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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Hassan Muwafaq Gheni
Al-Mustaqbal University college
Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

 1 
Efficient time-series forecasting of nuclear reactions using swarm intelligence algorithms Hala Shaker Mehdy; Nariman Jabbar Qasim; Haider Hadi Abbas; Israa Al_Barazanchi; Hassan Muwafaq Gheni
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5093-5103

Abstract

In this research paper, we focused on the developing a secure and efficient time-series forecasting of nuclear reactions using swarm intelligence (SI) algorithm. Nuclear radioactive management and efficient time series for casting of nuclear reactions is a problem to be addressed if nuclear power is to deliver a major part of our energy consumption. This problem explains how SI processing techniques can be used to automate accurate nuclear reaction forecasting. The goal of the study was to use swarm analysis to understand patterns and reactions in the dataset while forecasting nuclear reactions using swarm intelligence. The results obtained by training the SI algorithm for longer periods of time for predicting the efficient time series events of nuclear reactions with 94.58 percent accuracy, which is higher than the deep convolution neural networks (DCNNs) 93% accuracy for all predictions, such as the number of active reactions, to see how the results can improve. Our earliest research focused on determining the best settings and preprocessing for working with a certain nuclear reaction, such as fusion and fusion task: forecasting the time series as the reactions took 0-500 ticks being trained on 300 epochs
Electrocardiograph signal recognition using wavelet transform based on optimized neural network Ali Talib Jawad; Dalael Saad Abdul-Zahra; Hassan Muwafaq Gheni; Ali Najim Abdullah
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4944-4950

Abstract

Due to the growing number of cardiac patients, an automatic detection that detects various heart abnormalities has been developed to relieve and share physicians’ workload. Many of the depolarization of ventricles complex waves (QRS) detection algorithms with multiple properties have recently been presented; nevertheless, real-time implementations in low-cost systems remain a challenge due to limited hardware resources. The proposed algorithm finds a solution for the delay in processing by minimizing the input vector’s dimension and, as a result, the classifier’s complexity. In this paper, the wavelet transform is employed for feature extraction. The optimized neural network is used for classification with 8-classes for the electrocardiogram (ECG) signal this data is taken from two ECG signals (ST-T and MIT-BIH database). The wavelet transform coefficients are used for the artificial neural network’s training process and optimized by using the invasive weed optimization (IWO) algorithm. The suggested system has a sensitivity of over 70%, a specificity of over 94%, a positive predictive of over 65%, a negative predictive of more than 93%, and a classification accuracy of more than 80%. The performance of the classifier improves when the number of neurons in the hidden layer is increased.
A secure sharing control framework supporting elastic mobile cloud computing Aws Hamed Hamad; Adnan Yousif Dawod; Mohammed Fakhrulddin Abdulqader; Israa Al_Barazanchi; Hassan Muwafaq Gheni
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2270-2277

Abstract

In elastic mobile cloud computing (EMCC), mobile devices migrate some computing tasks to the cloud for execution according to current needs and seamlessly and transparently use cloud resources to enhance their functions. First, based on the summary of existing EMCC schemes, a generic EMCC framework is abstracted; it is pointed out that the migration of sensitive modules in the EMCC program can bring security risks such as privacy leakage and information flow hijacking to EMCC; then, a generic framework of elastic mobile cloud computing that incorporates risk management is designed, which regards security risks as a cost of EMCC and ensures that the use of EMCC is. Finally, it is pointed out that the difficulty of risk management lies in risk quantification and sensitive module labeling. In this regard, risk quantification algorithms are designed, an automatic annotation tool for sensitive modules of Android programs is implemented, and the accuracy of the automatic annotation is demonstrated through experiments.
Evaluation of wind-solar hybrid power generation system based on Monte Carlo method Yitong Niu; Ahmed Mohammed Merza; Suhad Ibraheem Kadhem; Jamal Fadhil Tawfeq; Poh Soon JosephNg; Hassan Muwafaq Gheni
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4401-4411

Abstract

The application of wind-photovoltaic complementary power generation systems is becoming more and more widespread, but its intermittent and fluctuating characteristics may have a certain impact on the system's reliability. To better evaluate the reliability of stand-alone power generation systems with wind and photovoltaic generators, a reliability assessment model for stand-alone power generation systems with wind and photovoltaic generators was developed based on the analysis of the impact of wind and photovoltaic generator outages and derating on reliability. A sequential Monte Carlo method was used to evaluate the impact of the wind turbine, photovoltaic (PV) turbine, wind/photovoltaic complementary system, the randomness of wind turbine/photovoltaic outage status and penetration rate on the reliability of Independent photovoltaic power generation system (IPPS) under the reliability test system (RBTS). The results show that this reliability assessment method can provide some reference for planning the actual IPP system with wind and complementary solar systems.
Co-Authors Adnan Yousif Dawod Ahmed Dheyaa Radhi Ahmed Mohammed Merza Ali Hussein Shamman Ali Najim Abdullah Ali Talib Jawad Alya'a R. Ali Asma Khazaal Abdulsahib Aws Hamed Hamad Dalael Saad Abdul-Zahra Fadya A. Habeeb Farah Jasim Mohammed Firas Hazzaa Geehan Sabah Hassan Ghadah M. Faisal Haider Hadi Abbas Haider Hadi Abbas Hala Shaker Mehdy Hasanain Abdalridha Abed Alshadoodee Hasanien Kariem Kuba Hussein Ali Alasadi Hussein Ali Ameen Intisar A. M. Al Sayed Israa Al-Barazanchi Israa Al-Barazanchi Israa Al_Barazanchi Jamal Fadhil Tawfeq Mohammed Fakhrulddin Abdulqader Muneer Sameer Gheni Mansoor Nariman jabbar qasim Noora Jamal Ali Poh Soon JosephNg Seba Aziz Sahy Shaymaa Mohammed Abdulameer Sinan Salih Suaad M. Saber Suhad Ibraheem Kadhem Sura Abdulmunem Mohammed Al-Juboori Sura Hammed Mahdi Yitong Niu Yitong Niu Zahraa A. Jaaz Zaid Ibrahim Rasol Zinah Sattar Jabbar