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

Use of social media is increasing day by day. A huge amount of textual data, as well as images, continue to explode to the web daily. Personality prediction is an evergreen area of research. Predicting personality with the help of data through social media is a promising approach as this method does not require any questionnaires to be filled by users thus reducing time and increasing credibility. In the current work we have fetched real-time Twitter data through a standard API and thereby created a sample experimental dataset. We have employed several popular machine learning algorithms such as kNN, Naive Bayes and SVM. It was observed that Naive Bayes yielded the highest accuracy of 71.67% as compared to the other classifiers. © 2019 IEEE.

World University root node

School of Computer Engineering & Technology

2019 Global Conference for Advancement in Technology, GCAT 2019

Analysis of Machine Learning Algorithms for Predicting Personality: Brief Survey and Experimentation

Various automated/semi-automated medical diagnosis systems based on human physiology have been gaining enormous popularity and importance in recent years. Physiological features exhibit several unique characteristics that contribute to reliability, accuracy and robustness of systems. There has also been significant research focusing on detection of conventional positive and negative emotions after presenting laboratory-based stimuli to participants. This paper presents a comprehensive survey on the following facets of mental stress detection systems: physiological data collection, role of machine learning in Emotion Detection systems and Stress Detection systems, various evaluation measures, challenges and applications. An overview of popular feature selection methods is also presented. An important contribution is the exploration of links between biological features of humans with their emotions and mental stress. The numerous research gaps in this field are highlighted which shall pave path for future research. © 2019 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences

Biocybernetics and Biomedical Engineering

A survey of machine learning techniques in physiology based mental stress detection systems

Data transmission over the wireless transmission channel is adversely affected by attenuation, distortion, interference, and noise, that hampers the ability of the receiver to correctly receive the transmitted message signal. Thus error detection and correction methods are implemented to mitigate these effects. Convolution encoder is one such channel encoding technique used at transmitter end for deep space and wireless communication whereas at receiver end the Viterbi decoder decodes the encoded data. Viterbi algorithm is based on principles of maximum likelihood where the optimal trellis path is identified that is followed at the encoder using cumulative hamming distance. This paper presents an SoC based Hardware-Software codesign approach of implementing the Viterbi Decoder along with the entire communication system comperising of random binary pattern generator, convolution encoder, QPSK modulator, QPSK demodulator and quantizer built on a Zynq-7000 All Programmable SoC chip. The blocks are designed using Verilog HDL (Hardware Description Language) using the tools Vivado IDE (Integrated Development Environment) 2017.4 by Xilinx and HDL coder toolbox of MATLAB 2018 a by MathWorks. Simulink model of the communication chain is implemented to simulate the design considering the effect of AWGN (Additive White Gaussian Noise) through the channel and then the partial design is translated to be implemented on a Zynq-7000 AP SoC. This design can find potential applications in Satellite communication, SDR (Software Defined Radio). © 2018 IEEE.

School of Electronics & Communication Engineering

Proceedings of the 2nd International Conference on Intelligent Computing and Control Systems, ICICCS 2018

Viterbi Decoder Using Zynq-7000 AP-SoC

International Journal of Computational Engineering Research 3 (2),

An Efficient Privacy Preserving, Enhanced and Innovative Ranked Keyword Search in Cloud Computing

In this paper, we have done an assessment to evaluate the efficiency of different feature descriptor algorithms like Local Binary Pattern (LBP), Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF) and Oriented FAST and Rotated BRIEF (ORB) on gender classification problem. The evaluation is done with and without using the feature descriptors and the results are compared to get a better understanding of the algorithms. Experiments are carried out on CAS-PEAL FRONTAL database. © 2019 IEEE.

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

Evaluation of different feature descriptor algorithms on classification task

In Wireless Sensor Networks (WSNs), Data Aggregation Trees (DATs) are employed for energy efficient data gathering. Energy efficient data collection is a primary requirement in the smart world of Internet of Things (IoT) as it facilitates to extend the survivability of the network. DATs gather data efficiently by employing data aggregation functions at the aggregator nodes. The employed aggregation function influences the cost of communication and cost of computation at a node. The study in this paper presents the techniques to estimate the communication and computation costs incurred for DAT construction, asymptotically. The strength of the proposed techniques is its ability to enable the estimation of best, average and worst case cost of DAT construction and rescheduling scenarios. Based on the asymptotic analysis, the study in this paper demonstrates the utilization of the proposed techniques to estimate the best and worst cases for communication and computation cost to meet the design objective of adhoc WSN deployments.

Proceedings - 2019 International Conference on Computing, Communication and Intelligent Systems, ICCCIS 2019

Asymptotic Cost Estimation for Scheduling Data Aggregation Trees in Sensor Networks

School of Mechanical Engineering

International Conference on Innovations in Engg Sciences, Management {\ldots}

Challenges in the Development of a new Obturation Device: A critical survey in Pune city

Opportunistic forwarding mechanism in delay tolerant networks (DTN) makes the network exposed to vulnerabilities. Providing security to existing routing protocols available for delay tolerant network becomes very difficult due to unavailability of sufficient contacts. It is necessary to design a secure routing protocol to overcome these issues. Energy is one of the crucial parameters in data forwarding and incentives should be offered to vehicular DTN nodes on the basis of energy spent for a particular message. This paper presents the secure routing mechanism which is based on the trust value of the vehicular DTN nodes. Incentive scheme is based on energy spent by the vehicular DTN node for forwarding data successfully. Routing is based on the trust value of the vehicular DTN nodes and incentive will be offered on successful delivery of the message. A source node offers the payment for successful delivery based on the time in which the message is delivered. The trust authority evaluates the credit and trust value for the participating vehicular DTN nodes based on energy and efforts put in successful delivery of the message.

Springer Journal of Computing

Energy Based Incentive Scheme for Secure Opportunistic Routing in Delay Tolerant Networks

Aesthetics is defined by the properties of arts and beauty, thus making it a very subjective domain. In our day to day lives, with the increase of multimedia requirements, the aesthetic appeal of images and videos has gained much importance in varied fields like advertising, film making, User-Interface design, social networking etc. Visual attributes greatly affect the aesthetic sense of the viewers. In this paper, to start with, we dive into the details of low level, middle level and high level image attributes that contribute towards the aesthetic appeal of images. Videos share their attributes with images except for the presence of motion in a video. Next, we proceed towards the handcrafted and deep learning techniques for assessing image and video attributes for their aesthetic appeal. Motion is an important but seldom explored visual attribute that affects video aesthetic appeal. Typically, slow motion creates an impact and appreciation amongst the viewers as they absorb the contents of the video better in comparison to faster motion in the video. Surveys conducted showcased the human inclination towards slowly paced videos in comparison to the fast-paced ones. We have experimented with the deep learning framework for detecting motion in nature based videos. Deep learning achieves an impressive performance in comparison to the handcrafted methods, thus reinforcing current trust in the deep learning frameworks for multimedia analysis. © 2019 IEEE.

2019 IEEE Bombay Section Signature Conference, IBSSC 2019

Deep Learning for motion based video aesthetics

Now a days the wireless sensor technologies are mostly being used in a typical application areas including environmental, defence, medical, military and commercial enterprises. In upcoming years, wireless sensor networks will integrate more tiny sensors into a variety of application scenarios like cyber physical systems, IoT etc. The security of sensor networks is an important constraint for the healthy operations of overall system in untrusted areas as these entities may disturb the proper functioning of applications by injecting wrong data into sensor networks. One way to address this issue is to provide more security to sensor nodes and encrypt the communication among them. This paper presents methodology to design attack-aware efficient secure keying mechanism that provides secure data transmission from one sensor node to another. © 2019, Springer Nature Switzerland AG.