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
In solving real life transportation problem we often face the state of uncertainty as well as hesitation due to various uncontrollable factors. To deal with uncertainty and hesitation many authors have suggested the intuitionistic fuzzy representation for the data. So, in this paper, we consider a transportation problem having uncertainty and hesitation in supply, demand and costs. We formulate the problem and utilize triangular intuitionistic fuzzy numbers (TrIFNs) to deal with uncertainty and hesitation. We propose a new method called PSK method for finding the intuitionistic fuzzy optimal solution for fully intuitionistic fuzzy transportation problem in single stage. Also the new multiplication operation on TrIFN is proposed to find the optimal object value in terms of TrIFN. The main advantage of this method is computationally very simple, easy to understand and also the optimum objective value obtained by our method is physically meaningful. Finally the effectiveness of the proposed method is illustrated by means of a numerical example which is followed by graphical representation of the finding.
Critical Paths Identification on Fuzzy Network Projectiosrjce
In this paper, a new approach for identifying fuzzy critical path is presented, based on converting the
fuzzy network project into deterministic network project, by transforming the parameters set of the fuzzy
activities into the time probability density function PDF of each fuzzy time activity. A case study is considered as
a numerical tested problem to demonstrate our approach.
UNDERSTANDING NEGATIVE SAMPLING IN KNOWLEDGE GRAPH EMBEDDINGijaia
Knowledge graph embedding (KGE) is to project entities and relations of a knowledge graph (KG) into a
low-dimensional vector space, which has made steady progress in recent years. Conventional KGE
methods, especially translational distance-based models, are trained through discriminating positive
samples from negative ones. Most KGs store only positive samples for space efficiency. Negative sampling
thus plays a crucial role in encoding triples of a KG. The quality of generated negative samples has a
direct impact on the performance of learnt knowledge representation in a myriad of downstream tasks,
such as recommendation, link prediction and node classification. We summarize current negative sampling
approaches in KGE into three categories, static distribution-based, dynamic distribution-based and custom
cluster-based respectively. Based on this categorization we discuss the most prevalent existing approaches
and their characteristics. It is a hope that this review can provide some guidelines for new thoughts about
negative sampling in KGE.
Min-based qualitative possibilistic networks are one of the effective tools for a compact representation of
decision problems under uncertainty. The exact approaches for computing decision based on possibilistic
networks are limited by the size of the possibility distributions. Generally, these approaches are based on
possibilistic propagation algorithms. An important step in the computation of the decision is the
transformation of the DAG (Direct Acyclic Graph) into a secondary structure, known as the junction trees
(JT). This transformation is known to be costly and represents a difficult problem. We propose in this paper
a new approximate approach for the computation of decision under uncertainty within possibilistic
networks. The computing of the optimal optimistic decision no longer goes through the junction tree
construction step. Instead, it is performed by calculating the degree of normalization in the moral graph
resulting from the merging of the possibilistic network codifying knowledge of the agent and that codifying
its preferences.
Data science is an area at the interface of statistics, computer science, and mathematics.
• Statisticians contributed a large inferential framework, important Bayesian perspectives, the bootstrap and CART and random forests, and the concepts of sparsity and parsimony.
• Computer scientists contributed an appetite for big, challenging problems.They also pioneered neural networks, boosting, PAC bounds, and developed programming languages, such as Spark and hadoop, for handling Big Data.
• Mathematicians contributed support vector machines, modern optimization, tensor analysis, and (maybe) topological data analysis.
A NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATIONmathsjournal
In Numerical analysis, interpolation is a manner of calculating the unknown values of a function for any conferred value of argument within the limit of the arguments. It provides basically a concept of estimating unknown data with the aid of relating acquainted data. The main goal of this research is to constitute a central difference interpolation method which is derived from the combination of Gauss’s third formula, Gauss’s Backward formula and Gauss’s forward formula. We have also demonstrated the graphical presentations as well as comparison through all the existing interpolation formulas with our propound method of central difference interpolation. By the comparison and graphical presentation, the new method gives the best result with the lowest error from another existing interpolationformula.
In solving real life transportation problem we often face the state of uncertainty as well as hesitation due to various uncontrollable factors. To deal with uncertainty and hesitation many authors have suggested the intuitionistic fuzzy representation for the data. So, in this paper, we consider a transportation problem having uncertainty and hesitation in supply, demand and costs. We formulate the problem and utilize triangular intuitionistic fuzzy numbers (TrIFNs) to deal with uncertainty and hesitation. We propose a new method called PSK method for finding the intuitionistic fuzzy optimal solution for fully intuitionistic fuzzy transportation problem in single stage. Also the new multiplication operation on TrIFN is proposed to find the optimal object value in terms of TrIFN. The main advantage of this method is computationally very simple, easy to understand and also the optimum objective value obtained by our method is physically meaningful. Finally the effectiveness of the proposed method is illustrated by means of a numerical example which is followed by graphical representation of the finding.
Critical Paths Identification on Fuzzy Network Projectiosrjce
In this paper, a new approach for identifying fuzzy critical path is presented, based on converting the
fuzzy network project into deterministic network project, by transforming the parameters set of the fuzzy
activities into the time probability density function PDF of each fuzzy time activity. A case study is considered as
a numerical tested problem to demonstrate our approach.
UNDERSTANDING NEGATIVE SAMPLING IN KNOWLEDGE GRAPH EMBEDDINGijaia
Knowledge graph embedding (KGE) is to project entities and relations of a knowledge graph (KG) into a
low-dimensional vector space, which has made steady progress in recent years. Conventional KGE
methods, especially translational distance-based models, are trained through discriminating positive
samples from negative ones. Most KGs store only positive samples for space efficiency. Negative sampling
thus plays a crucial role in encoding triples of a KG. The quality of generated negative samples has a
direct impact on the performance of learnt knowledge representation in a myriad of downstream tasks,
such as recommendation, link prediction and node classification. We summarize current negative sampling
approaches in KGE into three categories, static distribution-based, dynamic distribution-based and custom
cluster-based respectively. Based on this categorization we discuss the most prevalent existing approaches
and their characteristics. It is a hope that this review can provide some guidelines for new thoughts about
negative sampling in KGE.
Min-based qualitative possibilistic networks are one of the effective tools for a compact representation of
decision problems under uncertainty. The exact approaches for computing decision based on possibilistic
networks are limited by the size of the possibility distributions. Generally, these approaches are based on
possibilistic propagation algorithms. An important step in the computation of the decision is the
transformation of the DAG (Direct Acyclic Graph) into a secondary structure, known as the junction trees
(JT). This transformation is known to be costly and represents a difficult problem. We propose in this paper
a new approximate approach for the computation of decision under uncertainty within possibilistic
networks. The computing of the optimal optimistic decision no longer goes through the junction tree
construction step. Instead, it is performed by calculating the degree of normalization in the moral graph
resulting from the merging of the possibilistic network codifying knowledge of the agent and that codifying
its preferences.
Data science is an area at the interface of statistics, computer science, and mathematics.
• Statisticians contributed a large inferential framework, important Bayesian perspectives, the bootstrap and CART and random forests, and the concepts of sparsity and parsimony.
• Computer scientists contributed an appetite for big, challenging problems.They also pioneered neural networks, boosting, PAC bounds, and developed programming languages, such as Spark and hadoop, for handling Big Data.
• Mathematicians contributed support vector machines, modern optimization, tensor analysis, and (maybe) topological data analysis.
A NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATIONmathsjournal
In Numerical analysis, interpolation is a manner of calculating the unknown values of a function for any conferred value of argument within the limit of the arguments. It provides basically a concept of estimating unknown data with the aid of relating acquainted data. The main goal of this research is to constitute a central difference interpolation method which is derived from the combination of Gauss’s third formula, Gauss’s Backward formula and Gauss’s forward formula. We have also demonstrated the graphical presentations as well as comparison through all the existing interpolation formulas with our propound method of central difference interpolation. By the comparison and graphical presentation, the new method gives the best result with the lowest error from another existing interpolationformula.
The philosophy of fuzzy logic was formed by introducing the membership degree of a linguistic value or variable instead of divalent membership of 0 or 1. Membership degree is obtained by mapping the variable on the graphical shape of fuzzy numbers. Because of simplicity and convenience, triangular membership numbers (TFN) are widely used in different kinds of fuzzy analysis problems. This paper suggests a simple method using statistical data and frequency chart for constructing non-isosceles TFN when we are using direct rating for evaluating a variable in a predefined scale. In this method, the relevancy between assessment uncertainties and statistical parameters such as mean value and the standard deviation is established in a way that presents an exclusive form of triangle number for each set of data. The proposed method with regard to the graphical shape of the frequency chart distributes the standard deviation around the mean value and forms the TFN with the membership degree of 1 for mean value. In the last section of the paper modification of the proposed method is presented through a practical case study.
This Presentation discusses he following topics:
Introduction
Need for Problem formulation
Problem Solving Components
Definition of Problem
Problem Limitation
Goal or Solution
Solution Space
Operators
Examples of Problem Formulation
Well-defined Problems and Solution
Examples of Well-Defined Problems
Constraint satisfaction problems (CSPs)
Examples of constraint satisfaction problem
Decision problem
PERFORMANCE ANALYSIS OF HYBRID FORECASTING MODEL IN STOCK MARKET FORECASTINGIJMIT JOURNAL
This paper presents performance analysis of hybrid model comprise of concordance and Genetic
Programming (GP) to forecast financial market with some existing models. This scheme can be used for in
depth analysis of stock market. Different measures of concordances such as Kendall’s Tau, Gini’s Mean
Difference, Spearman’s Rho, and weak interpretation of concordance are used to search for the pattern in
past that look similar to present. Genetic Programming is then used to match the past trend to present
trend as close as possible. Then Genetic Program estimates what will happen next based on what had
happened next. The concept is validated using financial time series data (S&P 500 and NASDAQ indices)
as sample data sets. The forecasted result is then compared with standard ARIMA model and other model
to analyse its performance
ON AVERAGE CASE ANALYSIS THROUGH STATISTICAL BOUNDS : LINKING THEORY TO PRACTICEcscpconf
Theoretical analysis of algorithms involves counting of operations and a separate bound is provided for a specific operation type . Such a methodology is plagued with its inherent
limitations. In this paper we argue as to why we should prefer weight based statistical bounds,which permit mixing of operations, instead as a robust approach. Empirical analysis is an important idea and should be used to supplement and compliment its existing theoretical counterpart as empirically we can work on weights (e.g. time of an operation can be taken as its weight). Not surprisingly, it should not only be taken as an opportunity so as to amend the mistakes already committed knowingly or unknowingly but also to tell a new story.
EXPERT OPINION AND COHERENCE BASED TOPIC MODELINGijnlc
In this paper, we propose a novel algorithm that rearrange the topic assignment results obtained from topic
modeling algorithms, including NMF and LDA. The effectiveness of the algorithm is measured by how much
the results conform to expert opinion, which is a data structure called TDAG that we defined to represent the
probability that a pair of highly correlated words appear together. In order to make sure that the internal
structure does not get changed too much from the rearrangement, coherence, which is a well known metric
for measuring the effectiveness of topic modeling, is used to control the balance of the internal structure.
We developed two ways to systematically obtain the expert opinion from data, depending on whether the
data has relevant expert writing or not. The final algorithm which takes into account both coherence and
expert opinion is presented. Finally we compare amount of adjustments needed to be done for each topic
modeling method, NMF and LDA.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
This presentation discusses about following topics:
Types of Problems Solved Using Artificial Intelligence Algorithms
Problem categories
Classification Algorithms
Naive Bayes
Example: A person playing golf
Decision Tree
Random Forest
Logistic Regression
Support Vector Machine
Support Vector Machine
K Nearest Neighbors
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
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.
Offline signature identification using high intensity variations and cross ov...eSAT Publishing House
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
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
Geospatial information system for tourism management in aurangabad city a re...eSAT Publishing House
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
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.
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
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.
Detect and overcome the selfish problem in wifi network using energy sharingeSAT Publishing House
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
The philosophy of fuzzy logic was formed by introducing the membership degree of a linguistic value or variable instead of divalent membership of 0 or 1. Membership degree is obtained by mapping the variable on the graphical shape of fuzzy numbers. Because of simplicity and convenience, triangular membership numbers (TFN) are widely used in different kinds of fuzzy analysis problems. This paper suggests a simple method using statistical data and frequency chart for constructing non-isosceles TFN when we are using direct rating for evaluating a variable in a predefined scale. In this method, the relevancy between assessment uncertainties and statistical parameters such as mean value and the standard deviation is established in a way that presents an exclusive form of triangle number for each set of data. The proposed method with regard to the graphical shape of the frequency chart distributes the standard deviation around the mean value and forms the TFN with the membership degree of 1 for mean value. In the last section of the paper modification of the proposed method is presented through a practical case study.
This Presentation discusses he following topics:
Introduction
Need for Problem formulation
Problem Solving Components
Definition of Problem
Problem Limitation
Goal or Solution
Solution Space
Operators
Examples of Problem Formulation
Well-defined Problems and Solution
Examples of Well-Defined Problems
Constraint satisfaction problems (CSPs)
Examples of constraint satisfaction problem
Decision problem
PERFORMANCE ANALYSIS OF HYBRID FORECASTING MODEL IN STOCK MARKET FORECASTINGIJMIT JOURNAL
This paper presents performance analysis of hybrid model comprise of concordance and Genetic
Programming (GP) to forecast financial market with some existing models. This scheme can be used for in
depth analysis of stock market. Different measures of concordances such as Kendall’s Tau, Gini’s Mean
Difference, Spearman’s Rho, and weak interpretation of concordance are used to search for the pattern in
past that look similar to present. Genetic Programming is then used to match the past trend to present
trend as close as possible. Then Genetic Program estimates what will happen next based on what had
happened next. The concept is validated using financial time series data (S&P 500 and NASDAQ indices)
as sample data sets. The forecasted result is then compared with standard ARIMA model and other model
to analyse its performance
ON AVERAGE CASE ANALYSIS THROUGH STATISTICAL BOUNDS : LINKING THEORY TO PRACTICEcscpconf
Theoretical analysis of algorithms involves counting of operations and a separate bound is provided for a specific operation type . Such a methodology is plagued with its inherent
limitations. In this paper we argue as to why we should prefer weight based statistical bounds,which permit mixing of operations, instead as a robust approach. Empirical analysis is an important idea and should be used to supplement and compliment its existing theoretical counterpart as empirically we can work on weights (e.g. time of an operation can be taken as its weight). Not surprisingly, it should not only be taken as an opportunity so as to amend the mistakes already committed knowingly or unknowingly but also to tell a new story.
EXPERT OPINION AND COHERENCE BASED TOPIC MODELINGijnlc
In this paper, we propose a novel algorithm that rearrange the topic assignment results obtained from topic
modeling algorithms, including NMF and LDA. The effectiveness of the algorithm is measured by how much
the results conform to expert opinion, which is a data structure called TDAG that we defined to represent the
probability that a pair of highly correlated words appear together. In order to make sure that the internal
structure does not get changed too much from the rearrangement, coherence, which is a well known metric
for measuring the effectiveness of topic modeling, is used to control the balance of the internal structure.
We developed two ways to systematically obtain the expert opinion from data, depending on whether the
data has relevant expert writing or not. The final algorithm which takes into account both coherence and
expert opinion is presented. Finally we compare amount of adjustments needed to be done for each topic
modeling method, NMF and LDA.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
This presentation discusses about following topics:
Types of Problems Solved Using Artificial Intelligence Algorithms
Problem categories
Classification Algorithms
Naive Bayes
Example: A person playing golf
Decision Tree
Random Forest
Logistic Regression
Support Vector Machine
Support Vector Machine
K Nearest Neighbors
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
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.
Offline signature identification using high intensity variations and cross ov...eSAT Publishing House
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
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
Geospatial information system for tourism management in aurangabad city a re...eSAT Publishing House
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
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.
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
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.
Detect and overcome the selfish problem in wifi network using energy sharingeSAT Publishing House
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
The compensatation of unbalanced 3 phase currents in transmission systems on ...eSAT Publishing House
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.
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.
Key frame extraction for video summarization using motion activity descriptorseSAT Publishing House
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
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
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
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.
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.
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
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
A preliminary survey on optimized multiobjective metaheuristic methods for da...ijcsit
The present survey provides the state-of-the-art of research, copiously devoted to Evolutionary Approach
(EAs) for clustering exemplified with a diversity of evolutionary computations. The Survey provides a
nomenclature that highlights some aspects that are very important in the context of evolutionary data
clustering. The paper missions the clustering trade-offs branched out with wide-ranging Multi Objective
Evolutionary Approaches (MOEAs) methods. Finally, this study addresses the potential challenges of
MOEA design and data clustering, along with conclusions and recommendations for novice and
researchers by positioning most promising paths of future research.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
In recent years, consumers and legislation have been pushing companies to optimize their activities in such a way as to reduce negative environmental and social impacts more and more. In the other side, companies
must keep their total supply chain costs as low as possible to remain competitive.This work aims to develop a model to traveling salesman problem including environmental impacts and to identify, as far as possible, the contribution of genetic operator’s tuning and setting in the success and
efficiency of genetic algorithms for solving this problem with consideration of CO2 emission due to transport. This efficiency is calculated in terms of CPU time consumption and convergence of the solution. The best transportation policy is determined by finding a balance between financial and environmental
criteria.Empirically, we have demonstrated that the performance of the genetic algorithm undergo relevant
improvements during some combinations of parameters and operators which we present in our results part.
Text documents clustering using modified multi-verse optimizerIJECEIAES
In this study, a multi-verse optimizer (MVO) is utilised for the text document clus- tering (TDC) problem. TDC is treated as a discrete optimization problem, and an objective function based on the Euclidean distance is applied as similarity measure. TDC is tackled by the division of the documents into clusters; documents belonging to the same cluster are similar, whereas those belonging to different clusters are dissimilar. MVO, which is a recent metaheuristic optimization algorithm established for continuous optimization problems, can intelligently navigate different areas in the search space and search deeply in each area using a particular learning mechanism. The proposed algorithm is called MVOTDC, and it adopts the convergence behaviour of MVO operators to deal with discrete, rather than continuous, optimization problems. For evaluating MVOTDC, a comprehensive comparative study is conducted on six text document datasets with various numbers of documents and clusters. The quality of the final results is assessed using precision, recall, F-measure, entropy accuracy, and purity measures. Experimental results reveal that the proposed method performs competitively in comparison with state-of-the-art algorithms. Statistical analysis is also conducted and shows that MVOTDC can produce significant results in comparison with three well-established methods.
The potential role of ai in the minimisation and mitigation of project delayPieter Rautenbach
Artificial intelligence (AI) can have wide reaching application within the construction
industry, however, the actual application of this set of technologies is currently under exploited. This
paper considers the role that the application of AI can take in optimising the efficiencies of project
execution and how this can potentially reduce project duration and minimise and mitigate delay on
projects.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Artificial Intelligence in Robot Path Planningiosrjce
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.
Extended pso algorithm for improvement problems k means clustering algorithmIJMIT JOURNAL
The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.
The well-known Vehicle Routing Problem (VRP) consist of assigning routeswith a set
ofcustomersto different vehicles, in order tominimize the cost of transport, usually starting from a central
warehouse and using a fleet of fixed vehicles. There are numerousapproaches for the resolution of this kind of
problems, being the metaheuristic techniques the most used, including the Genetic Algorithms (AG). The
number of approachesto the different parameters of an AG (selection, crossing, mutation...) in the literature is
such that it is not easy to take a resolution of a VRP problem directly. This paper aims to simplify this task by
analyzing the best known approaches with standard VRP data sets, and showing the parameter configurations
that offer the best results.
A Mixture Model of Hubness and PCA for Detection of Projected OutliersZac Darcy
With the Advancement of time and technology, Outlier Mining methodologies help to sift through the large
amount of interesting data patterns and winnows the malicious data entering in any field of concern. It has
become indispensible to build not only a robust and a generalised model for anomaly detection but also to
dress the same model with extra features like utmost accuracy and precision. Although the K-means
algorithm is one of the most popular, unsupervised, unique and the easiest clustering algorithm, yet it can
be used to dovetail PCA with hubness and the robust model formed from Guassian Mixture to build a very
generalised and a robust anomaly detection system. A major loophole of the K-means algorithm is its
constant attempt to find the local minima and result in a cluster that leads to ambiguity. In this paper, an
attempt has done to combine K-means algorithm with PCA technique that results in the formation of more
closely centred clusters that work more accurately with K-means algorithm .This combination not only
provides the great boost to the detection of outliers but also enhances its accuracy and precision.
A MIXTURE MODEL OF HUBNESS AND PCA FOR DETECTION OF PROJECTED OUTLIERSZac Darcy
With the Advancement of time and technology, Outlier Mining methodologies help to sift through the large
amount of interesting data patterns and winnows the malicious data entering in any field of concern. It has
become indispensible to build not only a robust and a generalised model for anomaly detection but also to
dress the same model with extra features like utmost accuracy and precision. Although the K-means
algorithm is one of the most popular, unsupervised, unique and the easiest clustering algorithm, yet it can
be used to dovetail PCA with hubness and the robust model formed from Guassian Mixture to build a very
generalised and a robust anomaly detection system. A major loophole of the K-means algorithm is its
constant attempt to find the local minima and result in a cluster that leads to ambiguity. In this paper, an
attempt has done to combine K-means algorithm with PCA technique that results in the formation of more
closely centred clusters that work more accurately with K-means algorithm
A Mixture Model of Hubness and PCA for Detection of Projected OutliersZac Darcy
With the Advancement of time and technology, Outlier Mining methodologies help to sift through the large
amount of interesting data patterns and winnows the malicious data entering in any field of concern. It has
become indispensible to build not only a robust and a generalised model for anomaly detection but also to
dress the same model with extra features like utmost accuracy and precision. Although the K-means
algorithm is one of the most popular, unsupervised, unique and the easiest clustering algorithm, yet it can
be used to dovetail PCA with hubness and the robust model formed from Guassian Mixture to build a very
generalised and a robust anomaly detection system. A major loophole of the K-means algorithm is its
constant attempt to find the local minima and result in a cluster that leads to ambiguity. In this paper, an
attempt has done to combine K-means algorithm with PCA technique that results in the formation of more
closely centred clusters that work more accurately with K-means algorithm .
COMPARISON BETWEEN THE GENETIC ALGORITHMS OPTIMIZATION AND PARTICLE SWARM OPT...IAEME Publication
Close range photogrammetry network design is referred to the process of placing a set of
cameras in order to achieve photogrammetric tasks. The main objective of this paper is tried to find
the best location of two/three camera stations. The genetic algorithm optimization and Particle
Swarm Optimization are developed to determine the optimal camera stations for computing the three
dimensional coordinates. In this research, a mathematical model representing the genetic algorithm
optimization and Particle Swarm Optimization for the close range photogrammetry network is
developed. This paper gives also the sequence of the field operations and computational steps for this
task. A test field is included to reinforce the theoretical aspects.
Similar to A heuristic approach for optimizing travel planning using genetics algorithm (20)
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERSveerababupersonal22
It consists of cw radar and fmcw radar ,range measurement,if amplifier and fmcw altimeterThe CW radar operates using continuous wave transmission, while the FMCW radar employs frequency-modulated continuous wave technology. Range measurement is a crucial aspect of radar systems, providing information about the distance to a target. The IF amplifier plays a key role in signal processing, amplifying intermediate frequency signals for further analysis. The FMCW altimeter utilizes frequency-modulated continuous wave technology to accurately measure altitude above a reference point.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...
A heuristic approach for optimizing travel planning using genetics algorithm
1. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 03 Issue: 01 | Jan-2014, Available @ http://www.ijret.org 531
A HEURISTIC APPROACH FOR OPTIMIZING TRAVEL PLANNING
USING GENETICS ALGORITHM
Md. Lutful Islam1
, Danish Pandhare2
, Arshad Makhthedar3
, Nadeem Shaikh4
1
Assistant Professor, Computer Engineering, MHSSCOE, Maharashtra, India
2
Student, Computer Engineering, MHSSCOE, Maharashtra, India
3
Student, Computer Engineering, MHSSCOE, Maharashtra, India
4
Student, Computer Engineering, MHSSCOE, Maharashtra, India
Abstract
In today’s fast-paced society, everyone is caught up in the hustle and bustle of life which has resulted in ineffective Planning of
their very important vacation tour. Either they spend much time on deciding what to do next, or will take many unnecessary,
unfocused and inefficient steps. The main purpose of our project is to develop a Travel Planner that will allow the customer to
plan the entire tour so that he visits many places in less time. The concept would be implemented using Genetics Algorithm of
Artificial Intelligence which would be used as a search algorithm to find the nearest optimal travel path. Moreover, In order to
reduce the running time of GA, Parallelization of Genetics Algorithm would be demonstrated using Hadoop Framework.
Key Words: Genetics Algorithm, TSP, Hadoop, and MapReduce etc…
--------------------------------------------------------------------***----------------------------------------------------------------------
1. INTRODUCTION
Travel Planning has become an emerging topic and has
attracted much attention in recent years. It is becoming
increasingly important to provide travellers with craving
information to assist them in the tour. Travellers, whether
leisure -tourists- or business travellers will want on top of
the attractions that a given destination offers, a flexible and
convenient transport means to reach it. So the purpose of
this Travel Planning System is to provide the traveller with
optimal, feasible and personalized route between origin and
destination [1].
The Travel Planning System is quite allied to TSP. So we
would be implementing this by ruminating the Travelling
Salesman Problem. The TSP deals with finding a route
covering all cities so that the total distance travelled is
minimal. There are mainly three reasons why TSP has
attracted the attention of many researcher‟s and remains an
active research area. First, a large number of real-world
problems can be modelled by TSP and it falls in
distinguished category of hard problems. Second, it was
proved to be NP-Complete problem and cannot be solved
exactly in polynomial time. Third, NP-Complete problems
are intractable in the sense that no one has found any really
efficient way of solving them for large problem size. Also,
NP-complete problems are known to be more or less
equivalent to each other; if one knew how to solve one of
them one could solve the lot [2].
Conventional Linear Programming methods do find Optimal
Solution to TSP but are infeasible and impracticable as the
time required to obtain the solution is very huge. Since real
time applications of TSP has higher problem complexity as
it consists of hundreds and thousands of nodes, the primary
objective of obtaining exact optimal solutions have been
shifted to obtaining heuristically good solutions.
Genetics Algorithm (GA) is one such Heuristics based
Algorithm that tends to find good and novel solutions to
TSP within reasonable time. GA‟s are relatively new
paradigms in artificial intelligence which are based on the
principles of natural selection [3]. Based on a predefined
Fitness value, GA iteratively goes on detecting the fittest
solution which increases its performance thereby making it
more suitable for finding solutions for many optimization
problems.
One of the key points of GA is its ability of being
parallelized. Thus, the Travel Planner will attempt to
minimize the runtime of Travelling Salesman Problem using
Genetics Algorithm by parallelizing it on Hadoop. Hadoop‟s
MapReduce Framework provides better scalability,
robustness, fault tolerance and easy means of access and
use.
2. BACKGROUND
Before delivering the implementation details of how to
integrate MapReduce and Genetic algorithms, discussion
regarding certain concepts is highly significant. The
following shows a brief overview of various topics.
2.1 Travelling Salesman Problem
Many mathematicians and Computer Scientists have
immensely addressed the Travelling Salesman Problem
which is considered to be the basic, fundamental and one of
the toughest problems in Computer Science and Operations
Research. The first instance of the traveling salesman
problem was from Euler in 1759 whose problem was to
2. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 03 Issue: 01 | Jan-2014, Available @ http://www.ijret.org 532
move a knight to every position on a chess board exactly
once. The traveling salesman first gained fame in a book
written by German salesman BF Voigt in 1832 on how to be
a successful traveling salesman [4]. Its importance stems
from the fact there is a plethora of fields in which it finds
potential applications such as automatic drilling of printed
circuit boards, threading of scan cells in a testable VLSI
circuit, X-ray crystallography, DNA fragments and many
more.
It can be normally defined as :-
1. Known Facts: A network of „n‟ cities named {c1, c2,
c3..., cn} with c1 as the starting node or city. A Cost Matrix
„C‟= [Cij] denoting the calculated Euclidean distances
between the cities „i‟ and „j‟.
2. Constraints: Each city should be visited exactly once.
The traveller should return to the starting city.
3. Problem: To find the least cost Hamiltonian cycle. Thus,
the aim is to obtain an optimal path cost such that the final
sum of all distances between each node and its successor is
minimized. An important point to note is that the first node
becomes the successor of the last node in order to satisfy the
constraints of the problem.
The distances in the cost matrix can be Straight Line
Distance, Euclidean distance or City Block (Manhattan)
distance. Given two cities with co-ordinates c1=(x1, y1) and
c2=(x2, y2), the Euclidean distance say „d‟ can be calculated
as:
The Travelling Salesman Problem has some variations
which can be observed from the cost matrix „C‟.TSP is said
to be Symmetric if Cij equals Cji for all values of „i‟ and „j‟.
It will be asymmetric otherwise. Thus, if a TSP problem
with n-cities is considered then there will (n-1)! Possible
solutions. Out of which the one with the minimum cost has
to be selected. This becomes extremely difficult and
impracticable as the number of possible solutions becomes
too large even for a moderate value of „n‟.
2.2 Genetics Algorithm
Genetic Algorithms (GAs) are adaptive heuristic search
algorithm premised on the evolutionary ideas of natural
selection and genetics. As such they represent an intelligent
exploitation of a random search used to solve optimization
problems. Although randomized, GAs are by no means
random, instead they exploit historical information to direct
the search into the region of better performance within the
search space. The basic techniques of the GAs are designed
to simulate processes in natural systems necessary for
evolution, specially those follow the principles first laid
down by Charles Darwin of "survival of the fittest" [5].
Genetic algorithms are based on the principle of Genetics
and Evolution which implies that the fitter individuals are
more likely to survive and have a greater chance of passing
their good genetic features to the next generation [6].
“Genetics” is derived from Greek word “genesis” meaning
„to grow‟ or „to become‟. GA was first pioneered by John
Holland in 1975 in the book “Adaptation in Natural and
Artificial System” [7].
2.2.1 Why Genetics Algorithm?
GA is used since it is better than conventional AI systems.
Unlike older AI systems, they do not break easily even if the
input changes slightly, or in the presence of reasonable
noise. Also, in searching large state space , multi-modal
state-space , or n-dimensional surface , a genetic algorithm
may offer significant benefits over more typical search of
optimization techniques.(linear programming, heuristics,
depth-first, breadth-first, and praxis) [5]. Not only does GAs
provide an alternative methods to solving problem, it
consistently outperforms other traditional methods in most
of the problems link. Many of the real world problems
involved finding optimal parameters, which might prove
difficult for traditional methods but ideal for GAs. The
appeal of GAs comes from their simplicity and elegance as
robust search algorithms as well as from their power to
discover good solutions rapidly for difficult high-
dimensional problems. GAs are useful and efficient when
the search space is large, complex, or poorly understood,
domain knowledge is scarce or expert knowledge is difficult
to encode to narrow the search space, no mathematics
analysis is available and when the traditional search
methods fail [8]. The continuing improvement in the
performance value of GA‟s has made them attractive for
many types of problem solving optimization methods. In
particular, genetic algorithms work very well on mixed
(continuous and discrete) combinatorial problems. Moreover
they are also less susceptible to getting 'stuck' at local
optima than gradient search methods[3].
2.2.2 Working
GA begins by generating an initial population which is
called as „genome‟ or a „gene pool‟ which will then be used
to generate successive generations. This is achieved by
application of three genetic operations to generate new and
better population as compared to the older generations. A
fitness score is assigned to each solution representing the
abilities of an individual to „compete‟.
The very first operator is Selection operator in which the
individual with the optimal (or generally near optimal)
fitness score is sought. Parents are selected to mate, on the
basis of their fitness, producing offspring via a reproductive
plan. Consequently highly fit solutions are given more
opportunities to reproduce , so that the offspring inherit
characteristics from each parent [5].
The second operator is Crossover wherein intermixing of
alleles of two parents are performed to obtain the new
offspring.
The third operator is Mutation that maintains uniqueness
and diversity of the gene pool in order to prevent any loss of
3. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 03 Issue: 01 | Jan-2014, Available @ http://www.ijret.org 533
genetic information caused due to crossover. The process
repeats until an optimal solution is discovered. Individuals
in the population die and are replaced by the new solutions
,eventually creating a generation once all mating
opportunities in the old population have been exhausted. In
this the better solutions thrive while the least fit solutions
die [5].
3. PROPOSED GA FOR TSP
The aim of this paper is to develop a software for proper
panning of the entire tour. This will implicitly include
solution of Travelling Salesman Problem using Genetics
Algorithm. Details of different components and operators
required are as follows:
3.1 Initialization of the Gene Pool (Population)
A random population is initially generated that consists of
some tours associated with their respective costs. The
individuals in the pool should be different from each other
in order to maintain the population diversity.
Simultaneously, there should also be a control on the
population size in order to improve the performance of the
Algorithm. Therefore one constructs tours with a greedy
heuristic and improve this by a tour improvement heuristic
[9].
1. Fitness Function: There are two different way of
calculating fitness value of a particular chromosome.
GAs are used in maximation problem but as TSP is a
minimization problem i.e. minimium cost path is chosen
reciprocal of the maxima function can be used for deciding
the fitness value of an organism. Thus if „fi‟ denotes the
fitness values and „Di‟ represents the maximum total
Distance then fitness function can be stated as
fi = 1/ Di …(i)
A fitness function evaluation is incorporated to assigns a
value to each organism, noted as fi. This fi value is a figure
of merit which is calculated by using any domain knowledge
that applies. In principle, this is the only point in the
algorithm that domain knowledge is necessary. Organisms
are chosen using the fitness value as a guide, where those
with higher fitness values are chosen more often. Selecting
organisms based on fitness value is a major factor in the
strength of GAs as search algorithms. The method employed
here was to calculate the total Euclidean distance Di for
each organism first, then compute fi by using the following
equation
fi = D max− Di ...(ii)
where Dmax is the longest Euclidean distance over
organisms in the population [3].
3.2 Selection and Survival of the fittest
The selection operator chooses two members of the present
generation to participate in the next operations of crossover
and mutation.[3] Two different approaches for selection are
1. Roulette Selection: Assign a probability to each organism
i, computed as the proportion using the following equation
Pi=fi †Σfj ....{iii}
where j=1, 2, 3... n! [3].
2. Deterministic Sampling: Assign to each organism „i‟, a
value Si evaluated by the relation.
Si = ROUND (Pi ∗ POPSIZE ) +1 ....(iv)
(Where: ROUND means rounding off to integer and
POPSIZE means the population size).The selection operator
then assures that each organism participates as parent
exactly Si times [3].
Table -1: Fitness values and Selection Criteria
Individual
Chromosome
Di fi (as
per
eq.
(i)
fi (as
per eq.
(ii)
Pi Si
1 BCDEFA 255 0.003 120 0.133 2
2 DFEABC 175 0.005 200 0.222 2
3 CBDAEF 95 0.010 280 0.311 3
4 FEDCBA 375 0.002 0 0.001 1
5 ABCDEF 75 0.013 300 0.333 3
Σfi=900
It can easily be inferred that no matter which rule is used 5th
and 3rd individuals have higher fitness value as compared to
others and hence these two organisms will become parent
and further process continues as follows.
3.3 Crossover
Crossover is a process of generating an offspring from
selected two parents. The genes of the both the parents are
converged and combined in order to obtain two children.
Following are the different types of crossover operators.
3.3.1 Single Point Crossover
Only one cross point is used. Children are generated by
swapping the alleles after second cross point of both the
parents.
Example :-
3.3.2 Two Point Crossover
Two cross over points are used. Swapping any the middle
section will result in the production of new child. From the
examples it can be clearly seen that both Single point and
two point crossovers may produce an invalid child. Some
cities are visited more than once while some are not even
visited at least once.
Parent 1: ABCG|HBDE
Parent 2: ADEF|GCBH
Child 1: ABCG|GCBH
Child 1: ADEF|HBDE
4. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 03 Issue: 01 | Jan-2014, Available @ http://www.ijret.org 534
Thus, some modifications are desirable in these crossovers
in order to have valid organisms after reproduction.
Example:-
3.3.3 Order Crossover
To apply order crossover, two random cross points are
selected. Alleles from parent1 that fall between the two
cross points are copied into the same positions of the
offspring. The remaining allele order is determined by
parent2. Non duplicative alleles are copied from parent2 to
the offspring beginning at the position following the second
cross point. Both the parent2 and the offspring are traversed
circularly from that point.
Fig -1: Genetic Algorithm Flowchart
A copy of the parent′s next non duplicative allele is placed
in the next available child position. An Example of OX is
given below with two random cross points; 3 and 6, the
alleles in the crossing sites from parent1 (GHB) are copied
into the same positions of the child. The alleles after second
cross point in parent2 (BA), B is skipped since it already
exists in child; therefore only A is copied to child at position
7. Traverse parent2 circularly, the alleles (HDE), skip H to
D which is copied to child at position 8. Traversed Child
circularly, the alleles from parent2 (EF) are copied to child
at positions 1 and 2. Finally, skip G to C and copy it to child
at position 3 [9].
Example :-
3.3.4 Partially Matched Crossover
PMX proceeds just as OX. Alleles from parent1 that fall
between two randomly selected crossing sites are copied
into the same positions of the offspring. The remaining
allele‟s positions are determined by parent2 during a two
step process. First, alleles in parent2 not within crossing
sites are copied to the corresponding positions within the
offspring. Next each allele of parent2 within the crossing
sites is placed in the offspring at the position occupied in
parent2 by the allele from parent1 that displaced it. See the
example below. The random crossing points are 3 and 6, the
alleles in the crossing site of parent1 (AHG) displace in the
child (DEF), and then the alleles (BC) in parent2 which are
not in crossing site are copied into the same positions of the
child. D goes to position 1 which is the position with respect
to parent2, displacing allele A. Finally E and F are placed in
the positions of H and G, respectively [9].
Example :-
3.4 Mutation
In order to maintain the uniqueness and diversity in the
generated population. Mutation just alters the result by a
small amount. It randomly selects a location and performs
some modification. This modification can be swapping of a
single bit or inverting a bit value.
3.4.1 Need for Mutation
Organisms with less fitness values are discarded after every
successful iteration. This elimination process might result in
some loss of genetic information. Random occasional
modification by means of mutation ensures maintenance of
that lost genetic information which selection and crossover
cannot guarantee. Thus, Mutation helps in preventing the
loss of genetic information, maintains uniqueness and
ensures diversity in the newly generated population.
4. PARALLEL GA BASED ON HADOOP
MAPREDUCE
In this section we present the design of the proposed parallel
Genetics Algorithm using Hadoop Map Reduce.
4.1 Parallelizing Genetics Algorithm
Genetics Algorithm as stated earlier is an effective
mechanism to find acceptable solution to problems in
business, engineering and science. GA‟s are generally able
to find satisfactory solutions in reasonable amount of time,
but as they are applied to more complicated and bigger
problems there is an increase in the time required to find an
Parent 1: ABC|GHB|DE
Parent 2: ADE|FGC|BH
Child 1: ABC|FGC|DE
Child 1: ADE|GHB|BH
Parent 1: ABC|GHB|DE
Parent 2: HDE|FGC|BA
Child 1: EFC|GHB|AD
Child 1: ABH|FGC|DE
Parent 1: FBC|AHG|DE
Parent 2: ABC|DEF|GH
Child 1: DBC|AHG|FE
5. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 03 Issue: 01 | Jan-2014, Available @ http://www.ijret.org 535
acceptable solution. However their exist some problems in
their utilisation part which are as follows :-
1. For some kind of problems, the population needs to be
very large and the memory required to store each individual
may be considerable (for e.g:- genetics programming). In
such cases running and application using a single GA is
inefficient, so parallel form of GA is necessary.
2. Sequential GAs may get trapped in a sub-optimal region
of the search space thus becoming unable to find better
quality solutions. PGAs can search in parallel different
subspaces of the search space , thus making it less likely to
be trapped by low quality sub-spaces [11].
As a consequence, multiple efforts are made to make GA
faster, and one of the most promising choice is to use
parallel implementation of GA. So the parallelization of the
Genetics Algorithm is the most significant aspect of the
project and the parallelization of the genetic algorithm
would ultimately reduce the run time of the algorithm. GA
falls into three different classes which includes Global,
Coarse-Grained, Fine-Grained and Hybrid. Global PGAs
use a single population and simply parallelize the evaluation
of the fitness, and then sequentially produce the next
generation. Coarse-grained parallelization involves evolving
many separate subpopulations, called demes, in parallel.
Coarse-grained PGAs often also implement migration,
allowing an individual to move from one deme to another.
Fine-grained parallelization involves assigning one
individual per processor core. This is usually undertaken
with special hardware. Hybrid PGA is the combination of all
the above techniques [12]. Also, the advantages of using
various PGAs facilitates no changes in the traditional GA.
Parallel Genetic Algorithms divide the search space into
many smaller pieces to find the near optimal solution.
During this process the sub-optimal solutions need to avoid
local minima. Different frameworks are available for
parallelization such as Hadoop, Open-cl, Parallel Java, and
C-MPI. Among all these frameworks this project would be
implemented using Hadoop which is the most popular and
flexible one.
4.2 Hadoop
Hadoop is an Apache project being built and used by a
global community of contributors, using the Java*
programming language. It doesn‟t maintain indexes or
relationships; you don‟t need to decide how you want to
analyze your data in advance. It breaks data into manageable
chunks, replicates them, and distributes multiple copies
across all the nodes in a cluster so you can process your data
quickly and reliably later. Hadoop is also use to conduct
analysis of data [12]. It is licensed under the Apache 2.0.
Hadoop is very scalable and has a robust file system which
is designed to handle all the intra-communication between
different nodes in a cluster. It is fault tolerant and recovers
from failure if one of the data nodes suffers from failure.
Hadoop framework consists of two main layers Hadoop
Distributed File System (HDFS) and Execution engine
(MapReduce). Hadoop introduce many components like
MapReduce, Hive, H-base, HDFS, Pig, Chukwa, Avro,
Hive, ZooKeeper etc [12].Yahoo!, has been the largest
contributor to this project, and uses Apache Hadoop
extensively across its businesses. Other contributors and
users include Facebook, LinkedIn, eHarmony, and eBay.
4.2.1 MapReduce Framework
The MapReduce framework was developed by Google. It
enables highly fault-tolerant massively scalable computation
to occur on a network of commodity hardware. MapReduce
has proven successful at allowing computation over
terabytes of data to become routine [12]. MapReduce' is a
framework for processing parallelizable problems across
huge datasets using a large number of computers (nodes),
collectively referred to as a cluster or grid. Computational
processing can occur on data stored either in a files system
(unstructured) or in a database (structured). MapReduce is
an elegant and flexible paradigm which enables to develop
large-scale distributed applications [13].
4.2.2 Outline of MapReduce
The MapReduce model works by splitting large tasks into
smaller tasks. The smaller tasks are executed in parallel. The
Map Reduce framework handles the Intra cluster
communication. Map Reduce has two important steps, the
map and reduce function. The data flows from the mapper to
the Reducers. The parallelism occurs in the mapper phrase.
The data is pre-processed; it‟s converted to key value pairs.
The key value pairs are passed to the map function. The
results from different mappers are combined or merged in
the reducer phase. A reducer takes in a key value pair and
produces a collection of new values [14]. All the
fundamental steps of Genetics Algorithm are carried out in
parallel and the above process is repeated till the optimal
value is reached.
4.3.3 Logical View of MapReduce
The Map and Reduce functions of MapReduce are both
defined with respect to data structured in (key, value) pairs.
Map takes one pair of data with a type in one data domain,
and returns a list of pairs in a different domain: Map(k1,v1)
→ list(k2,v2). The Map function is applied in parallel to
every pair in the input dataset. This produces a list of pairs
for each call. After that, the MapReduce framework collects
all pairs with the same key from all lists and groups them
together, creating one group for Logical view of Map
Reduce: The Map and Reduce functions of MapReduce are
both defined with respect to data structured in (key, value)
pairs. Map takes one pair of data with a type in one data
domain, and returns a list of pairs in a different domain:
Map(k1,v1) → list(k2,v2). The Map function is applied in
parallel to every pair in the input dataset. This produces a
list of pairs for each call. After that, the MapReduce
framework collects all pairs with the same key from all lists
and groups them together, creating one group for each key
[15].
6. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 03 Issue: 01 | Jan-2014, Available @ http://www.ijret.org 536
Fig -2: Hadoop MapReduce Model
The Reduce function is then applied in parallel to each
group, which in turn produces a collection of values in the
same domain: Reduce(k2, list (v2)) → list(v3). Each Reduce
call typically produces either one value v3 or an empty
return, though one call is allowed to return more than one
value. The returns of all calls are collected as the desired
result list. Thus the MapReduce framework transforms a list
of (key, value) pairs into a list of values [15].
CONCLUSION
In this paper we have discussed the Travel Planning System
using Genetics Algorithm to assist the user to obtain
optimal tour to visit n cities. It has also proposed the use of
advanced operators of Genetic Algorithms in order to
enhance the rate of divergence, and achieved tours with
reasonable time. The obtained results highlighted that using
Parallel Genetics Algorithm allowed us to save more time.
and the Map Reduce model helps to increase the processing
speed. Certain aspects in the project such as the parallel
design of GA can be enhanced in the future. An enhanced
parallel design can incorporate more computation of
selection, crossover or mutation in the mapper phase. This
will put less pressure on the reducers. The architecture can
be improved further by adding more reducers
REFERENCES
[1]. Konstantinos G.Zografos and Michael A.Madas, “ A
Travel and Tourism System Providing Readl-Time,
Value Added Logistical Services on the Move”, Athens
University of Economics & Business(AUEB).
[2]. Dino Keco and Abdulhamit Subasi, “ Parallelization of
genetics algorithm using Hadoop Map/Reduce”.
[3]. Buthainah Fahran Al-Dulaimi, and Hamza A. Ali,
“Enhanced Travelling Salesman Problem Solving by
Genetic Algorithm Technique (TSPGA) in paper 2008.
[4]. Kylie Bryant and Arthur Benjamin, ”Genetics
Algorithm and the Travelling Salesman Problem”,
thesis, Department of Mathematics, Harvey Mudd
College, Dec. 2000.
[5]. Introduction to Genetics Algorithm (1996). [Online].
Available:
http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol1/h
mw/article1.html.
[6]. Noraini Mohd Razali and John Geraghty, “Genetic
Algorithm Performance with Different Selection
Strategies in Solving TSP flwcchart,” in Proceedings of
the World Congress on Engineering 2011 Vol II WCE
2011,paper 06.08.11, London, U.K.
[7]. Ashish Gupta and Shipra Khurana, “ Study of
Travelling Salesman Problem using Genetics
Algorithm”,vol 2,issue 5,pp 575-588, May 2012.
[8]. Genetics Algorithm (1996) Web page on
www.doc.ic.ac.uk. [Online]. Available:
http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/tc
w2/report.html.
[9]. Konstantinos G.Zografos and Michael A.Madas, “ A
Travel and Tourism System Providing Readl-Time,
Value Added Logistical Services on the Move”, Athens
University of Economics & Business(AUEB)..
[10].Varshika dwivedi, Taruna Chauhan, Sanu Saxena and
Princie Agrawal, “Travelling Salesman Problem using
Genetics Algorithm”.
[11].Mariusz Nowastawski and Riccardo Poli, “ Parallel
Genetic Algorithm Taxonomy”, Proc‟ 99,paper May
13,1999.
[12].Rahate Kanchan Sharadchandra and L.M.R.J Lobo, “
Parallelization of Genetics Algorithm using
Hadoop”,vol 1,paper Nov-12.
[13].Linda Di Geronimo, Filomena Ferrucci, Alfonso
Murolo and Federica Sarro, “ A Parallel Genetics
Algorithm Based on Hadoop MapReduce for the
Automatic Generation of JUnit Test Suites”.
[14].Akshat Mishra ,‟Genetic Algorithm for the Travelling
Salesman Problem on Hadoop”,Tech. Rep. 2011.
[15].Map Reduce (2014) Webpage on Wikipedia [Online].
Available: http://en.wikipedia.org/wiki/MapReduce.
[16].Introduction to Genetics Algorithm (2014) Webpage on
obitko [Online]. Available:
http://www.obitko.com/tutorials/genetic-algorithms/ga-
basic-description.php
BIOGRAPHIES
Md. Lutful Islam is an Assistant Professor
at M.H Saboo Siddik College of Engg. His
qualification includes M.Tech from Aligarh
Muslim University and M.C.A from
Rajasthan Vidyapeeth, Udaipur. He has a
teaching experience of 15 yrs. His key areas
of interests are Computer Graphics, Discrete Mathematics,
Robotics and Artificial Intelligence.
Danish Pandhare is a final year student(B.E)
of M.H.Saboo Siddik college of Engineering,
Byculla, Mumbai University. He holds a
Diploma in Computer Engineering from
Vidya Prasarak Mandals Polytechnic,
MSBTE. His key areas of interests include
Web Designing, Cloud Computing , Networkng, Analysis of
Algorithm and Design
7. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 03 Issue: 01 | Jan-2014, Available @ http://www.ijret.org 537
Arshad Makhthedar is a final year
student(B.E) of M.H.Saboo Siddik college
of Engineering, Byculla, Mumbai
University. He holds a Diploma in Computer
Engineering from M.H Saboo Siddik
Polytechnic, MSBTE. His key areas of
interests include AI and Soft Computing, Mathematics and
Statistics.
Nadeem Shaikh is a final year student(B.E)
of M.H.Saboo Siddik college of
Engineering, Byculla, Mumbai University.
His key areas of interests include
Distributed and Internet Computing
Systems, Web Security, Web based
Systems, and Web Security.