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2023 IEEE International Conference on Artificial Intelligence and Green Energy, ICAIGE 2023

作者： Zayene, Mohamed Amine Basly, Hend Sayadi, Fatma Ezahra National School of Engineering School of Sousse Lab. Networked Objects Control and Communication Systems Sousse Tunisia

ISBN: (纸本)9798350325539

Neurodegenerative disease especially dementia are reported as disease that leads to death, Alzheimer's Disease (AD) is kind dementia that cause progressive and irreversible brain disorder loss which leads to death. AD shows no symptoms in its early stages which makes diagnosing it its beginning a challenge and helpful for doctors as they can slow down its progress in its early stages. Computer-aided approaches such as machine learning which come up with several techniques to detect AD by extracting features from the given image data and use them to build a classifier. Recently, a subcategory of machine learning called deep learning has widely been employed to enhance the medical diagnosis by attempting notable performance. In fact, these approaches avoid the tricky manual feature extraction using Convolutional Neural Network (CNN) considered as a reference in the field of computer vision. This paper proposes a combination of machine-deep learning technics for early diagnosis of AD from positron emission tomography (PET). We first train our CNN on PET images to extract the most relevant features, then we select the most appropriate CNN's level from where the features will be extracted, which will be feed in a second step as input to a Support Vector Machine based classifier (SVM). The proposed approach achieves notable results that exceeded the performance obtained by various existing approaches. © 2023 IEEE.

关键词： Positron emission tomography

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Embracing the Future: The Evolution of Flexible Reconfigurable Manufacturing systems

Embracing the Future: The Evolution of Flexible Reconfigurab...

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systems, Advanced Technologies and Knowledge (ISSATK), International Symposium of

作者： Salah Hammedi Jalloul Elmeliani Lotfi Nabli National School of Engineers of Monastir Networked Objects Control and Communication Systems (NOCCS) ENISo University of Sousse Tunisia National Engineering School of Bizerte Networked Objects Control and Communication Systems (NOCCS) ENISo University of Sousse Tunisia

ISBN: (数字)9798331540173

ISBN: (纸本)9798331540180

Flexible Reconfigurable Manufacturing systems (FRMS) are a radical approach to modern manufacturing, focusing on adaptability and efficiency to respond to dynamic market demands. The fundamental principles and advantages of FRMS are discussed in this article. These systems offer a versatile solution to traditional, inflexible manufacturing setups thanks to their modularity, automation, reconfigurability and data-driven decision-making. The key benefits include the ability to adapt to changing production requirements, cost efficiency, improved product quality, and resource optimization. FRMS are found in various industries, such as automotive, electronics, aerospace, and pharmaceuticals, and they meet the needs of customization, precision, and rapid production shifts. FRMS’s future looks promising, as emerging trends like AI integration, sustainability, customization, and interconnectivity are poised to further enhance their capabilities. Manufacturers can excel in an ever-evolving industrial landscape thanks to FRMS’s position in meeting the challenges of an increasingly dynamic global market as they continue to evolve.

关键词： Adaptation models Scalability Decision making Market research Aerodynamics Software systems Security Vehicle dynamics Sustainable development Manufacturing systems

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Automatic Face Expression Neutralisation and Posture Correction via 3D Contrastive Learning 27

Automatic Face Expression Neutralisation and Posture Correct...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Wiem, Grina Ali, Douik University of Sousse Networked Objects Control and Communication Systems Laboratory National School of Engineers of Sousse Sousse Technology Center University of Monastir National Engineering School of Monastir Sahloul Belt Road 4054 Rue Ibn Jazzar Monastir5035 Tunisia University of Sousse Networked Objects Control and Communication Systems Laboratory National School of Engineers of Sousse Sousse Technology Center Sahloul Belt Road 4054 Tunisia

The performance of recognition systems can be significantly affected by various factors, with facial expression poses and lighting changes being the main confounding factors. In order to minimize their impact, we propose an intriguing method in this study that enables the generation of high-quality images specifically tailored to the target domain. Our objective is to utilize disentangled representation to effectively model the decomposition of data variations and generate neutral facial expression images with frontal posture and adaptive illumination. To achieve this, we incorporate 3D priors in the adversarial learning during the training process, simulating the generation of an analytical 3D face deformation as well as rendering operations. Additionally, we employ contrastive learning to control the disentanglement of the generated faces while preserving the essential properties of facial features. This technique enables us to learn an embedding space in which similar data samples are represented closely, while distinct samples are kept far apart from each other. Furthermore, we conduct an analysis of the learned latent space and introduce several other significant properties that enhance the reinforcement of factor disentanglement. These properties include an imitation learning algorithm, which facilitates the acquisition of meaningful patterns and characteristics. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： 3D contrastive learning disentanglement expression neutralization generative adversarial network

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Alzheimer's Disease Detection Based on Features Level Selection for CNN-SVM Combination

Alzheimer's Disease Detection Based on Features Level Select...

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Artificial Intelligence & Green Energy (ICAIGE), IEEE International Conference on

作者： Mohamed Amine Zayene Hend Basly Fatma Ezahra Sayadi Lab. Networked Objects Control and Communication Systems National School of Engineering School of Sousse Sousse Tunisia

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FFGAN: An Auto-supervised Approach for Frontal Face Generation Via Disentangled Contrastive Learning 28th

FFGAN: An Auto-supervised Approach for Frontal Face Generati...

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28th International Conference on Knowledge Based and Intelligent information and Engineering systems, KES 2024

作者： Wiem, Grina Ali, Douik National Engineering School of Monastir University of Monastir Rue Ibn Jazzar Monastir5035 Tunisia Networked Objects Control and Communication Systems Laboratory Sousse Technology Centre National School of Engineers of Sousse University of Sousse Sahloul Belt Road Sousse4054 Tunisia

This work proposes a novel generative model, FF-GAN (Frontal Face Generative Adversarial Network), for generating high-quality and diverse frontal faces. FF-GAN utilizes contrastive learning to effectively learn the underlying representation of frontal faces from unlabeled image datasets. This approach allows the model to capture the essential characteristics of frontal faces without the need for explicit pose annotations. We evaluate the performance of FF-GAN using established metrics like FID (Fréchet Inception Distance), IS (Inception Score), and SSIM (Structural Similarity Index Measure). The results demonstrate that FF-GAN achieves superior performance compared to existing methods, generating highly realistic and visually appealing frontal faces with exceptional structural coherence. This research contributes to the field of facial image generation by introducing an effective unsupervised learning approach based on contrastive learning for generating high-quality frontal faces. © 2024 The Authors.

关键词： Contrastive Learning

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An FPGA-SoC based Hardware Acceleration of Convolutional Neural Networks 9

An FPGA-SoC based Hardware Acceleration of Convolutional Neu...

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9th IEEE International Conference on Sciences of Electronics, Technologies of Information and Telecommunications, SETIT 2022

作者： Bouaafia, Soulef Messaoud, Seifeddine Khemiri, Randa Sayadi, Fatma Ezahra Monastir University Lab. Electronics and Microelectronics Faculty of Sciences of Monastir Monastir Tunisia Sousse University Lab. Networked Objects Control and Communication Systems National Engineering School of Sousse Sousse Tunisia

ISBN: (数字)9781728184425

ISBN: (纸本)9781728184425

Deep learning has evolved as a discipline that has demonstrated its capacity and usefulness in tackling complicated learning issues as a result of recent improvements in digital technology and the availability of authentic data. Convolutional neural networks (CNNs) in particular have demonstrated their usefulness in image processing and computer vision applications. They do, however, need heavy CPU operations and memory bandwidth, which prevents general-purpose CPUs from attaining desirable performance levels. To boost CNN throughput, hardware accelerators such as application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), and graphics processing units (GPUs) have been deployed. FPGAs, in particular, have lately been used to accelerate the development of deep learning networks due to their ability to optimize parallelism and power efficiency. Based on hardware-software architecture, this research provides a CNN acceleration model for video compression applications. Vivado High Level Synthesis is used to accelerate the CNN model in order to develop Intellectual Property (IP) cores. © 2022 IEEE.

关键词： Convolutional neural networks

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Face Expression Neutralisation Using Improved Variational Autoencoder 6

Face Expression Neutralisation Using Improved Variational Au...

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6th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2022

作者： Wiem, Grina Ali, Douik University of Sousse Networked Objects Control and Communication Systems Laboratory National School of Engineers of Sousse National Engineering School of Monastir Rue Ibn Jazzar Monastir5035 Tunisia University of Sousse Networked Objects Control and Communication Systems Laboratory National School of Engineers of Sousse Pôle Technologique de Sousse Route de Ceinture Sahloul Sousse4054 Tunisia

ISBN: (纸本)9781665451161

There is a high demand for realistic facial expressions in modern computer graphics and multimedia research. Unfortunately, synthesizing face expressions takes time, effort, and hard labour since high naturalism of face expressions is required. Despite recent advances in synthesizing realistic facial images, current generative models conflict to catch more involved image types, potentially due to their latent space simplified architecture. Furthermore, the union of a Generative Adversarial Network (GAN) with a Variational Autoencoder (VAE) by using usually the Adam optimizer has recently been studied and investigated in cutting-edge research. Hence, at tiem of learning the generator takes a long time and then overfit for a particular time instance. So, in this work, we will suggest integrating the VAE to GAN's architecture by using the Ranger optimizer into an unsupervised generative model that at once learns to encode, generate and compare dataset samples to bring out various real neutral faces in a reduced and optimal time. Next, we proposed the probability distributions called Kullback-Leibler Divergence (KLD) as an objective weighting scheme that helps us to measure just how much information we lose when we choose an approximation during training. Also, the Mean Square Error (MSE), the most commonly used loss function for regression is used as a metric of evaluation for the experiment tests. © 2022 IEEE.

关键词： Mean square error

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CNL-ResUnet: A novel deep learning architecture for stroke image segmentation

CNL-ResUnet: A novel deep learning architecture for stroke i...

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International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Nesrine Jazzar Besma Mabrouk Ali Douik Networked Objects Control and Communication Systems National Engineering School of Sousse University of Sousse Tunisia National Engineering School of Sfax University of Sfax Tunisia Department of Electrical and Computer Engineering Advanced Technologies for Medicine and Signals ATMS National Engineers School Sfax University Tunisia Networked Objects Control and Communication Systems National Engineering School Sousse Tunisia

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

Segmentation of stroke lesions holds significant importance in enabling timely diagnosis and subsequent treatment by medical professionals. Automating this process presents technical challenges given the diverse appearance and evolving nature of lesions, discrepancies among medical experts, limited dataset availability, and the necessity for multiple MRI modalities for comprehensive imaging. This research presents an innovative method called CNL-ResUNet, specifically designed for 2D stroke image segmentation. CNL-ResUNet ingeniously incorporates residual blocks into its architecture, capitalizing on their innate capacity to facilitate seamless information flow across layers, thereby empowering the network to efficiently distill complex patterns from sparse datasets. It integrates customized skip connections to reduce semantic disparities between the encoder and decoder layers. Additionally, the architecture incorporates an innovative Classifier and Localizer (CNL) module, providing supplementary classification and localization insights with enhanced accuracy. By combining these insights with the segmentation results, CNL-ResUNet adeptly reduces the occurrence of false positives and false negatives. To evaluate the model’s performance, experiments were conducted utilizing the SISS dataset from the Ischemic Stroke Lesion Segmentation Challenge 2015. The outcomes from these experiments highlight the superiority of our approach, revealing significant qualitative and quantitative improvements across standard evaluation metrics like the Dice coefficient and Accuracy. Our proposed network demonstrates potential for advancing automated tools in stroke segmentation.

关键词： Deep learning Training Image segmentation Accuracy Semantics Stroke (medical condition) Robustness

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Automatic Face Expression Neutralisation and Posture Correction via 3D Contrastive Learning

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Procedia Computer Science 2023年 225卷 269-278页

作者： Grina Wiem Douik Ali University of Sousse Networked Objects Control and Communication Systems Laboratory National School of Engineers of Sousse Sousse Technology Center Sahloul Belt Road 4054 University of Monastir National Engineering School of Monastir Rue Ibn Jazzar Monastir 5035 Tunisia University of Sousse Networked Objects Control and Communication Systems Laboratory National School of Engineers of Sousse Sousse Technology Center Sahloul Belt Road 4054 Tunisia

关键词： generative adversarial network expression neutralization disentanglement 3D contrastive learning

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Comparison of RNN-Based Models for Sign Language Recognition and Translation

Comparison of RNN-Based Models for Sign Language Recognition...

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Marah El Askri Riadh Bchir Hend Basly Fatma Ezahra Sayadi National Engineering School of Sousse (ENISO) Laboratory of Networked Objects Control and Communication Systems (NOCCS) University of Sousse Sousse Tunisia Faculty of Sciences of Monastir University of Monastir Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The recognition of human activities using vision based methods has become a key area of interest in video analytics research. In the past years, several sophisticated deep learning (DL) algorithms have surfaced, designed to recognize complex human actions depicted in video streams. These approaches have exhibited remarkable competence in tasks related to video analytics. This paper explores the field in greater depth by centering its focus on Sign Language (SL) as a unique and complex form of human activity. SL plays is pivotal in facilitating communication for people who are deaf or hard of hearing, enabling them to interact and engage. The main idea is to recognize Arabic Sign Language especially Tunisian accent, and subsequently translate it into spoken language utilizing artificial intelligence (AI) methods. The goal of this work is to facilitate communication between individuals who are deaf and those who are not. So, this paper presents the comparative results of four deep-learning models: Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Reccurent Unit (GRU), and Bidirectional Gated Reccurent Unit (Bi-GRU), aiming to improve the recognition accuracy of dynamic signs. The results indicate that the BiLSTM outperforms other variants, showcasing an accuracy of 98%.

关键词： Sign language Accuracy Recurrent neural networks Visual analytics Logic gates Streaming media Artificial intelligence Information technology Long short term memory Streams

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