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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Qahtan Makki Shallal
Southern Technical University

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Clustering similar time series data for the prediction the patients with heart disease Raid Luaibi Lafta; Mohanad S. AL-Musaylh; Qahtan Makki Shallal
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp947-954

Abstract

Developed intelligent technologies are become play a promising role in providing better decision-making and improving the medical services provided to the patients. A risk prediction task for short-term is big challenge task; however, it is a great importance for recommendation systems in health care field to provide patients with accurate and reliable recommendations. In this work, clustering method and least square support vector machine are used for prediction a short-term disease risk prediction. The clustering similar method is based on euclidean Distance which used to identify the similar sliding windows. The proposed model is trained by using the slide windows samples. Finally, the appropriate recommendations are generated for heart diseases patients who need to take a medical test or not for following day using least square support vector machine. A real dataset which collected from heart diseases patient is used for evaluation. The proposed method yields a good results related by the recommendations accuracy generated to chronicle heart patients and reduce the risk of incorrect recommendations.
Method to implement K-NN machine learning to classify data privacy in IoT environment Qahtan Makki Shallal; Zaid Alaa Hussien; Alaa Ahmed Abbood
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 2: November 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i2.pp985-990

Abstract

Internet of Things technology allows many devices to connect with each other. The interaction could be between humans and devices or between devices itself. In fact, the data are traveling between the devices through the media within the boundary, and it could be traveling outside the boundary when it required to be analyzed or stored in the cloud through the internet. Due the transmission media and internet, the data are vulnerable to attacks. Thus, the data need to be encrypted strongly for the purpose of protection. Usually, most of the encryption techniques will consume computer resources. In this work, we divide the data that are used in the IoT environment into three levels of sensitivity which are low, medium and high sensitive data to leverage the computer resources such as time of encryption and decryption, battery usage and so on. A framework is proposed in this work to encrypt the data depends on the level of sensitivity using the machine learning K nearest neighbors (K-NN).
Automated brain tumor classification using various deep learning models: a comparative study Alaa Ahmed Abbood; Qahtan Makki Shallal; Mohammed Abdulraheem Fadhel
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i1.pp252-259

Abstract

The brain tumor, the most common and aggressive disease, leads to a very shorter lifespan. Thus, planning treatments is a crucial step in improving a patient's quality of life. In general, several image techniques such as CT, MRI, and ultrasound have been used for assessing tumors in the prostate, breast, lung, brain, etc. Primarily, MRI images are applied to detect tumors in the brain during this work. The enormous amount of data produced by the MRI scan thwarts tumor vs. non-tumor manual classification at a particular time. Unfortunately, with a small number of images, it has certain limitations (i.e., precise quantitative measurements). Therefore, an automated classification system is necessary to avoid human mortality. The automatic categorization of brain tumors in the surrounding tumor region is a challenging task concerning space and structural variability. Four deep learning models: AlexNet, VGG16, GoogleNet, and RestNet50, are used in this comparative study to classify brain tumors. Based on accuracy, the results showed that RestNet50 is the best model with an accuracy of 95.8%, while AlexNet has the fast performance with a processing time of 1.2 seconds. In addition, a hardware parallel processing unit (GPU) is employed for real-time purposes, where AlexNet (the fastest model) has a processing time of only 8.3 msec.
Internet of things (IoT): a technology review, security issues, threats, and open challenges Alaa Ahmed Abbood; Qahtan Makki Shallal; Mohammed A. Fadhel
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 3: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i3.pp1685-1692

Abstract

Internet of things (IoT) devices are spread in different areas such as e-tracking, e-commerce, e-home, and e-health, etc. Thus, during the last ten years, the internet of things technology (IoT) has been a research focus. Both privacy and security are the key concerns for the applications of IoT, and still face a huge number of challenges. There are many elements used to run the IoT technology which include hardware and software such as sensors, GPS, cameras, applications, and so forth. In this paper, we have analyzed and explain the technology of IoT along with its elements, security features, security issues, and threats that attached to each layer of IoT to guide the consideration of researchers into solve and understand the most serious problems in IoT environment.
Co-Authors Alaa Ahmed Abbood Alaa Ahmed Abbood Mohammed A. Fadhel Mohammed Abdulraheem Fadhel Mohanad S. AL-Musaylh Raid Luaibi Lafta Zaid Alaa Hussien