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

Liquid–liquid equilibrium data for the system consisting of toluene, n-heptane and Aliquat 336 ionic liquid was obtained at 303.15 K and atmospheric pressure. The values of distribution coefficient and selectivity were evaluated. Mathematical model relating distribution coefficient and dispersion, dipolar interaction and hydrogen bonding has been developed. Hansen’s solubility parameters were used to determine the parameters. The model was applied for various ionic liquids as solvents. The model predicted the trends similar to the trend reported in the literature. Thus model can predict better solvent for the given extraction application. This methodology has great potential to act as a knowledge-based framework to aid development of new tailor-made solvents. This will save cost and time in experimentation and analysis.

School of Chemical Engineering

World University root node

School of Computer Engineering & Technology

Separation Science and Technology

Extraction of toluene and n- heptane mixture using ionic liquid Aliquat 336 and mathematical modeling for solvent selection

Heat transfer enhancement techniques as passive, active or a combination of passive and active methods arecommonly used in areas such as process industries, heating and cooling in evaporators, thermal power plants, airconditioning equipment, refrigerators, radiators for space vehicles, automobiles (in tracked vehicle it plays veryimportant roll), etc. Passive techniques, where inserts, extended surfaces like fins of various shapes and sizes are usedin the flow passage to enhance the heat transfer rate, are benefited compared with active techniques, because theinsert manufacturing process is easier and it can be easily applied in an existing heat exchanger. In design ofcompact heat exchangers, passive techniques can play an important role if a proper passiveconfiguration/combinations can be selected according to the heat exchanger working condition (both flow and heattransfer conditions). Ribs, fins, dimples, Vortices etc., are the most commonly passive heat transfer enhancementdevices. In this paper, major emphasis is given to work with different shapes of fins, and their arrangement because,these are found effective heat transfer enhancement tools.

School of Mechanical Engineering

International Journal of Current Engineering and Technology

Review on Heat Transfer Enhancement of a Plate Fin Type Heat Exchanger by Different Shapes of Fins

Currently Internet usage has increased a lot due to bandwidth availaility and technology advancements. Internet is widely used for knowledge sharing, online review of products etc. Many open forums, blogs are used for this purpose. Since many users are contributing their opinions towards any query submitted by information seeker, there is a possibility of confusion. Often opinions contradict with each other creating confusion in information seeker's mind. In these cases role of Opinion Leader(s) is very prominent. Opinion Leader is a person who has knowledge in the particular field, who's opinion makes difference and who can influence other's opinions. Identification of a person who has great experiences and/or knowledge, in a particular domain, is very helpful and useful in decision making, product marketing etc. This paper presents an approach for identification of Opinion Leader(s) using modified SPEAR (Spamming Resistant Expertise Analysis and Ranking) algorithm. The expertise of user is found out on different topics. Modified SPEAR algorithm effectively identifies Opinion Leader(s) by making use of additional influence measures in the form of credit score functions. It also analyses these measures and studies their effects while ranking the Opinion Leader(s) effectively. © 2016 IEEE.

International Conference on Computing, Analytics and Security Trends, CAST 2016

Identification of topic-specific Opinion Leader using SPEAR algorithm in Online Knowledge communities

Hadoop MapReduce has been proved to be an efficient model for distributed data processing. This model is widely used by different service providers, which create a challenge of maintaining same efficiency and performance level in different systems. One of the most critical problems for this model is how to overcome heterogeneity and scalability in different systems. The decreases of performance in heterogeneous environment occur due to inefficient scheduling of Map and Reduce tasks. Another important problem is how to minimize master node overhead and network traffic created by scheduling algorithm. In this paper, we introduce a lightweight adaptive scheduler in which we provide the classifier with information about jobs requirement and node capabilities. The scheduler classifies jobs into executable and nonexecutable according to the nodes capabilities. Then the scheduler assigns the tasks to appropriate nodes in the cluster to get highest performance. © Springer Science+Business Media Singapore 2017.

Advances in Intelligent Systems and Computing

An adaptive Mapreduce scheduler for scalable heterogeneous systems

Recently, many online attacks have increased due to the growing use of the Internet, Among these the most well-known attack is phishing. Phishing is a demonstration of getting delicate data by persuading the users to uncover their own data by pretending as a trusty source in the web transaction. Most phishing attacks work by sending a forged email that contains a URL which leads you to the fake website by clicking on it. In most of the cases, phisher chooses http URLs for the attack. Along these lines, we are proposing a novel algorithm which will detect whether a given http URL is of phishing site or not. Our algorithm performs Google's updated blacklist check, utilizes Google search engine results, Alexa Ranking and no of URL-based features, for detecting phishing URLs. It displays alert message if the URL found as possible phishing, otherwise it displays safe message. This algorithm enhances the performance when dealing with known/old phishing URLs. © 2016 IEEE.

International Conference on Automatic Control and Dynamic Optimization Techniques, ICACDOT 2016

A novel algorithm to detect phishing URLs

In this era, the web usage mining plays one of the important role in analyzing and improving performance of web applications. Initially in this paper, the concept of web usage mining has been introduced along with Improved AprioriAll algorithm which is the base algorithm for the proposed tool along with its limitation. The improved approach is discussed which integrates sequential pattern mining with graph theory. In the next part, design of web usage mining tool with improved approach is discussed. Implementation details of the tool are discussed along with the results. The last part covers how the raw result from the proposed tool can be further visualized and analyzed using new technologies like D3.js and Neo4j. The future scope of the proposed tool is mentioned at the end of the paper. © 2017 IEEE.

Proceedings - 1st International Conference on Intelligent Systems and Information Management, ICISIM 2017

Web usage mining tool by integrating sequential pattern mining with graph theory

Over the last decade social media platforms such as Facebook and Twitter have gained huge popularity. Usage of social media sites has increased leaps and bounds. An organization or institute can make use of the data obtained from various such social networking sites to their benefit. Digital Identity created by any person/customer on such sites can be targeted by the organization or institute for promoting their services as well as to target some new customers, thus providing them new business opportunities. Thus it can be a win-win situation for both the parties-the organization gets opportunity to increase their business footprint and the customers may be more than happy with the personalized offers from the organization. In this paper, we propose a method to understand customer's likings using public APIs made available by the social network sites such as Twitter and Facebook. As there is huge concern regarding privacy of data on social media, we have used only the data made available publicly by these platforms without using any unethical tools or methods. © 2016 IEEE.

Proceedings of the 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2016

Digital identity based recommendation system using social media

Cloud computing nowadays are gaining high importance from government services, private users and public sectors. The rise in cloud computing is due to the provided facilities such as flexibility, disaster recovery, security, etc. Moving data into cloud servers provide great convenience to users because they just have to upload their data and not care about the hardware management and security. But, the sensitive or privacy relevant data has some major implications. The techniques used so far for storage security are the cryptographic methods and some compression techniques such as compression sensing, JPEG compression technique, etc. to make more space for future data. In this paper we propose a system so that the cloud storage can be made more secure. To increase the efficiency, we are using DCT algorithm for compression and AES for encryption. The main concept of the proposed system is to increase the security so we are splitting the image in chunks. This Image splitting method, lowers the risk of image being retrieved by attacker as we upload the chunks instead of image. © 2017 IEEE.

2017 International Conference on Computer Communication and Informatics, ICCCI 2017

Image stack management and data access control for multi-authority cloud storage

Emotions are fundamental to human lives and their decision-making. Understanding an expression of emotional feeling between people forms an intricate web. There are systems, been developed that attempt to recognize aspects of emotion related behaviors and to respond to these, for example systems designed to improve the user experience or to change user behavior. The emotion recognition through facial images in recent times have proved to be an interesting topic for the researchers. Since two decades, a big research has been addressed to enhance Human Computer Interaction (HCI). There are a lot of applications and API-accessible software online that parallels the human ability to discern emotional behavior. The visual detection market is expanding enormously that can be seen noted from various system developed and described in this paper. This paper discuss various attributes, methods and emotional labels that are considered by various emotion API system. This paper also gives an overview of the databases available for inferring emotion through human facial features. The paper. © 2017 IEEE.

Proceedings of the 2017 International Conference on Intelligent Computing and Control Systems, ICICCS 2017

A survey: Software API and database for emotion recognition

The objective of this paper is to construct a model to predict stock value movement using the opinion mining and clustering method to predict National Stock Exchange (NSE). We have used domain specific approach to predict the stocks from each domain we have taken some stock with maximum capitalization. Proposed Method is Not at all like past methodologies where the general states of mind or sentiments are considered, sentiments of the particular subjects of the organization or sector are fused into the stock prediction model. Topics and related opinion of shareholders are automatically extracted from the writings in a message board by utilizing our proposed strategy alongside isolating clusters of comparable sort of stocks from others using clustering algorithms. Proposed methodology will give us two output set i.e. one from sentiment analysis and another from clustering based prediction with respect to some specialized parameters of stock exchange. By examining both the results an efficient prediction is produced. In this paper stocks with maximum capitalization within all the important sectors are taken into consideration for empirical analysis. © 2016 IEEE.