Bangalore University, Bangalore.
3Sangamithra A, Assistant Professor in Computer Dept., Kristu Jayanti College (Autonomous) Bangalore.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 1694-1689 | Volume-8 Issue-4, November 2019. | Retrieval Number: C5620098319/2019©BEIESP | DOI: 10.35940/ijrte.C5620.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Learning disabilities (LD) is turning into a major issue in various nations around the globe which can even contrarily influence human common advancement. The undertaking of this work is to help the specialized programme network in their task to be with the standard. The underlying section of the paper gives a comprehensive investigation of the distinctive components of diagnosing learning disabilities. Despite the fact that LD can be analysed early – before 5 years of age, most youngsters were not determined to have LD until the age of nine on account of its unpredictable side effects and unclear indication in children disorder issue. Fuzzy logic K-means clustering has inspired a tremendous transformation in Machine learning and can take and able to resolve a variation of problems. This paper is the elaboration on the strategy for utilizing this mix to encourage the early analysis of LD. Since Fuzzy Logic clustering in Machine Learning is generally considered and connected in different areas of science, we invite all the related analysts from the fields of computer science, engineering, statistics, social sciences, healthcare, and so on, etc. The result of the paper demonstrates that the previously mentioned methodology can possibly be the potential of the supporting decision-making system in LD investigating and diagnosing.
Keywords: Machine Learning, Learning Disabilities, Fuzzy Logic clustering.
Scope of the Article: Machine Learning.