Author
Anupam Shukla
Other affiliations: Indian Institutes of Information Technology
Bio: Anupam Shukla is an academic researcher from Indian Institute of Information Technology and Management, Gwalior. The author has contributed to research in topics: Artificial neural network & Motion planning. The author has an hindex of 22, co-authored 215 publications receiving 1896 citations. Previous affiliations of Anupam Shukla include Indian Institutes of Information Technology.
Papers published on a yearly basis
Papers
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29 Aug 20181 citations
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01 Jan 2010
TL;DR: This chapter would present how the problem modeling capabilities of the fuzzy systems combines with the learning ability of the neural networks to create the Adaptive Neuro Fuzzy Inference Systems.
Abstract: The neural networks are excellent means of learning where training algorithms may be used for the tuning of the various parameters of the neural network. The fuzzy systems are extensively used for their fuzzy approach to problem modeling and solving. In this chapter we would present how the problem modeling capabilities of the fuzzy systems combines with the learning ability of the neural networks to create the Adaptive Neuro Fuzzy Inference Systems. We later see how these systems may be evolved using an evolutionary approach to make evolutionary neuro fuzzy systems. The other part of the chapter would focus upon the mechanism of fuzzy neural networks. These are neural networks that take fuzzy inputs and generate fuzzy outputs. Here we would transform the various neural computations into fuzzy arithmetic for problem solving. The neural networks are many times regarded as black boxes. We hence need specialized mechanisms to extract out rules from these networks for understanding and implementation. This would be discussed as the last part of the chapter.
1 citations
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01 Jan 2010
TL;DR: This chapter studies a few intelligent systems in the domain of music for the identification of genres as well as the recognition of artists using Genetic Algorithms and Neural Networks.
Abstract: Music is an exciting area where the computational intelligence has cast a deep impact. In this chapter we study a few intelligent systems in the domain of music. The vast volume of music available in various formats has necessitated the need for their automated classification. Here we discuss the system for the identification of genres as well as the recognition of artists. These systems have a variety of application in playlist generation, music suggestion, music fetching etc. The other part of the chapter would focus upon the composition of music. Here also we discuss a variety of methods using Genetic Algorithms and Neural Networks. The manual assistive design of Genetic Algorithms enables the automated composition of music as per human demand. The neural approach uses a series prediction phenomenon to compose music when some part of it is known. These systems enable good composition techniques which may be employed to assist human composers in their task.
1 citations
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01 Jan 2016
TL;DR: The system was comparatively evaluated using different ensemble integration methods for breast cancer diagnosis namely weighted averaging, product, minimum, maximum, poling and fuzzy integration and different neural network approaches including MLP neural network, Radial Basis Function Network, Learning Vector Quantization and Recurrent Neural Network.
Abstract: Development of efficient, prompt and robust systems that are intelligent enough to replace/reduce human supervision in medical diagnosis is one of the primary objectives that have driven advancements in research for long. One area where this need for intelligent automation has been most acutely felt is related to the diagnosis of breast cancer in women. Mammography is the most widely used test for screening and early diagnosis of breast cancer. However, it is error-prone and hence cannot be used reliably for effective diagnosis of the said disease. In this paper, we describe how the above said problem could be efficiently solved through Ensemble Approach. The use of the approach has bestowed the expert system with significantly simple and swift learning, smaller requirement for storage space during classification, faster classification with added possibility of incremental learning. The system was comparatively evaluated using different ensemble integration methods for breast cancer diagnosis namely weighted averaging, product, minimum, maximum, poling and fuzzy integration and different neural network approaches including MLP neural network, Radial Basis Function Network, Learning Vector Quantization and Recurrent Neural Network. Detailed experimental analysis with the system shows that the best performance in terms of accuracy and specificity measures is achieved while used maximum integration technique with Radial Bass Function Network while finest performance in terms of sensitivity is achieved when MLP neural network with Minimum integration is used.
1 citations
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01 Sep 2013TL;DR: The novelties presented in the paper may significantly provide a cost-effective solution to the problem of area exploration and finding a known object in an unknown environment by demonstrating an innovative approach of using the infrared sensors instead of high cost long range sensors and cameras.
Abstract: This paper presents a proposed set of the novel technique, methods, and algorithm for simultaneous path planning, area exploration, area retrieval, obstacle avoidance, object detection, and object retrieval autonomously by a multi-robot system. The proposed methods and algorithms are built considering the use of low cost infrared sensors with the ultimate function of efficiently exploring the given unknown area and simultaneously identifying desired objects by analyzing the physical characteristics of several of the objects that come across during exploration. In this paper, we have explained the scenario by building a coordinative multi-robot system consisting of two autonomously operated robots equipped with low-cost and low-range infrared sensors to perform the assigned task by analyzing some of the sudden changes in their environment. Along with identifying and retrieving the desired object, the proposed methodology also provide an inclusive analysis of the area being explored. The novelties presented in the paper may significantly provide a cost-effective solution to the problem of area exploration and finding a known object in an unknown environment by demonstrating an innovative approach of using the infrared sensors instead of high cost long range sensors and cameras. Additionally, the methodology provides a speedy and uncomplicated method of traversing a complicated arena while performing all the necessary and inter-related tasks of avoiding the obstacles, analyzing the area as well as objects, and reconstructing the area using all these information collected and interpreted for an unknown environment. The methods and algorithms proposed are simulated over a complex arena to depict the operations and manually tested over a physical environment which provided 78% correct results with respect to various complex parameters set randomly.
1 citations
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01 Jan 2004
TL;DR: Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance and describes numerous important application areas such as image based rendering and digital libraries.
Abstract: 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.
3,627 citations
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TL;DR: This paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies which are adaptive, distributed, asynchronous, and verifiably correct.
Abstract: 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.
2,198 citations
09 Mar 2012
TL;DR: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems as mentioned in this paper, and they have been widely used in computer vision applications.
Abstract: 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.
2,069 citations