Abu Zitar, Raed

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Name
Abu Zitar, Raed
Main Affiliation
Email
raed.zitar@sorbonne.ae
ORCID
0000-0003-2693-2132
Scopus Author ID
8858522100

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  • Publication
    A Modified Oppositional Chaotic Local Search Strategy Based Aquila Optimizer to Design an Effective Controller for Vehicle Cruise Control System
    (2023)
    Ekinci, Serdar
    ;
    Izci, Davut
    ;
    Abualigah, Laith
    ;
    Abu Zitar, Raed 
    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.
      16
  • Publication
    A non-convex economic load dispatch problem using chameleon swarm algorithm with roulette wheel and Levy flight methods
    (2023)
    Abu Zitar, Raed 
    ;
    Braik, Malik Sh.
    ;
    Awadallah, Mohammed A.
    ;
    Al-Betar, Mohammed Azmi
    ;
    Hammouri, Abdelaziz I.
    An Enhanced Chameleon Swarm Algorithm (ECSA) by integrating roulette wheel selection and Lévy flight methods is presented to solve non-convex Economic Load Dispatch (ELD) problems. CSA has diverse strategies to move towards the optimal solution. Even so, this algorithm’s performance faces some hurdles, such as early convergence and slumping into local optimum. In this paper, several enhancements were made to this algorithm. First, it’s position updating process was slightly tweaked and took advantage of the chameleons’ randomization as well as adopting several time-varying functions. Second, the Lévy flight operator is integrated with roulette wheel selection method and both are combined with ECSA to augment the exploration behavior and lessen its bias towards exploitation. Finally, an add-on position updating strategy is proposed to develop a further balance between exploration and exploitation conducts. The optimization performance of ECSA is shown by testing it on five various real ELD cases with a generator having 3, 13, 40, 80 and 140 units, each with different constraints. The results of the ELD systems’ analysis depict that ECSA is better than the parent CSA and other state-of-the art methods. Further, the efficacy of ECSA was experimented on several benchmark test functions, and its performance was compared to other well-known optimization methods. Experimental results show that ECSA surpasses other methods on complex benchmark functions with modest computational burdens. The superiority and practicality of ECSA is demonstrated by getting new best solutions for large-scale ELD cases such as 40-unit and 140-unit test systems.
      20
  • Publication
    Correction to: Multiclass feature selection with metaheuristic optimization algorithms: a review
    (2023)
    Akinola, Olatunji O.
    ;
    Ezugwu, Absalom E.
    ;
    Agushaka, Jeffrey O.
    ;
    Abu Zitar, Raed 
    ;
    Abualigah, Laith
      2
  • Publication
    Diabetes Monitoring System in Smart Health Cities Based on Big Data Intelligence
    (2023)
    AlZu’bi, Shadi
    ;
    Elbes, Mohammad
    ;
    Mughaid, Ala
    ;
    Bdair, Noor
    ;
    Abualigah, Laith
    ;
    Forestiero, Agostino
    ;
    Abu Zitar, Raed 
    Diabetes is a metabolic disorder in which the body is unable to properly regulate blood sugar levels. It can occur when the body does not produce enough insulin or when cells become resistant to insulin’s effects. There are two main types of diabetes, Type 1 and Type 2, which have different causes and risk factors. Early detection of diabetes allows for early intervention and management of the condition. This can help prevent or delay the development of serious complications associated with diabetes. Early diagnosis also allows for individuals to make lifestyle changes to prevent the progression of the disease. Healthcare systems play a vital role in the management and treatment of diabetes. They provide access to diabetes education, regular check-ups, and necessary medications for individuals with diabetes. They also provide monitoring and management of diabetes-related complications, such as heart disease, kidney failure, and neuropathy. Through early detection, prevention and management programs, healthcare systems can help improve the quality of life and outcomes for people with diabetes. Current initiatives in healthcare systems for diabetes may fail due to lack of access to education and resources for individuals with diabetes. There may also be inadequate follow-up and monitoring for those who have been diagnosed, leading to poor management of the disease and lack of prevention of complications. Additionally, current initiatives may not be tailored to specific cultural or demographic groups, resulting in a lack of effectiveness for certain populations. In this study, we developed a diabetes prediction system using a healthcare framework. The system employs various machine learning methods, such as K-nearest neighbors, decision tree, deep learning, SVM, random forest, AdaBoost and logistic regression. The performance of the system was evaluated using the PIMA Indians Diabetes dataset and achieved a training accuracy of 82% and validation accuracy of 80%.
      5
  • Publication
    Hybrid model of alternating least squares and root polynomial technique for color correction
    (2023)
    Babbar, Geetanjali
    ;
    Bajaj, Rohit
    ;
    Mittal, Nitin
    ;
    Mahajan, Shubham
    ;
    Abu Zitar, Raed 
    ;
    Abualigah, Laith
    Color correction is an image-altering technique that modifies image color in such a way that it matches a reference image. Many approaches have already been proposed by various researchers; however, those models have been unable to reduce color errors between two images, which results in inefficiency and poor-quality images. This research paper presents an effective and improved color correction model wherein alternate least square (ALS) and root polynomial (RP) are used together. The main objective of the proposed model is to reduce the error between a reference image and a target image to enhance the image quality and make them look realistic. To achieve this objective, the proposed model used the Amsterdam library of object images which contains a picture of single objects captured under various illumination angles and colors. The main contribution of this paper is a hybrid ALS + RP color correction technique, implemented on the dataset image that fixes its color as per the reference image and enhances its quality. The target image is then converted into three color models, i.e., LAB, LUV, and RGB into XYZ format. Finally, the color difference between a reference image and a target image is observed by calculating values for parameters like Mean, median, 95% quantile, and maximum error. The effectiveness of the suggested hybrid color correction approach is assessed and validated in MATLAB software for each color model. Through extensive experiments, it is observed that the proposed hybrid model yields the least errors for the RGB color model. This is followed up by LUV and then LAB, to prove its supremacy over other models.
      13
  • Publication
    Improving clinical documentation: automatic inference of ICD-10 codes from patient notes using BERT model
    (2023)
    Abu Zitar, Raed 
    ;
    Al-Bashabsheh, Emran
    ;
    Alaiad, Ahmad
    ;
    Al-Ayyoub, Mahmoud
    ;
    Beni-Yonis, Othman
    ;
    Abualigah, Laith
    Electronic health records provide a vast amount of text health data written by physicians as patient clinical notes. The world health organization released the international classification of diseases version 10 (ICD-10) system to monitor and analyze clinical notes. ICD-10 is system physicians and other healthcare providers use to classify and code all diagnoses and symptom records in conjunction with hospital care. Therefore, the data can be easily stored, retrieved, and analyzed for decision-making. In order to address the problem, this paper introduces a system to classify the clinical notes to ICD-10 codes. This paper examines 7541 clinical notes collected from a health institute in Jordan and annotated by ICD-10’s coders. In addition, the research uses another outsource dataset to augment the actual dataset. The research presented many approaches, such as the baseline and pipeline models. The Baseline model employed several methods like Word2vec embedding for representing the text. The model structure also involves long-short-term memory a convolutional neural network, and two fully-connected layers. The second Pipeline approach adopts the transformer model, such as Bidirectional Encoder Representations from Transformers (BERT), which is pre-trained on a similar health domain. The Pipeline model builds on two BERT models. The first model classifies the category codes representing the first three characters of ICD-10. The second BERT model uses the outputs from the general BERT model (first model) as input for the special BERT (second model) to classify the clinical notes into total codes of ICD-10. Moreover, Baseline and Pipeline models applied the Focal loss function to eliminate the imbalanced classes. However, The Pipeline model demonstrates a significant performance by evaluating it over the F1 score, recall, precision, and accuracy metric, which are 92.5%, 84.9%, 91.8%, and 84.97%, respectively.
      10
  • 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.
      16
  • Publication
    Modified arithmetic optimization algorithm for drones measurements and tracks assignment problem
    (2023)
    Abu Zitar, Raed 
    ;
    Abualigah, Laith
    ;
    Barbaresco, Frederic
    ;
    Seghrouchni, Amal El Fallah
    This paper presents efforts to solve the multi-track measurement assignment problem in drone detection and tracking. In many cases, several radars are collectively used to track drones efficiently, generating measurements and several tracks under different circumstances. In this work, several measurements are simulated during a time frame accompanied by the generation of several tracks using the Linear Kalman Filter. The focus is on finding an optimum measurements/track assignment for the simulated measurements and track values. The measurements and track generation are simulated using Stone Soup software. On the other hand, the optimization of the problem is implemented using several evolutionary-based metaheuristic algorithms. This optimization problem is known to be computationally explosive, especially if long time frames are considered. In particular, a new modified method based on the Arithmetic Optimization Algorithm is proposed. The optimization is applied to a formulated cost function that considers uncertainty, false alarms, and existing clutters. Simulations and comparisons show the ability of those evolutionary-based algorithms to solve this kind of problem efficiently. The proposed method obtained promising results compared to other comparative methods used to solve this drone’s measurements and track assignment problem.
      16
  • Publication
    Solar power forecasting beneath diverse weather conditions using GD and LM-artificial neural networks
    (2023)
    Sharma, Neetan
    ;
    Puri, Vinod
    ;
    Mahajan, Shubham
    ;
    Abualigah, Laith
    ;
    Abu Zitar, Raed 
    ;
    Gandomi, Amir H.
    Large-scale solar energy production is still a great deal of obstruction due to the unpredictability of solar power. The intermittent, chaotic, and random quality of solar energy supply has to be dealt with by some comprehensive solar forecasting technologies. Despite forecasting for the long-term, it becomes much more essential to predict short-term forecasts in minutes or even seconds prior. Because key factors such as sudden movement of the clouds, instantaneous deviation of temperature in ambiance, the increased proportion of relative humidity and uncertainty in the wind velocities, haziness, and rains cause the undesired up and down ramping rates, thereby affecting the solar power generation to a greater extent. This paper aims to acknowledge the extended stellar forecasting algorithm using artificial neural network common sensical aspect. Three layered systems have been suggested, consisting of an input layer, hidden layer, and output layer feed-forward in conjunction with back propagation. A prior 5-min te output forecast fed to the input layer to reduce the error has been introduced to have a more precise forecast. Weather remains the most vital input for the ANN type of modeling. The forecasting errors might enhance considerably, thereby affecting the solar power supply relatively due to the variations in the solar irradiations and temperature on any forecasting day. Prior approximation of stellar radiations exhibits a small amount of qualm depending upon climatic conditions such as temperature, shading conditions, soiling effects, relative humidity, etc. All these environmental factors incorporate uncertainty regarding the prediction of the output parameter. In such a case, the approximation of PV output could be much more suitable than direct solar radiation. This paper uses Gradient Descent (GD) and Levenberg Maquarndt Artificial Neural Network (LM-ANN) techniques to apply to data obtained and recorded milliseconds from a 100 W solar panel. The essential purpose of this paper is to establish a time perspective with the greatest deal for the output forecast of small solar power utilities. It has been observed that 5 ms to 12 h time perspective gives the best short- to medium-term prediction for April. A case study has been done in the Peer Panjal region. The data collected for four months with various parameters have been applied randomly as input data using GD and LM type of artificial neural network compared to actual solar energy data. The proposed ANN based algorithm has been used for unswerving petite term forecasting. The model output has been presented in root mean square error and mean absolute percentage error. The results exhibit a improved concurrence between the forecasted and real models. The forecasting of solar energy and load variations assists in fulfilling the cost-effective aspects.
      4