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.

Intelligent Biometric System using PCA and R-LDA

Abstract: The paper presents a novel biometric authentication approach using Principal Component Analysis (PCA), Regularized-Linear Discriminant Analysis (R-LDA) and supervised neural networks. Low dimensional feature vectors of human face images are required to drive neural networks effectively. After histogram equalization process each image is presented to PCA or R-LDA for normalization and dimension reduction. The preprocessing steps of PCA or R-LDA produce Low dimensional feature vectors appropriate for training. Neural network has a great deal of nerve cell and can accomplish parallel distributing operation. Back Propagation (BP), Radial Basis Function(RBF) & Learning Vector Quantization (LVQ) are used as classifiers. The analysis of obtained results shown that R-LDA preprocessed feature vectors driven by supervised neural networks are having better recognition performance than PCA. While among supervised neural networks RBF gave most matched output during testing.

Analysis & modeling multi-breeded Mean-Minded ant colony optimization of agent based Road Vehicle Routing Management

Abstract: In this paper Road Vehicle Routing Management is being analyzed and modeled considering multi-parameter scheme and a new modified Mean-Minded ant colony optimization (ACO) heuristic is used to optimized the different options that several vehicle system can avail to reach its destination The model has taken care so that the busy roads are avoided and congestion never arises The aim of this work is to uniformly distribute the traffic and the movement of vehicles through some selected points is enumerated to see the distribution of vehicles in all paths Some modification of ant-colony optimization algorithm is made and instead of running one breed of ants, here multi breeds are being initialized to demarcate multi - objective and multi - capacitive vehicles The pheromone density no longer depends on the number of ants, but is actually a function of the parameters which it is seeking, instead of the traditional pheromone trail function used So in a nutshell the pheromone evaporation functions will a different one and evaporation criteria will be how much the ant is happy while passing through that road Analogy can be derived as a road with scattered food of different type and several types of ants are passing, and each time they see food of their liking they eat them and spread pheromone to attract more insects of its types, however that eaten food is refilled and the supply will never end The results obtained showed that the ACO has been successful up to a certain extent in channeling the traffic in various routes of the system irrespective of its kind and considering the road network as a dynamic system with varying parameters, the vehicle distribution has been near uniform except fluctuations arising due to dynamicity error

A Novel Approach to Classificatory Problem using Grammatical Evolution based Hybrid Algorithm

Abstract: Numerous problems that where intelligent systems find application are classificatory in nature. These include Face Recognition, Speaker Recognition, Word Recognition, etc. In this paper we propose a new model for these classificatory problems. This model is based on the Grammatical Evolution which is an Evolutionary Algorithm that uses chromosomes as a set of instructions over a predefined grammar. The model that we propose here is a type of fuzzy inference system. Rules are in form of a collection of points representing every class. The separation between the unknown input and these representative points determines the degree of belongingness of the unknown input to the specific class being considered. Multiple contributions from same classes are simply added together. The training data set is used for the purpose of generating the initial set of configurations of this fuzzy model. The fuzzy functions are parameterized by adding fuzzy parameters, like any neuro-fuzzy model. These parameters are trained by a validation data set using a training algorithm. The performance of the system over training and validation data set serve as the fitness function. Variable mutation rate is applied. We tested the effectiveness of the algorithm over the picture learning problem and received better results than numerous commonly used algorithms. General Terms Algorithms

A novel approach of speaker authentication by fusion of speech and image features using Artificial Neural Networks

Abstract: This paper presents a decision fusion technique for a bimodal biometric verification system that makes use of facial and speech biometrics. This report considers multimodal biometric systems and their applicability to access control, authentication and security applications. We have simulated three Artificial Neural Network (ANN) models: firstly, speaker identification by speech parameters, secondly person identification by image parameters and finally the person authentication by fusion of speech and image feature. All the three ANN models are trained by Back-propagation algorithm.

Sign Language Recognition Using ResNet50 Deep Neural Network Architecture

Abstract: Communication is a barrier between the deaf-mute community and the rest of the society. Sign Language is used for communication among such people who cannot speak and listen. The automation of sign language recognition has gained researchers attention in the last few years. Many complex and costly hardware systems have already been developed to assist the purpose. However, we propose to use deep learning approach for automated sign language recognition. We devised a novel 2-level ResNet50 based Deep Neural Network Architecture to classify fingerspelled words. The dataset used is the standard American Sign Language Hand gesture dataset by [1]. The dataset was first augmented using various augmentation techniques. In our 2-level ResNet50 based approach the Level 1 model classifies the input image into one of the 4 sets. After an image is classified into one of the sets it is provided as an input to the corresponding second level model for predicting the actual class of the image. Our approach yields an accuracy of 99.03% on 12,048 test images.

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.