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

An important requirement for many novel location based services, is to determine the locations of people, equipment, animals, etc. The accuracy and response time of estimation are critical issues in location estimation system. Most of the location estimation system suffers with the problem of scalability and unavailability of all the access points at all the location for large site. In this paper, we have proposed a distributed semi-supervised location estimation method, which divide the location estimation system into subsystems. Our method partition the input signal space and output location space into clusters on the basis of visibility of access points at various locations of the site area. Each cluster of input signal space together with output location subspace is used to learn the association between Received Signal Strength fingerprint and their respective location in a subsystem. Previous methods for location estimation in indoor wireless networks require a large amount of labeled data for learning the radio map. However, labeled instances are often difficult, expensive, or time consuming to obtain, as they require great efforts, meanwhile unlabeled data may be relatively easy to collect. So, the use of semi-supervised learning is more feasible. On each subsystem at first, we use Locally Linear Embedding to reduce the dimensions of data, and then we use semi-supervised learning algorithm to learn the radio map. The algorithm performs nonlinear mapping between the received signal strengths from nearby access points and the user's location. It is shown that the proposed Distributed Semi-Supervised Locally Linear Embedding scheme has the advantage of robustness, scalability, useful in large site application and is easy in training and implementation. We have compared our results with Distributed Subtract on Negative Add on Positive (DSNAP) and benchmark method RADAR. Experimental results show that our method provide better results in terms of accuracy and response time in comparison to centralized systems, in which a single system is used for large site as well as with DSNAP and benchmark method RADAR.

RSS Fingerprints Based Distributed Semi-Supervised Locally Linear Embedding (DSSLLE) Location Estimation System for Indoor WLAN

In the present work an attempt is made to develop a Decision support system (DSS) 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 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 Back-Propagation Algorithm (BPA), Radial Basis Function Network (RBFN) 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 and ANFIS gave accuracies of 93.75, 99.00 and 99.50 % respectively. Thus ANFIS is the best network for mentioned problem. This system will assist doctor to take decision at the critical time of fetal delivery.

Decision Support System for Fetal Delivery Using Soft Computing Techniques

Breast cancer is one of the major causes of death in women which accounts one out of eight. As primary cause is still unknown, early detection increases better treatment and improves total recovery. We present some novel hybrid approaches for classification of breast cancer. Artificial Neural Network (ANN) which suffers credit assignment problem can be avoided by modular and evolutionary artificial neural network which achieves simple and small individual neural network. Ensemble of ANN is to obtain a more reliable and accurate ANN. Evolutionary Neural Network (ENN) is used for optimization of neural network learning and design. The best accuracies achieved for diagnosis are around 99% using breast cancer datasets.

Hybrid computing based intelligent system for breast cancer diagnosis

H.264/AVC, the latest video coding standard, outperforms the other standards by maximizing the video quality and minimizing the bit rate. To achieve better coding efficiency, H.264/AVC uses several techniques such as inter mode and intra mode prediction with variable size motion compensation, which adopts Rate Distortion Optimization RDO). This increases the computational complexity of the encoder especially for devices with lower processing capabilities such as mobile and other handheld devices. In this paper, we propose an algorithm to reduce the number of mode and sub mode evaluations in inter mode prediction. Experimental results show that this fast intra mode selection algorithm can lessen about 72% encoding time with little loss of bitrate and visual quality.

An Efficient Mode Selection Algorithm for H.264 Encoder for Application in Low Computational Power Devices

Target tracking and searching are the very important problems in robotics. It can be used in many variations like path planning where the objective is to reach up to target without collide with obstacle, or it may be used in the places where some source is needed to find. Here nature inspired PSO algorithm is used to solve this problem with the help of multi robot system. Use of multi robots will find the target fast. Here new concept clustering based distributing factors (CBDF) is introduced to scatter the robots in environment to search and track the target. This CBDF method divides the area into regions. Different robots use coordination to reach up to these targets. For the movement of robots, PSO is used because it can be considered as minimization problem with respect to minimize the path length. Here, four parameters move, time, coverage, and energy are calculated to reach up to target. At last, we are showing the results for both known and unknown target problem.

Anupam Shukla

Papers

RSS Fingerprints Based Distributed Semi-Supervised Locally Linear Embedding (DSSLLE) Location Estimation System for Indoor WLAN

Decision Support System for Fetal Delivery Using Soft Computing Techniques

Hybrid computing based intelligent system for breast cancer diagnosis

An Efficient Mode Selection Algorithm for H.264 Encoder for Application in Low Computational Power Devices

CBDF-Based Target Searching and Tracking Using Particle Swarm Optimization