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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control)
J. Emerson Raja
Multimedia University
Computer Science & IT Electrical & Electronics Engineering

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An Efficient Microcontroller Based Sun Tracker Control for Solar Cell Systems E.M.H. Arif; J. Hossen; G. Ramana Murthy; Jesmeen M. Z. H.; J. Emerson Raja
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 4: August 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (25.088 KB) | DOI: 10.11591/ijece.v9i4.pp2743-2750

Abstract

The solar energy is fast becoming a different means of electricity resource. Now in world Fossil fuels are seriously depleting thus the need for another energy source is a necessity. To create effective utilization of its solar, energy efficiency must be maximized. An attainable way to deal with amplifying the power output of sun-powered exhibit is by sun tracking. This paper presents the control system for a solar cell orientation device which follows the sun in real time during daytime.
Automatic missing value imputation for cleaning phase of diabetic’s readmission prediction model Jesmeen Mohd Zebaral Hoque; Jakir Hossen; Shohel Sayeed; Chy. Mohammed Tawsif K.; Jaya Ganesan; J. Emerson Raja
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp2001-2013

Abstract

Recently, the industry of healthcare started generating a large volume of datasets. If hospitals can employ the data, they could easily predict the outcomes and provide better treatments at early stages with low cost. Here, data analytics (DA) was used to make correct decisions through proper analysis and prediction. However, inappropriate data may lead to flawed analysis and thus yield unacceptable conclusions. Hence, transforming the improper data from the entire data set into useful data is essential. Machine learning (ML) technique was used to overcome the issues due to incomplete data. A new architecture, automatic missing value imputation (AMVI) was developed to predict missing values in the dataset, including data sampling and feature selection. Four prediction models (i.e., logistic regression, support vector machine (SVM), AdaBoost, and random forest algorithms) were selected from the well-known classification. The complete AMVI architecture performance was evaluated using a structured data set obtained from the UCI repository. Accuracy of around 90% was achieved. It was also confirmed from cross-validation that the trained ML model is suitable and not over-fitted. This trained model is developed based on the dataset, which is not dependent on a specific environment. It will train and obtain the outperformed model depending on the data available.
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.
Co-Authors Abid Hossen Bhuvaneswari Thangavel Chy. Mohammed Tawsif K. E.M.H. Arif G. Ramana Murthy J. Hossen Jakir Hossen Jaya Ganesan Jesmeen M. Z. H. Jesmeen Mohd Zebaral Hoque S. Sayeed Shohel Sayeed Tawsif K.