Communications Internationales

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/11

Browse

Start date: 2000Subject: search.filters.subject.Deep learning

Search Results

Now showing 1 - 10 of 10
  • No Thumbnail Available
    Item
    Transformer-Based Approach for Intrusion Detection System
    (Institute of Electrical and Electronics, 2025) Senoussi, Nour El Houda; Salmi, Cheikh; Banouh, Nassim; Khalfi, Adem
    Intrusion Detection Systems (IDS) have been used for years to protect enterprise hosts from cyberattacks. Traditional IDSs are usually based on simple methods, such as signatures or heuristics, that do not adapt to reactions against new threats that are constantly increasing. The objective of this paper is to develop an IDS based on a deep learning technique which is transformers. Unlike conventional models and thanks to their self-attention mechanism, transformers are characterized by an excellent ability to support complex patterns by very accurately modeling the context in sequential data. A host-based dataset containing system logs and network activities is used to train the transformer model that forms the core of the developed IDS. A detailed evaluation is used to compare our approach against existing methods based on machine learning and deep learning, showing significant improvements in precision, recall, and false positive rate. These results are very encouraging for developing robust IDSs that can be fine-tuned in real time to take into account new attacks
  • Thumbnail Image
    Item
    Deep Learning Models to Analyze Sentiments of People Regarding New Vaccines
    (Institute of Electrical and Electronics Engineers, 2025) Khoudi, Asmaa; Draoui, Yasmine; Aoutou, Nadjet
    The COVID-19 pandemic has generated a vast corpus of online conversations regarding vaccines, predominantly on social media platforms like X (formerly known as Twitter). However, analyzing sentiment in Arabic text is challenging due to the diverse dialects and lack of readily available sentiment analysis resources for the Arabic language. This paper proposes an explainable Deep Learning (DL) approach designed for sentiment analysis of Arabic tweets related to COVID-19 vaccinations. The proposed approach utilizes a Bidirectional Long Short-Term Memory (BiLSTM) network with Multi-Self-Attention (MSA) mechanism for capturing contextual impacts over long spans within the tweets, while having the sequential nature of Arabic text constructively learned by the BiLSTM model. Moreover, the XLNet embeddings are utilized to feed contextual information into the model. Subsequently, two essential Explainable Artificial Intelligence (XAI) methods, namely Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), have been employed for gaining further insights into the features’ contributions to the overall model performance and accordingly achieving reasonable interpretation of the model’s output. Obtained experimental results indicate that the combined XLNet with BiLSTM model outperforms other implemented state-of-the-art methods, achieving an accuracy of 93.2% and an F-measure of 92% for average sentiment classification. The integration of LIME and SHAP techniques not only enhanced the model’s interpretability, but also provided detailed insights into the factors that influence the classification of emotions. These findings underscore the model’s effectiveness and reliability for sentiment analysis in low-resource languages such as Arabic
  • No Thumbnail Available
    Item
    Application of Deep Transfer Learning in Medical Imaging for Thyroid Lesion Diagnostic Assistance
    (Institute of Electrical and Electronics Engineers, 2024) Chaouchi, Lynda; Gaceb, Djamel; Touazi, Fayçal; Djani, Djouher; Yakoub, Assia
    This academic work evaluates and compares the performance of various deep convolutional neural network (DCNN) architectures in classifying thyroid nodules into two categories, malignant and benign, using ultrasound images. The dataset comprises 269 cases of benign lesions and 526 cases of malignant lesions. Given the limited dataset size, we employ a progressive learning approach with three established CNN models: VGG-16, ResNet-50, and EfficientNet. Initially pretrained on ImageNet, these models undergo further fine-tuning using a radiographic image dataset related to a different medical condition but similar to our domain. Different levels and fine-tuning strategies are applied to these models. A supervised softmax classifier is used for classifying lesions as malignant or benign, with the exception of the VGG-16 model. For the VGG-16 model, two additional classifiers, Support Vector Machine (SVM) and Random Forest (RF), are evaluated. The results obtained demonstrate the possibility of easily transitioning from the classification of one disease to another, even with a limited number of images, by leveraging the knowledge already acquired from another extensive database.
  • No Thumbnail Available
    Item
    Detect misinformation of COVID-19 using deep learning : a comparative study based on word embedding
    (IEEE, 2023) Khoudi, Asmaa; Yahiaoui, Nessrine; Rebahi, Feriel
    Since its emergence in December 2019, there have been numerous news of COVID-19 pandemic shared on social media, which contain information from both reliable and unreliable medical sources. News and misleading information spread quickly on social media, which can lead to anxiety, unwanted exposure to medical remedies, etc. Rapid detection of fake news can reduce their spread. In this paper, we aim to create an intelligent system to detect misleading information about COVID-19 using deep learning techniques based on LSTM and BLSTM architectures. Data used to construct the DL models are text type and need to be transformed to numbers. We test, in this paper the efficiency of three vectorization techniques: Bag of words, Word2Vec and Bert. The experimental study showed that the best performance was given by LSTM model with BERT by achieving an accuracy of 91% of the test set
  • No Thumbnail Available
    Item
    Aerial forest smoke’s fire detection using enhanced YOLOv5
    (Springer, 2023) Cherifi, Dalila; Bekkour, Belkacem; Benmalek, Assala; Bayou, Meroua; Mechti, Ines; Bekkouche, Abdelghani; Amine, Chaima; Halak, Ahmed
    Forest fires around the world are the main cause of devastating millions of forest hectares, destroying several infrastructures and unfortunately causing many human casualties among both fire fighting crews and civilians that might be accidentally surrounded by the fire. The early detection of more than 58,950 forest fires and the real-time fire perception are two key factors that allow the firefighting crews to act accordingly in order to prevent the fire from achieving unmanageable proportions [1]. Forest fire detection is such a challenging problem for the current world. Traditional methodologies depend on a set of expensive hardware and sensors that might be not accurate due to some environment parameters and weather fluctuations. This paper proposes an accurate intelligent deep learning-based YOLOv5 model to detect forest fires from a given aerial images
  • No Thumbnail Available
    Item
    Study and implementation of u-net encoder-decoder neural network for brain tumors segmentation
    (Springer, 2023) Cherifi, Dalila; Bekkouche, Abdelghani; Bayou, Meroua; Benmalek, Assala; Mechti, Ines; Bekkour, Belkacem; Amine, Chaima; Ahmed, Halak
    Emerging advanced technologies have seen a revolution of applications into medical field, in all its aspects and sides, this has helped healthcare practitioners and empowered them in achieving accurate diagnosis and treatment, specifically with the evolution of computer Aided Diagnosis systems which use image processing techniques, Computer vision,and deep learning applied on different medical images in order to diagnose the image, or sections of the image with particular diseases or illnesses. Medical images of multiples organs or parts of the body (Liver, brain, kidney, skin, etc..) can today be visualized thanks to the advanced medical imaging techniques that exists in the market (MRI, CT, etc…) these technologies uses high energy in order to acquire high quality images but high energy can harm human cells, this is why we us low energy and with this used we get slightly low quality medical images, and here technology intervenes where we can use preprocessing techniques in order to increase image resolution prior to perform diagnosis either by doctor or CAD system. We present in this paper a computer aided diagnosis system that provides an automated brain tissue segmentation applied on 3D MRI images with its four different modalities (T1, T1C, T2, T2 weighted) of BRatS 2020 challenge dataset, by implementing a U-Net like deep neural network which provides information about classification of brain tissue into healthy tissue, Edema, Enhancing tumour, Non enhancing tumour. The model achieved an accuracy of 99.01% and dice coefficient of 47.95% after 35 epochs of training
  • No Thumbnail Available
    Item
    Arabic speech recognition using deep learning and common voice dataset
    (IEEE, 2022) Oukas, Nourredine; Zerrouki, Taha; Haboussi, Samia; Djettou, Halima
    Speech recognition is critical in creating a natural voice interface for human-to-human communication with modern digital life equipment. Smart homes, vehicles, autonomous devices in the Internet of Things, and others need to recognize various spoken languages. Meanwhile, the Arabic language has a shortage of speech recognition systems. This study comes to develop an Arabic speech-to-text tool for Arabic language. Our solution uses DeepSpeech model which is a deep learning approach and uses a data set from the Common Voice Mozilla project. The results showed a 24.3 percent Word Error Rate and a 17.6 percent character error rate. So, the proposed model reduces the Word Error Rate by 11.7% compared to Bakheet's Wav2Vec model
  • No Thumbnail Available
    Item
    Super-resolution of document images using transfer deep learning of an ESRGAN model
    (IEEE, 2022) Kezzoula, Zakia; Gaceb, Djamel; Gritli, Nadjat
    This paper presents a novel super-resolution approach of document images. It is based on transfer deep learning of an ESRGAN model. This model, which showed good robustness on natural images, has been adapted to document images by using better levels of fine-tuning and a post-processing to enhance contrast. The experiments were carried out on our document image dataset that we built from document images presenting different challenges. Documents of different categories with different complexity levels and degradation kinds. The results obtained are better compared to ten existing approaches, which we have developed and tested on the same dataset with the same evaluation protocol
  • No Thumbnail Available
    Item
    Prediction of shear wave velocity in the williston basin using big data analysis and robust machine learning algorithms
    (2022) Laalam, A.; Mouedden, N.; Ouadi, H.; Chemmakh, A.; Merzoug, A.; Boualam, A.; Djezzar, S.; Aihar, A.; Berrehal, B. E.
    The shear velocity is one of the most critical parameters in determining the mechanical rock elastic properties, which serve as inputs for different studies such as wellbore stability, mechanical earth modeling, hydraulic fracturing, and reservoir characterization. However, the sonic log is not acquired in every drilled well. We analyzed the log data of more than 35000 wells in the Williston Basin, and we found that only very few wells had sonic logs. For this reason, several studies attempted to correlate the shear velocity (or slowness) to other easily accessible properties; these will be presented in the literature review, with their pros and cons. The focus of this paper is to apply machine learning algorithms to synthesize the shear slowness log. Our models are trained and tested with log data from 27 wells drilled in the Bakken petroleum system, Williston Basin. Logging data include Gamma Ray, Deep Resistivity, Density, Neutron Porosity, and Shear Slowness. Five different algorithms were developed and tested against blind data including Xtreme Gradient Booster, Random Forest Regressor, Linear Regression, Ada Boost Regression, and Bayesian Ridge Regression. Overall, the R2-score varied from 0.55 to 0.92, with the XGBoost outperforming the other algorithms
  • No Thumbnail Available
    Item
    A review of recent progress in deep learning-based methods for MRI brain tumor segmentation
    (Institute of Electrical and Electronics Engineers Inc, 2020) Chihati, S.; Gaceb, Djamel
    Brain tumor segmentation is a challenging task that involves delimiting cancerous tissues with heterogeneous and diffuse forms in brain medical images. This process is undoubtedly an important step in computer-aided diagnosis systems, in which tumor regions must be isolated for visualization and subsequent analysis. Recently, great progress has been made in brain tumor segmentation with the emergence of deep learning-based methods, which automatically learn hierarchical, and discriminative features from raw data. These methods outperformed the classical machine learning approaches where handcrafted features are used to describe the differences between pathological and healthy tissues. In this paper, we present a comprehensive overview of recent progress in deep learning-based methods for brain tumor segmentation from magnetic resonance images. Moreover, we discuss the most common challenges and suggest possible solutions