Ultrasonic Study of the Binary Liquid Mixture Containing 1, 4- Dioxane and Ethyl Formate
Authors: Sarwade M. P, Dr. Popalghat S. K
Abstract
Density ( ), viscosity ( ) and Ultrasonic velocity (U) of the binary mixture of 1, 4- Dioxane and Ethyl formate were measured over entire composition range. These measurements were done at constant temperature 303 K and at frequency 1MHZ. These were used to evaluate various acoustic parameters such as intermolecular free length ( ), intermolecular free volume ( ), internal pressure ( ), Wada’s constant (W) and specific acoustic impedance (Z).In addition to these thermo-acoustic parameters excess parameters were computed. These are excess intermolecular free length ( ), excess free volume ( ), excess internal pressure ( ) and excess specific acoustic impedance ( ). These parameters have been interpreted in terms of intermolecular interactions at frequency 1MHZ and at constant temperature 303K.
Review of Fire Detection and Localization Techniques in Video Surveillance Applications
Authors: Kumkum Kumari, Dr. Mohd Ahmed
Abstract
Because of high fire frequency and huge damage, the early fire detection is becoming more and more important. Due to the rapid development of image processing technology, video image in the application of detection technology has become more and more widely. Video based fire detection technology is becoming the focal point of research with its advantages of high intuitive, speed and anti- jamming capability. This paper reviewed the fire detection methods based on video images in recent years. Through the review, it is clear to see that video based fire detection technology can be divided into two main areas: the characteristics detection of flame and smoke. This fire detection method can improve the accuracy of the fire alarm, real-time and robustness. If the optimal algorithms can be adopted for each part of detecting motion area and extracting fire characteristics, the system performance will be further improved.
Question-answering Machine Learning Model For Covid-19
Authors: Rahimanuddin Shaik, Dr. V Nagagopiraju
Abstract
Covid-19 pandemic outbreak has affected everyone of our lives directly or indirectly. There is a lot of information and an equally good amount of misinformation filled out there on the internet. The only way we can mitigate the Covid-19 crisis and keep ourselves in a healthy condition is to be informed with the right information. Whatsapp forwards, YouTube clickbait videos have made the situation worse by propagating misinformation. Governments and NGOs are trying to give out the right information but these misinformation carriers have got more reach and popularity. In this thesis, I tried to come up with a machine learning model that would act as a question answering engine for Covid-19 queries. The Question answer engine is built on the Covid-19 Open Research Dataset (CORD-19 dataset in short), the largest open dataset corpus of Covid- 19 related research papers which is developed by Allen Institute for AI as one of the training datasets. Bidirectional Encoder Representations from Transformers (BERT), Global Vectors for Word Representation (GloVe) and Generative Pre-trained Transformer 3 (GPT-3) models were used to create required embeddings. The model combines Covid-19 related research texts’ mining and leverages the famed GPT-3 models capabilities with Natural Language Processing(NLP) tools to give a better set of answers than the previously known information retrieval Question Answering Systems. Evaluation of the model is done through comparison with a manually created dataset with accurate information based on reliable sources like WHO. It is hoped that the Machine Learning Model created through this research study would be able to help researchers and common people alike in their search for accurate knowledge and information..