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

Vehicle technologies have advanced significantly over the past 20 years, especially with respect to novel in-vehicle systems for route navigation, information access, infotainment, and connected vehicle advancements for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) connectivity and communications. While there is great interest in migrating to fully automated, self-driving vehicles, factors such as technology performance, cost barriers, public safety, insurance issues, legal implications, and government regulations suggest it is more likely that the first step in the progression will be multifunctional vehicles.Today, embedded controllers, as well as a variety of sensors and high-performance computing in present-day cars, allow for a smooth transition from complete human control toward semisupervised or assisted control, then to fully automated vehicles. Next-generation vehicles will need to be more active in assessing driver awareness, vehicle capabilities, and traffic and environmental settings, plus how these factors come together to determine a collaborative safe and effective driver–vehicle engagement for vehicle operation.This article reviews a range of issues pertaining to driver modeling for the detection and assessment of distraction. The areas addressed include 1) understanding driver behavior and distraction, 2) maneuver recognition and distraction analysis, 3) glance behavior and visual tracking, and 4) mobile platform advancements for in-vehicle data collection and human-machine interface 5) Alcohol Detection

World University root node

School of Computer Engineering & Technology

International Research Journal of Engineering and Technology

Survey : Automatic Understanding By Vehicle For Driver Distraction Problem

Distraction is nothing but the lacking of the attention to activities necessary for secure driving. Inattention can either be an intended or an unintended diversion of concentration from drivers side. Driver distraction can be defined as anything that is necessary for identification of information to securely sustain the lateral and longitudinal control of the vehicle due to any some event, the persons, inside or outside the vehicle that might be forced or tends cause the drivers shifting attention to another place from the fundamental driving task. The main cause of driver distraction is to compete for event activity that turns to reduce driving performance that outcome in road traffic crashes. The system defined in this paper given solution to the above problem and can reduce the road accident which is one of the main causes due to driver distraction. It also evaluates the driver's mind become active or not by accurately detecting yawning, face detection, eye detection and to find whether the driver consumed an alcoholic or not. To attain an alcohol detection through the sensor. It is done for the safety of people traveling by car. The Haar cascade algorithm can be used for object detection in real time. © 2018 IEEE.

Proceedings - 2018 4th International Conference on Computing, Communication Control and Automation, ICCUBEA 2018

Camera Based Driver Distraction System Using Image Processing

This paper review conditions of concrete bridge by taking visual inspection and non-destructive test. In India most of bridges was constructed after 1950. So, regular inspection of bridge is necessary. However, various factors such as large infrastructure development, the growth of population, has led to accelerated to detection of damage. Hence heavy traffic, the bridges are subjected to very high dynamic loading, to avoid Collapse Bridge it is mandatory to assess the conditions of existing bridge or health monitoring of bridge. Through this paper we recommending corrective measure for operation and maintenance. NDT method is to be use for verification of visual inspection in bridge management where measure damage occurred in the structure but cannot determine by visual. These techniques will allow inspector to locate damages earlier before any possible tragedies just like mahad bridge collapse in India, there exists a correlation between results of non-destructive test and condition states on visual inspections. There are many methods for non-destructive test (NDT) but every method has its own boundaries and which mean the method cannot afford an accurate and consistence result for difference cases and to detect different defect. The study also show that Ultrasonic pulse velocity test is the ideal NDT method to predict the deterioration in the structures and to determine the service life of the structures. The concept of non-destructive testing is to obtain material properties of in place specimens without the destruction of the specimen or the structure from which it is taken.

School of Civil Engineering

Journal of Advances and Scholarly Researches in Allied Education

Health Monitoring of Mundhwa Bridge

Prediction of daylight illuminance has become the prime concern for building designers. This paper presents the daylight illuminance frequency distribution, correlation coefficients amongst climate variables and linear regression models developed for moderate climate zone. The emphasis of the present study is to predict daylight illuminance intensities and to investigate its relationship amongst the important climatic variables such as temperature, percentage sky clearance and percentage relative humidity at specific timings of the day, observed during different climatic conditions and seasons. The daylight illuminance data is collected with the help of digital ‘Lux’ meter across the Pune city (India). The daylight illuminance frequency distribution, correlation coefficients and the linear regression models derived for four climate specific months are presented and explained. The minimum illuminance level of 6260 Lux is observed at 8.00 am whereas the maximum is 147300 Lux at 12.00 noon in the month of July and the highest frequency of illuminance intensities falls in the range 140000-145000 Lux. A better association (positive) of illuminance intensities has been observed with percentage sky clearance variable; the correlation coefficients in the month of July are 0.678, 0.656 and 0.453. The percentage error between predicted and measured values of daylight illuminance levels derived from the developed regression models varies from 4.47% to 12.33%.

Journal of Engineering Science and Technology

Predicting daylight illuminance in urban city using multiple regression techniques

Substantial accumulations of computerized photos are consistently made. Various collections of digital photos like in the area of technology, private organization, etc. are structured because of the result of digitizing existing accumulations of simple pictures, charts, outlines, canvases, and archives. For the most part, the main strategy for investigating these accumulations was by perusing or indexing of index words, Digital photo databases begin the best approach to searching based on content. In this paper, we have shown a strategy that has no past information about the picture inside the database, yet retrieval is carried out considering the content data of the pictures prone to be called as content based on image retrieval. Here we are attempting to enhance the image retrieval framework for more exactness and efficiency by utilizing Radial base Function neural system. This arrangement with multilayer feed forward system recognition. By utilizing these procedures we can get into know the exact relevant image gave by the client according to the query. The Scale Invariant Feature Transform (SIFT) is a standout amongst the most nearby feature detector and descriptors which is utilized as a part of the vast majority of the vision programming. In this paper in regards to CBIR framework we can use SIFT algorithm to concentrate the nearby feature of the pictures. The retrieval method has been implemented on two CBIR systems, one using multiple features and the other using only RBG feature. The results obtained are positive and we have obtained higher precision (81.93%) using multiple features than that of using only RGB (41.18%). Experiment demonstrates that the proposed system get better results of retrieval system.

International Journal of CiiT

“SIFT based Efficient Content based Image Retrieval System using Neural Network

The world wide web including social sites, blogs, forums etc. generate a huge amount of data in the form of views, emotions, opinions and arguments regarding different products, social activities, brands and politics. This has prompted increasing interests in analytical domain. Thus, all these comments have a great influence on readers, vendors politicians. In political domain, political parties and other experts make use of facebook, twitter and blogs to communicate with the people and to check voice of public. Hence, an appropriate model which gives correct analysis and results about peoples' sentiments related to political parties and political diplomats is built. Data is captured from social media sites and pre-processing is carried out. Word2vec model is used for word embedding process. For final evaluation neural network is used. After detailed study and comparison efficient neural network model is chosen and positive/negative sentiments are identified. Experimental evaluation signifies the effectiveness of the proposed model. This system is also helpful for understanding people's response to particular political decision which will help in better decision making during elections and political campaigns. © 2018 IEEE.

1st International Conference on Data Science and Analytics, PuneCon 2018 - Proceedings

Political Sentiment Assessment through Social Media

Wireless Communication has established itself as an efficient and reliable medium for data transfer. But with the number of end users increasing exponentially and the availability of radio spectrum diminishing at a fast pace there is a need of an alternate approach for data transfer. The optical wireless communication proves to be an efficient alternate solution to solve the radio frequency spectrum crisis and also to overcome the constraints of the Wireless-Fidelity (Wi-Fi) technology. The LEDs used in our daily lives for the purpose of providing light when integrated with Li-Fi technology can make an efficient communication network for data transfer. This paper proposes cost-effective and working application of the Li-Fi technology to overcome the constraints of the existing data transmission technologies and to provide an alternate data transmission medium which is built on the existing infrastructure. © 2018 IEEE.

A New Approach to Wireless Data Transmission Using Visible Light

Due to the scattered lighting and light absorbed conditions, the underwater images frequently suffered from the low contrast as well as color distortion. As underwater images with the different color tones are captured under different light conditions, it is difficult to perform restoration and enhancement of such images. There are number of conventional methods reported to improve the quality of underwater images, however they constantly performs poor. In this paper, we propose a novel underwater image restoration and enhancement algorithm. We proposed the novel modified depth estimation approach for underwater images based on image blurriness as well as light absorption. To improve the processing speed as well as image quality we proposed novel image blurriness estimation approach in first we performed the image normalization and double precision conversion to improve the processing speed, then apply the edge-stopping pyramid technique rather than Gaussian filtering to improve image quality. The proposed approach is simulated and evaluated against the state-of-art methods in terms of processing time, PSNR, SSIM etc. © 2018 IEEE.

School of Electronics & Communication Engineering

Underwater Image Restoration Based on Light Absorption

This android application is developed for the visually impaired. It has real-time objection recognition using a camera phone and gives voice output for the recognized object. The application uses a neural network for efficient recognition and quicker response time. The neural networks, similar to the processing of the brain, develop algorithms that can be used to model complex patterns and prediction problems. We have used MobileNets neural network which is used for mobile and embedded vision application. © 2018 IEEE.

School of Electrical Engineering

Android Application for Object Recognition Using Neural Networks for the Visually Impaired

Hydraulic fracturing is a method in which fluid is pumped at elevated pressures to break down the formation, and create a conductive pathway for production of hydrocarbon fluids. Understanding the stress environment of the rock is critical for a better design and successful execution of a fracturing treatment. More often than not, a formation breakdown pressure is equal to or in close approximation of minimum horizontal or in-situ stress, also termed as closure pressure. Various analytical methods such as G-Function plot, G-dP/dG plot, square-root of time plot etc. are used for the determination of closure pressure, and have been implemented since the inception of hydraulic fracturing as a way to better design fracture treatments. These methods are prone to have subjectivity due to the experience and knowledge of the person analyzing the data, which calls for a need to more objectively analyze such data, in order to better predict the closure pressure. Machine learning is a method to teach computers to implement a predesigned algorithm and execute tasks without having to explicitly program them. It helps create significantly complex mathematical models which automate processes based on critical learning parameters, and predict within a certain acceptable degree of accuracy. In this paper, Artificial Neural Networks (ANN), a machine learning methodology, has been applied in order to minimize the subjectivity in predicting the value of closure pressure. Artificial neural networks, similar to neural networks of the brain, are a system comprising of various neurons. These neurons are organized in layers namely input layer (consisting of input neurons), output layer (consisting of output neurons or results) and multiple hidden layers. The number of input, hidden and output neurons depend on the parameters affecting the end-result. An ANN has been designed, taking into consideration critical parameters on which closure pressure depends. The model identifies and learns from the patterns in the data and predicts the required output. This output is then compared with the actual results in order minimize the error. The objective is to minimize the error so as to get a close match for the given data. In this paper we have kept the ratio of learning to testing at 80:20, which means that of all the available data, 80% is used for training the model and the rest 20% is used for testing the model. Results from this work point to the fact that the ANN was able to predict the closure pressure with reasonable accuracy. © Copyright 2018, Society of Petroleum Engineers.