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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Indonesian Journal of Electrical Engineering and Computer Science
Muneera Altayeb
Al-Ahliyya Amman University
Computer Science & IT Electrical & Electronics Engineering

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Classification of three pathological voices based on specific features groups using support vector machine Muneera Altayeb; Amani Al-Ghraibah
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp946-956

Abstract

Determining and classifying pathological human sounds are still an interesting area of research in the field of speech processing. This paper explores different methods of voice features extraction, namely: Mel frequency cepstral coefficients (MFCCs), zero-crossing rate (ZCR) and discrete wavelet transform (DWT). A comparison is made between these methods in order to identify their ability in classifying any input sound as a normal or pathological voices using support vector machine (SVM). Firstly, the voice signal is processed and filtered, then vocal features are extracted using the proposed methods and finally six groups of features are used to classify the voice data as healthy, hyperkinetic dysphonia, hypokinetic dysphonia, or reflux laryngitis using separate classification processes. The classification results reach 100% accuracy using the MFCC and kurtosis feature group. While the other classification accuracies range between~60% to~97%. The Wavelet features provide very good classification results in comparison with other common voice features like MFCC and ZCR features. This paper aims to improve the diagnosis of voice disorders without the need for surgical interventions and endoscopic procedures which consumes time and burden the patients. Also, the comparison between the proposed feature extraction methods offers a good reference for further researches in the voice classification area.
Voice controlled Camera Assisted Pick and Place Robot Using Raspberry Pi Muneera Altayeb; Amani Al-Ghraibah
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 1: March 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i1.3636

Abstract

Modern monitoring systems or manufacturing machines have a major drawback as they depend on human operators who can easily get distracted or make mistakes, so a system is needed that can constantly monitor the desired area and make decisions while identifying a pre-trained object. Tracking objects with a camera is critical in any automated monitoring and tracking system. The main goal of this paper is to design and implement a robot that can distinguish objects based on their features, such as color and size, and based on artificial intelligence and image processing algorithms.  The robot will analyze the video stream to detect the colored object, and specify its location inside the video frame. Using the detected position, the raspberry pi will decide the rotation direction whether it is to the right to the left, or forward until it reaches the object, grabs it and puts it in the robot's pocket.  The main controlling unit of the system is the Raspberry Pi, the robot is equipped with a Wi-Fi modem to communicate with the mobile application, which is used to control the robot in two modes: manual mode, where the user can point the robot in any direction either by pressing function button or through voice commands. The second mode is the Automatic mode, where the user can ask the robot to detect an object according to a set of characteristics and grab it without any human intervention and based on a novel digital image processing object-tracking algorithm, the accuracy in voice command mode has reached 95%.   
Hand Gestures Replicating Robot Arm based on MediaPipe Muneera Altayeb
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 3: September 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i3.4491

Abstract

A robotic arm is any variety of programmable mechanical devices designed to operate items like a human arm and is one of the most beneficial innovations of the 20th century, quickly becoming a cornerstone of many industries. It can perform a variety of tasks and duties that may be time-consuming, difficult, or dangerous to humans. The gesture-based control interface offers many opportunities for more natural, configurable, and easy human-machine interaction. It can expand the capabilities of the GUI and command line interfaces that we use today with the mouse and keyboard. This work proposed changing the concept of remote controls for operating a hand-operated robotic arm to get rid of buttons and joysticks by replacing them with a more intuitive approach to controlling a robotic arm via the hand gestures of the user. The proposed system performs vision-based hand gesture recognition and a robot arm that can replicate the user's hand gestures using image processing. The system detects and recognizes hand gestures using Python and sends a command to the microcontroller which is the Arduino board connected to the robot arm to replicate the recognized gesture. Five servo motors are connected to the Arduino Nano to control the fingers of the robot arm; These servos are related to the robot arm prototype. It is worth noting that this system was able to repeat the user's hand gestures with an accuracy of up to 96%.
Assessing the effectiveness of data mining tools in classifying and predicting road traffic congestion Areen Arabiat; Muneera Altayeb
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1295-1303

Abstract

Traffic congestion is a significant issue in cities, impacting the environment, commuters, and the economy. Predicting congestion is crucial for efficient network operation, but high-quality data and computational techniques are challenging for scientists and engineers. The revolution of data mining and machine learning has enabled the development of effective prediction methods. Machine learning (ML) approaches have shown potential in predicting traffic congestion, with classification being a key area of study. Open-source software tools WEKA and Orange are used to predict and classify traffic congestion. However, there is no single best strategy for every situation. This study compared the effectiveness of both data mining tools for predicting congestion in one of the areas of the capital of the Hashemite Kingdom of Jordan, Amman, by testing several classifiers including support vector machine (SVM), K-nearest neighbors (KNN), logistic regression (LR), and random forest (RF) classifications. The results showed that the Orange mining tool was superior in predicting traffic congestion, with a prediction accuracy of 100% for Random forest, logistic regression, and 99.8% for KNN. On the other hand, results were better in WEKA for the SVM classifier with an accuracy of 99.7%.
Co-Authors Amani Al-Ghraibah Areen Arabiat