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.

DDoS Attack Detection Through Digital Twin Technique in Metaverse

Abstract: A digital twin (DT) is an electronic replica of a real-world item. It is built on top of asset-specific data items and is often enhanced using semantic technologies and simulation environments. The DT lays the way for anything from routine monitoring to hands-off administration of a physical entity. With the development of the metaverse concept of DT gains importance. As it helps to manage the physical entity in the metaverse. Therefore, it is beneficial to use DT for the detection and mitigation of different types of cyber attacks. In this context, we use the concept of DT for the identification and detection of DDoS attacks in the IoT network. Our proposed approach uses the concept of support vector machine (SVM) learning technique for the identification and detection of DDoS attacks. In our proposed approach, the DT of physical routers is stored in the metaverse, which makes it secure against any type of physical attack. Our proposed approach detected the malicious packets with an accuracy of 93.25% accuracy.

Optimized Graph Search Using Multi-Level Graph Clustering

Abstract: Graphs find a variety of use in numerous domains especially because of their capability to model common problems. The social networking graphs that are used for social networking analysis, a feature given by various social networking sites are an example of this. Graphs can also be visualized in the search engines to carry search operations and provide results. Various searching algorithms have been developed for searching in graphs. In this paper we propose that the entire network graph be clustered. The larger graphs are clustered to make smaller graphs. These smaller graphs can again be clustered to further reduce the size of graph. The search is performed on the smallest graph to identify the general path, which may be further build up to actual nodes by working on the individual clusters involved. Since many searches are carried out on the same graph, clustering may be done once and the data may be used for multiple searches over the time. If the graph changes considerably, only then we may re-cluster the graph.

Clinical Decision suppor t system for fetal Deliver y using

Abstract: In the present work an attempt is made to develop a Clinical Decision support system (CDSS) using the pathological attributes to predict the fetal delivery to be done normal or by surgical procedure. The pathological tests like Blood Sugar (BR), Blood pressure (BP), Resistivity Index (RI) and systolic / Diastolic (S/P) ratio will be recorded at the time of delivery. All attributes lie within a specific range for normal patient. The database consists of the attributes for cases i.e. normal and surgical delivery. Soft computing technique namely Artificial Neural Networks are used for simulator. The attributes from dataset are used for training & testing of ANN models. Three models of ANN are trained using Back-Propagation Algorithm (BPA), Radial Basis F unction network (RBF N) and Learning Vector Quantization Network (LVQN). The designing factors have been changed to get the optimized model, which gives highest recognition score. The optimized models of BPA, RBF N, and LVQN gave accuracies of 93.75, 99.00, and 87.5 % respectively. Thus RBF N is the best network for mentioned problem. This system will assist doctor to take decision at the critical time of fetal delivery.

Fusion Methods in Biometrics

Abstract: The use of speech as a biometric gives limited accuracy in the problems of speaker recognition and verification. The need of better recognition scores has resulted in the fusion of speech with other biometric modalities. This chapter discusses the fusion of speech with face which gives a high recognition score at the same time making the system convenient to be used for the user. We discuss three distinct ways to carry out this fusion. The first method is by directly mixing the attributes. This method has problems of excessive dimensionality of the resultant system. Hence many attributes from both modalities need to be deleted. The other method we discuss is the application of modular neural networks with division of attributes. In this technique the various attributes are divided between the various modules. The results are combined by an integrator. The last method is the use of clustering based division of input space by the system.

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.