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

The world today faces the grave crisis of a pandemic that threatens life, health, industries, economies and society as we know it. To tackle the spread of the COVID-19 disease, it is imperative that the individuals affected by it are identified efficiently. The medical workforce is overwhelmed in many countries, and doctors and nurses must be assisted with rapid automated systems that detect COVID-19 confidently without the need for trained personnel or special manufacturing processes. The currently available testing mechanisms are accurate but take up to 5 days to show results. Automated radiological testing is faster and does not require any testing kits. This study aims to simplify, speed up and validate the process of COVID-19 detection and differentiation from other lung diseases using Artificial Intelligence and Machine Learning principles to implement a cost effective and accurate COVID-19 test by detection of inflamed parts of lungs in chest X-Rays. Two approaches for separate target users are built. In the first approach, a customized Convolutional Neural Network model is built to classify chest X-ray images into two classes- 0(COVID-19 negative) and 1 (COVID-19 positive). This model reached an accuracy of 98%. The Inception v3 architecture (GoogLeNet) was trained for the second application, in which people infected by COVID-19 were distinguished from patients of other underlying lung diseases such as Atelectasis and Pneumonia. This approach demonstrates how reliably an automated COVID-19 test can be extended to people with other lung complications and showed a 91% accuracy. © 2021 IEEE.

World University root node

School of Computer Engineering & Technology

Engineering and Technology

2021 2nd Global Conference for Advancement in Technology, GCAT 2021

COVID-19 Detection Using Convolutional Neural Networks and InceptionV3

Emotion analysis plays a part in understanding the feelings of human beings. People's actions and speech express various feelings, behaviors and emotions which can have various impacts. Emotion and sentiment analysis is a broad research area for finding emotion which helps getting useful insight through text and speech. In most of previous work, nearly all projects have focused on analyzing the expression based on positive, negative and neutral classification. This research work analyzes the proposed system by categorizing the text into emotion classes called joy, sadness, anger, fear, love and surprise. This work helps us to label text emotions into multiple classes and categorize the text for better accuracy. This research work represents the enhancement of a novel Deep Learning model scheme, Long Short-Term Memory and Recurrent Neural Network which discuss various categories distribution on knowledgeable data. This will also summarize the previous works done on textual emotional classification based on various sentiment models and approach with comparative survey analysis.

Proceedings of the 6th International Conference on Communication and Electronics Systems, ICCES

Study and Analysis of Emotion Classification on Textual Data

Dynamic Textures are temporal extension of static textures. Texture is defined as an image with some repetitive pattern in it. Dynamic texture (DT) is nothing but video data with some stationary properties and some moving objects in it. DT segmentation is a broad branch of DT Analysis. Segmentation is separating disjoint regions in the image frames which have homogeneous properties such as texture, color, motion. An important role is played by it in the applications like forest fire detection, traffic density detection, crowd congestion detection before stampede, auto pilot airplanes. There are various approaches used for DT segmentation. In this paper, the approaches based on optical flow, local spatiotemporal technique (Local Binary Pattern) and Global spatiotemporal technique (Contourlet transform) are discussed. Each technique is used and modified or combined with some other technique by researchers. This paper gives an overview of all techniques along with variations in them and the benefits achieved by using them on DT dataset. Some experimental results are also presented.

Springer : Archives of Computational Methods in Engineering

Dynamic Texture Segmentation Approaches for Natural and Manmade Cases: Survey and Experimentation

Dynamic texture (DT) is temporal extension of static texture. There are two broad types of dynamic textures: natural and manmade. The examples of dynamic textures are waving tree, sea water, fountain, traffic, moving crowd, etc. Dynamic texture segmentation is a technique used to separate the moving objects from stationary content in the video. Majority of the techniques give good results either for natural or for manmade dynamic textures. The proposed approach for DT Segmentation is combination of local spatiotemporal technique, i.e., local binary pattern-Weber local descriptor and global spatiotemporal technique, i.e., contourlet transform. The local spatiotemporal technique considers the appearance and motion of the object for segmentation. The technique is computationally less complex than optical flow and gives good results for manmade dynamic textures. The global spatiotemporal technique is based on Laplacian pyramid and directional filters. It gives good results for natural dynamic textures. The proposed technique discussed in this paper is unified approach for any type of dynamic texture. The regions of images commonly segmented by both the techniques are considered as the final segmented output. The proposed system works equally well on any kind of dynamic texture. © 2019 Inderscience Enterprises Ltd.

International Journal of Reasoning-based Intelligent Systems

Unified dynamic texture segmentation system based on local and global spatiotemporal techniques

International Journal of Image, Graphics \& Signal Processing

Velocity and Orientation Detection of Dynamic Textures Using Integrated Algorithm.

Data mining also sometimes called data or knowledge discovery is the process of analyzing data from different perspectives and summarizing it into useful information. Image processing is related to Computer vision, which is a high-level image processing out of which a machine/computer/software intends to decipher the physical contents of an image or a sequence of images. One of the ways to do this is by comparing selected facial features from the image and a facial database. Recognizing emotion from images has become one of the active research themes in image processing and in applications based on human-computer interaction. This research conducts an experimental study on recognizing facial emotions. The flow of our emotion recognition system include the basic process in FER system. These include image acquisition, preprocessing of an image, face detection, feature extraction, classification and then when the emotions are classified the system assigns the user particular music according to his emotion. Our system focuses on live images taken from the webcam. The aim of this research is to develop automatic facial emotion recognition system for stressed individuals thus assigning them music therapy so as to relief stress. The emotions considered for the experiments include happiness, Sadness, Surprise, Fear, Disgust, and Anger that are universally accepted. © 2017 IEEE.

Proceedings of the 2017 International Conference on Intelligent Computing and Control Systems, ICICCS 2017

Facial emotion recognition system through machine learning approach

This paper proposes an innovative idea for segmenting a video sequence including vigorous textures in transform domain. The advanced mode is based on the contourlet transform. This transform method agreed to re-evaluate spatiotemporal structures for a given level and inclination. All advances examine and compare multi-scale dissolution to recognize dynamic texture in image sequences and then intend a simple efficient approach to dynamic texture segmentation using contourlet transform with measures to find the vibratory attribute. So it is competent to specify the traffic of an image sequence by calculating the parameters. The observation that, for objects that generally travel, it is normally easier to see them when they are moving than when they are stationary. However when there will be more traffic crowd, it is difficult to find the task features. The segmentation proficiency is well applied on video sequence of dynamic texture and also traffic classification is done by measuring the parameter. Future prospects are finally discussed. © 2015 IEEE.

International Conference on Emerging Trends in Engineering and Technology, ICETET

Traffic Video Surveillance Using Contourlet Transform

The texture which is in motion is known as Dynamic texture. As the texture can change in shape and direction over time, Segmentation of Dynamic Texture is a challenging task. Furthermore, features of Dynamic texture like spatial (i.e., appearance) and temporal (i.e., motion) may differ from each other. However, studies are mostly limited to characterization of single dynamic textures in the current literature. In this paper, the segmentation problem of image sequences consisting of cluttered dynamic textures is addressed. For the segmentation of dynamic texture, two local texture descriptor based techniques and Lucas-Kanade optical flow technique are combined together to achieve accurate segmentation. Two texture descriptor based techniques are Local binary pattern and Weber local descriptor. These descriptors are used in spatial as well as in temporal domain and it helps to segment a frame of video into distinct regions based on the histogram of the region. Lucas-Kanade based optical flow technique is used in temporal domain, which determines direction of motion of dynamic texture in a sequence. These three features are computed for every section of individual frame and equivalent histograms are obtained. These histograms are concatenated and compared with suitable threshold to obtain segmentation of dynamic texture. © Springer International Publishing Switzerland 2015.

Advances in Intelligent Systems and Computing

Dynamic texture segmentation using texture descriptors and optical flow techniques

Document image analysis analyzes the document images to extract the text and graphics information from image. Printed character recognition is important in the context of document image analysis. Machine learning Approach such as pattern recognition and matching can be applied to document image based printed character recognition. In this paper we discussed the Template Matching approach to printed character recognition. Template matching is found to be an effective technique to recognize printed character as compared to neural network and other classification techniques. © 2013 Springer.

An approach for document image based printed character recognition

International Journal of Scientific \& Engineering Research

Vehicle detection and tracking from video frame sequence

Printed character recognition is important in the context of document image analysis. Document image analysis analyzes the document images to extract the text and graphics information from image. Text extraction is followed by the character recognition .This paper reviews various phases of machine printed character recognition in document image. © 2012 Published by Elsevier Ltd.