Now showing 1 - 10 of 37
  • Publication
    A Modified Oppositional Chaotic Local Search Strategy Based Aquila Optimizer to Design an Effective Controller for Vehicle Cruise Control System
    (2023)
    Ekinci, Serdar
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    Izci, Davut
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    Abualigah, Laith
    ;
    In this work, we propose a real proportional-integral-derivative plus second-order derivative (PIDD2) controller as an efficient controller for vehicle cruise control systems to address the challenging issues related to efficient operation. In this regard, this paper is the first report in the literature demonstrating the implementation of a real PIDD2 controller for controlling the respective system. We construct a novel and efficient metaheuristic algorithm by improving the performance of the Aquila Optimizer via chaotic local search and modified opposition-based learning strategies and use it as an excellently performing tuning mechanism. We also propose a simple yet effective objective function to increase the performance of the proposed algorithm (CmOBL-AO) to adjust the real PIDD2 controller's parameters effectively. We show the CmOBL-AO algorithm to perform better than the differential evolution algorithm, gravitational search algorithm, African vultures optimization, and the Aquila Optimizer using well-known unimodal, multimodal benchmark functions. CEC2019 test suite is also used to perform ablation experiments to reveal the separate contributions of chaotic local search and modified opposition-based learning strategies to the CmOBL-AO algorithm. For the vehicle cruise control system, we confirm the more excellent performance of the proposed method against particle swarm, gray wolf, salp swarm, and original Aquila optimizers using statistical, Wilcoxon signed-rank, time response, robustness, and disturbance rejection analyses. We also use fourteen reported methods in the literature for the vehicle cruise control system to further verify the more promising performance of the CmOBL-AO-based real PIDD2 controller from a wider perspective. The excellent performance of the proposed method is also illustrated through different quality indicators and different operating speeds. Lastly, we also demonstrate the good performing capability of the CmOBL-AO algorithm for real traffic cases. We show the CmOBL-AO-based real PIDD2 controller as the most efficient method to control a vehicle cruise control system.
      30Scopus© Citations 25
  • Publication
    A Novel Deep Learning Technique for Detecting Emotional Impact in Online Education
    (2022) ;
    AlZu’bi, Shadi
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    Hawashin, Bilal
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    Abu Shanab, Samia
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    Zraiqat, Amjed
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    Mughaid, Ala
    ;
    Almotairi, Khaled H.
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    Abualigah, Laith
    Emotional intelligence is the automatic detection of human emotions using various intelligent methods. Several studies have been conducted on emotional intelligence, and only a few have been adopted in education. Detecting student emotions can significantly increase productivity and improve the education process. This paper proposes a new deep learning method to detect student emotions. The main aim of this paper is to map the relationship between teaching practices and student learning based on emotional impact. Facial recognition algorithms extract helpful information from online platforms as image classification techniques are applied to detect the emotions of student and/or teacher faces. As part of this work, two deep learning models are compared according to their performance. Promising results are achieved using both techniques, as presented in the Experimental Results Section. For validation of the proposed system, an online course with students is used; the findings suggest that this technique operates well. Based on emotional analysis, several deep learning techniques are applied to train and test the emotion classification process. Transfer learning for a pre-trained deep neural network is used as well to increase the accuracy of the emotion classification stage. The obtained results show that the performance of the proposed method is promising using both techniques, as presented in the Experimental Results Section.
    Scopus© Citations 20  52  7
  • Publication
    A Novel Methodology for Human Kinematics Motion Detection Based on Smartphones Sensor Data Using Artificial Intelligence
    (2023)
    Raza, Ali
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    Al Nasar, Mohammad Rustom
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    Hanandeh, Essam Said
    ;
    ;
    Nasereddin, Ahmad Yacoub
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    Abualigah, Laith
    Kinematic motion detection aims to determine a person’s actions based on activity data. Human kinematic motion detection has many valuable applications in health care, such as health monitoring, preventing obesity, virtual reality, daily life monitoring, assisting workers during industry manufacturing, caring for the elderly. Computer vision-based activity recognition is challenging due to problems such as partial occlusion, background clutter, appearance, lighting, viewpoint, and changes in scale. Our research aims to detect human kinematic motions such as walking or running using smartphones’ sensor data within a high-performance framework. An existing dataset based on smartphones’ gyroscope and accelerometer sensor values is utilized for the experiments in our study. Sensor exploratory data analysis was conducted in order to identify valuable patterns and insights from sensor values. The six hyperparameters, tunned artificial indigence-based machine learning, and deep learning techniques were applied for comparison. Extensive experimentation showed that the ensemble learning-based novel ERD (ensemble random forest decision tree) method outperformed other state-of-the-art studies with high-performance accuracy scores. The proposed ERD method combines the random forest and decision tree models, which achieved a 99% classification accuracy score. The proposed method was successfully validated with the k-fold cross-validation approach.
      8Scopus© Citations 10
  • Publication
    Adapted arithmetic optimization algorithm for multi-level thresholding image segmentation: a case study of chest x-ray images
    (2023) ;
    Otair, Mohammad
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    Abualigah, Laith
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    Tawfiq, Saif
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    Alshinwan, Mohammad
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    Ezugwu, Absalom E.
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    Sumari, Putra
    Particularly in recent years, there has been increased interest in determining the ideal thresholding for picture segmentation. The best thresholding values are found using various techniques, including Otsu and Kapur-based techniques. These techniques work well for bi-level thresholding, but when used to find the appropriate thresholds for multi-level thresholding, there will be issues with long calculation times, high computational costs, and the need for accuracy improvements. This work investigates the capability of the Arithmetic Optimization Algorithm to discover the best multilayer thresholding for picture segmentation to circumvent this issue. The leading mathematical arithmetic operators' distributional nature is used by the AOA method. The picture histogram was used to construct the candidate solutions in the modified algorithms, which were then updated according to the algorithm's features. The solutions are evaluated using Otsu's fitness function throughout the optimization process. The picture histogram is used to display the algorithm's potential solutions. The proposed approach is tested on five frequent photos from the Berkeley University database. The fitness function, root-mean-squared error, peak signal-to-noise ratio, and other widely used assessment metrics were utilized to assess the performance of the suggested segmentation approach. Many benchmark pictures were employed to verify the suggested technique's effectiveness and evaluate it against other well-known optimization methods described in the literature.
      16
  • Publication
    An efficient artificial intelligence approach for early detection of cross-site scripting attacks
    (2024)
    Younas, Faizan
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    Raza, Ali
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    Thalji, Nisrean
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    Abualigah, Laith
    ;
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    Jia, Heming
    Cross-Site Scripting (XSS) attacks continue to pose a significant threat to web applications, compromising the security and integrity of user data. XSS is a web application vulnerability where malicious scripts are injected into websites, allowing attackers to execute arbitrary code in the victim’s browser. The consequences of XSS attacks can be severe, ranging from financial losses to compromising sensitive user information. XSS attacks enable attackers to deface websites, distribute malware, or launch phishing campaigns, compromising the trust and reputation of affected organizations. This study proposes an efficient artificial intelligence approach for the early detection of XSS attacks, utilizing machine learning and deep learning approaches, including Long Short-Term Memory (LSTM). Additionally, advanced feature engineering techniques, such as the Term Frequency-Inverse Document Frequency (TFIDF), are applied and compared to evaluate results. We introduce a novel approach named LSTM-TFIDF (LSTF) for feature extraction, which combines temporal and TFIDF features from the cross-site scripting dataset, resulting in a new feature set. Extensive research experiments demonstrate that the random forest method achieved a high performance of 0.99, outperforming state-of-the-art approaches using the proposed features. A k-fold cross-validation mechanism is utilized to validate the performance of applied methods, and hyperparameter tuning further enhances the performance of XSS attack detection. We have applied Explainable Artificial Intelligence (XAI) to understand the interpretability and transparency of the proposed model in detecting XSS attacks. This study makes a valuable contribution to the growing body of knowledge on XSS attacks and provides an efficient model for developers and security practitioners to enhance the security of web applications.
    Scopus© Citations 1  1
  • Publication
    An intelligent cybersecurity system for detecting fake news in social media websites
    (2022) ;
    Mughaid, Ala
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    Al-Zu'bi, Shadi
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    Arjan, A
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    Al-Amrat, Rula
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    Alajmi, Rathaa
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    Abualigah, Laith
    ;
    Maalej, Ahmed
    People worldwide suffer from fake news in many life aspects, healthcare, transportation, education, economics, and many others. Therefore, many researchers have considered seeking techniques for automatically detecting fake news in the last decade. The most popular news agencies use e-publishing on their websites; even websites can publish any news they want. However, thus before quotation any news from a website, there should be a close look at news resource ranking by using a trusted websites classifier, such as the website world rank, which reflects the repute of these websites. This paper uses the world rank of news websites as the main factor of news accuracy by using two widespread and trusted websites ranking. Moreover, a secondary factor is proposed to compute the news accuracy similarity by comparing the current news with fakes news and getting the possible news accuracy. Experiments results are conducted on several benchmark datasets. The results showed that the proposed method got promising results compared to other comparative methods in defining the news accuracy.
    Scopus© Citations 25  43  78
  • Publication
    An investigation to identify the factors that cause failure in English essay, precis, and composition papers in CSS exams
    (2024)
    Gul, Kiran
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    Shahzad, Waheed
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    Raza, Ali
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    Hanandeh, Essam
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    Aldiabat, Khaled
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    Shboul, Rabah
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    Abualigah, Laith
    The research study aims to examine why candidates in Pakistan failed the English Essay, Precis, and Composition sections of the Central Superior Services (CSS) tests. Those candidates chosen for various civil service positions take the prestigious and difficult CSS exam. The study aims to discover candidates’ difficulties in these particular CSS exam sections and investigate methods for enhancing their English language ability. A mixed-methods strategy is used in the research process to collect both quantitative and qualitative data. Participants in the CSS exam who once took the English Essay, Precis, and Composition papers and got fail in it received a survey form to respond according to their experience. Other than this, we also conducted semi-structured interviews with CSS test winners currently working as officials, such as Deputy Commissioners, Assistant Commissioners, Assistant Superintendents of Police, and Deputy Superintendents of Police. Insights into the causes of failure and the experiences of successful candidates are sought after from both data sources. The research findings highlighted several key factors contributing to failure in English Essays, Precis, and Composition papers. These factors include lack of comprehension and understanding, grammatical errors, inadequate organization, poor handwriting, insufficient practice, lack of originality, difficulty in adapting to essay prompts and precis passages, poor organization, failure to understand and address the purpose, insufficient development of ideas, failure to reach the required word count, grammatical mistakes, neglecting proofreading and revision, poor writing expression, and weak induction and conclusion in essays, tough paper pattern old formatted curriculum. Participants reported struggling to express their ideas coherently, having limited language skills, facing challenges in managing time effectively, lacking proper precis structure understanding, inadequate expertise in the subject, lack of training and resources, lack of analytical and critical thinking abilities, inadequate exam preparation, time management issues, poor grammar abilities, exam phobia, and limited vocabulary as potential factors contributing to failure.
      1
  • Publication
    Augmented arithmetic optimization algorithm using opposite-based learning and lévy flight distribution for global optimization and data clustering
    (2022) ;
    Abualigah, Laith
    ;
    Abd Elaziz, Mohamed
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    Yousri, Dalia
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    Al-qaness, Mohammed A. A.
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    Ewees, Ahmed A.
    This paper proposes a new data clustering method using the advantages of metaheuristic (MH) optimization algorithms. A novel MH optimization algorithm, called arithmetic optimization algorithm (AOA), was proposed to address complex optimization tasks. Math operations inspire the AOA, and it showed significant performance in dealing with different optimization problems. However, the traditional AOA faces some limitations in its search process. Thus, we develop a new variant of the AOA, namely, Augmented AOA (AAOA), integrated with the opposition-based learning (OLB) and Lévy flight (LF) distribution. The main idea of applying OLB and LF is to improve the traditional AOA exploration and exploitation trends in order to find the best clusters. To evaluate the AAOA, we implemented extensive experiments using twenty-three well-known benchmark functions and eight data clustering datasets. We also evaluated the proposed AAOA with extensive comparisons to different optimization algorithms. The outcomes verified the superiority of the AAOA over the traditional AOA and several MH optimization algorithms. Overall, the applications of the LF and OLB have a significant impact on the performance of the conventional AOA.
    Scopus© Citations 7  29  1
  • Publication
    Autokeras Approach: A Robust Automated Deep Learning Network for Diagnosis Disease Cases in Medical Images
    (2023)
    Alaiad, Ahmed
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    Migdady, Aya
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    Al-Khatib, Raed M
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    Alzoubi, Omar
    ;
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    Abualigah, Laith
    Automated deep learning is promising in artificial intelligence (AI). However, a few applications of automated deep learning networks have been made in the clinical medical fields. Therefore, we studied the application of an open-source automated deep learning framework, Autokeras, for detecting smear blood images infected with malaria parasites. Autokeras is able to identify the optimal neural network to perform the classification task. Hence, the robustness of the adopted model is due to it not needing any prior knowledge from deep learning. In contrast, the traditional deep neural network methods still require more construction to identify the best convolutional neural network (CNN). The dataset used in this study consisted of 27,558 blood smear images. A comparative process proved the superiority of our proposed approach over other traditional neural networks. The evaluation results of our proposed model achieved high efficiency with impressive accuracy, reaching 95.6% when compared with previous competitive models.
      16Scopus© Citations 5
  • Publication
    Design Research Insights on Text Mining Analysis: Establishing the Most Used and Trends in Keywords of Design Research Journals
    (2022) ;
    Nusir, Muneer
    ;
    Louati, Ali
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    Louati, Hassen
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    Tariq, Usman
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    Abualigah, Laith
    ;
    Gandomi, Amir H
    Design research topics attract exponentially more attention and consideration among researchers. This study is the first research article that endeavors to analyze selected design research publications using an advanced approach called “text mining”. This approach speculates its results depending on the existence of a research term (i.e., keywords), which can be more robust than other methods/approaches that rely on contextual data or authors’ perspectives. The main aim of this research paper is to expand knowledge and familiarity with design research and explore future research directions by addressing the gaps in the literature; relying on the literature review, it can be stated that the research area in the design domain still not built-up a theory, which can unify the field. In general, text mining with these features allows increased validity and generalization as compared to other approaches in the literature. We used a text mining technique to collect data and analyzed 3553 articles collected in 10 journals using 17,487 keywords. New topics were investigated in the domain of design concepts, which included attracting researchers, practitioners, and journal editorial boards. Such issues as co-innovation, ethical design, social practice design, conceptual thinking, collaborative design, creativity, and generative methods and tools were subject to additional research. On the other hand, researchers pursued topics such as collaborative design, human-centered design, interdisciplinary design, design education, participatory design, design practice, collaborative design, design development, collaboration, design theories, design administration, and service/product design areas. The key categories investigated and reported in this paper helped in determining what fields are flourishing and what fields are eroding.
      25  10Scopus© Citations 2