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

Engineering and Technology

Mohsin, Shreerang Nandanwar “Home Automation and Security System Using Android ADK” in International Journal of Electronics Communication and Computer Technology (IJECCT) Volume

Mohd

This paper solves the problem of prevention of asthma attacks, and also it can predict a future asthma attack based on prevalent asthma triggers. The system forms an opinion of the everyday condition of the user, for example, if the user is doing fine today but might not be fine tomorrow. With the help of neural networks, it is possible to predict precisely and correct the user habits. A user who is not an asthma patient but thinks he may have the disease can also use this system. Monthly reports are generated of every user stating whether the patient's condition has worsened or got better. © 2015 IEEE.

World University root node

Proceedings of the 2015 IEEE 3rd International Conference on MOOCs, Innovation and Technology in Education, MITE 2015

Rule extraction for detection and prevention of asthma attacks

In recent years, the amount of online content has grown in enormous proportions. Users try to collect valuable information about contents in order to find their way to relevant web pages. And a lot of research is going on to collect valuable service usage data and process it using different methods to know their behaviors. Many systems and approaches have been proposed in the literature which tries to get information about the user’s interests by profiling the user. The objective of the paper is to profile users on their specific devices and the web usage patterns based on the keyboard and mouse usage, time spent on the web. By analyzing the usage patterns of various users, we prove that the patterns exhibited by any one user are different from other users. © Springer Science+Business Media Singapore 2016.

Proceedings of the International Congress on Information and Communication Technology

Uniqueness in User Behavior While Using the Web

Mobility of nodes plays a key role in Wireless sensor network (WSN). It comes up with challenges like energy efficiency and improve network lifetime. WSN contain nodes which are having capability of sensing, computation, and wireless communications. Every node has ability to communicate with other node or Base Station (BS). Sensor nodes have limited energy as compared to BS. Energy efficiency is very important in WSN, because sensor node cannot provide energy for long time while sensing and processing data in network. For this reason mobile sink is rich in energy supply and computational power. Mobile sink can be used to improve the performance of network by shifting the load of energy depletion from sensor node to sink. The effective way to increase the network lifetime is to reposition the sink node. It also helps to reduce energy-holes problems. It is necessary to keep track of mobile sink so that data distribution should take place correctly. But tracking the location of mobile sink and updating it frequently is also a challenge for energy conservation and it affects the network performance. Various sink mobility strategies exist to increase energy efficiency. This paper mainly focuses on study of different algorithms, which are used for efficient data processing using mobile sink. © 2016 ACM.

ACM International Conference Proceeding Series

A survey of various sink mobility based techniques in wireless sensor network

Pune

A study on handwritten marathi word recognition

Due to the continuous arrival of plenty of raw data, the big data classification is getting much attention in various fields, such as industry, medicine, and financial business. Due to the urgent need of big data classification in those fields, several standard classifiers are adapted to perform the big data classification, but they are not able to deal with these huge data problems. More commonly, Navie Bayes classifier is mostly preferred for big data classification because of its simple computation procedure. However, the main drawback behind this classifier is the independence hypothesis, in which the input data are conditionally independent of each other. To overcome this drawback, we propose a new classification algorithm using Correlative Naïve Bayes classifier and MapReduce framework (CNB-MRF). Here, the newly proposed correlation function with MapReduce framework is used to make the classification based on the dependent hypothesis to improve the classification performance. Here, the data classification can be easily performed for every new sample through the probability index table of the training data sample and the posterior probability of the testing data samples using MapReduce framework. Then, the classification by the proposed CNB-MRF classifier is performed using localization and skin dataset. It is concluded that, the proposed CNB-MRF classifier achieves a high classification accuracy of 74.77% and 61.35% for localization and skin dataset respectively, as compared with Naïve bayes classifier and MapReduce framework (NB-MRF).

International Review on Computers and Software

CNB-MRF: Adapting Correlative Naive Bayes Classifier and MapReduce Framework for Big Data Classification

X.509 certificates empower to reveal the unique identity of the parties participating in the conversation. Right now, during online exchanges, many people and groups are using X.509 certificates to represent their identity, so the level of excellence and reliability of these certificates become dubious. Hence, we introduced a framework which computes risk associated with X.509 certificates with the assistance of certain trust criteria and attributes. For assessing risk related with certificate, we utilized Random Forest ensemble machine learning algorithm, which categorizes risk in three levels- High, Medium and Low. User needs to input the certificate and the system will predict the risk associated with that certificate. If predicted risk is high or medium, system will specify the parameter due to which it triggers risk. Our framework can be applied in browser-server communication and identifying real-time phishing websites which have Https URLs. © Springer International Publishing AG 2016.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Risk evaluation of X.509 certificates–a machine learning application

Due to the sheer volume of data available on the internet, the problem of information overload is an ever increasing one. Personalization systems based on user profiles tend to assist the user in his day-to-day tasks by recommending 'relevant' content to the user based on his previous web usage and navigation patterns. These user profiles hold more and more information about the user, his likes, dislikes as it learns about the user over time. Privacy and security issues of the 'user profiles' becomes far the more important as the user profiles actually reflect the user itself albeit in a pseudo form. © 2016 IEEE.

2016 International Conference on Internet of Things and Applications (IOTA)

Personalization in user profiling: privacy and security issues

The Web is a vast pool of resources which comprises of a lot of web pages covering all aspects of life. Understanding a user’s interests is one of the major research areas towards understanding the web today. Identifying the relevance of the surfed web pages for the user is a tedious job. Many systems and approaches have been proposed in literature, to try and get information about the user’s interests by user profiling. This paper proposes an improvement in determining the relevance of the webpage to the user, which is an extension to the relevance formula that was proposed earlier. The current work aims to create user profiles automatically and implicitly depending on the various web pages a user browses over a period of time and the user’s interaction with them. This automatically generated user profile assigns weights to web pages proportional to the user interactions on the webpage and thus indicates relevancy of web pages to the user based on these weights. © Springer International Publishing Switzerland 2016.

Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 1

A Framework to Infer Webpage Relevancy for a User

User profiles are important in many areas in which it is essential to obtain knowledge about users of software applications. Knowledge about a user includes his likes, dislikes, even his emotional state can be determined by user profiling. In this paper we examine what information constitutes a user profile; and how the profile information is used to get the emotional state of a user. We also study the main issues regarding user profiles from the perspectives of these research fields. © 2016 IEEE.