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

In this paper we use car games as a simulator for real automobiles, and generate driving logs that contain the vehicle data. This includes values for parameters like gear used, speed, left turns taken, right turns taken, accelerator, braking and so on. From these parameters we have derived some more additional parameters and analyzed them. As the input from automobile driver is only routine driving, no explicit feedback is required; hence there are more chances of being able to accurately profile the driver. Experimentation and analysis from this logged data shows possibility that driver profiling can be done from vehicle data. Since the profiles are unique, these can be further used for a wide range of applications and can successfully exhibit typical driving characteristics of each user. © 2015 IEEE.

Engineering and Technology

2015 International Conference on Pervasive Computing (ICPC)

Profiling of automobile drivers using car games

The static allocation schemes have led to the problem of spectrum under-utilization. In many bands, spectrum access is a more significant problem than physical scarcity of spectrum, in large part due to legacy command-and-control regulation that limits the ability of potential spectrum users to obtain such access. Dynamic spectrum management allows adaptive allocation of spectrum to various users in a multiuser environment as a function of spatiotemporally varying physical environment. A Cognitive Radio Network is a multi-hop multi-channel network with dynamic channel set for each user. Establishing a selforganizing cognitive radio network requires extensive exchange of control messages, needed to coordinate various network functions such as cooperative sensing, channel access, topology management, and routing. In wireless networks, control messages are broadcasted over a channel known to all nodes, commonly referred to as the common control channel. The concept of a single frequency band as control channel is not practical. It is considered wise to form smaller group of secondary users such that the number of available channels that are common to them is maximum. This paper deals with the methodology of obtaining the optimum groups. The major thrust of this paper is on the formation of the groups in such a way that graceful tradeoffs is achieved between the number of nodes in a group and the number of idle channels common to all the members of the group. Keywords— Cognitive radio network, control channel, idle channels, graceful tradeoff, clustering.

World University root node

International Journal of computer science and mobile computing

Control Channel Allocation in Cognitive Radio Networks using Clustering

Wireless Sensor Networks (WSN) consists ofautonomous nodes which are static in nature and permanent inlocation for a lengthy period of time. The mobile WSN, havinga unique features like limited resources and non-rechargeablebatteries. Due to this constraints, the recent researches mainlyfocused to manage the energy consumption in order to extendthe lifetime of the network. Nowadays, the WSN may consistsof Heterogeneous and Mobile sensor nodes for manyapplications. For that, the important design issues are energy,scalability, production costs, data aggregation, fault tolerance,network lifetime, latency, node deployment, Quality of Service(QoS), mobility, throughput, packet delivery ratio and end toend delay etc. The advantage of allowing the sensors to bemobile increases the number of applications beyond those forwhich static WSNs are used.

International Journal of Advanced Information Science and Technology (IJAIST), ISSN: 2319-2682

A Survey on Energy Efficient Hierarchical Routing Protocol in Wireless Sensor Network

Smartphone applications are gaining popularity and with time many complex and computation/resource intensive applications are coming to market. These applications require more resources and consume lot of battery power, but the improvisation in battery capacity has been lagged far behind as compared to other components of mobile devices. On other hand, clouds are rich in resources and computational power. Therefore, by utilizing cloud resources for application execution we can reduce battery consumption and in turn increases battery charging intervals. This all is known as cyber foreign or computation offloading in Moble Cloud Computing. There are various factors affecting computation offloading like bandwidth. So, we had considered bandwidth as major factor in proposed offloading decision making algorithm. Our algorithm is based on divide and conquer strategy. This system focuses on time and energy saving. © 2015 ACM.

ACM International Conference Proceeding Series

Time and energy saving through computation offloading with bandwidth consideration for mobile cloud computing

Online health communities offer tremendous medical information that could be available to all. However as is the case with other data intensive applications, this information contains hidden patterns which if explored, analyzed and understood could be very useful for administrators, medical practitioner sand patients alike. There are websites where patients express their experiences or side-effects on drugs used. In this work we collect such real time health posts from reputed websites, and perform data mining to determine the various possible associations from these posts. Also, we shall perform knowledge discovery from user posts, whereby useful 'patterns' about groups like: disease to disease, disease to drug and drug to symptom are discovered. © 2015 IEEE.

Proceedings of 2015 IEEE 9th International Conference on Intelligent Systems and Control, ISCO 2015

Knowledge discovery from user health posts

Diagnosis is an important task in medical science because of its criticality, efficiency and accuracy in determining whether or not a patient has a particular disease. This shall further decide the most suitable line of treatment. There has been a large increase in the number of thyroid cases over the past few years. Since thyroid has a complex relation with metabolism and body weight, it is extremely important to diagnose thyroid disease as early as possible. This paper presents an exhaustive survey of work done in the past with respect to semi-automated medical diagnosis in general and thyroid disease diagnosis in particular. Medical Diagnosis encompassed the use of classifiers like Fuzzy Neural Networks, k-Nearest Neighbor and Decision Tree, while the latter included the use of Computer-Aided Diagnosis, different Neural Networks and Support Vector Machine. Amongst these, the impact of Feature Selection using Particle Swarm Optimization and Ant Colony Optimization on classification was also surveyed. © 2015 IEEE.

Thyroid disease diagnosis: A survey

-With the dawn of cultural and traditional revolution and the age of information technology, there came a serious threat of illicit/pornographic content, which disturbs the cultural harmony of our society. So with the growing network of these content there arouse a need of application which detects such content and take a legitimate action on the same. So taking this into the consideration, the primary goal of the present study is to develop technology for detecting the illicit/pornographic content and to take legitimate action on such things. The application domain of our project is image processing and this software is going to process the images for illicit/pornographic content by using skin-tone based technology. It is going to detect how much percentage of skin is revealed in an image and depending on this is going to decide that whether that image is acceptable or not. Since the platform used is java, our application will be platform independent. The images detected by our application are going to be encrypted so that if anybody claims for his/her images as personal, the admin can even decrypt the same which makes our application as user friendly and extra secured.Thus with added features and benefits this software stands ahead of any other software is currently in market.

IJCSN International Journal of Computer Science and Network

Illicit/Pornographic Content Detection &amp; Security

Analysis Of Crowdsourced Data Using Data Mining Approach For Decision Support

Due to rapid digitization and emergence of social media the movie industry is growing by leaps and bounds. The average number of movies produced per year is greater than 1000. So to make the movie profitable, it becomes a matter of concern that the movie succeeds. Given the low success rate, models and mechanisms to predict reliably the ranking and or box office collections of a movie can help de-risk the business significantly and increase average returns. The current predictive models available are based on various factors for assessment of the movie. These include the classical factors such as cast, producer, director etc. or the social factors in form of response of the society on various online platforms. This methodology lacks to harvest the required accuracy level. Our paper suggests that the integration of both the classical and the social factors (anticipation and user feedback) and the study of interrelation among the classical factors will lead to more accuracy. © 2015 IEEE.

2015 International Conference on Pervasive Computing: Advance Communication Technology and Application for Society, ICPC 2015

Role of different factors in predicting movie success

Recommendation Systems (RSs) are becoming tools of choice to select the online information relevant to a given user. Collaborative Filtering (CF) is the most popular approach to build Recommendation System and has been successfully employed in many applications. Collaborative Filtering algorithms are much explored technique in the field of Data Mining and Information Retrieval. In CF, past user behavior are analyzed in order to establish connections between users and items to recommend an item to a user based on opinions of other users. Those customers, who had similar likings in the past, will have similar likings in the future. In the past decades due to the rapid growth of Internet usage, vast amount of data is generated and it has becomea challenge for CF algorithms. So, CF faces issues with sparsity of rating matrix and growing nature of data. These challenges are well taken care of by Matrix Factorization (MF). In this paper we are going to discuss different Matrix Factorization models such as Singular Value Decomposition (SVD), Principal Component Analysis (PCA) and Probabilistic Matrix Factorization (PMF). This paper attempts to present a comprehensive survey of MF model like SVD to address the challenges of CF algorithms, which can be served as a roadmap for research and practice in this area. © 2015 The Authors.