Pub Date : 2020-12-01DOI: 10.1109/sccic51516.2020.9377333
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Pub Date : 2020-12-01DOI: 10.1109/SCCIC51516.2020.9377330
Teegwende Zougmore, Sadouanouan Malo, B. Gueye, S. Ouaro
We propose a conceptual framework to predict the risk of freshwater source infestation by Schistosomiasis parasites. Our approach aims to combine two sources of information which are outputs of prediction models. The proposed framework is broken down into three Y-shaped branches. The left branch is a water quality prediction model built on the basis of machine learning algorithms applied on data collected by an IoT platform. These data represent physical and chemical parameters of a freshwater source which affect the development of snails and parasites that cause Schistosomiasis. The branch on the right is a non autonomous mathematical model which through its derived reproduction number $R_{0}$ determines the density evolution of all actors involved in Schistosomiasis transmission life cycle. In the middle branch happens a fusion process which combines the two information by taking into account their uncertainty and complementary. The output of the fusion is the final decision about the risk of infestation. This work has focused on the identification of applicable machine learning algorithms for water quality prediction and the identification of a mathematical model. The work has consisted also to give the characteristics of the fusion problem to handle.
{"title":"Toward a Data Fusion Based Framework to Predict Schistosomiasis Infection","authors":"Teegwende Zougmore, Sadouanouan Malo, B. Gueye, S. Ouaro","doi":"10.1109/SCCIC51516.2020.9377330","DOIUrl":"https://doi.org/10.1109/SCCIC51516.2020.9377330","url":null,"abstract":"We propose a conceptual framework to predict the risk of freshwater source infestation by Schistosomiasis parasites. Our approach aims to combine two sources of information which are outputs of prediction models. The proposed framework is broken down into three Y-shaped branches. The left branch is a water quality prediction model built on the basis of machine learning algorithms applied on data collected by an IoT platform. These data represent physical and chemical parameters of a freshwater source which affect the development of snails and parasites that cause Schistosomiasis. The branch on the right is a non autonomous mathematical model which through its derived reproduction number $R_{0}$ determines the density evolution of all actors involved in Schistosomiasis transmission life cycle. In the middle branch happens a fusion process which combines the two information by taking into account their uncertainty and complementary. The output of the fusion is the final decision about the risk of infestation. This work has focused on the identification of applicable machine learning algorithms for water quality prediction and the identification of a mathematical model. The work has consisted also to give the characteristics of the fusion problem to handle.","PeriodicalId":120154,"journal":{"name":"2020 IEEE 2nd International Conference on Smart Cities and Communities (SCCIC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130662904","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}
Sensor network is a set of sensors nodes brought together for multi-hop data transmission to one or more sinks. Wireless sensor networks (WSN) are used in many areas such as smart cities, environmental monitoring, precision agriculture. Once deployed, WSNs are very rigid in terms of reconfiguration. Sofware-Defined Networking (SDN) technology is explored in order to facilitate reconfiguration of WSN nodes. Several architectures have been proposed, among which SDN-WISE. SDN-WISE uses centralized routing model which separates data plane executed by the sensor nodes and the control plane executed by a software program hosted in a controller. In SDN-WISE, data transmission path choice is the best route in terms of the number of hops. Improved variants of SND-WISE use other metrics such as node energy, but the problem with these approaches is that a chosen path is used until one of its nodes depletes its energy before a path change process is initiated. This impacts efficiency of the network and reduces life of the network. In this work, we propose Dynamic Energy Aware Routing Protocol (DEARP) that monitors residual energy of nodes in order to make routing decisions. This will prevent nodes on most stressed paths from running out of its energy quickly while other paths with nodes with higher residual energies could be used. Our approach optimizes lifetime of WSN by preventing most stressed nodes from running out sooner.
{"title":"DEARP: Dynamic Energy Aware Routing Protocol for Wireless Sensor Network","authors":"Mahamadi Boulou, Tiguiane Yélémou, Doda Afoussatou Rollande, Hamadoun Tall","doi":"10.1109/SCCIC51516.2020.9377331","DOIUrl":"https://doi.org/10.1109/SCCIC51516.2020.9377331","url":null,"abstract":"Sensor network is a set of sensors nodes brought together for multi-hop data transmission to one or more sinks. Wireless sensor networks (WSN) are used in many areas such as smart cities, environmental monitoring, precision agriculture. Once deployed, WSNs are very rigid in terms of reconfiguration. Sofware-Defined Networking (SDN) technology is explored in order to facilitate reconfiguration of WSN nodes. Several architectures have been proposed, among which SDN-WISE. SDN-WISE uses centralized routing model which separates data plane executed by the sensor nodes and the control plane executed by a software program hosted in a controller. In SDN-WISE, data transmission path choice is the best route in terms of the number of hops. Improved variants of SND-WISE use other metrics such as node energy, but the problem with these approaches is that a chosen path is used until one of its nodes depletes its energy before a path change process is initiated. This impacts efficiency of the network and reduces life of the network. In this work, we propose Dynamic Energy Aware Routing Protocol (DEARP) that monitors residual energy of nodes in order to make routing decisions. This will prevent nodes on most stressed paths from running out of its energy quickly while other paths with nodes with higher residual energies could be used. Our approach optimizes lifetime of WSN by preventing most stressed nodes from running out sooner.","PeriodicalId":120154,"journal":{"name":"2020 IEEE 2nd International Conference on Smart Cities and Communities (SCCIC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134563841","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}
Pub Date : 2020-12-01DOI: 10.1109/SCCIC51516.2020.9377332
J. Thiombiano, Yaya Traoré, Sadouanouan Malo, Patrice Koassa, Oumarou Sié
Semantic annotation of resources with ontologies plays a decisive role for semantic search, interoperability and data integration. In this paper, we focus on semi-automated web page annotation for a meningitis knowledge sharing platform. This web page annotation refers to the introduction of categories on a page or a set of tags on a page. Thus, we present a semantic web page annotation approach based on the use of an ontology. In this proposal, ontology is used to determine the categories on the web page. Our method extracts the relevant terms called keywords in the page to annotate. Our strategy for identifying the categories focus on ontology's concepts similarity with each keyword. The tags are identified among the keywords that are not mapped to the ontology's concepts. The results of simulation indicate the approach is feasible for practical use in semantic annotation of a new web page.
{"title":"Semantic annotation of resources based on ontologies:application to a knowledge sharing platform on meningitis","authors":"J. Thiombiano, Yaya Traoré, Sadouanouan Malo, Patrice Koassa, Oumarou Sié","doi":"10.1109/SCCIC51516.2020.9377332","DOIUrl":"https://doi.org/10.1109/SCCIC51516.2020.9377332","url":null,"abstract":"Semantic annotation of resources with ontologies plays a decisive role for semantic search, interoperability and data integration. In this paper, we focus on semi-automated web page annotation for a meningitis knowledge sharing platform. This web page annotation refers to the introduction of categories on a page or a set of tags on a page. Thus, we present a semantic web page annotation approach based on the use of an ontology. In this proposal, ontology is used to determine the categories on the web page. Our method extracts the relevant terms called keywords in the page to annotate. Our strategy for identifying the categories focus on ontology's concepts similarity with each keyword. The tags are identified among the keywords that are not mapped to the ontology's concepts. The results of simulation indicate the approach is feasible for practical use in semantic annotation of a new web page.","PeriodicalId":120154,"journal":{"name":"2020 IEEE 2nd International Conference on Smart Cities and Communities (SCCIC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120997335","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}
Pub Date : 2020-12-01DOI: 10.1109/SCCIC51516.2020.9377335
Abdoul Azize Kindo, Guidedi Kaladzavi, Sadouanouan Malo, G. Camara, T. Tapsoba, Kolyang
Fuzzy logic is an extension of Boolean logic created by Lotfi Zadeh in 1965 based on his mathematical theory of fuzzy sets, which is a generalization of classical set theory. By introducing the notion of degree or possibility, fuzzy logic confers very appreciable flexibility to the reasoning, which uses it, which makes it possible to take into account imprecision and uncertainties. Despite a beginning of reluctance and even rejection of the theory of fuzzy logic, it has been used in many fields. Today, its usefulness and its reputation are no longer to be demonstrated because more than 50 years after its appearance, it has been well adopted by engineers and part of the scientific world. In this paper, we present a state of the art on fuzzy logic and its use in the field of knowledge representation and more specifically in ontology modeling using OWL and SWRL.
{"title":"Fuzzy logic approach for knowledge modeling in an Ontology: A review","authors":"Abdoul Azize Kindo, Guidedi Kaladzavi, Sadouanouan Malo, G. Camara, T. Tapsoba, Kolyang","doi":"10.1109/SCCIC51516.2020.9377335","DOIUrl":"https://doi.org/10.1109/SCCIC51516.2020.9377335","url":null,"abstract":"Fuzzy logic is an extension of Boolean logic created by Lotfi Zadeh in 1965 based on his mathematical theory of fuzzy sets, which is a generalization of classical set theory. By introducing the notion of degree or possibility, fuzzy logic confers very appreciable flexibility to the reasoning, which uses it, which makes it possible to take into account imprecision and uncertainties. Despite a beginning of reluctance and even rejection of the theory of fuzzy logic, it has been used in many fields. Today, its usefulness and its reputation are no longer to be demonstrated because more than 50 years after its appearance, it has been well adopted by engineers and part of the scientific world. In this paper, we present a state of the art on fuzzy logic and its use in the field of knowledge representation and more specifically in ontology modeling using OWL and SWRL.","PeriodicalId":120154,"journal":{"name":"2020 IEEE 2nd International Conference on Smart Cities and Communities (SCCIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129725371","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}
Pub Date : 2020-12-01DOI: 10.1109/SCCIC51516.2020.9377334
Agbokpanzo Richard Gilles, Didavi Audace, H. Aristide, Oloulade Arouna, Espanet Christophe
This paper aims to show the influence of the data size and the number of meteorological data used in the prediction of the output power of a photovoltaic installation with neural networks. We trained with different input data with the 2019a MATLAB Neural Network Start (NNS) tool, three feedforward networks. To train these networks, we used the algorithm of Levenberg-Marquardt and as data meteorological data such as wind speed at 10m from the ground, air temperature at 2m from the ground, position of the sun, direct radiation on an inclined plane and diffuse radiation on an inclined plane downloaded in the PVGIS database for a period from January 1, 2005 to December 31, 2016 for Natitingou city in the Republic of Benin. The first network was trained with wind speed at 10m, air temperature at 2m and sun position as input, the second network with wind speed at 10m, air temperature at 2m, sun position and direct radiation on an inclined plane and the third network with wind speed at 10m, air temperature at 2m, sun position, direct radiation on an inclined plane and diffuse radiation on an inclined plane. For the three networks we took the best results from 10 trainings. Thus, we obtained for the three networks respectively as mean square error 6186, 191 and 0.46 and as regression values 0.95, 0.998 and 0.999 respectively. In descending order according to the data used, the best performance was obtained with: • wind speed at 10m, air temperature at 2m, position of the sun, radiation, direct on an inclined plane and diffuse radiation on an inclined plane; • wind speed at 10m, air temperature at 2m, position of the sun, and the direct radiation on an inclined plane; • wind speed at 10m, air temperature at 2m and position of the sun.
{"title":"Evaluation of the photovoltaic power prediction performance of a neural network based on input data","authors":"Agbokpanzo Richard Gilles, Didavi Audace, H. Aristide, Oloulade Arouna, Espanet Christophe","doi":"10.1109/SCCIC51516.2020.9377334","DOIUrl":"https://doi.org/10.1109/SCCIC51516.2020.9377334","url":null,"abstract":"This paper aims to show the influence of the data size and the number of meteorological data used in the prediction of the output power of a photovoltaic installation with neural networks. We trained with different input data with the 2019a MATLAB Neural Network Start (NNS) tool, three feedforward networks. To train these networks, we used the algorithm of Levenberg-Marquardt and as data meteorological data such as wind speed at 10m from the ground, air temperature at 2m from the ground, position of the sun, direct radiation on an inclined plane and diffuse radiation on an inclined plane downloaded in the PVGIS database for a period from January 1, 2005 to December 31, 2016 for Natitingou city in the Republic of Benin. The first network was trained with wind speed at 10m, air temperature at 2m and sun position as input, the second network with wind speed at 10m, air temperature at 2m, sun position and direct radiation on an inclined plane and the third network with wind speed at 10m, air temperature at 2m, sun position, direct radiation on an inclined plane and diffuse radiation on an inclined plane. For the three networks we took the best results from 10 trainings. Thus, we obtained for the three networks respectively as mean square error 6186, 191 and 0.46 and as regression values 0.95, 0.998 and 0.999 respectively. In descending order according to the data used, the best performance was obtained with: • wind speed at 10m, air temperature at 2m, position of the sun, radiation, direct on an inclined plane and diffuse radiation on an inclined plane; • wind speed at 10m, air temperature at 2m, position of the sun, and the direct radiation on an inclined plane; • wind speed at 10m, air temperature at 2m and position of the sun.","PeriodicalId":120154,"journal":{"name":"2020 IEEE 2nd International Conference on Smart Cities and Communities (SCCIC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131803676","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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