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

Breast cancer is the second leading cause of cancer deaths in women worldwide and occurs in nearly one out of eight women. In this paper we develop a hybrid intelligent system for diagnosis, prognosis and prediction for breast cancer using SANE (Symbiotic, Adaptive Neuro-evolution) and compare with ensemble ANN, modular neural network, fixed architecture evolutionary neural network (F-ENN) and Variable Architecture evolutionary neural network (V-ENN). While the monolithic neural and fuzzy systems have been extensively used for diagnosis, the individual limitations of the various models put a great threshold on prediction accuracies, which may be overcome with the use of SANE. The SANE system coevolves a population of neurons that cooperate to form a functioning neural network. Breast cancer database from the University of Wisconsin available at UCI Machine Learning Repository is used for conducting experimental work.

Breast cancer diagnostic system using Symbiotic Adaptive Neuro-evolution (SANE)

The main objective of this research work is to develop an expert system for the diagnosis and detection of Hepatitis and liver disorders based on various Artificial Neural Networks models. In this research work Artificial Neural Networks models like Back Propagation Algorithm, Probabilistic Neural Networks, Competitive learning Networks, Learning vector quantization and Elman Networks have been used for detection and diagnosis of Hepatitis and liver disorders. The various networks developed with the help of MATLAB. Required data has been chosen from trusty machine learning data base (UCI). This system in comparison with other traditional diagnostic systems is faster, more reliable and more accurate. One can use this system as a specialist assistant or for training medicine students.

Soft computing based expert system for Hepatitis and liver disorders

Expressions of Affect Alwin de Rooij, Joost Broekens and Maarten H. Lamers (2013). International Journal of Synthetic Emotions (pp. 1-31). www.igi-global.com/article/abstract-expressions-affect/77654?camid=4v1a Design of a Mobile Robot to Clean the External Walls of Oil Tanks Hernán González Acuña, Alfonso René Quintero Lara, Ricardo Ortiz Guerrero, Jairo de Jesús Montes Alvarez, Hernando González Acevedo and Elkin Yesid Veslin Diaz (2014). Robotics: Concepts, Methodologies, Tools, and Applications (pp. 743-753). www.igi-global.com/chapter/design-of-a-mobile-robot-to-clean-the-externalwalls-of-oil-tanks/84922?camid=4v1a

Optimization of Focused Wave Front Algorithm in Unknown Dynamic Environment for Multi-Robot Navigation

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) and Neuro-Fuzzy technique. In this approach the feed-forward neural network has been trained using Back propagation algorithm (BPA) and by Adaptive Neuro Fuzzy Inference System (ANFIS). First, all the data (from UCI machine learning repository) has been normalized so that the value of every attribute is between 0 and 1. Out of 265 instances, 200 instances have been used for training the system and 65 have been used for testing purposes. 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.

Intelligent System for the Diagnosis of Epilepsy

This study proposes a novel user-centric cyber-physical framework to achieve distributed demand response (DDR) in a distribution system where local schedulers (LS) present at individual buses program both local and non-local loads to reduce the maximum load within a sparse communication setting. A COnjoint Methodology for communication and controller DESign (COMDES) has been proposed to design the topology of the communication network while considering various cyber and physical properties of the smart grid like bandwidth, load profile and voltage controller stability margin. The designed communication network provides a unified pathway for exchanging multiple types of data among different points in the grid to consummate the advanced ideas of DDR and multi-input multi-output voltage control concurrently. Concepts of Lyapunov stability and linear matrix inequalities are employed to design optimisation frameworks for tuning the values of voltage controllers which, in turn, play a vital role in pooling the loads to be scheduled by the individual LS for execution. Established algorithms like earliest deadline first, least laxity first and dynamic rate priority are used to generate switching schedules for redistributed loads and achieve peak shaving. IEEE 4 bus and 14 bus models have been used to demonstrate the efficacy of the proposed framework.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-cps.2018.5021

Anupam Shukla

Papers

Breast cancer diagnostic system using Symbiotic Adaptive Neuro-evolution (SANE)

Soft computing based expert system for Hepatitis and liver disorders

Optimization of Focused Wave Front Algorithm in Unknown Dynamic Environment for Multi-Robot Navigation

Intelligent System for the Diagnosis of Epilepsy

Enabling cyber‐physical demand response in smart grids via conjoint communication and controller design