Browsing by Author "Bayou, Meroua"

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    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
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    Convolution neural network deployment for plant leaf diseases detection
    (Springer, 2023) Cherifi, Dalila; Bayou, Meroua; Benmalek, Assala; Mechti, Ines; Bekkouche, Abdelghani; Bekkour, Belkacem; Amine, Chaima; Ahmed, Halak
    The automated identification of plant diseases based on plant leaves is a huge breakthrough. Furthermore, early and accurate detection of plant diseases positively impacts crop productivity and quality. However, managing the accessibility of early plant disease detection is crucial. This work has environmental goals aiming to save plants from different threatening diseases by providing early detection of the affected leaves. We studied the performance of different Convolutional Neural Network (CNN) architectures in predicting 26 diseases for 14 plant species. The work studied the complexity of the system and compared the two main deep learning frameworks, TensorFlow and PyTorch, to get the most accurate results with higher accuracy. Using the “New PlantVillage Dataset” from Kaggle [1], the TensorFlow models achieved an accuracy of 90,94% for the basic CCN architecture, and 95,59% for the Transfer Learning architecture with VGG19. Whereas the PyTorch models achieved an accuracy of 93,47% for the basic CCN architecture, and 98,53% for the Transfer Learning architecture with ResNet34. Finally, after examining the feasibility of the model’s implementation and discussing the main problems that may be encountered, the models were deployed in a mobile application using the Tflite and torch mobile flutter SDK to let them as an internal feature in the mobile without the need for any access to the cloud, which is known as edge AI
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    Gas turbine trip prediction with time-Series data using RNN and LSTM.
    (Université M’hamed Bougara de Boumerdes : Institut de Genie Electrique et Electronique, 2024) Bayou, Meroua; Cherifi, Dalila (Supervisor)
    Gas turbine (GT) trip is one of the most disruptive occurrences that influenc eG Toperation, as it reduces the remaining useful life of the equipment and results in revenue loss due to business interruption. Thus, early diagnosis of early GT trip symptoms is critical for ensuring effective operation and lowering operating and maintenance expenses. In this work, we implement two neural network methods, RNN and LSTM, for gas turbine trip prediction using a time series sensors readings dataset and compare their performances in accurately predicting gas turbine trips within 60 seconds of their occurrence, allowing operators to take timely and effective actions to prevent trips and ensure the reliability and efficienc yo fpowe rgeneration systems. The objective of this work is to defin eth ebes tperformin gmode lfo rthi ssensitiv etas kand reach the highest possible accuracy and precision by implementing different architectures and exploring variations of hyperparameters such as the number of features, validation split, and the input sequence length. Our experimental results show that both RNN and LSTM are effective in achieving the goal of predicting gas turbine trips prior to their occurrence. The best-performing model is the bidirectional LSTM with multiple features input, a sequence length of 50, and 10% validation split, where we reached a test accuracy of 96.47%, precision 97.14%, recall 94.44%, F1 score 95.77%, and ROC-AUC of 0.96.
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    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