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

Hospital readmissions are recognized as indicators of poor quality of care, such as inadequate discharge planning and care coordination. Moreover, most experts believe that many readmissions are unnecessary and avoidable. In the present paper, we design a Recurrent Neural Network model to predict whether a patient would be readmitted in the hospital and compared its accuracy with basic classifiers such as SVM, Random Forest and with Simple Neural Networks. RNN showed highest prediction power in all the models used and thus this can be used by hospitals to target high risk patients and prevent recurrent admissions.

Recurrent Neural Networks with Non-Sequential Data to Predict Hospital Readmission of Diabetic Patients

We construct a mixture of experts model for medical diagnosis. Each of the experts is a complex modular neural network. The first modularity clusters the entire input space into a set of modules. The second modularity divides the number of attributes. Each cluster is a neural network that solves the problem. The individual neural networks are evolved using genetic algorithms, which optimise the architecture along with the weights and biases. The complete system is used for the diagnosis of breast cancer. Experimental results show that the proposed system outperforms the traditional simple and hybrid approaches.

Diagnosis of breast cancer by modular evolutionary neural networks

Handwriting Recognition enables a person to scribble something on a piece of paper and then convert it into text. If we look into the practical reality there are enumerable styles in which a character may be written. These styles can be self combined to generate more styles. Even if a small child knows the basic styles a character can be written, he would be able to recognize characters written in styles intermediate between them or formed by their mixture. This motivates the use of Genetic Algorithms for the problem. In order to prove this, we made a pool of images of characters. We converted them to graphs. The graph of every character was intermixed to generate styles intermediate between the styles of parent character. Character recognition involved the matching of the graph generated from the unknown character image with the graphs generated by mixing. Using this method we received an accuracy of 98.44%.

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Offline Handwriting Recognition using Genetic Algorithm

The past few years have seen a lot of applications of Hybrid Soft Computing approaches that seem to have completely replaced the traditional uni-system approaches. The added abilities that come from the hybrid approaches motivate their use in every system. Bio-Medical Engineering is yet another field which has seen a major change in he past few years. We find various new approaches being applied to this field as well as many new models being proposed. At this juncture, we study the effectiveness of various new hybrid approaches in the field of Bio-medicals in this paper. PIMA Indian database has been used for this purpose from the UCI Machine Learning Repository. The basic aim is to compare the various hybrid approaches from the recent literature and compare their performances. We have chosen 3 major Hybrid Systems and standard Back Propagation Algorithm for this purpose. These are Adaptive Neuro Fuzzy Inference Systems, Ensembles and Evolutionary Artificial Neural Networks. We also try to explain the results from our theoretical understanding of the individual Hybrid Systems.

Comparative analysis of intelligent hybrid systems for detection of PIMA indian diabetes

Texture classification is an active area of research in the field of pattern recognition. Convolutional neural networks (CNNs) have a remarkable capability of recognizing patterns and are one of the most efficient deep learning techniques. But, finding the optimal values of the different hyperparameters of the CNN is a major challenge. Nature-inspired algorithms (NIAs) are the meta-heuristic algorithms well-known for their optimizing capability. Whale optimization algorithm (WOA) is a recent nature-inspired algorithm (NIA) that is inspired by the hunting behaviour of the humpback whales. In this paper, we propose a novel deep learning technique for texture recognition using a CNN optimized through WOA. We apply WOA at the two different levels in the CNN: In the convolutional layer (for optimizing the values of the filters), and in the fully-connected layer (for optimizing the values of the weights and biases). For examining the performance of our technique, we apply it to the following three benchmark texture datasets: Kylberg v1.0, Brodatz, and Outex_TC_00012. Our model performs better than the most of the existing methods for the Kylberg and the Outex_TC_00012 datasets and gives competitive results for the Brodatz dataset. It is evident from the results that our model has the potential for application in the field of texture recognition.

/pdf/texture-classification-using-convolutional-neural-network-469eghzlso.pdf

Anupam Shukla

Papers

Recurrent Neural Networks with Non-Sequential Data to Predict Hospital Readmission of Diabetic Patients

Diagnosis of breast cancer by modular evolutionary neural networks

Offline Handwriting Recognition using Genetic Algorithm

Comparative analysis of intelligent hybrid systems for detection of PIMA indian diabetes

Texture classification using convolutional neural network optimized with whale optimization algorithm