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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Mostafa Abdulghafoor Mohammed
Imam Aadham University College
Computer Science & IT Electrical & Electronics Engineering

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Large scale data analysis using MLlib Ahmed Hussein Ali; Maan Nawaf Abbod; Mohammed Khamees Khaleel; Mostafa Abdulghafoor Mohammed; Tole Sutikno
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 5: October 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i5.21059

Abstract

Recent advancements in the internet, social media, and internet of things (IoT) devices have significantly increased the amount of data generated in a variety of formats. The data must be converted into formats that is easily handled by the data analysis techniques. It is mathematically and physically expensive to apply machine learning algorithms to big and complicated data sets. It is a resource-intensive process that necessitates a huge amount of logical and physical resources. Machine learning is a sophisticated data analytics technology that has gained in importance as a result of the massive amount of data generated daily that needs to be examined. Apache Spark machine learning library (MLlib) is one of the big data analysis platforms that provides a variety of outstanding functions for various machine learning tasks, spanning from classification to regression and dimension reduction. From a computational standpoint, this research investigated Apache Spark MLlib 2.0 as an open source, autonomous, scalable, and distributed learning library. Several real-world machine learning experiments are carried out in order to evaluate the properties of the platform on a qualitative and quantitative level. Some of the fundamental concepts and approaches for developing a scalable data model in a distributed environment are also discussed.
An effective classification approach for big data with parallel generalized Hebbian algorithm Ahmed Hussein Ali; Royida A. Ibrahem Alhayali; Mostafa Abdulghafoor Mohammed; Tole Sutikno
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.3135

Abstract

Advancements in information technology is contributing to the excessive rate of big data generation recently. Big data refers to datasets that are huge in volume and consumes much time and space to process and transmit using the available resources. Big data also covers data with unstructured and structured formats. Many agencies are currently subscribing to research on big data analytics owing to the failure of the existing data processing techniques to handle the rate at which big data is generated. This paper presents an efficient classification and reduction technique for big data based on parallel generalized Hebbian algorithm (GHA) which is one of the commonly used principal component analysis (PCA) neural network (NN) learning algorithms. The new method proposed in this study was compared to the existing methods to demonstrate its capabilities in reducing the dimensionality of big data. The proposed method in this paper is implemented using Spark Radoop platform.
A smart gas leakage monitoring system for use in hospitals Nadia Mahmood Hussien; Yasmin Makki Mohialden; Nada Thanoon Ahmed; Mostafa Abdulghafoor Mohammed; Tole Sutikno
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 2: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i2.pp1048-1054

Abstract

A gas leaks lead to personal and financial damage. Much effort has been dedicated to preventing such leaks and developing reliable techniques for leak detection and leakage localization using sensors. These sensors usually sound an alarm after detecting a dangerous gas in its vicinity. This paper describes a system for detecting a gas leakage from cylinders which notifies the user via the GSM network. The system consists of an LPG gas leakage detector which sends a warning signal to Arduino Uno Microcontroller. The system uses the GSM network to send notifications, a liquid crystal display (LCD) monitor to display the warning message and buzzer to sound the alert.
Co-Authors Ahmed Hussein Ali Ahmed Hussein Ali Maan Nawaf Abbod Mohammed Khamees Khaleel Nada Thanoon Ahmed Nadia Mahmood Hussien Royida A. Ibrahem Alhayali Tole Sutikno Yasmin Makki Mohialden