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

Bacteria Foraging Optimisation Algorithm is a collective behaviour-based meta-heuristics searching depending on the social influence of the bacteria co-agents in the search space of the problem. Th...

Discrete bacteria foraging optimization algorithm for graph based problems - a transition from continuous to discrete

There has been a considerable effort in the design of evolutionary systems for the automatic generation of neural networks. Symbiotic Adaptive Neuro Evolution (SANE) is a novel approach that carries co-evolution of neural networks at two levels of neuron and network. The SANE network is likely to face problems when the applied data set has high number of attributes or a high dimensionality. In this paper we build a modular neural network with probabilistic sum integration technique to solve this curse of dimensionality. Each module is a SANE network. The division of the problem involves the breaking up of the problem into sub-problems with different (may be overlapping) attributes. The algorithm was simulated for the Breast Cancer database from UCI machine learning repository. Simulation results show that the algorithm, keeping the dimensionality low, was able to effectively solve the problem.

/pdf/modular-symbiotic-adaptive-neuro-evolution-for-high-3ennfewooq.pdf

Modular symbiotic adaptive neuro evolution for high dimensionality classificatory problems

Gait is a biometric identification technology, which can identify people from distance, without user cooperation. This paper proposes a simple and efficient automatic gait recognition method taking frontal view silhouette of walking person using principal component analysis. Here for each image, background subtraction method is applied to extract the moving silhouette. These silhouette images are used to extract features using principal component analysis algorithm. Here principal component analysis method is applied to reduce the dimensionality of feature vectors. These reduce features vector represent the most relevant information of walking person, which are able to distinguish one people from others. Our result shows that taking frontal view image for recognition, gives good results. In our work the recognition rate is 97.50%.

Frontal view gait based recognition using PCA

To locate and find out the accurate position of a moving mobile sensor node is key component to establish a successful mobile wireless sensor network. In most cases its graphical representations were used to locate mobile node which is not acceptable when dealing with real time. We proposed an algorithm based on the RSSI and co-operative communication between nodes to find out the accurate localization of sensor node. The proposed algorithm increases efficiency in localization of the mobile sensor node in terms of time and Energy.

Energy efficient dynamic nearest node election for localizations of mobile node in wireless sensor networks

Epilepsy is a common chronic neurological disorder that is characterized by recurrent unprovoked seizures. About 50 million people worldwide have epilepsy at any one time. This paper presents an Intelligent Diagnostic System for Epilepsy using Artificial Neural Networks (ANNs). In this approach the feed-forward neural network has been trained using three ANN algorithms, the Back propagation algorithm (BPA,), the Radial Basis Function (RBF) and the Learning Vector Quantization (LVQ). The simulator has been developed using MATLAB and performance is compared by considering the metrics like accuracy of diagnosis, training time, number of neurons, number of epochs etc. The results obtained clearly shows that the presented methods have improved the inference procedures and are advantageous over the conventional architectures on both efficiency and accuracy.

Anupam Shukla

Papers

Discrete bacteria foraging optimization algorithm for graph based problems - a transition from continuous to discrete

Modular symbiotic adaptive neuro evolution for high dimensionality classificatory problems

Frontal view gait based recognition using PCA

Energy efficient dynamic nearest node election for localizations of mobile node in wireless sensor networks

Diagnosis of Epilepsy Disorders Using Artificial Neural Networks