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
Status
Currently affiliated

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  • Publication
    Comparative Study on Arabic Text Classification: Challenges and Opportunities
    (2023)
    Abu Zitar, Raed 
    ;
    Abualigah, Laith
    ;
    Oliva, Diego
    ;
    Hussien, Abdelazim G.
    ;
    Melhem, Mohammed K. Bani
    There have been great improvements in web technology over the past years which heavily loaded the Internet with various digital contents of different fields. This made finding certain text classification algorithms that fit a specific language or a set of languages a difficult task for researchers. Text Classification or categorization is the practice of allocating a given text document to one or more predefined labels or categories, it aims to obtain valuable information from unstructured text documents. This paper presents a comparative study based on a list of chosen published papers that focus on improving Arabic text classifications, to highlight the given models and the used classifiers besides discussing the faced challenges in these types of researches, then this paper proposes the expected research opportunities in the field of text classification research. Based on the reviewed researches, SVM and Naive Bayes were the most widely used classifiers for Arabic text classification, while more effort is needed to develop and to implement flexible Arabic text classification methods and classifiers.
      13
  • Publication
    Markisa/Passion Fruit Image Classification Based Improved Deep Learning Approach Using Transfer Learning
    (2023)
    Abu Zitar, Raed 
    ;
    Abdo, Ahmed
    ;
    Hong, Chin Jun
    ;
    Kuan, Lee Meng
    ;
    Pauzi, Maisarah Mohamed
    ;
    Sumari, Putra
    ;
    Abualigah, Laith
    Fruit recognition becomes more and more important in the agricultural industry. Traditionally, we need to manually identify and label all the fruits in the production line, which is labor intensive, error-prone, and ineffective. Therefore, a lot of fruit recognition systems are created to automate the process, but fruit recognition system for Malaysia local fruit is limited. Thus, this project will focus on classifying one of the Malaysia local fruits which is markisa/passion fruit. We proposed two CNN models for markisa classification. The performances of the proposed models are evaluated on our own dataset collection and produces an accuracy of 97% and 65% respectively. The results indicated that the architecture of CNN model is very important because different architecture can produce different results. Therefore, first CNN model is selected because it can classify 4 types of markisa with a higher accuracy. In the proposed work, we also inspected two transfer learning methods in the classification of markisa which are VGG-16 and InceptionV3. The results showed that the performance of the first proposed CNN model outperforms VGG-16 (95% accuracy) and InceptionV3 (65% accuracy).
      19
  • Publication
    Salak Image Classification Method Based Deep Learning Technique Using Two Transfer Learning Models
    (2023)
    Abu Zitar, Raed 
    ;
    Theng, Lau Wei
    ;
    San, Moo Mei
    ;
    Cheng, Ong Zhi
    ;
    Shen, Wong Wei
    ;
    Sumari, Putra
    ;
    Abualigah, Laith
    ;
    Izci, Davut
    ;
    Jamei, Mehdi
    ;
    Al-Zu’bi, Shadi
    Salak is one of the fruits plants in Southeast Asia; there are at least 30 cultivars of salak. The size, shape, skin color, sweetness or even flesh color will be different depending on the cultivar. Thus, classification of salak based on their cultivar become a daily job for the fruit farmers. There are many techniques that can be used for fruit classification using computer vision technology. Deep learning is the most promising algorithm compared to another Machine Learning (ML) algorithm. This paper presents an image classification method on 4 types of salak (salak pondoh, salak gading, salak sideempuan and salak affinis) using a Convolutional Neural Network (CNN), VGG16 and ResNet50. The dataset consists of 1000 images which having 250 of images for each type of salak. Pre-processing on the dataset is required to standardize the dataset by resizing the image into 224 * 224 pixels, convert into jpg format and augmentation. Based on the accuracy result from the model, the best model for the salak classification is ResNet50 which gave an accuracy of 84% followed by VGG16 that gave an accuracy of 77% and CNN which gave 31%.
      39