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

Energy plays very important role in any kind of network. As most of routing protocols and strategies are designed by considering the well-connected network but because of the mobility of nodes these routing protocols do not work effectively. For such kind of intermittent connectivity, Delay Tolerant Network is used. Amongst all the mobile nodes, many have a very limited amount of energy and to perform any kind of operation energy is required. So, it is necessary to reduce the consumption of energy. The effective and efficient use of energy increases the lifetime of node and network. Hence, there are some approaches which proposed different schemes to reduce the energy consumption of node and also increase the delivery probability. By using different aspects of the conservation of energy the energy efficiency has been increased. Amongst them, few recent approaches are explained in this papers which consider the energy prominently. © Springer Nature Singapore Pte Ltd. 2018.

World University root node

Engineering and Technology

Lecture Notes in Networks and Systems

Energy based recent trends in delay tolerant networks

Data analysis plays a significant role in handling a large amount of data in the healthcare. The previous medical researches based on handling and assimilate a huge amount of hospital data instead of prediction. Due to an enormous amount of data growth in the biomedical and healthcare field the accurate analysis of medical data becomes propitious for earlier detection of disease and patient care. However, the accuracy decreases when the medical data is partially missing. To overcome the problem of missing medical data, we perform data cleaning and imputation to transform the incomplete data to complete data. We are working on heart disease prediction on the basis of the dataset with help of Naïve bayes and KNN algorithm. To extend this work, we propose the disease risk prediction using structured data. We use convolutional neural network based unimodel disease risk prediction algorithm. The prediction accuracy of CNN-UDRP algorithm reaches more than 65%. Moreover, this system answers the question related to disease which people face in their life. © 2018 IEEE.

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

Disease risk prediction by using convolutional neural network

Changing perspective of Industries with Industry 4.

Binarization of Degraded Brahmi Script Estampage Images

Diabetes mellitus is a chronic disease and a health challenge worldwide. According to the International Diabetes Federation, 451 million people across the globe have diabetes, with this number anticipated to rise up to 693 million people by 2045. It has been shown that 80% of the complications arising from type II diabetes can be prevented or delayed by early identification of the people who are at risk. Diabetes is difficult to diagnose in the early stages as its symptoms grow subtly and gradually. In a majority of the cases, the patients remain undiagnosed until they are admitted for a heart attack or begin to lose their sight. This paper analyzes the different classification algorithms based on a patient's health history to aid doctors identify the presence of as well as promote early diagnosis and treatment. The experiments were conducted on Pima Indian Diabetes data set. Various classifiers used include K Nearest Neighbors, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, Support Vector Machine and Neural Network. Results demonstrate that Random Forests performed well on the data set giving an accuracy of 79.7%.

Analysis of Classifiers for Prediction of Type II Diabetes Mellitus

Mobile grid (MG) is emergi ng as a new computing paradigm due to the ubiquitous availability of mobile devices. With the advancement in the capability of these devices, computationally intensive tasks can be executed using a peer-to-peer grid of such devices. MG can provide an edifice to execute parallel computationally intensive tasks. Key challenges that crop up while computing on a MG are resource constrained environment, inefficient resource allocation, high failure probability, etc. As a result, selection of appropriate nodes for task execution becomes critical for successful execution of the application. In this paper, we propose an efficient resource allocation model (ERAM) which provides resource allocation with failure handling. We created a MG comprising of Wi-Fi Direct connected Android smartphones. Different scenarios are considered for the purpose of experimentation. Our approach performs well with respect to application completion time, % battery consumption and recovery time from failure in comparison with existing techniques.

International Journal of Information Technology

Efficient Resource Allocation scheme for on-the-fly computing based Mobile Grids

In this paper, 35 W driver power amplifier was designed and simulated using GaN HEMT for L-band radar. GaN HEMT is used because it can provide high output power and high gain as compared to other semiconductor technologies. The 35 W output power is generated using CGHV40030 GaN HEMT which is sufficient to drive further stages of power amplifier. The driver amplifier is designed at 1.3 GHz of center frequency. This amplifier is designed in class AB and 60.5% of PAE is achieved. © 2018, Springer Nature Singapore Pte Ltd.

Advances in Intelligent Systems and Computing

35 W GaN solid-state driver power amplifier for L-band radar applications

This paper proposes a framework for facial image quality estimation in order to address the limitation of real-time applicability of facial recognition. This framework determines whether an image is suitable for facial recognition. We first exploit machine learning algorithms to map the relationship between image quality features and performance of facial recognition. We extract a variety of features (like focus measure, brightness, obscured face) and study their influence on the accuracy of face recognition. After examining the results of this approach, we then used deep learning to build a binary classifier which accepts or rejects images before sending them for actual facial recognition. This decision is taken based on the probability of the facial recognition framework correctly matching a face from the image. We used images from the Chokepoint dataset, and OpenFace-an open source facial recognition software, for building our framework. © 2018 IEEE.

2018 3rd International Conference for Convergence in Technology, I2CT 2018

Towards Designing an Adaptive Framework for Facial Image Quality Estimation at Edge

New level of information sharing is enabled by different online communities like wikis, blogs, forums etc. which provides a platform for interaction with individuals which offers services like searching and posting queries or answers and share expertise with other information seekers. For recently posted query or searched query system furnishes the pool of answers with similar questions links, which could be a prolonged task for finding the significant answer. To overcome this, the system proposes an approach to effectively rank answers which are most relevant and best from historical archives based on similar queries found. It comprises of two components, one which contains training samples with positive, negative and neutral classes and other component retrieves similar questions to posted questions which are with their answer pools. Two data mining approaches were compared to retrieve similar questions. Our objective is to rank answer candidates based on pair wise comparison where question-Answer pairs are ranked using pair wise learning to a rank approach based on a trained model which provides the user with most relevant answers for a given posted question. © 2018 IEEE.

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

Answer selection in community question answering portals

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

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.