Sujatha Srinivasan | SRM UNIVERSITY (original) (raw)
Talks by Sujatha Srinivasan
Papers by Sujatha Srinivasan
International Journal of Data Mining & Knowledge Management Process, 2012
Cultural algorithms (CA) are inspired from the cultural evolutionary process in nature and use so... more Cultural algorithms (CA) are inspired from the cultural evolutionary process in nature and use social intelligence to solve problems. Cultural algorithms are composed of a belief space which uses different knowledge sources, a population space and a protocol that enables exchange of knowledge between these sources. Knowledge created in the population space is accepted into the belief space while this collective knowledge from these sources is combined to influence the decisions of the individual agents in solving problems. Classification rules comes under descriptive knowledge discovery in data mining and are the most sought out by users since they represent highly comprehensible form of knowledge. The rules have certain properties which make them useful forms of actionable knowledge to users. The rules are evaluated using these properties represented as objective and subjective measures. Objective measures are problem oriented while subjective measures are more user oriented. Evolutionary systems allow the user to incorporate different rule metrics into the solution of a multi objective rule mining problem. However the algorithms found in the literature allow only certain attributes of the system to be controlled by the user. Research gap exists in providing a complete user controlled system to experiment with evolutionary multi objective classification rule mining. In the current study a Cultural Algorithm Toolkit for Classification Rule Mining (CAT-CRM) is proposed which allows the user to control three different set of parameters. CAT-CRM allows the user to control the evolutionary parameters, the rule parameters as well as agent parameters and hence can be used for experimenting with an evolutionary system, a rule mining system or an agent based social system. Results of experiments conducted to observe the effect of different crossover rates and mutation rates on classification accuracy on a bench mark data set is reported.
International Journal of Engineering & Technology, 2018
Image processing, in the contemporary domain, is now emerging as a novel and an innovative space ... more Image processing, in the contemporary domain, is now emerging as a novel and an innovative space in computing research and applications. Today, the discipline of “computer science” may be termed as “image science”, why because in every aspect of computer application, either science or humanities or management, image processing plays a vital role in varied ways. It is broadly now used in all the industries, organizations, administrative divisions; various social organizations, economic/business institutions, healthcare, defense and so on. Image processing takes images as input and image processing techniques are used to process the images and the output is modified images, video, or collection of text, or features of the images. The resultant output by most image processing techniques creates a huge amount of data which is categorized as Big-data. In this technique, bulky information is processed and stored as either structured or unstructured data as a result of processing images th...
Intelligent Agent-Based Organization for Studying the Big Five Personality Traits
Intelligent Computing and Innovation on Data Science
Studying behavior of individuals and teams in an organization is termed organizational behavior (... more Studying behavior of individuals and teams in an organization is termed organizational behavior (OB) study and has a profound research literature under the area of social sciences. OB can be studied through empirical studies or using agent-based system (ABS). Using computing techniques has an advantage over empirical studies, since humans need not be involved in ABS studies and time can be incorporated into the system to study the evolution of the organization over a period. The recent literature on organization behavior shows research gap in using the Big Five personality traits in ABSs to study and evaluate them. An attempt has been made in this study to create a virtual organization incorporating the personality traits and applying it to a real-world problem. The solution obtained is used as a concrete method for evaluation. The observations are encouraging in modeling an organization using ABS and to gain insight into the individual and organizational evolution.
Automatic Pruning of Rules Through Multi-objective Optimization—A Case Study with a Multi-objective Cultural Algorithm
Intelligent Computing and Innovation on Data Science
Classification algorithms create an overwhelmingly large number of rules, sometimes exceeding the... more Classification algorithms create an overwhelmingly large number of rules, sometimes exceeding the number of data instances in the data set. Large number of rules hinders decision making. Therefore, there is a need for decreasing the rules before presenting it to the user. The process of removing unwanted rules is known in the data mining literature as rule pruning. In this pruning process, care should be taken to preserve the accuracy of the classifier. Mining compact classifiers with less number of rules that are also accurate and novel is a challenge. Thus, rule mining is a multi-objective optimization problem. Finding the best combination of subjective and objective metrics to present the user with compact, accurate and novel rules is the problem taken for study in the present study.
Hybrid Reduction Algorithm with Cat Swarm Optimization for Churn Prediction
Journal of critical reviews
Customer complexity is a main issue and for large companies is the main problem. Considering the ... more Customer complexity is a main issue and for large companies is the main problem. Considering the immediate impact on firms earnings, companies are trying to change strategies to calculate customer concerns. Consequently, it is very important to find a way to solve this problem by differentiating the factors that increase the client's depression. The chief involvement of this study is to progress an effective churn prediction prototypical using a hybrid approach. Here, initially, data is collected from the dataset and the missing data is removed at the pre-processing stage. Then, to reduce the problem, the input dataset is enhanced as a dimension reduction function. For dimensional reduction, the proposed method uses a hybrid technique. Here, PCA and LDA algorithm are hybridized to reduce dimensionality. After the dimensionality reduction process, the reduced dataset is provided to the optimal continuous neural network (ORNN). Here, the traditional RNA classifier is trained with Cat Swarm Optimization (CSO). In this work, Tera Data Center at Duke University churn set of predictive data for the calculation, the measured performance. Finally, the performance of the proposed model is estimated at different scales, and it is recognized that the proposed system, designed with dimensional reduction through optimal classification methods, performs better with 95.08% classification accuracy compared to other classification models.
International Journal of Engineering & Technology
Big data is a Firing Term in the recent era of the modern world, due to the information exploita-... more Big data is a Firing Term in the recent era of the modern world, due to the information exploita-tion; there is an enormous amount of data produced. Big data is a powerful momentum of infor-mation and communication technology field due to the effect of growing data in healthcare, IOT, cloud computing, online education, online businesses, and public management. The produced data is not only large but also complex. Big data has a large amount of unstructured data so that there is a need to develop advanced tools and techniques for handling big data. Machine Learning is a prominent area of Artificial Intelligence. It makes the system to make intelligent resolutions by giving the knowledge to achieve the goals. This study reviews the various challenges and innovative ideas for big data analytics with machine learning in different fields over the past ten years. This paper mainly organized to identify the research projects based on the discussions over machine learning techniques for big...
![Table 1: Summary of ML Techniques and Observations [16] Discussed about application ML in different categories in health care like Free-text Notes of doctors, stroke prediction diagnoses, CT scan diagnosis and teaches how to apply ML in healthcare application in a stepwise refinement and impressed to develop the health care system based on machine learning techniques in order to fulfill the challenges of biomedical research. [17] Explored that Magnetic Flux Leakage(MFL) sensors to examine defect nature of the oil and gas pipelines. The MFL sensors spread in every 3 millimeters over the pipelines produces more MFL signals growing as big data and a system developed with ANN algorithm to find different defect types by using Magnetic Flux Leakage(MFL) sensors to examine the oil and gas pipelines. The results obtained that best defect depth estimation accuracy and with better error percentage found in sample data. They motivated to develop the same system with very large data sets(big data).](https://figures.academia-assets.com/80169174/table_001.jpg)
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Computational Economics
A huge amount of data is being collected and stored by financial institutions like banks during t... more A huge amount of data is being collected and stored by financial institutions like banks during their operations. These data contain the most important facts about the institutions and its customers. A good and efficient data analytics system can find patterns in this huge data source that can be used in actionable knowledge creation. Actionable knowledge is the knowledge that can be put to decision making and take some positive action towards better performance of organizations. This actionable knowledge is termed Business Intelligence by data scientists. Business Intelligence and Analytics is the process of applying data mining techniques to organizational or corporate data to discover patterns. Business Intelligence and Business Analytics are emerging as important and essential fields both for data scientists and organizations. Risk analysis, fraud detection, customer retention, customer satisfaction analysis and actuarial analysis are some of the areas of application of business intelligence and analytics. Credit risk analysis is an important part of a successful financial institution particularly in the banking sector. The current study takes this risk analysis in financial institutions and reviews the state of the art in using data analytics or data mining techniques for financial risk analysis. The analysis of risk from financial data depends on several factors that are both objective and subjective. Hence it is a multi-criteria decision problem. The study also proposes a multi-objective genetic algorithm (MOGA) for analyzing financial data for risk analysis and prediction. The proposed MOGA is different from other evolutionary systems in that a memory component to hold the rules is added to the system while other systems B Sujatha Srinivasan
Evolutionary multi objective optimization for rule mining: a review
Evolutionary multi objective optimization (EMOO) systems are evolutionary systems which are used ... more Evolutionary multi objective optimization (EMOO) systems are evolutionary systems which are used for optimizing various measures of the evolving system. Rule mining has gained attention in the knowledge discovery literature. The problem of discovering rules with specific properties is treated as a multi objective optimization problem. The objectives to be optimized being the metrics like accuracy, comprehensibility, surprisingness, novelty to
Nugget Discovery with a Multi-Objective Cultural Algorithm
Computer Science & Engineering: An International Journal, 2012
Cultural Algorithm Toolkit for Multi-objective Rule Mining
International Journal on Computational Science & Applications, 2012
Dominators vs pure dominators on the accuracy of a classifier with a multi objective cultural algorithm
Proceedings of the International Conference on Advances in Computing, Communications and Informatics - ICACCI '12, 2012
"If-Then" classification rules are one of many types of knowled... more "If-Then" classification rules are one of many types of knowledge formats mined by data mining algorithms. These rules known as classifiers are evaluated based on objective and subjective metrics. Support, coverage, confidence, precision, recall, sensitivity and specificity are objective metrics which are used in measuring the accuracy of a classifier. Subjective measures like interest, surprise and comprehensibility are more user-oriented.
Rule Discovery with a Multi Objective Cultural Algorithm
Advances in Intelligent Systems and Computing, 2013
Cultural algorithms (CA) are evolutionary systems which utilize agent technology and which suppor... more Cultural algorithms (CA) are evolutionary systems which utilize agent technology and which supports any evolutionary strategy like evolutionary algorithm or swarm intelligence or ant algorithms. CA uses a basic set of five knowledge sources (KS’s) which are used in various animal species to guide the search toward best solutions and thus are better than evolutionary algorithms which are memory less blind search methods. The preserved knowledge in CA is disseminated throughout the system in future generations. Cultural algorithms have been used effectively in solving optimization problems, in engineering rule based systems, and combined with data mining to study complex social systems. However application of cultural algorithm for multi objective optimization of classification rules is hardly found in the literature. Research gap exists in using Cultural Algorithm for rule mining taking the various properties of rules as objectives for optimization. In the current study a cultural algorithm framework is proposed for rule mining considering it as a multi objective optimization problem.
Procedia Engineering, 2012
Classification rule mining is the most sought out by users since they represent highly comprehens... more Classification rule mining is the most sought out by users since they represent highly comprehensible form of knowledge. The rules are evaluated based on objective and subjective metrics. The user must be able to specify the properties of the rules. The rules discovered must have some of these properties to render them useful. These properties may be conflicting. Hence discovery of rules with specific properties is a multi objective optimization problem. Cultural Algorithm (CA) which derives from social structures, and which incorporates evolutionary systems and agents, and uses five knowledge sources (KS's) for the evolution process better suits the need for solving multi objective optimization problem. In the current study a cultural algorithm for classification rule mining is proposed for multi objective optimization of rules.
A hybrid agent based virtual organization for studying knowledge evolution in social systems
Artificial Intelligence Research, 2012
Social modeling applies computational methods and techniques to the analysis of social processes ... more Social modeling applies computational methods and techniques to the analysis of social processes and human behavior.Cultural algorithms (CA’s) are evolutionary systems which utilize agent technology and which supports any evolutionarystrategy like genetic algorithm, evolutionary algorithm or swarm intelligence or ant algorithms. CA’s have been used formodeling the evolution of complex social systems, for re-engineering rule based systems, for data mining, and for solvingoptimization problems. In the current study a cultural algorithm framework is used to model an Agent Based VirtualOrganization (ABVO) for studying the dynamics of a social system at micro as well as macro level. Research gap exists indefining a concrete and systematic method for evaluating and validating Agent Based Social Systems (ABSS). Also theknowledge evolution process at micro and macro levels of an organization needs further exploration. The proposed CA isapplied to the problem of multi-objective optimization ...
A social intelligent system for multi-objective optimization of classification rules using cultural algorithms
Computing, 2013
ABSTRACT Cultural algorithms (CA) use social intelligence to solve problems in optimization. The ... more ABSTRACT Cultural algorithms (CA) use social intelligence to solve problems in optimization. The CA is a class of evolutionary computational models inspired from observing the cultural evolutionary process in nature. Cultural algorithms employ a basic set of knowledge sources, each related to knowledge observed in various animal species. Knowledge from these sources is then combined to influence the decisions of the individual agents in solving problems. Classification using “IF-THEN” rules comes under descriptive knowledge discovery in data mining and is the most sought out by users since they represent highly comprehensible form of knowledge. The rules have certain properties which make them useful forms of actionable knowledge to the users. The rules are evaluated using these properties represented as objective and subjective measures. The rule properties may be conflicting. Hence discovery of rules with specific properties is considered as a multi-objective optimization problem. In the current study an extended cultural algorithm which applies social intelligence in the data mining domain to present users with a set of rules optimized according to user specified metrics is proposed. Preliminary experimental results using benchmark data sets reveal that the algorithm is promising in producing rules with specific properties.
Intelligent agent based artificial immune system for computer security—a review
Artificial Intelligence Review, 2009
Since its introduction in the 1990s the internet has proliferated in the life of human kind in ma... more Since its introduction in the 1990s the internet has proliferated in the life of human kind in many numbers of ways. The two by-products of the internet are intelligent agents and intrusions which are far away from each other in the intention of their creation while similar in their characteristics. With automated code roaming the network intruding the users on
The growing world has the transactions of finance mostly done by the transfer of amount through t... more The growing world has the transactions of finance mostly done by the transfer of amount through the cashless payments over the Internet. This growth of transactions led to the large amount of data which resulted in the creation of big data. The day-by-day transactions increase continuously which explored as big data with high speed, beyond the limit of transactions and variety. The fraudsters can also use anything to affect the systematic working of current fraud detection system (FDS). So, there is a challenge to improve the present FDS with maximum possible accuracy to fulfill the need of FDS. When the payment is made by using the credit cards, there is chance of misusing the credit cards by the fraudsters. Now, it is essential to find the system that detects the fraudulent transactions as a real-world challenge for FDS and report them to the corresponding people/organization to reduce the fraudulent rate to a minimal one. This paper gives an efficient study of FDS for credit card...
International Journal of Data Mining & Knowledge Management Process, 2012
Cultural algorithms (CA) are inspired from the cultural evolutionary process in nature and use so... more Cultural algorithms (CA) are inspired from the cultural evolutionary process in nature and use social intelligence to solve problems. Cultural algorithms are composed of a belief space which uses different knowledge sources, a population space and a protocol that enables exchange of knowledge between these sources. Knowledge created in the population space is accepted into the belief space while this collective knowledge from these sources is combined to influence the decisions of the individual agents in solving problems. Classification rules comes under descriptive knowledge discovery in data mining and are the most sought out by users since they represent highly comprehensible form of knowledge. The rules have certain properties which make them useful forms of actionable knowledge to users. The rules are evaluated using these properties represented as objective and subjective measures. Objective measures are problem oriented while subjective measures are more user oriented. Evolutionary systems allow the user to incorporate different rule metrics into the solution of a multi objective rule mining problem. However the algorithms found in the literature allow only certain attributes of the system to be controlled by the user. Research gap exists in providing a complete user controlled system to experiment with evolutionary multi objective classification rule mining. In the current study a Cultural Algorithm Toolkit for Classification Rule Mining (CAT-CRM) is proposed which allows the user to control three different set of parameters. CAT-CRM allows the user to control the evolutionary parameters, the rule parameters as well as agent parameters and hence can be used for experimenting with an evolutionary system, a rule mining system or an agent based social system. Results of experiments conducted to observe the effect of different crossover rates and mutation rates on classification accuracy on a bench mark data set is reported.
International Journal of Engineering & Technology, 2018
Image processing, in the contemporary domain, is now emerging as a novel and an innovative space ... more Image processing, in the contemporary domain, is now emerging as a novel and an innovative space in computing research and applications. Today, the discipline of “computer science” may be termed as “image science”, why because in every aspect of computer application, either science or humanities or management, image processing plays a vital role in varied ways. It is broadly now used in all the industries, organizations, administrative divisions; various social organizations, economic/business institutions, healthcare, defense and so on. Image processing takes images as input and image processing techniques are used to process the images and the output is modified images, video, or collection of text, or features of the images. The resultant output by most image processing techniques creates a huge amount of data which is categorized as Big-data. In this technique, bulky information is processed and stored as either structured or unstructured data as a result of processing images th...
Intelligent Agent-Based Organization for Studying the Big Five Personality Traits
Intelligent Computing and Innovation on Data Science
Studying behavior of individuals and teams in an organization is termed organizational behavior (... more Studying behavior of individuals and teams in an organization is termed organizational behavior (OB) study and has a profound research literature under the area of social sciences. OB can be studied through empirical studies or using agent-based system (ABS). Using computing techniques has an advantage over empirical studies, since humans need not be involved in ABS studies and time can be incorporated into the system to study the evolution of the organization over a period. The recent literature on organization behavior shows research gap in using the Big Five personality traits in ABSs to study and evaluate them. An attempt has been made in this study to create a virtual organization incorporating the personality traits and applying it to a real-world problem. The solution obtained is used as a concrete method for evaluation. The observations are encouraging in modeling an organization using ABS and to gain insight into the individual and organizational evolution.
Automatic Pruning of Rules Through Multi-objective Optimization—A Case Study with a Multi-objective Cultural Algorithm
Intelligent Computing and Innovation on Data Science
Classification algorithms create an overwhelmingly large number of rules, sometimes exceeding the... more Classification algorithms create an overwhelmingly large number of rules, sometimes exceeding the number of data instances in the data set. Large number of rules hinders decision making. Therefore, there is a need for decreasing the rules before presenting it to the user. The process of removing unwanted rules is known in the data mining literature as rule pruning. In this pruning process, care should be taken to preserve the accuracy of the classifier. Mining compact classifiers with less number of rules that are also accurate and novel is a challenge. Thus, rule mining is a multi-objective optimization problem. Finding the best combination of subjective and objective metrics to present the user with compact, accurate and novel rules is the problem taken for study in the present study.
Hybrid Reduction Algorithm with Cat Swarm Optimization for Churn Prediction
Journal of critical reviews
Customer complexity is a main issue and for large companies is the main problem. Considering the ... more Customer complexity is a main issue and for large companies is the main problem. Considering the immediate impact on firms earnings, companies are trying to change strategies to calculate customer concerns. Consequently, it is very important to find a way to solve this problem by differentiating the factors that increase the client's depression. The chief involvement of this study is to progress an effective churn prediction prototypical using a hybrid approach. Here, initially, data is collected from the dataset and the missing data is removed at the pre-processing stage. Then, to reduce the problem, the input dataset is enhanced as a dimension reduction function. For dimensional reduction, the proposed method uses a hybrid technique. Here, PCA and LDA algorithm are hybridized to reduce dimensionality. After the dimensionality reduction process, the reduced dataset is provided to the optimal continuous neural network (ORNN). Here, the traditional RNA classifier is trained with Cat Swarm Optimization (CSO). In this work, Tera Data Center at Duke University churn set of predictive data for the calculation, the measured performance. Finally, the performance of the proposed model is estimated at different scales, and it is recognized that the proposed system, designed with dimensional reduction through optimal classification methods, performs better with 95.08% classification accuracy compared to other classification models.
International Journal of Engineering & Technology
Big data is a Firing Term in the recent era of the modern world, due to the information exploita-... more Big data is a Firing Term in the recent era of the modern world, due to the information exploita-tion; there is an enormous amount of data produced. Big data is a powerful momentum of infor-mation and communication technology field due to the effect of growing data in healthcare, IOT, cloud computing, online education, online businesses, and public management. The produced data is not only large but also complex. Big data has a large amount of unstructured data so that there is a need to develop advanced tools and techniques for handling big data. Machine Learning is a prominent area of Artificial Intelligence. It makes the system to make intelligent resolutions by giving the knowledge to achieve the goals. This study reviews the various challenges and innovative ideas for big data analytics with machine learning in different fields over the past ten years. This paper mainly organized to identify the research projects based on the discussions over machine learning techniques for big...
Computational Economics
A huge amount of data is being collected and stored by financial institutions like banks during t... more A huge amount of data is being collected and stored by financial institutions like banks during their operations. These data contain the most important facts about the institutions and its customers. A good and efficient data analytics system can find patterns in this huge data source that can be used in actionable knowledge creation. Actionable knowledge is the knowledge that can be put to decision making and take some positive action towards better performance of organizations. This actionable knowledge is termed Business Intelligence by data scientists. Business Intelligence and Analytics is the process of applying data mining techniques to organizational or corporate data to discover patterns. Business Intelligence and Business Analytics are emerging as important and essential fields both for data scientists and organizations. Risk analysis, fraud detection, customer retention, customer satisfaction analysis and actuarial analysis are some of the areas of application of business intelligence and analytics. Credit risk analysis is an important part of a successful financial institution particularly in the banking sector. The current study takes this risk analysis in financial institutions and reviews the state of the art in using data analytics or data mining techniques for financial risk analysis. The analysis of risk from financial data depends on several factors that are both objective and subjective. Hence it is a multi-criteria decision problem. The study also proposes a multi-objective genetic algorithm (MOGA) for analyzing financial data for risk analysis and prediction. The proposed MOGA is different from other evolutionary systems in that a memory component to hold the rules is added to the system while other systems B Sujatha Srinivasan
Evolutionary multi objective optimization for rule mining: a review
Evolutionary multi objective optimization (EMOO) systems are evolutionary systems which are used ... more Evolutionary multi objective optimization (EMOO) systems are evolutionary systems which are used for optimizing various measures of the evolving system. Rule mining has gained attention in the knowledge discovery literature. The problem of discovering rules with specific properties is treated as a multi objective optimization problem. The objectives to be optimized being the metrics like accuracy, comprehensibility, surprisingness, novelty to
Nugget Discovery with a Multi-Objective Cultural Algorithm
Computer Science & Engineering: An International Journal, 2012
Cultural Algorithm Toolkit for Multi-objective Rule Mining
International Journal on Computational Science & Applications, 2012
Dominators vs pure dominators on the accuracy of a classifier with a multi objective cultural algorithm
Proceedings of the International Conference on Advances in Computing, Communications and Informatics - ICACCI '12, 2012
"If-Then" classification rules are one of many types of knowled... more "If-Then" classification rules are one of many types of knowledge formats mined by data mining algorithms. These rules known as classifiers are evaluated based on objective and subjective metrics. Support, coverage, confidence, precision, recall, sensitivity and specificity are objective metrics which are used in measuring the accuracy of a classifier. Subjective measures like interest, surprise and comprehensibility are more user-oriented.
Rule Discovery with a Multi Objective Cultural Algorithm
Advances in Intelligent Systems and Computing, 2013
Cultural algorithms (CA) are evolutionary systems which utilize agent technology and which suppor... more Cultural algorithms (CA) are evolutionary systems which utilize agent technology and which supports any evolutionary strategy like evolutionary algorithm or swarm intelligence or ant algorithms. CA uses a basic set of five knowledge sources (KS’s) which are used in various animal species to guide the search toward best solutions and thus are better than evolutionary algorithms which are memory less blind search methods. The preserved knowledge in CA is disseminated throughout the system in future generations. Cultural algorithms have been used effectively in solving optimization problems, in engineering rule based systems, and combined with data mining to study complex social systems. However application of cultural algorithm for multi objective optimization of classification rules is hardly found in the literature. Research gap exists in using Cultural Algorithm for rule mining taking the various properties of rules as objectives for optimization. In the current study a cultural algorithm framework is proposed for rule mining considering it as a multi objective optimization problem.
Procedia Engineering, 2012
Classification rule mining is the most sought out by users since they represent highly comprehens... more Classification rule mining is the most sought out by users since they represent highly comprehensible form of knowledge. The rules are evaluated based on objective and subjective metrics. The user must be able to specify the properties of the rules. The rules discovered must have some of these properties to render them useful. These properties may be conflicting. Hence discovery of rules with specific properties is a multi objective optimization problem. Cultural Algorithm (CA) which derives from social structures, and which incorporates evolutionary systems and agents, and uses five knowledge sources (KS's) for the evolution process better suits the need for solving multi objective optimization problem. In the current study a cultural algorithm for classification rule mining is proposed for multi objective optimization of rules.
A hybrid agent based virtual organization for studying knowledge evolution in social systems
Artificial Intelligence Research, 2012
Social modeling applies computational methods and techniques to the analysis of social processes ... more Social modeling applies computational methods and techniques to the analysis of social processes and human behavior.Cultural algorithms (CA’s) are evolutionary systems which utilize agent technology and which supports any evolutionarystrategy like genetic algorithm, evolutionary algorithm or swarm intelligence or ant algorithms. CA’s have been used formodeling the evolution of complex social systems, for re-engineering rule based systems, for data mining, and for solvingoptimization problems. In the current study a cultural algorithm framework is used to model an Agent Based VirtualOrganization (ABVO) for studying the dynamics of a social system at micro as well as macro level. Research gap exists indefining a concrete and systematic method for evaluating and validating Agent Based Social Systems (ABSS). Also theknowledge evolution process at micro and macro levels of an organization needs further exploration. The proposed CA isapplied to the problem of multi-objective optimization ...
A social intelligent system for multi-objective optimization of classification rules using cultural algorithms
Computing, 2013
ABSTRACT Cultural algorithms (CA) use social intelligence to solve problems in optimization. The ... more ABSTRACT Cultural algorithms (CA) use social intelligence to solve problems in optimization. The CA is a class of evolutionary computational models inspired from observing the cultural evolutionary process in nature. Cultural algorithms employ a basic set of knowledge sources, each related to knowledge observed in various animal species. Knowledge from these sources is then combined to influence the decisions of the individual agents in solving problems. Classification using “IF-THEN” rules comes under descriptive knowledge discovery in data mining and is the most sought out by users since they represent highly comprehensible form of knowledge. The rules have certain properties which make them useful forms of actionable knowledge to the users. The rules are evaluated using these properties represented as objective and subjective measures. The rule properties may be conflicting. Hence discovery of rules with specific properties is considered as a multi-objective optimization problem. In the current study an extended cultural algorithm which applies social intelligence in the data mining domain to present users with a set of rules optimized according to user specified metrics is proposed. Preliminary experimental results using benchmark data sets reveal that the algorithm is promising in producing rules with specific properties.
Intelligent agent based artificial immune system for computer security—a review
Artificial Intelligence Review, 2009
Since its introduction in the 1990s the internet has proliferated in the life of human kind in ma... more Since its introduction in the 1990s the internet has proliferated in the life of human kind in many numbers of ways. The two by-products of the internet are intelligent agents and intrusions which are far away from each other in the intention of their creation while similar in their characteristics. With automated code roaming the network intruding the users on
The growing world has the transactions of finance mostly done by the transfer of amount through t... more The growing world has the transactions of finance mostly done by the transfer of amount through the cashless payments over the Internet. This growth of transactions led to the large amount of data which resulted in the creation of big data. The day-by-day transactions increase continuously which explored as big data with high speed, beyond the limit of transactions and variety. The fraudsters can also use anything to affect the systematic working of current fraud detection system (FDS). So, there is a challenge to improve the present FDS with maximum possible accuracy to fulfill the need of FDS. When the payment is made by using the credit cards, there is chance of misusing the credit cards by the fraudsters. Now, it is essential to find the system that detects the fraudulent transactions as a real-world challenge for FDS and report them to the corresponding people/organization to reduce the fraudulent rate to a minimal one. This paper gives an efficient study of FDS for credit card...