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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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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.
Cost-effective resource and task scheduling in fog nodes Ali Hussein Shamman; Hussein Ali Alasadi; Hussein Ali Ameen; Zaid Ibrahim Rasol; Hassan Muwafaq Gheni
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp466-477

Abstract

Cloud services are the cutting edge technology, however the growing demand for the internet of things has certain limitations which are high latency expectation and high cost of cloud resources, and this is caused by long-distance between application and cloud. Fog computing is a distributed extension of the cloud, which provide storage and computation at the network level. It consists of an internet of things (IoT) application, a fog control node, and a fog access node. This research works towards minimizing the cloud cost in scheduling. For this purpose, a cost-effective task and user scheduling algorithm are performed. The first task scheduling model is composed based on composers' roles after that task scheduling algorithm is performed to handle the various task at the fog access node in an optimized manner. Finally, the reallocation mechanism reduces the time and service delay. For the analysis purpose extensive simulation is carried out and performance statistics were compared with other existing algorithms. It was observed that the proposed algorithm provides highly cost-optimized user and task scheduling with better performance statistics and reduces the delay in the task by providing optimization in the concurrent task at the fog node.
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.
Integrating security and privacy in mmWave communications Ghadah M. Faisal; Hasanain Abdalridha Abed Alshadoodee; Haider Hadi Abbas; Hassan Muwafaq Gheni; Israa Al-Barazanchi
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4314

Abstract

The aim of this paper is to integrate security and privacy in mmWave communications. MmWave communication mechanism access three major key components of secure communication (SC) operations. proposed design for mmWave communication facilitates the detection of the primary signal in physical (PHY) layer to find the spectrum throughput for primary user (PU) and secondary user (SU). The throughput of SC for PU with maximum throughput being recorded at 0.7934 while maximum throughput for SU is recorded at 0.7679. So, we will design a mmWave communication mechanism for solving this problem. The probability for sensing where the probability of detection (PD) is predicted at a defined range of 690 km with an estimated accuracy of 83.56% while the probability of false alarm (PFA) is predicted at a defined range of 230 km with an estimated accuracy of 81.39%. This conflicting but interrelated issue is investigated over three stages for the purpose of solving with a cross-layer model with MAC and PHY layers for a secure communication network (SCN) while reducing the collision effect concurrently with a 92.76% for both cross-layers. MATLAB 2019b would be forwarded in use as the increasing demand for augmenting the bandwidth in secure communications has actuated the evolutionary technology.
Detection of the patient with COVID-19 relying on ML technology and FAST algorithms to extract the features Seba Aziz Sahy; Sura Hammed Mahdi; Hassan Muwafaq Gheni; Israa Al-Barazanchi
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4355

Abstract

COVID-19 is unquestionably one of the most hazardous health issues of our century, and it is a significant cause of mortality for both men and women throughout the globe. Even with the most advanced pharmacological and technical innovations, cancer oncologists, and biologists still have a substantial problem treating COVID-19. For patients with COVID-19, it is critical to offer initial, precise, and effective indicative procedures to increase their survival and minimize morbidity and mortality, which is currently lacking. A COVID-19 detection method has been presented in this paper for the initial identification of COVID-19 hazard factors. Features from accelerated segment test (FAST), a robust feature was used to extract features in this suggested method. The experiments show that it is possible to identify FAST traits efficiently. A consequence was a high success rate (98%) for accuracy performance.
Design module for speech recognition graphical user interface browser to supports the web speech applications Fadya A. Habeeb; Suaad M. Saber; Shaymaa Mohammed Abdulameer; Hassan Muwafaq Gheni; Ahmed Dheyaa Radhi
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4346

Abstract

The web speech API has made it possible to integrate audio data into web applications and make it a unique experience for all customers and users of modern applications. The website can only be accessed through devices equipped with a which stands for graphical user interface (GUI) and screen. For this to be done, there must be a physical attraction with such devices. This paper presents speech recognition using a web browser (SRWB) which permits browsing or surfing the internet with the use of a standard voice-only and vocal user interface (VUL) development. The SRWB system input from the users in form of vocal commands and covers these voice commands to HTTP requests. The SRWB system will send the voice commands to the web server for processing purposes and when the processing is done, the converted or translated HTTP response is outputted to the end-users in a voice format made audible with the attached loudspeakers. SAPI, developed by Microsoft, allows the use of SRWB in Windows applications. The algorithm is implemented by the system to achieve its goal for web content, classifying, analyzing, and sending important parts of web pages back to the end-user.
Man-in-the-middle and denial of service attacks detection using machine learning algorithms Sura Abdulmunem Mohammed Al-Juboori; Firas Hazzaa; Zinah Sattar Jabbar; Sinan Salih; Hassan Muwafaq Gheni
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.v12i1.4555

Abstract

Network attacks (i.e., man-in-the-middle (MTM) and denial of service (DoS) attacks) allow several attackers to obtain and steal important data from physical connected devices in any network. This research used several machine learning algorithms to prevent these attacks and protect the devices by obtaining related datasets from the Kaggle website for MTM and DoS attacks. After obtaining the dataset, this research applied preprocessing techniques like fill the missing values, because this dataset contains a lot of null values. Then we used four machine learning algorithms to detect these attacks: random forest (RF), eXtreme gradient boosting (XGBoost), gradient boosting (GB), and decision tree (DT). To assess the performance of the algorithms, there are many classification metrics are used: precision, accuracy, recall, and f1-score. The research achieved the following results in both datasets: i) all algorithms can detect the MTM attack with the same performance, which is greater than 99% in all metrics; and ii) all algorithms can detect the DoS attack with the same performance, which is greater than 97% in all metrics. Results showed that these algorithms can detect MTM and DoS attacks very well, which is prompting us to use their effectiveness in protecting devices from these attacks.
Research on fault adaptive fault tolerant control of distributed wind solar hybrid generator Yitong Niu; Intisar A. M. Al Sayed; Alya'a R. Ali; Israa Al_Barazanchi; Poh Soon JosephNg; Zahraa A. Jaaz; Hassan Muwafaq Gheni
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4242

Abstract

Due to the poor accessibility, poor operating conditions, high failure rate, long maintenance time, and difficult maintenance of wind hybrid generators, the economic loss is huge once the failure stops. To this end, the fault adaptive fault-tolerant control of distributed wind and wind hybrid generators is studied, the historical operation data of offshore wind and wind hybrid generators and onshore wind and wind hybrid generators are counted and compared, and the fault characteristics of key components of offshore wind and wind hybrid generators are analyzed. The generator sets are summarized, and the common electrical faults of wind turbines and their impacts on the system are analyzed. This paper summarizes the current research status of fault-tolerant operation of existing offshore wind and wind complementary generators in terms of software fault tolerance and hardware fault tolerance, summarizes the current fault tolerance schemes for offshore wind and wind complementary generators, and analyzes the application feasibility of existing fault tolerance schemes. In addition, the main problems of fault-tolerant offshore wind and solar complementary generator sets are pointed out, and future research hotspots are foreseen.
The role of artificial intelligence in enhancing administrative decision support systems by depend on knowledge management Hasanain Abdalridha Abed Alshadoodee; Muneer Sameer Gheni Mansoor; Hasanien Kariem Kuba; Hassan Muwafaq Gheni
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4243

Abstract

This study illustrates the role of artificial intelligence in enhancing administrative decision support systems by depend on knowledge management. As per new technologies are evolving and the workflow need more concious approach of implementation, thus the role of artificial intelligence is evolved in support to decision making. The study takes privates college administration as a varible on which the results rely. The upgrades in innovation have upgraded most techniques for leading business tasks that further develop organizations and administration conveyance. Companies in this area need to wander into digitizing of all industry cycles, business sequences linked to administration and more essential services in educational institutes over time. The need for a proper decision-making support using knowledge management stills create a big gap in the foundation of an effective and efficient eductaional system for the good governance and to improve the image of some institute. The examination interaction has been intended to follow an iterative methodology of information revelation chose for the review. Using the statistical package for social sciences (IBM-SPSS) version 23 logic instrument, the illustrative research was completed with insights into the segment profile of the respondents. Hayes' process macro v3.3 with SPSS was used to analyze the interceding effect.
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