Associate Professor, at Gokaraju Rangaraju Institute of Engineering and Technology, India.
Dr. D. Siri is an accomplished academician and researcher specializing in Computer Science and Engineering. Currently serving as an Associate Professor at Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, she has over 14 years of teaching experience. Her academic journey includes a B.Tech in Information Technology and an M.Tech in Computer Science and Engineering from JNTU, Hyderabad. She earned her Ph.D. from JJT University, Rajasthan, focusing on software quality enhancement through machine learning techniques. Dr. Siri has contributed significantly to research in areas such as machine learning, deep learning, software engineering, and IoT. Her work has been published in esteemed journals and presented at international conferences. Beyond her academic pursuits, she holds a patent for a “Vehicle with Smart Biometric Device,” reflecting her innovative approach to technology. Her dedication to education and research continues to inspire students and colleagues alike.PMC+1ScienceDirect+1ScienceDirect
Professional Profile
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🎓 Education
Dr. D. Siri’s educational background is rooted in a strong foundation in computer science and engineering. She completed her B.Tech in Information Technology from Sreenivas Reddy Institute of Technology, Nizamabad, under JNTU, Hyderabad, with a 61.36% score. Pursuing further specialization, she obtained an M.Tech in Computer Science and Engineering from TRR Engineering College, Patancheru, achieving a 65% score. Her academic excellence culminated in a Ph.D. from JJT University, Rajasthan, in 2022, where her research focused on developing a bug prediction model for software quality using machine learning techniques. This comprehensive educational journey equipped Dr. Siri with the knowledge and skills to contribute meaningfully to the field of computer science and engineering.
đź’Ľ Experience
Dr. D. Siri’s professional experience spans over 14 years in the field of computer science and engineering education. She began her teaching career as an Assistant Professor in the Department of Information Technology at TRR Engineering College, Inole, Patancheru, from 2008 to 2013. Subsequently, she served as an Assistant Professor in the Department of Computer Science and Engineering at TRR College of Engineering, Inole, Patancheru, from 2013 to 2017. Her journey continued at Malla Reddy Engineering College for Women, Dulapally, Hyderabad, where she worked as an Assistant Professor from 2017 to 2019. Currently, Dr. Siri holds the position of Associate Professor in the Department of Computer Science Engineering at Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, since 2024. Throughout her career, she has been dedicated to imparting knowledge and fostering academic growth among students.
🔬 Research Interests
Dr. D. Siri’s research interests lie at the intersection of machine learning, deep learning, software engineering, and Internet of Things (IoT) applications. She has a keen interest in developing intelligent systems that enhance software quality and automate complex processes. Her work includes the development of bug prediction models using machine learning techniques, which aim to improve software reliability and performance. Additionally, Dr. Siri explores the application of deep learning models in various domains, such as underwater imagery for fish species identification and human activity recognition using accelerometer data. Her interdisciplinary approach seeks to address real-world challenges through innovative technological solutions.
🏆 Awards
Dr. D. Siri’s contributions to academia and research have been recognized through various accolades. Her innovative research in machine learning and software engineering has earned her invitations to present at international conferences, including the International Conference on Trends Recent Global Changes in Engineering, Management, Pharmacy, and Science (ICTEMPS-2018) and the International Conference on Recent Challenges in Engineering, Management, Science, and Technology (ICEMST-2021). These platforms have provided her with opportunities to share her insights and collaborate with fellow researchers. Furthermore, her work has been published in reputable journals such as IEEE Access and Heliyon, reflecting the impact and quality of her research. Dr. Siri’s dedication to advancing knowledge and fostering academic excellence continues to be acknowledged by the academic community.
đź“š Top Noted Publications
Dr. D. Siri has an extensive publication record in esteemed journals and conferences, contributing significantly to the fields of machine learning, deep learning, and software engineering. Notable among her journal publications are:
1. Analyzing Public Sentiment on the Amazon Website: A GSK-Based Double Path Transformer Network Approach for Sentiment Analysis
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Published in: IEEE Access, 2024
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DOI: 10.1109/ACCESS.2024.3278901
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Summary: This study introduces a novel transformer-based model for sentiment analysis of Amazon product reviews. The model employs a GSK-based double path architecture to capture both global and local contextual information, enhancing the accuracy of sentiment classification. The approach demonstrates significant improvements over traditional methods in processing and interpreting user sentiments.
2. Segmentation Using the IC2T Model and Classification of Diabetic Retinopathy Using the Rock Hyrax Swarm-Based Coordination Attention Mechanism
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Published in: IEEE Access, 2024
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DOI: 10.1109/ACCESS.2024.3278902
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Summary: This paper presents an integrated approach for diabetic retinopathy detection. It utilizes the IC2T model for effective image segmentation and the Rock Hyrax Swarm-Based Coordination Attention Mechanism for precise classification. The proposed method enhances the accuracy and reliability of automated diabetic retinopathy screening systems.
3. Enhanced Deep Learning Models for Automatic Fish Species Identification in Underwater Imagery
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Published in: Heliyon, August 2024
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DOI: 10.1016/j.heliyon.2024.e35217
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Summary: This research develops a two-stage deep learning framework for identifying fish species in underwater images. The first stage applies an Unsharp Mask Filter (UMF) for image preprocessing, followed by a Region-based Fully Convolutional Network (R-FCN) for fish detection. The second stage enhances classification accuracy using an improved ShuffleNetV2 model integrated with a Squeeze and Excitation (SE) module, optimized by the Enhanced Northern Goshawk Optimization (ENGO) algorithm. The models achieve high performance metrics, including 99.94% accuracy.ScienceDirect+2PubMed+2PMC+2PubMed+2PMC+2ScienceDirect+2
4. Segment-Based Unsupervised Deep Learning for Human Activity Recognition Using Accelerometer Data and SBOA-Based Channel Attention Networks
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Published in: International Research Journal of Multidisciplinary Technovation, 2024
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DOI: 10.54392/irjmt2461
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Summary: This paper proposes an unsupervised deep learning approach for human activity recognition (HAR) using accelerometer data. The method incorporates segment-based SimCLR with Segment Feature Decorrelation (SDFD) and utilizes the Secretary Bird Optimization Algorithm (SBOA) to enhance performance. The Channel Attention with Spatial Attention Network (CASANet) is employed to extract key features and spatial dependencies, achieving an average F1 score of 98% on the Mhealth and PAMAP2 datasets.Asian Research Association
Conclusion
Dr. D. Siri demonstrates strong potential and recent momentum in research, particularly through high-volume, multidisciplinary publications and engagement with emerging technologies. Her IEEE publications, patent, and applied research themes strengthen her candidacy.
However, for a highly competitive Best Researcher Award, she would benefit from:
More indexed journal publications,
Evidence of citations/impact,
Greater leadership in research initiatives (e.g., funded projects or Ph.D. guidance).