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

School of Computer Engineering & Technology

World University root node

Engineering and Technology

International Journal for Research in Applied Science & Engineering Technology

Link Prediction on Social Media: Survey

The goal of education is not to increase the amount of knowledge but to create the possibilities for a child to invent and discover, to create men who are capable of doing new things. Jean Piaget Education plays an important role in development nation by creating good, responsible, and intelligent individuals who are politically, socially and economically sound. Effective teaching is important because it transforms the student from one level to other higher level which makes the students independent learners. Effective teaching does not mean being perfect or giving the wonderful performance, but bringing out best from the students which will not only make the students technically strong but also make them good and responsible citizen who in turn will contributes back for nation development. Today Machine learning algorithms or Artificial Intelligence plays very vital role for technical development in every domain for example medical, engineering, teaching, agriculture, military etc. Professor 4.0 is a virtual professor developed by using various animated software like Maya, Alice, Blender etc. , trained by machine learning algorithms and which delivers the lecture exactly same as the real professor taking the class. The main objective of this research is to create a virtual professor who can take a class on particular session, answer students queries, control the class and give best technical knowledge to the students. Virtual Professor 4.0 is trained through machine learning algorithm in such a way that it can answer all the student queries on a particular topic. The outcome of this research is that the combination of this virtual professor with the WPU professor who are physically fit mentally alert and spiritually enlightened can definitely contribute for effective teaching/learning process to make our smart WPU student to responsible citizen contributing for nations growth.

Global Journal of Innovative Higher Learning

Professor 4.0 An Approach For Effective Education 4.0

The Share Market Prices fluctuates very often because of multiple external variables like crude oil price change, gold, government policy change, etc. This paper describes the Deep Neural Network (DNN) for the actual-time financial fluctuations (stock market) and also for future market price situation prediction. Authors have design the model by using two related important concepts of Deep Learning (DL) and Reinforcement Learning (RL). For informative feature learning, deep learning is used to automatically sense the dynamic market condition. Then, using this deep information, RL module takes decisions about trading to collect the rewards in strange environment. Many researchers have developed various Deep Learning Models and Algorithms to predict the Share Market fluctuations. This paper talks about the understanding of the dynamic market and the deep learning techniques to understand and predict the future situation in Share Market Prices.

Universal Review

Understanding Dynamic Market Condition Using Deep Direct Reinforcement Learning

International Engineering Research Journal(IERJ)

Rumour Detection on Twitter

Big data is the recent imminent technology, which can provide large benefits to the business administration. Owing to such huge volume, it becomes very complicated to ensure effective analysis by the existing techniques. The complications can be related to analyze, capture, sharing, storage, and visualization of the data. To tackle these challenges, a novel classification technique using Holoentropy based Correlative Naive Bayes classifier and MapReduce Model (HCNB-MRM) is proposed. The proposed HCNB, which is designed by combining the Holoentropy function with the correlative based Naive Bayes classifier deals with both high-dimensional data sets as well as extensive datasets to improve the benchmark, and classify the data based on dependent assumption. Therefore, the proposed HCNB-MRM is used to make the process simpler and to choose the best features from big dataset. The proposed HCNB with the MapReduce Model maximizes the performance of big data classification using probability index table, and posterior probability of the testing data samples. The performance of the proposed HCNB-MRM is evaluated using three metrics, such as accuracy, sensitivity, and specificity. From the experimental results, it is analyzed that the proposed HCNB-MRM obtains a high classification accuracy of 93.5965% and 94.3369% for the localization dataset, and skin dataset when compared with the existing techniques.

Evolutionary Intelligence

Holoentropy based Correlative Naive Bayes classifier and MapReduce model for classifying the big data

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.

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

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.