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

There is a large number of credit card payments take place that is targeted by fraudulent activities. Companies which are responsible for the processing of electronic transactions need to efficiently detect the fraudulent activity to maintain customers’ trust and the continuity of their own business. In this paper, the developed algorithm detects credit card fraud. Prediction of any algorithm is based on certain attribute like customer’s buying behavior, a network of merchants that customer usually deals with, the location of the transaction, amount of transaction, etc. But these attribute changes over time. So, the algorithmic model needs to be updated periodically to reduce this kind of errors. Proposed System provides two solutions for handling concept drift. One is an Active solution and another one is Passive. Active solution refers to triggering mechanisms by explicitly detecting a change in statistics. Passive solution suggests updating the model continuously in order to consider newly added records. The proposed and developed system filters 80% fraudulent transactions and acts as a support system for the society at a large. © 2018, Springer Nature Singapore Pte Ltd.

World University root node

School of Computer Engineering & Technology

Advances in Intelligent Systems and Computing

Implementation of credit card fraud detection system with concept drifts adaptation

AVAYA Interaction Center is developed for communication between customer and the company. The Interaction Center provides business-class control of contact center communications across several channels like voice, email, web chat. Chat is a progressively popular communication channel due to growth in the era of Internet. Existing chat generator tool supports only IE browser. So, there is a need that the tool should be browser independent. The paper proposes the tool based on REST API. In the proposed system, the REST API of Chat media of CSPortal is captured through SOAPUI tool. The request–response of REST API is captured for different resources like/csportal/cometd/of CSPortal Chat Media. The result shows the proposed approach is more efficient than existing tool and chat is live for more than 2 min. © 2018, Springer Nature Singapore Pte Ltd.

Implementation of REST API automation for interaction center

In this invention the wind turbine is fixed to the tree stem. With this mechanism, the need of foundation of the wind turbine is eliminated. The invention is related to small wind turbine. The turbine location is above the top of the tree to get sufficient wind speed. The pole may be attached to the tree with the help of springs to facilitate the growth of the tree. The pole may be attached to a tree stem at four to five different locations. The energy generated may be stored in a battery kept in a house near by the tree. This may be used for temporary electrical supply for research work in the industry, forest, rural area etc. © 2017 IEEE.

School of Electrical Engineering

2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017

Tree mounted wind turbine

In this paper high the ice accumulated on the wind turbine blade is removed by passing hot air through the blade. The hollow tubes are embedded in the wind turbine blades. The hollow tubes may be circular on cross section or of any suitable shape. The air is heated by an electrical heating or by gas geyser. First the air is compressed with help of a compressor. Then that air is passed through the electric heater or gas heater. The hot air is then passed through the wind turbine blade. When the hot air is passéd through the wind turbine blade, the ice melts and falls down. Hot air is passed through the wind turbine blade after frequent intervals of time when the environmental conditions are favorable for icing. The process of deicing is fast. © 2017 IEEE.

Ice extraction from wind turbine using flow of hot air through blade

In this paper deicing mechanism for wind turbine is developed. Hot water is used to remove the ice accumulated on the wind turbine blades. The pipes are embedded in the wind turbine blades. The pipes which are embedded may be made up of copper or steel or any other thermally conducting material. The water pipe is connected to the blade from the water tank placed at the ground. The water is heated by water heater. The water heater may be an electric heater. The water may also be heated by gas geyser. The water pump is used to circulate the water from the water tank to the wind turbine blades. When the water pump is started it circulates the water through the water heater and the wind turbine blades. The pump keeps the water circulating through the wind turbine blade until the ice melts and falls down. In the environmental conditions favorable for ice accretion on the wind turbine blades, the pump circulates water through wind turbine blade after frequent intervals of time to prevent ice accretion on the wind turbine blades. © 2017 IEEE.

Ice removal from wind turbine using hot water flow through blade

One of the most effective techniques for early breast cancer detection is Digital mammography. In this technique, the electronic images of breast are taken and stored in the computer. Abnormalities in these images can be found by Gray-Level Co-occurrence Matrix (GLCM) and its textural features. The calculation of GLCM and it's textural features is a time consuming process. The affordable solution is Graphical Processing Unit (GPU) based Compute Unified Device Architecture (CUDA) framework where the calculations can be made parallel. The sequential program on CPU as well as parallel program on GPU of GLCM and textural feature calculation have been implemented. The results of optimized parallel implementation on GPU show up to 12 times faster calculation over sequential implementation on CPU. © 2017 IEEE.

School of Electronics & Communication Engineering

Proceedings - International Conference on Trends in Electronics and Informatics, ICEI 2017

Efficient implementation of GLCM based texture feature computation using CUDA platform

Among the numerous factors that are responsible for increasing road accidents, the second most common cause is drowsiness. In an attempt to reduce the rate of accidents, we propose a system which would efficiently handle the timely detection of drowsiness and would accordingly curb the speed of the vehicle being driven. As a proof of concept of the proposed method, we have trained the SVM classifier on the EEG (electroencephalogram) waves derived from “Analysis of a sleep-dependent neuronal feedback loop: the slow-wave micro continuity of the EEG” by Kemp et al. [1, 2]. The data is obtained from a wireless EEG headset. The classification results will determine whether the EEG data corresponds to drowsiness or alertness. This level of drowsiness is then used to determine the maximum speed limit. As the work in [3] has stated, there is a strong correlation between the number of accidents and the speed limit. Hence altogether, the proposed system integrates EEG waves for sleep level detection, and speed lock as a preventive measure to reduce the number of plausible accidents. © 2019, Springer Nature Switzerland AG.

International Conference on Innovations in Bio-Inspired Computing and Applications

BMI Application: Accident Reduction Using Drowsiness Detection

In this paper, we attempt to suppress noise from mouse input due to involuntary tremors from patients with motor disabilities. In order to achieve this task, we used techniques like breakpoint detection and mean filtering to preprocess our data which was then fed into a deep recurrent neural network. Additionally, we tested various architectures and explored existing hardware and software based assistive technologies. © 2019, Springer Nature Switzerland AG.

LSTM Approach to Cancel Noise from Mouse Input for Patients with Motor Disabilities

The purpose of this paper is to investigate stress distribution pattern around implant-bone interface of commercially available different thread profile dental implants using finite element analysis and experimental verification by photo elastic stress analysis. A two-dimensional Finite Element Model of implant prosthesis (Implant, abutment, screw, &amp; human bone) is modeled using commercial software code. Occlusal load on teeth is applied as static load on the abutment to find the shear stress around implant-bone interface. Photo elastic model is prepared using acrylic material, having homogenous mixture of Araldite Resin CY 230 and hardener HY 951. Screwing and drilling of implant in the photo elastic model is done by dentist in the lab. Tardy’s method is used for noting the fringe order at a particular point to predict the stress intensity in the acrylic model. Scaling method is used to predict the shear stress in the cancellous bone from the measured stress intensity of photo elastic model. The current research work reveals stress distribution pattern with different implant systems. The highest value of stress is observed in the model fitted with reverse buttress thread profile at apical portion of implant while least value was observed for implant fitted with V thread profile at critical section, and is found to be the best profile amongst the other two from maximum shear stress criteria.

School of Mechanical Engineering

Materials Today: Proceedings

Finite element analysis and experimental investigations on stress distribution of dental implants around implant-bone interface

Usage of social sites like Facebook, Twitter is increasing rapidly. People are using these sites for getting feedback about any product or service. Social data is the best data which business analyst can use for getting analysis results. From social data the investor can predict in which products the users are more interested in or what changes the users want in service. The social data analysis will definitely increase the sale or profit gained by the investor. The use of machine learning algorithms for analysing market data will add more knowledge into the knowledge of investor. In this study, we have proposed a method for analysing market data collected from social sites. We have shown the behaviour of different machine learning algorithms against market data. It is found that AdaBoost algorithm with one level decision trees perform best against market data. We have used AdaBoost to boost the performance of decision trees and proved with results. Different phases of social media data mining are also explained in detail.