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  • Publication
    Optimazting Dynamics for Voluntry Retirement and Sustainable Living
    (2023)
    Aldulaimi, Saeed Hameed
    ;
    Abdeldayem, Marwan
    ;
    Abu-AlSondos, Ibrahim A.
    ;
    ;
    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.
      7
  • Publication
    Deepfakes Signatures Detection in the Handcrafted Features Space
    (2023)
    Hamadene, Assia
    ;
    Ouahabi, Abdeldjalil
    ;
    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.
      4
  • Publication
    Van der Pol-informed Neural Networks for Multi-step-ahead Forecasting of Extreme Climatic Events
    (2023)
    Dutta, Anurag
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    Panja, Madhurima
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    Kumar, Uttam
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    Hens, Chittaranjan
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    Deep learning has produced excellent results in several applied domains including computer vision, natural language processing, speech recognition, etc. Physics-informed neural networks (PINN) are a new family of deep learning models that combine prior knowledge of physics in the form of high-level abstraction of natural phenomena with data-driven neural networks. PINN has emerged as a flourishing area of scientific computing to deal with the challenges of shortage of training data, enhancing physical plausibility, and specifically aiming to solve complex differential equations. However, building PINNs for modeling and forecasting the dynamics of extreme climatic events of geophysical systems remains an open scientific problem. This study proposes Van der Pol-informed Neural Networks (VPINN), a physics-informed differential learning approach, for modeling extreme nonlinear dynamical systems such as climatic events, exploiting the physical differentials as the physics-derived loss function. Our proposal is compared to state-of-the-art time series forecasting models, showing superior performance.The codes and dataset used for the experiments are made available at https: //github.com/mad-stat/VPINN.
      38  1
  • Publication
    Probabilistic AutoRegressive Neural Networks for Accurate Long-Range Forecasting
    (2023)
    Panja, Madhurima
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    ;
    Kumar, Uttam
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    Forecasting time series data is a critical area of research with applications spanning from stock prices to early epidemic prediction. While numerous statistical and machine learning methods have been proposed, real-life prediction problems often require hybrid solutions that bridge classical forecasting approaches and modern neural network models. In this study, we introduce a Probabilistic AutoRegressive Neural Network (PARNN), capable of handling complex time series data exhibiting non-stationarity, nonlinearity, non-seasonality, long-range dependence, and chaotic patterns. PARNN is constructed by improving autoregressive neural networks (ARNN) using autoregressive integrated moving average (ARIMA) feedback error. Notably, the PARNN model provides uncertainty quantification through prediction intervals and conformal predictions setting it apart from advanced deep learning tools. Through comprehensive computational experiments, we evaluate the performance of PARNN against standard statistical, machine learning, and deep learning models. Diverse real-world datasets from macroeconomics, tourism, epidemiology, and other domains are employed for short-term, medium-term, and long-term forecasting evaluations. Our results demonstrate the superiority of PARNN across various forecast horizons, surpassing the state-of-the-art forecasters. The proposed PARNN model offers a valuable hybrid solution for accurate long-range forecasting. The ability to quantify uncertainty through prediction intervals further enhances the model’s usefulness in various decision-making processes.
      10
  • Publication
    Analysis of the Performance of Four Filter Types for Drone Tracking
    (2023) ;
    Segrouchni, Amal El Fallah
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    Barbaresco, Frederic
    ;
    In this work, extensive simulations are done to compare the performance of the 4 filter types; Linear Kalman filter (LKF), Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF). A simple nearly constant velocity (NCV) motion model is used with a Gaussian noise measurement model. Simulations were done with different ground truths, different measurements covariance matrices, and different speeds of the drone. Stone soup software was used in the simulations. The analyses revealed informative results that gave us more understanding of the behavior of the four filters when a common type of motion model such as the NCV model is used.
      9