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

In this paper we have continued the introduction and application of a new nature inspired meta-heuristics algorithm called Egyptian Vulture Optimization Algorithm (EVOA) which primarily favors combinatorial optimization problems and graph based problems. The algorithm is derived from the nature, behavior and key skills of the Egyptian Vultures for acquiring food for leading their livelihood. These spectacular, innovative and adaptive acts make Egyptian Vultures as one of the most intelligent of its kind among birds. The details of the bird’s habit and the mathematical modeling steps of the algorithm are illustrated demonstrating how the meta-heuristics can be applied on the route planning for a graph based road network depending on the multi-parametric optimization of distance (travel time) and waiting time. Due to the dynamically changing behavior of the waiting time for the various crossings of the network, the system is dynamic system and the best optimized path tend to change with time and will help in diverging the vehicle flow through the various routes of the road network. The road network problem is considered as a special case of Travelling Salesman Problem based combinatorial problem with changes and constraint imposed and also the steps of the algorithm is also changed to suit and quicken the solution finding process and imbibe the theory of chance and rejection subsequently. The results of application of the algorithm on the road network and its comparison with Ant Colony Optimization Algorithm & Intelligent Water Drops Algorithm show that the algorithm works well and provides the scope of utilization in similar kind of problems like path planning, scheduling, routing, and other constraint driven problems. EVOA is one of the very few algorithms which are readily applicable for discrete domain problems.

Road Traffic Management Using Egyptian Vulture Optimization Algorithm: A New Graph Agent-Based Optimization Meta-Heuristic Algorithm

In this paper a new approach for the prediction of breast cancer has been made by reducing the features of the data set using PCA principal component analysis technique and prediction results by simulating different models namely SANE Symbiotic, Adaptive Neuro-evolution, Modular neural network, Fixed architecture evolutionary neural network F-ENN, and Variable Architecture evolutionary neural network V-ENN. The dimensionality reduction of the inputs achieved by PCA technique to an extent of 33% and further different models of the soft computing technique simulated and tested based on efficiency to find the optimum model. The SANE model includes maximum number of connections per neuron as 24, evolutionary population size of 1000, maximum neurons in hidden layer as 12, SANE elite value of 200, mutation rate of 0.2, and number of generations as 100. The simulated results reflect that this is the best model for the prediction of the breast cancer disease among the other models considered in the experiment and it can effectively assist the doctors for taking the diagnosis results as its efficiency found to be 98.52% accuracy which is highest.

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Breast Cancer Data Prediction by Dimensionality Reduction Using PCA and Adaptive Neuro Evolution

Financial time series has been standard complex problem in the field of forecasting due to its non-linearity and high volatility. Though various neural networks such as backpropagation, radial basis, recurrent and evolutionary etc. can be used for time series forecasting, each of them suffer from some flaws. Performances are more varied for different time series with loss of generalization. Each of the method poses some pros and cons for it. In this paper, we use ensembles of neural networks to get better performance for the financial time series forecasting. For neural network ensemble four different modules has been used and results of them are finally integrated using integrator to get the final output. Gating has been used as integration techniques for the ensembles modules. Empirical results obtained from ensemble approach confirm the outperformance of forecast results than single module results.

Financial time series forecast using neural network ensembles

H.264/AVC, the newest international video coding standard achieves higher compression efficiency as compared to all other existing coding standard such as MPEG-4 and H.263. However this efficiency comes with a dramatic increase in computational complexity due to several advanced techniques, such as inter mode and intra mode prediction with variable size motion compensation. It adopts rate distortion optimization (RDO), while maximizing visual quality and minimizing the required bit rate. In this paper, we propose a fast inter mode selection algorithm. The aim is to reduce the number of modes in intra and inter mode prediction. Experimental results show that this algorithm reduces the total encoding time with little loss of bit rate and visual quality.

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A Fast Mode Selection Algorithm Using Texture Analysis for H.264/AVC

In the present work an attempt is made to develop an intelligent Decision support system (IDSS) using the pathological attributes to predict the fetal delivery to be done normal or by surgical procedure. The pathological tests like Blood Sugar (BR), Blood pressure (BP), Resistivity Index (RI) and systolic/ Diastolic (S/P) ratio will be recorded at the time of delivery. All attributes lie within a specific range for normal patient. The database consists of the attributes for cases 2 (i.e. normal and surgical delivery). Soft computing technique namely Artificial Neural Networks (ANN) are used for simulator. The attributes from dataset are used for training & testing of ANN models. Three models of ANN are trained using BackPropagation Algorithm (BPA), Radial Basis Function Network (RBFN), Learning Vector Quantization Network (LVQN) and one hybrid approach is Adaptive Neuro-Fuzzy Inference System (ANFIS). The designing factors have been changed to get the optimized model, which gives highest recognition score. The optimized models of BPA, RBFN, LVQN and ANFIS gave accuracies of 93.75, 99.00, 87.50 and 99.50% respectively. Hence in our present research the ANFIS is the model whom efficiency and result are best.The ANFIS is the best network for mentioned problem. This system will assist doctor to take decision at the critical time of fetal delivery. DOI: 10.4018/978-1-4666-2455-9.ch057

Anupam Shukla

Papers

Road Traffic Management Using Egyptian Vulture Optimization Algorithm: A New Graph Agent-Based Optimization Meta-Heuristic Algorithm

Breast Cancer Data Prediction by Dimensionality Reduction Using PCA and Adaptive Neuro Evolution

Financial time series forecast using neural network ensembles

A Fast Mode Selection Algorithm Using Texture Analysis for H.264/AVC

Intelligent Decision Support System for Fetal Delivery using Soft Computing Techniques