@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.}
}
@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.}
}
@article {7391589120120201,
	title = {Software Productivity: Harmonization in ISO/IEEE Software Engineering Standards.},
	journal = {Journal of Software (1796217X)},
	volume = {7},
	number = {2},
	year = {2012},
	pages = {462 - 470},
	abstract = {The software productivity is an important key of software quality factors. The productivity measure has become a tool for managers since it is used to compare the performance between different companies (benchmarking) and to compare the efficiency of different developers in the same company. Therefore, it allows doing strategic planning and decision making based on such measurement. A variety of international standardization bodies such as IEEE and ISO as well as software engineering researchers have proposed a set of factors which influence the software productivity attribute, and also a set of measures to evaluate it. However, there is no unique model that integrates all the software productivity best practices. The aim of this paper is to survey the available international standards and research work on software productivity and figure out the key differences in order to propose a standards-based model. Such model will include the set of quality attributes that could be used to ref},
	keywords = {Benchmark problems (Computer science), Computer programmers, Decision making, IEEE 802.11 (Standard), IEEE Std. 1045, International Organization for Standardization, ISO 9126, Measurement, Measurements, Productivity Drivers, Quality Attributes, Quality Models, Software Developers Productivity, Standards},
	issn = {1796217X},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=iih\&AN=73915891\&site=ehost-live},
	author = {Cheikhi, Laila and Al-Qutaish, Rafa E. and Idri, Ali}
}
