Narula Institute of Technology

About: Narula Institute of Technology is a based out in . It is known for research contribution in the topics: Quantum dot cellular automaton & Cognitive radio. The organization has 288 authors who have published 490 publications receiving 2258 citations. The organization is also known as: NiT.

A Linear Time Series Analysis of Fetal Heart Rate to Detect the Variability: Measures Using Cardiotocography

Abstract: Real time analysis and interpretation of fetal heart rate (FHR) is the challenge posed to every clinician. Different algorithms had been developed, tried and subsequently incorporated into Cardiotocograph (CTG) machines for automated diagnosis. Feature extraction and accurate detection of baseline and its variability has been the focus of this chapter. Algorithms by Dawes and Redman and Ayres-de-Campos have been discussed in this chapter. The authors are pleased to propose an algorithm for extracting the variability of fetal heart. The algorithm’s accuracy and degree of agreement with clinician’s diagnosis had been established by various statistical methods. This algorithm has been compared with an algorithm proposed by Nidhal and the new algorithm is found to be better at detecting variability in both ante-partum and intra-partum period.

Measures of Linear and Nonlinear Interval-Valued Hexagonal Fuzzy Number

Abstract: In the view of significant exposure of multifarious interval-valued fuzzy numbers in neoteric studies, different measures of interval-valued generalized hexagonal fuzzy numbers (IVGHFN) associated ...

Crack identification in beam-type structural elements using a piezoelectric sensor

Abstract: A crack or any kind of damage induces a sharp discontinuity in the rotational displacement curve in a structure under load. However, if the beam is not loaded sufficiently or crack depth is very sm...

A BFS-Based Pruning Algorithm for Disease-Symptom Knowledge Graph Database

Abstract: Breadth-first search (BFS) is one of the most fundamental algorithms for searching a graph. In our previous work, a mobile-assisted diagnosis scheme has been proposed and we have designed a Disease-Symptom data model as a large knowledge base. In this work, we present the design and implementation of a pruning algorithm to capture a part of the large graph-based data model. Generally, to search a large graph, most of the graph queries are too long and very cumbersome to write and sometimes very difficult to implement. Pruning algorithm is one of the prominent solutions of this problem. It results a subgraph or forest for the desired input. Here, our proposed pruning algorithm is multi-source sequential BFS algorithm and it is demonstrated on Disease-Symptom graph database.