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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Abid Hossen
Khulna University
Computer Science & IT

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AUTO-CDD: automatic cleaning dirty data using machine learning techniques Jesmeen M. Z. H.; Abid Hossen; J. Hossen; J. Emerson Raja; Bhuvaneswari Thangavel; S. Sayeed; Tawsif K.
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 4: August 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i4.12780

Abstract

Cleaning the dirty data has become very critical significance for many years, especially in medical sectors. This is the reason behind widening research in this sector. To initiate the research, a comparison between currently used functions of handling missing values and Auto-CDD is presented. The developed system will guarantee to overcome processing unwanted outcomes in data Analytical process; second, it will improve overall data processing. Our motivation is to create an intelligent tool that will automatically predict the missing data. Starting with feature selection using Random Forest Gini Index values. Then by using three Machine Learning Paradigm trained model was developed and evaluated by two datasets from UCI (i.e. Diabetics and Student Performance). Evaluated outcomes of accuracy proved Random Forest Classifier and Logistic Regression gives constant accuracy at around 90%. Finally, it concludes that this process will help to get clean data for further analytical process.
Towards machine learning-based self-tuning of Hadoop-Spark system Md. Armanur Rahman; Abid Hossen; J. Hossen; Venkataseshaiah C; Thangavel Bhuvaneswari; Aziza Sultana
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 2: August 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i2.pp1076-1085

Abstract

Apache Spark is an open source distributed platform which uses the concept of distributed memory for processing big data. Spark has more than 180 predominant configuration parameter. Configuration settings directly control the efficiency of Apache spark while processing big data, to get the best outcome yet a challenging task as it has many configuration parameters.  Currently, these predominant parameters are tuned manually by trial and error. To overcome this manual tuning problem in this paper proposed and developed a self-tuning approach using machine learning. This approach can tune the parameter value when it’s required. The approach was implemented on Dell server and experiment was done on five different sizes of the dataset and parameter. A comparison is provided to highlight the experimented result of the proposed approach with default Spark configuration system. The results demonstrate that the execution is speeded-up by about 33% (on an average) compared to the default configuration.
Co-Authors Aziza Sultana Bhuvaneswari Thangavel J. Emerson Raja J. Hossen Jesmeen M. Z. H. Md. Armanur Rahman S. Sayeed Tawsif K. Thangavel Bhuvaneswari Venkataseshaiah C