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

Cardiovascular diseases are one of the most vital causes offatality. Cardiovascular disease prediction is a critical challenge in the area of clinical data analysis. Machine learning and Neural Networks are more promising in assisting decide and predict from the massive data produced by healthcare. We have noted different features had used in recent developments of the machine learning model. In this paper, we proposed machine learning techniques to predict cardiovascular disease using features. BMI is one of the highlighting features we used for prediction. BMI is important in predicting cardiovascular disease. The main focus of the article isthe effect ofBMI onthe prediction of cardiovascular disease. The model has proposed with different features as well as regression and classification techniques. We conclude that BMI is a significant factor while predicting cardiovascular disease. © 2020 IEEE.

World University root node

School of Computer Engineering & Technology

2020 IEEE Pune Section International Conference, PuneCon 2020

Cardiovascular Disease Prediction Using Machine Learning Models

School of Mechanical Engineering

Materials Today: Proceedings

Using numerical simulation to investigate the effect of injection configurations over the scramjet performance

eLearning has completely transformed the way in which learning is imparted to students. Relevance of the topic being taught is often found lacking in learning material. Students can access lot of contents or video for learning different topics but keeping track of its relevance for him is a tedious job. Recommendation system plays an important role in such situations. The system which provides recommendation is a reference system that would recommend an education work to a student based on the works already done. This paper reviews the main paradigms of recommendation systems using explicit and implicit feedback and the various methodologies that have been implemented to design recommender systems to enhance learning. The concepts of eLearning and recommendation systems are summarized. eLearning recommendation systems will be useful to enhance learning.

School of Polytechnic and Skill Development

Proceeding of International Conference on Computational Science and {\ldots}

Recommender System in eLearning: A Survey

Increasing network lifetime (NL) is an important requirement in wireless sensor networks (WSNs). One of the techniques to extend NL is to use Data Aggregation Trees (DATs). DATs improve NL by combining the energy efficiency benefits of both Data Aggregation (DA) and tree-based routing. While centralized and distributed strategies for DAT construction are widely used, we propose a combined approach for DAT construction to improve NL. The approach reduces the communication overhead and relaxes the requirement of complete network information at the sink. In the proposed work, this collaborative approach is termed as Extended Local View (ELV) approach. Two ELV-based DAT construction algorithms termed as ELV with Fixed sink (ELVF) and ELV with Random sink (ELVR) are proposed. Both ELVF and ELVR use heuristics-based technique of Local Path Reestablishment (LPR) and greedy-based technique of Extended Path Reestablishment (EPR). Using these techniques a sequence of DATs are scheduled that collectively improve NL and also reduce the associated DAT reconstruction overhead. Performance of ELVF and ELVR is evaluated with rigorous experiments and the simulation results show that the proposed algorithms have improved NL and are scalable across different DA ratio values. DAT schedule analysis further demonstrates reduced DAT reconstruction overhead of the proposed algorithms which illustrates its suitability for hostile and critical environments.

International Journal of Communication Systems

Scheduling data aggregation trees to extend network lifetime in sensor networks

This paper surveys tools and techniques used in the Big-data and Big-data computing. The principles upon which Big-data and dataintensive computations work have been explored with some outlines provided by various researchers in the field. The paper starts with the traditionalcluster computing and further explores about the Hadoop system as a tool to solve Big-data issues. It also covers the significant growth in cloudcomputing towards hosting MapReduce to use for Data-intensive computations as one of the services available through clouds. It has brief coverageabout Microsoft Azure and Amazon clouds for MapReduce services to be provided through Internet.

International Journal of Scientific & Technology Research

Survey Of Technologies, Tools, Concepts And Issues In Big Data

In this work, a new finishing process has been proposed for polishing a work piece made of AISI D3 steel material with the use of magnetorheological (MR) fluid as working medium. Magnetorheological fluid finishing process is considered as one of the important non-conventional methods for polishing the complicated shapes as well as hard material components. The experimentation is carried out on predominating factors like the ratio of CIPs to Al2O3 abrasive particles, magnetizing current, the rotating speed of the tool, feed rate of work piece, working gap and hardness of work pieces to get the response as a percentage change in surface roughness. To design the experiments, a full factorial design is employed and the results are analyzed with the help of regression and ANOVA method. The results of experimentation reveal that the major contribution of the parameters to improve the surface finish is of working gap, current applied to the electromagnet and the hardness of the workpiece followed by the rotating speed of the tool, the ratio of CIP to abrasive and feed rate of the workpiece. Further, results showed that the average percentage deviation between the observed and the predicted value is less than 4%. © Springer Nature Singapore Pte Ltd. 2020.

Advances in Intelligent Systems and Computing

Experimental Study of Process Parameters on Finishing of AISI D3 Steel Using Magnetorheological Fluid

Texture segmentation is a well-known research domain and a large number of researchers are working on it across the globe due to its wide varieties of applications in various domains such as medical imaging, shape extraction, product inspection, remote sensing, and segmentation of natural images. Many times, images are corrupted by various types of noises such as Gaussian, speckle, and salt-pepper noise. Most often Gaussian noise is a source of corruption in many applications. Prague texture dataset is extensively used by researchers due to wide varieties of multi-class textures in it. The present study offers the restoration of Prague texture database benchmark images. These images are corrupted with Gaussian noise with different variance values. The state-of-the-art algorithm viz. Colour Block Matching 3D (C-BM3D) filter, which achieves the first rank among 15 algorithms reported in the most recent literature, is used for the restoration of noisy texture benchmark images. Image restoration performance metrics used are peak signal to noise ratio (PSNR) and structural similarity index (SSIM). © 2020 IEEE.

School of Electronics & Communication Engineering

Proceedings of the International Conference on Intelligent Computing and Control Systems, ICICCS 2020

Quantitative Restoration of Noisy Colour Texture Segmentation Benchmark Images using State-of-the-Art Algorithm

Fake news classification on twitter using flume, n-gram analysis, and decision tree machine learning technique

International Journal of Scientific Research in Engineering and Management (IJSREM)

AI Estimating Viable Locations for Onshore Wind Turbine Installations

Detection, and mapping of potholes in a precise and punctual manner is an essential task in avoiding road accidents. Today, roadway distresses are manually detected, which requires time and labor. In this paper, we introduce a system which uses deep learning algorithms and is integrated with smartphones to detect potholes in real-time. The user interface of the system is a smartphone application which maps all potholes on a route that the user is traveling. Simultaneously, deep learning object detection algorithm: Single Shot Multi-box Detector (SSD) looks for potholes using a mobile camera in the background. As soon as an unregistered pothole is detected by SSD, coordinates of the pothole are updated to the database in real-time. Accelerometer and gyroscope readings are continuously taken and assessed by a Deep Feed Forward Neural Network model to detect unregistered potholes. This dual mechanism of camera-based as well as accelerometer-gyroscope based detection not only cross validates detections but also provides stable results even if one mechanism fails. The pothole co-ordinates are rendered on the map user interface that can be accessed in the same application. This system with map/navigation feature as front end and two-fold deep learning pothole detection algorithm in backend is an efficient and a zero cost solution for real-time pothole detection.