Data Mining - Concepts and Techniques.

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

International Journal of Computer and Applications

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Genetic algorithms are widely used in the field of optimization. Schema theory forms the foundational basis for the success of genetic algorithms. Traditional genetic algorithms involve only a single mutation phase per iteration of the algorithm. In this paper, a novel concept of genetic algorithms involving two mutation steps per iteration is proposed. The purpose of adding a second mutation phase is to improve the explorative power of the genetic algorithms. All the possible cases regarding the working of the proposed variant of the genetic algorithms are explored. After a meticulous analysis of all these cases, three lemmas are proposed regarding the survival of a schema after the application of the dual mutation. Based on these three lemmas, a theorem is proved, and a mathematical expression representing the probability of survival of a schema after the application of the crossover and dual mutation is derived. This expression provides a new insight about the penetration of a schema for such scenario and improves our understanding of the functioning of this modified form of the genetic algorithm.

Mathematical analysis of schema survival for genetic algorithms having dual mutation

Diagnosis of diseases is well known problem in the medical field. Past research shows that medical database of disease can be train by using various neural network models. Many medical problems face the problem of curse of dimensionality due to the excessively large number of input attributes. Breast cancer is one such problem. We propose the use of modular neural network for effective diagnosis. In the proposed methodology four modules are made; each module gets half the problem attributes which are trained and tested by two neural network models, Back Propagation Neural Network (BPNN) and Radial Basis Function (RBFN). Integration is done using a probabilistic sum rule. The modular neural network gave an accuracy of 95.75% over training data and 98.22% over testing accuracy, which was experimentally determined to be better than monolithic neural networks.

Diagnosis of breast cancer by modular neural network

Mathematical analysis of the cumulative effect of novel ternary crossover operator and mutation on probability of survival of a schema

Various parameters affect the performance of Genetic Algorithms in terms of the accuracy of the optimal solution achieved and convergence rate. In this paper, effect of one such important parameter (elite count) on the behavior of Genetic Algorithms is meticulously analyzed, A standard benchmark function 'Rastrigin's Function' is used for the purpose of the study, and the results indicate that the extremely high values of elite count result in premature convergence on local minima, while low values of elite count result in much better solutions, near to the global optima.

Analysis of the Effect of Elite Count on the Behavior of Genetic Algorithms: A Perspective

In this paper we have applied Multi-Objective Intelligent Water Drops Algorithm (MO-IWDA) for optimized route determination of the vehicle through all the underutilized paths available in a road graph exploiting optimization of dynamic parameter based path planning for the vehicle users. Due to the tendency of all the vehicles to follow the same path and also the shortest distance being the implied choice, there occurs tremendous increase in waiting time and high congestion which leads to pollution and stress. Hence it becomes necessary to revise the traffic management system and with the emergence of advanced smart gadgets it is now possible to locally investigate the network information and provide the best decisions for the users so that there is not only enhancement of time factor but also there is optimal usage of fuel and power. IWDA has emerged as a successful graph based meta-heuristics and has been used in NP-hard combinatorial problems like travelling salesman problem, knapsack problems etc. The success of IWDA lies more on probabilistic exploration as there is time linked cooperation and communication between the droplets in terms of its velocity and carried sand. Here we have involved multiobjective optimization of distance and waiting time minimization and used non-weighted fitness function for decision making and evaluation of the best combinations. Here another problem has been resolved of whether there is requirement of keeping different sand quality for both distance and waiting time or the same sand can be dealt with both. A comparative study of the two schemes will clearly reveal the individuality of the IWDA in handling multi-parameter optimization or there is requirement of different types of sand types for each kind of optimization. Analysis of results shows that the optimization level depends on sand investigation criteria which are dependent on how the parameter scaling is done. For multi-objective optimization the relative ratio of scaling is the prime factor. However due to the introduction of the exploration and adaptive parameters the algorithm will sought out the explored path and lead to global optimization.

Anupam Shukla

Papers

Mathematical analysis of schema survival for genetic algorithms having dual mutation

Diagnosis of breast cancer by modular neural network

Mathematical analysis of the cumulative effect of novel ternary crossover operator and mutation on probability of survival of a schema

Analysis of the Effect of Elite Count on the Behavior of Genetic Algorithms: A Perspective

Multi-objective adaptive intelligent water drops algorithm for optimization & vehicle guidance in road graph network