Rounak Raman | Information Technology | Research Excellence Award

Published-on by Indian Scientist

Mr. Rounak Raman | Information Technology | Research Excellence Award

Netaji Subhas University of Technology | India

Rounak Raman is a technology researcher and engineering innovator with a strong academic background in Information Technology and a multidisciplinary portfolio spanning artificial intelligence, machine learning, generative AI, computer vision, IoT systems, wireless sensor networks, and network security. He has gained significant research and development experience through contributions at DRDO-INMAS, where he worked on EEG-based cognitive analytics and neurofeedback systems, and at NSUT, where he designed advanced IoT protocols including energy-aware clustering, trust-based opportunistic communication, and secure hierarchical key-rotation mechanisms. His technical work extends into generative AI solutions, semantic search systems, real-time computer vision applications, and large-scale image and document intelligence models. He has led impactful projects such as PRAGATI smart parking, SyntheX document analysis framework, and QuickTag for AI-driven product taxonomy. His achievements include securing positions in national innovation challenges, hackathons, entrepreneurship competitions, and international fellowship programs. Alongside industry-recognized certifications in data science, system design, and cybersecurity, he has also held leadership roles in student organizations, mentoring teams, guiding open-source contributions, and facilitating research collaboration. His research interests include GenAI, NLP, IoT security, WSNs, cryptography, edge intelligence, and autonomous network optimization. He remains committed to creating scalable, socially impactful technological solutions.

Profiles: Google Scholar

Featured Publication

Raman, R., Yadav, A., Kukreja, D., & Sharma, D. K. (2025). CONTEXT-NET: A context-aware nexus-based aggregation protocol for opportunistic networks. Internet of Things, 101809.

Raman, R., Yadav, A., & Kukreja, D. (2025). ARMor-IoT: Aggregated reliable mechanism for optimized trust in IoT. In International Conference on Artificial Intelligence and Its Application (pp. 229–241).

Banushri S | Deep Learning | Best Researcher Award

Published-on by Indian Scientist

Mrs. Banushri S | Deep Learning | Best Researcher Award

Impact College of Engineering and Applied Sciences| India

Dr. Banushri S is an accomplished academician and researcher in Computer Science and Engineering, recognized for her contributions to machine learning, deep learning, image processing, and human activity recognition. She has published impactful research across reputed journals and conferences, contributing to the scientific community with multiple documents, citations, and an evolving h-index that reflects her growing influence in the field. Her academic journey includes strong foundational degrees in engineering and digital electronics, shaping her expertise in logic design, embedded systems, computer architecture, networking, operating systems, programming, and advanced AI-driven technologies. With extensive teaching experience as an Assistant Professor in leading engineering institutions, she has guided numerous undergraduate and postgraduate learners, supervised research projects, and supported curriculum development and departmental academic activities. Her research works span areas such as fall detection, wearable sensor data analysis, transfer learning, and long-range context modeling for human activity recognition, including contributions indexed in Scopus and other reputed platforms. She has actively participated in faculty development programs, workshops, and conferences focusing on artificial intelligence, computer vision, and full-stack technologies, while also being a member of professional bodies like ISTE and CSI. Her work reflects a commitment to advancing intelligent systems research and contributing to academic excellence.

Profile: Scopus

Featured Publications

Banushri, S. (2026). Attention-guided residual shrinkage with gated recurrent unit for human activity recognition. Information Processing and Management.

Runu Banerjee Roy | Artificial Intelligence | Best Researcher Award

Published-on by Indian Scientist

Dr. Runu Banerjee Roy | Artificial Intelligence | Best Researcher Award

Jadavpur University| India

Dr. Runu Banerjee Roy is a Professor in Instrumentation and Electronics Engineering at Jadavpur University, widely recognized for her contributions to electronic olfaction, taste sensing, molecular imprinting, sensor development, and artificial intelligence–based instrumentation. She has an extensive research profile with 1213 citations, an h-index of 17, and an i10-index of 25, reflecting her strong scholarly impact and research productivity. Her publication record includes more than 50 peer-reviewed journal papers, around 40 conference papers, multiple book chapters, and several patents that are granted, published, or filed. She has successfully guided doctoral and postgraduate research scholars and completed multiple sponsored research projects funded by major scientific agencies, focusing on portable sensing devices, electronic nose and tongue systems, and electrochemical detection technologies for applications in food quality and safety. In academics, she has served in leadership roles such as Head of Department, NBA accreditation coordinator, and curriculum committee member, contributing to program development and quality enhancement. Her work has earned recognition through competitive research awards, scientific prizes, and support for international research presentations. With strong expertise spanning instrumentation, intelligent sensing systems, and applied electronics, she continues to advance innovative research, academic excellence, and technology-driven solutions in modern sensor engineering.

Profile: Google Scholar | Scopus

Featured Publications

Roy, R. B., Tudu, B., Shaw, L., Jana, A., Bhattacharyya, N., & Bandyopadhyay, R. (2012). Instrumental testing of tea by combining the responses of electronic nose and tongue. Journal of Food Engineering, 110(3), 356–363.

Banerjee, M. B., Roy, R. B., Tudu, B., Bandyopadhyay, R., & Bhattacharyya, N. (2019). Black tea classification employing feature fusion of E-Nose and E-Tongue responses. Journal of Food Engineering, 244, 55–63.

Banerjee, R., Tudu, B., Bandyopadhyay, R., & Bhattacharyya, N. (2016). A review on combined odor and taste sensor systems. Journal of Food Engineering, 190, 10–21.

Roy, R. B., Chattopadhyay, P., Tudu, B., Bhattacharyya, N., & Bandyopadhyay, R. (2014). Artificial flavor perception of black tea using fusion of electronic nose and tongue response: A Bayesian statistical approach. Journal of Food Engineering, 142, 87–93.

Nag, S., Pradhan, S., Naskar, H., Roy, R. B., Tudu, B., Pramanik, P., … et al. (2021). A simple nano cerium oxide modified graphite electrode for electrochemical detection of formaldehyde in mushroom. IEEE Sensors Journal, 21(10), 12019–12026.

Pritam BIKRAM | Artificial Intelligence | Best Researcher Award

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Mr. Pritam BIKRAM | Artificial Intelligence | Best Researcher Award

Indian Institute of Engineering Science and Technology, Shibpur, India

Author Profile

SCOPUS

ORCID

GOOGLE SCHOLAR

🎓 EARLY ACADEMIC PURSUITS

Mr. Pritam Bikram began his academic journey with a B.Tech in Information Technology from the Government College of Engineering and Leather Technology, graduating in 2020. With a strong foundation in computational systems and programming, he advanced his studies by pursuing an M.Tech in Information Technology at the prestigious Indian Institute of Engineering Science and Technology (IIEST). His dedication and academic excellence earned him a GATE scholarship during this period. Currently, he is pursuing his Ph.D. at IIEST, where his research is focused on advanced applications of Artificial Intelligence (AI) in real-world domains.

🏢 PROFESSIONAL ENDEAVORS

Throughout his academic tenure, Mr. Bikram has remained consistently involved in cutting-edge research and collaborative scientific inquiry. Working under a result-oriented and interdisciplinary professional environment, he has contributed to the development of novel AI-based solutions in Intelligent Transportation Systems (ITS), remote sensing, environmental monitoring, and precision agriculture. His analytical mindset and logical problem-solving approach have enabled him to tackle multifaceted challenges across domains.

📚 CONTRIBUTIONS AND RESEARCH FOCUS IN ARTIFICIAL INTELLIGENCE

Mr. Bikram’s core research lies in the realm of Artificial Intelligence, with specific focus areas including:

  • Trajectory prediction using graph neural networks

  • Missing spatio-temporal data imputation

  • Deep learning applications for traffic forecasting

  • AI-driven spectral analysis for crop disease monitoring

His work is characterized by graph-based encoder-decoder learning frameworks, attentive graph structure learning, and dynamic attention mechanisms, reflecting a deep command over neural architectures and real-world data-driven applications.

🏅 ACADEMIC CITATIONS, ACCOLADES AND RECOGNITION

Mr. Bikram is the first author of multiple high-impact peer-reviewed journal articles, including:

  • Expert Systems with Applications (Elsevier, 2025)

  • Neurocomputing (Elsevier, 2024)

  • Applied Intelligence (Springer, 2024)

  • Remote Sensing Applications: Society and Environment (Elsevier, 2025)

His research has begun gaining strong academic traction and citations, contributing to the broader scientific discourse in AI and its applications. He has also received:

  • 🎓 GATE Scholarship (2020)

  • 🧠 Ph.D. Institute Scholarship (2022)

🌍 IMPACT AND INFLUENCE

Mr. Bikram’s contributions have significant implications for smart city development, environmental sustainability, and agritech innovations. His models have demonstrated real-world applicability in enhancing urban mobility, early disease detection in agriculture, and data imputation for intelligent decision-making systems. By addressing societal challenges with AI, he continues to drive forward impactful and sustainable technological solutions.

🧭 LEGACY AND FUTURE CONTRIBUTIONS

Looking ahead, Mr. Bikram envisions integrating interdisciplinary AI methodologies with climate science, transportation modeling, and remote sensing technologies. His role as a reviewer for esteemed journals like Knowledge-Based Systems, Neurocomputing, Applied Intelligence, and IEEE Transactions on Intelligent Vehicles marks him as a thought leader shaping future research directions.

He is also preparing to become an IEEE Member (December 2025), aligning with a global network of innovators.

 ✅CONCLUSION

With a powerful combination of technical expertise, research depth, and real-world problem-solving focus, Mr. Pritam Bikram stands as an emerging scholar in the Artificial Intelligence community. His contributions are expected to influence diverse fields from urban mobility to climate-resilient agriculture, leaving a lasting legacy.

🔬NOTABLE PUBLICATION:

Effective message-passing scheme and aggregation technique embedded in graph-based encoder-decoder learning framework for trajectory prediction
Author(s): Pritam Bikram, Shubhajyoti Das, Arindam Biswas
Journal: Expert Systems with Applications
Year: 2025

Multilayer optimized deep learning model to analyze spectral indices for predicting the condition of rice blast disease
Author(s): Shubhajyoti Das, Pritam Bikram, Arindam Biswas, Vimalkumar C., Parimal Sinha
Journal: Remote Sensing Applications: Society and Environment
Year: 2025

Dynamic attention aggregated missing spatial–temporal data imputation for traffic speed prediction
Author(s): Pritam Bikram, Shubhajyoti Das, Arindam Biswas
Journal: Neurocomputing
Year: 2024

Attentive graph structure learning embedded in deep spatial-temporal graph neural network for traffic forecasting
Author(s): Pritam Bikram, Shubhajyoti Das, Arindam Biswas
Journal: Applied Intelligence
Year: 2024

Taveena | Computer Science | Best Researcher Award

Published-on by Indian Scientist

Ms. Taveena | Computer Science | Best Researcher Award

Indian Institute of Technology Roorkee, India

Author Profile

SCOPUS

ORCID

🎓 EARLY ACADEMIC PURSUITS

Ms. Taveena began her academic journey in Computer Science and Engineering at Punjabi University, Patiala, where she completed her undergraduate studies. She pursued her M.Tech. in CSE from IIT (ISM) Dhanbad, where she first ventured into deep learning for audio classification using recurrent neural networks. She is currently pursuing her Ph.D. at IIT Roorkee, focusing on decoding mental imagery through physiological signals under the guidance of Prof. Partha Pratim Roy.

🏢 PROFESSIONAL ENDEAVORS

Ms. Taveena has over 6 years of rigorous research experience, combining deep learning, physiological signal processing, and multimodal data analysis. She is adept at designing efficient neural architectures, contributing to diverse research areas such as neural architecture search (NAS), EEG-fMRI fusion, audio–EEG classification, and cross-modal generation tasks. She has been a key contributor to cutting-edge projects involving parameter-efficient tuning and low-rank adaptation techniques.

📚 CONTRIBUTIONS AND RESEARCH FOCUS IN COMPUTER SCIENCE

Her primary focus lies in developing deep learning frameworks for complex time-series and multimodal signals, particularly in:

  • EEG-based motor imagery and speech imagery classification

  • Silent speech decoding and mental imagery task adaptation

  • Cross-session classification using self-supervised contrastive learning

  • Multi-modal EEG–fMRI and image–EEG fusion

  • Lightweight neural tuning with adapters

Her contributions extend into broader domains of natural language processing (NLP) and computer vision, focusing on foundational model architectures and cross-modal learning.

🏅 ACADEMIC CITATIONS, ACCOLADES AND RECOGNITION

  • 📌 Journal Articles:

    • Biomedical Signal Processing and Control (IF: 4.9)

    • IEEE Transactions on Industrial Informatics (Under review, IF: 9.9)

    • International Journal of Activity and Behavior Computing

  • 📌 Conference Presentations:

    • ICPR 2022 (Canada) and ICPR 2025 (India)

    • ABC Conference 2025 (Winner of SSDC challenge and Best Paper Award)

  • 📌 Preprints & Community Contribution:

    • Active on arXiv with high-engagement preprints

🌍 IMPACT AND INFLUENCE

Ms. Taveena’s work addresses real-world challenges in neurotechnology, enhancing human–computer interaction, silent communication, and cognitive state monitoring. Her research on EEG signal decoding is paving the way for assistive technologies, particularly benefiting neurologically impaired individuals. She has influenced the academic and open research community through arXiv preprints and open-access contributions.

🧭 LEGACY AND FUTURE CONTRIBUTIONS

Ms. Taveena is poised to leave a lasting impact through:

  • Novel deep learning tools for brain signal decoding

  • Foundational model adaptation for low-resource and multimodal scenarios

  • Open scientific collaboration via preprints and peer reviewing

  • Future goals include bridging neuroscience and AI, making cognitive computing more accessible and inclusive

She aims to further contribute to large-scale cross-modal learning, AI for healthcare, and foundational model efficiency for real-time applications.

 ✅CONCLUSION

Ms. Taveena represents the next-generation AI researcher, combining deep theoretical knowledge with practical application in neuroscience, speech, and multimodal learning. With an excellent academic pedigree and significant contributions to EEG decoding and multimodal AI, she is a promising leader in Computer Science and Brain–AI interfaces.

🔬NOTABLE PUBLICATION:

Native Arabic EEG-based Silent Speech Decoding Using Deep Learning Techniques
Authors: Taveena Lotey, Salini Yadav, Partha Pratim Roy
Journal: International Journal of Activity and Behavior Computing
Year: 2025

EEG-Based Mental Imagery Task Adaptation via Ensemble of Weight-Decomposed Low-Rank Adapters
Authors: Taveena Lotey, Aman Verma, Partha Pratim Roy
Journal: Lecture Notes in Computer Science
Year: 2024

Cross-Session Motor Imagery EEG Classification using Self-Supervised Contrastive Learning
Authors: Taveena Lotey, Prateek Keserwani, Gaurav Wasnik, Partha Pratim Roy
Journal: 2022 26th International Conference on Pattern Recognition (ICPR)
Year: 2022