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

In the present experimental study incremental sheet forming is combined with stretch forming in order to obtain uniform thickness distribution, minimise thinning and to improve geometric accuracy of formed part. Preforming or stretch forming is initially done in order to obtain initial thickness distribution. Incremental sheet forming is then employed to obtain final part shape and thickness distribution. Forming time is also minimised considerably because of initial stretch forming process. The experiments are designed using central composite design (CCD) of response surface methodology. From the results, it is observed that preforming and preform tool shape have significant influence on sheet thinning. Also the combination of ISF and stretch forming processes yields in reduced thinning and uniform thickness distribution. Multi-performance optimisation using desirability function is also performed to improve thinning and geometric accuracy. Further mathematical models are developed which are in good agreement with the experimental results.

School of Mechanical Engineering

World University root node

Fulltext

International Journal of Materials Engineering Innovation

Optimisation and modelling of thinning and geometric accuracy in incremental sheet forming combined with stretch forming

Problems associated with asphaltene deposition are key challenges for the upstream industry. In this research work, systematic experimental studies of asphaltene precipitation were conducted for two different Iranian crude reservoirs. Though gas injection in the reservoir enhances the crude productivity, it leads to higher precipitation of asphaltene compared to the natural depletion conditions. State of the art software tool namely CMG was utilized to predict the performance of the reservoirs for natural depletion condition as well as at high gas injection conditions. The CMG based predictions matched well with the experimental observations for both the reservoirs with reasonable accuracy. © 2019 Petroleum and Coal.

School of Petroleum Engineering

Petroleum and Coal

Experimental and CMG study of asphaltene precipitation under natural depletion and gas injection conditions

Malaria is a serious worldwide health issue which causes an expected 13,444 individuals in danger of malaria in 2017. The estimated cost of detection of malaria in India is 11,640 crores per year. So there is an urgent need for a new tool to diagnose malaria. Malaria is completely preventable and treatable disease. In this project, we make a new tool to diagnose malaria using regional descriptor and PSO-SVM classifier. The proposed work used various image processing techniques like image acquisition, image pre-processing, image segmentation, feature extraction and classification. The implementation work is mainly focusing on detection accuracy, computational time, less estimation time for parasite detection. In this way, the new tool for the detection of malaria parasites gives faster and accurate results, and by using this proposed methods pathologists can easily detect malaria parasites, and they can achieve 98% accuracy. This new tool is useful to reduce deaths. © Springer Nature Singapore Pte Ltd. 2019.

School of Computer Engineering & Technology

Advances in Intelligent Systems and Computing

An implementation of malaria detection using regional descriptor and PSO-SVM classifier

Internet of thing is new communication system where globally identifiable real world objects are exchanging information with each other over an Internet infrastructure. The major objective of IOT is development of smart environments and self-aware things applications like smart transport, products, cities, buildings, rural areas, energy, health, living etc.As the Internet of Things (IoT) evolves, applications rely on devices without much human intervention. In order to well maintain these sensor devices, it is required to remotely monitor and control devices without consuming significant resources. Managing these remote devices effectively can lead to cost savings and operational efficiency, and designers must create solutions that allow customers to monitor and make changes on the fly instead of manually sending technicians to make changes in the field.This Paper focused on different aspects of Device Management and survey of different protocols used for Device Management in Internet of Things.This paper proposes Device Management life cycle in Internet of Things and provide overview of Device Management user interface. © 2018 IEEE.

Proceedings - 2018 IEEE Global Conference on Wireless Computing and Networking, GCWCN 2018

An Efficient Way to Manage Constrained Devices in Internet of Things

Satellites all around the globe generate huge quantities of data periodically on a daily basis. The management of such large quantities of data poses significant problems ranging from data storage, retrieval and manipulation. The current scenario of managing Indian Space Research Organisation's (ISRO) ASTROSAT-CZTI satellite data involves an elementary and unsophisticated approach which causes a lot of delay in data retrieval and further causes noticeable data portability issues. The evaluation and research on the above mentioned complication paved a way for us to introduce a new architectural system to solve this complex optimization problem. We propose the creation of an independent application server and an underlying Django REST API based web service which would facilitate the efficient management and retrieval of this data. This also extends the scope, making the system more dynamic and helps in load balancing. The application server helps the system to regulate the turbulent flow of huge data quantities. The Django web framework provides feature distribution which helps manage unorganised data cost effectively. Thus, it removes the possible causes of deterioration of response in the existing system. The REST API extends the scope of the system by providing umpteen useful features which makes universal interfacing of our system possible. The techniques used in the development of our proposed system including data migration, prefetching of data and minimizing the time trade-off, address all existing challenges and show a significant improvement in the overall performance of the system. © 2019 IEEE.

2019 10th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2019

Architectural Optimization of Large Scale Astronomical Data

The cloud servers help in sharing data among the people within an organization or outside it. The outsourcing of one’s data over the Internet has become much easier with the help of the newer technology, the cloud servers. The cloud servers help in sharing data among the people within an organization or outside it. As security over the cloud has become a major challenge and also a very important aspect, providing security for the data that is being stored has become crucial. Hence, encryption of data that would be outsourced has become a very prominent talk recently. Similarly, fetching of the documents over the cloud should give faster and better results which would eventually give a better performance result. The number of keywords defined can be numerous, and hence the term multi-keyword. © Springer Nature Singapore Pte Ltd. 2019.

Lecture Notes in Networks and Systems

Multi-keyword Ranked Fuzzy Keyword Search Over Encrypted Cloud Data

A dual polarized (DP) 2 × 2 MIMO antenna with high gain and isolation is proposed for WLAN band (IEEE 802.11a/b/g/n) and C-band (spectrum sharing) applications. The proposed MIMO antenna design consists of two DP pentagonal microstrip elements. A slot on a ground is introduced to escalation the isolation among two antennas. The measured frequency bands extend from 2.43 to 2.54 GHz, 4.05 to 4.13 GHz and 5.22 to 5.37 GHz with VSWR &amp;lt;2. The achieved isolation between two ports is more than 30 dB. The measured peak gains at 2.45 GHz, 4.09 GHz and 5.29 GHz resonant frequencies are 7.95 dBd, 3.39 dBd and 4.18 dBd, respectively. The RT druid 5880 substrate is used for fabricating the MIMO antenna. Measured results agree with the simulation ones. The diversity performances in terms of diversity gain (DG), efficiency, and envelope correlation coefficient (ECC) have also been reported. The proposed MIMO antenna is tested using Wi-Fi router. © 2019, Springer Nature Switzerland AG.

School of Electronics & Communication Engineering

Lecture Notes on Data Engineering and Communications Technologies

Tri-band Dual Polarized 2 × 2 MIMO Antenna for Wireless Communications

In the agricultural sector, identification of plant diseases is extremely crucial as they hamper robustness and health of the plant which play a vital role in agricultural productivity. These problems are common in plants, if proper prevention methods are not taken it might seriously affect the cultivation. The current method of detecting disease is done by an expert's opinion and physical analysis, which is time-consuming and costly in the real world. Hence, computer-based detection has become a necessity. This paper comprises of an overview of image segmentation using K-means clustering and HSV dependent classification for recognizing infected part of the leaf and feature extraction using GLCM. The efficiency of the proposed methodology is able to detect and classify the plant diseases successfully with an accuracy of 98% when processed by Random Forest classifier. © 2019 IEEE.

CSITSS 2019 - 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution, Proceedings

Plant Diseases Detection and Classification using Machine Learning Models

We live in an era of information explosion where media for information communication and information sharing have proliferated due to the onset of a digital age. One such important source and medium of consolidated, comprehensive and reliable information, which has persisted through centuries, is news articles. News articles have been considered as a primary source of information, ranging from local to global scope, and encompassing a wide array of domains, by most of the human population. However, as the technology for dissemination of information attracts innovations, it becomes equally important that the consumption and utilization of this information becomes smart as well. If processed properly and treated in an intelligent manner, the information contained in news articles can reveal useful insights and hidden patterns that might prove to be of critical importance in certain decision making pertaining to the topics and domains in question. The paper puts forward a novel framework created by combining Natural Language Processing methodologies like Sentiment Analysis and Information Retrieval, which are performed on top of a Deep Learning based classification with aptly defined classes for generating useful insights from a news article dataset. The paper contains the details regarding the implementation of this system and also a presentation of results on a particular data set of news articles - articles on HIV-AIDS, which have been categorized into meaningful classes and have been processed using aforementioned NLP tasks. Through this paper, a curated dataset of 3, 000 news articles related to HIV-AIDS has also been introduced, which will be open sourced so as to inspire further research and experimentation in this specific domain. © 2019 IEEE.

Proceedings - 2019 5th International Conference on Computing, Communication Control and Automation, ICCUBEA 2019

DeepDiseaseInsight: A deep learning NLP based novel framework for generating useful insights from disease news articles

3D Printing is a technology which is based on additive manufacturing. This technology has a potential to change the manufacturing industry by manufacturing product in the shortest time period where at first the same product required days, months or years. The study on these printers and their software is to reduce the cost of printers as compared to one already present in the market. This paper is based on the types of 3D printing methods used from the early 1980s till today and their hardware and software used. © 2018 IEEE.