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Mutasem K. Alsmadi

Imam Abdulrahman Bin Faisal University

Author-ID : 2376382

Computer Science & IT Electrical & Electronics Engineering

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Robust features extraction for general fish classification Mutasem K. Alsmadi; Mohammed Tayfour; Raed A. Alkhasawneh; Usama Badawi; Ibrahim Almarashdeh; Firas Haddad
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 6: December 2019
Publisher : Institute of Advanced Engineering and Science

Image recognition process could be plagued by many problems including noise, overlap, distortion, errors in the outcomes of segmentation, and impediment of objects within the image. Based on feature selection and combination theory between major extracted features, this study attempts to establish a system that could recognize fish object within the image utilizing texture, anchor points, and statistical measurements. Then, a generic fish classification is executed with the application of an innovative classification evaluation through a meta-heuristic algorithm known as Memetic Algorithm (Genetic Algorithm with Simulated Annealing) with back-propagation algorithm (MA-B Classifier). Here, images of dangerous and non-dangerous fish are recognized. Images of dangerous fish are further recognized as Predatory or Poison fish family, whereas families of non-dangerous fish are classified into garden and food family. A total of 24 fish families were used in testing the proposed prototype, whereby each family encompasses different number of species. The process of classification was successfully undertaken by the proposed prototype, whereby 400 distinct fish images were used in the experimental tests. Of these fish images, 250 were used for training phase while 150 were used for testing phase. The back-propagation algorithm and the proposed MA-B Classifier produced a general accuracy recognition rate of 82.25 and 90% respectively.

Bivariate modified hotelling’s T2 charts using bootstrap data Firas Haddad; Mutasem K. Alsmadi; Usama Badawi; Tamer Farag; Raed Alkhasawneh; Ibrahim Almarashdeh; Walaa Hassan
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 6: December 2019
Publisher : Institute of Advanced Engineering and Science

The conventional Hotelling’s charts are evidently inefficient as it has resulted in disorganized data with outliers, and therefore, this study proposed the application of a novel alternative robust Hotelling’s charts approach. For the robust scale estimator , this approach encompasses the use of the Hodges-Lehmann vector and the covariance matrix in place of the arithmetic mean vector and the covariance matrix, respectively. The proposed chart was examined performance wise. For the purpose, simulated bivariate bootstrap datasets were used in two conditions, namely independent variables and dependent variables. Then, assessment was made to the modified chart in terms of its robustness. For the purpose, the likelihood of outliers’ detection and false alarms were computed. From the outcomes from the computations made, the proposed charts demonstrated superiority over the conventional ones for all the cases tested.

Fog computing scheduling algorithm for smart city Ahmad Mohammad Alsmadi; Roba Mahmoud Ali Aloglah; Nisrein Jamal sanad Abu-darwish; Ahmad Al Smadi; Muneerah Alshabanah; Daniah Alrajhi; Hanouf Alkhaldi; Mutasem K. Alsmadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2219-2228

With the advent of the number of smart devices across the globe, increasing the number of users using the Internet. The main aim of the fog computing (FC) paradigm is to connect huge number of smart objects (billions of object) that can make a bright future for smart cities. Due to the large deployments of smart devices, devices are expected to generate huge amounts of data and forward the data through the Internet. FC also refers to an edge computing framework that mitigates the issue by applying the process of knowledge discovery using a data analysis approach to the edges. Thus, the FC approaches can work together with the internet of things (IoT) world, which can build a sustainable infrastructure for smart cities. In this paper, we propose a scheduling algorithm namely the weighted round-robin (WRR) scheduling algorithm to execute the task from one fog node (FN) to another fog node to the cloud. Firstly, a fog simulator is used with the emergent concept of FC to design IoT infrastructure for smart cities. Then, spanning-tree routing (STP) protocol is used for data collection and routing. Further, 5G networks are proposed to establish fast transmission and communication between users. Finally, the performance of our proposed system is evaluated in terms of response time, latency, and amount of data used.

The students’ acceptance of learning management systems in Saudi Arabian Universities Mutasem K. Alsmadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

For distance learners, continuous official education is very important for improving knowledge and learning experience in order to meet the career challenges in the modern world. This work studies the success factors which affect the use of LMS and evaluates the ability to apply the proposed model in the field of distance learning (DL) particularly in higher education. The survey was carried out on higher education learners who were included in the DL instructions. This work has utilized a questionnaire that was modified from literature to inspect three measurements, system design, system usage, and system outcome. Utilizing the obtained survey data for students of DL (N=149), the path analysis discovered that the design of the system has a significant effect on the satisfaction of users and intention for using LMS which affects the use of the system. Consequently, the satisfaction of users and the system used has a great impact on the net benefit.

Hybrid feature selection method based on particle swarm optimization and adaptive local search method Malek Alzaqebah; Sana Jawarneh; Rami Mustafa A. Mohammad; Mutasem K. Alsmadi; Ibrahim Al-marashdeh; Eman A. E. Ahmed; Nashat Alrefai; Fahad A. Alghamdi
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2414-2422

Machine learning has been expansively examined with data classification as the most popularly researched subject. The accurateness of prediction is impacted by the data provided to the classification algorithm. Meanwhile, utilizing a large amount of data may incur costs especially in data collection and preprocessing. Studies on feature selection were mainly to establish techniques that can decrease the number of utilized features (attributes) in classification, also using data that generate accurate prediction is important. Hence, a particle swarm optimization (PSO) algorithm is suggested in the current article for selecting the ideal set of features. PSO algorithm showed to be superior in different domains in exploring the search space and local search algorithms are good in exploiting the search regions. Thus, we propose the hybridized PSO algorithm with an adaptive local search technique which works based on the current PSO search state and used for accepting the candidate solution. Having this combination balances the local intensification as well as the global diversification of the searching process. Hence, the suggested algorithm surpasses the original PSO algorithm and other comparable approaches, in terms of performance.

Neighborhood search methods with Moth Optimization algorithm as a wrapper method for feature selection problems Malek Alzaqebah; Nashat Alrefai; Eman A. E. Ahmed; Sana Jawarneh; Mutasem K. Alsmadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Feature selection methods are used to select a subset of features from data, therefore only the useful information can be mined from the samples to get better accuracy and improves the computational efficiency of the learning model. Moth-flam Optimization (MFO) algorithm is a population-based approach, that simulates the behavior of real moth in nature, one drawback of the MFO algorithm is that the solutions move toward the best solution, and it easily can be stuck in local optima as we investigated in this paper, therefore, we proposed a MFO Algorithm combined with a neighborhood search method for feature selection problems, in order to avoid the MFO algorithm getting trapped in a local optima, and helps in avoiding the premature convergence, the neighborhood search method is applied after a predefined number of unimproved iterations (the number of tries fail to improve the current solution). As a result, the proposed algorithm shows good performance when compared with the original MFO algorithm and with state-of-the-art approaches.

Emergent situations for smart cities: A survey Ahmad Mohamad Al-Smadi; Mutasem K. Alsmadi; Abdel Karim Baareh; Ibrahim Almarashdeh; Hayam Abouelmagd; Osman Saad Shidwan Ahmed
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 6: December 2019
Publisher : Institute of Advanced Engineering and Science

A smart city is a community that uses communication and information technology to improve sustainability, livability, and feasibility. As any community, there are always unexpected emergencies, which must be treated to preserve the regular order. However, a smart system is needed to be able to respond effectively to these emergent situations. The contribution made in this survey is twofold. Firstly, it provides a comprehensive exhaustive and categorized overview of the existing surveys for smart cities. The categorization is based on several criteria such as structures, benefits, advantages, applications, challenges, issues, and future directions. Secondly, it aims to analyze several studies with respect to emergent situations and management to smart cities. The analysis is based on several factors such as the challenges and issues discussed, the solutions proposed, and opportunities for future research. The challenges include security, privacy, reliability, performance, scalability, heterogeneity, scheduling, resource management, and latency. Few studies have investigated the emergent situations of smart cities and despite the importance of latency factor for smart city applications, it is rarely discussed.

Comparison of specific segmentation methods used for copy move detection Eman Abdulazeem Ahmed; Malek Alzaqebah; Sana Jawarneh; Jehad Saad Alqurni; Fahad A. Alghamdi; Hayat Alfagham; Lubna Mahmoud Abdel Jawad; Usama A. Badawi; Mutasem K. Alsmadi; Ibrahim Almarashdeh
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.pp2363-2374

In this digital age, the widespread use of digital images and the availability of image editors have made the credibility of images controversial. To confirm the credibility of digital images many image forgery detection types are arises, copy-move forgery is consisting of transforming any image by duplicating a part of the image, to add or hide existing objects. Several methods have been proposed in the literature to detect copy-move forgery, these methods use the key point-based and block-based to find the duplicated areas. However, the key point-based and block-based have a drawback of the ability to handle the smooth region. In addition, image segmentation plays a vital role in changing the representation of the image in a meaningful form for analysis. Hence, we execute a comparison study for segmentation based on two clustering algorithms (i.e., k-means and super pixel segmentation with density-based spatial clustering of applications with noise (DBSCAN)), the paper compares methods in term of the accuracy of detecting the forgery regions of digital images. K-means shows better performance compared with DBSCAN and with other techniques in the literature.

Susceptible exposed infectious recovered-machine learning for COVID-19 prediction in Saudi Arabia Mutasem K. Alsmadi; Ghaith M. Jaradat; Sami A. Abahussain; Mohammed Fahed Tayfour; Usama A. Badawi; Hayat Alfagham; Muneerah Ebrahem Alshabanah; Daniah Abdulrahman Alrajhi; Hanouf Naif ALkhaldi; Njoud Ahmad Altuwaijri; Hany Answer ShoShan; Hayah Mohamed Abouelnaga; Ahmed Baz Mohamed Metwally
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.pp4761-4776

Susceptible exposed infectious recovered (SEIR) is among the epidemiological models used in forecasting the spread of disease in large populations. SEIR is a fitting model for coronavirus disease (COVID-19) spread prediction. Somehow, in its original form, SEIR could not measure the impact of lockdowns. So, in the SEIR equations system utilized in this study, a variable was included to evaluate the impact of varying levels of social distance on the transmission of COVID-19. Additionally, we applied artificial intelligence utilizing the deep neural network machine learning (ML) technique. On the initial spread data for Saudi Arabia that were available up to June 25th, 2021, this improved SEIR model was used. The study shows possible infection to around 3.1 million persons without lockdown in Saudi Arabia at the peak of spread, which lasts for about 3 months beginning from the lockdown date (March 21st). On the other hand, the Kingdom's current partial lockdown policy was estimated to cut the estimated number of infections to 0.5 million over nine months. The data shows that stricter lockdowns may successfully flatten the COVID-19 graph curve in Saudi Arabia. We successfully predicted the COVID-19 epidemic's peaks and sizes using our modified deep neural network (DNN) and SEIR model.

Co-Authors Abdel Karim Baareh Ahmad AL Smadi Ahmad Mohamad Al-Smadi Ahmad Mohammad Alsmadi Ahmed Baz Mohamed Metwally Daniah Abdulrahman Alrajhi Daniah Alrajhi Eman A. E. Ahmed Eman Abdulazeem Ahmed Fahad A. Alghamdi Firas Haddad Ghaith M. Jaradat Hanouf Alkhaldi Hanouf Naif ALkhaldi Hany Answer ShoShan Hayah Mohamed Abouelnaga Hayam Abouelmagd Hayat Alfagham Ibrahim Al-Marashdeh Ibrahim Almarashdeh Jehad Saad Alqurni Lubna Mahmoud Abdel Jawad Malek Alzaqebah Mohammed Fahed Tayfour Mohammed Tayfour Muneerah Alshabanah Muneerah Ebrahem Alshabanah Nashat Alrefai Nisrein Jamal sanad Abu-darwish Njoud Ahmad Altuwaijri Osman Saad Shidwan Ahmed Raed A. Alkhasawneh Raed Alkhasawneh Rami Mustafa A. Mohammad Roba Mahmoud Ali Aloglah Sami A. Abahussain Sana Jawarneh Tamer Farag Usama A. Badawi Usama Badawi Walaa Hassan

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