PROGRAM TEST DATA GENERATION FOR BRANCH COVERAGE WITH GENETIC ALGORITHM: COMP...cscpconf
In search based test data generation, the problem of test data generation is reduced to that of
function minimization or maximization.Traditionally, for branch testing, the problem of test data
generation has been formulated as a minimization problem. In this paper we define an alternate
maximization formulation and experimentally compare it with the minimization formulation. We
use a genetic algorithm as the search technique and in addition to the usual genetic algorithm
operators we also employ the path prefix strategy as a branch ordering strategy and memory and elitism. Results indicate that there is no significant difference in the performance or the coverage obtained through the two approaches and either could be used in test data generation when coupled with the path prefix strategy, memory and elitism.
Trust Enhanced Role Based Access Control Using Genetic Algorithm IJECEIAES
Improvements in technological innovations have become a boon for business organizations, firms, institutions, etc. System applications are being developed for organizations whether small-scale or large-scale. Taking into consideration the hierarchical nature of large organizations, security is an important factor which needs to be taken into account. For any healthcare organization, maintaining the confidentiality and integrity of the patients’ records is of utmost importance while ensuring that they are only available to the authorized personnel. The paper discusses the technique of Role-Based Access Control (RBAC) and its different aspects. The paper also suggests a trust enhanced model of RBAC implemented with selection and mutation only ‘Genetic Algorithm’. A practical scenario involving healthcare organization has also been considered. A model has been developed to consider the policies of different health departments and how it affects the permissions of a particular role. The purpose of the algorithm is to allocate tasks for every employee in an automated manner and ensures that they are not over-burdened with the work assigned. In addition, the trust records of the employees ensure that malicious users do not gain access to confidential patient data.
Improving the effectiveness of information retrieval system using adaptive ge...ijcsit
Traditional Genetic Algorithm which is used in previous studies depends on fixed control parameters
especially crossover and mutation probabilities, but in this research we tried to use adaptive genetic
algorithm.
Genetic algorithm started to be applied in information retrieval system in order to optimize the query by
genetic algorithm, a good query is a set of terms that express accurately the information need while being
usable within collection corpus, the last part of this specification is critical for the matching process to be
efficient, that is why most research efforts are actually put toward the query improvement.
We investigated the use of adaptive genetic algorithm (AGA) under vector space model, Extended Boolean
model, and Language model in information retrieval (IR), the algorithm used crossover and mutation
operators with variable probability, where a traditional genetic algorithm (GA) uses fixed values of those,
and remain unchanged during execution. GA is developed to support adaptive adjustment of mutation and
crossover probability; this allows faster attainment of better solutions. The paper has been tested using
242 Arabic abstracts collected from the proceedings of the Saudi Arabian National conference.
ENHANCED BREAST CANCER RECOGNITION BASED ON ROTATION FOREST FEATURE SELECTIO...cscpconf
Optimization problems are dominantly being solved using Computational Intelligence. One of
the issues that can be addressed in this context is problems related to attribute subset selection
evaluation. This paper presents a computational intelligence technique for solving the
optimization problem using a proposed model called Modified Genetic Search Algorithms
(MGSA) that avoids local bad search space with merit and scaled fitness variables, detecting
and deleting bad candidate chromosomes, thereby reducing the number of individual
chromosomes from search space and subsequent iterations in next generations. This paper aims
to show that Rotation forest ensembles are useful in the feature selection method. The base
classifier is multinomial logistic regression method integrated with Haar wavelets as projection
filter and reproducing the ranks of each features with 10 fold cross validation method. It also
discusses the main findings and concludes with promising result of the proposed model. It
explores the combination of MGSA for optimization with Naïve Bayes classification. The result
obtained using proposed model MGSA is validated mathematically using Principal Component
Analysis. The goal is to improve the accuracy and quality of diagnosis of Breast cancer disease
with robust machine learning algorithms. As compared to other works in literature survey,
experimental results achieved in this paper show better results with statistical inferenc
PROGRAM TEST DATA GENERATION FOR BRANCH COVERAGE WITH GENETIC ALGORITHM: COMP...cscpconf
In search based test data generation, the problem of test data generation is reduced to that of
function minimization or maximization.Traditionally, for branch testing, the problem of test data
generation has been formulated as a minimization problem. In this paper we define an alternate
maximization formulation and experimentally compare it with the minimization formulation. We
use a genetic algorithm as the search technique and in addition to the usual genetic algorithm
operators we also employ the path prefix strategy as a branch ordering strategy and memory and elitism. Results indicate that there is no significant difference in the performance or the coverage obtained through the two approaches and either could be used in test data generation when coupled with the path prefix strategy, memory and elitism.
Trust Enhanced Role Based Access Control Using Genetic Algorithm IJECEIAES
Improvements in technological innovations have become a boon for business organizations, firms, institutions, etc. System applications are being developed for organizations whether small-scale or large-scale. Taking into consideration the hierarchical nature of large organizations, security is an important factor which needs to be taken into account. For any healthcare organization, maintaining the confidentiality and integrity of the patients’ records is of utmost importance while ensuring that they are only available to the authorized personnel. The paper discusses the technique of Role-Based Access Control (RBAC) and its different aspects. The paper also suggests a trust enhanced model of RBAC implemented with selection and mutation only ‘Genetic Algorithm’. A practical scenario involving healthcare organization has also been considered. A model has been developed to consider the policies of different health departments and how it affects the permissions of a particular role. The purpose of the algorithm is to allocate tasks for every employee in an automated manner and ensures that they are not over-burdened with the work assigned. In addition, the trust records of the employees ensure that malicious users do not gain access to confidential patient data.
Improving the effectiveness of information retrieval system using adaptive ge...ijcsit
Traditional Genetic Algorithm which is used in previous studies depends on fixed control parameters
especially crossover and mutation probabilities, but in this research we tried to use adaptive genetic
algorithm.
Genetic algorithm started to be applied in information retrieval system in order to optimize the query by
genetic algorithm, a good query is a set of terms that express accurately the information need while being
usable within collection corpus, the last part of this specification is critical for the matching process to be
efficient, that is why most research efforts are actually put toward the query improvement.
We investigated the use of adaptive genetic algorithm (AGA) under vector space model, Extended Boolean
model, and Language model in information retrieval (IR), the algorithm used crossover and mutation
operators with variable probability, where a traditional genetic algorithm (GA) uses fixed values of those,
and remain unchanged during execution. GA is developed to support adaptive adjustment of mutation and
crossover probability; this allows faster attainment of better solutions. The paper has been tested using
242 Arabic abstracts collected from the proceedings of the Saudi Arabian National conference.
ENHANCED BREAST CANCER RECOGNITION BASED ON ROTATION FOREST FEATURE SELECTIO...cscpconf
Optimization problems are dominantly being solved using Computational Intelligence. One of
the issues that can be addressed in this context is problems related to attribute subset selection
evaluation. This paper presents a computational intelligence technique for solving the
optimization problem using a proposed model called Modified Genetic Search Algorithms
(MGSA) that avoids local bad search space with merit and scaled fitness variables, detecting
and deleting bad candidate chromosomes, thereby reducing the number of individual
chromosomes from search space and subsequent iterations in next generations. This paper aims
to show that Rotation forest ensembles are useful in the feature selection method. The base
classifier is multinomial logistic regression method integrated with Haar wavelets as projection
filter and reproducing the ranks of each features with 10 fold cross validation method. It also
discusses the main findings and concludes with promising result of the proposed model. It
explores the combination of MGSA for optimization with Naïve Bayes classification. The result
obtained using proposed model MGSA is validated mathematically using Principal Component
Analysis. The goal is to improve the accuracy and quality of diagnosis of Breast cancer disease
with robust machine learning algorithms. As compared to other works in literature survey,
experimental results achieved in this paper show better results with statistical inferenc
Analysis of Parameter using Fuzzy Genetic Algorithm in E-learning SystemHarshal Jain
The aim of this project is to analyze the parameter, for the inputs to find an optimization problem than the candidate solution we have. This will help us to find more accurate knowledge level of user, using Genetic Algorithm (GA). In this algorithm a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions.
GA is a search technique that depends on the natural selection and genetics principles and which determines a optimal solution for even a hard issue.genetic algorithm crossover and genetic algorithm for optimization
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
ON FEATURE SELECTION ALGORITHMS AND FEATURE SELECTION STABILITY MEASURES: A C...ijcsit
Data mining is indispensable for business organizations for extracting useful information from the huge volume of stored data which can be used in managerial decision making to survive in the competition. Due to the day-to-day advancements in information and communication technology, these data collected from ecommerce and e-governance are mostly high dimensional. Data mining prefers small datasets than high dimensional datasets. Feature selection is an important dimensionality reduction technique. The subsets selected in subsequent iterations by feature selection should be same or similar even in case of small perturbations of the dataset and is called as selection stability. It is recently becomes important topic of research community. The selection stability has been measured by various measures. This paper analyses the selection of the suitable search method and stability measure for the feature selection algorithms and also the influence of the characteristics of the dataset as the choice of the best approach is highly problem dependent.
Survival of the Fittest: Using Genetic Algorithm for Data Mining OptimizationOr Levi
Presented at the eBay Inc Data Conference 2013:
“Survival of the Fittest: Using Genetic Algorithm for Data Mining Optimization”
Showed a Genetic Algorithm based method to optimize cluster analysis and developed a demo, applying this algorithm, for grouping similar items on eBay into a catalog of unique products.
Stable Drug Designing by Minimizing Drug Protein Interaction Energy Using PSO csandit
Each and every biological function in living organism happens as a result of protein-protein interactions. The diseases are no exception to this. Identifying one or more proteins for a
particular disease and then designing a suitable chemical compound (known as drug) to destroy these proteins has been an interesting topic of research in bio-informatics. In previous methods,drugs were designed using only seven chemical components and were represented as a fixedlength
tree. But in reality, a drug contains many chemical groups collectively known as
pharmacophore. Moreover, the chemical length of the drug cannot be determined before
designing the drug.
In the present work, a Particle Swarm Optimization (PSO) based methodology has been
proposed to find out a suitable drug for a particular disease so that the drug-protein interaction
becomes stable. In the proposed algorithm, the drug is represented as a variable length tree and essential functional groups are arranged in different positions of that drug. Finally, the structure of the drug is obtained and its docking energy is minimized simultaneously. Also, the
orientation of chemical groups in the drug is tested so that it can bind to a particular active site of a target protein and the drug fits well inside the active site of target protein. Here, several inter-molecular forces have been considered for accuracy of the docking energy. Results showthat PSO performs better than the earlier methods.
Each and every biological function in living organism occurs due to protein-protein interactions. The
diseases are no exception to this. Identifying one or more proteins for a particular disease and then
designing a suitable chemical compound (which is known as drug or ligand) to destroy those proteins is a
challenging topic of research in computational biology. In earlier methods, drugs were designed using only
a few chemical components and were represented as a fixed-length tree. But in reality, a drug contains
many chemical groups collectively known as pharmacophore. Moreover, the chemical length of the drug
cannot be determined before designing that drug.
In the present work, a Particle Swarm Optimization (PSO) based methodology has been proposed to find
out a suitable drug for a particular disease so that the drug-target protein interaction energy becomes
minimum. In the proposed algorithm, the drug is represented as a variable length tree and essential
functional groups are arranged in different positions of that drug. Finally, the structure of the drug is
obtained and its docking energy is minimized simultaneously. Also, the orientation of chemical groups in
the drug is tested so that it can bind to a particular active site of a target protein and the drug fits well
inside the active site of target protein. Here, several inter-molecular forces have been considered for
accuracy of the docking energy. Results are demonstrated for three different target proteins both
numerically and pictorially. Results show that PSO performs better than the earlier methods.
A Non-Revisiting Genetic Algorithm for Optimizing Numeric Multi-Dimensional F...ijcsa
Genetic Algorithm (GA) is a robust and popular stochastic optimization algorithm for large and complex search spaces. The major shortcomings of Genetic Algorithms are premature convergence and revisits to individual solutions in the search space. In other words, Genetic algorithm is a revisiting algorithm that escorts to duplicate function evaluations which is a clear wastage of time and computational resources. In this paper, a non-revisiting genetic algorithm with adaptive mutation is proposed for the domain of MultiDimensional numeric function optimization. In this algorithm whenever a revisit occurs, the underlined search point is replaced with a mutated version of the best/random (chosen probabilistically) individual from the GA population. Furthermore, the recommended approach is not using any extra memory resources to avoid revisits. To analyze the influence of the method, the proposed non-revisiting algorithm is evaluated using nine benchmarks functions with two and four dimensions. The performance of the proposed genetic algorithm is superior as contrasted to simple genetic algorithm as confirmed by the experimental results.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Building a Classifier Employing Prism Algorithm with Fuzzy LogicIJDKP
Classification in data mining is receiving immense interest in recent times. As the knowledge is based on
historical data, classifications of data are essential for discovering the knowledge. To decrease the
classification complexity, the quantitative attributes of data need splitting. But the splitting using the
classical logic is less accurate. This can be overcome by the use of fuzzy logic. This paper illustrates how to
build up the classification rules using the fuzzy logic. The fuzzy classifier is built on by using the prism
decision tree algorithm. This classifier produces more realistic results than the classical one. The
effectiveness of this method is justified over a sample dataset.
Mathematical Modelling: A Comparatively Mathematical Study Model Base between...IOSR Journals
In this paper, we have studied on the topic of „Corruption‟. Also, I will try to find or study the effect of corruption on the Development of the country or any country of the world. Therefore, how find the solution of the problem of corruption will be destroyed completely from the society. We have observed that the Development of the country depends upon Corruption. That is, when the Corruption increases, Development decreases automatically of any country of the world. Therefore, I will try to find the formula on the problem of „Relation between the Corruption and Development of any field or any country of the world‟. Also, I have to highlight the concept of „Application of Mathematical modeling in the interesting problem “corruption” in every field of our country or world .Also, Applied Mathematics focuses on the formulation and study of Mathematical Models .Thus the activity of Applied Mathematics is vitally connected with Research in Pure Mathematics. So I will try to study on it and find, what is corruption and quantity of corruption and also find the growth of corruption and how it will decay? Now we convert this areal world problem to mathematics problem and find some formulae on it such as Mathematical Corruption Growth formula, Mathematical Constant corruption level formula and Mathematical decay of corruption formula.
Analysis of Parameter using Fuzzy Genetic Algorithm in E-learning SystemHarshal Jain
The aim of this project is to analyze the parameter, for the inputs to find an optimization problem than the candidate solution we have. This will help us to find more accurate knowledge level of user, using Genetic Algorithm (GA). In this algorithm a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions.
GA is a search technique that depends on the natural selection and genetics principles and which determines a optimal solution for even a hard issue.genetic algorithm crossover and genetic algorithm for optimization
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
ON FEATURE SELECTION ALGORITHMS AND FEATURE SELECTION STABILITY MEASURES: A C...ijcsit
Data mining is indispensable for business organizations for extracting useful information from the huge volume of stored data which can be used in managerial decision making to survive in the competition. Due to the day-to-day advancements in information and communication technology, these data collected from ecommerce and e-governance are mostly high dimensional. Data mining prefers small datasets than high dimensional datasets. Feature selection is an important dimensionality reduction technique. The subsets selected in subsequent iterations by feature selection should be same or similar even in case of small perturbations of the dataset and is called as selection stability. It is recently becomes important topic of research community. The selection stability has been measured by various measures. This paper analyses the selection of the suitable search method and stability measure for the feature selection algorithms and also the influence of the characteristics of the dataset as the choice of the best approach is highly problem dependent.
Survival of the Fittest: Using Genetic Algorithm for Data Mining OptimizationOr Levi
Presented at the eBay Inc Data Conference 2013:
“Survival of the Fittest: Using Genetic Algorithm for Data Mining Optimization”
Showed a Genetic Algorithm based method to optimize cluster analysis and developed a demo, applying this algorithm, for grouping similar items on eBay into a catalog of unique products.
Stable Drug Designing by Minimizing Drug Protein Interaction Energy Using PSO csandit
Each and every biological function in living organism happens as a result of protein-protein interactions. The diseases are no exception to this. Identifying one or more proteins for a
particular disease and then designing a suitable chemical compound (known as drug) to destroy these proteins has been an interesting topic of research in bio-informatics. In previous methods,drugs were designed using only seven chemical components and were represented as a fixedlength
tree. But in reality, a drug contains many chemical groups collectively known as
pharmacophore. Moreover, the chemical length of the drug cannot be determined before
designing the drug.
In the present work, a Particle Swarm Optimization (PSO) based methodology has been
proposed to find out a suitable drug for a particular disease so that the drug-protein interaction
becomes stable. In the proposed algorithm, the drug is represented as a variable length tree and essential functional groups are arranged in different positions of that drug. Finally, the structure of the drug is obtained and its docking energy is minimized simultaneously. Also, the
orientation of chemical groups in the drug is tested so that it can bind to a particular active site of a target protein and the drug fits well inside the active site of target protein. Here, several inter-molecular forces have been considered for accuracy of the docking energy. Results showthat PSO performs better than the earlier methods.
Each and every biological function in living organism occurs due to protein-protein interactions. The
diseases are no exception to this. Identifying one or more proteins for a particular disease and then
designing a suitable chemical compound (which is known as drug or ligand) to destroy those proteins is a
challenging topic of research in computational biology. In earlier methods, drugs were designed using only
a few chemical components and were represented as a fixed-length tree. But in reality, a drug contains
many chemical groups collectively known as pharmacophore. Moreover, the chemical length of the drug
cannot be determined before designing that drug.
In the present work, a Particle Swarm Optimization (PSO) based methodology has been proposed to find
out a suitable drug for a particular disease so that the drug-target protein interaction energy becomes
minimum. In the proposed algorithm, the drug is represented as a variable length tree and essential
functional groups are arranged in different positions of that drug. Finally, the structure of the drug is
obtained and its docking energy is minimized simultaneously. Also, the orientation of chemical groups in
the drug is tested so that it can bind to a particular active site of a target protein and the drug fits well
inside the active site of target protein. Here, several inter-molecular forces have been considered for
accuracy of the docking energy. Results are demonstrated for three different target proteins both
numerically and pictorially. Results show that PSO performs better than the earlier methods.
A Non-Revisiting Genetic Algorithm for Optimizing Numeric Multi-Dimensional F...ijcsa
Genetic Algorithm (GA) is a robust and popular stochastic optimization algorithm for large and complex search spaces. The major shortcomings of Genetic Algorithms are premature convergence and revisits to individual solutions in the search space. In other words, Genetic algorithm is a revisiting algorithm that escorts to duplicate function evaluations which is a clear wastage of time and computational resources. In this paper, a non-revisiting genetic algorithm with adaptive mutation is proposed for the domain of MultiDimensional numeric function optimization. In this algorithm whenever a revisit occurs, the underlined search point is replaced with a mutated version of the best/random (chosen probabilistically) individual from the GA population. Furthermore, the recommended approach is not using any extra memory resources to avoid revisits. To analyze the influence of the method, the proposed non-revisiting algorithm is evaluated using nine benchmarks functions with two and four dimensions. The performance of the proposed genetic algorithm is superior as contrasted to simple genetic algorithm as confirmed by the experimental results.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Building a Classifier Employing Prism Algorithm with Fuzzy LogicIJDKP
Classification in data mining is receiving immense interest in recent times. As the knowledge is based on
historical data, classifications of data are essential for discovering the knowledge. To decrease the
classification complexity, the quantitative attributes of data need splitting. But the splitting using the
classical logic is less accurate. This can be overcome by the use of fuzzy logic. This paper illustrates how to
build up the classification rules using the fuzzy logic. The fuzzy classifier is built on by using the prism
decision tree algorithm. This classifier produces more realistic results than the classical one. The
effectiveness of this method is justified over a sample dataset.
Mathematical Modelling: A Comparatively Mathematical Study Model Base between...IOSR Journals
In this paper, we have studied on the topic of „Corruption‟. Also, I will try to find or study the effect of corruption on the Development of the country or any country of the world. Therefore, how find the solution of the problem of corruption will be destroyed completely from the society. We have observed that the Development of the country depends upon Corruption. That is, when the Corruption increases, Development decreases automatically of any country of the world. Therefore, I will try to find the formula on the problem of „Relation between the Corruption and Development of any field or any country of the world‟. Also, I have to highlight the concept of „Application of Mathematical modeling in the interesting problem “corruption” in every field of our country or world .Also, Applied Mathematics focuses on the formulation and study of Mathematical Models .Thus the activity of Applied Mathematics is vitally connected with Research in Pure Mathematics. So I will try to study on it and find, what is corruption and quantity of corruption and also find the growth of corruption and how it will decay? Now we convert this areal world problem to mathematics problem and find some formulae on it such as Mathematical Corruption Growth formula, Mathematical Constant corruption level formula and Mathematical decay of corruption formula.
Mechanism of the Reaction of Plasma Albumin with Formaldehyde in Ethanol - Wa...IOSR Journals
The Spectrophotometric determination of the acid dissociation/ionisation constant (pKa) of plasma albumin-formaldehyde adduct in both water solution and Ethanol solutions was carried out in this study. The pKa values obtained in both media were used to establish the Bronsted-linear type constants from plots of pKa against logarithm of second order rate constants obtained at varying pHs in the study. The result of the pKa values obtained in both water solution and ethanol-water mixtures were found to be in the range of 5.0 - 8.0. This pointed to the fact that only lysine residue with pKa value 8.3 that might have possibly reacted with formaldehyde in this reaction of all the known amino acid residues in plasma albumin. The corresponding Brønsted-type plots proportionality constants (β) for the reaction in water and ethanol-water mixtures were found to be β = 0.059 and 0.0057 respectively. The reaction mechanisms that have low values for proportionality constants α or β are considered to have a transition state closely resembling the reactant with little proton transfer (Cox et al, 1988). Thus, one would suggest that the cross-linking of formaldehyde with plasma albumin in water and ethanol-water mixtures proceeds through little proton transfer
Exact Analytical Expression for Outgoing Intensity from the Top of the Atmosp...IOSR Journals
This research is a part of the work devoted on the application of analytical Discrete Ordinate (ADO) method to the polarized monochromatic radiative transfer equation undergoing anisotropic scattering with source function matrix in a finite coupled Atmosphere –Ocean media having flat interface boundary conditions involving specular reflection and transmission matrix. Discontinuities in the derivatives of the Stokes vector with respect to the cosine of the polar angle at smooth interface between the two media with different refractive indices (air and water) is tackled by using a suitable quadrature scheme devised earlier. Atmosphere and ocean are assumed to be homogeneous. No stratification is adopted in the two media. Exact expression for the
emergent radiation intensity vector from the top of the atmosphere is derived. Exact expressions for the emergent polarized radiation intensity vector from the air-water interface as well as from any point of the two medium in any direction can also be derived in terms of eigenvectors and eigenvalues.
I- Function and H -Function Associated With Double IntegralIOSR Journals
The object of this paper is to discuss certain integral properties of a I -function and H -function,
proposed by Inayat-Hussain which contain a certain class of Feynman integrals, the exact partition of a Gaussian
model in Statistical Mechanics and several other functions as its particular cases. During the course of finding,
we establish certain new double integral relation pertaining to a product involving I function and H function.
These double integral relations are unified in nature and act as a key formulae from which we can obtain as their
special case, double integral relations concerning a large number of simple special functions. For the sake of
illustration, we record here some special cases of our main results which are also new and of interest by
themselves. All the result which are established in this paper are basic in nature and are likely to find useful
applications in several fields notably electrical network, probability theory and statistical mechanics.
Abstract: Synthesis of 2-(1, 3- dihydro- 3 - oxo- 2H - pyridylpyrr- 2- ylidene)-1, 2-dihydro- 3H- pyridylpyrrol- 3- one, polycyclic bi-indolinedione vat dye was carried out using a heterocyclic compound, 2-aminopyridine, chlorinated ethanoic acid and sodium hydroxide. The aromatic glycine that resulted as an intermediate to this new polycyclic bi-indolinedione compound was fused with admixture sodamide: sodium hydroxide: potassium hydroxide and oxidized by acidified concentrated solution of ferric chloride using hydrochloric acid. This dye showed good fastness properties on cotton, polyester, dacron, silk, wool and paper. The spectral analysis was in agreement with the proposed structure of the compound. UV: (DMSO) λmax nm; 609. IR:(KBr) ʋ cm-1; 3402, 2251.66, 2078.00, 1650.25, 1398.18, 1005.50 825.37, 764.03, 627.00. 1H-NMR :( DMSO-d6) δppm; 6.6, 7.2, 8.0; 13C- NMR: (DMSO-d6) δppm; 190.11, 188.20, 157.01, 155.21, 149.60, 148.11, 136.02, 134.50, 130.0, 128.94, 27.53, 126.31, 124.30, 123.91. MS: m/z; 32, 77, 105,181, 264 (M+).
Nuclear Magnetic Resonance (NMR) Analysis of D - (+) - Glucose: A Guide to Sp...IOSR Journals
NMR spectroscopy has a wide range of applications including exchange phenomena, the
identification and structural studies of complex biomolecules. 1D 1H-NMR without water suppression, 1D
Carbon, 1D 13C-DEPT135, 2D Cosy, 2D HSQC, 2D TOCSY, 2D HMQC, and 2D HMBC techniques were used
to completely elucidate the structure of glucose with spectral induced at 400MHz.. The spectral were analysed
using spinworks 3. The results obtained from the spectral data were systematically combined to elucidate the
structure of the D-glucose. Full characterisation of D-glucose was achieved by assigning 1H and 13C signals,
starting from the known to unknown signals.
“Relationship of Kinematic Variables with the Performance of Standing Broad J...IOSR Journals
Abstract: The purpose of investigation was to study the relationship of kinematics variables with the
performance of standing broad jump. Subjects were randomly selected from J.N.V. University, Jodhpur and
M.D.S. University, Ajmer. The criterion measure used for this study was the performance in standing broad
jump and selected kinematics variables. To analyze the raw data coefficient of correlation (r) were calculated
and results were compared with the help of Analysis of variance (ANOVA) technique where level of significance
was set at .05.
The Protective Role Of High Dietary Protein On Arsenic Induced Hepatotoxicity...IOSR Journals
The objective of the present investigation was to study the protective role of High dietary protein on arsenic induced hepatotoxicity model in adult male albino rats. Hepatotoxicity in rats was caused by arsenic tri oxide at a dose of 3mg- /ml/kg body weight. Hepamerz, a drug used as standard hepatoprotective agent, was administered orally as standard hepatoprotective agent for 14 consecutive days prior to arsenic treatment at a dose of 10mg- /ml/kg body weight. This drug has many side effects. These side effects have prompted the scientific world for the search of alternative natural remedies of liver damage. The High dietary protein was administered orally to rats along with arsenic. The biochemical parameters were investigated. The results indicated that biochemical changes produced by arsenic were restored to almost normal by High protein diet. The High protein diet produced hepatoprotective effect through the modulation of antioxidant - mediated mechanism by altering serum glutamate oxaloacetate transaminase (SGOT), serum glutamate pyruvate transaminase (SGPT), alkaline phosphatase (ALP), superoxide dismutase (SOD) and catalase (CAT) activities and reduced glutathione (GSH) and lipid peroxidation (LPO) levels - against arsenic induced hepatotoxicity model in rats.
In this research, a hybrid wrapper model is proposed to identify the featured gene subset from the gene expression data. To balance the gap between exploration
and exploitation, a hybrid model with a popular meta-heuristic algorithm named
spider monkey optimizer (SMO) and simulated annealing (SA) is applied. In the proposed model, ReliefF is used as a filter to obtain the relevant gene subset
from dataset by removing the noise and outliers prior to feeding the data to the
wrapper SMO. To enhance the quality of the solution, simulated annealing is
deployed as local search with the SMO in the second phase, which will guide to the detection of the most optimal feature subset. To evaluate the performance of the proposed model, support vector machine (SVM) as a fitness function to recognize the most informative biomarker gene from the cancer datasets along with University of California, Irvine (UCI) datasets. To further evaluate the model, 4 different classifiers (SVM, na¨ıve Bayes (NB), decision tree (DT), and k-nearest neighbors (KNN)) are used. From the experimental results and analysis, it’s noteworthy to accept that the ReliefF-SMO-SA-SVM performs relatively better than its state-of-the-art counterparts. For cancer datasets, our model performs better in terms of accuracy with a maximum of 99.45%.
A Genetic Algorithm on Optimization Test FunctionsIJMERJOURNAL
ABSTRACT: Genetic Algorithms (GAs) have become increasingly useful over the years for solving combinatorial problems. Though they are generally accepted to be good performers among metaheuristic algorithms, most works have concentrated on the application of the GAs rather than the theoretical justifications. In this paper, we examine and justify the suitability of Genetic Algorithms in solving complex, multi-variable and multi-modal optimization problems. To achieve this, a simple Genetic Algorithm was used to solve four standard complicated optimization test functions, namely Rosenbrock, Schwefel, Rastrigin and Shubert functions. These functions are benchmarks to test the quality of an optimization procedure towards a global optimum. We show that the method has a quicker convergence to the global optima and that the optimal values for the Rosenbrock, Rastrigin, Schwefel and Shubert functions are zero (0), zero (0), -418.9829 and -14.5080 respectively
The Evaluated Measurement of a Combined Genetic Algorithm and Artificial Immu...IJECEIAES
This paper demonstrates a hybrid between two optimization methods which are the Artificial Immune System (AIS) and Genetic Algorithm (GA). The novel algorithm called the immune genetic algorithm (IGA), provides improvement to the results that enable GA and AIS to work separately which is the main objective of this hybrid. Negative selection which is one of the techniques in the AIS, was employed to determine the input variables (populations) of the system. In order to illustrate the effectiveness of the IGA, the comparison with a steady-state GA, AIS, and PSO were also investigated. The testing of the performance was conducted by mathematical testing, problems were divided into single and multiple objectives. The five single objectives were then used to test the modified algorithm, the results showed that IGA performed better than all of the other methods. The DTLZ multi-objective testing functions were then used. The result also illustrated that the modified approach still had the best performance.
This paper presents a set of methods that uses a genetic algorithm for automatic test-data generation in
software testing. For several years researchers have proposed several methods for generating test data
which had different drawbacks. In this paper, we have presented various Genetic Algorithm (GA) based test
methods which will be having different parameters to automate the structural-oriented test data generation
on the basis of internal program structure. The factors discovered are used in evaluating the fitness
function of Genetic algorithm for selecting the best possible Test method. These methods take the test
populations as an input and then evaluate the test cases for that program. This integration will help in
improving the overall performance of genetic algorithm in search space exploration and exploitation fields
with better convergence rate.
APPLYING GENETIC ALGORITHMS TO INFORMATION RETRIEVAL USING VECTOR SPACE MODEL IJCSEA Journal
Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users’ needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in 1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used cosine similarity and jaccards to compute similarity between the query and documents, and used two proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study concluded that we might have several improvements when using adaptive genetic algorithms.
Applying genetic algorithms to information retrieval using vector space modelIJCSEA Journal
Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users’ needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on
mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in
1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used
cosine similarity and jaccards to compute similarity between the query and documents, and used two
proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at
evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study
concluded that we might have several improvements when using adaptive genetic algorithms.
Applying Genetic Algorithms to Information Retrieval Using Vector Space ModelIJCSEA Journal
Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users’ needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in 1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used cosine similarity and jaccards to compute similarity between the query and documents, and used two proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study concluded that we might have several improvements when using adaptive genetic algorithms.
Optimal rule set generation using pso algorithmcsandit
Classification and Prediction is an important resea
rch area of data mining. Construction of
classifier model for any decision system is an impo
rtant job for many data mining applications.
The objective of developing such a classifier is to
classify unlabeled dataset into classes. Here
we have applied a discrete Particle Swarm Optimizat
ion (PSO) algorithm for selecting optimal
classification rule sets from huge number of rules
possibly exist in a dataset. In the proposed
DPSO algorithm, decision matrix approach was used f
or generation of initial possible
classification rules from a dataset. Then the propo
sed algorithm discovers important or
significant rules from all possible classification
rules without sacrificing predictive accuracy.
The proposed algorithm deals with discrete valued d
ata, and its initial population of candidate
solutions contains particles of different sizes. Th
e experiment has been done on the task of
optimal rule selection in the data sets collected f
rom UCI repository. Experimental results show
that the proposed algorithm can automatically evolv
e on average the small number of
conditions per rule and a few rules per rule set, a
nd achieved better classification performance
of predictive accuracy for few classes.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
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Particle Swarm Optimization based K-Prototype Clustering Algorithm iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
AUTOMATED TEST CASE GENERATION AND OPTIMIZATION: A COMPARATIVE REVIEWijcsit
Software testing is the primary phase, which is performed during software development and it is carried by a sequence of instructions of test inputs followed by expected output. Evolutionary algorithms are most popular in the computational field based on population. The test case generation process is used to identify
test cases with resources and also identifies critical domain requirements. The behavior of bees is based on
population and evolutionary method. Bee Colony algorithm (BCA) has gained superiority in comparison to other algorithms in the field of computation. The Harmony Search (HS) algorithm is based on the enhancement process of music. When musicians compose the harmony through different possible combinations of the music, at that time the pitches are stored in the harmony memory and the optimization
can be done by adjusting the input pitches and generate the perfect harmony. Particle Swarm Optimization (PSO) is an intelligence based meta-heuristic algorithm where each particle can locate their source of food at different position.. In this algorithm, the particles will search for a better food source position in the hope of getting a better result. In this paper, the role of Artificial Bee Colony, particle swarm optimization
and harmony search algorithms are analyzed in generating random test data and optimized those test data.
Test case generation and optimization through bee colony, PSO and harmony search (HS) algorithms which are applied through a case study, i.e., withdrawal operation in Bank ATM and it is observed that these algorithms are able to generate suitable automated test cases or test data in a client manner. This
section further gives the brief details and compares between HS, PSO, and Bee Colony (BC) Optimization
methods which are used for test case or test data generation and optimization.
GPCODON ALIGNMENT: A GLOBAL PAIRWISE CODON BASED SEQUENCE ALIGNMENT APPROACHijdms
The alignment of two DNA sequences is a basic step in the analysis of biological data. Sequencing a long
DNA sequence is one of the most interesting problems in bioinformatics. Several techniques have been
developed to solve this sequence alignment problem like dynamic programming and heuristic algorithms.
In this paper, we introduce (GPCodon alignment) a pairwise DNA-DNA method for global sequence
alignment that improves the accuracy of pairwise sequence alignment. We use a new scoring matrix to
produce the final alignment called the empirical codon substitution matrix. Using this matrix in our
technique enabled the discovery of new relationships between sequences that could not be discovered using
traditional matrices. In addition, we present experimental results that show the performance of the
proposed technique over eleven datasets of average length of 2967 bps. We compared the efficiency and
accuracy of our techniques against a comparable tool called “Pairwise Align Codons” [1].
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Designing Great Products: The Power of Design and Leadership by Chief Designe...
B017410916
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 4, Ver. I (July – Aug. 2015), PP 09-16
www.iosrjournals.org
DOI: 10.9790/0661-17410916 www.iosrjournals.org 9 | Page
Fuzzy Inference Rule Generation Using Genetic Algorithm
Variant
Shruti S. Jamsandekar1
, Ravindra R. Mudholkar 2
1
(Department of Computer Studies, CSIBER(Kolhapur) ,India)
2
(Department of Electronics, Shivaji University(Kolhapur), India)
Abstract: In essence of fuzzy inference system (FIS) for classification, Genetic algorithm (GA) which is an
optimal searching technique is used for generation rules in the proposed work. This paper develops an FIS with
rules generated using GA, the GA is developed with rule importance as fitness criteria. The rule importance of
each rule is calculated by its rule support in each rule class. Encoding of rules is using the fuzzy membership set
no for antecedent and consequent. Also the stopping criteria is a combination ofgenerations specified and
Minimum rules fired count. The Proposed system using GA approach for rule generation giving consistent
results with optimal rules.
Keywords: fuzzy rules, genetic algorithm, rule_support, rule importance, fitness function.
I. INTRODUCTION
Fuzzy Inference have been applied to various problem, of which classification problem apart of data
mining task [12] is of one which has application in fields, like medicine, education, business etc.
classification process can be improved by using fuzzy sets having overlapping class definitions and if-
then rules for inference that provide better reasoning in ambiguous. Fuzzy rules are usually
constructed by expert based on their domain knowledge but at times this can be tedious and
cumbersome.
Many researchers have worked and still trying to do automatic generation of rules using neural
network, association rule mining, genetic algorithm (GA) [1][9][6][5]. The reason why GA is
preferred is its global-search nature, GAs work with a population of candidate solutions
(individuals).Second, in GAs a candidate solution is evaluated as a whole by the fitness function. These
characteristics are in contrast with most greedy rule induction algorithms, which work with a single
candidate solution at a time based on local information only. [11]In this proposed work we have
intended to construct an classification system with fuzzy inference . The approach is towards automatic
construction of fuzzy system. The automatic construction of fuzzy inference system(FIS) for
classification is developed in 2 phases.
1.Self generated membership function for FIS
2.Generation of Fuzzy Rules.
The automatic construction of fuzzy classification system by self generated membership function
already done in preceding research work by us [13].
A data driven approach for automatic generation of fuzzy rules is the focus of the experiment in this
paper. Initial population of rules is built in the form of matrix and input membership functions are built
with membership function parameters stored in array form. These parameter arrays are generated using
clustering algorithm. Both the rules and input, output FIS variables parameters and data are passed as
arguments to proposed algorithm to generate rules using genetic algorithm approach.
This paper is organized as follows: Section2 gives the state of the art for mining rules using fuzzy
genetic approach. Section 3 is devoted to Rule_Gen, the algorithm we propose for generating fuzzy
rules using genetic algorithm. Experimental tests on real-life datasets are described in Section4. We
conclude in Section5with a summary of our contribution.
II. RELATED WORK
The authors in [5] proposed a algorithm for association rule mining named Quant-Miner using genetic
algorithm, the algorithm dynamically chooses good intervals for each rule with categorical attributes. The
approach used by them is evolutionary optimizing support and confidence with fitness function based on gain
measure and the algorithm looking for positive gain. Soumadip Ghosh et.al have proposed in [6] an genetic
algorithm approach for finding frequent item sets which caters to positive and negative association mining.
2. Fuzzy Inference Rule Generation Using Genetic Algorithm Variant…
DOI: 10.9790/0661-17410916 www.iosrjournals.org 10 | Page
In[7] authors have proposed an algorithm to measure the quality of categorical data in association rule
mining, they have converted the categorical data to asymmetric binary form by introducing new items as per the
distinct attribute pair, apriori association algorithm is applied to find the frequent itemset with minimum
support. Quality of data in the frequent itemset obtained is measured in terms of confidence factor,
completeness, interestingness, comprehensibility, the rules with optimum values for all measures is searched
using genetic algorithm. The author in [8] tried to explore the different methods of using genetic algorithm with
K- nearest neighbor algorithm to improve the classification accuracy and minimize the training error. Soraj
et.al in [10] proposed a genetic-fuzzy approach for the discovery of fuzzy decision rules from datasets
containing categorical as well as continuous attributes. They have fuzzified the continuous attributes using the
triangular fuzzy number and also designed a matching process while computing the fitness of an individual in
Genetic algorithm population.
III. METHODOLOGY
The proposed algorithm for generation of Fuzzy rules comprises of four procedures .
1. The genetic algorithm for rule generation: Genetic _ Rule_Gen
2. The fitness calculation function comprising of two procedures: support_Rule_class and
rule_ratio_importance
3. Evaluation of FIS with rule population generated:GARule
The algorithm for generation of fuzzy rules by using genetic algorithm is as follows.
Repeatedly for n generations following steps are carried out
1. Built the FIS with rule population and evaluate the FIS rules against the output class to return rule
firing strength for given data.
2. Calculate and return the support for each rule and the classification class.
3. Calculate and return the importance of each rule which is the fitness value of each rule.
4. Select two parent chromosomes p1 and p2 from the rule population where p1 is parent chromosome
with best fitness value and p2 is any parent chromosome from rule population, such that p1 ≠ p2.
Usually according to GA approach 2 best fitness parent chromosomes are chosen but in this experiment
we have chosen one best fitness value parent chromosome and other parent chromosome with any
fitness value as this lead to more variations in children chromosome than in usual approach.
5. Select two rule chromosomes with least fitness value.
6. Crossover points are generated using 2 points crossover method.
7. 2 Offspring chromosomes are generated at the crossover points of parent chromosomes.
8. Each of the offspring generated is checked if present in current population either partially or fully.
9. If offspring chromosomes are not fully or partially matched with current population rule chromosome
then replace the offsprings with selected least fitness value chromosomes to built new rule population.
10. If both offsprings are present fully in current population do not replace the offsprings with the least
value chromosomes, simply go through next generation.
11. If any one or both of the offspring chromosome/s is in partial form matching with any of the rule
chromosome/s in current population, then evaluate the fitness function of the offspring chromosome
and compare it with matched rule chromosome/s.
12. If offspring chromosome is having better fitness value than matched rule chromosome then replace the
offspring chromosome/s with respective matched rule chromosome to built new rule population.
13. Pass the new rule population to the next generation
There has been many research on termination criteria of genetic algorithm, with simplest being the
iteration based on specific number of times or time based where after specific time with ever is best
fitness value obtain is taken as final [2][3][9], also bhandari et.al [4] proposed variance as stopping
criteria for genetic algorithm where they have proposed that if ai be the best fitness function value
obtained at the end of ith iteration of an GA. Then, a1 ≤ a2 ≤ a3 ……..≤ an ……≤ F1, as F1 is the
global optimal value of the fitness function if
an =
1
n
𝑎𝑖
𝑛
𝑖=1
(1)
be the average of the ai’s and
a¯2n = 1/n 𝑎2𝑖𝑛
𝑖=1 (2)
be the average of the a2i’s up to the nth iteration, then variance of the best fitness values obtained up to
the nth iteration, defined by bn, is 𝑏𝑛 =
1
𝑛
𝑎𝑖 − 𝑎𝑛𝑛
𝑖=1 2
=1/𝑛 (𝑎2𝑖 − 𝑎2𝑛)𝑛
𝑖=1 (3)
3. Fuzzy Inference Rule Generation Using Genetic Algorithm Variant…
DOI: 10.9790/0661-17410916 www.iosrjournals.org 11 | Page
= a¯2n - a¯n2
bn can be used as a stopping criterion for a GA. GA is stopped or terminated after N iterations when
bN<ϵ , where ϵ(> 0) is a user defined small quantity.
3.1 Genetic _ Rule_Gen Procedure
Step 1: Input the initial rule population as matrix P[] n x m (where n is the no of rules and m is the no of rule
parameter in form [a1,a2…an, o, w, c ] a is input variables of a rule with i input variables; i =1…n,o is the
output variable, w is the rule weight and c is connection and/or. ) , membership function generated for fuzzy
input variables as matrix input(i)[]1xmf ( where i is input no. and mf=1,2,… j) and input data
Step 2: declare the two parents chromosome structures p1,p2 and two offspring chromosome structures
off1,off2.
Step 3: initial the generation count to 1
Step 4: Repeat step 4 to step 10 until generation_count<100 and stop_flag ==false
Step 5: calculate the fitness of each rule from rule population
Step 5.1 : call GaRuleprocedure to automatically generate the FIS and evaluate the FIS rules against the output
class to return rule firing strength for given data
Step 5.2 : call support_Rule_class procedure to calculate and return the support for each rule and for each
classification class
Step 5.3: call rule_ratio_importance procedure to calculate and return the importance of each rule which is the
fitness value of each rule.
Step 5.4: count the rules with importance as zero store in count variable.(indicating rules not fired)
Step 5.5 : if count <min_count specified then set stop_flag = true else stop_flag=false
Step 6: Select two parent chromosomes from the population where p1 [aij1,aij2…aijn,oi,wi,ci] is parent
chromosome with best fitness value and p2 is any other parent chromosome from rule population P[] , p1 ≠ p2 .
Step 7: Compute the crossover points crp1,crp2 as follows
crpi = floor(1 + (rand(1) * n)) i=1,2 (4)
Step 8: Generate offsprings at the crossover points crp1,crp2
off1[X1,X2,….Xn,w,c]
off2[Y1,Y2,….Yn,w,c]
Step 9: Select the least fitness value chromosomes from the P[]
d1= rule chromosome no with min rule importance
d2= rule chromosome no with second last min rule importance
Step 10: check if offspring off1 and off2 chromosome pattern generated are fully or partially in P[] .
Step 10.1: if (∃ ( off1 ^off2 ) ∈P) then set flag_both =true
Step 10.2: if (∃ T[x1,x2…xn - 1]∈ P==off1[x1,x2…..xn-1] ) then call GaRule, support_Rule_class,
rule_ratio_importance procedure for off1
Step10.2.1:ifrule_ratio_importance(off1)>rule_ratio_importance(T) then d1 = T;
Step10.3:if(∃ T[x1,x2…xn-1]∈P==off2[x1,x2…..xn-1]) then callGaRule, support_Rule_class
,rule_ratio_importance procedure for off2
Step10.3.1: if rule_ ratio_ importance(off2)>rule_ratio_importance(T) then d2 = T;
Step 11: if(flag_both == true) thenno replacement of off1 and off2 with d1 and d2 in P.else
P[ ] = P[ ] – d1 and P[ ] = P[ ] – d2
P[ ] = P[ ] ∪off1∪off2
Step 12: return P;// new rule population.
4. Fuzzy Inference Rule Generation Using Genetic Algorithm Variant…
DOI: 10.9790/0661-17410916 www.iosrjournals.org 12 | Page
3.2 GARuleProcedure
The procedure builds up a FIS automatically based on the input data by constructing the input and output
membership function and also the rule generated. Returns FIS evaluation parameters (output, IRR,ORR,ARR)
Following are the steps of the GARule procedure.
Step1: Input the membership function array created using clustering technique for input and output variables,
input data arrays and rule population matrix.
Step 2: calculate the input membership function degree of input membership functions using evalmf matlab
function and store it in eval_input matrix.
Step 3: create an FISclustclass using newfis Matlab function.
Step 3.1: add input/s and output variables to the FIS using addvarmatlab function.
Step 3.2: add input/s and output membership functions to the FIS using addmf matlab function with input
membership array data generated.
Step 3.3: add rule population to the FIS using addrule matlab function
Step 4 : evaluate the FIS created with the input data using evalfis matlab function which returns.
output: the output matrix of size M-by-L, where M represents the number of input values specified previously,
and L is the number of output variables for the FIS.[14 ].
IRR: the result of evaluating the input values through the membership functions. This matrix is of the
sizenumRules-by-N, where numRules is the number of rules, and N is the number of input variables.[14
ORR: the result of evaluating the output values through the membership functions. This matrix is of the
sizenumPts-by-numRules*L, where numRules is the number of rules, and L is the number of outputs. The
firstnumRules columns of this matrix correspond to the first output, the next numRules columns of this matrix
correspond to the second output, and so forth.[14 ]
ARR: the numPts-by-L matrix of the aggregate values sampled at numPts along the output range for each
output. [14]
Step 5: End
3.3 Support_Rule_Class Procedure
Function support_Rule_class(OR, POP, n)
OR : array consisting result of evaluating the output values through the membership functions
POP : rule population
n : number of rules
Step 1: declare and initialize array for support_rule(Number of data points supporting individual rule),
rule_class(class support for each rule),total_support_rule(total inference values by support_rule), V( is a
temporary vector to store all OR values), support_class(Number of data points supporting rule class).
Step 2 : loop through all rows s of OR matrix
Step 2.1: if OR[s,t] >0 then V[t] = OR[s,t]; Else V(t)=-1 ∀ i ∈{ 1,2……n}
Step 2.2: Find the rule no I with maximum OR value in vector V
Step 2.3: support_rule(I)= support_rule(I)+1;
Step 2.4: total_support_rule(I)= total_support_rule(I) + V(I);
Step 2.5: end loop
Step 3: calculate the support_class for each rule by using formula
support_rule[i] >0→support_class[POP[i,3] ]
= support_class[POP[i,3] ] + support_rule[i] ∀i∈{ 1,2……n} (5)
5. Fuzzy Inference Rule Generation Using Genetic Algorithm Variant…
DOI: 10.9790/0661-17410916 www.iosrjournals.org 13 | Page
Step 4: calculate the rule_class for each rule by using formula
rule_class(i)= support_class(POP(i,3)) ∀i∈{ 1,2……n} (6)
Step 5 : end
3.4 Rule_Ratio_ImportanceProcedure
Function rule_ratio_importance(SR,RC,TSR,n)
SR: rule support
RC: rule class
TSR: total support for the rule in each class
n: number of rules
Step1: declare ratio and importance array for n rules
Step2: calculate ratio and importance
RC[i]==0 -> ratio[i]= SR[i] ie( if rule class is not defined assign rule support as the ratio for the rule) else
ratio[i] = SR[i]/RC[i] (7)
( ratio will give the number of data points supporting rule with the rule class) and
importance [i] = ratio[i]*TSR[i] (8)
(if ratio is bigger more the importance of the rule ∀ i ∈{ 1,2……n})
Step 3: return ratio and importance
Step 4: end
IV. Experimental Results
The experiment is carried out using 51 student data set of MSc. Course of Shivaji university
on fuzzy tool box of MATLAB Rb2009 and genetic algorithm is implemented using MATLAB programming
.The GA Population comprises of the fuzzy rule list with 25 candidate rules coded in the form of if-then rules in
table 1:
Rule
no.
Input
1
Input
2
Input
3
Input
4
Class
Output
Rule
Weight
Implication
(And/Or)
1 1 1 1 1 1 1 1
2 1 2 2 1 2 1 1
3 1 3 2 3 3 1 1
4 1 4 3 3 3 1 1
5 1 5 3 5 4 1 1
6 2 1 1 2 1 1 1
7 2 2 2 2 2 1 1
8 2 3 2 2 2 1 1
9 2 4 3 3 3 1 1
10 2 5 4 3 4 1 1
11 3 1 2 3 2 1 1
12 3 2 3 3 3 1 1
13 3 3 3 3 3 1 1
14 3 4 4 4 4 1 1
15 3 4 4 2 3 1 1
16 4 1 2 4 3 1 1
17 4 2 2 3 2 1 1
18 4 3 3 4 2 1 1
19 4 4 4 4 4 1 1
20 4 5 5 5 5 1 1
21 5 1 2 3 3 1 1
22 5 2 2 3 4 1 1
23 5 3 3 3 4 1 1
24 5 4 4 4 5 1 1
25 5 5 5 5 5 1 1
Table 1: Initial Rule List
6. Fuzzy Inference Rule Generation Using Genetic Algorithm Variant…
DOI: 10.9790/0661-17410916 www.iosrjournals.org 14 | Page
Parent Chromosome 1:
3 3 3 3 3 1 1
Parent chromosome 2:
4 5 5 5 5 1 1
Cross over points: two point crossover operation at 2 – 4
Offspring chromosome 1 :
4 3 3 3 5 1 1
Offspring chromosome 2:
3 5 5 5 3 1 1
The rules generated using GA is in Table 2 where each rule antecedent, consequent and rule importance is
shown.
The proposed system was tested for many epochs and was giving the almost same optimal rules but
terminated at after different no of generation. 11 epoch result are shown in Table 3
V. Conclusion
The Proposed GA algorithm with rule importance as fitness criteria and with termination
criteria as combination of specified generations and rules firing count is stable and has shown consistent
results.It is been observed that out the 25 rules from initial population only 16 rules have been fired with good
rule importance in classifying the items. GA terminated at different generations for each epoch but the rules
identified with good rule importance ie with higher firing strength are almost same for each epoch. Also fuzzy
inference of the 51 data sets using the rules generated a consistent result for each epoch generated rules. The
results are tabulated in Table 4. The Figure 1 shows the epoch wise results for 6 data values.
Figure 1: Epoch Wise Results Of Data
0
50
100
1 2 3 4 5 6
Epoch1
Epoch2
Epoch 3
Epoch 4
epoch 5
Rule
No.
Input
1
Input
2
Input
3
Input
4
Class
Output
Rule
Weight
Implication
(And/Or)
Rule
Importance
1 1 1 1 1 1 1 1 0.059
2 1 2 2 1 2 1 1 0.077
3 1 3 2 3 3 1 1 0.009
4 1 4 3 3 3 1 1 0.011
5 1 5 3 5 4 1 1 0.022
6 2 1 1 2 1 1 1 0.073
7 2 2 2 2 2 1 1 0.213
8 2 3 2 2 2 1 1 0.358
9 2 4 3 3 3 1 1 2.570
10 2 5 4 3 4 1 1 1.184
11 3 1 2 3 2 1 1 0
12 3 2 3 3 3 1 1 0
13 3 3 3 3 3 1 1 0