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

We mean to use the new headway in innovation to foster dietary as evaluation arrangements which is a more advantageous way to deal with screen every day food admission and control dietary patterns. Food, calorie and sustenance estimation framework is extremely useful for people groups to gauge and deal with their every day food admission. AI will make proposals dependent on the things that are of client's advantage or loved by them before. Additionally these calculations are utilized to suggest the food that the client might like. The suggested food and food in standard plans set by client's own circumstance are in a similar group, which meets the client's healthful equilibrium. We feature the issue of determination of legitimate eating regimen that should satisfy everybody's nourishing prerequisites. © Grenze Scientific Society, 2022.

World University root node

School of Computer Engineering & Technology

Engineering and Technology

13th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2022

Diet Recommendation by Machine Learning

Detection, and mapping of potholes in a precise and punctual manner is an essential task in avoiding road accidents. Today, roadway distresses are manually detected, which requires time and labor. In this paper, we introduce a system which uses deep learning algorithms and is integrated with smartphones to detect potholes in real-time. The user interface of the system is a smartphone application which maps all potholes on a route that the user is traveling. Simultaneously, deep learning object detection algorithm: Single Shot Multi-box Detector (SSD) looks for potholes using a mobile camera in the background. As soon as an unregistered pothole is detected by SSD, coordinates of the pothole are updated to the database in real-time. Accelerometer and gyroscope readings are continuously taken and assessed by a Deep Feed Forward Neural Network model to detect unregistered potholes. This dual mechanism of camera-based as well as accelerometer-gyroscope based detection not only cross validates detections but also provides stable results even if one mechanism fails. The pothole co-ordinates are rendered on the map user interface that can be accessed in the same application. This system with map/navigation feature as front end and two-fold deep learning pothole detection algorithm in backend is an efficient and a zero cost solution for real-time pothole detection.

IEEE Xplore

Deep Learning Approach to Detect Potholes in Real-Time using Smartphone

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.

Lecture Notes on Data Engineering and Communications Technologies

Efficient and Secure Keying Mechanism for Communication in Sensor Networks

It has been observed that, the use of wireless sensor network platforms have procured vast popularity over last few years. The wireless sensor networks are being widely used in agriculture, home security, health care monitoring and traffic management systems. Usage of these systems has increased rapidly. An organization or institute can make use of the data obtained from various such sensor network systems for their analysis. Data obtained from such networks can be misused by any person if the systems are not well secured. Thus misuse of valuable information raises many problems for organization or institute. So to tackle this problem, we need to secure the sensor network systems from vulnerable attacks. In this paper, we propose a new novel approach for securing wireless sensor networks with different layers of security along with data encryption / decryption strategies. As there is a huge concern regarding privacy and integrity of data, this approach will help the organizations to secure their information and make underlying wireless networks more secure and reliable. This paper also brings focus on design and implementation of reliable secure sensor network for the health care monitoring in medical field. The architecture will allow smooth and efficient interconnection of patient's body area network and stationary wireless networks. It presents native solutions for event management, time synchronization and security. © 2016 IEEE.

Proceedings - International Conference on Global Trends in Signal Processing, Information Computing and Communication, ICGTSPICC 2016

Novel approach towards securing wireless sensor network systems for health care

In this era, the web usage mining plays one of the important role in analyzing and improving performance of web applications. Initially in this paper, the concept of web usage mining has been introduced along with Improved AprioriAll algorithm which is the base algorithm for the proposed tool along with its limitation. The improved approach is discussed which integrates sequential pattern mining with graph theory. In the next part, design of web usage mining tool with improved approach is discussed. Implementation details of the tool are discussed along with the results. The last part covers how the raw result from the proposed tool can be further visualized and analyzed using new technologies like D3.js and Neo4j. The future scope of the proposed tool is mentioned at the end of the paper. © 2017 IEEE.

Proceedings - 1st International Conference on Intelligent Systems and Information Management, ICISIM 2017

Web usage mining tool by integrating sequential pattern mining with graph theory

Over the last decade social media platforms such as Facebook and Twitter have gained huge popularity. Usage of social media sites has increased leaps and bounds. An organization or institute can make use of the data obtained from various such social networking sites to their benefit. Digital Identity created by any person/customer on such sites can be targeted by the organization or institute for promoting their services as well as to target some new customers, thus providing them new business opportunities. Thus it can be a win-win situation for both the parties-the organization gets opportunity to increase their business footprint and the customers may be more than happy with the personalized offers from the organization. In this paper, we propose a method to understand customer's likings using public APIs made available by the social network sites such as Twitter and Facebook. As there is huge concern regarding privacy of data on social media, we have used only the data made available publicly by these platforms without using any unethical tools or methods. © 2016 IEEE.

Proceedings of the 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology, iCATccT 2016

Digital identity based recommendation system using social media

We present staff notation recognition using deep learning technique. To satisfy the purpose, the application requires a user to provide input image of the staff notations. As the staff notations are predominantly black in color, algorithms like desaturation and decomposition can be used to convert colored images into gray scale equivalent (Image Processing). The converted images will undergo the processing of Convolutional Neural Network algorithms which will equip multilayer approach to perform convolution and pooling. The task achievement involves feature extraction to generate an output classification in the form of text. © 2016 IEEE.

A novel approach for staff notation recognition using deep learning

Now a days wireless sensors are attracting strong interest for different monitoring and controlling applications as they are integrated with the function of sensing, processing and wireless communication. The wireless sensor networks have been deployed for agricultural, industrial, remote monitoring and traffic management purposes. As energy shortage is a worldwide problem so that more attention has been placed on incorporating energy harvesting devices in wireless sensor networks. The main purpose of this paper is to focus on the systematic study of the design principles of wireless sensor networks and to provide technical approaches to address three key challenges in designing of reliable and endurable wireless sensor networks. The key challenges are namely communication reliability, operation with significantly low and effective power sources and multi-tier network architecture. © 2017 IEEE.