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.

Evolutionary Neural and Fuzzy Systems

Abstract: Artificial Neural Networks are valuable tools for problem of machine learning and problem solving. A major problem in the use of neural networks is that the architecture needs to be fixed. Further the training algorithm is needed to fix the various parameters. The training algorithm may many times give a sub-optimal performance by getting trapped in local minima. In this chapter we make use of the optimization powers of the evolutionary algorithms and use them for the construction of the neural network. We would first present the application of evolutionary algorithms in setting the weights and biases of the neural networks. We later make use of evolutionary algorithms for fixing the architecture as well along with the weights and biases. Here we would describe a connectionist approach where the optimal connections evolve. We would also describe an incremental evolution technique in the same problem. We then make use of Grammatical Evolution for evolving the neural networks. At the end we give a similar treatment to the Fuzzy Systems as well.

An energy efficient platform for mobile robot

Abstract: Exploration of an unknown environment by autonomous mobile robot is a fundamental concern in mobile robotics. Today most of the mobile robots are powered by batteries so their energy and operation times are limited. Therefore, how to minimize energy consumption and obstacle avoidance becomes an important problem. Frontier-based method is known to be most efficient for robot exploration system. In this paper, we propose a novel energy efficient platform based on the notion of frontiers for mobile robot. The robot selects the next frontier to visit based on the robot's current direction and relative location of frontier cells. Our frontier selection method can reduce duplicate coverage and thus save energy and reduce the distance also. Distance to frontier are computed using energy efficient A* algorithm. We estimate the energy consumption and choose the most energy-efficient route to move to that frontier considering energy of stop and turnings. We tested the algorithm on various types of environment maps. The experiments were conducted assuming equal velocity for the robot during whole exploration. All paths generated were optimal in terms of energy consumption and turns. The robot was easily able to escape a variety of obstacles and reach the goal in an optimal manner.

Feature Engineering Combined with 1 D Convolutional Neural Network for Improved Mortality Prediction

Abstract: The intensive care units (ICUs) are responsible for generating a wealth of useful data in the form of Electronic Health Record (EHR). This data allows for the development of a prediction tool with perfect knowledge backing. We aimed to build a mortality prediction model on 2012 Physionet Challenge mortality prediction database of 4000 patients admitted in ICU. The challenges in the dataset, such as high dimensionality, imbalanced distribution, and missing values were tackled with analytical methods and tools via feature engineering and new variable construction. The objective of the research is to utilize the relations among the clinical variables and construct new variables which would establish the effectiveness of 1-Dimensional Convolutional Neural Network (1- D CNN) with constructed features. Its performance with the traditional machine learning algorithms like XGBoost classifier, Support Vector Machine (SVM), K-Neighbours Classifier (K-NN), and Random Forest Classifier (RF) is compared for Area Under Curve (AUC). The investigation reveals the best AUC of 0.848 using 1-D CNN model.

Accoustic Modeling for Development of Accented Indian English ASR

Abstract: This paper investigates Indian English from the point of view of a speech recognition problem. A novel approach towards building an Automated Speech Recognition System (ASR) for Indian English using PocketSphinx has been proposed. The system was trained with a database of English words spoken by Indians in three different accents using continuous as well as semi-continuous models. We have compared the performances in each case and the optimum case performance comes close to 98 % accurate. Based on this study, we tweaked the original PocketSphinx Android application in order to incorporate our results and present it as an Indian English-based SMS sending application. We are working further on this approach to identify ways of successfully training a speech recognition system to recognize a much wider variety of Indian accents with much more significant accuracy.

SSLLE: Semi-supervised Locally Linear Embedding based Localization Method for Indoor Wireless Networks.

Abstract: Due to vast applications of mobile devices and local area wireless networks, location based services are popularized and location information use has become important . The paper proposes a method based on Semisupervised Locally Linear Embedding for localization in indoor wireless networks. Previous methods for location estimation in indoor wireless networks require a large amount of labeled data for learning the radio map. However labeled instances are often difficult, expensive, or time consuming to obtain, as they require great efforts, meanwhile unlabeled data may be relatively easy to collect. So the use of semi-supervised learning is more feasible. In the experiment 101 access points (APs) have been deployed so the Received Signal Strength (RSS) vector received by the mobile station has large dimensions (i.e.101). First we have used Locally Linear Embedding, a dimensional reduction technique to reduce the dimensions of data, and then we have used semi-supervised learning algorithm to learn the radio map. The algorithm performs nonlinear mapping between the received signal strengths from nearby access points and the user’s location. It is shown that the proposed scheme is easy in training and implementation. Experimental results are presented to demonstrate the feasibility of the proposed SSLLE algorithm.

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.