Browsing by Type "review article"

Now showing 1 - 4 of 4
  • Publication
    Big Data Maturity Assessment Models: A Systematic Literature Review
    (2023)
    Al-Sai, Zaher Ali
    ;
    Husin, Mohd Heikal
    ;
    Syed-Mohamad, Sharifah Mashita
    ;
    Abdullah, Rosni
    ;
    Abu Zitar, Raed 
    ;
    Abualigah, Laith
    ;
    Gandomi, Amir H.
    Big Data and analytics have become essential factors in managing the COVID-19 pandemic. As no company can escape the effects of the pandemic, mature Big Data and analytics practices are essential for successful decision-making insights and keeping pace with a changing and unpredictable marketplace. The ability to be successful in Big Data projects is related to the organization’s maturity level. The maturity model is a tool that could be applied to assess the maturity level across specific key dimensions, where the maturity levels indicate an organization’s current capabilities and the desirable state. Big Data maturity models (BDMMs) are a new trend with limited publications published as white papers and web materials by practitioners. While most of the related literature might not have covered all of the existing BDMMs, this systematic literature review (SLR) aims to contribute to the body of knowledge and address the limitations in the existing literature about the existing BDMMs, assessment dimensions, and tools. The SLR strategy in this paper was conducted based on guidelines to perform SLR in software engineering by answering three research questions: (1) What are the existing maturity assessment models for Big Data? (2) What are the assessment dimensions for Big Data maturity models? and (3) What are the assessment tools for Big Data maturity models? This SLR covers the available BDMMs written in English and developed by academics and practitioners (2007–2022). By applying a descriptive qualitative content analysis method for the reviewed publications, this SLR identified 15 BDMMs (10 BDMMs by practitioners and 5 BDMMs by academics). Additionally, this paper presents the limitations of existing BDMMs. The findings of this paper could be used as a grounded reference for assessing the maturity of Big Data. Moreover, this paper will provide managers with critical insights to select the BDMM that fits within their organization to support their data-driven decisions. Future work will investigate the Big Data maturity assessment dimensions towards developing a new Big Data maturity model.
      104  82Scopus© Citations 9
  • Publication
    Driver’s facial expression recognition: A comprehensive survey
    (2024)
    Saadi, Ibtissam
    ;
    Cunningham, Douglas W.
    ;
    Taleb-Ahmed, Abdelmalik
    ;
    Hadid, Abdenour 
    ;
    El Hillali, Yassin
    Driving is an integral part of daily life for millions of people worldwide, and it has a profound impact on road safety and human health. The emotional state of the driver, including feelings of anger, happiness, or fear, can significantly affect their ability to make safe driving decisions. Recognizing the facial expressions of drivers(DFER) has emerged as a promising technique for improving road safety and can provide valuable information about their emotions, This information can be used by intelligent transportation systems (ITS), like advanced driver assistance systems (ADAS) to take appropriate decision, such as alerting the driver or intervening in the driving process, to prevent the potential risks. This survey paper presents a comprehensive survey of recent studies that focus on the problem of recognizing the facial expression of driver recognition in the driving context from 2018 to March 2023. Specifically, we examine studies that address the recognition of the driver's emotion using facial expressions and explore the challenges that exist in this field, such as illumination conditions, occlusion, and head poses. Our survey includes an analysis of different techniques and methods used to identify and categorize specific expressions or emotions of the driver. We begin by reviewing and comparing available datasets and summarizing state-of-the-art methods, including machine learning-based methods, deep learning-based methods, and hybrid methods. We also identify limitations and potential areas for improvement. Overall, our survey highlights the importance of recognizing driver facial expressions in improving road safety and provides valuable insights into recent developments and future research directions in this field.
    Scopus© Citations 1  13
  • Publication
    Intensive Review of Drones Detection and Tracking: Linear Kalman Filter Versus Nonlinear Regression, an Analysis Case
    (2023)
    Abu Zitar, Raed 
    ;
    Mohsen, Amani 
    ;
    Seghrouchni, Amal ElFallah
    ;
    Barbaresco, Frederic
    ;
    Al-Dmour, Nidal A.
    In this paper, an extensive review for objects and drones (AUVs) detection and tracking is presented. The article presents state of the art methods used in detection and tracking of drones with adequate analysis and comparisons summarizing the findings of the most recent research material in that field. The most famous technique used in drones tracking is Kalman Filters (KFs) in its different forms. The paper presents analysis and comparisons for drones tracking based on Linear Kalman Filters (LKF) compared to tracking using Nonlinear Polynomial Regression (NPR) techniques. Interesting findings reflect the need for both methods at different circumstances depending on the noise conditions of the measurements. On the other hand, many new methods such as Artificial Intelligence (AI) based techniques are recently used in drones detection and recognition. Detection methods could come separate or combined with tracking techniques. The work presents broad and deep literature review with critical analysis of most famous methods used in drones detection and tracking.
      42Scopus© Citations 27
  • Publication
    Recent Advances of Chimp Optimization Algorithm: Variants and Applications
    (2023)
    Daoud, Mohammad Sh.
    ;
    Shehab, Mohammad
    ;
    Abualigah, Laith
    ;
    Alshinwan, Mohammad
    ;
    Abd Elaziz, Mohamed
    ;
    Shambour, Mohd Khaled Yousef
    ;
    Oliva, Diego
    ;
    Alia, Mohammad A.
    ;
    Abu Zitar, Raed 
    Chimp Optimization Algorithm (ChOA) is one of the recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods: it has very few parameters, and no derivation information is required in the initial search. Also, it is simple, easy to use, flexible, scalable, and has a special capability to strike the right balance between exploration and exploitation during the search which leads to favorable convergence. Therefore, the ChOA has recently gained a very big research interest with tremendous audiences from several domains in a very short time. Thus, in this review paper, several research publications using ChOA have been overviewed and summarized. Initially, introductory information about ChOA is provided which illustrates the natural foundation context and its related optimization conceptual framework. The main operations of ChOA are procedurally discussed, and the theoretical foundation is described. Furthermore, the recent versions of ChOA are discussed in detail which are categorized into modified, hybridized, and paralleled versions. The main applications of ChOA are also thoroughly described. The applications belong to the domains of economics, image processing, engineering, neural network, power and energy, networks, etc. Evaluation of ChOA is also provided. The review paper will be helpful for the researchers and practitioners of ChOA belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining, and clustering. As well, it is wealthy in research on health, environment, and public safety. Also, it will aid those who are interested by providing them with potential future research.
      25Scopus© Citations 6