Browsing by Author "Marzouq, Manal"
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- A comprehensive review of solar irradiation estimation and forecasting using artificial neural networks: data, models and trendsPublication . El-Amarty, Naima; Marzouq, Manal; El Fadili, Hakim; Bennani, Saad Dosse; Ruano, AntonioSolar irradiation data are imperatively required for any solar energy-based project. The non-accessibility and uncertainty of these data can greatly affect the implementation, management, and performance of photovoltaic or thermal systems. Developing solar irradiation estimation and forecasting approaches is an effective way to overcome these issues. Practically, prediction approaches can help anticipate events by ensuring good operation of the power network and maintaining a precise balance between the demand and supply of the power at every moment. In the literature, various estimation and forecasting methods have been developed. Artificial Neural Network (ANN) models are the most commonly used methods in solar irradiation prediction. This paper aims to firstly review, analyze, and provide an overview of different aspects required to develop an ANN model for solar irradiation prediction, such as data types, data horizon, data preprocessing, forecasting horizon, feature selection, and model type. Secondly, a highly detailed state of the art of ANN-based approaches including deep learning and hybrid ANN models for solar irradiation estimation and forecasting is presented. Finally, the factors influencing prediction model performances are discussed in order to propose recommendations, trends, and outlooks for future research in this field.
- Join security and block watermarking-based evolutionary algorithm and Racah moments for medical imagingPublication . Chekira, Chaimae; Marzouq, Manal; Fadili, Hakim El; Lakhliai, Zakia; Ruano, MariaEnsuring the security of medical images containing confidential patient health information has become crucial. This paper presents a multipurpose medical image system based on a join of security and watermarking to achieve high data protection. We propose a block-splitting technique applied to large-scale cover medical images and hidden watermarks. In each subspace, we calculate Racah moments and apply a new evolutionary algorithm to automatically select the best positions for moments to be used for watermark hiding. This selection is based on a set of chromosomes with a specific coding strategy and evolutionary operators. The entire watermarked medical image is transmitted, and the extraction process is applied on the receiving side to recover the hidden watermark. Our method aims to achieve an optimal trade-off between watermarking requirements (imperceptibility and robustness) and enhance data protection by combining the proposed method with the three security services (confidentiality, integrity, and authentication). This hybrid combination uses high-performance encryption algorithms applied to the watermark components: the patient’s fingerprint, Electronic Record Patient, and doctor’s face. Watermarking performance and security services are evaluated using different medical datasets (DICOM, Figshare Brain Tumor, Covid-19, CXR, and MuRa images) in the absence of noise and against local or global noise. A comparative analysis with reliable and recent methods in medical image watermarking shows that our method achieves high performance: imperceptibility with a maximum PSNR of 46 dB, robustness with a maximum NC of 0.98, authentication with a maximum PSNR of 36.2 dB, and integrity with a maximum similarity of 0.97.
- A new evolutionary forest model via incremental tree selection for short-term global solar irradiance forecasting under six various climatic zonesPublication . El-Amarty, Naima; Marzouq, Manal; El Fadili, Hakim; Bennani, Saad Dosse; Ruano, Antonio; Rabehi, AbdelazizThe increasing integration of solar sources into the energy mix presents significant challenges, particularly in short-term energy management. Accurate solar irradiance forecasts can greatly assist solar power plant operators and energy network managers in making informed decisions about energy production and consumption. This paper aims to develop a new accurate forecasting model for short-term global solar irradiance based on an innovative evolutionary forest approach. Our model, baptized EFITS, performs incremental tree selection through appropriate evolutionary operators maintaining a good tradeoff between accuracy and diversity, generating progressively near-optimal decision trees to construct the final evolutionary forest forecaster. This new evolution process also automatically selects near-optimal input parameters, enhancing the overall model accuracy and generalization ability. Six climatically diverse locations in Morocco and three types of inputs (endogenous, exogenous, and hybrid) are used to assess the performance of the proposed. The results demonstrate that our proposed model exhibits excellent performance across all studied sites and horizons. Among all input types, hybrid inputs delivered the best forecasting accuracy across all studied sites and horizons. Notably, the continental climate site (Bni Mellal) achieved the highest accuracy, with nRMSE ranging from 4.94% to 7.54% and nMBE from 0.71% to -0.46% for 1 to 6 h forecasts. Conversely, Ifrane city, characterized by a humid temperate climate, showed the lowest accuracy, with nRMSE ranging from 10.34% to 18.94% and nMBE from 1.21% to -1.54%. Finally, a detailed comparison with benchmarking models (random forest, bagging, gradient boosting, single decision tree, bidirectional long short-term memory network, and scaled persistence models), revealed that our model consistently outperforms them across all tested scenarios, locations, and forecasting horizons.