SUAD Institutional Repository
by SUAD Library

Your reference for the Sorbonne University Abu Dhabi research output and research impact

 
Research outputs
598
Disciplines
9
Researchers
91
Recent Additions
  • Publication
    Drone Detection with Improved Precision in Traditional Machine Learning and Less Complexity in Single Shot Detectors
    (2024)
    Kassab, Mohamad
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    Barbaresco, Frederic
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    Seghrouchni, Amal El Fallah
    This work presents a broad study of drone detection based on a variety of machine-learning methods including traditional and deep-learning techniques. The data sets used are images obtained from sequences of video frames in both RGB and IR formats, filtered and unfiltered. First, traditional machine learning techniques such as SVM and RF were investigated to discover their drawbacks and study their feasibility in drone detection. It was evident that those techniques are not suitable for complex data sets (sets with several non-drone objects and clutter in the background). It was observed that the sliding window size results in a bias toward the selection of the bounding box when using the traditional NMS method. Therefore, to address this issue, a modified NMS is proposed and tested on SVM and RF. SVM and RF with modified NMS managed to achieve a relative improvement of up to 25% based on the evaluation metric. The Deep Learning techniques, on the other hand, showed better detection performance but less improvement when using the proposed NMS method. Since their biggest drawback is complexity, a modified deep learning paradigm was proposed to mitigate the usual complexity associated with deep learning methods. The proposed paradigm uses (Single Shot Detector) SSD and AdderNet filters in an attempt to avoid excessive multiplications in the convolutional layers. To demonstrate our method, the most common deep-learning techniques were comparatively tested to create a baseline for evaluating the proposed SSD/AdderNet. The training and testing of the deep learning models were repeated six times to investigate the consistency of learning in terms of parameters and performance. The proposed model was able to achieve better results with respect to the IR data set compared to its counterpart while reducing the number of multiplications at the convolutional layers by 43.42%. Moreover, and as a result of lower complexity, the proposed SSD/AdderNet showed fewer training and inference times compared to its counterpart.
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  • Publication
    Urban Growth, Migration and Social Diversification in the Arab-Persian Gulf
    With the development of oil exploration and production around the Arabo-Persian Gulf from the end of World-War II, the towns linked with this activity have experienced a rapid growth, leading to the significant changes in pre-existed urban hierarchy. This article aims to describe and analyse the process of growth of all the cities and towns located on the two sides of the Gulf. During the last few years, the urban growth appears to be linked with new urban policies using oil revenues for building a post-oil economy. Divided between rich social groups constituted by local families and highly qualified migrants, often North-Americans on one side and on the other side poor workers, mainly Asians, far from their family and on temporary contracts along with various social groups of new migrants. The present analysis pinpoints the importance of a large and hierarchized middle class as well as a large popular class in the social fabrics, which determine local social life as well as consumption pattern. It demonstrates the social complexity of the society in large urban agglomerations of the Gulf countries, which are getting strongly integrated with the globalization process and benefitted by large investments from the states and international companies. It also shows how the middle and small towns are greatly marginalised in this lopsided development process.
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  • Publication
    Erratum to: Recent Advances of Chimp Optimization Algorithm: Variants and Applications
    (2023)
    Daoud, Mohammad Sh.
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    Shehab, Mohammad
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    Abualigah, Laith
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    Alshinwan, Mohammad
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    Abd Elaziz, Mohamed
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    Shambour, Mohd Khaled Yousef
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    Oliva, Diego
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    Alia, Mohammad A.
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  • Publication
    Optimazting Dynamics for Voluntry Retirement and Sustainable Living
    (2023)
    Aldulaimi, Saeed Hameed
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    Abdeldayem, Marwan
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    Abu-AlSondos, Ibrahim A.
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    ;
    Ahmed, Mohamed Alsayed Haider
    The current study aims to investigate the role of voluntary retirement decisions by employees of governmental sector in the Bahrain on sustainable living, particularly on the level of financial balance and quality of life. Investigate whether the results of the motives of the voluntary retirement decision on the quality of life of government sector employees. The Methodology used in this study is the descriptive-analytical study. Findings revealed that the main reason/motive for voluntary retirement was personal and health and psychological motives. Also, the causes of the voluntary retirement decision and the financial balance are positively correlated. The motives of the voluntary retirement decision and the quality of life are positively correlated. The financial motives were the most influential dimension of the voluntary retirement decision on the quality of life. The study recommends improving the conditions of voluntary retirement employees of government sector and handing more roles and responsibilities mainly to those with long years of experience to run the work and involve them in the decision-making process to motivate them at work.
      1
  • Publication
    Deepfakes Signatures Detection in the Handcrafted Features Space
    (2023)
    Hamadene, Assia
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    Ouahabi, Abdeldjalil
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    In the Handwritten Signature Verification (HSV) literature, several synthetic databases have been developed for data-augmentation purposes, where new specimens and new identities were generated using bio-inspired algorithms, neuromotor synthesizers, Generative Adversarial Networks (GANs) as well as several deep learning methods. These synthetic databases contain synthetic genuine and forgeries specimens which are used to train and build signature verification systems. Researches on generative data assume that synthetic data are as close as possible to real data, this is why, they are either used for training systems when used for data augmentation tasks or are used to fake systems as synthetic attacks. It is worth, however, to point out the existence of a relationship between the handwritten signature authenticity and human behavior and brain. Indeed, a genuine signature is characterised by specific features that are related to the owner’s personality. The fact which makes signature verification and authentication achievable. Handcrafted features had demonstrated a high capacity to capture personal traits for authenticating real static signatures. We, therefore, Propose in this paper, a handcrafted feature based Writer-Independent (WI) signature verification system to detect synthetic writers and signatures through handcrafted features. We also aim to assess how realistic are synthetic signatures as well as their impact on HSV system’s performances. Obtained results using 4000 synthetic writers of GPDS synthetic database show that the proposed handcrafted features have considerable ability to detect synthetic signatures vs. two widely used real individuals signatures databases, namely CEDAR and GPDS-300, which reach 98.67% and 94.05% of successful synthetic detection rates respectively.