Mostafa Abdulghafoor Mohammed
Imam Aadham University College

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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.
Distributed denial of service attack defense system-based auto machine learning algorithm Mohammad Aljanabi; Russul Hayder; Shatha Talib; Ahmed Hussien Ali; Mostafa Abdulghafoor Mohammed; Tole Sutikno
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4537

Abstract

The use of network-connected gadgets is rising quickly in the internet age, which is escalating the number of cyberattacks. The detection of distributed denial of service (DDoS) attacks is a tedious task that has necessitated the development of a number of models for its identification recently. Nonetheless, because of major fluctuations in subscriptions and traffic rates, it continues to be a difficult challenge. A novel automatic detection technique was created to address this issue in this work, which reduces the feature space and consequently minimizes the computational time and model overfitting. Data preprocessing is done first to increase the model's generalizability; then, a feature selection method is used to choose the most pertinent features to increase the accuracy of the classification process. Additionally, hyperparameter tuning-choosing the proper parameters for the learning approach-improved model performance. Finally, the support vector machine (SVM) is compatible with the optimization and the hyperparameters offered by supervised learning methods. The CICDDoS2019 dataset was used to evaluate each of these assays, and the experimental findings demonstrated that, with an accuracy of 99.95%, the suggested model performs well when compared to more modern techniques.
The effectiveness of big data classification control based on principal component analysis Mostafa Abdulghafoor Mohammed; Mostafa Mahmood Akawee; Ziyad Hussien Saleh; Raed Abdulkareem Hasan; Ahmed Hussein Ali; Tole Sutikno
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4405

Abstract

Large-scale datasets are becoming more common, yet they can be challenging to understand and interpret. When dealing with big datasets, principal component analysis (PCA) is used to minimize the dimensionality of the data while maintaining interpretability and avoiding information loss. It accomplishes this by producing new uncorrelated variables that gradually reduce the variance of the system. In the field of data analysis, PCA is a multivariate statistical technique commonly used to obtain rules explaining the separation of groups in a given situation. Classes are predicted using a classification algorithm, a supervised learning technique that indicates which type of data points will be presented. Creating a classification model using classification algorithms is required before any successful classification can be achieved. It is possible to predict the future using a variety of categorized strategies. It is necessary to reduce the dimensionality of data sets using the PCA approach. This article will begin by introducing the basic ideas of PCA and discussing what it can and cannot do. It will then describe some variants of PCA and their application and then shows how PCA improves the performance using a series of experiments.
A proposed software for controlling operating system-dependent functionality Sundos Abdulameer Alazawi; Nadia Mahmood Hussien; Yasmin Makki Mohialden; Mostafa Abdulghafoor Mohammed
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4536

Abstract

When the operating system environment temperature rises above safety, the CPU may become unresponsive or even malfunction. To address this problem, to achieve this goal a two-part system was designed. The first, consists of a controlled sensor that constantly monitors the room environment temperature and alerts the user if it rises above acceptable levels for computer use. The second part adopts a Python that uses the OS module, which provides a portable interface for OS-dependent tasks and shuts down the device to prevent it from behaving unexpectedly. A series of experiments at different temperatures demonstrated the ability of the device to alert the user.
The software requirements process for designing a microcontroller-based voice-controlled system Nadia Mahmood Hussien; Yasmin Makki Mohialden; Mustafa Mahmood Akawee; Mostafa Abdulghafoor Mohammed
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4407

Abstract

Smartphones of today are capable of controlling motors, music systems, and lighting. This project's objective is to construct a robot car for the elderly and disabled that is based on the Arduino platform. Voice instructions can be used to wirelessly control the robotic car that the user is riding. The robot is able to move to the left and right, as well as forward and backward, and it can also stop. The voice-controlled robot vehicle built using Arduino and operated by an HC-05 module is connected to Bluetooth. The exact spoken commands are sent to the robot through the phone via an application that runs on android. The Arduino, which is in charge of controlling the robotic automobile, gets commands through a Bluetooth transceiver module, which then relays them to the Arduino. The hardware consists of an android phone, an android-powered motor drive, an Arduino, and Bluetooth. This system was developed with the help of Arduino C and the android-meets-robot framework. The primary goals of this piece of writing are to gain an understanding of how to create the criteria for a voice-controlled system that is based on Arduino.