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Benkrama, Soumia
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Ensemble learning weighted average meta-classifier for palm diseases identification Abden, Sofiane; Bendjima, Mostefa; Benkrama, Soumia
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp303-311

Abstract

Crop diseases lead to significant losses for farmers and threaten the global food supply. The date palm, valued for its nutritional benefits and drought resistance in desert climates, is a vital export crop for many countries in the Middle East and North Africa, second only to hydrocarbons. However, various diseases pose a threat to this important plant. Therefore, early disease prediction using deep learning (DL) is essential to prevent the deterioration of date palm crops. The aim of this paper is to apply a robust ensemble method (EL) combining tree transfer learning (TL) models Resnet50, DenseNet201, and InceptionV3, and compares its performance with the CNN-SVM model and the tree TL models mentioned previously. The models were applied to a date palm dataset containing three classes: White scale, brown spot, and healthy leaf. The training and validation sets were applied to a public dataset, while the testing set was applied to a local dataset captured manually to check the model’s performance. As a result, we considered that the ensemble method gave very satisfactory results compared to other methods. Our hybrid model reached a testing accuracy of 98% while achieving an amazing training and validation accuracy of 99.94% and 98.14%, respectively.
A comparative study of CNN architectures for the detection of tomato leaf diseases Benkrama, Soumia; Ahmed, Benyamina; Hemdani, Nour El Houda
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1587-1594

Abstract

Recent advancements in computer vision and machine learning (ML) have revolutionised various sectors, including precision agriculture (PA). In our study, we focused on detecting tomato leaf diseases (TLD) using deep learning (DL) techniques. Using a convolutional neural network (CNN) model, we developed an agricultural image index to accurately detect TLD. By utilizing available datasets from Kaggle, we trained our model to recognize various TLDs. To determine the most effective one, we compared multiple architectures, including VGG, ResNet, and EfficientNetB1. The obtained results demonstrated a classification accuracy of over 99% on the test set. This approach has allowed us to accelerate and enhance the disease detection process, positively impacting agricultural communities by reducing crop losses and enabling early intervention in case of disease outbreaks. Our study highlights the effectiveness of CNN models in the detection of TLD, paving the way for future applications in PA.
Early detection of food safety risks using BERT and large language models Gasbaoui, Mohammed El Amin; Benkrama, Soumia; Bendjima, Mostefa
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1683-1692

Abstract

Sentiment analysis can be a powerful tool in safeguarding public health. This allows authorities to investigate and take action before a foodborne illness outbreak spreads. This paper introduces a novel system that proactively empowers restaurants to identify potential food safety hazards and hygiene regulation violations. The system leverages the power of natural language processing (NLP) to analyze Arabic restaurant reviews left by customers. By fine-tuning a pre-trained BERT mini-Arabic model on three targeted datasets: Sentiment Twitter Corpus, an Algerian dialect dataset, and an Arabic restaurant dataset, the system achieves an impressive accuracy of 91%. Additionally, the system caters to spoken feedback by accepting audio reviews. We utilized Whisper AI for accurate text transcription, followed by classification using a fine-tuned Gemini model from Google on Algerian local comments and others generated using large language models (LLMs) through few-shot learning techniques, reaching an accuracy of 93%. Notably, both models operate independently and concurrently. Leveraging RESTful APIs, the system integrates the solved sub-solutions from each microservice into a fusion layer for a comprehensive restaurant evaluation. This multifaceted approach delivers remarkable results for both modern standard Arabic (MSA) and the Algerian dialect, demonstrating its effectiveness in addressing restaurant food safety concerns.
Co-Authors Abden, Sofiane Ahmed, Benyamina Bendjima, Mostefa Gasbaoui, Mohammed El Amin Hemdani, Nour El Houda