Contribution to Decision Tree Induction with Python: A Review

Among the learning algorithms, one of the most popular and easiest to understand is the decision tree induction. The popularity of this method is related to three nice characteristics: interpretability, efficiency, and flexibility. Decision tree can be used for both classification and regression kind of problem. Automatic learning of a decision tree is characterised by the fact that it uses logic and mathematics to generate rules instead of selecting them based on intuition and subjectivity. In this review, we present essential steps to understand the fundamental concepts and mathematics behind decision tree from training to building. We study criteria and pruning algorithms, which have been proposed to control complexity and optimize decision tree performance. A discussion around several works and tools will be exposed to analyze the techniques of variance reduction, which do not improve or change the representation bias of decision tree. We chose Pima Indians Diabetes dataset to cover essential questions to understand pruning process. The paper’s original contribution is to provide an up-to-date overview that is fully focused on implemented algorithms to build and optimize decision trees. This contributes to evolve future developments of decision tree induction

A Survey of Deep Learning Methods for WTP Control and Monitoring

Drinking water is vital for everyday life. We are dependent on water for everything from cooking to sanitation. Without water, it is estimated that the average, healthy human won’t live more than 3–5 days. The water is therefore essential for the productivity of our community. The water treatment process (WTP) may vary slightly at different locations, depending on the technology of the plant and the water it needs to process, but the basic principles are largely the same. As the WTP is complex, traditional laboratory methods and mathematical models have limitations to optimize this type of operations. These pose challenges for water-sanitation services and research community. To overcome this matter, deep learning is used as an alternative to provide various solutions in WTP optimization. Compared to traditional machine learning methods and because of its practicability, deep learning has a strong learning ability to better use data sets for data mining and knowledge extraction. The aim of this survey is to review the existing advanced approaches of deep learning and their applications in WTP especially in coagulation control and monitoring. Besides, we also discuss the limitations and prospects of deep learning

Gestion supervisée d’une unité de coagulation pour la potabilisation des eaux à partir d’une méthodologie d’apprentissage et d’expertise

Le travail présenté propose une méthodologie de classification par apprentissage qui permet l’identification des états fonctionnels sur une unité de coagulation impliquée dans le traitement des eaux de surface. La supervision et le diagnostic de ce procédé ont été réalisés en utilisant la méthode de classification LAMDA (Learning Algorithm for Multivariate Data Analysis). Cette méthodologie d’apprentissage et d’expertise permet d’exploiter et d’agréger toutes les informations provenant du procédé et de son environnement ainsi que les connaissances de l’expert. L’étude montre qu’il est possible d’ajouter aux informations issues des capteurs classiques (température, matières en suspension, pH, conductivité, oxygène dissous), la valeur de la dose de coagulant calculée par un capteur logiciel développé dans une étude antérieure afin d’affiner le diagnostic. Le site d’application choisi pour l’identification des états fonctionnels est la station de production d’eau potable Rocade de la ville de Marrakech, Maroc.The present work proposes a learning classification method to identify the functional states of a coagulation process for the treatment of surface water and production of drinking water. Supervisory control and diagnosis were performed using the LAMDA (Learning Algorithm for Multivariate Data Analysis) classification technique. This expert learning method involves the processing and aggregation of all information stemming from an environmental process, and it allows the incorporation of the user’s knowledge. The study shows that it is possible to refine the diagnosis by taking into account the information obtained from common sensors (e.g., temperature, suspended solids, pH, conductivity, dissolved oxygen) together with the predicted coagulant dosage, as computed with an intelligent software sensor developed previously. The Rocade drinking water plant located at Marrakech, Morocco was chosen to test the method

Classer les supports pour préserver les connaissances : l'exemple des 'patchs' dans le domaine de la création musicale contemporaine

cote interne IRCAM: Lamrini11aNone / NoneInternational audienceEn s'appuyant sur l'analyse des expressions algébriques contenues dans des documents transcrits à partir des processus existants, conçus dans l'environnement Max/MSP, un lien systématique entre classification conceptuelle et classification par apprentissage automatique est établi afin d'établir les prémices d'une organologie des traitements musicaux et audio numériques

Quand la préservation passe par la classification : le cas des documents sonores et musicaux

cote interne IRCAM: Lamrini10aNone / NoneNational audienceLa création artistique contemporaine fait aujourd’hui largement appel aux technologies électroacoustiques et numériques. Dans la création musicale spécialement, les dispositifs et les outils logiciels permettant de manipuler les sons en temps réel sont apparus voici une trentaine d’années, et notamment les « patchs » processus numériques temps réel utilisés lors de performances ou de concerts en live. Soumis aux difficultés de la préservation, ces modules logiciels de traitement sonore sont souvent considérés comme des véritables documents numériques, ils sont à la fois supports de création et supports de constitution de connaissances dans la création artistique contemporaine. Pour soutenir les échanges et la construction d'une interprétation collective autour de ce document, nous proposons dans cet article une approche d’analyse et de classification, par les techniques du data-mining, de ces processus numériques afin de former une ontologie du domaine voire une organologie des traitements musicaux et audio numériques

Energetical and rheological approaches of wheat flour dough mixing with a spiral mixer

Wheat flour dough was mixed in a spiral mixer for different operating conditions, at a speed ranging from 80 to 320 rpm, for a period of 7-15 min. The specific mechanical energy E, delivered and the dough temperature T-d were continuously recorded and varied from 7 to 82 kJ/kg and T-d of 13.5 to 36 degrees C, respectively. E-s and T-d were strongly correlated because of viscous dissipation but variations of ingredients initial temperature allowed to uncouple them. The energy balance during mixing process was set through a simple model assuming constant heat transfer (h = 75 W/(m(2) degrees C)) which took into account thermal losses. Shear viscosity curves of the dough were determined by correlating volumic power to angular speed; by comparison to typical dough shear flow curve, a constant characteristic of the mixer geometry (K-s = 1.55) was determined like for models of mixer power consumption. The impact of mixing on small deformation rheological properties was assessed by DMA and the variations of the ratio, of maximum (75 degrees C) to minimum (50 degrees C) elastic modulus, was interpreted in relation with gluten network development. (C) 2011 Elsevier Ltd. All rights reserved

A decision support tool for technical processes optimization in drinking water treatment

International audienceIn water treatment, the technical processes study aims generally to deal with problems that natural processes are unable or only inadequate to perform. The technical systems aim for a good control of process and therefore a good stability. This is the case of coagulation process in drinking water treatment by removing suspended particles. It requires a good knowledge of raw water characteristics to ensure adequate choice of the coagulant rate. Without the adequate coagulant, this method is not effective. The good coagulation control is therefore essential to guarantee the reliability of the water treatment and the final quality of water produced. This paper presents a neural approach in combination with a fuzzy methodology to study the impact of raw water characteristics on the coagulation process control. Using the concepts of evolutionary algorithms, we developed a decision support tool using fault detection, data validation-reconstruction, and predictive control methods to predict the optimum coagulant dosage to be used in a drinking water treatment plant. Simulation results using experimental data stemming from four treatment plants show the reliability of this system to optimize one of critical processes in drinking water treatment

Calibration study of HDM-4 Model of structural cracking models for flexible pavements in Moroccan Context

International audienceLe présent travail a pour objet, l'étude de calage du sous-modèle de fissuration structurelle qui a une influence assez notable sur le processus de détérioration des chaussées routières, pour ce faire on est basé sur l'exploitation de la base de données routière, résultant du relevé visuel effectué par le CNER (Centre National d'Eudes et de Recherches Routières). Un suivi étalé sur 6 ans de 99 sections test implantées sur le territoire national en considérant 10 familles de structures modifiant les matériaux constituant le corps de chaussée ainsi que leurs épaisseurs. Méthode de calage des sous-modèles de comportement de chaussées recommandée par les concepteurs du modèle HDM-4 est la méthode "windows". Afin d'établir des facteurs d'ajustement notés Kcia pour l'initiation de fissures et Kcpa pour la propagation de fissures. Cette méthode est basée sur un traitement des données en utilisant la régression linéaire simple avec la ligne d'égalité. En utilisant les équations de fissuration structurelle du modèle HDM-4 par une simulation sur Excel, on aboutit lors de cette étude à des résultats permettant un bon calage par l'utilisation les coefficients suivants : Kcia = 1,27 et Kcpa = 0.79. Abstract The present work aims the study of timing of structural crack Sub-model that has a fairly significant influence on the process of deterioration of road pavements, to do this it is based on the exploitation of the road database resulting visual survey conducted by the NCSRR (National Centre of Studies and Road Research). A follow-up spread over 6 years of 99 test sections located on national territory by considering 10 family's structures modifying the floor body constituting materials and their thicknesses. Calibration method of road behavior of sub-models recommended by the designers of the HDM-4 model is the method "windows". To establish adjustment factors noted Kcia for initiation of cracks and Kcpa for crack propagation. This method is based on a data processing using simple linear regression with equal line. Using the equations of structural cracking HDM-4 model a simulation on Excel, we arrive in this study to results allowing good timing by using the following coefficients: Kcia = 1.27 and Kcpa = 0.79