MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

Engineering and Technology

The major source of noise pollution was an internal combustion engine. Henceforth, the design of the engine exhaust system was a tough challenge for automotive industries. The mufflers have been used to moderate the exhaust noise from the engine to the neighboring atmosphere. So in domestic as well as the industrial application, it is required to reduce noise levels to prevent human hazards. The performance of muffler is measured in terms of transmission loss. The transmission loss of reactive muffler is depending on its geometry. This article describes the effects of pores at the inlet pipe and outlet pipe of the reactive muffler on its performance capacity at a target frequency. It is found that the muffler performance is dependent on the number and position of pores. The single chamber and double chamber reactive mufflers are used for the study. The numerical analysis is performed by COMSOL Multiphysics software. The numerical analysis result of the different models of the muffler is compared with experimental analysis. The purpose of the study is to investigate the effect of pores in inlet and outlet pipe to maximize the TL at the target frequency of reactive muffler.

School of Mechanical Engineering

Noise and Vibrations worldwide

Analysis for effect of pores on acoustic performance of reactive muffler

For cancer prediction, the prognostic stage is the main factor that helps medical experts to decide the optimal treatment for a patient. Specialists study prognostic stage information from medical reports, often in an unstructured form, and take a larger review time. The main objective of this study is to suggest a generic clinical decision-unifying staging method to extract the most reliable prognostic stage information of breast cancer from medical records of various health institutions. Additional prognostic elements should be extracted from medical reports to identify the cancer stage for getting an exact measure of cancer and improving care quality. This study has collected 465 pathological and clinical reports of breast cancer sufferers from India’s reputed medical institutions. The unstructured records were found distinct from each institute. Anatomic and biologic factors are extracted from medical records using the natural language processing, machine learning and rule-based method for prognostic stage detection. This study has extracted anatomic stage, grade, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) from medical reports with high accuracy and predicted prognostic stage for both regions. The prognostic stage prediction’s average accuracy is found 92% and 82% in rural and urban areas, respectively. It was essential to combine biological and anatomical elements under a single prognostic staging method. A generic clinical decision-unifying staging method for prognostic stage detection with great accuracy in various institutions of different regional areas suggests that the proposed research improves the prognosis of breast cancer.

School of Computer Engineering & Technology

Med Biol Eng Comput 59

Information extraction for prognostic stage prediction from breast cancer medical records using NLP and ML

COVID-19 (SARS CoV-2) has already claimed more than 3.3 million lives worldwide. Many patients of COVID-19 are suffering from Mucormycosis. The use of technology helps to improve accuracy, saves time and energy to make precise decisions that will save lives of patients. Day by day new problems arise and IoT, Cloud Computing, Big Data, Machine Learning, Artificial Intelligence, etc. have been helping to analyze new problems more quickly and supports better design strategies. The sensor-based devices feeds the data to process, analyze and provide a solution. Machine Learning Algorithms are used to make precise decisions with the help of existing data repository with similar kinds of symptoms in patients, history of allergies, surgeries, and treatment. This proposed model will help to guide medical professionals to provide solutions to the problems arising in the treatment of COVID-19 and post-treatment diseases like Mucormycosis (also known as Zygomycosis).

i-manager’s Journal on Software Engineering,

SMART HEALTHCARE USING INTERNET OF THINGS (IoT) FOR REMOTE DIAGNOSIS OF COVID-19 PATIENTS

Retaining walls are structures that can be used as a temporary as well as permanent support in underground metro stations and it resist the lateral forces imposed by surcharge load, rock movement and water level. Stability failure of retaining wall has been worked out in sliding by considering the water level ,surcharge load at the front face of retaining walls. In these paper an attempt has been made to study the effect of different diameter secant pile with strut and ground anchor. In this study the researcher has taken two condition into account, the first condition is comparing 600mm diameter secant pile with strut and ground anchor and second condition is comparing 800mm diameter secant pile with strut and ground anchor. The analysis is carried out using finite element modelling on both the prescribed conditions and the result showed us that strut displacement is less than ground anchor.&nbsp;

School of Civil Engineering

World University root node

GIS Science journal

<a href="https://drive.google.com/file/d/1WYBlyrAxMR66oW6xTYjzO_6Vl26VTZkK/view?usp=sharing">Comparative Behaviour Of Retaining Wall With Strut And Ground Anchor Used In Underground Metro Station</a>

Synthesis of Bioethanol and organic manure from nonedible deoiled cake.

School of Petroleum Engineering

Distributed Generation and Alternate  Energy Journal

The Study of Biofuel Generation and Charecteristics Analysis from Jatropha, Karanja and Styrax Officinalis L Seed Cakes

Human actions consist of a sequence of similar patterns which are difficult to classify using traditional image processing algorithms. Video Analytics is a major research area that adds brains to eyes which means analytics to the camera. It monitors the video contents and extracts intelligent information from it. The human action analysis and its detection is a challenging task. The proposed method focuses on detections of normal human activity using Long-Short Term Memory (LSTM) as a deep neural architecture. The preprocessing technique of redundant frame detection along with pretrained Convolutional Neural Network (CNN) is implemented for classifying the activities efficiently. Transfer learning approach is used followed by Long-Short Term Memory (LSTM) network to generate hybrid framework which further enhances the activity detection. Proposed method shows improvement in accuracy as compared to reference method. This method can be further implemented for on edge processing in embedded platforms for real time applications.

School of Electronics & Communication Engineering

International Journal of Computing and Digital Systems

Intelligent video analytics for human action detection: a deep learning approach with transfer learning

In Delhi, according to CPCB, solid waste generation is predicted to be 43.5 million metric tonnes/year. In that, the waste generated from the construction industry is considered to be about 13.6 million metric tonnes. These solid waste materials formed by demolished structures are discarded by dumping them as landfills or disposal areas. Relinquish of this solid wastes is causing scarcity of disposal areas in urban areas. Hence, it's essential to start re-using and recycling waste demolition concrete to save lots of money, energy, and the environment. Employing recycled coarse aggregates assists in sustainable and economic growth through recycling. Durability parameters of Recycled Aggregate Concrete (RAC) are considered in this research to find out the optimum quantity of recycled aggregates which acts as a replacement for natural aggregates and finding out the mathematical equation by Analytical Modelling. Fly Ash here is employed as a replacement for cement without affecting its strength of concrete. This experimental investigation includes various durability tests viz. Water Absorption test (WAT), Abrasion Test, and Compressive strength. Recycled aggregate concrete of M50 and M60 grades with 0, 20, 40, and 60 % substitute of natural aggregate with recycled concrete aggregates along with 25% of cement is substituted by Fly Ash. The tests were executed at 28 days and 56 days of curing. From the above durability test, Mathematical modeling has to be found out to study concrete durability. Analysis and correlation were done by MLR (Multiple Linear Regression). The equations are formed of all three tests with their regression graph. So this research assists in the use of recycled aggregate concrete in the construction sector.

GIS Science Journal

DURABILITY STUDY OF RECYCLED AGGREGATE CONCRETE WITH PARTIAL REPLACEMENT OF CEMENT BY FLY ASH

According to the building material promotion council (BMPTC) India generates 150 million tons of construction and demolition waste per year. From this demolition waste, we can recycle the concrete aggregates replacing natural coarse aggregate to make it more sustainable. This experimental research aims to study the rheological behavior of M50 and M60 grade concrete. The replacement of NCA with RCA in this study was 0%, 20%, 40%, and 60% along with the cement replacement of 25% by fly ash. The polycarbonate (PC) based superplasticizer was used for the concrete. The tests performed to study the rheology of concrete were slump test, Vee-Bee test, compaction factor test &amp; flow table test. The mathematical equation is been formulated showing the interrelation between the results found in the above tests. The promising results will reduce the cost of concrete on-site significantly and the mathematical equation formulated will help for on-site calculations of the quantity of recycled concrete aggregate for the expected rheological behavior of concrete.

RHEOLOGY OF RECYCLED AGGREGATE CONCRETE

Retinopathy of prematurity (ROP) is a potentially blinding disorder seen in low birth weight preterm infants. In India, the burden of ROP is high, with nearly 200,000 premature infants at risk. Early detection through screening and treatment can prevent this blindness. The automatic screening systems developed so far can detect "severe ROP" or "plus disease," but this information does not help schedule follow-up. Identifying vascularized retinal zones and detecting the ROP stage is essential for follow-up or discharge from screening. There is no automatic system to assist these crucial decisions to the best of the authors' knowledge. The low contrast of images, incompletely developed vessels, macular structure, and lack of public data sets are a few challenges in creating such a system. In this paper, a novel method using an ensemble of "U-Network" and "Circle Hough Transform" is developed to detect zones I, II, and III from retinal images in which macula is not developed. The model developed is generic and trained on mixed images of different sizes. It detects zones in images of variable sizes captured by two different imaging systems with an accuracy of 98%. All images of the test set (including the low-quality images) are considered. The time taken for training was only 14 min, and a single image was tested in 30 ms. The present study can help medical experts interpret retinal vascular status correctly and reduce subjective variation in diagnosis.Keywords: Artificial Intelligence; Automatic zone detection; Machine learning; Retinopathy of prematurity(ROP); Segmentation; U-Net.

Journal of Digital Imaging

Assistive Framework for Automatic Detection of All the Zones in Retinopathy of Prematurity Using Deep Learning

Heavy metals such as lead,copper,nickel,etc contamination and their toxicity is a serious concern in India.Many methods of treatment are available. But an adsorption process with activated carbon is unique and most efficientmethods for removing the pollution agents.Researchers are working to identify various cost-effective,efficient,environmentally friendly, easily and locally available adsorbent materials. This research work aimedto investigate the application of orange peel (citrus Sinensispeel) as an adsorbent material which is locally and easily available agricultural by-product, and verifying its results with the results of PAC in exclusion of Pb 2+ ions from aqueous solutions.Batch mode adsorption method has been followedin investigating the effect of various parameters such as pH, contact time, adsorbent dose, Pb 2+ ions concentration, and adsorbent of varying particle size on the adsorption of Pb 2+ ions by both OP and PAC. The evaluation of adsorption data is essential for the development of an empirical relationship based on the results collected throughout the course of experimental work. Several equilibrium isotherms, including the Langmuir, Freundlich, BET, and Modified Freundlich Isotherms, were analysed in order to find the isotherm that provided the best fit.

Design Engineering (Toronto)

<a href="http://thedesignengineering.com/index.php/DE/article/view/2528">Equilibrium Studies on Adsorption of Pb2+ ions using Citrus Sinensis Peel</a>