@article {Ahajjam2022,
	title = {Experimental investigation of variational mode decomposition and deep learning for short-term multi-horizon residential electric load forecasting},
	journal = {Applied Energy},
	volume = {326},
	year = {2022},
	note = {cited By 0},
	abstract = {With the booming growth of advanced digital technologies, it has become possible for users as well as distributors of energy to obtain detailed and timely information about the electricity consumption of households. These technologies can also be used to forecast the household{\textquoteright}s electricity consumption (a.k.a. the load). In this paper, Variational Mode Decomposition and deep learning techniques are investigated as a way to improve the accuracy of the load forecasting problem. Although this problem has been studied in the literature, selecting an appropriate decomposition level and a deep learning technique providing better forecasting performance have garnered comparatively less attention. This study bridges this gap by studying the effect of six decomposition levels and five distinct deep learning networks. The raw load profiles are first decomposed into intrinsic mode functions using the Variational Mode Decomposition in order to mitigate their non-stationary aspect. Then, day, hour, and past electricity consumption data are fed as a three-dimensional input sequence to a four-level Wavelet Decomposition Network model. Finally, the forecast sequences related to the different intrinsic mode functions are combined to form the aggregate forecast sequence. The proposed method was assessed using load profiles of five Moroccan households from the Moroccan buildings{\textquoteright} electricity consumption dataset (MORED) and was benchmarked against state-of-the-art time-series models and a baseline persistence model. {\textcopyright} 2022 Elsevier Ltd},
	keywords = {data set, decomposition, Decomposition level, Deep learning, Electric load forecasting, Electric power plant loads, Electric power utilization, electricity, electricity supply, Electricity-consumption, Experimental investigations, forecasting method, Housing, Intrinsic mode functions, learning, Learning algorithms, Learning systems, Learning techniques, Load profiles, Multi-horizon forecasting, Residential load forecasting, Short-term residential load forecasting, Variational mode decomposition, Wavelet decomposition},
	doi = {10.1016/j.apenergy.2022.119963},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138343844\&doi=10.1016\%2fj.apenergy.2022.119963\&partnerID=40\&md5=322ce14ee40d2de7bbdfa72a4aabc718},
	author = {Ahajjam, M.A. and Bonilla Licea, D. and Ghogho, M. and Kobbane, A.}
}
@article {Gryech2022,
	title = {An Exploration of Features Impacting Respiratory Diseases in Urban Areas},
	journal = {International Journal of Environmental Research and Public Health},
	volume = {19},
	number = {5},
	year = {2022},
	note = {cited By 3},
	abstract = {Air pollution exposure has become ubiquitous and is increasingly detrimental to human health. Small Particulate matter (PM) is one of the most harmful forms of air pollution. It can easily infiltrate the lungs and trigger several respiratory diseases, especially in vulnerable populations such as children and elderly people. In this work, we start by leveraging a retrospective study of 416 children suffering from respiratory diseases. The study revealed that asthma prevalence was the most common among several respiratory diseases, and that most patients suffering from those diseases live in areas of high traffic, noise, and greenness. This paved the way to the construction of the MOREAIR dataset by combining feature abstraction and micro-level scale data collection. Unlike existing data sets, MOREAIR is rich in context-specific components, as it includes 52 temporal or geographical features, in addition to air-quality measurements. The use of Random Forest uncovered the most important features for the understanding of air-quality distribution in Moroccan urban areas. By linking the medical data and the MOREAIR dataset, we observed that the patients included in the medical study come mostly from neighborhoods that are characterized by either high average or high variations of pollution levels. {\textcopyright} 2022 by the authors. Licensee MDPI, Basel, Switzerland.},
	keywords = {aged, air pollutant, Air Pollutants, Air pollution, Air quality, air quality control, Article, asthma, breathing disorder, child, controlled study, disease risk assessment, environmental exposure, environmental risk, exploratory research, geographic information system, GIS, health risk, human, Humans, major clinical study, Measurement, medical information, Moroccan, neighborhood, noise injury, Particulate Matter, prevalence, public health, random forest, Respiration Disorders, respiratory disease, respiratory tract disease, Retrospective Studies, retrospective study, Risk assessment, risk factor, traffic pollution, urban area},
	doi = {10.3390/ijerph19053095},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125596721\&doi=10.3390\%2fijerph19053095\&partnerID=40\&md5=011cdce4b84286477d86b54655147f71},
	author = {Gryech, I. and Ghogho, M. and Mahraoui, C. and Kobbane, A.}
}
@article {Ben-Aboud2021107149,
	title = {Electro-Smog Monitoring Using Low-Cost Software-Defined Radio Dongles},
	journal = {IEEE Access},
	volume = {9},
	year = {2021},
	note = {cited By 1},
	pages = {107149-107158},
	abstract = {With the increasing use of wireless communication technologies, it is important to monitor electromagnetic exposure (ideally with high temporal and spatial resolutions). In this paper, we explore the use of low-cost software-defined radio dongle for electro-smog measurements and more specifically for electro-magnetic fields power measurements and the estimation of the incident power density. We describe how the raw data is collected and then compute the average electromagnetic field power. We then compensate for the non-linearity of the amplifier and the antenna gain to get the corrected electromagnetic field power measurements. We use these measurements to estimate the incident power density which is the metric that we use to evaluate the electro-smog. The results show that the considered low-cost SDR dongle is stable and provides good quality power measurements. The estimation of the incident power density is shown to be accurate enough for monitoring the electro-smog. {\textcopyright} 2013 IEEE.},
	keywords = {Air pollution, Analog circuits, Antenna gains, Antennas, Costs, Electromagnetic exposure, Electromagnetic fields, Incident power, Low costs, Radio, Radio receivers, Software radio, Software-defined radios, Temporal and spatial, Wireless communication technology},
	doi = {10.1109/ACCESS.2021.3100773},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112673496\&doi=10.1109\%2fACCESS.2021.3100773\&partnerID=40\&md5=b7b53e18cf471222f475a67fe957cbee},
	author = {Ben-Aboud, Y. and Ghogho, M. and Pollin, S. and Kobbane, A.}
}
@conference {Ahajjam20201701,
	title = {Electric Power Quality Disturbances Classification based on Temporal-Spectral Images and Deep Convolutional Neural Networks},
	booktitle = {2020 International Wireless Communications and Mobile Computing, IWCMC 2020},
	year = {2020},
	note = {cited By 7},
	pages = {1701-1706},
	abstract = {We propose a deep learning based technique for power quality disturbances (PQD) detection and identification that aims at mimicking the reasoning of human field experts. This technique consists of processing small-size images containing superimposed time and frequency representations of the electric signal. The classification of PQD is performed with a convolutional neural network (CNN) trained with synthetic signals containing various single and multiple PQDs. Simulation results show that our technique is able to detect and identify with a high accuracy, in addition to pure sinusoidal, eight single PQDs and 20 of their combinations (up to four PQDs in the same signal) even in the presence of noise. Features such as lower computational load and simplicity while maintaining high performance sets the proposed technique apart from previous ones. {\textcopyright} 2020 IEEE.},
	keywords = {Computational loads, Convolution, Convolutional neural networks, Deep learning, Deep neural networks, Detection and identifications, Electric power quality disturbances, Electric signal, Image classification, Mobile computing, Power quality, Power quality disturbances, Spectral images, Spectroscopy, Synthetic signals, Time and frequencies},
	doi = {10.1109/IWCMC48107.2020.9148438},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089652241\&doi=10.1109\%2fIWCMC48107.2020.9148438\&partnerID=40\&md5=a479607523102d68ad4d7d0188079ac8},
	author = {Ahajjam, M.A. and Licea, D.B. and Ghogho, M. and Kobbane, A.}
}
@conference {Zakroum2018175,
	title = {Exploratory data analysis of a network telescope traffic and prediction of port probing rates},
	booktitle = {2018 IEEE International Conference on Intelligence and Security Informatics, ISI 2018},
	year = {2018},
	pages = {175-180},
	doi = {10.1109/ISI.2018.8587323},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061067648\&doi=10.1109\%2fISI.2018.8587323\&partnerID=40\&md5=76f77a31025e393df1d23b123bcb8dee},
	author = {Zakroum, M. and Houmz, A. and Ghogho, M. and Mezzour, G. and Lahmadi, A. and Francois, J. and Koutbi, M.E.}
}
