
Altaf AlaouiIbn Tofaïl University · Department of Mathematics
Altaf Alaoui
Doctor of Engineering
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26
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Publications
Publications (26)
Spatial data enables researchers to assess various factors of urban livability. Regarding the development of remote sensing technics and advances in Geographical Information Systems (GIS), large collections of valuable spatial datasets become available for livability researchers. This paper aims to identify relevant spatial data sources in use in l...
Precision agriculture techniques have been increasingly adopted worldwide to optimize cultivation practices and achieve sustainable crop production. In this study, we developed a Machine Learning approach to identify optimal cultivation practices for sustainable apple production in precision agriculture in the Msemrir town Morocco. We collected a d...
In Morocco, agriculture is an important sector that contributes to the country's economy and food security. Accurately predicting crop yields is crucial for farmers, policy makers, and other stakeholders to make informed decisions regarding resource allocation and food security. This paper investigates the potential of Machine Learning algorithms f...
Agricultural productivity is a critical component of sustainable economic growth, particularly in developing countries. Morocco, with its vast agricultural potential, is in need of advanced technologies to optimize crop productivity. Precision farming is one such technology, which incorporates the use of artificial intelligence and machine learning...
Learning models used for prediction are mostly developed without taking into account the size of datasets that can produce models of high accuracy and better performance. Although, the general believe is that, large dataset is needed to construct a predictive learning model. To describe a data set as large in size depends on the circumstances and c...
The implementation of Artificial Intelligence in the aerospace field is fairly new and various domains of the aerospace discipline are to be explored. This paper provides a general review on the upbringing and improvements of Artificial Intelligence as a pilar of the next generation of scientific research. Applications and developments brought by A...
This paper provides a general review on the upbringing and improvements of Artificial Intelligence
(AI) as a pilar of the next generation of scientific research. Applications and developments brought
by AI and Deep Learning to the aerospace industry generally, mainly the prediction of
aerodynamic coefficients and design optimization. This paper d...
Arthropod-borne infections are a medical and economic threat to humans and livestock. Over the last three decades, several unprecedented viral outbreaks have been recorded in the Western part of the Arabian Peninsula. However, little is known about the circulation and diversity of arthropod-borne viruses in this region. To prepare for new outbreaks...
The process of data discovery is an approach to extracting knowledge, valid, and usable information from large amounts of data, using automatic or semi-automatic methods. This article is an inventory of the different information extraction processes encountered in the literature for different fields of application and for the development of environ...
A high number of wetlands were irreversibly lost in arid regions, mainly because their conservation raises the great conflict between maintaining wetland hydrology and satisfying water needs of the human population. In Yemen, as an arid developing country, the poor knowledge on wetlands is another challenge that faces their conservation. The presen...
The high use of social media has led to a new form of political involvement and participation. In this paper, we use Latent Class Analysis to identify participants' behavior regarding political participation and engagement based on the nature of their interaction on social media. The LCA findings reveal three statistically distinct and behavioral c...
Random Forests are an ensemble learning method that refers to train individual classifiers and aggregates their predictors to produce one optimal predictive model. In this paper, we compare the accuracy metric of six Random Forest methods implemented in the ‘CARET package’ of the R language. We explore the Time Resolution Universe (HTRU2) dataset c...
Ensemble methods are a machine learning technique that combines several base models in order to produce one optimal predictive model. In this paper, we compare accuracy metric of three ensemble methods: Bagging, Random Forest, and Boosting. Then, We use the “CARET package”, implemented in R language, to experiment the Time Resolution Universe (HTRU...
The use of social media as a tool for political and electoral communication is not fully explored by Moroccan researchers. This study aims to identify the influencers of the political conversation, on YouTube, during the period of the Moroccan election campaign of 2016 by using centrality measures, and discuss the nature of information shared by th...
The pulsar classification represents a major issue in the astrophysical area. The Bagging Algorithm is an ensemble method widely used to improve the performance of classification algorithms, especially in the case of pulsar search. In this way, our paper tries to prove how the Bagging Method can improve the performance of pulsar candidate detection...
Random Forest Algorithm is a method of machine learning that refers to train individual classifiers and aggregates their predictors. It is specifically reserved to decision tree classifiers and used for classification and regression problems in several areas. Beyond the choice of the most appropriate algorithm to the study context and the database...
The conception and treatment of complex classifications or typologies (as hierarchical tables), mainly of nature components, has for long constituted a major concern for researchers. Hence, several codification methods were developed in order to address and facilitate the management of such tables. Most of these methods uses alphanumeric codes, tha...
Viral haemorrhagic fevers (VHFs) are a group of infectious, devastating and severe diseases caused by enveloped single-stranded RNA viruses. The endemicity, emergence or re-emergence of different VHF viruses and lack of vaccines and antiviral therapy for most VHFs result in a significant global threat. Most VHF viruses are restricted to specific pa...
Purpose of Review
Rift valley fever (RVF) is a debilitating disease leading to economic loss in livestock, severe clinical forms, and fatal cases in humans. The re-emergence of RVF in some countries of the Middle East and North Africa (MENA) region has caused global public concern. The aim of this review is to highlight the different outbreaks in t...
Viral hemorrhagic fever (VHF) refers to a group of diseases characterized by an acute febrile syndrome with hemorrhagic manifestations and high mortality rates caused by several families of viruses that affect humans and animals.
These diseases are typically endemic in certain geographical regions and sometimes cause major outbreaks. The history of...
This paper presents a solution based on the unsupervised classification for the multiple-criteria analysis problems of data, where the characteristics and the number of clusters are not predefined, and the objects of data sets are described by several criteria, and the latter can be contradictory, of different nature and varied weights. This work f...
Equine influenza is an infectious and contagious disease of horses. Studies on this topic are rare in the Maghreb countries. Therefore, the aim of this work is to present the various studies conducted on serological and molecular equine influenza virus since 1972 in the Maghreb region in particular in Morocco, Algeria and Tunisia.
A total of four e...