Dharmapuri Siri | Computer Science | Best Researcher Award

Published on by Award and Honors

Dr. Dharmapuri Siri | Computer Science | Best Researcher Award 

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

Scopus

ORCID

Google Scholar

🎓 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.

💼 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.

📚 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

  • Published in: IEEE Access, 2024

  • DOI: 10.1109/ACCESS.2024.3278901

  • 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

  • Published in: IEEE Access, 2024

  • DOI: 10.1109/ACCESS.2024.3278902

  • 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

  • Published in: Heliyon, August 2024

  • DOI: 10.1016/j.heliyon.2024.e35217

  • 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

  • Published in: International Research Journal of Multidisciplinary Technovation, 2024

  • DOI: 10.54392/irjmt2461

  • 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).

Jiseong Byeon | Computer Science | Best Researcher Award

Published on by Award and Honors

Mr. Jiseong Byeon | Computer Science | Best Researcher Award 

Mr. Jiseong Byeon at Department of Industrial and Systems Engineering, Dongguk University, South Korea.

Jiseong Byeon is a passionate and emerging researcher in the field of artificial intelligence and computer vision, currently pursuing an M.S. in Industrial and Systems Engineering at Dongguk University, Seoul. With a multidisciplinary academic background combining global business and systems engineering, Jiseong brings a unique blend of strategic thinking and technical expertise. His research is centered around the development of intelligent image-based systems, particularly in the medical domain. He has experience working with advanced deep learning frameworks and has contributed to projects involving 3D human modeling and predictive analytics. Known for his curiosity and collaborative spirit, he aims to advance healthcare and human-computer interaction through innovative AI models. 📸🧠💡

Professional Profile

ORCID

🎓 Education

Jiseong Byeon is currently enrolled in a Master’s program in Industrial and Systems Engineering at Dongguk University, Seoul, beginning in September 2024. He previously earned his Bachelor of Arts in Global Business from Dong-A University in Busan, graduating in August 2024. His educational journey has been a unique blend of global business principles and technical problem-solving, giving him a diverse perspective on interdisciplinary research. During his undergraduate years, Jiseong began exploring data science and AI applications, which led him to transition fully into research-focused engineering. Through academic coursework, hands-on lab experiences, and independent study, he has built a solid foundation in data analytics, deep learning, and applied computer vision techniques. 🏫📚🧑‍🎓

💼 Experience

Jiseong Byeon has amassed valuable research experience across both undergraduate and graduate levels. Currently serving as a Graduate Researcher at Dongguk University since September 2024, he is engaged in developing models for 3D human body reconstruction using Vision Transformer architectures. This cutting-edge work aims to transform how AI interprets and renders human anatomy in digital formats. Previously, from March 2022 to August 2024, he worked as an Undergraduate Research Assistant at Dong-A University. There, he contributed to building encoding-based click prediction models and performed in-depth crime factor analysis using Seoul city data. These diverse experiences have honed his data interpretation skills and technical creativity, preparing him for advanced research and real-world AI application. 🖥️🔍📊

🔬 Research Interests

Jiseong Byeon’s research interests lie at the intersection of artificial intelligence, computer vision, and human modeling. His key areas include Image-to-Image Translation using the Pix2Pix framework, 3D Human Body Modeling, and Vision Transformers for medical applications. He is deeply motivated to apply deep learning algorithms to tasks that require detailed visual interpretation—especially those in the medical field where accurate prediction can significantly enhance outcomes. His work also explores how AI can be used for real-time inference and post-surgical visualization, such as predicting body shape changes. Additionally, Jiseong is keen on exploring the scalability of such models for widespread, ethical, and efficient implementation. 🤖🧬👨‍⚕️

🏆 Awards

While still early in his research career, Jiseong Byeon has shown exceptional promise and has been consistently recognized by his academic mentors for his innovation and diligence. He has been nominated for several internal research awards at Dong-A University, particularly for his work on crime prediction modeling and click prediction systems. His transition to graduate-level research was also supported by faculty recommendations based on the excellence of his undergraduate research projects. With his first peer-reviewed publication accepted and increasing involvement in high-impact research domains, he is a strong candidate for early-career research recognition and award nominations. 🏅📈🌟

📚 Top Noted Publications

Byeon has contributed to a peer-reviewed article that showcases the application of deep learning in medical image analysis:

The paper titled “Predicting Post-Liposuction Body Shape Using RGB Image-to-Image Translation” by Kim, M., Byeon, J., Chang, J., and Youm, S., published in Applied Sciences in 2025, presents a novel approach to forecasting post-liposuction body contours using RGB image-to-image translation techniques.

Key Details:

  • Authors: M. Kim, J. Byeon, J. Chang, and S. Youm

  • Publication Year: 2025

  • Journal: Applied Sciences

  • Citation Count: Cited by 3 articles as of 2025

Research Highlights:

The study focuses on leveraging RGB image-to-image translation methods to predict the outcomes of liposuction procedures. By utilizing preoperative images, the model aims to generate realistic visualizations of post-surgical body shapes, enhancing patient consultations and surgical planning.

Related Works:

While direct citations of this paper are limited, related research in the domain includes:

  • Development of a Non-Contact Sensor System for Converting 2D Images into 3D Body Data: This study introduces a deep learning approach to generate 3D body models from 2D images, facilitating obesity monitoring and body shape analysis. scholarworks.dongguk.edu+2Dongguk University+2MDPI+2

  • Development of an Obesity Information Diagnosis Model Reflecting Body Type Information Using 3D Body Information Values: This research emphasizes the use of 3D body data to enhance obesity diagnosis models, reflecting detailed body type information. MDPI+4ResearchGate+4MDPI+4

  • Predictive Model for Abdominal Liposuction Volume in Patients with Obesity Using Machine Learning: This study develops a machine learning model to predict liposuction volumes, aiding in surgical planning for obese patients.

Conclusion

Jiseong Byeon is a highly promising early-career researcher with a strong foundation in computer vision, deep learning, and real-world applications. His current trajectory suggests significant potential for future impact in both academic and applied AI research. While it may be slightly early for a top-tier “Best Researcher Award”, he is exceptionally well-positioned for a “Rising Star” or “Promising Researcher” recognition. With continued publication, international exposure, and leadership development, he could become a strong contender for major awards in the near future.