Anupam Shukla

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.

Hybrid Intelligent Systems for Medical Diagnosis

Abstract: Bio-Medical Engineering is a rapidly growing field as a result of the need and rise of automation. This field calls for the collaboration between the people from the medical background and the engineers to develop intelligent systems for the various tasks in bio-medicals. These systems are used for the detection of the various diseases. These act as Clinical Decision Support Systems (CDSS) in order to assist the doctors in their task of identification of the presence of the diseases. They hence act as valuable tools for the doctors in the analysis of the diseases. This is especially important considering the work load over the doctors and the vast presence of the diseases. The increasing health consciousAbstrAct

Robot Path Planning using Dynamic Programming with Accelerating Nodes

Abstract: We solve the problem of robot path planning using Dynamic Programming (DP) designed to perform well in case of a sudden path blockage. A conventional DP algorithm works well for real time scenarios only when the update frequency is high i.e. changes can be readily propagated. In case updates are costly, for a sudden blockage the robot continues moving along the wrong path or stands stationary. We propose a modified DP that has nodes with additional processing (called accelerating nodes) to enable different segments of the map to become informed about the blockage rapidly. We further quickly compute an alternative path in case of a blockage. Experimental results verify that usage of accelerating nodes makes the robot follow optimal paths in dynamic environments.

Gender Identification in Human Gait Using Neural Network

Abstract: Biometrics is an advanced way of person recognition as it establishes more direct and explicit link with humans than passwords, since biometrics use measurable physiological and behavioural features of a person. In this paper gender recognition from human gait in image sequence have been successfully investigated. Silhouette of 15 males and 15 females from the database collected from CASIR site have been extracted. The computer vision based gender classification is then carried out on the basis of standard deviation, centre of mass and height from head to toe using Feed Forward Back Propagation Network with TRAINLM as training functions, LEARNGD as adaptation learning function and MSEREG as performance function. Experimental results demonstrate that the present gender recognition system achieve recognition performance of 93.4%, 94.6%, and 94.7% with 2 layers/20 neurons, 3 layers/30 neurons and 4 layers/30 neurons respectively. When the performance function is replaced with SSE the recognition performance is increased by 2%, 2.4% and 3% respectively for 2 layers/20 neurons, 3 layers/30 neurons and 4 layers/30 neurons.The above study indicates that Gait based gender recognition is one of the best reliable biometric technology that can be used to monitor people without their cooperation. Controlled environments such as banks, military installations and even airports need to quickly detect threats and provide differing levels of access to different user groups.

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

Abstract: 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.

Multi robot exploration using a modified a algorithm

Abstract: Exploration of an unknown environment is one of the major applications of multi robot systems. A popular concept for the exploration problem is based on the notion of frontiers: the boundaries of the current map from where target points are allocated to multiple robots. Exploring an environment is then about entering into the unexplored area by moving towards the targets. To do so they must have an optimal path planning algorithm that chooses the shortest route with minimum energy consumption. Aiming at the problem, we discuss a modification to the well known A* algorithm that satisfies these requirements. Furthermore, we discuss improvements to the target allocation strategy, by pruning the frontier cells, because the computation burden for optimal allocation is increases with the number of frontier cells. The proposed approach has been tested with a set of environments with different levels of complexity depending on the density of the obstacles. All exploration paths generated were optimal in terms of smoothness and crossovers.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

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.

Coverage control for mobile sensing networks

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.

Artificial neural networks

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.