Browsing by Author "Fadili, Hakim El"
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- Energy disaggregation using multi-objective genetic algorithm designed neural networksPublication . Habou Laouali, Inoussa; Gomes, Isaías; Ruano, Maria; Bennani, Saad Dosse; Fadili, Hakim El; Ruano, AntonioEnergy-saving schemes are nowadays a major worldwide concern. As the building sector is a major energy consumer, and hence greenhouse gas emitter, research in home energy management systems (HEMS) has increased substantially during the last years. One of the primary purposes of HEMS is monitoring electric consumption and disaggregating this consumption across different electric appliances. Non-intrusive load monitoring (NILM) enables this disaggregation without having to resort in the profusion of specific meters associated with each device. This paper proposes a low-complexity and low-cost NILM framework based on radial basis function neural networks designed by a multi-objective genetic algorithm (MOGA), with design data selected by an approximate convex hull algorithm. Results of the proposed framework on residential house data demonstrate the designed models’ ability to disaggregate the house devices with excellent performance, which was consistently better than using other machine learning algorithms, obtaining F1 values between 68% and 100% and estimation accuracy values ranging from 75% to 99%. The proposed NILM approach enabled us to identify the operation of electric appliances accounting for 66% of the total consumption and to recognize that 60% of the total consumption could be schedulable, allowing additional flexibility for the HEMS operation. Despite reducing the data sampling from one second to one minute, to allow for low-cost meters and the employment of low complexity models and to enable its real-time implementation without having to resort to specific hardware, the proposed technique presented an excellent ability to disaggregate the usage of devices.
- 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.
- Non-intrusive load monitoring of household devices using a hybrid deep learning model through convex hull-based data selectionPublication . Habou Laouali, Inoussa; Ruano, Antonio; Ruano, Maria da Graça; Bennani, Saad Dosse; Fadili, Hakim ElThe availability of smart meters and IoT technology has opened new opportunities, ranging from monitoring electrical energy to extracting various types of information related to household occupancy, and with the frequency of usage of different appliances. Non-intrusive load monitoring (NILM) allows users to disaggregate the usage of each device in the house using the total aggregated power signals collected from a smart meter that is typically installed in the household. It enables the monitoring of domestic appliance use without the need to install individual sensors for each device, thus minimizing electrical system complexities and associated costs. This paper proposes an NILM framework based on low frequency power data using a convex hull data selection approach and hybrid deep learning architecture. It employs a sliding window of aggregated active and reactive powers sampled at 1 Hz. A randomized approximation convex hull data selection approach performs the selection of the most informative vertices of the real convex hull. The hybrid deep learning architecture is composed of two models: a classification model based on a convolutional neural network trained with a regression model based on a bidirectional long-term memory neural network. The results obtained on the test dataset demonstrate the effectiveness of the proposed approach, achieving F1 values ranging from 0.95 to 0.99 for the four devices considered and estimation accuracy values between 0.88 and 0.98. These results compare favorably with the performance of existing approaches.