March 2023 - March 2025 - Assistant Professor - Vignan’s University, Guntur
Research Interest
Focusing on low resource machine translation, multilingual representation learning, transfer learning, dialogue, decision-making, question answering, summarization, ontologies, information retrieval, text decipherment
Focusing on fundamental research, including AI robustness, adversarial machine learning, anti-spoofing, domain adaptation and federated learning, and applied research in application areas such as biomedical sciences, biometric authentication, computational social science and cybersecurity
Memberships
Publications
MU-Net: Modified U-Net Architecture for Automatic Ocean Eddy Detection
Article, IEEE Geoscience and Remote Sensing Letters, 2022, DOI Link
View abstract ⏷
Ocean eddies have a significant effect on the maritime environment. They are necessary for carrying a variety of ocean traces across the ocean. Although deep learning algorithms for detecting eddies are a relatively new trend, it is still in their infancy. In this letter, a deep learning method for ocean eddy identification based on semantic segmentation is proposed. In semantic segmentation, understanding the context efficiently for pixel-level recognition is crucial. Two attention modules are proposed to tackle this problem. The proposed work consists of VGG16-based U-Net architecture with two attention modules to show a contextual correlation in the channel and spatial dimensions. Every pixel or channel adapts to include context from every other pixel or channel based on their correlations. Furthermore, a new residual path is proposed to replace the conventional skip connection between encoder and decoder modules. The findings of the experiments show that adopting an attention-based deep framework and new residual path improves the model performance over the existing state-of-the-art techniques.
Patents
Projects
Scholars
Interests
Deep Learning
Federated Machine Learning
Natural Language Processing
Thought Leaderships
There are no Thought Leaderships associated with this faculty.
Top Achievements
Research Area
No research areas found for this faculty.
Recent Updates
No recent updates found.
Education
2010
B.Tech
NIET, JNTU K
India
2012
M.Tech
NIET, JNTU K
India
2024
Ph.D
NIT Rourkela
India
Experience
March 2023 - March 2025 - Assistant Professor - Vignan’s University, Guntur
Research Interests
Focusing on low resource machine translation, multilingual representation learning, transfer learning, dialogue, decision-making, question answering, summarization, ontologies, information retrieval, text decipherment
Focusing on fundamental research, including AI robustness, adversarial machine learning, anti-spoofing, domain adaptation and federated learning, and applied research in application areas such as biomedical sciences, biometric authentication, computational social science and cybersecurity
Awards & Fellowships
Memberships
Publications
MU-Net: Modified U-Net Architecture for Automatic Ocean Eddy Detection
Article, IEEE Geoscience and Remote Sensing Letters, 2022, DOI Link
View abstract ⏷
Ocean eddies have a significant effect on the maritime environment. They are necessary for carrying a variety of ocean traces across the ocean. Although deep learning algorithms for detecting eddies are a relatively new trend, it is still in their infancy. In this letter, a deep learning method for ocean eddy identification based on semantic segmentation is proposed. In semantic segmentation, understanding the context efficiently for pixel-level recognition is crucial. Two attention modules are proposed to tackle this problem. The proposed work consists of VGG16-based U-Net architecture with two attention modules to show a contextual correlation in the channel and spatial dimensions. Every pixel or channel adapts to include context from every other pixel or channel based on their correlations. Furthermore, a new residual path is proposed to replace the conventional skip connection between encoder and decoder modules. The findings of the experiments show that adopting an attention-based deep framework and new residual path improves the model performance over the existing state-of-the-art techniques.
March 2023 - March 2025 - Assistant Professor - Vignan’s University, Guntur
Research Interests
Focusing on low resource machine translation, multilingual representation learning, transfer learning, dialogue, decision-making, question answering, summarization, ontologies, information retrieval, text decipherment
Focusing on fundamental research, including AI robustness, adversarial machine learning, anti-spoofing, domain adaptation and federated learning, and applied research in application areas such as biomedical sciences, biometric authentication, computational social science and cybersecurity
Awards & Fellowships
Memberships
Publications
MU-Net: Modified U-Net Architecture for Automatic Ocean Eddy Detection
Article, IEEE Geoscience and Remote Sensing Letters, 2022, DOI Link
View abstract ⏷
Ocean eddies have a significant effect on the maritime environment. They are necessary for carrying a variety of ocean traces across the ocean. Although deep learning algorithms for detecting eddies are a relatively new trend, it is still in their infancy. In this letter, a deep learning method for ocean eddy identification based on semantic segmentation is proposed. In semantic segmentation, understanding the context efficiently for pixel-level recognition is crucial. Two attention modules are proposed to tackle this problem. The proposed work consists of VGG16-based U-Net architecture with two attention modules to show a contextual correlation in the channel and spatial dimensions. Every pixel or channel adapts to include context from every other pixel or channel based on their correlations. Furthermore, a new residual path is proposed to replace the conventional skip connection between encoder and decoder modules. The findings of the experiments show that adopting an attention-based deep framework and new residual path improves the model performance over the existing state-of-the-art techniques.
