Delhi Technological University

About: Delhi Technological University is a education organization based out in New Delhi, India. It is known for research contribution in the topics: Computer science & Control theory. The organization has 4427 authors who have published 6761 publications receiving 71035 citations. The organization is also known as: Delhi College of Engineering & DTU.

Blockchain Technology for Cloud Storage: A Systematic Literature Review

Abstract: The demand for Blockchain innovation and the significance of its application has inspired ever-progressing exploration in various scientific and practical areas. Even though it is still in the initial testing stage, the blockchain is being viewed as a progressive solution to address present-day technology concerns, such as decentralization, identity, trust, character, ownership of data, and information-driven choices. Simultaneously, the world is facing an increase in the diversity and quantity of digital information produced by machines and users. While effectively looking for the ideal approach to storing and processing cloud data, the blockchain innovation provides significant inputs. This article reviews the application of blockchain technology for securing cloud storage.

Analysing the interaction of factors for resilient humanitarian supply chain

Abstract: Emergencies and disasters place an exceptional demand on the managerial skills of the humanitarian aid community. Most of the developing countries lack in such kind of resilience and effective huma...

An Optimal Fuzzy System for Edge Detection in Color Images Using Bacterial Foraging Algorithm

Abstract: This paper presents a fuzzy system for edge detection, using smallest univalue segment assimilating nucleus (USAN) principle and bacterial foraging algorithm (BFA). The proposed algorithm fuzzifies the USAN area obtained from the original image, using a USAN area histogram-based Gaussian membership function. A parametric fuzzy intensification operator (FINT) is proposed to enhance the weak edge information, which results in another fuzzy set. The fuzzy measures, i.e., fuzzy edge quality factor and sharpness factor, are defined on fuzzy sets. The BFA is used to optimize the parameters involved in the fuzzy membership function and the FINT. The fuzzy edge map is obtained using optimized parameters. The adaptive thresholding is used to defuzzify the fuzzy edge map to obtain a binary edge map. The experimental results are analyzed qualitatively and quantitatively. The quantitative measures, i.e., Pratt's figure of merit, Cohen’ Kappa, Shannon's entropy, and edge strength similarity-based edge quality metric, are used. The quantitative results are statistically analyzed using t-test. The proposed algorithm outperforms many of the traditional and state-of-the-art edge detectors.