Dr. Rama Devi Burri
@iare.ac.in
Professor, Information Technology
Institute of Aeronautical Engineering
EDUCATION
Ph.D from Acharya Nagarjuna University
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Artificial Intelligence, Computer Science, Computer Science
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
V. Suryanarayana, B. Prabhu Shankar, Rama Devi Burri, T. Priyanka, Ravi Kumar Saidala, A. Sasi Kumar, Piyush Chauhan, and Jagdish Chandra Patni
Elsevier BV
Rama Devi Burri, Lalitha Aradhini Kusampudi, Shaik Mohammed Sharfuddin, and Nandigam Venkata Siva Sai
IEEE
In these days there has been greater concern about the driver's drowsiness on road safety. According to the survey of the National Highway Traffic Safety Administration (NHTSA) a greater percentage of fatalities, injuries and even deaths every year is because of drowsy driving. So, there is an immediate necessity to implement a system which detects the drowsiness of the driver and alerts the driver. These systems, which rely on visual behaviour analysis, hold the potential to significantly decrease accidents by providing timely alerts when drivers exhibit signs of drowsiness. These systems make use of cameras and computer vision algorithms, such as the Haar cascade classifier and CNN. These systems scrutinize facial features, eye movements, and other indicators to assess levels of alertness and identify signs of drowsiness. The cameras that are integrated continuously capture facial expressions, enabling the evaluation of eyelid closure for the Eye Aspect Ratio (EAR) and Mouth aspect ratio (MAR) across frames. If predefined thresholds for EAR values are surpassed, an alert system triggers, notifying both the driver and passengers. The real-time detection of driver drowsiness, reliant on visual behaviour analysis, carries immense potential to save lives, curtail accidents, and enhance economic outcomes. By promptly alerting drivers to their drowsy state, these systems serve as crucial preventatives of accidents while promoting safer driving practices.
Praveen Vardhan Kuppili
Science Research Society
In this paper, we introduce the notation of a vague strong implicative filter of lattice wajsberg algebra. Also, we investigate some of its properties with illustrations. Further, we obtain the relation between vague implicative filter and anti vague strong implicative filter In lattice wajssberg algebra. Finally, we establish the equivalent condition of a vague strong implicative filter.
V. V. N. S. Tejaswi, V. B. V. N. Prasad, T. Rama Rao, and Rama Devi Burri
AIP Publishing
M Gopikrishna, Rama Devi Burri, Nellore Manoj Kumar, D. Mahesh, G. Siva Sankar, and Nynalasetti Kondala Kameswara Rao
IEEE
Machine learning techniques grounded on mathematical linguistics, such as Natural Language Processing, may detect shifts in public sentiment. This article explores the potential of using Latent Dirichlet Allocation (LDA) and natural language processing (NLP) methods to gauge the public’s rising concern for ecological and conservation issues. It will serve as the foundation upon which factories may base their eco-marketing choices. Massive expenditures are needed to refocus manufacturing on green commodities creation, market introduction of new ecological products, and promotion of energy-saving technology. Capturing the consistent demand from contractors and customers for decolonization is essential for generating a profit. The article contrasts the traditional data collection techniques (such as surveys) with the more modern machine learning techniques. Popularity, media coverage, and the variety of people actively engaged in the discussion are used to determine the importance of various data sources. The World Health Organization (WHO) has made the connection between pollution in the environment and deteriorating health. The author has identified foundational classes and terms that shape how people think about environmental marketing. This report uses NLP to gauge how people feel about ecological marketing concepts.
Sai Manvitha Enadula, Akshith Sriram Enadula, and Rama Devi Burri
IEEE
Online education system was developed due to the Covid-19 pandemic. The core idea of this paper is to map the connection between teaching practices to student learning in an online environment. Face to face evaluation techniques are fairly quick and easy for formative assessments to check student understanding in existent environment. Prevailing studies illustrate that a person's facial expressions and emotions are closely related. In order to make the teaching-learning process more effective, teachers usually collect day to day feedback from the students. This feedback can be used to improve teaching skills and make the process more interactive. In a virtual learning mode, there is a need to identify and understand the emotions of people. Constructive information can be extracted from online platforms using facial recognition algorithms. An online course connected with students is used for examination; the results have shown that this technique performs strongly.
V. B. V. N. Prasad, K. Prasad, M. Ramesh, R. D. Burri, and T. Rama Rao
Union of Researchers of Macedonia
The accessibility of implicit sensors in cellular telephones has empowered a large agency of imaginitive applications. individual elegance of use manages spotting a purchaser's feelings. beyond programs have essentially depended on account and showing self-introduced emotions. This paper indicates a practical feeling recognition framework for cell phones finished as a terrific console that surmises a patron's enthusiastic nation using device mastering structures. The framework utilizes accelerometer readings and exceptional a part of composing behavior like pace and postponement among letters to prepare a classifier to foresee emotions. Credulous Bayes, J48, IBK, Multi-reaction without delay relapse and SVM were assessed and J48 modified into seemed to be the finest classifier with over 90% exactness and accuracy. but giving emotive input to singular customers, the framework likewise makes use of geocategorized facts to collect and show passionate conditions of locales or countries via a website.
Now a day's Data is playing a central role and is carrying the big asset in the insurance industry. In today's journey insurance industry has a vital role. Insurance transporters have access to more information than ever before. From the past 700+ years in the insurance industry we can consider the three major eras Starting from 15th century to 1960, industry followed the manual era, from1960s to 2000 we are in the systems era, now we are in digital era i.e. 2001-20X0.The highest corporate object in all three eras is that the fundamental insurance industry has been determined by believing the data analytics in adopting the changing technologies to better and keep the ways and keep capital together. In advanced analysis the main challenge is the analytical models and algorithms which are being insufficient to support insurers; only by machines we can overcome this challenge