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.

Conflict Resolutions for Trajectory Co-ordination of Multiple Mobile Robots

Abstract: Prioritized planning approaches are commonly used for finding coordinated trajectories for multiple robots so as to prevent collision among multiple robots. Classical methods for the same fail to solve the problem instance when there are conflict situations. In this paper, we analyze such conflict situations and offer heuristic solutions for the multiple robots to reach their destination or goal positions. The contributions in this paper are: (1) Identification of conflict situations where classical prioritized path planning methods failed to provide deterministic solution. (2) Propose heuristic solutions for overcoming the conflict situations.

Backup manager — An Android application for storing messages and apps information online

Abstract: Essential communication in this fast moving world is always been a major concern. Although we have messengers like gtalk, Facebook, WhatsApp who are a good source of circulating messages but they kind of deviates us from the imperative purpose of communication. There has been no services available which allows us to store important messages and apps information to cloud rather than storing it to phone itself so we can recover them anytime whenever our cell is not with us or gets damage or any mishap. The application we developed provide the functionality to store messages to cloud and anyone anytime can retrieve those messages, provided he/she knows the key. Also, the user can store the apps information installed in device and retrieve them whenever there is a need. Additionally, it can also be effective in case of system failure. This would create a new revolution in smartphone era, where no one will lose his apps and reduce the time of cost related to circulating a particular message among many. This application is highly beneficial in academic institutions and private organization to propagate a message to vast number of people. Message reaches only to those who are actually interested. The main feature of this app is that it doesn't require any sort of registration.

Human-Machine Interface System for pre-Diagnosis of Diseasesusing Machine Learning

Abstract: The rapid growth of applications of latest information technology into the field of medical sciences have founded the idea to develop such a platform through which pre-diagnosis of diseases could be easy, efficient and less time consuming. This paper talks about two frameworks designed using machine learning algorithms such as ANN, SVM and Decision Tree Induction to develop the models through which a number of diseases can be pre-diagnosed simultaneously with the analysis of symptoms initially recorded in the patient's body. These symptoms and physical readings have been taken as inputs to produce the output i.e. the predicted disease. The most important factors contributing for multiple disease prediction were determined such as age, sex, body temperature, blood pressure and symptoms like nausea, vomiting and fever. Data sets were collected from different hospitals in India during this research. All the models used were able to perform with an accuracy above 85%.

Structure stability analysis of Ninn = 2 - 22 using nature inspired algorithms: a performance study

Abstract: Using nature inspired algorithms NIAs with plane-wave self-consistent field PWSCF, density functional theory DFT and pseudo-potentials, the minimisation of potential energy and relative stability for Ni

Data-Driven Stillbirth Prediction and Analysis of Risk Factors in Pregnancy

Abstract: One of the main issues in developing countries is the lack of policies for ensuring good public health conditions in rural areas. Maternal and child health care is one such area that has not improved in developing countries. Although child health has improved noticeably over the years, infant or under-5-mortality has not become any better. There remain major knowledge gaps in our understanding of how factors such as prenatal care, antenatal care, social and economic backgrounds, living conditions and lifestyle of pregnant women and their family members affect the pregnancy outcomes. Understanding such factors that affect the poor pregnancy outcome helps in formulating plans to prevent such issues and to treat them effectively. Determining health policies will be easier from a deeper analysis of such factors involved. This paper discusses some of the key machine learning techniques to predict the pregnancy outcome as a stillbirth or not and analyze some of the factors that majorly cause stillbirth.

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.