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

Challenge of finding the shortest route visiting each member of a collection of locations and returning to starting point is an NP-hard problem. It is also known as Traveling salesman problem, TSP is specific problem of combinatorial optimization studied in computer science and mathematical applications. In our work we present a hybrid version of Evolutionary algorithm to solve TSP problem. In this method we extend Genetic Algorithm with Artificial Bee Colony operators i:e Employed Bees and Onlooker Bees to improve the solution space named as Real Genetic Bee Colony Algorithm (RGBCA). Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution. Artificial Bee Colony (ABC) is an optimization algorithm based on the intelligent behavior of honey bee swarm. In the proposed method we extend GA with two operators of ABC for local search strategy. In this hybrid procedure (RGBCA), the exploitation process in the ABC algorithm improves classical Genetic Algorithm. The experimental results show that compared to original GA, our GBCA model can reach broader domains in the search space and show improvements in both precision and computational time.

RGBCA-genetic bee colony algorithm for travelling salesman problem

There are two major challenges in mobile sink based wireless sensor networks (WSNs) concerning to the query-driven scenarios, first, dissemination of queries to their respective region of interests (RoIs), and second, routing the data towards mobile sink. Due to sink mobility, routing of data packets to the sink becomes difficult because sink's query injection location and data collection location (current location of sink) may not be the same. Moreover, advertising mobile sink's location by flooding, introduces extensive burden on sensor nodes. In this paper, we propose a virtual ring infrastructure based query-driven ring routing protocol (QRRP) to reduce the overhead of updating mobile sink location information as well as routing the data towards current location of the sink. QRRP takes the advantage of proposed angle based routing to route the queries from mobile sink to their respective RoIs, and data from sensor nodes to the sink. Simulation results on the proposed approach show that energy consumption and data delivery delay are significantly reduced as compared to state-of-the-art mechanisms.

QRRP: A Query-driven Ring Routing Protocol for Mobile Sink based Wireless Sensor Networks

Bat Algorithm (BA) has recently emerged as an efficient nature inspired meta-heuristics due to its added parameters and searching features. In this paper modified versions of discrete bat algorithm is being proposed for the first time which will suit the discrete domain problems. Here the BA has utilized the famous three variable dependent Weibull Cumulative Distribution Function as Weibull Coded Binary Bat Algorithm (WCBBA), another as Real Bat Algorithm (RBA) and the third as the hybrid of the two, for search process with its modeling according to a road network management system where it is being tried to optimize the travel route and produce a vehicle load balancing structure of the network through optimized path establishment. In bat algorithm apart from the search criteria where the virtual bats move, they also utilize their Echolocation property for further investigation of the search space for prey. This makes the heuristic more probabilistic, dynamic and adaptive and the bats can reach a better solution through continuous analysis and exploration. Here the bat algorithm is modeled to handle a multi-objective optimization model where each bat tries to optimize its own route criteria or rather tries to find its liking prey. The enhanced search criteria of bats helps in reducing the number of bats for the search process, but as the Echolocation process spreads out it increases the complexity of the search process that is parallelism is traded off with complexity. The results show that the algorithm has potential for better results and has been compared with the converging rate of Ant Colony Optimization (ACO) & Intelligent Water Drops (IWD) algorithms.

Adaptive a Discrete Real Bat Algorithms for Route Search Optimization of Graph Based Road Network

Following the nature and its processes has been proved to be very fruitful when it comes to tackling the difficult hardships and making life easy Yet again the nature and its processes has been proven to be worthy of following, but this time the discrete family is being facilitated and another member is added to the bio-inspired computing family A new biological phenomenon following meta-heuristics called Green Heron Optimization Algorithm (GHOA) is being introduced for the first time which acquired its potential and habit from an intelligent bird called Green Heron whose diligence, skills, perception analysis capability and procedure for food acquisition has overwhelmed many zoologists This natural skillset of the bird has been transferred into operations which readily favor the graph based and discrete combinatorial optimization problems, both unconstrained and constraint though the latter requires safe guard and validation check so that the generated solutions are acceptable With proper modifications and modeling it can also be utilized for other wide variety of real world problems and can even optimize benchmark equations In this work we have mainly concentrated on the algorithm introduction with establishment, illustration with minute details of the steps and performance validation of the algorithm for a wide range of dimensions of the Travelling Salesman Problem combinatorial optimization problem datasets to clearly validate its scalability performance and also on a road network for optimized graph based path planning The result of the simulation clearly stated its capability for combination generation through randomization and converging global optimization and thus has contributed another important member of the bio-inspired computation family

New Bio-inspired Meta-Heuristics - Green Herons Optimization Algorithm - for Optimization of Travelling Salesman Problem and Road Network

Biometric authentication techniques such as lips, face, and eyes are more reliable and efficient than conventional authentication techniques such as password authentication, token, cards, personal identification number, etc. In this research paper, the emphasis has been laid on the speaker identification based on lip features. In this study, we have presented a detailed comparative analysis for speaker identification by using lip features, Principal Component Analysis (PCA), and neural network classifiers. PCA has been used for feature extraction from the six geometric lip features which are height of the outer corners of the mouth, width of the outer corners of the mouth, height of the inner corners of the mouth, width of the inner corners of the mouth, height of the upper lip, and height of the lower lip. These features are then used for training of the network by using different neural network classifiers such as Back Propagation (BP), Radial Basis Function (RBF) and Learning Vector Quantization (LVQ). These approaches are incorporated on "TULIPS1 database, (Movellan, 1995)" which is a small audiovisual database of 12 subjects saying the first 4 digits in English. After the detailed analysis and evaluation a maximum of 91.07% accuracy in speaker recognition is obtained using PCA and RBF. Speaker identification has a wide range of applications such as Audio Processing, Medical data, Finance, Array processing, etc.

Anupam Shukla

Papers

RGBCA-genetic bee colony algorithm for travelling salesman problem

QRRP: A Query-driven Ring Routing Protocol for Mobile Sink based Wireless Sensor Networks

Adaptive a Discrete Real Bat Algorithms for Route Search Optimization of Graph Based Road Network

New Bio-inspired Meta-Heuristics - Green Herons Optimization Algorithm - for Optimization of Travelling Salesman Problem and Road Network

Expert System for Speaker Identification Using Lip Features with PCA