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  • Publication
    Markisa/Passion Fruit Image Classification Based Improved Deep Learning Approach Using Transfer Learning
    (2023) ;
    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).
      8
  • Publication
    Salak Image Classification Method Based Deep Learning Technique Using Two Transfer Learning Models
    (2023) ;
    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%.
      5
  • Publication
    Arabic Text Classification Using Modified Artificial Bee Colony Algorithm for Sentiment Analysis: The Case of Jordanian Dialect
    (2023) ;
    Habeeb, Abdallah
    ;
    Otair, Mohammed A
    ;
    Abualigah, Laith
    ;
    Alsoud, Anas Ratib
    ;
    Elminaam, Diaa Salama Abd
    ;
    Ezugwu, Absalom E
    ;
    Jia, Heming
    Arab customers give their comments and opinions daily, and it increases dramatically through online reviews of products or services from companies, in both Arabic, and its dialects. This text describes the user’s condition or needs for satisfaction or dissatisfaction, and this evaluation is either negative or positive polarity. Based on the need to work on Arabic text sentiment analysis problem, the case of the Jordanian dialect. The main purpose of this paper is to classify text into two classes: negative or positive which may help the business to maintain a report about service or product. The first phase has tools used in natural language processing; the stemming, stop word removal, and tokenization to filtering the text. The second phase, modified the Artificial Bee Colony (ABC) Algorithm, with Upper Confidence Bound (UCB) Algorithm, to promote the exploitation ability for the minimum dimension, to get the minimum number of the optimal feature, then using forward feature selection strategy by four classifiers of machine learning algorithms: (K-Nearest Neighbors (KNN), Support vector machines (SVM), Naïve-Bayes (NB), and Polynomial Neural Networks (PNN). This proposed model has been applied to the Jordanian dialect database, which contains comments from Jordanian telecom company’s customers. Based on the results of sentiment analysis few suggestions can be provided to the products or services to discontinue or drop, or upgrades it. Moreover, the proposed model is applied to the database of the Algerian dialect, which contains long Arabic texts, in order to see the efficiency of the proposed model for short and long texts. Four performance evaluation criteria were used: precision, recall, f1-score, and accuracy. For a future step, in order to build on or use for the classification of Arabic dialects, the experimental results show that the proposed model gives height accuracy up to 99% by applying to the Jordanian dialect, and a 82% by applying to the Algerian dialect.
  • Publication
    Comparative Study on Arabic Text Classification: Challenges and Opportunities
    (2023) ;
    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.
      1
  • Publication
    Comparative Study on Arabic Text Classification: Challenges and Opportunities
    (2023) ;
    Melhem, Mohammed K. Bani
    ;
    Abualigah, Laith
    ;
    Hussien, Abdelazim G.
    ;
    Oliva, Diego
      2