Fuzzy C-means Model and Algorithm for Data Clustering

Pattern recognition has become a very important field over the last decade since automation and computerization in many systems has led to large amount of data being stored in the databases.  The primary intention of pattern recognition is to automatically assist humans in analyzing the vast amount of available data and extracting useful knowledge from it. Many algorithms have been developed for many applications, especially for static pattern recognition.  Since the information of these processes can be non-deterministic over the time period, fuzzy approach can be applied to deal with this. In this work, fuzzy approach for optimization techniques in the pattern recognition will be implemented. It will show a fuzzy model for data clustering and feature extraction that best suits for the process of pattern recognition when we deal with non-crisp data

Bifurcation Analysis for Metapopulation Models

Two models with four components of chain populations are considered. In the model, a prey population X is predated by individuals of a specialist predator population Y, and another prey population Z is predated by individuals of a generalist predator population U. This model is governed by a system of four nonlinear first order ordinary differential equations. To study the dynamics of the food chain model, the mentioned system of ordinary differential equations solved numerically. One of the biological parameters varied in a sufficiently large range and its effects on the dynamics of the system are observed.  Along the axis of the predating rate of the specialist predator, around four points  we meet chaos. At each time chaos precedes period doublings.

Out-of-stock problem: possible classification schemes

An out-of-stock (OOS) event is referred as one of the biggest supply-chain management problem concerning retailers, distributors and consumers. We present available PCG data and discuss how to determine the importance of some features (fields), their interconnections and compare them with standard data fields used in other publicly accessible studies and recommendations from Efficient Consumer Response (ECR). We propose several models and algorithms to predict and solve Out of stock problem and at the end the computational results of these models are presented

Optimization of Transport Problems with Fuzzy Coefficients

In this paper, we concentrate on three kinds of fuzzy linear programming problems: linear programming problems with only fuzzy technological coefficients, linear programming problems with fuzzy right-hand sides and linear programming problems in which both the right-hand side and the technological coefficients are fuzzy numbers. We consider here only the case of fuzzy numbers with linear membership functions. The symmetric method of Bellman and Zadeh [2] is used for a defuzzification of these problems. The crisp problems obtained after the defuzzification are non-linear and even non-convex in general. Finally, we give illustrative examples and their numerical solutions

A Stochastic Programming Approach for Multi-Period Portfolio Optimization

presented in this paper. The basic model involves Multi-Period decisions (portfolio optimization) and deals with the usual uncertainty of investment returns and future liabilities. Therefore, is it well suited to a stochastic programming approach. We consider the problem of rebalancing policy to accomplish some investment’s criteria. Transaction costs have also been a subject of concern in this paper. In particular, a large amount of transactions usually make asset price move in an unfavorable direction. Therefore, the first problem neglects transactions cost while the second does not

Classification of chromosomes using nearest neighbor classifier

This paper addresses automated classification of human chromosomes using k nearest neighbor classifier. k nearest neighbor classifier classifies objects according to the closest training sample in the feature space.  Various distance functions can be used in computation of how close the object is to the training sample. In this work various different distance functions are used to compare the performance of each. It was found that Euclidean distance function produces the best results

Authorship Categorization With Neural Network

This paper explores the use of neural networks in author classification. Also exploring the effect of stylometry is another aim of the research. Choosing the algorithm and descriptors are important issues in the research. In this paper methods for the multi-topic machine learning of an authorship attribution classifier were investigated using texts from novels as the data set. Artificial neural network is proposed to classify the texts of authors using a set of lexical descriptors and feed-forward neural network using back propagation. The result shows that Turkish authors Peyami Safa, Orhan Pamuk and Mustafa Necati Sepetcioglu’s two novels are successfully classified