Benitha Christinal J | Computer Science | Women Researcher Award

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Mrs. Benitha Christinal J | Computer Science | Women Researcher Award

Assistant Professor | Presidency University | India

Mrs. Benitha Christinal J is an accomplished academic and researcher specializing in Computer Science and Engineering with a strong focus on Artificial Intelligence, Deep Learning, and Internet of Things (IoT). She has extensive professional experience in higher education, demonstrating excellence in teaching, curriculum development, and academic coordination. Her research interests include deep learning applications for cybersecurity, decentralized systems, and intelligent data analysis. She has published numerous papers in reputed international journals such as Oxidation Communications, Ain Shams Engineering Journal, Journal of Supercomputing, and Optical Fiber Technology, addressing challenges in areas like federated learning, SDN-IoT security frameworks, and evolutionary intrusion detection systems. She has also presented her work at several international conferences, contributing to advancements in AI-based healthcare, blockchain-enabled sustainability, and smart network optimization. A published author of a textbook on Database Management Systems, she has guided multiple undergraduate and postgraduate projects that have gained recognition at academic and professional levels. Her technical proficiency spans programming languages like Python, Java, and C++, and tools for web and data driven applications. Beyond research and teaching, she has been actively involved in organizing academic events, fostering industry collaborations, and mentoring students toward innovation. Her commitment to advancing technology education and research underscores her vision of shaping the next generation of computer science professionals through excellence, creativity, and applied intelligence.

Profiles: Scopus | Orcid

Featured Publications

Benitha Christinal, J., Betsee Natasha, A., Nivethitha, M., Asmitha, E., & Kaviya, N. (2025). A modern generative AI framework for Alzheimer detection leveraging autoencoders and softmax classifier. In Proceedings of the 3rd International Conference on Augmented Intelligence and Sustainable Systems (ICAISS 2025). IEEE.

Benitha Christinal, J., Jagadeesh, S., Ajai, M., Lakshman, A., & Betsee Natasha, A. (2025). Memory Montage: Amnesia support appa. In Proceedings of the International Conference on Emerging Trends in Engineering and Technology (ICETET 2025). IEEE.

Benitha Christinal, J., & Ameelia Roseline, A. (2025, September). Securing SDON with hybrid evolutionary intrusion detection system: An ensemble algorithm for feature selection and classification. Optical Fiber Technology, 104206.

Benitha Christinal, J., Chandran, V., Srinic, J., & Prasannasrinivasan, A. (2024). A distributed node clustering coalition game for mobile ad hoc networks. In Proceedings of the Asia Pacific Conference on Innovation in Technology (APCIT 2024). IEEE.

Sumanth, V., Anitha, K., Christinal, J. B., Sekhar, G. S., Khekare, G., Patil, H., Kumar, N. M., & Rajaram, A. (2024). Advanced communications and networking for environmental protection monitoring in remote wilderness areas. Journal of Environmental Protection and Ecology, 25(3), 1012–1023.

Jun Tang | Computer Science | Best Researcher Award

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Mr. Jun Tang | Computer Science | Best Researcher Award

AI Algorithm Researcher | Chengdu Zhihui Heneng City Technology | China

Mr. Jun Tang is a researcher specializing in intelligent transportation and autonomous driving, with a strong focus on the integration of computer vision and artificial intelligence to enhance vehicular perception and decision making systems. His research primarily explores large vision foundation models and their applications in object detection, scene understanding, and adaptive driving environments. He has contributed to developing advanced detection frameworks that leverage reinforcement learning to improve recognition accuracy, robustness, and real time responsiveness in dynamic traffic conditions. Mr. Tang’s recent interests include prompt-guided object detection methods that utilize natural language and contextual cues to refine visual understanding within autonomous systems. Through his work at Chengdu Zhihui Heneng City Technology, he plays a key role in bridging the gap between theoretical AI models and practical intelligent mobility applications, fostering innovations that advance the safety, efficiency, and scalability of next generation transportation systems. His interdisciplinary approach combines deep learning, machine perception, and cognitive automation, contributing to the development of more adaptive and human like decision making in autonomous vehicles.

Profile: Orcid

Featured Publications

Tang, J., Li, D., Yang, J., Chen, J., & Yuan, R. (2025). Leveraging large visual models for enhanced object detection: An improved SAM-YOLOv5 model. Knowledge-Based Systems, 114757.

Tang, J. (2025, August 29). RT-DETR-based intelligent transportation object detection optimization method and system with prompt mechanism fusion.

Tang, J. (2025, May 27). Object detection method and system based on prompt engineering and regional text description.

Tang, J. (2025, April 11). Quantitative evaluation method and system for multimodal large models.

Tang, J. (2025, January 17). Evaluation method and system for urban governance multimodal large models based on text labeling.

Srinivasa Rao Gundu | Computer Science | Best Researcher Award

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Dr. Srinivasa Rao Gundu | Computer Science | Best Researcher Award

Assistant Professor at Malla Reddy University, India

Dr. Srinivasa Rao Gundu is an Assistant Professor at the School of Sciences, Malla Reddy University, Hyderabad, with over 18 years of academic and professional experience in computer science. He earned his Ph.D. in Computer Science from Dravidian University in 2021, focusing on hybrid algorithms for load balancing in cloud computing environments. His thesis introduced and tested novel RT, RTE, and RTEAH hybrid algorithms to enhance cloud resource allocation and performance.

Profile:

🎓 Academic Background:

  • Ph.D. in Computer Science
    Dravidian University, India
    Thesis: “Hybrid Approach to Load Balancing in Cloud Environment”
    His doctoral research focused on designing and testing hybrid algorithms (RT, RTE, RTEAH) to optimize load balancing in cloud computing environments, considering parameters like response time, execution time, and network delay.

  • Master of Computer Applications (M.C.A.)
    Osmania University, Hyderabad

  • Bachelor of Science (Computer Science)
    Osmania University, Hyderabad

🧠 Areas of Expertise:

  • Cloud Computing & Load Balancing

  • Quantum Computing & Cryptography

  • Artificial Intelligence & Machine Learning

  • Big Data Analytics

  • Internet of Things (IoT)

  • Operating Systems & Networking Global Journals

👨‍🏫 Teaching Experience:

  • Assistant Professor, Malla Reddy University (2023–Present)
    Courses: Applied Cryptography & Network Security, Operating Systems, Quantum ComputingAcademia.edu+6MR University+6ResearchGate+6

  • Guest Lecturer, Government Degree College, Hyderabad (2021–2023)
    Courses: C, C++, Java, DBMS, Oracle, Web Designing

  • Senior Software Trainer, Key Soft Computer Education (2019–2021, 2009–2013)

  • Programmer, JKR Softech Pvt. Ltd. (2013–2014)

  • Software Trainer, S3K Software Solutions (2006–2009)IGI Global+7Academia.edu+7LinkedIn+7

🏅 Professional Memberships:

  • Computer Science Teachers Association (CSTA) – USA

  • International Association of Engineers (IAENG) – Hong Kong

  • Indian Science Congress Association (ISCA) – India

  • Internet Society (ISOC) – USA

  • American Physical Society (APS) – USA

  • American Mathematical Society (AMS) – USA

  • Institute of Mathematical Statistics (IMS) – USA

  • Education Research and Development Association (ERDA) – India

  • International Association of Innovation Professionals (IAOIP) – USA

  • Scope Database – International Advisory Board (SD-IAB) – Singapore

Citation Metrics:

  • Total Citations: 208

  • Citations Since 2020: 206

  • h-index: 8

  • i10-index: 6

Publication Top Notes:

  1. Hybrid IT and multi cloud: An emerging trend and improved performance in cloud computing
    Gundu, S.R., Panem, C.A., & Thimmapuram, A. (2020). SN Computer Science, 1(5), 256. [Citations: 43]

  2. Sixth‐Generation (6G) Mobile Cloud Security and Privacy Risks for AI System Using High‐Performance Computing Implementation
    Gundu, S.R., Charanarur, P., Chandelkar, K.K., Samanta, D., Poonia, R.C., et al. (2022). Wireless Communications and Mobile Computing, 2022(1), 4397610. [Citations: 33]

  3. Real-time cloud-based load balance algorithms and an analysis
    Gundu, S.R., Panem, C.A., & Thimmapuram, A. (2020). SN Computer Science, 1(4), 187. [Citations: 27]

  4. The dynamic computational model and the new era of cloud computation using Microsoft Azure
    Gundu, S.R., Panem, C.A., & Thimmapuram, A. (2020). SN Computer Science, 1(5), 264. [Citations: 13]

  5. Robotic technology-based cloud computing for improved services
    Gundu, S.R., Panem, C.A., & Timmapuram, A. (2020). SN Computer Science, 1(4), 190. [Citations: 11]

  6. Improved Hybrid Algorithm Approach based Load Balancing Technique in Cloud Computing
    Anuradha, B.S.R.G.T. (2019). Global Journal of Computer Science and Technology: B Cloud and Distributed, [Citations: 11]

  7. Emerging computational challenges in cloud computing and RTEAH algorithm based solution
    Gundu, R.S.G., Thimmapuram, A., & Panem, C.A. (2021). Journal of Ambient Intelligence and Humanized Computing, 11. [Citations: 9]

  8. Machine-learning-based spam mail detector
    Charanarur, P., Jain, H., Rao, G.S., Samanta, D., Sengar, S.S., & Hewage, C.T. (2023). SN Computer Science, 4(6), 858. [Citations: 8]

  9. High‐Performance Computing‐Based Scalable “Cloud Forensics‐as‐a‐Service” Readiness Framework Factors—A Review
    Gundu, S.R., Panem, C., & Satheesh, S. (2022). Cyber Security and Network Security, 27-45. [Citations: 8]

  10. Improved implementation of hybrid approach in cloud environment
    Rao, G.S., & Anuradha, T. (2018). Network, 3, 3.312. [Citations: 6]

  11. Improved hybrid approach for load balancing in virtual machine
    Rao, G.S., & Anuradha, T. (2018). International Journal of Computer Science Engineering, 6(10), 730-733. [Citations: 5]

  12. Intelligence Using Automata-Based Nature’s Digital Philosophy
    Gundu, S.R., Panem, C.A., & Thimmapuram, A. (2020). SN Computer Science, 1, 1-6. [Citations: 4]

  13. Fuzzy Logic Applications in Computer Science and Mathematics
    Kar, R., Le, D.N., Mukherjee, G., Mallik, B.B., & Shaw, A.K. (2023). John Wiley & Sons. [Citations: 3]

  14. The role of machine learning and artificial intelligence in detecting the malicious use of cyber space
    Panem, C., Gundu, S.R., & Vijaylaxmi, J. (2023). Robotic Process Automation, 19-32. [Citations: 3]

  15. Digital Data Growth and the Philosophy of Digital Universe in View of Emerging Technologies
    Gundu, T.A.S.R. (2020). International Journal of Scientific Research in Computer Science, [Citations: 3]

  16. A comprehensive study on cloud computing and its security protocols and performance enhancement using artificial intelligence
    Gundu, S.R., Panem, C., & Vijaylaxmi, J. (2023). Robotic Process Automation, 1-17. [Citations: 2]

  17. Deception preclusion, discretion, and data safety for contemporary business
    Gundu, S.R., Charanarur, P., & Vijaylaxmi, J. (2023). Fraud Prevention, Confidentiality, and Data Security for Modern Businesses. [Citations: 2]

  18. Protection of personal data and internet of things security
    Charanarur, P., Gundu, S.R., & Vijaylaxmi, J. (2023). Fraud Prevention, Confidentiality, and Data Security for Modern Businesses. [Citations: 2]

  19. Cloud Computing and its Service Oriented Mechanism
    Gundu, C.S.R., & Panem (2022). Akinik Publications, New Delhi. [Citations: 2]

  20. Observed issues in cloud-based web commerce adoption for the financial transactions in Hyderabad
    Gundu, T.A.S.R., & Panem, C.A. (2021). Journal of Mechanics of Continua and Mathematical Sciences, 16(9), 1-13. [Citations: 2]

 

Xiang Li | Computer Science | Best Researcher Award

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Dr. Xiang Li | Computer Science | Best Researcher Award

Associate Researcher Qilu University of Technology (Shandong Academy of Sciences) China

Dr. Xiang Li is an accomplished Associate Researcher at the Qilu University of Technology (Shandong Academy of Sciences) in China, where he has been serving since 2019. With a strong academic foundation in computer science and a research focus spanning EEG-based emotion recognition, multimodal sentiment analysis, and contrastive learning, Dr. Li has published widely in high-impact journals and conferences. His work is recognized internationally, with over 1,700 citations on Google Scholar, reflecting his significant influence in the field of artificial intelligence and affective computing.

Profile

Google Scholar

Orcid

🎓 Education

Dr. Li earned his Ph.D. in Computer Science from the College of Intelligence and Computing at Tianjin University in 2019. He previously received his Master’s degree in Computer Science (2014) and a Bachelor’s degree in Network Engineering (2011), both from the School of Information Science and Technology at Shandong University of Science and Technology.

💼 Experience

Since 2019, Dr. Xiang Li has held the position of Associate Researcher at the Qilu University of Technology. In addition to his research role, he contributes significantly to teaching, instructing several undergraduate and graduate-level courses since 2020, including English for Computer Science, Information Retrieval, and Data Mining, Analysis, and Visualization. His multidisciplinary expertise allows him to merge theory with practice, especially in the intersection of artificial intelligence, neuroscience, and ocean data analytics.

🔬 Research Interests

Dr. Li’s research interests lie in EEG-based emotion recognition, multimodal deep learning, contrastive learning, and affective computing. He has also made substantial contributions to intelligent quality control in ocean observation, shipborne wind speed correction, and biomedical signal processing. His innovative approaches often employ supervised and self-supervised learning frameworks, with a focus on enhancing data-driven decision-making using limited or noisy data.

🏆 Awards

  • 🧠 ESI Highly Cited Paper for “EEG based emotion recognition: A tutorial and review” (2022)
  • 🏅 Recognized for over 1,700 citations on Google Scholar
  • 📈 Several publications with >100 citations, such as his works on quantum-inspired sentiment analysis and cross-subject EEG emotion recognition
  • 🧪 Multiple papers published in SCI Tier-1 journals and top CCF-ranked conferences

📚 Publications Top Notes:  

Below is a selection of Dr. Xiang Li’s publications, presented with hyperlinks, publication years, journals/conferences, and citation data (when available):

📘 2025: Multi-Affection Prompt Learning for Sentiment, Emotion and Sarcasm Joint Detection in Conversations – Tsinghua Science and Technology [SCI-1]

📘 2024: A Supervised Information Enhanced Multi-granularity Contrastive Learning Framework for EEG based Emotion Recognition – ICASSP 2024 [CCF-B]

📘 2024: Self-Supervised Pretraining-Enhanced Intelligent Quality Control for Ocean Observations – ICONIP 2024 [CCF-C]

📘 2024: An Adaptive Time-convolutional Network Online Prediction Method for Ocean Observation Data – SEKE 2024 [CCF-C]

📘 2024: Fusion of Time-Frequency Features in Contrastive Learning for Wind Speed Correction – Journal of Ocean University of China [SCI-3]

📘 2023: EEG-based Parkinson Detection through Supervised Contrastive Learning – BIBM 2023 [CCF-B]

📘 2022: EEG based Emotion Recognition: A Tutorial and Review – ACM Computing Surveys, 55(4) [SCI-1, IF=23.8, Cited by: 272]

📘 2021: Emotion Recognition via Dual-pipeline Graph Attention Network – BIBM 2021 [CCF-B]

📘 2020: Latent Factor Decoding of Multi-channel EEG through Neural Networks – Frontiers in Neuroscience, [SCI-2, Cited by: 81]

📘 2018: Exploring EEG Features in Cross-subject Emotion Recognition – Frontiers in Neuroscience, [SCI-2, Cited by: 369]

📘 2016: Emotion Recognition from Multi-channel EEG via CNN-RNN – BIBM 2016 [CCF-B, Cited by: 320]

📘 2015: EEG-based Emotion Identification Using Deep Feature Learning – ACM SIGIR NeuroIR Workshop [CCF-A Workshop, Cited by: 92]