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

The medical field is very versatile field and one of the interested research areas for the scientist. It deals with many medical disease problems starting with the diagnosis of the disease, preventing from the disease and treatment for the disease. There are various types of medical disease and accordingly various types of treatment methods. In this paper we mostly concern about the diagnosis of the heart disease. Mainly two types of the diagnosis method are used one is manual and other is automatic diagnosis which consists of diagnosis of disease with the help of intelligent expert system. In this paper the modular neural network is used to diagnosis the heart disease. The attributes are divided and given to the two neural network models Backpropagation Neural Network (BPNN) and Radial Basis Function Neural Network (RBFNN) for training and testing. The two integration techniques are used two integrate the results and provide the final training accuracy and testing accuracy. The modular neural network with probabilistic product method gave an accuracy of 87.02% over training data and 85.88% over testing accuracy and with probabilistic product method gave an accuracy of 89.72% over training data and 84.70% over testing accuracy, which was experimentally determined to be better than monolithic neural networks.

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Use of Modular Neural Network for Heart Disease

Cyber physical systems like smart grid are largely migrating towards distributed control philosophy to achieve high reliability. The design of communication network between various sensors and controllers plays an important role in control of these systems. The design process involves examining a number of topological combinations, which increase exponentially with the number of nodes in the considered system. Moreover, for a practical system, the different characteristics and availability of various physical and communication resources in the network pose multiple constraints on this design. In this work, a generalised constraint-based sensor controller connection design methodology has been developed, which effectively reduces the number of combinations, to design more stable cyber-physical controllers. To handle variations in multiple parameters in physical and communication domain, different controllers have been developed for different operating conditions that are scheduled as per requirement. The methodology has been shown to stabilise bus voltages in a smart grid scenario under variations in load, communication delays and loss of communication links.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-cps.2017.0024

Handling multi‐parametric variations in distributed control of cyber‐physical energy systems through optimal communication design

Recently health care researchers are working a lot on outcome prediction on Intensive Care Unit (ICU) and trauma. Outcome prediction in intensive care is a difficult process. Accurate synthesis of quality data and application of prior experience to the analysis is required to solve it. In this paper we will review some of the recent advancements in the mortality prediction of ICU patients using machine learning techniques. Mainly the research covered in this survey will be on predicting readmission in Intensive care unit, mortality rate after ICU discharge and life expectancy rate for 5 years. In order to analyse how much a patient will survive in next five years, this expectancy rate is used. These predictions are very useful because using these results doctors can take decision about extending the stay of patient in hospital and also helps in taking decision about the particular treatment needed. An extensive survey of the related research is presented with a stress on the major novelties of their work. At last, the research gaps inferred after analyzing the previous works are highlighted along with the future prospects in the field of research.

Mortality Prediction of ICU patients using Machine Leaning: A survey

Robot path planning is about finding a collision free motion from one position to another. The wave front expansion is commonly used for path planning tasks and appreciated for its efficiency. However, this approach requires full wave expansion, which takes considerable amount of time and process, in large scale environments. This paper presents a new method for motion planning of mobile robots based on wave expansion approach which avoids full wave expansion. This method imposes a cost function, that focuses on some of the waves for expansion instead of trying to expand the entire waves, as socalled focused wave front expansion algorithm. The proposed approach has been tested through computer simulation, with a set of environments with different levels of complexity depending on the density of the obstacles. Finally, we compare the results of our proposed method to that of the existing wave front method.

A focused wave front algorithm for mobile robot path planning

Feature based Recognition Systems has been an area of intense research for long. The creation of a reliable, robust and sufficiently efficient recognition system has been tried using features from several sources including textual and image sources. Speech based sources have also been used for the creation of such a recognition system. However, variations caused due to differences in individual speaker characteristics, mood variations and inter-mingled noise disturbances make the realization of such a system very difficult. This paper proposes a recognition system for identification of the speaker, language and the words spoken. The system makes use of Adaptive Neuro-Fuzzy Inference paradigm for the same. First, the sampling frequency and the speech features are extracted from the speech database to form speech feature vectors. The features used are LPC, LPCC, RC, LAR, LSF and ARSCIN. The speech database is prepared using 25 speakers including male and female speakers. Five different speaking texts of different languages having same meaning are used to get the best speaker identification accuracy. The languages spoken by the speakers include English, Hindi, Punjabi, Sanskrit and Telugu. The Feature vectors, thus prepared, are fed to an Adaptive Neuro-Fuzzy Inference System for speaker, language and word recognition. The experimental results show the system to be amply efficient and successful in the recognition tasks involved.

Anupam Shukla

Papers

Use of Modular Neural Network for Heart Disease

Handling multi‐parametric variations in distributed control of cyber‐physical energy systems through optimal communication design

Mortality Prediction of ICU patients using Machine Leaning: A survey

A focused wave front algorithm for mobile robot path planning

Multilingual speaker recognition using ANFIS