Étude des performances épuratoires de la technique du lagunage aéré appliquée à la station d’épuration de la ville d’Errachidia - Maroc

Dans le but d’évaluer le rendement de la nouvelle station d’épuration de la ville d’Errachidia type lagunage aéré, nous avons étudié les paramètres physico-chimiques et bactériologiques des eaux brutes et épurées de la station. Pour cela, nous avons réalisé un ensemble de mesures tels que : la température, le pH et la conductivité (paramètres sur places), la demande biochimique en oxygène DBO5, la demande chimique en oxygène DCO et les matières en suspension MES (paramètres physico-chimiques), les coliformes fécaux (CF) et les coliformes totaux (CT) (paramètres bactériologiques). Les résultats d’analyses ont montré une évolution des rendements épuratoires de la nouvelle station par rapport à l’ancienne de type lagunage naturel. Ces rendements mesurés à partir de la DBO5, DCO et MES donnent des valeurs respectivement de 82%, 83% et 88%. D’autre part la qualité bactériologique des eaux épurées est conforme à une réutilisation agricole.Mots-clés : station d’épuration, MES, DCO, DBO5, paramètres bactériologiques

An evaluation of neural networks performance for job scheduling in a public cloud environment

Artificial Neural Networks (ANNs) represent a family of powerful machine learning-based techniques used to solve many real-world problems. The various applications of ANNs can be summarized into classification or pattern recognition, prediction and modeling. As with other machine learning techniques, ANNs are getting momentum in the Big Data era for analysing, predicting and Big Data analytics from large data sets. ANNs bring new opportunities for Big Data analysis for extracting accurate information from the data, yet there are also several challenges to be faced not known before with traditional data sets. Indeed, the success of learning and modeling Big Data by ANNs varies with training sample size, depends on data dimensionality, complex data formats, data variety, etc. In particular, ANNs performance is directly influenced by data size, requiring more memory resources. In this context, and due to the assumption that data set may no longer fit into main memory, it is interesting to investigate the performance of ANNs when data is read from main memory or from the disk. This study represents a performance evaluation of Artificial Neural Network (ANN) with multiple hidden layers, when training data is read from memory or from disk. The study shows also the trade-offs between processing time and data size when using ANNs.Peer ReviewedPostprint (author's final draft

Supporting academic decision making at higher educational institutions using machine learning-based algorithms

Decisions made by deans and university managers greatly impact the entire academic community as well as society as a whole. In this paper, we present survey results on which academic decisions they concern and the variables involved in them. Using machine learning algorithms, we predicted graduation rates in a real case study to support decision making. Real data from five undergraduate engineering programs at District University Francisco Jose de Caldas in Colombia illustrate our results. The comparison between support vector machine and artificial neural network is held using the confusion matrix and the receiver operating characteristic curve. The algorithm methods and architecture are presented