Chandigarh University

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

An experimental investigation of heat transfer characteristics of water based Al2O3 nanofluid operated shell and tube heat exchanger with air bubble injection technique

Abstract: The thermal performance of shell and tube heat exchangers has been enhanced with the use of different techniques. Air bubble injection is one such promising and inexpensive technique that enhances the heat transfer characteristics inside shell and tube heat exchanger by creating turbulence in the flowing fluid. In this paper, experimental study on heat transfer characteristics of shell and tube heat exchanger was done with the injection of air bubbles at the tube inlet and throughout the tube with water based Al 2 O 3 nanofluids i.e. (0.1%v/v and 0.2%v/v). The outcomes obtained for both the concentrations at two distinct injection points were compared with the case when air bubbles were not injected. The outcomes revealed that the heat transfer characteristics enhanced with nanoparticles volumetric concentration and the air bubble injection. The case where air bubbles were injected throughout the tube gave maximum enhancement followed by the cases of injection of air bubbles at the tube inlet and no air bubble injection. Besides this, water based Al 2 O 3 nanofluid with 0.2%v/v of Al 2 O 3 nanoparticles gave more enhancement than Al 2 O 3 nanofluid with 0.1%v/v of Al 2 O 3 nanoparticles as the enhancement in the heat transfer characteristics is directly proportional to the volumetric concentration of nanoparticles in the base fluid. The heat transfer rate showed an enhancement of about 25-40% and dimensionless exergy loss showed an enhancement of about 33-43% when air bubbles were injected throughout the tube. Moreover, increment in the heat transfer characteristics was also found due to increase in the temperature of the hot fluid keeping the flow rate of both the heat transfer fluids constant.

Automatic segmentation and area calculation of optic disc in ophthalmic images

Abstract: The diseases related to eye such as glaucoma, retinopathy etc. are mostly detected at the later stages when there is very little scope of treatment. This is because the symptoms presented by these diseases are not significant until the disease at some advanced level. So there is a need of regular and less time consuming diagnosis of the eye. The diagnosis can be done by observing the shape and other characteristics of the different parts of the retinal image; optic disc is the important one. This paper presents a technique to automatically segment the optic disc from retinal images of any size. This technique preserves the shape of the disc after carefully removing the blood vessels. Also, the area of the optic disc is calculated.

Cardiac Diagnostic Feature and Demographic Identification (CDF-DI): An IoT Enabled Healthcare Framework Using Machine Learning.

Abstract: The incidence of cardiovascular diseases and cardiovascular burden (the number of deaths) are continuously rising worldwide. Heart disease leads to heart failure (HF) in affected patients. Therefore any additional aid to current medical support systems is crucial for the clinician to forecast the survival status for these patients. The collaborative use of machine learning and IoT devices has become very important in today’s intelligent healthcare systems. This paper presents a Public Key Infrastructure (PKI) secured IoT enabled framework entitled Cardiac Diagnostic Feature and Demographic Identification (CDF-DI) systems with significant Models that recognize several Cardiac disease features related to HF. To achieve this goal, we used statistical and machine learning techniques to analyze the Cardiac secondary dataset. The Elevated Serum Creatinine (SC) levels and Serum Sodium (SS) could cause renal problems and are well established in HF patients. The Mann Whitney U test found that SC and SS levels affected the survival status of patients (p 0.05). The Cox regression model also found a significant association of age group with the survival status using follow-up months. Furthermore, the present study also proposed important features of Cardiac disease that identified the patient’s survival status, age group, and gender. The most prominent algorithm was the Random Forest (RF) suggesting five key features to determine the survival status of the patient with an accuracy of 96%: Follow-up months, SC, Ejection Fraction (EF), Creatinine Phosphokinase (CPK), and platelets. Additionally, the RF selected five prominent features (smoking habits, CPK, platelets, follow-up month, and SC) in recognition of gender with an accuracy of 94%. Moreover, the five vital features such as CPK, SC, follow-up month, platelets, and EF were found to be significant predictors for the patient’s age group with an accuracy of 96%. The Kaplan Meier plot revealed that mortality was high in the extremely old age group (χ2 (1) = 8.565). The recommended features have possible effects on clinical practice and would be supportive aid to the existing medical support system to identify the possibility of the survival status of the heart patient. The doctor should primarily concentrate on the follow-up month, SC, EF, CPK, and platelet count for the patient’s survival in the situation.

Nature-inspired optimization algorithms for different computing systems: novel perspective and systematic review

Abstract: Nature inspired algorithm plays a very vibrant role in solving the different optimization problems these days. The fundamental attitude of naturalistic approaches is to boost the competence, improvement, proficiency, success in the task except from it to help in underrating the energy use, cost, size. Several computing techniques are taking the benefits from nature inspired algorithms for solving their problems related to load balancing, scheduling and many others. These algorithms have come up with lots of improvements in the results. The aim of this analysis is to make efforts in the betterment in different areas of computing and help in solving various problems related to load balancing, scheduling and energy efficiency. The structure of the paper includes an introduction, contribution to the work, background study, which includes the role of nature inspired techniques in a different computing environment, research challenges and its applications. The sustainable goal and objective of the article is to perform the energy efficiency, load balancing and scheduling on different computing systems which include grid, cloud, distributed, fog and edge computing by using various nature inspired algorithms. This comprehensive study gives the awareness and valuable provision for the researchers in this area by providing a thorough study of different computing techniques in different research fields.