Pub Date : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208375
I. Julie, E. Kirubakaran
The most important application of Microarray for gene expression analysis is used to discover or classify the unknown tissue samples with the help of known tissue samples. Several Data Mining Classifiers have been proposed recently to predict/identify the cancer patterns. In this research work, we have focused and studied a few Classification Techniques such as Support Vector Machine (SVM), Nearest Neighbor Classifier (k-NN), ICS4, Non-Parallel Plane Proximal Classifier (NPPC), NPPC-SVM, and Margin-based Feature Elimination-SVM (MFE-SVM). The performances of these classifiers have been analyzed in terms of Threshold Level, Execution Time, Memory Usage and Memory Utilization. From our experimental results, we revealed that the Threshold level and Execution Time to predict the Cancer Patterns are different for different Classifiers. Our experimental results established that among the above identified classifiers, the k-NN classifier achieves less Threshold to predict the cancer pattern, but however it consumes more execution time, whereas the MFE-SVM achieves less execution time to predict the cancer pattern, but it still needs more threshold to predict the Pattern. That is to find the best single classifier in terms of Threshold and Execution Time is still complicated. To address this major issue, we have proposed an efficient Classifier called Maximizing Feature Elimination Technique based Hybrid Classifier (MFE-HC), which is the combination of both k-NN and SVM classifiers. From the results, it is established that our proposed work performs better than both the k-NN and MFE-SVM Classifiers interms of Threshold and Execution Time.
{"title":"MFE-HC: The maximizing feature elimination technique based hybrid classifier for cancer molecular pattern discovery","authors":"I. Julie, E. Kirubakaran","doi":"10.1109/ICPRIME.2012.6208375","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208375","url":null,"abstract":"The most important application of Microarray for gene expression analysis is used to discover or classify the unknown tissue samples with the help of known tissue samples. Several Data Mining Classifiers have been proposed recently to predict/identify the cancer patterns. In this research work, we have focused and studied a few Classification Techniques such as Support Vector Machine (SVM), Nearest Neighbor Classifier (k-NN), ICS4, Non-Parallel Plane Proximal Classifier (NPPC), NPPC-SVM, and Margin-based Feature Elimination-SVM (MFE-SVM). The performances of these classifiers have been analyzed in terms of Threshold Level, Execution Time, Memory Usage and Memory Utilization. From our experimental results, we revealed that the Threshold level and Execution Time to predict the Cancer Patterns are different for different Classifiers. Our experimental results established that among the above identified classifiers, the k-NN classifier achieves less Threshold to predict the cancer pattern, but however it consumes more execution time, whereas the MFE-SVM achieves less execution time to predict the cancer pattern, but it still needs more threshold to predict the Pattern. That is to find the best single classifier in terms of Threshold and Execution Time is still complicated. To address this major issue, we have proposed an efficient Classifier called Maximizing Feature Elimination Technique based Hybrid Classifier (MFE-HC), which is the combination of both k-NN and SVM classifiers. From the results, it is established that our proposed work performs better than both the k-NN and MFE-SVM Classifiers interms of Threshold and Execution Time.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130594397","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208377
S. Vivek, J. Aravinth, S. Valarmathy
Biometrics consists of methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits. This paper describes the feature extraction techniques for three modalities viz. fingerprint, iris and face. The extracted information from each modality is stored as a template. The information are fused at the match score level using a density based score level fusion, GMM followed by the Likelihood ratio test. GMM parameters are estimated from training data using the iterative Expectation-Maximization (EM) algorithm.
{"title":"Feature extraction for multimodal biometric and study of fusion using Gaussian mixture model","authors":"S. Vivek, J. Aravinth, S. Valarmathy","doi":"10.1109/ICPRIME.2012.6208377","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208377","url":null,"abstract":"Biometrics consists of methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits. This paper describes the feature extraction techniques for three modalities viz. fingerprint, iris and face. The extracted information from each modality is stored as a template. The information are fused at the match score level using a density based score level fusion, GMM followed by the Likelihood ratio test. GMM parameters are estimated from training data using the iterative Expectation-Maximization (EM) algorithm.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"22 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130668397","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208299
P. Anitha, G. Pavithra, P. S. Periasamy
Wireless Mesh Networks (WMNs) have become one of the important domains in wireless communications. They comprise of a number of static wireless routers which form an access network for end users to IP-based services. Unlike conventional WLAN deployments, wireless mesh networks offer multihop routing, facilitating an easy and cost-effective deployment. In this paper, an efficient and secure multicast routing on such wireless mesh networks is concentrated. This paper identifies novel attacks against high throughput multicast protocols in wireless mesh networks through S-ODMRP protocol. Recently, Sybil attack is observed to be the most harmful attack in WMNs, where a node illegitimately claims multiple identities. This paper systematically analyzes the threat posed by the Sybil attack to WMN. The Sybil attack is encountered by the defense mechanism called Random Key Predistribution technique (RKP). The performance of the proposed approach which integrates the S-ODMRP and RKP is evaluated using the throughput performance metric. It is observed from the experimental results that the proposed approach provides good security against Sybil attack with very high throughput.
{"title":"An improved security mechanism for high-throughput multicast routing in wireless mesh networks against Sybil attack","authors":"P. Anitha, G. Pavithra, P. S. Periasamy","doi":"10.1109/ICPRIME.2012.6208299","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208299","url":null,"abstract":"Wireless Mesh Networks (WMNs) have become one of the important domains in wireless communications. They comprise of a number of static wireless routers which form an access network for end users to IP-based services. Unlike conventional WLAN deployments, wireless mesh networks offer multihop routing, facilitating an easy and cost-effective deployment. In this paper, an efficient and secure multicast routing on such wireless mesh networks is concentrated. This paper identifies novel attacks against high throughput multicast protocols in wireless mesh networks through S-ODMRP protocol. Recently, Sybil attack is observed to be the most harmful attack in WMNs, where a node illegitimately claims multiple identities. This paper systematically analyzes the threat posed by the Sybil attack to WMN. The Sybil attack is encountered by the defense mechanism called Random Key Predistribution technique (RKP). The performance of the proposed approach which integrates the S-ODMRP and RKP is evaluated using the throughput performance metric. It is observed from the experimental results that the proposed approach provides good security against Sybil attack with very high throughput.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134222888","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208374
T. Balasubramanian, R. Umarani
Data Mining is the process of extracting information from large data sets through using algorithms and Techniques drawn from the field of Statistics, Machine Learning and Data Base Management Systems. Traditional data analysis methods often involve manual work and interpretation of data which is slow, expensive and highly subjective Data Mining, popularly called as knowledge discovery in large data, enables firms and organizations to make calculated decisions by assembling, accumulating, analyzing and accessing corporate data. It uses variety of tools like query and reporting tools, analytical processing tools, and Decision Support System. [1][2] This article explores data mining techniques in health care. In particular, it discusses data mining and its application in areas where people are affected severely by using the under-ground drinking water which consist of high levels of fluoride in Krishnagiri District, Tamil Nadu State, India. This paper identifies the risk factors associated with the high level of fluoride content in water, using clustering algorithms and finds meaningful hidden patterns which give meaningful decision making to this socio-economic real world health hazard.
{"title":"An analysis on the impact of fluoride in human health (dental) using clustering data mining technique","authors":"T. Balasubramanian, R. Umarani","doi":"10.1109/ICPRIME.2012.6208374","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208374","url":null,"abstract":"Data Mining is the process of extracting information from large data sets through using algorithms and Techniques drawn from the field of Statistics, Machine Learning and Data Base Management Systems. Traditional data analysis methods often involve manual work and interpretation of data which is slow, expensive and highly subjective Data Mining, popularly called as knowledge discovery in large data, enables firms and organizations to make calculated decisions by assembling, accumulating, analyzing and accessing corporate data. It uses variety of tools like query and reporting tools, analytical processing tools, and Decision Support System. [1][2] This article explores data mining techniques in health care. In particular, it discusses data mining and its application in areas where people are affected severely by using the under-ground drinking water which consist of high levels of fluoride in Krishnagiri District, Tamil Nadu State, India. This paper identifies the risk factors associated with the high level of fluoride content in water, using clustering algorithms and finds meaningful hidden patterns which give meaningful decision making to this socio-economic real world health hazard.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134563670","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208289
M. Rejusha, K. Jayanthi
This paper presents a fast, efficient, lossless image compression algorithm named FELICS. This consists of two techniques named simplified adjusted binary code and GOLOMB-Rice code which provide lossless compression for high throughput applications. Two-level parallelism with four-stage pipelining is adopted. Pseudo Dual-Port RAM is used which improves the processing speed and decreases area and power consumption. The proposed architecture can be used for high definition display applications.
{"title":"Implementation of VLSI-oriented FELICS algorithm using Pseudo Dual-Port RAM","authors":"M. Rejusha, K. Jayanthi","doi":"10.1109/ICPRIME.2012.6208289","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208289","url":null,"abstract":"This paper presents a fast, efficient, lossless image compression algorithm named FELICS. This consists of two techniques named simplified adjusted binary code and GOLOMB-Rice code which provide lossless compression for high throughput applications. Two-level parallelism with four-stage pipelining is adopted. Pseudo Dual-Port RAM is used which improves the processing speed and decreases area and power consumption. The proposed architecture can be used for high definition display applications.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128205859","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208279
R. Kavitha, A. Vijaya, D. Saraswathi
Due to the advancement in information and communication technology and vast varied learner group, e-learning has become popular. For achieving the adaptivity in learning, predefined concept map is used to provide proper guidance. In most of the researches, weight of concepts in each learning item is not properly considered. In this study, a three phase prerequisite concept map formulation for adaptivity is proposed. This approach is a most efficient one, since the first phase discards all the unrelated items which may distract the further analysis. Here, Norm-referencing in Item Analysis approach is used to find the item discrimination for elimination of irrelevant items. The second phase, computes all the grade association rules. The weight of concept in each learning item is considered and prerequisite concept sets are found which is not having any redundancy and cyclic in its map, thereby facilitating the next step of procedure. The final phase constructs the concept map with maximum confidence in a capable manner. Finally, the Prerequisite concept map can be used in tutoring system, thereby enhancing the adaptivity in e-learning.
{"title":"An augmented prerequisite concept relation map design to improve adaptivity in e-learning","authors":"R. Kavitha, A. Vijaya, D. Saraswathi","doi":"10.1109/ICPRIME.2012.6208279","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208279","url":null,"abstract":"Due to the advancement in information and communication technology and vast varied learner group, e-learning has become popular. For achieving the adaptivity in learning, predefined concept map is used to provide proper guidance. In most of the researches, weight of concepts in each learning item is not properly considered. In this study, a three phase prerequisite concept map formulation for adaptivity is proposed. This approach is a most efficient one, since the first phase discards all the unrelated items which may distract the further analysis. Here, Norm-referencing in Item Analysis approach is used to find the item discrimination for elimination of irrelevant items. The second phase, computes all the grade association rules. The weight of concept in each learning item is considered and prerequisite concept sets are found which is not having any redundancy and cyclic in its map, thereby facilitating the next step of procedure. The final phase constructs the concept map with maximum confidence in a capable manner. Finally, the Prerequisite concept map can be used in tutoring system, thereby enhancing the adaptivity in e-learning.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134014766","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208370
R. Gopalakrishnan, A. Krishnan
A power system operation at minimum cost is no longer the only measure for electrical power dispatch. Combined Economic Emission Dispatch (CEED) problem is attained by considering both the economy and emission objectives with necessary constraints. The stability of the power system is also considered as an important factor in the performance of the power system. Several optimization techniques are slow for such complex optimization tasks and are not suitable for on-line use. This paper presents an effective optimization algorithm, for solving security constrained combined economic emission dispatch problem. Also, the power system stability plays an important role in power system. Power system stability criteria are also considered in this paper for better performance of the entire power system. In this paper, an efficient optimization technique called Modified Artificial Bee Colony Optimization is used to solve the CEED problem. The performance of the proposed approach is compared with the other optimization techniques. The experimental result shows that the proposed system results in better solution for CEED problem with the maintenance of system stability.
{"title":"Intelligence technique to solve combined economic and emission dispatch","authors":"R. Gopalakrishnan, A. Krishnan","doi":"10.1109/ICPRIME.2012.6208370","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208370","url":null,"abstract":"A power system operation at minimum cost is no longer the only measure for electrical power dispatch. Combined Economic Emission Dispatch (CEED) problem is attained by considering both the economy and emission objectives with necessary constraints. The stability of the power system is also considered as an important factor in the performance of the power system. Several optimization techniques are slow for such complex optimization tasks and are not suitable for on-line use. This paper presents an effective optimization algorithm, for solving security constrained combined economic emission dispatch problem. Also, the power system stability plays an important role in power system. Power system stability criteria are also considered in this paper for better performance of the entire power system. In this paper, an efficient optimization technique called Modified Artificial Bee Colony Optimization is used to solve the CEED problem. The performance of the proposed approach is compared with the other optimization techniques. The experimental result shows that the proposed system results in better solution for CEED problem with the maintenance of system stability.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"695 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115391024","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208290
D. Saraswathi, A. V. Kathiravan, R. Kavitha
Search engine spam is a webpage that has been designed to artificially inflating its search engine ranking. Recently this search engine spam has been increased dramatically and creates problem to the search engine and the web surfer. It degrades the search engine's results, occupies more memory and consumes more time for creating indexes, and frustrates the user by giving irrelevant results. Search engines have tried many techniques to filter out these spam pages before they can appear on the query results page. Spammers intend to increase the PageRank of certain spam pages by creating a large number of links pointing to them. We have designed and develop a system, spamcity score that detects spam hosts or pages on the Web. The UK Web Spam UK 2007 data set has been used for experimentation. It is a public web spam dataset annotated at the level of hosts, for all results reported here. System uses the key features of popular link based algorithms to detect spam in improved manner. In this paper, various ways of creating spam pages, a collection of current methods that are being used to detect spam and a new approach to build a tool for improving link spam detection using spamcity score of term spam. This new approach uses SVMLight tool to detect the link spam which considers the link structure of Web and page contents. These statistical features are used to build a classifier that is tested over a large collection of Web link spam. The link farm can be identifying based on Web Graph, classification by using SVMLight Tool, Degree based measure, page Rank, Trust Rank, and Truncated PageRank. The spam classifier makes use of the Wordnet word database and SVMLight tool to classify web links as either spam or not spam. These features are not only related to quantitative data extracted from the Web pages, but also to qualitative properties, mainly of the page links.
{"title":"A new enhanced technique for link farm detection","authors":"D. Saraswathi, A. V. Kathiravan, R. Kavitha","doi":"10.1109/ICPRIME.2012.6208290","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208290","url":null,"abstract":"Search engine spam is a webpage that has been designed to artificially inflating its search engine ranking. Recently this search engine spam has been increased dramatically and creates problem to the search engine and the web surfer. It degrades the search engine's results, occupies more memory and consumes more time for creating indexes, and frustrates the user by giving irrelevant results. Search engines have tried many techniques to filter out these spam pages before they can appear on the query results page. Spammers intend to increase the PageRank of certain spam pages by creating a large number of links pointing to them. We have designed and develop a system, spamcity score that detects spam hosts or pages on the Web. The UK Web Spam UK 2007 data set has been used for experimentation. It is a public web spam dataset annotated at the level of hosts, for all results reported here. System uses the key features of popular link based algorithms to detect spam in improved manner. In this paper, various ways of creating spam pages, a collection of current methods that are being used to detect spam and a new approach to build a tool for improving link spam detection using spamcity score of term spam. This new approach uses SVMLight tool to detect the link spam which considers the link structure of Web and page contents. These statistical features are used to build a classifier that is tested over a large collection of Web link spam. The link farm can be identifying based on Web Graph, classification by using SVMLight Tool, Degree based measure, page Rank, Trust Rank, and Truncated PageRank. The spam classifier makes use of the Wordnet word database and SVMLight tool to classify web links as either spam or not spam. These features are not only related to quantitative data extracted from the Web pages, but also to qualitative properties, mainly of the page links.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128775381","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208339
R. Narayanan, S. Karthikeyan
Security has become one of the major concerns in the present scenario because of the increase in quantity of data and theft. Due to these reasons, an efficient security system is very much necessary for protecting the authorized properties. This leads to the usage of password, key, etc., for securing the system. This security system can be easily broken because the password and key can be easily lost by the user. Also, the users have to memorize the password and key for access to the system and sometimes the user may forget the password and key. This has forced the researchers for developing a better security system. Then, the usage of biometrics evolved to be better technique for security purpose as biometrics is unique for every individual. Mostly used biometrics features for security purpose are fingerprint, iris, retina, palm, face, etc. This paper focuses on using the fingerprint as biometrics for developing a security system. The security is enhanced in this paper by using double encryption technique by means of combining symmetric key and a symmetric key generation. Also, Reed and Solomon codes are used to provide tolerance for decryption. The experimental result shows that the proposed Fuzzy Vault construction results in proving better security.
{"title":"Double encryption based secure fuzzy vault construction using fingerprint biometric features","authors":"R. Narayanan, S. Karthikeyan","doi":"10.1109/ICPRIME.2012.6208339","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208339","url":null,"abstract":"Security has become one of the major concerns in the present scenario because of the increase in quantity of data and theft. Due to these reasons, an efficient security system is very much necessary for protecting the authorized properties. This leads to the usage of password, key, etc., for securing the system. This security system can be easily broken because the password and key can be easily lost by the user. Also, the users have to memorize the password and key for access to the system and sometimes the user may forget the password and key. This has forced the researchers for developing a better security system. Then, the usage of biometrics evolved to be better technique for security purpose as biometrics is unique for every individual. Mostly used biometrics features for security purpose are fingerprint, iris, retina, palm, face, etc. This paper focuses on using the fingerprint as biometrics for developing a security system. The security is enhanced in this paper by using double encryption technique by means of combining symmetric key and a symmetric key generation. Also, Reed and Solomon codes are used to provide tolerance for decryption. The experimental result shows that the proposed Fuzzy Vault construction results in proving better security.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125322559","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 : 2012-03-21DOI: 10.1109/ICPRIME.2012.6208293
C. Aswani Kumar, K. Sumangali
FCA is a mathematical framework that depicts knowledge derived from the data represented as formal context. The objective of this paper is to apply Formal Concept Analysis (FCA) to analyze the key performance areas (KPA) of faculty of an institute. While constructing the formal context we have considered the faculties as objects and their KPA as attributes. This context is processed using FCA and the knowledge derived is analyzed to measure the performance of the faculty.
{"title":"Performance evaluation of employees of an organization using formal concept analysis","authors":"C. Aswani Kumar, K. Sumangali","doi":"10.1109/ICPRIME.2012.6208293","DOIUrl":"https://doi.org/10.1109/ICPRIME.2012.6208293","url":null,"abstract":"FCA is a mathematical framework that depicts knowledge derived from the data represented as formal context. The objective of this paper is to apply Formal Concept Analysis (FCA) to analyze the key performance areas (KPA) of faculty of an institute. While constructing the formal context we have considered the faculties as objects and their KPA as attributes. This context is processed using FCA and the knowledge derived is analyzed to measure the performance of the faculty.","PeriodicalId":148511,"journal":{"name":"International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012)","volume":"262 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124657728","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：