Pub Date : 2017-12-01DOI: 10.1109/ICSOFTCOMP.2017.8280090
J. Roman, Devarshi Mehta, P. Sajja
This paper considers a graph based adaptive approach for sequencing of learning objects for Special Needs Learners (SNL) in customized manner. The proposed algorithm traverses the nodes of the graph containing learning content topics in effective manner. This approach ensures not only customized learning for special needs learners, but also imparts some level of intelligence in the process of learning. The SNL goes through the necessary nodes on the graph form the first node of the learning module (priority based) to the last learning module considering the priorities and needs of the learners and obtains the optimal solution for the SNL. The algorithm is designed in such a way that the learning process and outcome are inline with the predefined curriculum. The curriculum is a tailor made collaboration of various independent and reusable learning modules as per the personalized requirements and the learning ability of the SNL. The paper also proposes the parameters that would contribute for the personalized learning of the SNL.
{"title":"Learning needs for special needs learners: A graph based adaptive approach for content sequencing","authors":"J. Roman, Devarshi Mehta, P. Sajja","doi":"10.1109/ICSOFTCOMP.2017.8280090","DOIUrl":"https://doi.org/10.1109/ICSOFTCOMP.2017.8280090","url":null,"abstract":"This paper considers a graph based adaptive approach for sequencing of learning objects for Special Needs Learners (SNL) in customized manner. The proposed algorithm traverses the nodes of the graph containing learning content topics in effective manner. This approach ensures not only customized learning for special needs learners, but also imparts some level of intelligence in the process of learning. The SNL goes through the necessary nodes on the graph form the first node of the learning module (priority based) to the last learning module considering the priorities and needs of the learners and obtains the optimal solution for the SNL. The algorithm is designed in such a way that the learning process and outcome are inline with the predefined curriculum. The curriculum is a tailor made collaboration of various independent and reusable learning modules as per the personalized requirements and the learning ability of the SNL. The paper also proposes the parameters that would contribute for the personalized learning of the SNL.","PeriodicalId":118765,"journal":{"name":"2017 International Conference on Soft Computing and its Engineering Applications (icSoftComp)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132911248","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 : 2017-12-01DOI: 10.1109/ICSOFTCOMP.2017.8280087
K. Suthar, Hiral B. Patel
In current era of Computer world everyone wants a quick access of data from anywhere and on anytime. Cloud computing provides lots of benefits to user at cheaper cost and whenever required. On one side thousand number of users information become available on central premises and able to achieve location independency where as on other side this generate issues related to data security as well as quick retrieval of information by analyzing user documents. These issues are not directly under the control of user. If we don't take proper care while retrieving of relevant information from the document of thousands of users then it becomes very tedious process. It is also crucial to secure user information on cloud storage from unauthorized access. When user needs to search something, it will be searched in every available document which takes large amount of time making user job wearisome. Hence to accost revealed important issues, here we proposed an efficient secure searching mechanism in which user can get require details quickly without getting any type of burden. Our proposed scheme deals with efficient searching and securing user information in Cloud environment which increase trust level as well as adoption of Cloud.
{"title":"Secure and efficient group based data retrieval from cloud storage using obfuscation and data mining techniques","authors":"K. Suthar, Hiral B. Patel","doi":"10.1109/ICSOFTCOMP.2017.8280087","DOIUrl":"https://doi.org/10.1109/ICSOFTCOMP.2017.8280087","url":null,"abstract":"In current era of Computer world everyone wants a quick access of data from anywhere and on anytime. Cloud computing provides lots of benefits to user at cheaper cost and whenever required. On one side thousand number of users information become available on central premises and able to achieve location independency where as on other side this generate issues related to data security as well as quick retrieval of information by analyzing user documents. These issues are not directly under the control of user. If we don't take proper care while retrieving of relevant information from the document of thousands of users then it becomes very tedious process. It is also crucial to secure user information on cloud storage from unauthorized access. When user needs to search something, it will be searched in every available document which takes large amount of time making user job wearisome. Hence to accost revealed important issues, here we proposed an efficient secure searching mechanism in which user can get require details quickly without getting any type of burden. Our proposed scheme deals with efficient searching and securing user information in Cloud environment which increase trust level as well as adoption of Cloud.","PeriodicalId":118765,"journal":{"name":"2017 International Conference on Soft Computing and its Engineering Applications (icSoftComp)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117324898","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 : 2017-12-01DOI: 10.1109/ICSOFTCOMP.2017.8280096
I. Khanykov, M. Kharinov, Chirag Patel
The paper focuses on image segmentation by means of approaching by piecewise-constant approximations. The objective is the improvement of the segmented image, expressed in the noticeable drop of image approximation error (total squared error). The proposed method involves segment dividing into two in some image part and merge of a pair of adjacent segments in another image part, keeping a total segment number constant. For further enhancement of the segmentation, the proposed method is used in combination with advanced K-means method. The steep decline of the approximation error is provided along with obvious increase of perceptual quality of image segmentation. The effect is achieved owing to binary adaptive hierarchy of nested segments, generated for each segment. The segmentation improvement method is usable for the advancement of the computer vision systems using conventional segmentation by partitioning image into connected segments.
{"title":"Image segmentation improvement by reversible segment merging","authors":"I. Khanykov, M. Kharinov, Chirag Patel","doi":"10.1109/ICSOFTCOMP.2017.8280096","DOIUrl":"https://doi.org/10.1109/ICSOFTCOMP.2017.8280096","url":null,"abstract":"The paper focuses on image segmentation by means of approaching by piecewise-constant approximations. The objective is the improvement of the segmented image, expressed in the noticeable drop of image approximation error (total squared error). The proposed method involves segment dividing into two in some image part and merge of a pair of adjacent segments in another image part, keeping a total segment number constant. For further enhancement of the segmentation, the proposed method is used in combination with advanced K-means method. The steep decline of the approximation error is provided along with obvious increase of perceptual quality of image segmentation. The effect is achieved owing to binary adaptive hierarchy of nested segments, generated for each segment. The segmentation improvement method is usable for the advancement of the computer vision systems using conventional segmentation by partitioning image into connected segments.","PeriodicalId":118765,"journal":{"name":"2017 International Conference on Soft Computing and its Engineering Applications (icSoftComp)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129059161","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 : 2017-12-01DOI: 10.1109/ICSOFTCOMP.2017.8280095
R. Perumal, P. Mouli
This paper affords an analysis of a novel local descriptor-dimensionality reduced local directional Pattern (DR-LDP) on different scales. DR-LDP extracts the features of the face by partitioning the image into 3 χ 3 sub-regions and the sub-region was convoluted with a set of eight Kirsch masks. The single eight-bit code generated for each sub-region. The histogram features are extracted by partitioning the resultant DR-LDP encoded image into 8 × 8 regions. The features of each regions are combined to form a feature vector for the given facial image. For any query image, the same process is carried out to extract the feature vector. A chi-square test is used to measure the dissimilarity of the feature, the dissimilarity of feature vector in the database with the feature vector of query image was determined to recognize the face. The experiments had been accomplished on well-known benchmark databases. In this paper, an analysis of DR-LDP on 3 χ 3, 5 χ 5, 7 χ 7 regions and convultes each region with 3 × 3, 5 × 5, 7 × 7 eight Kirsch masks are performed to test the robustness of it. From the analysis, it is evident that the DR-LDP performs the best for the scale 3 χ 3.
{"title":"Analysis of dimensionality reduced local directional pattern on different scales","authors":"R. Perumal, P. Mouli","doi":"10.1109/ICSOFTCOMP.2017.8280095","DOIUrl":"https://doi.org/10.1109/ICSOFTCOMP.2017.8280095","url":null,"abstract":"This paper affords an analysis of a novel local descriptor-dimensionality reduced local directional Pattern (DR-LDP) on different scales. DR-LDP extracts the features of the face by partitioning the image into 3 χ 3 sub-regions and the sub-region was convoluted with a set of eight Kirsch masks. The single eight-bit code generated for each sub-region. The histogram features are extracted by partitioning the resultant DR-LDP encoded image into 8 × 8 regions. The features of each regions are combined to form a feature vector for the given facial image. For any query image, the same process is carried out to extract the feature vector. A chi-square test is used to measure the dissimilarity of the feature, the dissimilarity of feature vector in the database with the feature vector of query image was determined to recognize the face. The experiments had been accomplished on well-known benchmark databases. In this paper, an analysis of DR-LDP on 3 χ 3, 5 χ 5, 7 χ 7 regions and convultes each region with 3 × 3, 5 × 5, 7 × 7 eight Kirsch masks are performed to test the robustness of it. From the analysis, it is evident that the DR-LDP performs the best for the scale 3 χ 3.","PeriodicalId":118765,"journal":{"name":"2017 International Conference on Soft Computing and its Engineering Applications (icSoftComp)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133270770","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 : 2017-12-01DOI: 10.1109/ICSOFTCOMP.2017.8280088
Bhaumik Vaidya, C. Paunwala
Object detection and tracking in the video sequence is a challenging task and time consuming process. Intrinsic factors like pose, appearance, variation in scale and extrinsic factors like variation in illumination, occlusion and clutter are major factors effecting this task. The main aim of this work is to implement and compare different algorithms in challenging conditions and find the algorithm that performs very efficiently on real time videos. In this paper, two motion based algorithms Zivkovic Adaptive Gaussian Mixture Model (ADGMM) and Grimson Gaussian Mixture Models (GGMM) and two feature based algorithms Speeded up Robust features (SURF) and Haar Cascade are implemented. The comparison of these algorithms in real life challenges and application is done to find out suitable algorithm for a particular application.
{"title":"Comparative analysis of motion based and feature based algorithms for object detection and tracking","authors":"Bhaumik Vaidya, C. Paunwala","doi":"10.1109/ICSOFTCOMP.2017.8280088","DOIUrl":"https://doi.org/10.1109/ICSOFTCOMP.2017.8280088","url":null,"abstract":"Object detection and tracking in the video sequence is a challenging task and time consuming process. Intrinsic factors like pose, appearance, variation in scale and extrinsic factors like variation in illumination, occlusion and clutter are major factors effecting this task. The main aim of this work is to implement and compare different algorithms in challenging conditions and find the algorithm that performs very efficiently on real time videos. In this paper, two motion based algorithms Zivkovic Adaptive Gaussian Mixture Model (ADGMM) and Grimson Gaussian Mixture Models (GGMM) and two feature based algorithms Speeded up Robust features (SURF) and Haar Cascade are implemented. The comparison of these algorithms in real life challenges and application is done to find out suitable algorithm for a particular application.","PeriodicalId":118765,"journal":{"name":"2017 International Conference on Soft Computing and its Engineering Applications (icSoftComp)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130497524","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 : 2017-12-01DOI: 10.1109/ICSOFTCOMP.2017.8280084
Mhidi Bousselham, Abderrahim Abdellaoui, H. Chaoui
Recently Vehicular Cloud Computing (VCC) has become a significant research area, due to its potential to ensure passenger comfort and improve road safety. It has emerged as a promising technology that leverages the cloud computing functionalities to exploit vehicles underutilized computational, storage and communications. However, it has recently shown that the VCC might be vulnerable to various kinds of attacks, due to the public nature of its network. Therefore, ensuring security and privacy in this paradigm is considered as one of its most important challenges. In this paper, we exploit software-defined network (SDN) technology to design a new security approach that protects vehicles from malicious nodes, using pseudonyms, key management and revocation list which provides authentication, confidentiality, integrity and Availability.
{"title":"Security against malicious node in the vehicular cloud computing using a software-defined networking architecture","authors":"Mhidi Bousselham, Abderrahim Abdellaoui, H. Chaoui","doi":"10.1109/ICSOFTCOMP.2017.8280084","DOIUrl":"https://doi.org/10.1109/ICSOFTCOMP.2017.8280084","url":null,"abstract":"Recently Vehicular Cloud Computing (VCC) has become a significant research area, due to its potential to ensure passenger comfort and improve road safety. It has emerged as a promising technology that leverages the cloud computing functionalities to exploit vehicles underutilized computational, storage and communications. However, it has recently shown that the VCC might be vulnerable to various kinds of attacks, due to the public nature of its network. Therefore, ensuring security and privacy in this paradigm is considered as one of its most important challenges. In this paper, we exploit software-defined network (SDN) technology to design a new security approach that protects vehicles from malicious nodes, using pseudonyms, key management and revocation list which provides authentication, confidentiality, integrity and Availability.","PeriodicalId":118765,"journal":{"name":"2017 International Conference on Soft Computing and its Engineering Applications (icSoftComp)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133126427","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 : 2017-12-01DOI: 10.1109/ICSOFTCOMP.2017.8280086
Kinjal Rabadiya, Ashwin Makwana, S. Jardosh
Nowadays Internet of Things gives a high comfort level to human life by providing connectivity between objects with a human being. People are also connected with each other via Social Network. There are many researches are being conducted on an integration of Social Network with Internet of Things. This new paradigm knows as “Social Internet of Things”. Internet of Things has many limitations so to overcome it Social IoT can be used. Social IoT provides network of objects with security, navigability and scalability by converting “Smart” objects to “Social” object. Social IoT provides all social network services and best comfort level using IoT. In this way, Social IoT has two separate layers as a network of people and network of smart objects and still, Social IoT paradigm can be represented as a field of state of art in IoT and Social Network and simulations. In this paper, we have contributed the implementation of Social IoT with history of Intranet of Things to Social IoT, Social IoT architectures, various relationships of Smart objects with each other, different policies, challenges and applications etc.
{"title":"Revolution in networks of smart objects: Social Internet of Things","authors":"Kinjal Rabadiya, Ashwin Makwana, S. Jardosh","doi":"10.1109/ICSOFTCOMP.2017.8280086","DOIUrl":"https://doi.org/10.1109/ICSOFTCOMP.2017.8280086","url":null,"abstract":"Nowadays Internet of Things gives a high comfort level to human life by providing connectivity between objects with a human being. People are also connected with each other via Social Network. There are many researches are being conducted on an integration of Social Network with Internet of Things. This new paradigm knows as “Social Internet of Things”. Internet of Things has many limitations so to overcome it Social IoT can be used. Social IoT provides network of objects with security, navigability and scalability by converting “Smart” objects to “Social” object. Social IoT provides all social network services and best comfort level using IoT. In this way, Social IoT has two separate layers as a network of people and network of smart objects and still, Social IoT paradigm can be represented as a field of state of art in IoT and Social Network and simulations. In this paper, we have contributed the implementation of Social IoT with history of Intranet of Things to Social IoT, Social IoT architectures, various relationships of Smart objects with each other, different policies, challenges and applications etc.","PeriodicalId":118765,"journal":{"name":"2017 International Conference on Soft Computing and its Engineering Applications (icSoftComp)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123263828","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 : 2017-12-01DOI: 10.1109/ICSOFTCOMP.2017.8280074
Ashish Ghosh
The Internet used today can be described as a network of computers, which connecats one user to others around the globe. Most of the usage and application of the “Internet of Computers” involves human intervention. The future of the Internet would be — a world where manual intervention for the objects on the network could be minimized and its functionalities would be automatic and smart. This internet would not only connect computers and smart phones; it would be a network of smart objects, the “Internet of Things”. These “things” would be smart enough to sense, process and decide a corresponding action, Examples include smart appliances (refrigerator, lights, air conditioners), traffic signals, smart body monitors, etc. The individual objects along with the network would collect process and exchange data strategically. This interconnected network along with all the smart objects working together in correspondence with each other form a larger “Cyber Physical System” (like smart cities, smart hospital, etc.). A working CPS would generate tons of data, hence efficient processing and effective use of this data is very crucial. There will be data from everywhere like climate data, social network data, video data, medical data, scientific data, etc. Storing these data for analytics may not always be feasible and analyzing them in real time will also be too difficult. Traditional analysis tools are not well suited to capture the complete essence of this massive data. The volume, velocity and variety is too large for comprehensive analysis, and the range of potential correlations and relationships between disparate data sources are too great for any analyst to test all hypotheses and derive all the value buried in the data. Some algorithms already have good capability of letting computers do the heavy thinking for us in case of smaller data. But, we are striving for more to deal with large volumes of such data in a short time. Therefore, we need to revisit old algorithms from statistics, machine learning, data mining and big data analytics and improvise them to tame such big data. Major innovations in big data analytics are still to take place; but, it is believed that emergence of such novel analytics is to come in near future from various domains. Soft computing tools, nature inspired algorithms, are likely to play a key role in this regard.
{"title":"Keynote: Next generation internet, data science, & soft computing","authors":"Ashish Ghosh","doi":"10.1109/ICSOFTCOMP.2017.8280074","DOIUrl":"https://doi.org/10.1109/ICSOFTCOMP.2017.8280074","url":null,"abstract":"The Internet used today can be described as a network of computers, which connecats one user to others around the globe. Most of the usage and application of the “Internet of Computers” involves human intervention. The future of the Internet would be — a world where manual intervention for the objects on the network could be minimized and its functionalities would be automatic and smart. This internet would not only connect computers and smart phones; it would be a network of smart objects, the “Internet of Things”. These “things” would be smart enough to sense, process and decide a corresponding action, Examples include smart appliances (refrigerator, lights, air conditioners), traffic signals, smart body monitors, etc. The individual objects along with the network would collect process and exchange data strategically. This interconnected network along with all the smart objects working together in correspondence with each other form a larger “Cyber Physical System” (like smart cities, smart hospital, etc.). A working CPS would generate tons of data, hence efficient processing and effective use of this data is very crucial. There will be data from everywhere like climate data, social network data, video data, medical data, scientific data, etc. Storing these data for analytics may not always be feasible and analyzing them in real time will also be too difficult. Traditional analysis tools are not well suited to capture the complete essence of this massive data. The volume, velocity and variety is too large for comprehensive analysis, and the range of potential correlations and relationships between disparate data sources are too great for any analyst to test all hypotheses and derive all the value buried in the data. Some algorithms already have good capability of letting computers do the heavy thinking for us in case of smaller data. But, we are striving for more to deal with large volumes of such data in a short time. Therefore, we need to revisit old algorithms from statistics, machine learning, data mining and big data analytics and improvise them to tame such big data. Major innovations in big data analytics are still to take place; but, it is believed that emergence of such novel analytics is to come in near future from various domains. Soft computing tools, nature inspired algorithms, are likely to play a key role in this regard.","PeriodicalId":118765,"journal":{"name":"2017 International Conference on Soft Computing and its Engineering Applications (icSoftComp)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127475714","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 : 2017-12-01DOI: 10.1109/ICSOFTCOMP.2017.8280093
Jyotirmoy Bhardwaj, K. K. Gupta, R. Gupta
Modern techniques are replacing traditional methods of water quality parameter measurement systems. Continuous effective online monitoring with user friendly decision support system is one of the essential challenges of water quality monitoring system. To achieve the goal of development of user friendly decision support system for monitoring of potable water in distribution network, this paper introduces soft computing framework, mainly consist of Python programming framework and fuzzy sets. In so far, we have exploited the properties of NumPy and Matplotlib libraries of Python for user interface and fuzzy sets for decision support system. The proposed decision support system collects and utilizes the data points generated from integrated Multi Sensor Array and process the obtained data set through rule based fuzzy sets. Effective user interface and decision making are essential prerequisite of any decision support system. Therefore, we developed Rule Based Decision Support System (RBDSS) strategy to measure the extent of potability of water in distribution network. Based on extensive research, five water quality parameters has been considered to implement decision support system i.e pH, Dissolved Oxygen (D.O.), Electrical Conductivity (E.C.), Oxygen Reduction Potential (O.R.P) and Temperature. The conducted study to test the feasibility of proposed decision support system testify the plausibility of framework in water quality distribution network.
{"title":"Soft computing framework for assessment of water quality in distribution network","authors":"Jyotirmoy Bhardwaj, K. K. Gupta, R. Gupta","doi":"10.1109/ICSOFTCOMP.2017.8280093","DOIUrl":"https://doi.org/10.1109/ICSOFTCOMP.2017.8280093","url":null,"abstract":"Modern techniques are replacing traditional methods of water quality parameter measurement systems. Continuous effective online monitoring with user friendly decision support system is one of the essential challenges of water quality monitoring system. To achieve the goal of development of user friendly decision support system for monitoring of potable water in distribution network, this paper introduces soft computing framework, mainly consist of Python programming framework and fuzzy sets. In so far, we have exploited the properties of NumPy and Matplotlib libraries of Python for user interface and fuzzy sets for decision support system. The proposed decision support system collects and utilizes the data points generated from integrated Multi Sensor Array and process the obtained data set through rule based fuzzy sets. Effective user interface and decision making are essential prerequisite of any decision support system. Therefore, we developed Rule Based Decision Support System (RBDSS) strategy to measure the extent of potability of water in distribution network. Based on extensive research, five water quality parameters has been considered to implement decision support system i.e pH, Dissolved Oxygen (D.O.), Electrical Conductivity (E.C.), Oxygen Reduction Potential (O.R.P) and Temperature. The conducted study to test the feasibility of proposed decision support system testify the plausibility of framework in water quality distribution network.","PeriodicalId":118765,"journal":{"name":"2017 International Conference on Soft Computing and its Engineering Applications (icSoftComp)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116839921","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 : 2017-12-01DOI: 10.1109/ICSOFTCOMP.2017.8280085
Shefali Naik
Use of appropriate indexing improves the performance of transactions in heterogeneous distributed database whereas inappropriate or no indexing deteriorates the same. Properly designed index leads to faster data access which ultimately improves the execution of transactions. Various relational database management systems and third party tools exist which provide suggestion for index management, but up to certain limits. These tools provide index suggestion with limited and simple queries. They do not analyze or suggest index for aggregate queries, sub queries and other complicated queries. The applications which access data from heterogeneous databases using such type queries need an index evaluator and recommender. To develop a good index evaluator and recommender a model is proposed in this paper on the basis of survey and literature review of existing methods. The survey has been conducted from the experienced people of IT industry to find out the feasibility of proposed model. The tool which is going to be developed from this model will be useful to improve the performance of heterogeneous distributed database transactions and will contribute in resolving index Selection Problem.
{"title":"TIER: Table index evaluator and recommender — A proposed model to improve transaction performance in distributed heterogeneous database","authors":"Shefali Naik","doi":"10.1109/ICSOFTCOMP.2017.8280085","DOIUrl":"https://doi.org/10.1109/ICSOFTCOMP.2017.8280085","url":null,"abstract":"Use of appropriate indexing improves the performance of transactions in heterogeneous distributed database whereas inappropriate or no indexing deteriorates the same. Properly designed index leads to faster data access which ultimately improves the execution of transactions. Various relational database management systems and third party tools exist which provide suggestion for index management, but up to certain limits. These tools provide index suggestion with limited and simple queries. They do not analyze or suggest index for aggregate queries, sub queries and other complicated queries. The applications which access data from heterogeneous databases using such type queries need an index evaluator and recommender. To develop a good index evaluator and recommender a model is proposed in this paper on the basis of survey and literature review of existing methods. The survey has been conducted from the experienced people of IT industry to find out the feasibility of proposed model. The tool which is going to be developed from this model will be useful to improve the performance of heterogeneous distributed database transactions and will contribute in resolving index Selection Problem.","PeriodicalId":118765,"journal":{"name":"2017 International Conference on Soft Computing and its Engineering Applications (icSoftComp)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128252456","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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