This paper introduces a new stock portfolio selection model in non-stochastic environment.Following the principle of maximum entropy, a new entropy-cost ratio function is introduced as
the objective function. The uncertain returns, risks and ividends of the securities are considered as interval numbers. Along with the objective function, eight different types of constraints are used in the model to convert it into a pragmatic one. Three different models have been proposed by defining the future inancial market optimistically, pessimistically and in hecombined form to model the portfolio selection problem. To illustrate the effectiveness and tractability of the proposed models, these are tested on a set of data from Bombay Stock Exchange (BSE). The solution has been done by genetic algorithm.
Heptagonal Fuzzy Numbers by Max Min MethodYogeshIJTSRD
In this paper, we propose another methodology for the arrangement of fuzzy transportation problem under a fuzzy environment in which transportation costs are taken as fuzzy Heptagonal numbers. The fuzzy numbers and fuzzy values are predominantly used in various fields. Here, we are converting fuzzy Heptagonal numbers into crisp value by using range technique and then solved by the MAX MIN method for the transportation problem. M. Revathi | K. Nithya "Heptagonal Fuzzy Numbers by Max-Min Method" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38280.pdf Paper URL: https://www.ijtsrd.com/mathemetics/applied-mathamatics/38280/heptagonal-fuzzy-numbers-by-maxmin-method/m-revathi
Logistic regression for ordered dependant variable with more than 2 levelsArup Guha
This document discusses multinomial logistic regression models. Multinomial logistic regression can handle dependent variables with more than two categories that may be ordinal (ordered categories) or nominal (unordered categories). The document focuses on proportional odds cumulative logit models, which model ordinal dependent variables by considering the natural ordering of categories. It provides an example of using SAS code to fit a proportional odds model to model the impact of radiation exposure on human health.
This document summarizes a research paper that proposes a dynamic approach to improving the k-means clustering algorithm. The proposed approach aims to address two weaknesses of the standard k-means algorithm: its requirement of prior knowledge of the number of clusters k, and its sensitivity to initialization. The approach determines initial cluster centroids by segmenting the data space and selecting high-frequency segments. It then uses the silhouette validity index to dynamically determine the optimal number of clusters k, rather than requiring the user to specify k. The approach is compared to the standard k-means algorithm and other modified approaches, and is shown to improve initial center selection and reduce computation time.
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.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Inventory Model with Price-Dependent Demand Rate and No Shortages: An Interva...orajjournal
In this paper, an interval-valued inventory optimization model is proposed. The model involves the price dependent
demand and no shortages. The input data for this model are not fixed, but vary in some real bounded intervals. The aim is to determine the optimal order quantity, maximizing the total profit and minimizing the holding cost subjecting to three constraints: budget constraint, space constraint, and
budgetary constraint on ordering cost of each item. We apply the linear fractional programming approach based on interval numbers. To apply this approach, a linear fractional programming problem is modeled with interval type uncertainty. This problem is further converted to an optimization problem with interval valued
objective function having its bounds as linear fractional functions. Two numerical examples in crisp
case and interval-valued case are solved to illustrate the proposed approach.
This document summarizes a research paper that proposes using a fuzzy ant colony system approach to solve the fuzzy vehicle routing problem with time windows (VRPTW). The paper models the fuzzy VRPTW using credibility theory to handle uncertain travel times represented as triangular fuzzy numbers. An improved ant colony system is then used as a metaheuristic to find high-quality routes that satisfy time window constraints at a desired level of confidence. Computational results on benchmark problems demonstrate the effectiveness of integrating fuzzy concepts with the ant colony system to solve the fuzzy VRPTW.
Heptagonal Fuzzy Numbers by Max Min MethodYogeshIJTSRD
In this paper, we propose another methodology for the arrangement of fuzzy transportation problem under a fuzzy environment in which transportation costs are taken as fuzzy Heptagonal numbers. The fuzzy numbers and fuzzy values are predominantly used in various fields. Here, we are converting fuzzy Heptagonal numbers into crisp value by using range technique and then solved by the MAX MIN method for the transportation problem. M. Revathi | K. Nithya "Heptagonal Fuzzy Numbers by Max-Min Method" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38280.pdf Paper URL: https://www.ijtsrd.com/mathemetics/applied-mathamatics/38280/heptagonal-fuzzy-numbers-by-maxmin-method/m-revathi
Logistic regression for ordered dependant variable with more than 2 levelsArup Guha
This document discusses multinomial logistic regression models. Multinomial logistic regression can handle dependent variables with more than two categories that may be ordinal (ordered categories) or nominal (unordered categories). The document focuses on proportional odds cumulative logit models, which model ordinal dependent variables by considering the natural ordering of categories. It provides an example of using SAS code to fit a proportional odds model to model the impact of radiation exposure on human health.
This document summarizes a research paper that proposes a dynamic approach to improving the k-means clustering algorithm. The proposed approach aims to address two weaknesses of the standard k-means algorithm: its requirement of prior knowledge of the number of clusters k, and its sensitivity to initialization. The approach determines initial cluster centroids by segmenting the data space and selecting high-frequency segments. It then uses the silhouette validity index to dynamically determine the optimal number of clusters k, rather than requiring the user to specify k. The approach is compared to the standard k-means algorithm and other modified approaches, and is shown to improve initial center selection and reduce computation time.
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.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Inventory Model with Price-Dependent Demand Rate and No Shortages: An Interva...orajjournal
In this paper, an interval-valued inventory optimization model is proposed. The model involves the price dependent
demand and no shortages. The input data for this model are not fixed, but vary in some real bounded intervals. The aim is to determine the optimal order quantity, maximizing the total profit and minimizing the holding cost subjecting to three constraints: budget constraint, space constraint, and
budgetary constraint on ordering cost of each item. We apply the linear fractional programming approach based on interval numbers. To apply this approach, a linear fractional programming problem is modeled with interval type uncertainty. This problem is further converted to an optimization problem with interval valued
objective function having its bounds as linear fractional functions. Two numerical examples in crisp
case and interval-valued case are solved to illustrate the proposed approach.
This document summarizes a research paper that proposes using a fuzzy ant colony system approach to solve the fuzzy vehicle routing problem with time windows (VRPTW). The paper models the fuzzy VRPTW using credibility theory to handle uncertain travel times represented as triangular fuzzy numbers. An improved ant colony system is then used as a metaheuristic to find high-quality routes that satisfy time window constraints at a desired level of confidence. Computational results on benchmark problems demonstrate the effectiveness of integrating fuzzy concepts with the ant colony system to solve the fuzzy VRPTW.
On Intuitionistic Fuzzy Transportation Problem Using Pentagonal Intuitionisti...YogeshIJTSRD
In this paper a new method is proposed for finding an optimal solution for Pentagonal intuitionistic fuzzy transportation problems, in which the cost values are Pentagonal intuitionistic fuzzy numbers. The procedure is illustrated with a numerical example. P. Parimala | P. Kamalaveni "On Intuitionistic Fuzzy Transportation Problem Using Pentagonal Intuitionistic Fuzzy Numbers Solved by Modi Method" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd41094.pdf Paper URL: https://www.ijtsrd.com/mathemetics/applied-mathematics/41094/on-intuitionistic-fuzzy-transportation-problem-using-pentagonal-intuitionistic-fuzzy-numbers-solved-by-modi-method/p-parimala
The document summarizes statistical pattern recognition techniques. It is divided into 9 sections that cover topics like dimensionality reduction, classifiers, classifier combination, and unsupervised classification. The goal of pattern recognition is supervised or unsupervised classification of patterns based on features. Dimensionality reduction aims to reduce the number of features to address the curse of dimensionality when samples are limited. Multiple classifiers can be combined through techniques like stacking, bagging, and boosting. Unsupervised classification uses clustering algorithms to construct decision boundaries without labeled training data.
A LEAST ABSOLUTE APPROACH TO MULTIPLE FUZZY REGRESSION USING Tw- NORM BASED O...ijfls
A least absolute approach to multiple fuzzy regression using Tw-norm based arithmetic operations is
discussed by using the generalized Hausdorff metric and it is investigated for the crisp input- fuzzy output
data. A comparative study based on two data sets are presented using the proposed method using shape
preserving operations with other existing method.
Fuzzy Inventory Model for Constantly Deteriorating Items with Power Demand an...iosrjce
IOSR Journal of Mathematics(IOSR-JM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
The document discusses machine learning algorithms for symbol-based learning. It introduces a general framework for symbol-based learning that includes representing data and goals, representation languages, operations for manipulating representations, concept spaces to search, and heuristics for guiding the search. It then describes two specific algorithms: version space search and ID3 decision tree induction. Version space search uses a candidate elimination algorithm to iteratively generalize from positive examples and specialize from negative examples to identify the target concept.
The document discusses using genetic algorithms for financial forecasting. It begins with an abstract that notes genetic algorithms have been used extensively in various domains including finance to generate profitable trading rules. The document then provides background on genetic algorithms and their basic functions like selection, crossover and mutation. It explains how genetic algorithms can be used to develop a model for financial forecasting by evaluating trading rules based on historical data to determine which rules would have yielded the highest returns.
IRJET- Optimization of 1-Bit ALU using Ternary LogicIRJET Journal
This document summarizes a research paper that proposes a novel approach to implementing a 1-bit arithmetic logic unit (ALU) using ternary logic. Ternary logic offers potential advantages over binary logic, including reduced transistor count and hardware. The authors designed a 1-bit ALU using ternary logic gates (T-gates) for ternary arithmetic and logic operations. Simulation results showed the ternary logic ALU design achieved a 25% reduction in transistor usage compared to an equivalent binary logic ALU design. The ternary logic ALU design approach could potentially be extended to multi-bit ALUs for applications where reduced transistor count is important.
A PSO-Based Subtractive Data Clustering AlgorithmIJORCS
There is a tremendous proliferation in the amount of information available on the largest shared information source, the World Wide Web. Fast and high-quality clustering algorithms play an important role in helping users to effectively navigate, summarize, and organize the information. Recent studies have shown that partitional clustering algorithms such as the k-means algorithm are the most popular algorithms for clustering large datasets. The major problem with partitional clustering algorithms is that they are sensitive to the selection of the initial partitions and are prone to premature converge to local optima. Subtractive clustering is a fast, one-pass algorithm for estimating the number of clusters and cluster centers for any given set of data. The cluster estimates can be used to initialize iterative optimization-based clustering methods and model identification methods. In this paper, we present a hybrid Particle Swarm Optimization, Subtractive + (PSO) clustering algorithm that performs fast clustering. For comparison purpose, we applied the Subtractive + (PSO) clustering algorithm, PSO, and the Subtractive clustering algorithms on three different datasets. The results illustrate that the Subtractive + (PSO) clustering algorithm can generate the most compact clustering results as compared to other algorithms.
Medical Conferences, Pharma Conferences, Engineering Conferences, Science Conferences, Manufacturing Conferences, Social Science Conferences, Business Conferences, Scientific Conferences Malaysia, Thailand, Singapore, Hong Kong, Dubai, Turkey 2014 2015 2016
Global Research & Development Services (GRDS) is a leading academic event organizer, publishing Open Access Journals and conducting several professionally organized international conferences all over the globe annually. GRDS aims to disseminate knowledge and innovation with the help of its International Conferences and open access publications. GRDS International conferences are world-class events which provide a meaningful platform for researchers, students, academicians, institutions, entrepreneurs, industries and practitioners to create, share and disseminate knowledge and innovation and to develop long-lasting network and collaboration.
GRDS is a blend of Open Access Publications and world-wide International Conferences and Academic events. The prime mission of GRDS is to make continuous efforts in transforming the lives of people around the world through education, application of research and innovative ideas.
Global Research & Development Services (GRDS) is also active in the field of Research Funding, Research Consultancy, Training and Workshops along with International Conferences and Open Access Publications.
International Conferences 2014 – 2015
Malaysia Conferences, Thailand Conferences, Singapore Conferences, Hong Kong Conferences, Dubai Conferences, Turkey Conferences, Conference Listing, Conference Alerts
Reduct generation for the incremental data using rough set theorycsandit
n today’s changing world huge amount of data is ge
nerated and transferred frequently.
Although the data is sometimes static but most comm
only it is dynamic and transactional. New
data that is being generated is getting constantly
added to the old/existing data. To discover the
knowledge from this incremental data, one approach
is to run the algorithm repeatedly for the
modified data sets which is time consuming. The pap
er proposes a dimension reduction
algorithm that can be applied in dynamic environmen
t for generation of reduced attribute set as
dynamic reduct.
The method analyzes the new dataset, when it become
s available, and modifies
the reduct accordingly to fit the entire dataset. T
he concepts of discernibility relation, attribute
dependency and attribute significance of Rough Set
Theory are integrated for the generation of
dynamic reduct set, which not only reduces the comp
lexity but also helps to achieve higher
accuracy of the decision system. The proposed metho
d has been applied on few benchmark
dataset collected from the UCI repository and a dyn
amic reduct is computed. Experimental
result shows the efficiency of the proposed method
A NEW APPROACH IN DYNAMIC TRAVELING SALESMAN PROBLEM: A HYBRID OF ANT COLONY ...ijmpict
Nowadays swarm intelligence-based algorithms are being used widely to optimize the dynamic traveling salesman problem (DTSP). In this paper, we have used mixed method of Ant Colony Optimization (AOC) and gradient descent to optimize DTSP which differs with ACO algorithm in evaporation rate and innovative data. This approach prevents premature convergence and scape from local optimum spots and also makes it possible to find better solutions for algorithm. In this paper, we’re going to offer gradient descent and ACO algorithm which in comparison to some former methods it shows that algorithm has significantly improved routes optimization.
This document discusses using particle swarm optimization based on variable neighborhood search (PSO-VNS) to attack classical cryptography ciphers. PSO is a population-based optimization algorithm inspired by bird flocking behavior. VNS is a metaheuristic algorithm that explores neighborhoods of solutions to escape local optima. The paper proposes improving PSO with VNS to find better solutions. It evaluates PSO-VNS on substitution and transposition ciphers, finding it recovers keys better than standard PSO and other variants.
Transportation Problem with Pentagonal Intuitionistic Fuzzy Numbers Solved Us...IJERA Editor
This paper presents a solution methodology for transportation problem in an intuitionistic fuzzy environment in
which cost are represented by pentagonal intuitionistic fuzzy numbers. Transportation problem is a particular
class of linear programming, which is associated with day to day activities in our real life. It helps in solving
problems on distribution and transportation of resources from one place to another. The objective is to satisfy
the demand at destination from the supply constraints at the minimum transportation cost possible. The problem
is solved using a ranking technique called Accuracy function for pentagonal intuitionistic fuzzy numbers and
Russell’s Method
GENETIC ALGORITHM FOR FUNCTION APPROXIMATION: AN EXPERIMENTAL INVESTIGATIONijaia
Function Approximation is a popular engineering problems used in system identification or Equation
optimization. Due to the complex search space it requires, AI techniques has been used extensively to spot
the best curves that match the real behavior of the system. Genetic algorithm is known for their fast
convergence and their ability to find an optimal structure of the solution. We propose using a genetic
algorithm as a function approximator. Our attempt will focus on using the polynomial form of the
approximation. After implementing the algorithm, we are going to report our results and compare it with
the real function output.
The paper talks about the pentagonal Neutrosophic sets and its operational law. The paper presents the cut of single valued pentagonal Neutrosophic numbers and additionally introduced the arithmetic operation of single-valued pentagonal Neutrosophic numbers. Here, we consider a transportation problem with pentagonal Neutrosophic numbers where the supply, demand and transportation cost is uncertain. Taking the benefits of the properties of ranking functions, our model can be changed into a relating deterministic form, which can be illuminated by any method. Our strategy is easy to assess the issue and can rank different sort of pentagonal Neutrosophic numbers. To legitimize the proposed technique, some numerical tests are given to show the adequacy of the new model.
New Method for Finding an Optimal Solution of Generalized Fuzzy Transportatio...BRNSS Publication Hub
In this paper, a proposed method, namely, zero average method is used for solving fuzzy transportation problems by assuming that a decision-maker is uncertain about the precise values of the transportation costs, demand, and supply of the product. In the proposed method, transportation costs, demand, and supply are represented by generalized trapezoidal fuzzy numbers. To illustrate the proposed method, a numerical example is solved. The proposed method is easy to understand and apply to real-life transportation problems for the decision-makers.
Linear algebra application in linear programming Lahiru Dilshan
Linear programming is used to maximize or minimize quantities subject to constraints. It can be applied to problems with any number of variables and constraints, as long as the relationships are linear. Key aspects include defining an objective function to optimize, determining the feasible region where all constraints are satisfied, and finding extreme points where the objective function may be maximized or minimized. An example problem involves determining how to allocate candy mixtures to maximize revenue given constraints on available ingredients. The optimal solution is found at an extreme point within the bounded feasible region.
An Approach to Mathematically Establish the Practical Use of Assignment Probl...ijtsrd
The assignment problem is a discrete and combinatorial problem where agents are assigned to perform tasks for efficiency maximization or cost time minimization. Assignment Problem is a part of human resource project management..The aim of the current study is to describe the importance of an researching solution method for the classical assignment problem the outcomes from the study show that both classical methods and the Greedy method provides the optimal or near optimal results Suraj Kumar Sahu "An Approach to Mathematically Establish the Practical Use of Assignment Problem in Real Life" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45217.pdf Paper URL: https://www.ijtsrd.com/mathemetics/other/45217/an-approach-to-mathematically-establish-the-practical-use-of-assignment-problem-in-real-life/suraj-kumar-sahu
The New Ranking Method using Octagonal Intuitionistic Fuzzy Unbalanced Transp...ijtsrd
In this paper a new ranking method is proposed for finding an optimal solution for intuitionistic fuzzy unbalanced transportation problem, in which the costs, supplies and demands are octagonal intuitionistic fuzzy numbers. The procedure is illustrated with a numerical example. Dr. P. Rajarajeswari | G. Menaka "The New Ranking Method using Octagonal Intuitionistic Fuzzy Unbalanced Transportation Problem" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31675.pdf Paper Url :https://www.ijtsrd.com/mathemetics/applied-mathamatics/31675/the-new-ranking-method-using-octagonal-intuitionistic-fuzzy-unbalanced-transportation-problem/dr-p-rajarajeswari
IRJET- Novel based Stock Value Prediction MethodIRJET Journal
This document presents a novel stock value prediction method using machine learning techniques. It uses a metaheuristic firefly algorithm to optimize the hyperparameters of a least squares support vector regression model for time series prediction of stock prices. A sliding window approach is used to reconstruct the phase space of the time series data and make single-day stock price predictions. The performance of the metaheuristic optimized model is evaluated using various error metrics to assess predictive accuracy.
A Fuzzy Mean-Variance-Skewness Portfolioselection Problem.inventionjournals
A fuzzy number is a normal and convex fuzzy subsetof the real line. In this paper, based on membership function, we redefine the concepts of mean and variance for fuzzy numbers. Furthermore, we propose the concept of skewness and prove some desirable properties. A fuzzy mean-variance-skewness portfolio se-lection model is formulated and two variations are given, which are transformed to nonlinear optimization models with polynomial ob-jective and constraint functions such that they can be solved analytically. Finally, we present some numerical examples to demonstrate the effectiveness of the proposed models
The document proposes applying robust techniques like support vector clustering to portfolio optimization models to address uncertainties. It outlines constructing a robust semi-mean absolute deviation optimization model that uses support vector clustering to simulate an uncertainty set capturing uncertain asset returns from historical data. The methodology involves collecting market data, cleaning the data, training and testing the robust portfolio optimization model on different datasets and analyzing the results to capture uncertainties better than fixed uncertainty sets.
On Intuitionistic Fuzzy Transportation Problem Using Pentagonal Intuitionisti...YogeshIJTSRD
In this paper a new method is proposed for finding an optimal solution for Pentagonal intuitionistic fuzzy transportation problems, in which the cost values are Pentagonal intuitionistic fuzzy numbers. The procedure is illustrated with a numerical example. P. Parimala | P. Kamalaveni "On Intuitionistic Fuzzy Transportation Problem Using Pentagonal Intuitionistic Fuzzy Numbers Solved by Modi Method" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd41094.pdf Paper URL: https://www.ijtsrd.com/mathemetics/applied-mathematics/41094/on-intuitionistic-fuzzy-transportation-problem-using-pentagonal-intuitionistic-fuzzy-numbers-solved-by-modi-method/p-parimala
The document summarizes statistical pattern recognition techniques. It is divided into 9 sections that cover topics like dimensionality reduction, classifiers, classifier combination, and unsupervised classification. The goal of pattern recognition is supervised or unsupervised classification of patterns based on features. Dimensionality reduction aims to reduce the number of features to address the curse of dimensionality when samples are limited. Multiple classifiers can be combined through techniques like stacking, bagging, and boosting. Unsupervised classification uses clustering algorithms to construct decision boundaries without labeled training data.
A LEAST ABSOLUTE APPROACH TO MULTIPLE FUZZY REGRESSION USING Tw- NORM BASED O...ijfls
A least absolute approach to multiple fuzzy regression using Tw-norm based arithmetic operations is
discussed by using the generalized Hausdorff metric and it is investigated for the crisp input- fuzzy output
data. A comparative study based on two data sets are presented using the proposed method using shape
preserving operations with other existing method.
Fuzzy Inventory Model for Constantly Deteriorating Items with Power Demand an...iosrjce
IOSR Journal of Mathematics(IOSR-JM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mathemetics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mathematics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
The document discusses machine learning algorithms for symbol-based learning. It introduces a general framework for symbol-based learning that includes representing data and goals, representation languages, operations for manipulating representations, concept spaces to search, and heuristics for guiding the search. It then describes two specific algorithms: version space search and ID3 decision tree induction. Version space search uses a candidate elimination algorithm to iteratively generalize from positive examples and specialize from negative examples to identify the target concept.
The document discusses using genetic algorithms for financial forecasting. It begins with an abstract that notes genetic algorithms have been used extensively in various domains including finance to generate profitable trading rules. The document then provides background on genetic algorithms and their basic functions like selection, crossover and mutation. It explains how genetic algorithms can be used to develop a model for financial forecasting by evaluating trading rules based on historical data to determine which rules would have yielded the highest returns.
IRJET- Optimization of 1-Bit ALU using Ternary LogicIRJET Journal
This document summarizes a research paper that proposes a novel approach to implementing a 1-bit arithmetic logic unit (ALU) using ternary logic. Ternary logic offers potential advantages over binary logic, including reduced transistor count and hardware. The authors designed a 1-bit ALU using ternary logic gates (T-gates) for ternary arithmetic and logic operations. Simulation results showed the ternary logic ALU design achieved a 25% reduction in transistor usage compared to an equivalent binary logic ALU design. The ternary logic ALU design approach could potentially be extended to multi-bit ALUs for applications where reduced transistor count is important.
A PSO-Based Subtractive Data Clustering AlgorithmIJORCS
There is a tremendous proliferation in the amount of information available on the largest shared information source, the World Wide Web. Fast and high-quality clustering algorithms play an important role in helping users to effectively navigate, summarize, and organize the information. Recent studies have shown that partitional clustering algorithms such as the k-means algorithm are the most popular algorithms for clustering large datasets. The major problem with partitional clustering algorithms is that they are sensitive to the selection of the initial partitions and are prone to premature converge to local optima. Subtractive clustering is a fast, one-pass algorithm for estimating the number of clusters and cluster centers for any given set of data. The cluster estimates can be used to initialize iterative optimization-based clustering methods and model identification methods. In this paper, we present a hybrid Particle Swarm Optimization, Subtractive + (PSO) clustering algorithm that performs fast clustering. For comparison purpose, we applied the Subtractive + (PSO) clustering algorithm, PSO, and the Subtractive clustering algorithms on three different datasets. The results illustrate that the Subtractive + (PSO) clustering algorithm can generate the most compact clustering results as compared to other algorithms.
Medical Conferences, Pharma Conferences, Engineering Conferences, Science Conferences, Manufacturing Conferences, Social Science Conferences, Business Conferences, Scientific Conferences Malaysia, Thailand, Singapore, Hong Kong, Dubai, Turkey 2014 2015 2016
Global Research & Development Services (GRDS) is a leading academic event organizer, publishing Open Access Journals and conducting several professionally organized international conferences all over the globe annually. GRDS aims to disseminate knowledge and innovation with the help of its International Conferences and open access publications. GRDS International conferences are world-class events which provide a meaningful platform for researchers, students, academicians, institutions, entrepreneurs, industries and practitioners to create, share and disseminate knowledge and innovation and to develop long-lasting network and collaboration.
GRDS is a blend of Open Access Publications and world-wide International Conferences and Academic events. The prime mission of GRDS is to make continuous efforts in transforming the lives of people around the world through education, application of research and innovative ideas.
Global Research & Development Services (GRDS) is also active in the field of Research Funding, Research Consultancy, Training and Workshops along with International Conferences and Open Access Publications.
International Conferences 2014 – 2015
Malaysia Conferences, Thailand Conferences, Singapore Conferences, Hong Kong Conferences, Dubai Conferences, Turkey Conferences, Conference Listing, Conference Alerts
Reduct generation for the incremental data using rough set theorycsandit
n today’s changing world huge amount of data is ge
nerated and transferred frequently.
Although the data is sometimes static but most comm
only it is dynamic and transactional. New
data that is being generated is getting constantly
added to the old/existing data. To discover the
knowledge from this incremental data, one approach
is to run the algorithm repeatedly for the
modified data sets which is time consuming. The pap
er proposes a dimension reduction
algorithm that can be applied in dynamic environmen
t for generation of reduced attribute set as
dynamic reduct.
The method analyzes the new dataset, when it become
s available, and modifies
the reduct accordingly to fit the entire dataset. T
he concepts of discernibility relation, attribute
dependency and attribute significance of Rough Set
Theory are integrated for the generation of
dynamic reduct set, which not only reduces the comp
lexity but also helps to achieve higher
accuracy of the decision system. The proposed metho
d has been applied on few benchmark
dataset collected from the UCI repository and a dyn
amic reduct is computed. Experimental
result shows the efficiency of the proposed method
A NEW APPROACH IN DYNAMIC TRAVELING SALESMAN PROBLEM: A HYBRID OF ANT COLONY ...ijmpict
Nowadays swarm intelligence-based algorithms are being used widely to optimize the dynamic traveling salesman problem (DTSP). In this paper, we have used mixed method of Ant Colony Optimization (AOC) and gradient descent to optimize DTSP which differs with ACO algorithm in evaporation rate and innovative data. This approach prevents premature convergence and scape from local optimum spots and also makes it possible to find better solutions for algorithm. In this paper, we’re going to offer gradient descent and ACO algorithm which in comparison to some former methods it shows that algorithm has significantly improved routes optimization.
This document discusses using particle swarm optimization based on variable neighborhood search (PSO-VNS) to attack classical cryptography ciphers. PSO is a population-based optimization algorithm inspired by bird flocking behavior. VNS is a metaheuristic algorithm that explores neighborhoods of solutions to escape local optima. The paper proposes improving PSO with VNS to find better solutions. It evaluates PSO-VNS on substitution and transposition ciphers, finding it recovers keys better than standard PSO and other variants.
Transportation Problem with Pentagonal Intuitionistic Fuzzy Numbers Solved Us...IJERA Editor
This paper presents a solution methodology for transportation problem in an intuitionistic fuzzy environment in
which cost are represented by pentagonal intuitionistic fuzzy numbers. Transportation problem is a particular
class of linear programming, which is associated with day to day activities in our real life. It helps in solving
problems on distribution and transportation of resources from one place to another. The objective is to satisfy
the demand at destination from the supply constraints at the minimum transportation cost possible. The problem
is solved using a ranking technique called Accuracy function for pentagonal intuitionistic fuzzy numbers and
Russell’s Method
GENETIC ALGORITHM FOR FUNCTION APPROXIMATION: AN EXPERIMENTAL INVESTIGATIONijaia
Function Approximation is a popular engineering problems used in system identification or Equation
optimization. Due to the complex search space it requires, AI techniques has been used extensively to spot
the best curves that match the real behavior of the system. Genetic algorithm is known for their fast
convergence and their ability to find an optimal structure of the solution. We propose using a genetic
algorithm as a function approximator. Our attempt will focus on using the polynomial form of the
approximation. After implementing the algorithm, we are going to report our results and compare it with
the real function output.
The paper talks about the pentagonal Neutrosophic sets and its operational law. The paper presents the cut of single valued pentagonal Neutrosophic numbers and additionally introduced the arithmetic operation of single-valued pentagonal Neutrosophic numbers. Here, we consider a transportation problem with pentagonal Neutrosophic numbers where the supply, demand and transportation cost is uncertain. Taking the benefits of the properties of ranking functions, our model can be changed into a relating deterministic form, which can be illuminated by any method. Our strategy is easy to assess the issue and can rank different sort of pentagonal Neutrosophic numbers. To legitimize the proposed technique, some numerical tests are given to show the adequacy of the new model.
New Method for Finding an Optimal Solution of Generalized Fuzzy Transportatio...BRNSS Publication Hub
In this paper, a proposed method, namely, zero average method is used for solving fuzzy transportation problems by assuming that a decision-maker is uncertain about the precise values of the transportation costs, demand, and supply of the product. In the proposed method, transportation costs, demand, and supply are represented by generalized trapezoidal fuzzy numbers. To illustrate the proposed method, a numerical example is solved. The proposed method is easy to understand and apply to real-life transportation problems for the decision-makers.
Linear algebra application in linear programming Lahiru Dilshan
Linear programming is used to maximize or minimize quantities subject to constraints. It can be applied to problems with any number of variables and constraints, as long as the relationships are linear. Key aspects include defining an objective function to optimize, determining the feasible region where all constraints are satisfied, and finding extreme points where the objective function may be maximized or minimized. An example problem involves determining how to allocate candy mixtures to maximize revenue given constraints on available ingredients. The optimal solution is found at an extreme point within the bounded feasible region.
An Approach to Mathematically Establish the Practical Use of Assignment Probl...ijtsrd
The assignment problem is a discrete and combinatorial problem where agents are assigned to perform tasks for efficiency maximization or cost time minimization. Assignment Problem is a part of human resource project management..The aim of the current study is to describe the importance of an researching solution method for the classical assignment problem the outcomes from the study show that both classical methods and the Greedy method provides the optimal or near optimal results Suraj Kumar Sahu "An Approach to Mathematically Establish the Practical Use of Assignment Problem in Real Life" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45217.pdf Paper URL: https://www.ijtsrd.com/mathemetics/other/45217/an-approach-to-mathematically-establish-the-practical-use-of-assignment-problem-in-real-life/suraj-kumar-sahu
The New Ranking Method using Octagonal Intuitionistic Fuzzy Unbalanced Transp...ijtsrd
In this paper a new ranking method is proposed for finding an optimal solution for intuitionistic fuzzy unbalanced transportation problem, in which the costs, supplies and demands are octagonal intuitionistic fuzzy numbers. The procedure is illustrated with a numerical example. Dr. P. Rajarajeswari | G. Menaka "The New Ranking Method using Octagonal Intuitionistic Fuzzy Unbalanced Transportation Problem" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31675.pdf Paper Url :https://www.ijtsrd.com/mathemetics/applied-mathamatics/31675/the-new-ranking-method-using-octagonal-intuitionistic-fuzzy-unbalanced-transportation-problem/dr-p-rajarajeswari
IRJET- Novel based Stock Value Prediction MethodIRJET Journal
This document presents a novel stock value prediction method using machine learning techniques. It uses a metaheuristic firefly algorithm to optimize the hyperparameters of a least squares support vector regression model for time series prediction of stock prices. A sliding window approach is used to reconstruct the phase space of the time series data and make single-day stock price predictions. The performance of the metaheuristic optimized model is evaluated using various error metrics to assess predictive accuracy.
A Fuzzy Mean-Variance-Skewness Portfolioselection Problem.inventionjournals
A fuzzy number is a normal and convex fuzzy subsetof the real line. In this paper, based on membership function, we redefine the concepts of mean and variance for fuzzy numbers. Furthermore, we propose the concept of skewness and prove some desirable properties. A fuzzy mean-variance-skewness portfolio se-lection model is formulated and two variations are given, which are transformed to nonlinear optimization models with polynomial ob-jective and constraint functions such that they can be solved analytically. Finally, we present some numerical examples to demonstrate the effectiveness of the proposed models
The document proposes applying robust techniques like support vector clustering to portfolio optimization models to address uncertainties. It outlines constructing a robust semi-mean absolute deviation optimization model that uses support vector clustering to simulate an uncertainty set capturing uncertain asset returns from historical data. The methodology involves collecting market data, cleaning the data, training and testing the robust portfolio optimization model on different datasets and analyzing the results to capture uncertainties better than fixed uncertainty sets.
PREDICTIVE EVALUATION OF THE STOCK PORTFOLIO PERFORMANCE USING FUZZY CMEANS A...ijfls
The aim of this paper is to investigate the trend of the return of a portfolio formed randomly or for any
specific technique. The approach is made using two techniques fuzzy: fuzzy c-means (FCM) algorithm and
the fuzzy transform, where the rules used at fuzzy transform arise from the application of the FCM
algorithm. The results show that the proposed methodology is able to predict the trend of the return of a
stock portfolio, as well as the tendency of the market index. Real data of the financial market are used from
2004 until 2007.
Financial time series forecasting has received
tremendous interest by both the individual and institutional
investors and hence by the researchers. But the high noise and
complexity residing in the financial data makes this job extremely
challenging. Over the years many researchers have used support
vector regression (SVR) quite successfully to conquer this
challenge. As the latent high noise in the data impairs the
performance, reducing the noise could be effective while
constructing the forecasting model. To accomplish this task,
integration of principal component analysis (PCA) and SVR is
proposed in this research work. In the first step, a set of technical
indicators are calculated from the daily transaction data of the
target stock and then PCA is applied to these values aiming to
extract the principle components. After filtering the principal
components, a model is finally constructed to forecast the future
price of the target stocks. The performance of the proposed
approach is evaluated with 16 years’ daily transactional data of
three leading stocks from different sectors listed in Dhaka Stock
Exchange (DSE), Bangladesh. Empirical results show that the
proposed model enhances the performance of the prediction
model and also the short-term prediction gains more accuracy
than long-term prediction.
Towards An Enhanced Semantic Approach Based On Formal Concept Analysis And Li...ijccsa
The volume of stored data increases rapidly. Therefore, the battery of extracted association heavily prohibits the better support of the decision maker. In this context, backboned on the Formal Concept Analysis, we propose to extend the notion of Formal Concept through the generalization of the notion of item set aiming to consider the item set as an intent, its support as the cardinality of the extent. Accordingly, we propose a new approach to extract interesting item sets through the concept coverage. This approach uses an original quality-criterion of a rule namely the profit improving the classical formal concept analysis through the addition of semantic value in order to extract meaningful association rules.
Study on Evaluation of Venture Capital Based onInteractive Projection Algorithminventionjournals
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, 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.
Particle Swarm Optimization in the fine-tuning of Fuzzy Software Cost Estimat...Waqas Tariq
Software cost estimation deals with the financial and strategic planning of software projects. Controlling the expensive investment of software development effectively is of paramount importance. The limitation of algorithmic effort prediction models is their inability to cope with uncertainties and imprecision surrounding software projects at the early development stage. More recently, attention has turned to a variety of machine learning methods, and soft computing in particular to predict software development effort. Fuzzy logic is one such technique which can cope with uncertainties. In the present paper, Particle Swarm Optimization Algorithm (PSOA) is presented to fine tune the fuzzy estimate for the development of software projects . The efficacy of the developed models is tested on 10 NASA software projects, 18 NASA projects and COCOMO 81 database on the basis of various criterion for assessment of software cost estimation models. Comparison of all the models is done and it is found that the developed models provide better estimation
Supplier Selection and Evaluation by Fuzzy-AHP Extent Analysis: A Case Study ...Dr. Amarjeet Singh
The ready-made garments (RMG)is a rapid
growing industry in Bangladesh and contributing
significantly in the country’s economy. Effective supplier
selection policy has significant strategic importance in the
performance of such fast moving consumer goods industry.
The supplier selection process is essentially a multi-criterion
decision making problem which, therefore, must be
developed systematically. Many models have been developed
and proposed to find optimum solutions of this complex
decision-making problem. Fuzzy Analytic Hierarchy
Process (Fuzzy-AHP), which is a derived extension of
classical Analytical Hierarchy Process (AHP),is an excellent
method for deciding among the complex structure at
different levels. In this paper an extent analysis of FuzzyAHP has been applied to evaluate and select the best
supplier agency providing most satisfaction. The evaluation
criteria are developed particularly for an RMG
manufacturer in Bangladesh context and used successfully
in the proposed model. A detailed implementation process is
presented in this paper and finally the best supplier agency
has been proposed from the outcome of the model.
This paper proposes using a "shrinkage" estimator as an alternative to the traditional sample covariance matrix for portfolio optimization. The shrinkage estimator combines the sample covariance matrix with a structured "shrinkage target" using a shrinkage constant to minimize distance from the true covariance matrix. The paper finds this shrinkage estimator significantly increases the realized information ratio of active portfolio managers compared to the sample covariance matrix. An empirical study on historical stock return data confirms the shrinkage method leads to higher ex post information ratios in portfolio optimization. However, the shrinkage target assumes identical pairwise correlations that may not fully reflect market characteristics.
This study introduces and compares different methods for estimating the two parameters of generalized logarithmic series distribution. These methods are the cuckoo search optimization, maximum likelihood estimation, and method of moments algorithms. All the required derivations and basic steps of each algorithm are explained. The applications for these algorithms are implemented through simulations using different sample sizes (n = 15, 25, 50, 100). Results are compared using the statistical measure mean square error.
USING CUCKOO ALGORITHM FOR ESTIMATING TWO GLSD PARAMETERS AND COMPARING IT WI...ijcsit
This study introduces and compares different methods for estimating the two parameters of generalized logarithmic series distribution. These methods are the cuckoo search optimization, maximum likelihood estimation, and method of moments algorithms. All the required derivations and basic steps of each algorithm are explained. The applications for these algorithms are implemented through simulations using different sample sizes (n = 15, 25, 50, 100). Results are compared using the statistical measure mean square error.
This document compares different estimation methods for parameters of the generalized logarithmic series distribution (GLSD), including cuckoo search optimization (CSO), maximum likelihood estimation (MLE), and method of moments (MOM). The CSO algorithm is introduced and applied to estimate the two GLSD parameters. Simulation results using different sample sizes show that CSO performs best for small sample sizes while MLE is best for large sample sizes, based on mean square error. The document concludes that CSO is the best estimator for small sample sizes.
This document discusses modeling claim amounts in insurance using probability distributions and simulations. It begins with an introduction to fitting distributions to insurance claims data. The key steps are outlined as selecting an appropriate loss distribution, estimating its parameters using maximum likelihood estimation, and testing the fit using goodness of fit tests. The Pareto distribution is discussed in more detail as it is commonly used to model insurance claim amounts. The document concludes by describing how to simulate claim amounts above a deductible using the Pareto distribution and calculate reinsurance premiums.
Novel approach for predicting the rise and fall of stock index for a specific...IAEME Publication
The document summarizes a proposed approach to predict the rise and fall of a stock index using subtractive clustering, neuro-fuzzy inference systems, and a novel contribution factor algorithm. The approach involves obtaining historical stock market data, performing subtractive clustering on graphs of the data, generating initial rules from the clusters, predicting outcomes using ANFIS modeling, applying the contribution factor algorithm, and making a stock index rise or fall prediction. The goal is to help intraday traders efficiently analyze stock market movements.
Symbolic Computation via Gröbner BasisIJERA Editor
The purpose of this paper is to find the orthogonal projection of a rational parametric curve onto a rational parametric surface in 3-space. We show that the orthogonal projection problem can be reduced to the problem of finding elimination ideals via Gröbnerbasis. We provide a computational algorithm to find the orthogonal projection, and include a few illustrative examples. The presented method is effective and potentially useful for many applications related to the design of surfaces and other industrial and research fields.
VALIDATION METHOD OF FUZZY ASSOCIATION RULES BASED ON FUZZY FORMAL CONCEPT AN...cscpconf
The document proposes a new validation method for fuzzy association rules based on three steps: (1) applying the EFAR-PN algorithm to extract a generic base of non-redundant fuzzy association rules using fuzzy formal concept analysis, (2) categorizing the extracted rules into groups, and (3) evaluating the relevance of the rules using structural equation modeling, specifically partial least squares. The method aims to address issues with existing fuzzy association rule extraction algorithms such as large numbers of extracted rules, redundancy, and difficulties with manual validation.
A Mathematical Programming Approach for Selection of Variables in Cluster Ana...IJRES Journal
The document presents a mathematical programming approach for selecting important variables in cluster analysis. It formulates a nonlinear binary model to minimize the distance between observations within clusters, using indicator variables to select important variables. The model is applied to a sample dataset of 30 observations across 5 variables, correctly identifying variables 3, 4 and 5 as most important for clustering the observations into two groups. The results are compared to an existing variable selection heuristic, with the mathematical programming approach achieving a 100% correct classification versus 97% for the other method.
Understanding the effect of Financing Variability Using Chance- Constrained A...IRJET Journal
1) The document discusses using chance-constrained programming (CCP) to model the impact of financing variability on time-cost tradeoffs in construction projects. CCP allows incorporating the probability of events into an optimization model.
2) Previous studies have used linear programming and other approaches to address time-cost tradeoffs but have not considered uncertainties like financing variability.
3) The study aims to develop a new mathematical model that comprehensively addresses precedence constraints, financing variability, and time-cost optimization for construction projects. CCP is used to quantify the effect of cost uncertainty from variable financing.
An Empirical Investigation Of The Arbitrage Pricing TheoryAkhil Goyal
The study empirically tests the Arbitrage Pricing Theory (APT) developed by Ross in 1976 using daily stock return data from 1962-1972. It finds:
1) Factor analysis identifies 5 factors that explain stock returns within industry groups, supporting the APT.
2) Cross-sectional regressions show factor loadings can explain expected stock returns, as the APT predicts.
3) Adding total return variance to the regressions does not eliminate the explanatory power of factor loadings, supporting the APT over alternatives.
4) Tests across industry groups find no evidence factor structures differ, as the APT assumes consistent factors across stocks.
In this paper, the black-litterman model is introduced to quantify investor’s views, then we expanded
the safety-first portfolio model under the case that the distribution of risk assets return is ambiguous. When
short-selling of risk-free assets is allowed, the model is transformed into a second-order cone optimization
problem with investor views. The ambiguity set parameters are calibrated through programming
Similar to ENTROPY-COST RATIO MAXIMIZATION MODEL FOR EFFICIENT STOCK PORTFOLIO SELECTION USING INTERVAL ANALYSIS (20)
ANALYSIS OF LAND SURFACE DEFORMATION GRADIENT BY DINSAR cscpconf
The progressive development of Synthetic Aperture Radar (SAR) systems diversify the exploitation of the generated images by these systems in different applications of geoscience. Detection and monitoring surface deformations, procreated by various phenomena had benefited from this evolution and had been realized by interferometry (InSAR) and differential interferometry (DInSAR) techniques. Nevertheless, spatial and temporal decorrelations of the interferometric couples used, limit strongly the precision of analysis results by these techniques. In this context, we propose, in this work, a methodological approach of surface deformation detection and analysis by differential interferograms to show the limits of this technique according to noise quality and level. The detectability model is generated from the deformation signatures, by simulating a linear fault merged to the images couples of ERS1 / ERS2 sensors acquired in a region of the Algerian south.
4D AUTOMATIC LIP-READING FOR SPEAKER'S FACE IDENTIFCATIONcscpconf
A novel based a trajectory-guided, concatenating approach for synthesizing high-quality image real sample renders video is proposed . The lips reading automated is seeking for modeled the closest real image sample sequence preserve in the library under the data video to the HMM predicted trajectory. The object trajectory is modeled obtained by projecting the face patterns into an KDA feature space is estimated. The approach for speaker's face identification by using synthesise the identity surface of a subject face from a small sample of patterns which sparsely each the view sphere. An KDA algorithm use to the Lip-reading image is discrimination, after that work consisted of in the low dimensional for the fundamental lip features vector is reduced by using the 2D-DCT.The mouth of the set area dimensionality is ordered by a normally reduction base on the PCA to obtain the Eigen lips approach, their proposed approach by[33]. The subjective performance results of the cost function under the automatic lips reading modeled , which wasn’t illustrate the superior performance of the
method.
MOVING FROM WATERFALL TO AGILE PROCESS IN SOFTWARE ENGINEERING CAPSTONE PROJE...cscpconf
Universities offer software engineering capstone course to simulate a real world-working environment in which students can work in a team for a fixed period to deliver a quality product. The objective of the paper is to report on our experience in moving from Waterfall process to Agile process in conducting the software engineering capstone project. We present the capstone course designs for both Waterfall driven and Agile driven methodologies that highlight the structure, deliverables and assessment plans.To evaluate the improvement, we conducted a survey for two different sections taught by two different instructors to evaluate students’ experience in moving from traditional Waterfall model to Agile like process. Twentyeight students filled the survey. The survey consisted of eight multiple-choice questions and an open-ended question to collect feedback from students. The survey results show that students were able to attain hands one experience, which simulate a real world-working environment. The results also show that the Agile approach helped students to have overall better design and avoid mistakes they have made in the initial design completed in of the first phase of the capstone project. In addition, they were able to decide on their team capabilities, training needs and thus learn the required technologies earlier which is reflected on the final product quality
PROMOTING STUDENT ENGAGEMENT USING SOCIAL MEDIA TECHNOLOGIEScscpconf
This document discusses using social media technologies to promote student engagement in a software project management course. It describes the course and objectives of enhancing communication. It discusses using Facebook for 4 years, then switching to WhatsApp based on student feedback, and finally introducing Slack to enable personalized team communication. Surveys found students engaged and satisfied with all three tools, though less familiar with Slack. The conclusion is that social media promotes engagement but familiarity with the tool also impacts satisfaction.
A SURVEY ON QUESTION ANSWERING SYSTEMS: THE ADVANCES OF FUZZY LOGICcscpconf
In real world computing environment with using a computer to answer questions has been a human dream since the beginning of the digital era, Question-answering systems are referred to as intelligent systems, that can be used to provide responses for the questions being asked by the user based on certain facts or rules stored in the knowledge base it can generate answers of questions asked in natural , and the first main idea of fuzzy logic was to working on the problem of computer understanding of natural language, so this survey paper provides an overview on what Question-Answering is and its system architecture and the possible relationship and
different with fuzzy logic, as well as the previous related research with respect to approaches that were followed. At the end, the survey provides an analytical discussion of the proposed QA models, along or combined with fuzzy logic and their main contributions and limitations.
DYNAMIC PHONE WARPING – A METHOD TO MEASURE THE DISTANCE BETWEEN PRONUNCIATIONS cscpconf
Human beings generate different speech waveforms while speaking the same word at different times. Also, different human beings have different accents and generate significantly varying speech waveforms for the same word. There is a need to measure the distances between various words which facilitate preparation of pronunciation dictionaries. A new algorithm called Dynamic Phone Warping (DPW) is presented in this paper. It uses dynamic programming technique for global alignment and shortest distance measurements. The DPW algorithm can be used to enhance the pronunciation dictionaries of the well-known languages like English or to build pronunciation dictionaries to the less known sparse languages. The precision measurement experiments show 88.9% accuracy.
INTELLIGENT ELECTRONIC ASSESSMENT FOR SUBJECTIVE EXAMS cscpconf
In education, the use of electronic (E) examination systems is not a novel idea, as Eexamination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity. The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.
TWO DISCRETE BINARY VERSIONS OF AFRICAN BUFFALO OPTIMIZATION METAHEURISTICcscpconf
African Buffalo Optimization (ABO) is one of the most recent swarms intelligence based metaheuristics. ABO algorithm is inspired by the buffalo’s behavior and lifestyle. Unfortunately, the standard ABO algorithm is proposed only for continuous optimization problems. In this paper, the authors propose two discrete binary ABO algorithms to deal with binary optimization problems. In the first version (called SBABO) they use the sigmoid function and probability model to generate binary solutions. In the second version (called LBABO) they use some logical operator to operate the binary solutions. Computational results on two knapsack problems (KP and MKP) instances show the effectiveness of the proposed algorithm and their ability to achieve good and promising solutions.
DETECTION OF ALGORITHMICALLY GENERATED MALICIOUS DOMAINcscpconf
In recent years, many malware writers have relied on Dynamic Domain Name Services (DDNS) to maintain their Command and Control (C&C) network infrastructure to ensure a persistence presence on a compromised host. Amongst the various DDNS techniques, Domain Generation Algorithm (DGA) is often perceived as the most difficult to detect using traditional methods. This paper presents an approach for detecting DGA using frequency analysis of the character distribution and the weighted scores of the domain names. The approach’s feasibility is demonstrated using a range of legitimate domains and a number of malicious algorithmicallygenerated domain names. Findings from this study show that domain names made up of English characters “a-z” achieving a weighted score of < 45 are often associated with DGA. When a weighted score of < 45 is applied to the Alexa one million list of domain names, only 15% of the domain names were treated as non-human generated.
GLOBAL MUSIC ASSET ASSURANCE DIGITAL CURRENCY: A DRM SOLUTION FOR STREAMING C...cscpconf
The document proposes a blockchain-based digital currency and streaming platform called GoMAA to address issues of piracy in the online music streaming industry. Key points:
- GoMAA would use a digital token on the iMediaStreams blockchain to enable secure dissemination and tracking of streamed content. Content owners could control access and track consumption of released content.
- Original media files would be converted to a Secure Portable Streaming (SPS) format, embedding watermarks and smart contract data to indicate ownership and enable validation on the blockchain.
- A browser plugin would provide wallets for fans to collect GoMAA tokens as rewards for consuming content, incentivizing participation and addressing royalty discrepancies by recording
IMPORTANCE OF VERB SUFFIX MAPPING IN DISCOURSE TRANSLATION SYSTEMcscpconf
This document discusses the importance of verb suffix mapping in discourse translation from English to Telugu. It explains that after anaphora resolution, the verbs must be changed to agree with the gender, number, and person features of the subject or anaphoric pronoun. Verbs in Telugu inflect based on these features, while verbs in English only inflect based on number and person. Several examples are provided that demonstrate how the Telugu verb changes based on whether the subject or pronoun is masculine, feminine, neuter, singular or plural. Proper verb suffix mapping is essential for generating natural and coherent translations while preserving the context and meaning of the original discourse.
EXACT SOLUTIONS OF A FAMILY OF HIGHER-DIMENSIONAL SPACE-TIME FRACTIONAL KDV-T...cscpconf
In this paper, based on the definition of conformable fractional derivative, the functional
variable method (FVM) is proposed to seek the exact traveling wave solutions of two higherdimensional
space-time fractional KdV-type equations in mathematical physics, namely the
(3+1)-dimensional space–time fractional Zakharov-Kuznetsov (ZK) equation and the (2+1)-
dimensional space–time fractional Generalized Zakharov-Kuznetsov-Benjamin-Bona-Mahony
(GZK-BBM) equation. Some new solutions are procured and depicted. These solutions, which
contain kink-shaped, singular kink, bell-shaped soliton, singular soliton and periodic wave
solutions, have many potential applications in mathematical physics and engineering. The
simplicity and reliability of the proposed method is verified.
AUTOMATED PENETRATION TESTING: AN OVERVIEWcscpconf
The document discusses automated penetration testing and provides an overview. It compares manual and automated penetration testing, noting that automated testing allows for faster, more standardized and repeatable tests but has limitations in developing new exploits. It also reviews some current automated penetration testing methodologies and tools, including those using HTTP/TCP/IP attacks, linking common scanning tools, a Python-based tool targeting databases, and one using POMDPs for multi-step penetration test planning under uncertainty. The document concludes that automated testing is more efficient than manual for known vulnerabilities but cannot replace manual testing for discovering new exploits.
CLASSIFICATION OF ALZHEIMER USING fMRI DATA AND BRAIN NETWORKcscpconf
Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing
attention of neuroscientists and computer scientists, since it opens a new window to explore
functional network of human brain with relatively high resolution. BOLD technique provides
almost accurate state of brain. Past researches prove that neuro diseases damage the brain
network interaction, protein- protein interaction and gene-gene interaction. A number of
neurological research paper also analyse the relationship among damaged part. By
computational method especially machine learning technique we can show such classifications.
In this paper we used OASIS fMRI dataset affected with Alzheimer’s disease and normal
patient’s dataset. After proper processing the fMRI data we use the processed data to form
classifier models using SVM (Support Vector Machine), KNN (K- nearest neighbour) & Naïve
Bayes. We also compare the accuracy of our proposed method with existing methods. In future,
we will other combinations of methods for better accuracy.
PROBABILITY BASED CLUSTER EXPANSION OVERSAMPLING TECHNIQUE FOR IMBALANCED DATAcscpconf
In many applications of data mining, class imbalance is noticed when examples in one class are
overrepresented. Traditional classifiers result in poor accuracy of the minority class due to the
class imbalance. Further, the presence of within class imbalance where classes are composed of
multiple sub-concepts with different number of examples also affect the performance of
classifier. In this paper, we propose an oversampling technique that handles between class and
within class imbalance simultaneously and also takes into consideration the generalization
ability in data space. The proposed method is based on two steps- performing Model Based
Clustering with respect to classes to identify the sub-concepts; and then computing the
separating hyperplane based on equal posterior probability between the classes. The proposed
method is tested on 10 publicly available data sets and the result shows that the proposed
method is statistically superior to other existing oversampling methods.
CHARACTER AND IMAGE RECOGNITION FOR DATA CATALOGING IN ECOLOGICAL RESEARCHcscpconf
Data collection is an essential, but manpower intensive procedure in ecological research. An
algorithm was developed by the author which incorporated two important computer vision
techniques to automate data cataloging for butterfly measurements. Optical Character
Recognition is used for character recognition and Contour Detection is used for imageprocessing.
Proper pre-processing is first done on the images to improve accuracy. Although
there are limitations to Tesseract’s detection of certain fonts, overall, it can successfully identify
words of basic fonts. Contour detection is an advanced technique that can be utilized to
measure an image. Shapes and mathematical calculations are crucial in determining the precise
location of the points on which to draw the body and forewing lines of the butterfly. Overall,
92% accuracy were achieved by the program for the set of butterflies measured.
SOCIAL MEDIA ANALYTICS FOR SENTIMENT ANALYSIS AND EVENT DETECTION IN SMART CI...cscpconf
Smart cities utilize Internet of Things (IoT) devices and sensors to enhance the quality of the city
services including energy, transportation, health, and much more. They generate massive
volumes of structured and unstructured data on a daily basis. Also, social networks, such as
Twitter, Facebook, and Google+, are becoming a new source of real-time information in smart
cities. Social network users are acting as social sensors. These datasets so large and complex
are difficult to manage with conventional data management tools and methods. To become
valuable, this massive amount of data, known as 'big data,' needs to be processed and
comprehended to hold the promise of supporting a broad range of urban and smart cities
functions, including among others transportation, water, and energy consumption, pollution
surveillance, and smart city governance. In this work, we investigate how social media analytics
help to analyze smart city data collected from various social media sources, such as Twitter and
Facebook, to detect various events taking place in a smart city and identify the importance of
events and concerns of citizens regarding some events. A case scenario analyses the opinions of
users concerning the traffic in three largest cities in the UAE
SOCIAL NETWORK HATE SPEECH DETECTION FOR AMHARIC LANGUAGEcscpconf
The anonymity of social networks makes it attractive for hate speech to mask their criminal
activities online posing a challenge to the world and in particular Ethiopia. With this everincreasing
volume of social media data, hate speech identification becomes a challenge in
aggravating conflict between citizens of nations. The high rate of production, has become
difficult to collect, store and analyze such big data using traditional detection methods. This
paper proposed the application of apache spark in hate speech detection to reduce the
challenges. Authors developed an apache spark based model to classify Amharic Facebook
posts and comments into hate and not hate. Authors employed Random forest and Naïve Bayes
for learning and Word2Vec and TF-IDF for feature selection. Tested by 10-fold crossvalidation,
the model based on word2vec embedding performed best with 79.83%accuracy. The
proposed method achieve a promising result with unique feature of spark for big data.
GENERAL REGRESSION NEURAL NETWORK BASED POS TAGGING FOR NEPALI TEXTcscpconf
This article presents Part of Speech tagging for Nepali text using General Regression Neural
Network (GRNN). The corpus is divided into two parts viz. training and testing. The network is
trained and validated on both training and testing data. It is observed that 96.13% words are
correctly being tagged on training set whereas 74.38% words are tagged correctly on testing
data set using GRNN. The result is compared with the traditional Viterbi algorithm based on
Hidden Markov Model. Viterbi algorithm yields 97.2% and 40% classification accuracies on
training and testing data sets respectively. GRNN based POS Tagger is more consistent than the
traditional Viterbi decoding technique.
APPLYING DISTRIBUTIONAL SEMANTICS TO ENHANCE CLASSIFYING EMOTIONS IN ARABIC T...cscpconf
Most of the recent researches have been carried out to analyse sentiment and emotions found in
English texts, where few studies have been conducted on Arabic contents, which have been
focused on analysing the sentiment as positive and negative, instead of the different emotions’
classes. Therefore this paper has focused on analysing different six emotions’ classes in Arabic
contents, especially Arabic tweets which have unstructured nature that make it challenging task
compared to the formal structured contents found in Arabic journals and books. On the other
hand, the recent developments in the distributional sematic models, have encouraged testing the
effect of the distributional measures on the classification process, which was not investigated by
any other classification-related studies for analysing Arabic texts. As a result, the model has
successfully improved the average accuracy to more than 86% using Support Vector Machine
(SVM) compared to the different sentiments and emotions studies for classifying Arabic texts
through the developed semi-supervised approach which has employed the contextual and the
co-occurrence information from a large amount of unlabelled dataset. In addition to the
different remarkable achieved results, the model has recorded a high average accuracy,
85.30%, after removing the labels from the unlabelled contextual information which was used in
the labelled dataset during the classification process. Moreover, due to the unstructured nature
of Twitter contents, a general set of pre-processing techniques for Arabic texts was found which
has resulted in increasing the accuracy of the six emotions’ classes to 85.95% while employing
the contextual information from the unlabelled dataset.
The chapter Lifelines of National Economy in Class 10 Geography focuses on the various modes of transportation and communication that play a vital role in the economic development of a country. These lifelines are crucial for the movement of goods, services, and people, thereby connecting different regions and promoting economic activities.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
2. 120 Computer Science & Information Technology (CS & IT)
However, most of them assumed the security returns as random variables. In 1965, one of the
innovative theories of mathematics, Fuzzy set theory, is proposed by Zadeh [14]. Realizing the
limitations of stochastic approach to handle such a non-stochastic factor (risky assets), researchers
start to involve fuzzy approach to portfolio selection models formulation. Ramaswamy [15], Parra
et al. [16], Zhang and Nie [17], Bilbao-Terol et al. [18], Gupta et al. [19], Huang [20, 21], Lin
and Liu [22], Bhattacharyya et al. [23, 24, 25], Li et al. [26] and others study fuzzy portfolio
selection models.
A special approach, based on interval approximation of fuzzy numbers, assumes that the
information of a decision making problem are not well defined but may vary in given intervals.
Interval valued fuzzy set theory is introduced separately in the mid-seventies by Grattan-Guinness
[27], Jahn [28], Sambuc [29] and Zadeh [30]. Many researchers use interval programming to
portfolio selection problem. Parra et al. [16] develop a goal programming (GP) model for
portfolio selection, based on the expected intervals of fuzzy numbers that define the objectives
and target values. Lai et al. [31] extend the Markowitz’s model to an interval programming model
by quantifying the expected return and the covariance as intervals. Ida [32, 33], Giove et al. [34],
Fang et al. [35], Ehrgott et al. [36], Bhattacharya et al. [37] use interval programming model in
various ways. Dividend, transaction cost, liquidity, short and long term return have been used in
these models along with mean, variance, skewness.
Maximization entropy principle and minimization cross-entropy principle motivated Kapur and
Hesavan [38] to propose an entropy maximization model in 1992. The principle of maximum
entropy states that, subject to precisely stated prior data like a proposition that expresses testable
information, the probability distribution which best represents the current state of knowledge is
the one with largest information-theoretical entropy. The principle was first expounded by E.T.
Jaynes in two papers [39, 40] in 1957, where he emphasized a natural correspondence
between statistical mechanics and information theory. Later, Fang et al. [41], Qin et al. [42], Li et
al. [43], Liu [44] and others also investigate entropy optimization models, which are widely
accepted.
The purpose of this paper is to build a new portfolio selection model by making a bridge between
these two concepts. Here we have proposed a new objective function as a ratio of entropy to total
transaction cost which we want to maximize under the usual constraints on return, risk, dividend
etc., but the constraints are treated here in fuzzy interval analytic way. The well known theory of
interval numbers and the reason to use it in portfolio models are discussed in the following two
sections. In next sections the objective function and constraints are proposed. Then the interval
model and an algorithm to solve the problem are discussed. The algorithm is based on the well
known Genetic Algorithm; some improvisations have been done to make the algorithm more
effective for the particular models. Finally real life market data extracted from Bombay Stock
Exchange (BSE) are used to solve the stated models to prove the efficiency of this new approach.
2. FUZZY INTERVAL ANALYSIS
Let R be the set of all real numbers. An order pair in a bracket defines an interval
[ ] { }, : ,A x x x Rα α α α= = ≤ ≤ ∈ ,
where α is lower bound and α is upper bound of interval A . The centre and the width of A are
defined as,
( )
2
m A
α α+
= and ( ) .
2
w A
α α−
=
3. Computer Science & Information Technology (CS & IT) 121
A can also be denoted by its centre and width as
{ }( ), ( ) : ( ) ( ) ( ) ( ), .A m A w A x m A w A x m A w A x R= = − ≤ ≤ + ∈
The annex of ordinary arithmetic to closed intervals is recognized as interval arithmetic. First, we
extract some fundamental concepts, as follows:
Definition 1. Let { }, , ,ο∈ + − × ÷ be a binary operation on R . Let A and B be two closed
intervals. Then the binary operation on the set of all closed intervals is defined by,
{ }: , .A B x y x A y Bο ο= ∈ ∈
In the case of division, it is always assumed that0 B∉ . The operations on the intervals are as
follows:
, ,A B α β α β + = + +
, ,A B α β α β − = − −
[ ], ,A k k k k Rα α± = ± ± ∈
[ ]
[ ]
[ ]
, , 0
, ,
, , 0
k k k
kA k k R
k k k
α α
α α
α α
≥
= = ∈
≤
An interval number is considered as a special fuzzy number whose membership function takes the
value 1 over the interval, and 0 anywhere else. Clearly, the above four operations of the intervals
are equivalent to the operations of addition, subtraction and scalar multiplication of fuzzy
numbers by means of the extension principle of Zadeh [45]. Rommelfanger et al. [46] inspect the
interval programming problem like a fuzzy programming problem. Ishibuchi and Tanaka [47]
suggest an order relation among two intervals.
Definition 2. For any two interval numbers [ ],A α α= and ,B β β = , there is an interval
inequality relation A B≤ among the two interval numbers A and B if and only if, ( ) ( ).m A m B≤
.Furthermore, if α β≤ , the interval inequality relation A B≤ between A and B is said to be
optimistic satisfactory; ifα β≥ , the interval inequality relation A B≤ between A and B is said to
be pessimistic satisfactory.
3. USING INTERVAL ANALYSIS IN PORTFOLIO MODELLING
We know that, in any emerging market, future returns of securities cannot be accurately
predicted. Generally, researchers consider the arithmetic mean of historical returns as the
expected return of the security. Thus the expected return of the security is a crisp value. Two
main problems arise here:
Firstly, if the time horizon of the historical data of a security is very long, the influence of the
recent data becomes negligible over that of the historical data.
Secondly, if the historical data of a security is not available in a large scale, one cannot accurately
estimate the statistical parameters, due to data scarcity.
Considering these two problems, it is better to consider the expected return of a security as an
interval number, rather than a crisp value, based on the arithmetic mean of historical data.
Financial reports and the security’s historical data can be used to determine the expected return
4. 122 Computer Science & Information Technology (CS & IT)
interval’s range. To determine the range of change in expected returns of securities, the following
three factors can be considered:
Arithmetic mean: Arithmetic means of returns of securities should not be expressed as expected
returns directly, but can be used as a good approximation. Denote the arithmetic mean return
factor as ( )iA r , which can be calculated with historical data.
Historical return tendency: If recent returns of a security have been increasing, the expected
return of the security is greater than the arithmetic mean based on historical data. However, if
recent returns of a security have been declining, the expected return of the security is smaller than
the arithmetic mean based on historical data. Denote the historical return tendency factor as ( )iH r
, which reflects the tendency of the return on the security. We can use the arithmetic mean of
recent returns as ( )iH r
.
Forecast of future returns of a security: The third factor influencing the expected return of a
security is its estimated future returns. In a risky modern market scenario the expected return is
highly influenced by the present financial condition of a particular corporation which may be
highly anomalous. Denote the forecast return factor as ( )iF r . Computation of derivation of ( )iF r
requires some forecasts based on the financial reports and individual experiences of experts of the
market.
Based on the above three factors, we can derive lower and upper limits of the expected return of
the security. We can put the minimum of the three factors ( ),iA r ( )iH r and ( )iF r as the lower
limit of the expected return, while we can put the maximum value of the three factors as the upper
limit of the expected return of the security.
Similarly, in fuzzy environment the risk and dividend are also unpredictable. So the variances,
covariance and dividends are also considered here as fuzzy interval numbers.
4. THE PORTFOLIO SELECTION MODEL
Here we first describe the assumptions and notations used in this paper. Then in the subsequent
subsections the objective function and the constraints have been constructed. The fourth
subsection presents three different mathematical models for different situations. Finally the
optimization algorithm, which is used here to solve the models, has been discussed.
4.1 Assumptions and notations
Let us consider a financial market with n risky assets. An investor wants to allocate his wealth
among these risky assets. For i th
risky asset ( 1,2,3,..........., )i n= , let us use the following
notations:
ix = the proportion of the total capital to be invested,
id = the estimated annual dividend in the next year,
ir = the expected return,
ijσ = cov( ,i jr r ), the covariance between ir and jr ,
ic = the transaction cost,
ik = the constant cost per change in a proportion, 0ik ≥ .
5. Computer Science & Information Technology (CS & IT) 123
4.2 Formulation of objective function
The Principle of Maximum Entropy: Entropy is used to measure the uncertainty associated
with each random variable. The Principle of Maximum Entropy is based on the premise that when
estimating the probability distribution, you should select that distribution which leaves you the
largest remaining uncertainty (i.e., the maximum entropy) consistent with your constraints. That
way you have not introduced any additional assumptions or biases into your calculations.
Following the spirit of maximum entropy principle, the total entropy of the allocated proportions
of investor’s wealth,
1
ln
n
i i
i
E x x
=
= −∑ is introduced in the objective function. Kapur [38]
introduces it earlier as a single term in that objective function.
Total transaction cost: Let me consider the transaction cost ic to be a V-shaped function of the
difference between a given portfolio ( )0 0 0 0 0
1 2 3, , ,................., nx x x x x= and a new portfolio
( )1 2 3, , ,................., .nx x x x x= .Hence, the total transaction cost is,
0
1 1
n n
i i i i
i i
C c k x x
= =
= = −∑ ∑ ,
which we want to minimize.
Taking these two aspects in consideration, the objective function is formulated as:
1
0
1
ln
max max
n
i i
i
n
i i i
i
x x
E
C
k x x
=
=
−
=
−
∑
∑
.
4.3 Construction of the constraints with interval coefficients
The expected return of portfolio ( )1 2 3, , ,................., nx x x x x= with transaction cost is given by,
0
1 1
( )
n n
i i i i i
i i
R x r x k x x
= =
= − −∑ ∑ .
Variance of the portfolio is,
2
1 1
( ) ,
n n
ij i j
i j
x x xσ σ
= =
= ∑∑
And the estimated annual dividend of the portfolio is,
1
( ) .
n
i i
i
D x d x
=
= ∑
For a new investor it can be assumed that 0
0, 1,2,3,......., .ix i n= ∀ =
From our previous discussion on interval analysis, let { }min ( ), ( ), ( )il i i i ir r A r H r F r∆ = − and
{ }max ( ), ( ), ( ) .ig i i i ir A r H r F r r∆ = − .
Then the fuzzy expected return for the ith
security is represented by the interval number:
6. 124 Computer Science & Information Technology (CS & IT)
,i i il i igr r r r r = − ∆ + ∆ % .
Similarly, ijσ and id , respectively are represented by:
, , , .ij ij ijl ij ijg i i il i igd d d d dσ σ σ σ σ = − ∆ + ∆ = − ∆ + ∆
%%
Then the fuzzy expected return, risk and dividend are respectively defined by,
0
1 1
( )
n n
i i i i i
i i
R x r x k x x
= =
= − −∑ ∑% % ,
2
1 1
( )
n n
ij i j
i j
x x xσ σ
= =
= ∑∑% % ,
1
( ) .
n
i i
i
D x d x
=
= ∑ %%
Since 0ix ≥ , we have,
2 2 2
( ) ( ) ( ), ( ) ( ) ,
( ) ( ) ( ), ( ) ( ) ,
( ) ( ) ( ), ( ) ( ) ,
Rl Rg
Vl Vg
Dl Dg
R x R x x R x x
x x x x x
D x D x x D x x
σ σ σ
= − ∆ + ∆
= − ∆ + ∆
= − ∆ + ∆
%
%
%
where,
0
1 1
0
1 1
( ) ( ) ( ) ,
( ) ( ) ( ) ;
n n
Rl i il i i i i
i i
n n
Rg i ig i i i i
i i
R x x r r x k x x
R x x r r x k x x
= =
= =
− ∆ = − ∆ − −
+ ∆ = + ∆ − −
∑ ∑
∑ ∑
2
1 1
2
1 1
1
1
( ) ( ) ( ) ,
( ) ( ) ( ) ;
( ) ( ) ( ) ,
( ) ( ) ( ) .
n n
Vl ij ijl i j
i j
n n
Vg ij ijg i j
i j
n
Dl i il i
i
n
Dg i ig i
i
x x x x
x x x x
D x x d d x
D x x d d x
σ σ σ
σ σ σ
= =
= =
=
=
− ∆ = − ∆
+ ∆ = + ∆
− ∆ = − ∆
+ ∆ = + ∆
∑∑
∑∑
∑
∑
Now, an investor always prefers a portfolio with having a minimum expected return, a maximum
risk tolerance and a higher dividend. So, the following constraints are proposed:
2
( ) ................................................ 1
( ) .............................................. 2
( ) ................................................ 3
R x C
x C
D x C
α
σ β
γ
≥
≤
≥
%
%
%
Here , ,α β γ will be allocated by the investor according to the preference.
The well known capital budget constraint on the assets is presented by:
7. Computer Science & Information Technology (CS & IT) 125
1
1................................................. 4
n
i
i
x C
=
=∑
The maximum and minimum fractions of the capital budget being allocated to each of the assets
in the portfolio depend upon factors like price relative to the asset in comparison with the average
of the price of all the assets in the chosen portfolio, minimal lot size that can be traded in the
market, the past performance of the price of the asset, information available about the issuer of
the asset, trends in the business of which it is a division etc. Different investors having different
views may allocate the same overall capital budget differently.
Let the maximum fraction of the capital that can be invested in a single asset i is M . Then
,( 1,2,3,............, )................... 5ix M i n C≤ ∀ =
Let the minimum fraction of the capital that can be invested in a single asset i is m . Then
,( 1,2,3,............, ).................... 6ix m i n C≥ ∀ =
The above two constraints ensure that neither a huge amount nor a very tiny amount of the assets
are assigned in a single stock of the portfolio. Huge amount of investment of the asset in a single
stock opposes the motto of selecting a portfolio (i.e., diversification of investment). On the other
hand, negligible amount of investment in a portfolio is impractical. For example, investing neither
80% nor 0.5% of the asset in a single stock of the portfolio is good. Note that for ( )n k− number
of stocks, we have 0ix = .
No short selling is considered in the portfolio here. So we have,
0,( 1,2,3,..............., )...................... 7ix i n C≥ ∀ =
Moreover, it is very difficult for an investor to maintain capital allocations for all available assets
in the market, so one can decide to deal with any k no. of assets out of a total of n assets. This
leads to the following constraint:
1
8
n
i
i
sign( x ) k.....................................................C
=
=∑ ,
Here isign( x ) represents the signum functional value of ix .
These are the eight main constraints to be considered in the model discussed in this paper.
Obviously, different investors have different approaches towards a problem.
4.4 Construction of the portfolio selection models
Using the objective function and the constraints obtained in the previous subsections, the
following fuzzy investment problem is constructed:
8. 126 Computer Science & Information Technology (CS & IT)
2
1
1
1
1 0 1 2 3
n
i
i
n
i i i i
i
E
max
C
subject to
R( x ) , ( x ) , D( x ) , sign( x ) k
x , x M , x m, x ,( i , , ,...............,n ).
α σ β γ
=
=
≥ ≤ ≥ =
= ≤ ≥ ≥ ∀ =
∑
∑
% %%
Now, denote:
2 2 2 2
( ) ( ) ( ), ( ) ( ) ( )
( ) ( ) ( ), ( ) ( ) ( )
( ) ( ) ( ), ( ) ( ) ( )
l Rl g Rg
l Vl g Vg
l Dl g Dg
R x R x x R x R x x
x x x x x x
D x D x x D x D x x
σ σ σ σ
= − ∆ = + ∆
= − ∆ = + ∆
= − ∆ = + ∆
Then, clearly,
2 2 2
( ) ( ), ( ) , ( ) ( ), ( ) , ( ) ( ), ( ) .l g l g l gR x R x R x x x x D x D x D xσ σ σ = = =
% %%
From the fuzzy interval optimization model 1 , let us construct the following three crisp
optimization models ( 2 , 3 , 4 ), with the help of interval programming by Lai et al. [31].
In the following model 2 , the investor estimates the return, risk and dividend pessimistically
and aims to optimize the objective based on such constraints.
2
1
1
max
2
( ) , ( ) , ( ) , ( )
1, , , 0,( 1,2,3,..............., ).
n
g l g i
i
n
i i i i
i
E
C
subject to
R x x D x sign x k
x x M x m x i n
α σ β γ
=
=
≥ ≤ ≥ =
= ≤ ≥ ≥ ∀ =
∑
∑
In the following model 3 , the investor estimates the return, risk and dividend optimistically and
aims to optimize the objective based on such constraints.
2
1
1
max
3
( ) , ( ) , ( ) , ( )
1, , , 0,( 1,2,3,..............., ).
n
l g l i
i
n
i i i i
i
E
C
subject to
R x x D x sign x k
x x M x m x i n
α σ β γ
=
=
≥ ≤ ≥ =
= ≤ ≥ ≥ ∀ =
∑
∑
9. Computer Science & Information Technology (CS & IT) 127
In following the model 4 , the investor chooses a balanced one; to some extent she/he is
optimistic, but to some extent she/he is pessimistic too.
2 2
1 1 2 2
3 3
1
1
max
( ) (1 ) ( ) , ( ) (1 ) ( ) ,
4
( ) (1 ) ( ) , 1,
, , 0,( 1,2,3,..............., ),
( ) .
l g g l
n
l g i
i
i i i
n
i
i
E
C
subject to
R x R x x x
D x D x x
x M x m x i n
sign x k
λ λ α λ σ λ σ β
λ λ γ
=
=
+ − ≥ + − ≤
+ − ≥ =
≤ ≥ ≥ ∀ =
=
∑
∑
Here, ( ) [ ]0 1j , j = 1, 2, 3λ ∈ would be assigned by the investor.
4.5 The optimization algorithm
Genetic algorithm (GA) is a stochastic search method used in computer science field of artificial
intelligence and is based on the principles of natural genetic systems. It performs a
multidimensional search in providing an optimal solution for evaluation function of an
optimization problem mimicking the process of natural evolution. Genetic algorithms belong to
the larger class of evolutionary algorithms (EA), which generate solutions to optimization
problems using techniques inspired by natural evolution, such as inheritance, mutation, selection,
and crossover. Since Holland [48] first proposed it in 1975, genetic algorithm has been widely
studied, experimented and applied in many fields like operations research, finance, industrial
engineering, VLSI design, pattern recognition, image processing etc.
While solving an optimization problem using genetic algorithm, a population of solutions is
evolved towards better solutions. Each population has a set of individuals,
its chromosomes or genotypes, which can be mutated and altered. The evolution usually starts
from a population of randomly generated individuals and are changed in future generations. In
each generation, the fitness of every individual in the population is evaluated, the more fit
individuals are stochastically selected from the current population, and each individual's genome
is modified using three basic operations on the individuals of the population. The operations are
selection, crossing over and mutation. The new population obtained after selection, cross over and
mutation is then to generate another population. The new population is then used in the next
iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number
of generations has been produced, or a satisfactory fitness level has been reached for the
population. If the knowledge about the best string is preserved within the population, such a
model is called a genetic algorithm with an elitist model (EGA). An EGA converges to the global
solution with any choice of initial population (c.f. Bhandari et al. [49]). To find the optimal
portfolio, we integrate fuzzy simulation into the GA. The GA procedure has been introduced in
detail in Huang [50]. Here, we summarize the algorithm as follows:
1. In the GA, a solution ( )1 2 3 nx x ,x ,x ,...............,x= is represented by the chromosome:
10. 128 Computer Science & Information Technology (CS & IT)
( )1 2 3 nC c ,c ,c ,............,c= , where the genes 1 2 3 nc ,c ,c ,............,c are in the interval [0, 1]. The
matching between the solution and the chromosome is through
( )1 2 3 1 2 3i i nx c / c c c ............ c ,i , , ,..........,n= + + + + = , which ensures that,
1 2 3 1nx x x .............. x+ + + + = always holds.
Randomly generate a pointC . Use fuzzy simulation to calculate the values of return, risk and
dividend to check the feasibility of the chromosomes. Take the feasible chromosomes as the
initial population.
2. Calculate the objective values for all chromosomes by fuzzy simulation. Then, select the best
among the chromosomes according to the objective values. The chromosome having the
maximum z-value is kept in memory by continuous update. Next, pass the population to perform
crossover and mutation.
3. Update the chromosomes by crossover and mutation operations. Check the feasibility of the
chromosomes to build a feasible new population.
4. Once again calculate the objective values for all chromosomes by fuzzy simulation. Select the
best among the chromosomes according to the objective values. The chromosome having the
maximum z-value is kept in memory by continuous update. Next, again pass the population to
perform crossover and mutation.
5. Repeat steps 2 to 4 a number of times.
6. The best chromosome obtained at the last iteration is the required solution.
5. CASE STUDY: APPLICATION TO BOMBAY STOCK EXCHANGE (BSE)
Now the portfolio selection model discussed above will be applied to the stock market of Bombay
stock exchange (BSE). Bombay Stock Exchange is a stock exchange located on Dalal
Street, Mumbai, Maharashtra, India. It is the 10th
largest stock exchanges in the world by market
capitalization. Established in 1875, BSE Ltd. (formerly known as Bombay Stock Exchange Ltd.),
is Asia’s first Stock Exchange and one of India’s leading exchange groups. Over the past 137
years, BSE has facilitated the growth of the Indian corporate sector by providing it an efficient
capital-raising platform. Popularly known as BSE, the bourse was established as "The Native
Share & Stock Brokers' Association" in 1875. Around 5000 companies are listed on BSE making
it world's No. 1 exchange in terms of listed members. The companies listed on BSE Ltd command
a total market capitalization of USD Trillion 1.2 as of October 31, 2012. BSE Ltd is world's fifth
most active exchange in terms of number of transactions handled through its electronic trading
system. It is also one of the world’s leading exchanges (3rd largest in July 2012) for Index
options trading (Source: World Federation of Exchanges).
Here, we have taken 10 companies which are included in BSE index. Their return, dividend and
other history has been collected and analyzed carefully for January, 2005 to March, 2010. The
data collected is shown in Table 1. The covariance matrix of the return rates of these risky assets
are shown in tables 2 and 3.
13. Computer Science & Information Technology (CS & IT) 131
SBI
0.107403
0.326497
-0.0183
-0.03459
-0.09612
0.071607
0.027155
0.041984
0.003937
-0.04269
WIPRO
-0.19038
-0.50519
0.108887
1.472226
0.598989
0.027155
1.239608
1.070363
0.262355
2.196997
TATA
STEEL
-0.13287
-0.33455
0.087728
1.256278
0.491615
0.041984
1.070363
0.929748
0.229462
1.870106
ONGC
-0.0312
-0.0483
0.0191
0.3257
0.1441
0.0040
0.2624
0.2295
0.0724
0.4513
TATA
MOTORS
-0.4914
-1.39089
0.223644
2.682954
1.187544
-0.04269
2.196997
1.870106
0.451286
4.025142
Let 0.001,ik = 0
0 8 0 005 8 0 1 2 3 10iM . , m= . ,k , x , i , , ,..........., .= = = ∀ =
Also let α = − 0.8 ; β = 0.9 ; γ = 0.5 ; λ1 = 0.4 ; λ2 = 0.5 ; λ3 = 0.3 .
Using these data, in this scenario let us solve the three portfolio optimization models 2 , 3 , 4 .
The solution is done by MATLAB and result obtained after 50 crossover and mutations in each
model is shown in Table 4.
Table 4. Solutions & Constraints of models 2 , 3 , 4
Models 2 3 4
Return [-0.3820,1.1265] [-0.3883,1.0744] [-0.3749,1.1028]
Risk [0.0524,0.3078] [0.0379,0.3953] [0.0416,0.2031]
Dividend [0.8214,1.9853] [1.0880,2.1498] [1.1245,2.1178]
Portfolio
TATA POWER 0.1259 0 0.1273
RELIANCE INFRA 0.1258 0.1129 0.1248
ITC 0 0.1298 0.1181
M&M 0 0.1418 0
GRASIM 0.1236 0.1129 0.1278
SBI 0.1223 0.1259 0.1245
WIPRO 0.1263 0 0
TATA STEEL 0.1272 0.1142 0.1276
ONGC 0.1281 0.1259 0.1173
TATA MOTORS 0.1208 0.1366 0.1326
In figure 1, the different portfolios for models 2 -4 are shown in the form of bar diagrams.
14. 132 Computer Science & Information Technology (CS & IT)
Figure 1. Allocation of capital for the three models
It is clear from the results shown in Table 4 for models 2-4 that in model 2 the investor used
the constraints pessimistically and optimistically in model 3 ,while in case of model 4 ,he was
neither too optimistic nor too pessimistic, but a mixed one. This is prominent from the results of
returns, risks and dividends obtained in each case:
RETURN: [ ] [ ]0 3749 1 1028 0 3883 1 1265. , . . , .− ⊂ − ,
RISK: [ ] [ ]0 0416 0 2031 0 0379 0 3953. , . . , .⊂ ,
DIVIDEND: [ ] [ ]1 1245 2 1178 0 8214 2 1498. , . . , . .⊂
From Figure 1, it is also evident that the investor is always allowed to handle with only 8 assets,
and also each allocation is inside the desired margins.
6. CONCLUSION
This article has introduced a new framework for fuzzy portfolio selection using interval valued
fuzzy numbers. A new objective function has been defined as the ratio of the entropy of the
allocated proportions of investor’s wealth to the total transaction costs. In addition to the
objective function, eight different constraints including constraints on return, risk and dividends
have been introduced in the models. Three different models representing pessimistic, optimistic
and balanced preference of the investors have been proposed. The return, covariance and
dividends of the stocks are represented by interval numbers. The models are tested on stock data
from BSE. The solution is done by the software MATLAB.
In future, the methodology presented here can be extended to the portfolio selection problems in
fuzzy, hybrid and general uncertain environments. In addition, genetic algorithm, hybrid
intelligent algorithm, tabu search, simulated annealing, ant-colony optimization and particle
swam optimization may be employed to solve the non-linear programming problem.
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
x 1 x 2 x 3 x 4 x 5 x 6 x 7 x 8 x 9 x 10
MODEL 1
MODEL 2
MODEL 3
15. Computer Science & Information Technology (CS & IT) 133
REFERENCES
[1] L. Bachelier, Theorie de la Speculation: Thesis: Annales Scientifiques de l’École Normale Superieure
(1900); I I I 17; 21–86.
[2] H. Markowitz, Portfolio selection, Journal of Finance 7 (1952) 77–91.
[3] F.D. Arditti, Risk and the required return on equity, Journal of Finance 22 (1967) 19–36.
[4] P. Samuelson, The fundamental approximation theorem of portfolio analysis in terms of means, variances
an higher moments, Review of Economic Studies 25 (1958) 65–86.
[5] A. Kraus, R. Litzenberger, Skewness preference and the valuation of risky assets, Journal of Finance 21
(1976) 1085–1094.
[6] H. Konno, H. Shirakawa, H. Yamazaki, A mean-absolute deviation-skewness portfolio optimization model,
Annals of Operations Research 45 (1993) 205–220.
[7] H. Konno, K. Suzuki, A mean-variance-skewness optimization model, Journal of the Operations Research
Society of Japan 38 (1995) 137–187.
[8] S.C. Liu, S.Y. Wang, W.H. Qiu, A mean-variance-skewness model for portfolio selection with transaction
costs, International Journal of Systems Science 34 (2003) 255–262.
[9] A.J. Prakash, C. Chang, T.E. Pactwa, Selecting a portfolio with skewness: Recent evidence from US,
European, and Latin American equity markets, Journal of Banking and Finance 27 (2003) 1375–1390.
[10] T. Lai, Portfolio selection with skewness: a multiple–objective approach, Review of the Quantitative
Finance and Accounting 1 (1991) 293–305.
[11] P. Chunhachinda, K. Dandapani, S. Hamid, A.J. Prakash, Portfolio selection and skewness: evidence from
international stock markets, Journal of Banking and Finance 21 (1997) 143–167.
[12] W. Briec, K. Kerstens, O. Jokung, Mean–variance–skewness portfolio performance gauging: a general
shortage function and dual approach, Management Science 53 (2007) 135–149.
[13] L. Yu, S.Y. Wang, K. Lai, Neural network based mean–variance–skewness model for portfolio selection,
Computers and Operations Research 35 (2008) 34–46.
[14] L. A. Zadeh ,1965, Fuzzy Sets, Information and Control, 8, 338-353.
[15] S. Ramaswamy, Portfolio selection using fuzzy decision theory, Working paper of Bank for International
Settlements, 59, 1998.
[16] M.A. Parra, A.B. Terol, M.V.R. Uría, A fuzzy goal programming approach to portfolio selection, European
Journal of Operational Research 133 (2001) 287–297.
[17] W. Zhang, Z. Nie, On admissible efficient portfolio selection problem, Applied Mathematics and
Computation 159 (2004) 357–371.
[18] A. Bilbao-Terol, B. Perez-Gladish, M. Arenas-Parra, M.R. Urfa, Fuzzy compromise programming for
portfolio selection, Applied Mathematics and Computation 173 (2006) 251–264.
[19] P. Gupta, M.K. Mehlawat, A. Saxena, Asset portfolio optimization using fuzzy mathematical
programming, Information Sciences 178 (2008) 1734–1755.
[20] X. Huang, Mean–semivariance models for fuzzy portfolio selection, Journal of Computational and Applied
Mathematics 217 (2008) 1–8.
[21] X. Huang, Risk curve and fuzzy portfolio selection, Computers and Mathematics with Applications 55
(2008) 1102–1112.
[22] C.C. Lin, Y.T. Liu, Genetic algorithms for portfolio selection problems with minimum transaction lots,
European Journal of Operational Research 185 (2008) 393–404.
[23] R. Bhattacharyya, M.B. Kar, S. Kar, D. Dutta Majumder, in: S. Chaudhury, et al. (Eds.), Mean–Entropy–
Skewness Fuzzy Portfolio Selection by Credibility Theory Approach, in: LNCS 5909, PReMI 2009, 2009,
pp. 603–608.
[24] R. Bhattacharyya, S. Kar, Possibilistic mean-variance- skewness portfolio selection models, International
Journal of Operations Research, 8(3), 44- 56, 2011.
[25] R. Bhattacharyya, S. Kar, Multi-objective fuzzy optimization for portfolio selection: an embedding
theorem approach, Turkish Journal of Fuzzy Systems, 2(1), 14 – 35, 2011.
[26] X. Li, Z. Qin, S. Kar, Mean–variance–skewness model for portfolio selection with fuzzy returns, European
Journal of Operational Research 202 (2010) 239–247.
[27] I. Grattan-Guinness, Fuzzy membership mapped onto interval and many valued quantities, Mathematical
Logic Quarterly 22 (1976) 149–160.
[28] K.U. Jahn, Interval-wertige mengen, Mathematische Nachrichten 68 (1975) 115–132.
[29] R. Sambuc, Functions Φ-floues, Applications á l’aide au diagonostic en pathologie thyroidienne, Ph.D.
Thesis, University of Marseille, 1975.
[30] L.A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning-I, Information
Sciences 8 (1975) 199–249.
16. 134 Computer Science & Information Technology (CS & IT)
[31] K.K. Lai, S.Y. Wang, J.P. Xu, S.S. Zhu, Y. Fang, A class of linear interval programming problems and its
application to portfolio selection, IEEE Transactions on Fuzzy Systems 10 (2002) 698–704.
[32] M. Ida, Portfolio selection problem with interval coefficients, Applied Mathematics Letters 16 (2003) 709–
713.
[33] M. Ida, Solutions for the portfolio selection problem with interval and fuzzy coefficients, Reliable
Computing 10 (2004) 389–400.
[34] S. Giove, S. Funari, C. Nardelli, An interval portfolio selection problems based on regret function,
European Journal of Operational Research 170 (2006) 253–264.
[35] Y. Fang, K.K. Lai, S.Y. Wang, Portfolio rebalancing model with transaction costs based on fuzzy decision
theory, European Journal of Operational Research 175 (2006) 879–893.
[36] M. Ehrgott, K. Klamroth, C. Schwehm, An MCDM approach to portfolio optimization, European Journal
of Operational Research 155 (2004) 752–770.
[37] R. Bhattacharyya, et al., Fuzzy mean-variance-skewness portfolio selection models by interval analysis,
Computers and Mathematics with Applications, 61 (2011), 126 – 137.
[38] Kapur J, and Kesavan H,Entropy Optimization Principles with Applications, Academic Press, New York,
1992.
[39] Jaynes, E.T. (1957), "Information theory and statistical mechanics", The Physical Review, Volume 106,
No. 4, 620-630, May 15, 1957.
[40] Jaynes, E.T. (1957), "Information theory and statistical mechanics II", The Physical Review, Volume 108,
No. 2, 171–190, October 15, 1957.
[41] Fang S, Rajasekera J, and Tsao H, Entropy Optimization and Mathematical Programming, Kluwer
Academic, Boston, 1997.
[42] Qin Z, Li X, and Ji X, Portfolio selection based on Fuzzy cross-entropy, JCAM, Vol. 228, 139-149, 2009.
[43] Li X and Liu B, Maximum Entropy Principle for fuzzy variables, International Journal of Uncertainity,
Fuzziness & Knowledge-Based Systems, Vol. 15, Supp. 2, 43-52, 2007.
[44] Liu B, A survey of entropy of fuzzy variables, Journal of uncertain systems, Vol. 1, No. 1, 4-13, 2007.
[45] L.A. Zadeh, Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets and Systems 1 (1978) 3–28.
[46] H. Rommerlfanger, R. Hanscheck, J. Wolf, Linear programming with fuzzy objectives, Fuzzy Sets and
Systems 29 (1989) 31–48.
[47] H. Ishihuchi, M. Tanaka, Multiobjective programming in optimization of the interval objective function,
European Journal of Operational Research 48 (1990) 219–225.
[48] J. H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor,
1975.
[49] D. Bhandari, C.A. Murthy, S.K. Pal, Genetic algorithm with elitist model and its convergence, International
Journal of Pattern Recognition and Artificial Intelligence 10 (6) (1996) 731–747.
[50] X. Huang, Fuzzy chance-constrained portfolio selection, Applied Mathematics and Computation 177
(2006) 500–507.
AUTHORS
Mr. Mainak Dey is presently working as an Assistant Professor in the Department of
Mathematics of Camellia Institute of Engineering, Madhyamgram, West Bengal, India.
He received his MSc. degree from Indian Institute of Technology, Roorkee, India and
BSc. from Jadavpur U niversity, Kolkata. Currently he is pursuing research work
leading to PhD degree under the necessary guidance of Dr. Rupak Bhattacharyya. His
research interests include Fuzzy Mathematics, Portfolio Management, Optimization and
Operation Research.
Dr. Rupak Bhattacharyya is presently working as an Associate Professor (Mathematics)
and HOD in the Department of Applied Science & Humanities of Global Institute of
Management & Technology, Krishnagar, West Bengal, India. He received his PhD
degree from National Institute of Technology, Durgapur. The Operational Research
Society of Indi a confers the Professor M N Gopalan Award on Dr. Bhattacharyya for
the best Doctoral Thesis in Operational Research of 2011. He has more than eight years
of teaching and research experience. He has more than 20 International publications. His
research interest includes Soft Computing, Financial Mathematics and Optimization.
Details on Dr. Bhattacharyya can be viewed from:
https://sites.google.com/site/rupakmath