August 2021-September 2022 - Assistant Professor - Department of Information Technology, V. R. Siddhartha Engineering College, Kanuru, Vijayawada, AP.
Jan 2018-Feb 2021 - Senior Research Fellow - Centre of Payment Systems LAB, Institute for Development and Research in Banking Technology (IDRBT)-Hyderabad- TS.
Jan 2016-Jan 2018 -Junior Research Fellow - Centre of Excellence in Analytics LAB, Institute for Development and Research in Banking Technology (IDRBT)- Hyderabad-TS.
July 2012-Jan 2016 - Assistant Professor - Department of Computer Science and Engineering, Koneru Lakshmaiah University, Vijayawada-AP.
Dec 2007-June 2012 - Assistant Professor - Department of Computer Science and Engineering, Gayatri Vidya Parishad College of Engineering (Autonomous), Visakhapatnam-AP.
Research Interest
Developing Machine learning & Deep learning models to solve various problems in the area of Natural Languaging Processing, Image Processing & Computer Vision.
Developing novel and innovative models in Machine learning and Deep learning.
Developing Machine learning & Deep learning models for “Anomaly detection in surveillance videos” & for “Face recognition in surveillance videos”.
Developing Machine learning & Deep learning models for “Learning low resource languages” & for “Social media text analysis”.
Awards
2006 – PGECET–32nd Rank (State Rank)
2010 - Ratified as an Assistant Professor–JNTU Kakinda
January 2016- January 2022- Ph.D. Fellowship - Institute for Development and Research in Banking Technology (IDRBT), Telangana, Hyderabad.
Source Title: Lecture Notes in Networks and Systems, Quartile: Q4, DOI Link
View abstract ⏷
The news is the most crucial resource for the general population to learn about what is occurring across the world. Even if newspapers remain a reliable source of news, social media is currently the next frontier in news. Regular individuals may simply alter the news to produce fake news since these social networks are so accessible. These fictitious news stories may be utilized for both political and commercial gains. It may be used as a vehicle to stir up neighborhood animosity, which is detrimental to society. In order to mitigate its impacts, it is crucial to recognize fake news. A platform that can validate and classify news is currently unavailable. In this essay, a technique is presented for figuring out whether or not news is reliable in the present. To train the features that were retrieved from the data using natural language processing techniques, this system makes use of ML classifiers including Decision Tree, Random Forest (RF), and Logistic Regression (LR). We evaluate each classifiers performance using a variety of parameters. The best classifier will provide the outcome for real-time news prediction.
Source Title: Lecture Notes in Networks and Systems, Quartile: Q4, DOI Link
View abstract ⏷
Face Recognition (FR) and surveillance video analytics are well-defined and solved problems in the applications of Computer Vision. FR aims to identify an already known person in a given image. Surveillance video analytics seeks to identify the occurrence of abnormal events or things in public places. But, recognizing the movements of most wanted criminals or suspects in public areas using FR systems with unclear surveillance video inputs is a very challenging problem. This work analyses the performance of three existing popular machine learning-based FR systems. They are (i) ViolaJones detector, (ii) HOG-based FR, and (iii) PCA-based FR. This work analyzes the performance of these FR models on two different datasets. One is a benchmark dataset that has only the frontal view of the faces of various subjects. Another dataset we created with 10000 images. These images are collected from 50 subjects. From each subject, 200 images are taken from various angles. This work observes that the above models will improve their performance from 7 to 10% in terms of accuracy by training them on the proposed dataset.
Patents
Projects
Scholars
Interests
Artificial Intelligence
Computer Vision
Data Science
Deep Learning
Image Processing
Natural Language Processing
Theoretical Computer Science
Thought Leaderships
There are no Thought Leaderships associated with this faculty.
Top Achievements
Education
2004
BTech
VSCE, Kakatiya University India
India
2008
MTech
Andhra University India
India
2022
National Institute of Technology-Warangal, India
India
Experience
August 2021-September 2022 - Assistant Professor - Department of Information Technology, V. R. Siddhartha Engineering College, Kanuru, Vijayawada, AP.
Jan 2018-Feb 2021 - Senior Research Fellow - Centre of Payment Systems LAB, Institute for Development and Research in Banking Technology (IDRBT)-Hyderabad- TS.
Jan 2016-Jan 2018 -Junior Research Fellow - Centre of Excellence in Analytics LAB, Institute for Development and Research in Banking Technology (IDRBT)- Hyderabad-TS.
July 2012-Jan 2016 - Assistant Professor - Department of Computer Science and Engineering, Koneru Lakshmaiah University, Vijayawada-AP.
Dec 2007-June 2012 - Assistant Professor - Department of Computer Science and Engineering, Gayatri Vidya Parishad College of Engineering (Autonomous), Visakhapatnam-AP.
Research Interests
Developing Machine learning & Deep learning models to solve various problems in the area of Natural Languaging Processing, Image Processing & Computer Vision.
Developing novel and innovative models in Machine learning and Deep learning.
Developing Machine learning & Deep learning models for “Anomaly detection in surveillance videos” & for “Face recognition in surveillance videos”.
Developing Machine learning & Deep learning models for “Learning low resource languages” & for “Social media text analysis”.
Awards & Fellowships
2006 – PGECET–32nd Rank (State Rank)
2010 - Ratified as an Assistant Professor–JNTU Kakinda
January 2016- January 2022- Ph.D. Fellowship - Institute for Development and Research in Banking Technology (IDRBT), Telangana, Hyderabad.
Source Title: Lecture Notes in Networks and Systems, Quartile: Q4, DOI Link
View abstract ⏷
The news is the most crucial resource for the general population to learn about what is occurring across the world. Even if newspapers remain a reliable source of news, social media is currently the next frontier in news. Regular individuals may simply alter the news to produce fake news since these social networks are so accessible. These fictitious news stories may be utilized for both political and commercial gains. It may be used as a vehicle to stir up neighborhood animosity, which is detrimental to society. In order to mitigate its impacts, it is crucial to recognize fake news. A platform that can validate and classify news is currently unavailable. In this essay, a technique is presented for figuring out whether or not news is reliable in the present. To train the features that were retrieved from the data using natural language processing techniques, this system makes use of ML classifiers including Decision Tree, Random Forest (RF), and Logistic Regression (LR). We evaluate each classifiers performance using a variety of parameters. The best classifier will provide the outcome for real-time news prediction.
Source Title: Lecture Notes in Networks and Systems, Quartile: Q4, DOI Link
View abstract ⏷
Face Recognition (FR) and surveillance video analytics are well-defined and solved problems in the applications of Computer Vision. FR aims to identify an already known person in a given image. Surveillance video analytics seeks to identify the occurrence of abnormal events or things in public places. But, recognizing the movements of most wanted criminals or suspects in public areas using FR systems with unclear surveillance video inputs is a very challenging problem. This work analyses the performance of three existing popular machine learning-based FR systems. They are (i) ViolaJones detector, (ii) HOG-based FR, and (iii) PCA-based FR. This work analyzes the performance of these FR models on two different datasets. One is a benchmark dataset that has only the frontal view of the faces of various subjects. Another dataset we created with 10000 images. These images are collected from 50 subjects. From each subject, 200 images are taken from various angles. This work observes that the above models will improve their performance from 7 to 10% in terms of accuracy by training them on the proposed dataset.
August 2021-September 2022 - Assistant Professor - Department of Information Technology, V. R. Siddhartha Engineering College, Kanuru, Vijayawada, AP.
Jan 2018-Feb 2021 - Senior Research Fellow - Centre of Payment Systems LAB, Institute for Development and Research in Banking Technology (IDRBT)-Hyderabad- TS.
Jan 2016-Jan 2018 -Junior Research Fellow - Centre of Excellence in Analytics LAB, Institute for Development and Research in Banking Technology (IDRBT)- Hyderabad-TS.
July 2012-Jan 2016 - Assistant Professor - Department of Computer Science and Engineering, Koneru Lakshmaiah University, Vijayawada-AP.
Dec 2007-June 2012 - Assistant Professor - Department of Computer Science and Engineering, Gayatri Vidya Parishad College of Engineering (Autonomous), Visakhapatnam-AP.
Research Interests
Developing Machine learning & Deep learning models to solve various problems in the area of Natural Languaging Processing, Image Processing & Computer Vision.
Developing novel and innovative models in Machine learning and Deep learning.
Developing Machine learning & Deep learning models for “Anomaly detection in surveillance videos” & for “Face recognition in surveillance videos”.
Developing Machine learning & Deep learning models for “Learning low resource languages” & for “Social media text analysis”.
Awards & Fellowships
2006 – PGECET–32nd Rank (State Rank)
2010 - Ratified as an Assistant Professor–JNTU Kakinda
January 2016- January 2022- Ph.D. Fellowship - Institute for Development and Research in Banking Technology (IDRBT), Telangana, Hyderabad.
Source Title: Lecture Notes in Networks and Systems, Quartile: Q4, DOI Link
View abstract ⏷
The news is the most crucial resource for the general population to learn about what is occurring across the world. Even if newspapers remain a reliable source of news, social media is currently the next frontier in news. Regular individuals may simply alter the news to produce fake news since these social networks are so accessible. These fictitious news stories may be utilized for both political and commercial gains. It may be used as a vehicle to stir up neighborhood animosity, which is detrimental to society. In order to mitigate its impacts, it is crucial to recognize fake news. A platform that can validate and classify news is currently unavailable. In this essay, a technique is presented for figuring out whether or not news is reliable in the present. To train the features that were retrieved from the data using natural language processing techniques, this system makes use of ML classifiers including Decision Tree, Random Forest (RF), and Logistic Regression (LR). We evaluate each classifiers performance using a variety of parameters. The best classifier will provide the outcome for real-time news prediction.
Source Title: Lecture Notes in Networks and Systems, Quartile: Q4, DOI Link
View abstract ⏷
Face Recognition (FR) and surveillance video analytics are well-defined and solved problems in the applications of Computer Vision. FR aims to identify an already known person in a given image. Surveillance video analytics seeks to identify the occurrence of abnormal events or things in public places. But, recognizing the movements of most wanted criminals or suspects in public areas using FR systems with unclear surveillance video inputs is a very challenging problem. This work analyses the performance of three existing popular machine learning-based FR systems. They are (i) ViolaJones detector, (ii) HOG-based FR, and (iii) PCA-based FR. This work analyzes the performance of these FR models on two different datasets. One is a benchmark dataset that has only the frontal view of the faces of various subjects. Another dataset we created with 10000 images. These images are collected from 50 subjects. From each subject, 200 images are taken from various angles. This work observes that the above models will improve their performance from 7 to 10% in terms of accuracy by training them on the proposed dataset.
