The presence of wireless sensor networks and their appliance in monitoring the different types of... more The presence of wireless sensor networks and their appliance in monitoring the different types of systems is growing in recent years. Considering the importance of a variety of monitoring systems in the open outdoor environments, such as agriculture, traffic monitoring, etc., the application of Wireless Sensor Networks (WSNs) with the integration of Unmanned Aerial Vehicles (UAVs) is important as well. Those networks can be used as support for monitoring the target systems, with the extensive data exchange between UAVs and clusters of fixed wireless sensors deployed in the wide area. In such systems, the planning of the deployment of sensor nodes gives a good starting point for ensuring the effectiveness of the data communications, good area coverage, and cost-effective solutions. When planning the interaction of UAVs and wireless sensor nodes, the mobility of the UAVs, as well as the network configuration and their influence on the signal quality, should be taken into concern. The good planning of wireless sensor nodes can increase the coverage of the system and it can decrease implementation costs as well. This paper has investigated the influence of UAV mobility as well as the network configuration on IEEE 802.15.4 network performance and signal quality. Presented analyses show that there is a clear difference in the influence of various wireless sensor network parameters on the received signal strength (RSSI) at the receiver side. In all four scenarios, the distance between the sensor nodes and UAV has the highest influence on RSSI, with the values ranging from 0.50 to 0.57 in scenarios 1, 3, and 4, and a value of 0.37 for scenario 2. The correlation of two parameters (drone height and elevation) is the same for both scenarios when the real distance is between 400 and 500m, while the drone height influence is slightly higher compared to elevation for scenarios with real distances up to 400m, 0.24 and 0.34 compared to 0.16 and 0.22.
The preprocessing of data is an important task in rough set theory as well as in Entropy. The dis... more The preprocessing of data is an important task in rough set theory as well as in Entropy. The discretization of data as part of the preprocessing of data is a very influential process. Is there a connection between the segmentation of the data histogram and data discretization? The authors propose a novel data segmentation technique based on a histogram with regard to the quality of a data discretization. The significance of a cut’s position has been researched on several groups of histograms. A data set reduct was observed with respect to the histogram type. Connections between the data histograms and cuts, reduct and the classification rules have been researched. The result is that the reduct attributes have a more irregular histogram than attributes out of the reduct. The following discretization algorithms were used: the entropy algorithm and the Maximal Discernibility algorithm developed in rough set theory. This article presents the Cuts Selection Method based on histogram seg...
This paper presents the comparison of the classification rules generated by See5.0/C5.0 and SSCO ... more This paper presents the comparison of the classification rules generated by See5.0/C5.0 and SSCO systems. See5.0/C5.0 system is based on C4.5 algorithm, while SSCO system is based on an algorithm, theoretically correlated to Rough Set Theory. Both systems generate classification rules in the IF THEN form. The goal of comparison of the classification rules, generated by those two systems is detection and extraction of important rules in the terms of classification power. Some experimental comparison of two systems has been done using the Wisconsin Breast Cancer Database (January 8, 1991), obtained from UCI Machine Learning Repository.
This paper deals with techniques of data analyses based on the rough sets theory and similarity r... more This paper deals with techniques of data analyses based on the rough sets theory and similarity relations. Local and global similarity relations have been described and used to analyze data. The process of the aggregation of local similarities has also been described. The technique is very useful when data is collected by various small and medium-sized enterprises. It is well
2011 IEEE 9th International Symposium on Intelligent Systems and Informatics, 2011
The paper presents a new form of indiscernibility relation based on graph. Based on widely accept... more The paper presents a new form of indiscernibility relation based on graph. Based on widely accepted definitions of indiscernibility relation and its matrix representation, it has been shown how the indiscernibility relations can be obtained by a graph. The application of the indiscernibility graph enables the partitioning of the universe of objects represented by their attributes. This is in connection
2012 IEEE 10th Jubilee International Symposium on Intelligent Systems and Informatics, 2012
ABSTRACT Data mining techniques and their applications are widely recognized as powerful tools in... more ABSTRACT Data mining techniques and their applications are widely recognized as powerful tools in various domains. Educational data mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. In the domain of education, there are varieties of data of different types collected during the educational process. The main question is: Is it possible to process the collected data with the data mining system and what are main advantages of data mining and e-learning interaction? In this paper, we present an insight into the possible interaction between course management system and data mining techniques. The main goal of this work is to investigate some data mining techniques in order to deliver most appropriate learning object to the learner. In this paper, visualization as a data mining technique is investigated. For this purpose, free data mining tool Weka was used.
This paper presents the comparison of the classification rules generated by See5.0/C5.0 and SSCO ... more This paper presents the comparison of the classification rules generated by See5.0/C5.0 and SSCO systems. See5.0/C5.0 system is based on C4.5 algorithm, while SSCO system is based on an algorithm, theoretically correlated to Rough Set Theory. Both systems generate classification rules in the IF THEN form. The goal of comparison of the classification rules, generated by those two systems is detection and extraction of important rules in the terms of classification power. Some experimental comparison of two systems has been done using the Wisconsin Breast Cancer Database (January 8, 1991), obtained from UCI Machine Learning Repository.
The paper deals with the decision rules synthesis i n the domain of expert systems and knowledge ... more The paper deals with the decision rules synthesis i n the domain of expert systems and knowledge based systems. These systems incorporate expert knowledge which is often expressed in the If Then form. As i t is very hard for experts to formally articulate their knowledge, automated decision rule composing algorithms have been used. The rule composing algorithms are often based on the rough sets theory. Originally, ru les are composed from data table by equivalence relation, while in this paper we investigate the rules based o n dominance relation. The main goal of this paper is to single out possible benefits and advantages of dominance relation based rules over equivalence rel ation based rules.
Rough set theory has become an important mathematical tool for dealing with uncertainty in data. ... more Rough set theory has become an important mathematical tool for dealing with uncertainty in data. The data discretization is one of the main problems to be solved in the process of synthesis of decision rules from table-organized data. In this paper, we present a new discretization method in the context of supervised training. This method is based on the neighborhood graph. To evaluate supervised discretization , we used data sets obtained from the UCI Machine Learning Repository. We have used the Rosetta system and proposed SSCO system. The experimental results show that our method is effective.
The table-organized data can be analyzed by various algorithms; some of them are capable of gener... more The table-organized data can be analyzed by various algorithms; some of them are capable of generating IF THEN decision rules which comprises of condition attributes and decision attributes. However, it is possible to reduce the set of condition attributes but without information loss. By analysis of the condition attributes set and cuts histogram obtained by discretization and rule consistency, it is possible to choose condition attributes. This paper gives some directions and the practical example.
2021 International Telecommunications Conference (ITC-Egypt)
The presence of wireless sensor networks and their appliance in monitoring the different types of... more The presence of wireless sensor networks and their appliance in monitoring the different types of systems is growing in recent years. Considering the importance of a variety of monitoring systems in the open outdoor environments, such as agriculture, traffic monitoring, etc., the application of Wireless Sensor Networks (WSNs) with the integration of Unmanned Aerial Vehicles (UAVs) is important as well. Those networks can be used as support for monitoring the target systems, with the extensive data exchange between UAVs and clusters of fixed wireless sensors deployed in the wide area. In such systems, the planning of the deployment of sensor nodes gives a good starting point for ensuring the effectiveness of the data communications, good area coverage, and cost-effective solutions. When planning the interaction of UAVs and wireless sensor nodes, the mobility of the UAVs, as well as the network configuration and their influence on the signal quality, should be taken into concern. The good planning of wireless sensor nodes can increase the coverage of the system and it can decrease implementation costs as well. This paper has investigated the influence of UAV mobility as well as the network configuration on IEEE 802.15.4 network performance and signal quality. Presented analyses show that there is a clear difference in the influence of various wireless sensor network parameters on the received signal strength (RSSI) at the receiver side. In all four scenarios, the distance between the sensor nodes and UAV has the highest influence on RSSI, with the values ranging from 0.50 to 0.57 in scenarios 1, 3, and 4, and a value of 0.37 for scenario 2. The correlation of two parameters (drone height and elevation) is the same for both scenarios when the real distance is between 400 and 500m, while the drone height influence is slightly higher compared to elevation for scenarios with real distances up to 400m, 0.24 and 0.34 compared to 0.16 and 0.22.
The presence of wireless sensor networks and their appliance in monitoring the different types of... more The presence of wireless sensor networks and their appliance in monitoring the different types of systems is growing in recent years. Considering the importance of a variety of monitoring systems in the open outdoor environments, such as agriculture, traffic monitoring, etc., the application of Wireless Sensor Networks (WSNs) with the integration of Unmanned Aerial Vehicles (UAVs) is important as well. Those networks can be used as support for monitoring the target systems, with the extensive data exchange between UAVs and clusters of fixed wireless sensors deployed in the wide area. In such systems, the planning of the deployment of sensor nodes gives a good starting point for ensuring the effectiveness of the data communications, good area coverage, and cost-effective solutions. When planning the interaction of UAVs and wireless sensor nodes, the mobility of the UAVs, as well as the network configuration and their influence on the signal quality, should be taken into concern. The good planning of wireless sensor nodes can increase the coverage of the system and it can decrease implementation costs as well. This paper has investigated the influence of UAV mobility as well as the network configuration on IEEE 802.15.4 network performance and signal quality. Presented analyses show that there is a clear difference in the influence of various wireless sensor network parameters on the received signal strength (RSSI) at the receiver side. In all four scenarios, the distance between the sensor nodes and UAV has the highest influence on RSSI, with the values ranging from 0.50 to 0.57 in scenarios 1, 3, and 4, and a value of 0.37 for scenario 2. The correlation of two parameters (drone height and elevation) is the same for both scenarios when the real distance is between 400 and 500m, while the drone height influence is slightly higher compared to elevation for scenarios with real distances up to 400m, 0.24 and 0.34 compared to 0.16 and 0.22.
The preprocessing of data is an important task in rough set theory as well as in Entropy. The dis... more The preprocessing of data is an important task in rough set theory as well as in Entropy. The discretization of data as part of the preprocessing of data is a very influential process. Is there a connection between the segmentation of the data histogram and data discretization? The authors propose a novel data segmentation technique based on a histogram with regard to the quality of a data discretization. The significance of a cut’s position has been researched on several groups of histograms. A data set reduct was observed with respect to the histogram type. Connections between the data histograms and cuts, reduct and the classification rules have been researched. The result is that the reduct attributes have a more irregular histogram than attributes out of the reduct. The following discretization algorithms were used: the entropy algorithm and the Maximal Discernibility algorithm developed in rough set theory. This article presents the Cuts Selection Method based on histogram seg...
This paper presents the comparison of the classification rules generated by See5.0/C5.0 and SSCO ... more This paper presents the comparison of the classification rules generated by See5.0/C5.0 and SSCO systems. See5.0/C5.0 system is based on C4.5 algorithm, while SSCO system is based on an algorithm, theoretically correlated to Rough Set Theory. Both systems generate classification rules in the IF THEN form. The goal of comparison of the classification rules, generated by those two systems is detection and extraction of important rules in the terms of classification power. Some experimental comparison of two systems has been done using the Wisconsin Breast Cancer Database (January 8, 1991), obtained from UCI Machine Learning Repository.
This paper deals with techniques of data analyses based on the rough sets theory and similarity r... more This paper deals with techniques of data analyses based on the rough sets theory and similarity relations. Local and global similarity relations have been described and used to analyze data. The process of the aggregation of local similarities has also been described. The technique is very useful when data is collected by various small and medium-sized enterprises. It is well
2011 IEEE 9th International Symposium on Intelligent Systems and Informatics, 2011
The paper presents a new form of indiscernibility relation based on graph. Based on widely accept... more The paper presents a new form of indiscernibility relation based on graph. Based on widely accepted definitions of indiscernibility relation and its matrix representation, it has been shown how the indiscernibility relations can be obtained by a graph. The application of the indiscernibility graph enables the partitioning of the universe of objects represented by their attributes. This is in connection
2012 IEEE 10th Jubilee International Symposium on Intelligent Systems and Informatics, 2012
ABSTRACT Data mining techniques and their applications are widely recognized as powerful tools in... more ABSTRACT Data mining techniques and their applications are widely recognized as powerful tools in various domains. Educational data mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. In the domain of education, there are varieties of data of different types collected during the educational process. The main question is: Is it possible to process the collected data with the data mining system and what are main advantages of data mining and e-learning interaction? In this paper, we present an insight into the possible interaction between course management system and data mining techniques. The main goal of this work is to investigate some data mining techniques in order to deliver most appropriate learning object to the learner. In this paper, visualization as a data mining technique is investigated. For this purpose, free data mining tool Weka was used.
This paper presents the comparison of the classification rules generated by See5.0/C5.0 and SSCO ... more This paper presents the comparison of the classification rules generated by See5.0/C5.0 and SSCO systems. See5.0/C5.0 system is based on C4.5 algorithm, while SSCO system is based on an algorithm, theoretically correlated to Rough Set Theory. Both systems generate classification rules in the IF THEN form. The goal of comparison of the classification rules, generated by those two systems is detection and extraction of important rules in the terms of classification power. Some experimental comparison of two systems has been done using the Wisconsin Breast Cancer Database (January 8, 1991), obtained from UCI Machine Learning Repository.
The paper deals with the decision rules synthesis i n the domain of expert systems and knowledge ... more The paper deals with the decision rules synthesis i n the domain of expert systems and knowledge based systems. These systems incorporate expert knowledge which is often expressed in the If Then form. As i t is very hard for experts to formally articulate their knowledge, automated decision rule composing algorithms have been used. The rule composing algorithms are often based on the rough sets theory. Originally, ru les are composed from data table by equivalence relation, while in this paper we investigate the rules based o n dominance relation. The main goal of this paper is to single out possible benefits and advantages of dominance relation based rules over equivalence rel ation based rules.
Rough set theory has become an important mathematical tool for dealing with uncertainty in data. ... more Rough set theory has become an important mathematical tool for dealing with uncertainty in data. The data discretization is one of the main problems to be solved in the process of synthesis of decision rules from table-organized data. In this paper, we present a new discretization method in the context of supervised training. This method is based on the neighborhood graph. To evaluate supervised discretization , we used data sets obtained from the UCI Machine Learning Repository. We have used the Rosetta system and proposed SSCO system. The experimental results show that our method is effective.
The table-organized data can be analyzed by various algorithms; some of them are capable of gener... more The table-organized data can be analyzed by various algorithms; some of them are capable of generating IF THEN decision rules which comprises of condition attributes and decision attributes. However, it is possible to reduce the set of condition attributes but without information loss. By analysis of the condition attributes set and cuts histogram obtained by discretization and rule consistency, it is possible to choose condition attributes. This paper gives some directions and the practical example.
2021 International Telecommunications Conference (ITC-Egypt)
The presence of wireless sensor networks and their appliance in monitoring the different types of... more The presence of wireless sensor networks and their appliance in monitoring the different types of systems is growing in recent years. Considering the importance of a variety of monitoring systems in the open outdoor environments, such as agriculture, traffic monitoring, etc., the application of Wireless Sensor Networks (WSNs) with the integration of Unmanned Aerial Vehicles (UAVs) is important as well. Those networks can be used as support for monitoring the target systems, with the extensive data exchange between UAVs and clusters of fixed wireless sensors deployed in the wide area. In such systems, the planning of the deployment of sensor nodes gives a good starting point for ensuring the effectiveness of the data communications, good area coverage, and cost-effective solutions. When planning the interaction of UAVs and wireless sensor nodes, the mobility of the UAVs, as well as the network configuration and their influence on the signal quality, should be taken into concern. The good planning of wireless sensor nodes can increase the coverage of the system and it can decrease implementation costs as well. This paper has investigated the influence of UAV mobility as well as the network configuration on IEEE 802.15.4 network performance and signal quality. Presented analyses show that there is a clear difference in the influence of various wireless sensor network parameters on the received signal strength (RSSI) at the receiver side. In all four scenarios, the distance between the sensor nodes and UAV has the highest influence on RSSI, with the values ranging from 0.50 to 0.57 in scenarios 1, 3, and 4, and a value of 0.37 for scenario 2. The correlation of two parameters (drone height and elevation) is the same for both scenarios when the real distance is between 400 and 500m, while the drone height influence is slightly higher compared to elevation for scenarios with real distances up to 400m, 0.24 and 0.34 compared to 0.16 and 0.22.
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Papers by Visnja Ognjenovic