Chandigarh University

About: Chandigarh University is a education organization based out in Mohali, India. It is known for research contribution in the topics: Computer science & Chemistry. The organization has 1358 authors who have published 2104 publications receiving 10050 citations.

Machinability improvement in Inconel-718 by enhanced tribological and thermal environment using textured tool

Abstract: Machinability of Inconel-718 superalloy in conventional approach is poor—this fact necessitates advanced technological adoption such as improved surface topography over used cutting tool. Recently, the performance of textured tool has been investigated to explore its potential benefits in achieving favourability in machining of superalloy. In that context, the impact of tool texturing, cutting speed and machining time on some of the prominent machinability indices like cutting forces, tool wear, surface finish and chip morphology has been deliberately investigated. The performance comparison of non-textured and textured tool has been conducted at cutting speed of 80, 120 and 180 m min−1 and at successive increment of machining times up to 10 min. Moreover, the scanning electron microscope analysis of worn tool edges was carried out to comprehend the wear mechanism. Furthermore, the thermal analysis was done for dedicated textured tooling condition. Results revealed that the textured tool performs better to ensure lower tool wear (VB), reduced cutting forces (Fc), lower surface roughness (Ra) and acceptable form of chips. The spots of textured tool acted as fins to promote efficient heat transfer from cutting zone and reduced the effective chip–tool contact length to cause less friction.

Intent-Based Network for Data Dissemination in Software-Defined Vehicular Edge Computing

Abstract: With the surge in the demand for online services and multimedia applications, the traffic on the underlying network infrastructure has escalated (multi-folded) in recent years To meet the strict latency requirements, Software-defined Networking (SDN) provides flexible network control (and possible intelligence) that can act as an enabler for application-oriented service industry However, the crippling gap between the business needs and the network delivery potential necessitates the underlying network to constantly (and consistently) adapt, protect, and inform across all strands of the service-oriented landscape Intent-based network has emerged as a recent solution to the cover the above gap by capturing business intent and thereafter activating and assuring it networkwide Motivated from these facts, in this article, an Intent-based network control framework has been designed over the SDN architecture for data dissemination in the vehicular edge computing ecosystem In this framework, a tensor-based mechanism is used to reduce the dimensionality of the incoming elephant-like traffic and then classifying the specific-attribute data traffic according to the defined priority requirement of the underlying applications Here, the network policies are configured using the intent-based controller according to the application requirement and then forwarded to the SDN controller to enable intelligent data dissemination (through an optimal route) at the data plane Convolution Neural Network is used to train the flow table to allocate the route dynamically for the classified traffic queues The proposed framework has been evaluated through extensive simulations and the results supports the claims in terms of the quality of service requirements

Comparative Analysis on Load Balancing Techniques in Cloud Computing

Abstract: Background/Objectives: Cloud computing is an arena that is ruling the world of information technology. Every user has its own definition for this technology as per their use. This paper is properly discussed document that describes the complete evolution of cloud computing from its beginning. Findings: With the presence of vast literature in field of load balancing, it was found confusion for the new scholars to find the startup point for their research in this field. Therefore, an exhaustive comparison has been made for the superior understanding of cloud evolution through various proposed algorithms from the past many decades, which will make the researchers possible to analyze the existing scenarios and a better way out to overcome the unsolved queries. Application/Improvements: The assessments between the algorithms will help the new researchers to analyze and opt for the parameters those need much more concentration to meet the required targets for better outcomes in the field.

DLRS: Deep Learning-Based Recommender System for Smart Healthcare Ecosystem

Abstract: Nowadays, the conventional healthcare domain has witnessed a paradigm shift towards patient-driven healthcare 40 ecosystem In this direction, healthcare recommender systems provide ubiquitous healthcare services to the end users even on the move However, there are various challenges for the design of patient driven healthcare recommender systems Some of the major challenges are: a) handling huge amount of data generated by smart devices and sensors, b) dynamic network management for real-time data transmission, and c) lack of knowledge gathering and aggregation methods For these reasons, in this paper; DLRS: A Deep Learning based Recommender System using software defined networking (SDN) is designed for smart healthcare ecosystem DLSR works in the following phases: a) a tensor-based dimensionality reduction algorithm is proposed for removing unwanted dimensions in the acquired data, b) a decision tree-based classification scheme is presented for categorization of the patient queries on the basis of different diseases, and c) a convolutional neural network based system is designed for providing recommendations about the patient health On evaluation, the results obtained prove the superiority of the proposed scheme in contrast to existing competing schemes