@article {Asaad2022,
	title = {AsthmaKGxE: An asthma{\textendash}environment interaction knowledge graph leveraging public databases and scientific literature},
	journal = {Computers in Biology and Medicine},
	volume = {148},
	year = {2022},
	note = {cited By 1},
	abstract = {Motivation: Asthma is a complex heterogeneous disease resulting from intricate interactions between genetic and non-genetic factors related to environmental and psychosocial aspects. Discovery of such interactions can provide insights into the pathophysiology and etiology of asthma. In this paper, we propose an asthma knowledge graph (KG) built using a hybrid methodology for graph-based modeling of asthma complexity with a focus on environmental interactions. Using a heterogeneous set of public sources, we construct a genetic and pharmacogenetic asthma knowledge graph. The construction of this KG allowed us to shed more light on the lack of curated resources focused on environmental influences related to asthma. To remedy the lack of environmental data in our KG, we exploit the biomedical literature using state-of-the-art natural language processing and construct the first Asthma{\textendash}Environment interaction catalog incorporating a continuously updated ensemble of environmental, psychological, nutritional and socio-economic influences. The catalog{\textquoteright}s most substantiated results are then integrated into the KG. Results: The resulting environmentally rich knowledge graph {\textquotedblright}AsthmaKGxE{\textquotedblright} aims to provide a resource for several potential applications of artificial intelligence and allows for a multi-perspective study of asthma. Our insight extraction results indicate that stress is the most frequent asthma association in the corpus, followed by allergens and obesity. We contend that studying asthma{\textendash}environment interactions in more depth holds the key to curbing the complexity and heterogeneity of asthma. Availability: A user interface to browse and download the extracted catalog as well as the KG are available at http://asthmakgxe.moreair.info/. The code and supplementary data are available on github (https://github.com/ChaiAsaad/MoreAIRAsthmaKGxE). {\textcopyright} 2022 Elsevier Ltd},
	keywords = {allergen, Article, Artificial intelligence, Association reactions, asthma, Automated, automated pattern recognition, data base, data extraction, Databases, Diseases, environmental factor, Factual, factual database, Gene-Environment Interaction, Genetic factors, genotype environment interaction, Graphic methods, Heterogeneous disease, human, Humans, Knowledge graph, Knowledge graphs, Knowledge management, Language processing, Learning algorithms, Machine learning, Machine-learning, NAtural language processing, Natural language processing systems, Natural languages, nutritional assessment, obesity, pathophysiology, Pattern recognition, pharmacogenetics, physiological stress, psychological aspect, Public database, Scientific literature, socioeconomics, User interfaces},
	doi = {10.1016/j.compbiomed.2022.105933},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135702024\&doi=10.1016\%2fj.compbiomed.2022.105933\&partnerID=40\&md5=2022f55e1de0bbaa947ba6699af6a143},
	author = {Asaad, C. and Ghogho, M.}
}
@article {Adil2022,
	title = {COVID-19-Related Scientific Literature Exploration: Short Survey and Comparative Study},
	journal = {Biology},
	volume = {11},
	number = {8},
	year = {2022},
	note = {cited By 1},
	abstract = {The urgency of the COVID-19 pandemic caused a surge in the related scientific literature. This surge made the manual exploration of scientific articles time-consuming and inefficient. Therefore, a range of exploratory search applications have been created to facilitate access to the available literature. In this survey, we give a short description of certain efforts in this direction and explore the different approaches that they used. {\textcopyright} 2022 by the authors.},
	doi = {10.3390/biology11081221},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137364249\&doi=10.3390\%2fbiology11081221\&partnerID=40\&md5=1602d312118c2b7347846773ddd73594},
	author = {Adil, B. and Lhazmir, S. and Ghogho, M. and Benbrahim, H.}
}
@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 {Hammouchi2022,
	title = {STRisk: A Socio-Technical Approach to Assess Hacking Breaches Risk},
	journal = {IEEE Transactions on Dependable and Secure Computing},
	year = {2022},
	note = {cited By 0},
	abstract = {Data breaches have begun to take on new dimensions and their prediction is becoming of great importance to organizations. Prior work has addressed this issue mainly from a technical perspective and neglected other interfering aspects such as the social media dimension. To fill this gap, we propose a STRisk predictive system where we expand the scope of the prediction task by bringing into play the social media dimension. We study over 3800 US organizations including both victim and non-victim organizations. For each organization, we design a profile composed of a variety of externally measured technical indicators and social factors. In addition, to account for unreported incidents, we consider the non-victim sample to be noisy and propose a noise correction approach to correct mislabeled organizations. We then build several machine learning models to predict whether an organization is exposed to experience a hacking breach. By exploiting both technical and social features, we achieve an Area Under Curve (AUC) score exceeding 98\%, which is 12\% higher than the AUC achieved using only technical features. Furthermore, our feature importance analysis reveals that open ports and expired certificates are the best technical predictors, while spreadability and agreeability are the best social predictors. IEEE},
	keywords = {Blogs, Cybe risk assessment, Cybe risk prediction, Data breach, Forecasting, Hacking breach, Learning systems, Malware, Noise measurements, Personal computing, Predictive models, Risk assessment, Risk predictions, Risks assessments, Security, Social networking (online)},
	doi = {10.1109/TDSC.2022.3149208},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124712380\&doi=10.1109\%2fTDSC.2022.3149208\&partnerID=40\&md5=f6b798bf0fc97476d3214d3efbc3597a},
	author = {Hammouchi, H. and Nejjari, N. and Mezzour, G. and Ghogho, M. and Benbrahim, H.}
}
@conference {Ben-Aboud2021,
	title = {Acquisition and time-series analysis of electromagnetic pollution data},
	booktitle = {2021 17th International Conference on Intelligent Environments, IE 2021 - Proceedings},
	year = {2021},
	note = {cited By 0},
	abstract = {With the increasing use of wireless communication technologies, it is important to monitor electromagnetic exposure, ideally with high temporal and spatial resolutions. This paper presents our low-cost electro-smog measurement process, covering hardware selection, RF power measurement, and RF power correction. Then, a time series analysis is performed on the electromagnetic exposure data collected in the city of Sala Al Jadida - Morocco for seven days. The results show that the electro-smog exposure has a strong predictable pattern and a preliminary time series model is derived. {\textcopyright} 2021 IEEE.},
	keywords = {Air pollution, Electro magnetic pollution, Electromagnetic exposure, Hardware selection, Harmonic analysis, Intelligent agents, Low costs, Measurement process, Temporal and spatial, Time series analysis, Time series modeling, Wireless communication technology},
	doi = {10.1109/IE51775.2021.9486565},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114504253\&doi=10.1109\%2fIE51775.2021.9486565\&partnerID=40\&md5=8958a981795705a145d39c5225a3dd66},
	author = {Ben-Aboud, Y. and Ghogho, M. and Pollin, S. and Kobbane, A.}
}
@article {Houmz2021,
	title = {Detecting the impact of software vulnerability on attacks: A case study of network telescope scans},
	journal = {Journal of Network and Computer Applications},
	volume = {195},
	year = {2021},
	note = {cited By 1},
	abstract = {Network scanning is one of the first steps in gathering information about a target before launching attacks. It is used to scan for vulnerable devices and exposed services in order to exploit them. Such exploits can result in data breaches or network disruption, which can be very costly for organizations. There are many factors, including technical and non-technical, affecting the volume of scanning activities. In this paper, we study the impact of vulnerability disclosure on the volume of scans over time and propose a machine learning-based approach to predict this impact. We conducted a comprehensive data collection of network scans from two network telescopes hosted in different countries, as well as the disclosed vulnerabilities from 2014 to 2019. We then designed a set of features to characterize the disclosed vulnerabilities and used several classifiers to predict whether a vulnerability will impact the volume of daily scans. The resulting classifier achieves over 85\% accuracy in predicting the impact. In addition, we performed an analysis of the key characteristics of vulnerabilities that directly affect scanning activities. Our findings show that this approach is able to classify vulnerabilities that have an impact on network scans. The implementation of our model and validation tests proved the efficiency of the selected features, as well as the robustness of our model to classify vulnerabilities{\textquoteright} impact on scans. {\textcopyright} 2021 Elsevier Ltd},
	keywords = {Case-studies, Classification algorithm, CVE, Forecasting, Machine learning, Network scan, Network scanning, Network security, Network telescopes, NVD, OR-networks, Scanning, Software vulnerabilities, Telescopes, Times series},
	doi = {10.1016/j.jnca.2021.103230},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116335984\&doi=10.1016\%2fj.jnca.2021.103230\&partnerID=40\&md5=1d7f8d5d4111761b27fc93badc70f925},
	author = {Houmz, A. and Mezzour, G. and Zkik, K. and Ghogho, M. and Benbrahim, H.}
}
@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 {Cherqi2021,
	title = {Leveraging Open Threat Exchange (OTX) to Understand Spatiooral Trends of Cyber Threats: Covid-19 Case Study},
	booktitle = {Proceedings - 2021 IEEE International Conference on Intelligence and Security Informatics, ISI 2021},
	year = {2021},
	note = {cited By 0},
	abstract = {Understanding the properties exhibited by Spatialoral evolution of cyber attacks improve cyber threat intelligence. In addition, better understanding on threats patterns is a key feature for cyber threats prevention, detection, and management and for enhancing defenses. In this work, we study different aspects of emerging threats in the wild shared by 160,000 global participants form all industries. First, we perform an exploratory data analysis of the collected cyber threats. We investigate the most targeted countries, most common malwares and the distribution of attacks frequency by localisation. Second, we extract attacks{\textquoteright} spreading patterns at country level. We model these behaviors using transition graphs decorated with probabilities of switching from a country to another. Finally, we analyse the extent to which cyber threats have been affected by the COVID-19 outbreak and sanitary measures imposed by governments to prevent the virus from spreading. {\textcopyright} 2021 IEEE.},
	keywords = {Attack analysis, Case-studies, Computer viruses, COVID-19, Crime, Cybe attack analyse, Cybe threat forecasting, Cyber attacks, Cyber threats, Cyber-attacks, Cyber-crimes, Information analysis, Network security, Spatialoral data analyse for cybe-crime analyse, Threat intelligence, Viruses},
	doi = {10.1109/ISI53945.2021.9624677},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123484404\&doi=10.1109\%2fISI53945.2021.9624677\&partnerID=40\&md5=729113fca1f2d9817bbaa46a6f5d0036},
	author = {Cherqi, O. and Hammouchi, H. and Ghogho, M. and Benbrahim, H.}
}
@article {Jeddi2021,
	title = {Machine learning for predicting the risk for childhood asthma using prenatal, perinatal, postnatal and environmental factors},
	journal = {Healthcare (Switzerland)},
	volume = {9},
	number = {11},
	year = {2021},
	note = {cited By 3},
	abstract = {The prevalence rate for childhood asthma and its associated risk factors vary significantly across countries and regions. In the case of Morocco, the scarcity of available medical data makes scientific research on diseases such as asthma very challenging. In this paper, we build machine learning models to predict the occurrence of childhood asthma using data from a prospective study of 202 children with and without asthma. The association between different factors and asthma diagnosis is first assessed using a Chi-squared test. Then, predictive models such as logistic regression analysis, decision trees, random forest and support vector machine are used to explore the relationship between childhood asthma and the various risk factors. First, data were pre-processed using a Chi-squared feature selection, 19 out of the 36 factors were found to be significantly associated (p-value < 0.05) with childhood asthma; these include: history of atopic diseases in the family, presence of mites, cold air, strong odors and mold in the child{\textquoteright}s environment, mode of birth, breastfeeding and early life habits and exposures. For asthma prediction, random forest yielded the best predictive performance (accuracy = 84.9\%), followed by logistic regression (accuracy = 82.57\%), support vector machine (accuracy = 82.5\%) and decision trees (accuracy = 75.19\%). The decision tree model has the advantage of being easily interpreted. This study identified important maternal and prenatal risk factors for childhood asthma, the majority of which are avoidable. Appropriate steps are needed to raise awareness about the prenatal risk factors. {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.},
	doi = {10.3390/healthcare9111464},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118143874\&doi=10.3390\%2fhealthcare9111464\&partnerID=40\&md5=ed53157bb344c4ea98eaef10d4ade531},
	author = {Jeddi, Z. and Gryech, I. and Ghogho, M. and Hammoumi, M.E.L. and Mahraoui, C.}
}
@conference {Ahajjam2021,
	title = {On Multi-Label Classification for Non-Intrusive Load Identification using Low Sampling Frequency Datasets},
	booktitle = {Conference Record - IEEE Instrumentation and Measurement Technology Conference},
	volume = {2021-May},
	year = {2021},
	note = {cited By 0},
	abstract = {Non-intrusive load monitoring (NILM) aims to infer information about the electric consumption of individual loads using the premises{\textquoteright} aggregate consumption. In this work, we target supervised multi-label classification for non-intrusive load identification. We describe how we have created a new dataset from Moroccan households using a low sampling frequency. Then, we analyze the performance of three machine learning models for NILM, and investigate the impact of signal input length on performance. {\textcopyright} 2021 IEEE.},
	keywords = {Aggregate consumption, Classification (of information), Electric consumption, Load identification, Machine learning models, Measurement, Multi label classification, Non-intrusive, Nonintrusive load monitoring, Sampling frequencies},
	doi = {10.1109/I2MTC50364.2021.9460059},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113709883\&doi=10.1109\%2fI2MTC50364.2021.9460059\&partnerID=40\&md5=2f6ca1a0b8790dce2a085d0e0ff10cf4},
	author = {Ahajjam, M.A. and Essayeh, C. and Ghogho, M. and Kobbane, A.}
}
@conference {Gryech2021,
	title = {On spatial prediction of urban air pollution},
	booktitle = {2021 17th International Conference on Intelligent Environments, IE 2021 - Proceedings},
	year = {2021},
	note = {cited By 0},
	abstract = {Air pollution continues to draw global attention, and still causes adverse environmental issues and health effects. Questions about how air pollution evolves spatially are also still unsolved. Many air quality monitoring stations are deployed in several countries to give insight about air quality. However, it is quite frequent for these stations to go out of order during their long life time. These incidents may lead to a significant loss of pollution data. To mitigate this issue, we propose to leverage spatial correlation between pollution monitoring stations to predict the lost data. To reduce the complexity of the prediction model, for each monitoring station, we identify the best set of other stations to use in the prediction model. Correlation-based station selection is shown to outperform distance-based station selection and provides an R2 above 0.8 when applied to the Airparif datasets. {\textcopyright} 2021 IEEE.},
	keywords = {Air quality, Air quality monitoring stations, Environmental issues, Forecasting, Intelligent agents, Monitoring stations, Pollution monitoring, Prediction model, Predictive analytics, Spatial correlations, Spatial prediction, Urban air pollution},
	doi = {10.1109/IE51775.2021.9486618},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114499832\&doi=10.1109\%2fIE51775.2021.9486618\&partnerID=40\&md5=10a04db251b0bca163abb4d34f0c721a},
	author = {Gryech, I. and Ben-Aboud, Y. and Ghogho, M. and Kobbane, A.}
}
@article {Khaouja2021118134,
	title = {A Survey on Skill Identification from Online Job Ads},
	journal = {IEEE Access},
	volume = {9},
	year = {2021},
	note = {cited By 8},
	pages = {118134-118153},
	abstract = {A changing job market, influenced by different factors such as globalization and demographic growth, urges close monitoring. The digitization of the job market has given the opportunity to researchers to better understand job market needs as job postings/ads become more accessible. However, such postings are submitted in unstructured text and need further processing to identify the required skills. The aim of this survey is to review current research on skill identification from job ads and to discuss possible future research directions. In this study, we systematically reviewed 108 research articles on the topic. In particular, we evaluated and classified the prior work aiming to identify the skill bases used for analyzing job market needs; the type of extracted skills; the skill identification methods; the studied sector and the skill identification granularity. Then, we categorized the existent applications and goals of skill identification. Finally, we present key challenges and discuss recent trends. {\textcopyright} 2013 IEEE.},
	keywords = {Commerce, Demographic growth, Employment, Identification method, Job market, Job postings, Possible futures, Recent trends, Surveys, Unstructured texts},
	doi = {10.1109/ACCESS.2021.3106120},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113305279\&doi=10.1109\%2fACCESS.2021.3106120\&partnerID=40\&md5=95e3da9f3201c5b0ca744ba8d8af7012},
	author = {Khaouja, I. and Kassou, I. and Ghogho, M.}
}
@article {Drissi2021594,
	title = {A Systematic Literature Review on e-Mental Health Solutions to Assist Health Care Workers during COVID-19},
	journal = {Telemedicine and e-Health},
	volume = {27},
	number = {6},
	year = {2021},
	note = {cited By 27},
	pages = {594-602},
	abstract = {Background: e-Mental health is an established field of exploiting information and communication technologies for mental health care. It offers different solutions and has shown effectiveness in managing many psychological issues. Introduction: The coronavirus disease 2019 (COVID-19) pandemic has critically influenced health care systems and health care workers (HCWs). HCWs are working under hard conditions, and are suffering from different psychological issues, including anxiety, stress, and depression. Consequently, there is an undeniable need of mental care interventions for HCWs. Under the circumstances caused by COVID-19, e-health interventions can be used as tools to assist HCWs with their mental health. These solutions can provide mental health care support remotely, respecting the recommended safety measures. Materials and Methods: This study aims to identify e-mental health interventions, reported in the literature, that are developed for HCWs during the COVID-19 pandemic. A systematic literature review was conducted following the PRISMA protocol by searching the following digital libraries: IEEE, ACM, ScienceDirect, Scopus, and PubMed. Results and Discussion: Eleven publications were selected. The identified e-mental health interventions consisted of social media platforms, e-learning content, online resources and mobile applications. Only 27\% of the studies included empirical evaluation of the reported interventions, 55\% listed challenges and limitations related to the adoption of the reported interventions. And 45\% presented interventions developed specifically for HCWs in China. The overall feedback on the identified interventions was positive, yet a lack of empirical evaluation was identified, especially regarding qualitative evidence. Conclusions: The COVID-19 pandemic has highlighted the importance and need for e-mental health solutions for HCWs. {\textcopyright} Copyright 2021, Mary Ann Liebert, Inc., publishers 2021.},
	keywords = {China, COVID-19, Digital libraries, e-learning, E-learning contents, Empirical evaluations, Health care, health care personnel, Health care workers, Health Personnel, Health-care system, human, Humans, Information and Communication Technologies, Mental health, Mobile applications, pandemic, Pandemics, SARS-CoV-2, Social media platforms, Systematic literature review},
	doi = {10.1089/tmj.2020.0287},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108029620\&doi=10.1089\%2ftmj.2020.0287\&partnerID=40\&md5=abb17cd8cdd22c6edcf3e914fcc6e8e2},
	author = {Drissi, N. and Ouhbi, S. and Marques, G. and De La Torre D{\'\i}ez, I. and Ghogho, M. and Janati Idrissi, M.A.}
}
@article {Drissi2020,
	title = {An analysis on self-management and treatment-related functionality and characteristics of highly rated anxiety apps},
	journal = {International Journal of Medical Informatics},
	volume = {141},
	year = {2020},
	note = {cited By 19},
	abstract = {Background and objective: Anxiety is a common emotion that people often feel in certain situations. But when the feeling of anxiety is persistent and interferes with a person{\textquoteright}s day to day life then this may likely be an anxiety disorder. Anxiety disorders are a common issue worldwide and can fall under general anxiety, panic attacks, and social anxiety among others. They can be disabling and can impact all aspects of an individual{\textquoteright}s life, including work, education, and personal relationships. It is important that people with anxiety receive appropriate care, which in some cases may prove difficult due to mental health care delivery barriers such as cost, stigma, or distance from mental health services. A potential solution to this could be mobile mental health applications. These can serve as effective and promising tools to assist in the management of anxiety and to overcome some of the aforementioned barriers. The objective of this study is to provide an analysis of treatment and management-related functionality and characteristics of high-rated mobile applications (apps) for anxiety, which are available for Android and iOS systems. Method: A broad search was performed in the Google Play Store and App Store following the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) protocol to identify existing apps for anxiety. A set of free and highly rated apps for anxiety were identified and the selected apps were then installed and analyzed according to a predefined data extraction strategy. Results: A total of 167 anxiety apps were selected (123 Android apps and 44 iOS apps). Besides anxiety, the selected apps addressed several health issues including stress, depression, sleep issues, and eating disorders. The apps adopted various treatment and management approaches such as meditation, breathing exercises, mindfulness and cognitive behavioral therapy. Results also showed that 51\% of the selected apps used various gamification features to motivate users to keep using them, 32\% provided social features including chat, communication with others and links to sources of help; 46\% offered offline availability; and only 19\% reported involvement of mental health professionals in their design. Conclusions: Anxiety apps incorporate various mental health care management methods and approaches. Apps can serve as promising tools to assist large numbers of people suffering from general anxiety or from anxiety disorders, anytime, anywhere, and particularly in the current COVID-19 pandemic. {\textcopyright} 2020 Elsevier B.V.},
	keywords = {Android (operating system), Anxiety, anxiety disorder, Betacoronavirus, breathing exercise, cognitive behavioral therapy, Cognitive-behavioral therapies, Coronavirus infection, Coronavirus Infections, data extraction, Disease Outbreaks, eating disorder, Eating disorders, epidemic, Health care, human, Humans, iOS (operating system), isolation and purification, language, Mental health, mental health care personnel, mental health service, Mental health services, mobile application, Mobile applications, organization and management, pandemic, Pandemics, Personal relationships, Pneumonia, priority journal, psychology, Review, self care, Self-Management, sleep, Social anxieties, stress, Systematic Review, telemedicine, therapy, Viral, virology, virus pneumonia},
	doi = {10.1016/j.ijmedinf.2020.104243},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088938984\&doi=10.1016\%2fj.ijmedinf.2020.104243\&partnerID=40\&md5=ec0c4f4cdf72007694d3cc8e8f50bb97},
	author = {Drissi, N. and Ouhbi, S. and Janati Idrissi, M.A. and Ghogho, M.}
}
@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.}
}
@article {Ahajjam2020,
	title = {IMPEC: An integrated system for monitoring and processing electricity consumption in buildings},
	journal = {Sensors (Switzerland)},
	volume = {20},
	number = {4},
	year = {2020},
	note = {cited By 4},
	abstract = {Non-intrusive Load Monitoring (NILM) systems aim at identifying and monitoring the power consumption of individual appliances using the aggregate electricity consumption. Many issues hinder their development. For example, due to the complexity of data acquisition and labeling, datasets are scarce; labeled datasets are essential for developing disaggregation and load prediction algorithms. In this paper, we introduce a new NILM system, called Integrated Monitoring and Processing Electricity Consumption (IMPEC). The main characteristics of the proposed system are flexibility, compactness, modularity, and advanced on-board processing capabilities. Both hardware and software parts of the system are described, along with several validation tests performed at residential and industrial settings. {\textcopyright} 2020 by the authors. Licensee MDPI, Basel, Switzerland.},
	keywords = {Article, Data acquisition, Disaggregation, Electric load management, Electric power utilization, electricity, Electricity-consumption, Energy monitoring, Hardware and software, Industrial settings, Integrated monitoring, Labeled data, Monitoring, Nonintrusive load monitoring, On-board processing, software, Software testing},
	doi = {10.3390/s20041048},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079573681\&doi=10.3390\%2fs20041048\&partnerID=40\&md5=a145afa634dd9bf882c3c570823158c9},
	author = {Ahajjam, M.A. and Licea, D.B. and Ghogho, M. and Kobbane, A.}
}
@article {Gryech2020379,
	title = {Machine learning for air quality prediction using meteorological and traffic related features},
	journal = {Journal of Ambient Intelligence and Smart Environments},
	volume = {12},
	number = {5},
	year = {2020},
	note = {cited By 4},
	pages = {379-391},
	abstract = {The presence of pollutants in the air has a direct impact on our health and causes detrimental changes to our environment. Air quality monitoring is therefore of paramount importance. The high cost of the acquisition and maintenance of accurate air quality stations implies that only a small number of these stations can be deployed in a country. To improve the spatial resolution of the air monitoring process, an interesting idea is to develop data-driven models to predict air quality based on readily available data. In this paper, we investigate the correlations between air pollutants concentrations and meteorological and road traffic data. Using machine learning, regression models are developed to predict pollutants concentration. Both linear and non-linear models are investigated in this paper. It is shown that non-linear models, namely Random Forest (RF) and Support Vector Regression (SVR), better describe the impact of traffic flows and meteorology on the concentrations of pollutants in the atmosphere. It is also shown that more accurate prediction models can be obtained when including some pollutants{\textquoteright} concentration as predictors. This may be used to infer the concentrations of some pollutants using those of other pollutants, thereby reducing the number of air pollution sensors. {\textcopyright} 2020 - IOS Press and the authors. All rights reserved.},
	keywords = {Accurate prediction, Air quality, Air quality monitoring, Air quality prediction, Data-driven model, Decision trees, Forecasting, Machine learning, Non-linear model, Nonlinear systems, Pollution sensors, Predictive analytics, Spatial resolution, Support vector regression, Support vector regression (SVR)},
	doi = {10.3233/AIS-200572},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093821240\&doi=10.3233\%2fAIS-200572\&partnerID=40\&md5=f8b7d726f054f01a2be432560d359fc5},
	author = {Gryech, I. and Ghogho, M. and Elhammouti, H. and Sbihi, N. and Kobbane, A.}
}
@article {Gryech2020,
	title = {Moreair: A low-cost urban air pollution monitoring system},
	journal = {Sensors (Switzerland)},
	volume = {20},
	number = {4},
	year = {2020},
	note = {cited By 25},
	abstract = {MoreAir is a low-cost and agile urban air pollution monitoring system. This paper describes the methodology used in the development of this system along with some preliminary data analysis results. A key feature of MoreAir is its innovative sensor deployment strategy which is based on mobile and nomadic sensors as well as on medical data collected at a children{\textquoteright}s hospital, used to identify urban areas of high prevalence of respiratory diseases. Another key feature is the use of machine learning to perform prediction. In this paper, Moroccan cities are taken as case studies. Using the agile deployment strategy of MoreAir, it is shown that in many Moroccan neighborhoods, road traffic has a smaller impact on the concentrations of particulate matters (PM) than other sources, such as public baths, public ovens, open-air street food vendors and thrift shops. A geographical information system has been developed to provide real-time information to the citizens about the air quality in different neighborhoods and thus raise awareness about urban pollution. {\textcopyright} 2020 by the authors. Licensee MDPI, Basel, Switzerland.},
	keywords = {Agile deployments, Agile manufacturing systems, Air quality, Costs, Decision trees, Geographic information systems, Information systems, Information use, Learning systems, Machine learning, Mobile sensing, Monitoring, Particles (particulate matter), Particulate Matter, Pollution detection, Pollution monitoring, Random forests, Real-time information, Sensor deployment, Urban air pollution, Urban pollutions},
	doi = {10.3390/s20040998},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079609813\&doi=10.3390\%2fs20040998\&partnerID=40\&md5=dc90b4357fcdc64d82d12f57a45f971f},
	author = {Gryech, I. and Ben-Aboud, Y. and Guermah, B. and Sbihi, N. and Ghogho, M. and Kobbane, A.}
}
@article {Ahajjam2020,
	title = {Mored: A moroccan buildings{\textquoteright} electricity consumption dataset},
	journal = {Energies},
	volume = {13},
	number = {24},
	year = {2020},
	note = {cited By 6},
	abstract = {This paper consists of two parts: An overview of existing open datasets of electricity consumption and a description of the Moroccan Buildings{\textquoteright} Electricity Consumption Dataset, a first of its kind, coined as MORED. The new dataset comprises electricity consumption data of various Moroccan premises. Unlike existing datasets, MORED provides three main data components: Whole premises (WP) electricity consumption, individual load (IL) ground-truth consumption, and fully labeled IL signatures, from affluent and disadvantaged neighborhoods. The WP consumption data were acquired at low rates (1/5 or 1/10 samples/s) from 12 households; the IL ground-truth data were acquired at similar rates from five households for extended durations; and IL signature data were acquired at high and low rates (50 k and 4 samples/s) from 37 different residential and industrial loads. In addition, the dataset encompasses non-intrusive load monitoring (NILM) metadata. {\textcopyright} 2020 by the authors.},
	keywords = {Data components, Electric load management, Electric power utilization, Electricity-consumption, Ground truth, Ground truth data, Industrial loads, Low rates, Nonintrusive load monitoring, Signature data},
	doi = {10.3390/en13246737},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106664615\&doi=10.3390\%2fen13246737\&partnerID=40\&md5=2d7b881b6d24a1d21b672b26fa9ee62f},
	author = {Ahajjam, M.A. and Licea, D.B. and Essayeh, C. and Ghogho, M. and Kobbane, A.}
}
@conference {Jeddi20205531,
	title = {Practical Acoustic Energy-Based Estimation of Inhalation Flow Rate for Asthma Monitoring},
	booktitle = {Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS},
	volume = {2020-July},
	year = {2020},
	note = {cited By 1},
	pages = {5531-5536},
	abstract = {The estimation of inhalation flow rate (IFR) using acoustic devices has recently received attention. While existing work often assumes that the microphone is placed at a fixed distance from the acoustic device, this assumption does not hold in real settings. This leads to poor estimation of the IFR since the received acoustic energy varies significantly with the distance. Despite the fact that the acoustic source is passive and only one microphone is used, we show in this paper that the distance can be estimated by exploiting the inhaler actuation sound, generated when releasing the medication. Indeed, this sound is used as a reference acoustic signal which is leveraged to estimate the distance in real settings. The resulting IFR estimation is shown to be highly accurate (R2 = 80.3\%). {\textcopyright} 2020 IEEE.},
	keywords = {Acoustic devices, Acoustic energy, Acoustic signals, Acoustic sources, Highly accurate, Microphones},
	doi = {10.1109/EMBC44109.2020.9175314},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091027116\&doi=10.1109\%2fEMBC44109.2020.9175314\&partnerID=40\&md5=33b9d02826be90a4bdc1aae587e1a8e3},
	author = {Jeddi, Z. and Ghogho, M. and Bohr, A. and Boetker, J. and Li, Y. and Kassou, I.}
}
@conference {Ben-Aboud20201324,
	title = {A research-oriented low-cost air pollution monitoring IoT platform},
	booktitle = {2020 International Wireless Communications and Mobile Computing, IWCMC 2020},
	year = {2020},
	note = {cited By 1},
	pages = {1324-1329},
	abstract = {This paper presents an IoT platform designed for air pollution monitoring. It aiming to facilitate the testing of different data collection strategies, to simplify the air quality monitoring process, to provide the citizens with real-time information about air pollution, to allow citizens to participate in the air quality monitoring process, and to help the authorities identify zones of high air pollution and take the most appropriate measures to improve air quality. The sensor nodes have been developed using low-cost off-the-shelf hardware. Both nomadic and mobile sensor nodes have been developed. A novel sensor management middleware has been designed and developed to have the flexibility to remotely control the operational settings of the sensor nodes and to reduce the volume of transmitted data. Finally, two applications have been developed and implemented for data visualization. The first is a mobile friendly air pollution meter. The second offers a spatial visualization of air pollution levels using a Geographic Information System (GIS). The developed platform has been tested in multiple measurement campaigns. The results of one of these campaigns (conducted in Hay Nahda II, Rabat, Morocco) is presented in this paper to showcase the platform. {\textcopyright} 2020 IEEE.},
	keywords = {Air pollution monitoring, Air quality, Air quality monitoring, Costs, Data collection strategies, Data visualization, Information use, Internet of things, Middleware, Mobile computing, Mobile sensor node, Multiple measurements, Off-the-shelf hardwares, Pollution detection, Real-time information, Sensor nodes, Spatial visualization, Visualization},
	doi = {10.1109/IWCMC48107.2020.9148176},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089690465\&doi=10.1109\%2fIWCMC48107.2020.9148176\&partnerID=40\&md5=6b9c355d625a664f7d5ac062470cf5f0},
	author = {Ben-Aboud, Y. and Ghogho, M. and Kobbane, A.}
}
@conference {Drissi2020593,
	title = {Sensor-based solutions for mental healthcare: A systematic literature review},
	booktitle = {HEALTHINF 2020 - 13th International Conference on Health Informatics, Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020},
	year = {2020},
	note = {cited By 2},
	pages = {593-600},
	abstract = {Mental well-being is a crucial aspect of the person{\textquoteright}s general health, compromised mental health impairs the person{\textquoteright}s functioning, decreases the quality of life, and limits the person{\textquoteright}s contribution to society. The mental health industry is still facing some barriers to healthcare delivery such as costs, mental health illiteracy, and stigma. Incorporating technological interventions in the treatment and the diagnosis processes might help overcome these barriers. Sensors are devices that have been used for healthcare since the 1990s and have been incorporated into mental healthcare in different forms. In this study, we conducted a systematic literature review to identify and analyze sensor-based solutions for mental healthcare. 12 studies were identified and analyzed. The majority of the selected studies presented methods and models and were empirically evaluated and showed promising accuracy results. Different types of sensors were used to collect different types of data about the patient such as physical and behavioral information. The selected studies mainly addressed the use of sensors for common mental issues like stress and depression or the analysis of general mental status. Some studies reported some limitations mainly related to technological issues and lack of standards. {\textcopyright} 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.},
	keywords = {Biomedical engineering, Health care, Healthcare delivery, Medical informatics, Mental health, Quality of life, Systematic literature review, Well being},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083705930\&partnerID=40\&md5=335f69fa969e09929802b364d7f6d696},
	author = {Drissi, N. and Ouhbi, S. and Garc{\'\i}a-Bern{\'a}, J.A. and Idrissi, M.A.J. and Ghogho, M.}
}
@conference {Cherqi201879,
	title = {Analysis of hacking related trade in the darkweb},
	booktitle = {2018 IEEE International Conference on Intelligence and Security Informatics, ISI 2018},
	year = {2018},
	pages = {79-84},
	doi = {10.1109/ISI.2018.8587311},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061056307\&doi=10.1109\%2fISI.2018.8587311\&partnerID=40\&md5=3f2dbe2fc3377bce7e35dd18dfb3710f},
	author = {Cherqi, O. and Mezzour, G. and Ghogho, M. and El Koutbi, M.}
}
@article {Rezzouqi20181145,
	title = {Analyzing the accuracy of historical average for urban traffic forecasting using google maps},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {868},
	year = {2018},
	pages = {1145-1156},
	doi = {10.1007/978-3-030-01054-6_79},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057079293\&doi=10.1007\%2f978-3-030-01054-6_79\&partnerID=40\&md5=5c985c898807cd5eb68ed52c62ea6335},
	author = {Rezzouqi, H. and Gryech, I. and Sbihi, N. and Ghogho, M. and Benbrahim, H.}
}
@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.}
}
@conference {ElBouziady20181,
	title = {Vehicle speed estimation using extracted SURF features from stereo images},
	booktitle = {2018 International Conference on Intelligent Systems and Computer Vision, ISCV 2018},
	volume = {2018-May},
	year = {2018},
	pages = {1-6},
	doi = {10.1109/ISACV.2018.8354040},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050956891\&doi=10.1109\%2fISACV.2018.8354040\&partnerID=40\&md5=53370a3035d03bc86531c0e0e81069f5},
	author = {El Bouziady, A. and Oulad Haj Thami, R. and Ghogho, M. and Bourja, O. and El Fkihi, S.}
}
@conference {Boumkheld2016108,
	title = {Intrusion detection system for the detection of blackhole attacks in a smart grid},
	booktitle = {2016 4th International Symposium on Computational and Business Intelligence, ISCBI 2016},
	year = {2016},
	note = {cited By 0},
	pages = {108-111},
	abstract = {Smart grid is the new electrical grid that will change forever the way we use energy, thanks to the deployment of sensing and communication technologies. It allows consumers to become more aware and in control of their energy consumption. However, the pervasive communication infrastructure deployed inside the smart grid makes it a target to different types of attacks, which is why security is an essential element in building a robust grid. In this article we develop an Intrusion Detection System (IDS) that uses data mining techniques for the detection of a Denial of service (DOS) attack in a smart grid, the so called blackhole attack. {\textcopyright} 2016 IEEE.},
	doi = {10.1109/ISCBI.2016.7743267},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85005943412\&doi=10.1109\%2fISCBI.2016.7743267\&partnerID=40\&md5=5c0ce5fd5e8c611326382b2cf80da730},
	author = {Boumkheld, N. and Ghogho, M. and El Koutbi, M.}
}
