Submission to Episciences Lot sizing is important in production planning. It consists of determining a production plan that meets the orders and other constraints while minimizing the production cost. Here, we consider a Discrete Lot Sizing and Scheduling Problem (DLSP), specifically the Pigment Sequencing Problem (PSP). We have developed a solution that uses Genetic Algorithms to tackle the PSP. Our approach introduces adaptive techniques for each step of the genetic algorithm, including initialization, selection, crossover, and mutation. We conducted a series of experiments to assess the performance of our approach across some multiple trials using publicly available instances of the PSP. Our experimental results demonstrate that Genetic Algorithms are practical and effective approaches for solving DLSP. Le dimensionnement de lots tient une place importante en planification de production en industrie. Il consiste à trouver un plan de production qui à la fois satisfait les demandes et autres contraintes tout en minimisant les coûts de production. Dans cet article, nous étudions une instance de problèmes de dimension discret (DLSP), le Pigment Sequencing Problem (PSP). Nous avons développé une approche basée sur les algorithmes génétiques afin de le résoudre. Notre approche propose des méthodes adaptatives pour chacune des étapes des algorithmes génétiques que sont l'initialisation, la sélection, le croisement et la mutation. Les expériences menées nous ont permis d'évaluer la performance de cette approche sur des instances en accès publique de PSP. Les résultats obtenus montrent que les algorithmes génétiques constituent une approche intéressante et effective dans la résolution des DLSP.
{"title":"Genetic Algorithms for Solving the Pigment Sequencing Problem","authors":"V. Houndji, Tafsir Gna","doi":"10.46298/arima.11382","DOIUrl":"https://doi.org/10.46298/arima.11382","url":null,"abstract":"Submission to Episciences\u0000 Lot sizing is important in production planning. It consists of determining a production plan that meets the orders and other constraints while minimizing the production cost. Here, we consider a Discrete Lot Sizing and Scheduling Problem (DLSP), specifically the Pigment Sequencing Problem (PSP). We have developed a solution that uses Genetic Algorithms to tackle the PSP. Our approach introduces adaptive techniques for each step of the genetic algorithm, including initialization, selection, crossover, and mutation. We conducted a series of experiments to assess the performance of our approach across some multiple trials using publicly available instances of the PSP. Our experimental results demonstrate that Genetic Algorithms are practical and effective approaches for solving DLSP.\u0000 Le dimensionnement de lots tient une place importante en planification de production en industrie. Il consiste à trouver un plan de production qui à la fois satisfait les demandes et autres contraintes tout en minimisant les coûts de production. Dans cet article, nous étudions une instance de problèmes de dimension discret (DLSP), le Pigment Sequencing Problem (PSP). Nous avons développé une approche basée sur les algorithmes génétiques afin de le résoudre. Notre approche propose des méthodes adaptatives pour chacune des étapes des algorithmes génétiques que sont l'initialisation, la sélection, le croisement et la mutation. Les expériences menées nous ont permis d'évaluer la performance de cette approche sur des instances en accès publique de PSP. Les résultats obtenus montrent que les algorithmes génétiques constituent une approche intéressante et effective dans la résolution des DLSP.","PeriodicalId":517660,"journal":{"name":"Revue Africaine de Recherche en Informatique et Mathématiques Appliquées","volume":" 43","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140383557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Teodor Knapik, A. Ratiarison, Hasina Razafindralambo
Six time series related to atmospheric phenomena are used as inputs for experiments offorecasting with singular spectrum analysis (SSA). Existing methods for SSA parametersselection are compared throughout their forecasting accuracy relatively to an optimal aposteriori selection and to a naive forecasting methods. The comparison shows that awidespread practice of selecting longer windows leads often to poorer predictions. It alsoconfirms that the choices of the window length and of the grouping are essential. Withthe mean error of rainfall forecasting below 1.5%, SSA appears as a viable alternative forhorizons beyond two weeks. Six séries temporelles ont servi pour des évaluations expérimentales, en fonction des paramètres choisis, d'exactitude de prévisions de phénomènes atmosphériques par la méthode d'analyse de spectre singulier (SSA). Les méthodes les plus connues de sélection automatique des ces paramètres ont été comparées avec une sélection optimale a posteriori et des méthodes de prévision naïves. On constate notamment qu'une pratique répandue d'utiliser des fenêtres plus larges conduit souvent à des prévisions de médiocre qualité. On confirme aussi que le choix du groupement est capital. Avec l'erreur moyenne observée en dessous de 1,5% de prévisions de pluviométrie pour des horizons au delà de deux semaines, la SSA apparaît comme une alternative viable à d'autres méthodes de prévision.
{"title":"An experimental evaluation of choices of SSA forecasting parameters","authors":"Teodor Knapik, A. Ratiarison, Hasina Razafindralambo","doi":"10.46298/arima.9641","DOIUrl":"https://doi.org/10.46298/arima.9641","url":null,"abstract":"Six time series related to atmospheric phenomena are used as inputs for experiments offorecasting with singular spectrum analysis (SSA). Existing methods for SSA parametersselection are compared throughout their forecasting accuracy relatively to an optimal aposteriori selection and to a naive forecasting methods. The comparison shows that awidespread practice of selecting longer windows leads often to poorer predictions. It alsoconfirms that the choices of the window length and of the grouping are essential. Withthe mean error of rainfall forecasting below 1.5%, SSA appears as a viable alternative forhorizons beyond two weeks.\u0000 Six séries temporelles ont servi pour des évaluations expérimentales, en fonction des paramètres choisis, d'exactitude de prévisions de phénomènes atmosphériques par la méthode d'analyse de spectre singulier (SSA). Les méthodes les plus connues de sélection automatique des ces paramètres ont été comparées avec une sélection optimale a posteriori et des méthodes de prévision naïves. On constate notamment qu'une pratique répandue d'utiliser des fenêtres plus larges conduit souvent à des prévisions de médiocre qualité. On confirme aussi que le choix du groupement est capital. Avec l'erreur moyenne observée en dessous de 1,5% de prévisions de pluviométrie pour des horizons au delà de deux semaines, la SSA apparaît comme une alternative viable à d'autres méthodes de prévision.","PeriodicalId":517660,"journal":{"name":"Revue Africaine de Recherche en Informatique et Mathématiques Appliquées","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140381882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fidy Heritiana Andrianarivony, Anne Cortella, J. Salone, Viviane Durand-Guerrier, Angelo Raherinirina
soumission à Episciences This article proposes a method for extracting knowledge in association rules using the classical measure of implication intensity. We then applied our method to data from mathematics didactics studies. The aim of the didactic study was to identify the relationships between students' difficulties and skills when demonstrating a mathematical proposition formulated in French. The results of our study show that our methodology is effective in extracting interesting rules. In addition, the results of our didactic analysis showed the dependency between understanding a mathematical statement in French, competence in translating it formally and proving it. Cet article propose une méthode pour extraire des connaissances en règles d’association en utilisant la mesure classique de l’intensité d’implication. Nous avons ensuite appliqué notre méthode dans des données issues de travaux en didactique des mathématiques. L’objectif de l’étude en didactique est de connaitre les relations entre les difficultés et les compétences des élèves lorsque ceux-ci démontrent une proposition mathématique formulée en langue française. Le résultat de notre étude nous a démontré que notre méthodologie est efficace pour extraire les règles intéressantes. De plus les résultats d’analyse didactique ont montré la dépendance entre compréhension d’un énoncé mathématique en français, compétence à le traduire formellement et à le prouver.
{"title":"Extraction of association rules based on the classical measure of intensity of involvement: application to mathematics didactics","authors":"Fidy Heritiana Andrianarivony, Anne Cortella, J. Salone, Viviane Durand-Guerrier, Angelo Raherinirina","doi":"10.46298/arima.12231","DOIUrl":"https://doi.org/10.46298/arima.12231","url":null,"abstract":"soumission à Episciences\u0000 This article proposes a method for extracting knowledge in association rules using the classical measure of implication intensity. We then applied our method to data from mathematics didactics studies. The aim of the didactic study was to identify the relationships between students' difficulties and skills when demonstrating a mathematical proposition formulated in French. The results of our study show that our methodology is effective in extracting interesting rules. In addition, the results of our didactic analysis showed the dependency between understanding a mathematical statement in French, competence in translating it formally and proving it.\u0000 Cet article propose une méthode pour extraire des connaissances en règles d’association en utilisant la mesure classique de l’intensité d’implication. Nous avons ensuite appliqué notre méthode dans des données issues de travaux en didactique des mathématiques. L’objectif de l’étude en didactique est de connaitre les relations entre les difficultés et les compétences des élèves lorsque ceux-ci démontrent une proposition mathématique formulée en langue française. Le résultat de notre étude nous a démontré que notre méthodologie est efficace pour extraire les règles intéressantes. De plus les résultats d’analyse didactique ont montré la dépendance entre compréhension d’un énoncé mathématique en français, compétence à le traduire formellement et à le prouver.","PeriodicalId":517660,"journal":{"name":"Revue Africaine de Recherche en Informatique et Mathématiques Appliquées","volume":"117 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140386951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yves Fotso Fotso, S. Touzeau, B. Tsanou, F. Grognard, S. Bowong
The coffee berry borer (CBB) Hypothenemus hampei (Coleoptera: Scolytidae) is the most important insect pest affecting coffee production worldwide and generating huge economic losses. As most of its life cycle occurs inside the coffee berry, its control is extremely difficult. To tackle this issue, we solve an optimal control problem based on a berry age-structured dynamical model that describes the infestation dynamics of coffee berries by CBB during a cropping season. This problem consists in applying a bio-insecticide at discrete times in order to maximise the economic profit of healthy coffee berries, while minimising the CBB population for the next cropping season. We derive analytically the first-order necessary optimality conditions of the control problem. Numerical simulations are provided to illustrate the effectiveness of the optimal control strategy.
{"title":"Optimal impulsive control of coffee berry borers in a berry age-structured epidemiological model","authors":"Yves Fotso Fotso, S. Touzeau, B. Tsanou, F. Grognard, S. Bowong","doi":"10.46298/arima.11338","DOIUrl":"https://doi.org/10.46298/arima.11338","url":null,"abstract":"The coffee berry borer (CBB) Hypothenemus hampei (Coleoptera: Scolytidae) is the most important insect pest affecting coffee production worldwide and generating huge economic losses. As most of its life cycle occurs inside the coffee berry, its control is extremely difficult. To tackle this issue, we solve an optimal control problem based on a berry age-structured dynamical model that describes the infestation dynamics of coffee berries by CBB during a cropping season. This problem consists in applying a bio-insecticide at discrete times in order to maximise the economic profit of healthy coffee berries, while minimising the CBB population for the next cropping season. We derive analytically the first-order necessary optimality conditions of the control problem. Numerical simulations are provided to illustrate the effectiveness of the optimal control strategy.","PeriodicalId":517660,"journal":{"name":"Revue Africaine de Recherche en Informatique et Mathématiques Appliquées","volume":"71 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140394102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: The fundamental need for authentication and identification of humans using their physiological, behavioral or biological characteristics, continues to be applied extensively to secure localities, property, financial transactions, etc. Biometric systems based on face characteristics, continue to attract the attention of researchers, major public and private services. In the literature, many methods have been deployed by different authors. The best performance must be found in order to be able to recommend the most effective method. So, the main objective of thisarticle is to make a comparative study of different existing techniques.Methods: A biometric system is generally composed of four stages: acquisition of facial images, preprocessing, extraction of characteristics and finally classification. In this work, the focus is on machine learning algorithms for classification. These algorithms are: Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Random Forests (RF), Logistic Regression (LR), Naive Bayesian Classification (NB: Naive Bayes’ Classifiers) and deep learning techniques such as Convolutional Neural Networks (CNN). The comparison criterion is the average performance, calculated using three performance measures: recognition rate, confusion matrix, and the Area Under Receiver Operating Characteristic (ROC) curve.Results: Based on this criterion, the performance comparison of selected machine learning algorithms, shows that CNN is the best, with an average performance of 100.00% On ORL face database. However, on the YALE database, classical algorithms such as artificial neural networks have obtained the best performances, the highest being a rate of 100%.Discussion: Deep learning techniques are very efficient in image classification as proven by the results on the ORL database. However, their inefficiency on YALE face database is due to the small size of this database which is inappropriate for some deep learning algorithms. But this weakness can be corrected by image augmentation techniques. The comparison of these results with existing state-of-the-art methods is nearly the same. Authors achieved performances of 94.82%, 95.79%, 96.15%, 96.44%, 97.27%, 98.52% and 98.95% for NB, KNN, RF, LR, ANN, SVM and CNN classifiers, respectively. Finally, in depth discussion, it is concluded that between all these approaches which are useful in face recognition, the CNN is the best classification algorithm.
{"title":"Comparative study of machine learning algorithms for face recognition","authors":"Atsu Alagah Komlavi, Kadri Chaibou, H. Naroua","doi":"10.46298/arima.9291","DOIUrl":"https://doi.org/10.46298/arima.9291","url":null,"abstract":"Background: The fundamental need for authentication and identification of humans using their physiological, behavioral or biological characteristics, continues to be applied extensively to secure localities, property, financial transactions, etc. Biometric systems based on face characteristics, continue to attract the attention of researchers, major public and private services. In the literature, many methods have been deployed by different authors. The best performance must be found in order to be able to recommend the most effective method. So, the main objective of thisarticle is to make a comparative study of different existing techniques.Methods: A biometric system is generally composed of four stages: acquisition of facial images, preprocessing, extraction of characteristics and finally classification. In this work, the focus is on machine learning algorithms for classification. These algorithms are: Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Random Forests (RF), Logistic Regression (LR), Naive Bayesian Classification (NB: Naive Bayes’ Classifiers) and deep learning techniques such as Convolutional Neural Networks (CNN). The comparison criterion is the average performance, calculated using three performance measures: recognition rate, confusion matrix, and the Area Under Receiver Operating Characteristic (ROC) curve.Results: Based on this criterion, the performance comparison of selected machine learning algorithms, shows that CNN is the best, with an average performance of 100.00% On ORL face database. However, on the YALE database, classical algorithms such as artificial neural networks have obtained the best performances, the highest being a rate of 100%.Discussion: Deep learning techniques are very efficient in image classification as proven by the results on the ORL database. However, their inefficiency on YALE face database is due to the small size of this database which is inappropriate for some deep learning algorithms. But this weakness can be corrected by image augmentation techniques. The comparison of these results with existing state-of-the-art methods is nearly the same. Authors achieved performances of 94.82%, 95.79%, 96.15%, 96.44%, 97.27%, 98.52% and 98.95% for NB, KNN, RF, LR, ANN, SVM and CNN classifiers, respectively. Finally, in depth discussion, it is concluded that between all these approaches which are useful in face recognition, the CNN is the best classification algorithm.","PeriodicalId":517660,"journal":{"name":"Revue Africaine de Recherche en Informatique et Mathématiques Appliquées","volume":"6 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140285579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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