Sivakumar Ramakrishnan

Evolutionary multi objective optimization for rule mining: a review

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 name a few. There are a variety of EMOO algorithms in the literature. The performance of these EMOO algorithms is influenced by various characteristics including evolutionary technique used, chromosome representation, parameters like population size, number of generations, crossover rate, mutation rate, stopping criteria, Reproduction operators used, objectives taken for optimization, the fitness function used, optimization strategy, the type of data, number of class attributes and the area of application. This study reviews EMOO systems taking the above criteria into consideration. There are other hybridization strategies like use of intelligent agents, fuzzification, meta data and meta heuristics, parallelization, interactiveness with the user, visualization, etc., which further enhance the performance and usability of the system. Genetic Algorithms (GAs) and Genetic Programming (GPs) are two widely used evolutionary strategies for rule knowledge discovery in Data mining. Thus the proposed study aims at studying the various characteristics of the EMOO systems taking into consideration the two evolutionary strategies of Genetic Algorithm and Genetic programming.

Intelligent agent based artificial immune system for computer security--a review

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 one side as worms, viruses, and Trojans and autonomous agents tending to help the users on the other side, the internet has given great research challenges to the computer scientists. The greatest challenge of the internet is intrusion, which has increased and never decreased. There are various security systems for the internet. As the Human Immune System protects human body from external attacks, these security systems tend to protect the internet from intruders. Thus the internet security systems are comparable with human immune systems in which autonomous cells move throughout the body to protect it while learning to tackle new threats and keeping them in their memory for the future. These properties are comparable with that of autonomous agents in the internet. Thus intelligent agent technology combined with ideas from human immune system is a great area of research which is still in its developing phase. In this paper, state of the art of security systems which use both these technologies of intelligent agents and artificial immune system i.e., Agent Based Artificial Immune System (ABAIS) for security are reviewed, paying special attention to features of human immune system used in the system, the role of the agents in the ABAIS and the security mechanisms provided against intrusions.

A social intelligent system for multi-objective optimization of classification rules using cultural algorithms

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.

Optimization of Conductive Thin Film Epoxy Composites Properties Using Desirability Optimization Methodology

Multiwalled carbon nanotubes (MWCNTs)/epoxy thin film nanocomposites were prepared using spin coating technique. The effects of process parameters such as sonication duration (5–35 min) and filler loadings (1-2 vol%) were studied using the design of experiment (DOE). Full factorial design was used to create the design matrix for the two factors with three-level experimentation, resulting in a total of 9 runs () of experimentation. Response surface methodology (RSM) combined with E.C. Harrington’s desirability function called desirability optimization methodology (DOM) was used to optimize the multiple properties (tensile strength, elastic modulus, elongation at break, thermal conductivity, and electrical conductivity) of MWCNTs/epoxy thin film composites. Based on response surface analysis, quadratic model was developed. Analysis of variance (ANOVA), -squared (-Sq), and normal plot of residuals were applied to determine the accuracy of the models. The range of lower and upper limits was determined in an overlaid contour plot. Desirability function was used to optimize the multiple responses of MWCNTs/epoxy thin film composites. A global solution of 12.88 min sonication and 1.67 vol% filler loadings was obtained to have maximum desired responses with composite desirability of 1.

Fuzzy preference-based multi-objective optimization method

Multiobjective evolutionary computation is still quite young and there are many open research problems. This paper is an attempt to design a hybridized Multiobjective Evolutionary Optimization Algorithm with fuzzy logic called Fuzzy Preference-Based Multi–Objective Optimization Method (FPMOM). FPMOM as an integrated components of Multiobjective Optimization Technique, Evolutionary Algorithm and Fuzzy Inference System able to search and filter the pareto-optimal and provide a good trade-off solution for the multiobjective problem using fuzzy inference method to choose the user intuitive based specific trade-off requirement. This paper will provide a new insight into the behaviourism of interactive Multiobjective Evolutionary Algorithm optimization problems using fuzzy inference method.

Cultural Algorithm Toolkit for Multi-objective Rule Mining

Cultural algorithm is a kind of evolutionary algorithm inspired from societal evolution and is composed of a belief space, 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 namely the rule metrics. 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 namely 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 number and type of metrics on the performance of the algorithm on bench mark data sets is reported.

Kinetic modeling of liquefied petroleum gas (LPG) reduction of titania in MATLAB

In the present study, reduction of Titania (TiO2) by liquefied petroleum gas (LPG)-hydrogen-argon gas mixture was investigated by experimental and kinetic modelling in MATLAB. The reduction experiments were carried out in the temperature range of 1100-1200°C with a reduction time from 1-3 hours and 10-20 minutes of LPG flowing time. A shrinking core model (SCM) was employed for the kinetic modelling in order to determine the rate and extent of reduction. The highest experimental extent of reduction of 38% occurred at a temperature of 1200°C with 3 hours reduction time and 20 minutes of LPG flowing time. The SCM gave a predicted extent of reduction of 82.1% due to assumptions made in the model. The deviation between SCM and experimental data was attributed to porosity, thermodynamic properties and minute thermal fluctuations within the sample. In general, the reduction rates increased with increasing reduction temperature and LPG flowing time.

Kinetic modelling of chlorination of nitrided ilmenite using MATLAB

In the present study, chlorination of nitride ilmenite using 2k factorial design was investigated. The reduction experiments were carried out in a temperature range of 400°C to 500°C, chlorination duration from 1 hour to 3 hours and using different type of carbon reactant. Phases of raw materials and reduced samples were analyzed by X-ray diffraction (XRD). Ilmenite was reduced to TiOxCyNz through carbothermal and nitridation for further chlorination into titanium tetrachloride. The Design of Experiment analysis suggested that the types of carbon reactant contribute most influence to the extent of chlorination of nitride ilmenite. The extent of chlorination was highest at 500°C with 3 hours chlorination time and carbon nanotube as carbon reactant.

Rule Discovery with a Multi Objective Cultural Algorithm

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.

Linguistic Competency Model for Intentional Agent

The linguistic competency structure which has been modeled as Linguistic Competency Model (LCM) is a new and an extensive work of discourse analysis of an Intentional Intelligent Agent. This model is able to explain explicitly the role of linguistic competency in a discourse and incorporated the intention as an embodied entity into the cognitive process of discourse. This model managed to address the hermeneutical process grammar as instrument of linguistic competency skill in the social interactive structure of discourse. This LCM has established a discourse structure which can semantically interpret and hermeneutically analyze the psychological temporal semantic- pragmatic linkages and movement of discourse. The embedded structure of physical spatio-temporal contiguity of verbals or events linkages also can be systematically associated with psychological temporal semantic- pragmatic movement in this LCM. Therefore this LCM model will give a new insight into an understanding of the composition and the characteristic of a hermeneutic discourse especially to define the linguistic competency in it.