Nirmal Varghese Babu | Artificial Intelligence | Best Researcher Award 

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Dr. Nirmal Varghese Babu | Artificial Intelligence | Best Researcher Award 

Dr. Nirmal Varghese Babu | Karunya Institute of Technology and Sciences | India

Author Profiles

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Early Academic Pursuits

Dr. Nirmal Varghese Babu began his academic journey with a strong inclination toward computer science and technology. He completed his B.Tech in Information Technology from Karunya Institute of Technology and Sciences, Coimbatore, in 2017 with a CGPA of 8.0. His passion for research and innovation in computing led him to pursue an M.Tech in Computer Science & Engineering from Amal Jyothi College of Engineering, Kanjirappally (2017–2019), graduating with an impressive CGPA of 8.72. Currently, he is pursuing a Ph.D. in Computer Science & Engineering from Karunya Institute of Technology and Sciences, expected to be completed in 2025. His academic excellence was rooted in his formative years at Mathews Mar Athanasius Residential School, Chengannur, where he built a strong foundation in analytical and computational thinking.

Professional Endeavors

Dr. Nirmal Varghese Babu is currently serving as an Assistant Professor at the School of Computer Science and Technology, Karunya Institute of Technology and Science, Coimbatore (since July 26, 2022). His professional endeavors include teaching, research, and mentoring in various areas of computer science and Artificial Intelligence. He has delivered lectures on Artificial Intelligence: Principles and Techniques, Cloud Computing for Data Analytics, AI for Games, AI for Food Processing Engineering, AI for Biotechnology, and MLOps. Alongside teaching, he mentors undergraduate students, coordinates final-year projects, and supervises academic research initiatives. His teaching methodology emphasizes experiential learning, guiding students to bridge theoretical knowledge with real-world technological applications.

Contributions and Research Focus

Dr. Nirmal’s research contributions revolve around Artificial Intelligence, machine learning, data analytics, and real-time systems. His M.Tech project, Multiclass Sentiment Analysis of Social Media Data using Neural Networks, explored advanced deep learning algorithms like CNN and RNN for classifying sentiment across social media platforms, specifically Twitter. This study integrated text and emoticon data for multiclass classification using one-hot encoding and neural networks. His earlier project, Real-Time Traffic Incident Detection using Social Media Data, demonstrated innovative use of Natural Language Processing to detect and analyze traffic incidents using Twitter data, integrating AI for real-time decision-making. His work exemplifies how Artificial Intelligence can transform data into actionable insights for societal and industrial benefit.

Impact and Influence

Dr. Nirmal Varghese Babu’s impact as an educator and researcher extends across academia and applied technology. At Karunya Institute, he plays a vital role in shaping the next generation of AI-driven engineers and data scientists. As a mentor and coordinator, he has successfully guided numerous B.Tech projects, fostering innovation in the domains of Artificial Intelligence, MLOps, and machine learning. His pedagogical style emphasizes research-based learning, promoting creative problem-solving and real-world application of AI. Through his leadership in academic project coordination and curriculum development, he has significantly influenced the integration of AI-based methodologies into modern engineering education.

Academic Cites

Dr. Nirmal’s academic contributions are recognized through his published works, research projects, and student-guided studies. His projects on sentiment analysis and traffic incident detection have been well-cited and appreciated within the AI and data analytics community. The relevance of his research is reflected in growing academic references to his work in areas such as neural networks, data mining, and sentiment classification. His scholarly achievements continue to inspire students and researchers pursuing advanced studies in Artificial Intelligence and computational learning.

Legacy and Future Contributions

Looking ahead, Dr. Nirmal Varghese Babu aims to expand his research in Artificial Intelligence, focusing on its integration with real-time analytics, smart systems, and cognitive computing. His future contributions are expected to advance the use of AI in multidisciplinary fields such as biotechnology, healthcare, and environmental systems. As an educator, his legacy lies in his ability to inspire and mentor young researchers, promoting a culture of innovation and ethical AI development. His ongoing research and academic leadership will undoubtedly continue to shape the evolution of AI-driven solutions and their transformative potential across industries.

Artificial Intelligence

Dr. Nirmal Varghese Babu’s expertise in Artificial Intelligence is evident through his teaching, research, and innovation in deep learning, neural networks, and data analytics. His projects and mentorship highlight the transformative role of Artificial Intelligence in addressing real-world challenges. The continued advancement of Artificial Intelligence under his guidance promises to create meaningful impact in both academic and applied technological domains.

Featured Publications

Babu, N. V., & Kanaga, E. G. M. (2022). Sentiment analysis in social media data for depression detection using artificial intelligence: A review. SN Computer Science, 3(1), 1–15. https://doi.org/10.1007/s42979-021-00921-2

Babu, D. E. G. M. K. N. V. (2022). Sentiment analysis in social media data for depression detection using artificial intelligence: A review. SN Computer Science, 3, 350.

Babu, N. V., & Rawther, F. A. (2021). Multiclass sentiment analysis in text and emoticons of Twitter data: A review. Proceedings of the Second International Conference on Networks and Advances in Computational Technologies (NetACT).

Prince, S. C., & Babu, N. V. (2024). Advancing multiclass emotion recognition with CNN-RNN architecture and illuminating module for real-time precision using facial expressions. Proceedings of the 2024 International Conference on Advances in Modern Age Technologies for Sustainable Development (AMATS).

Babu, N. V., Kanaga, E. G. M., Kattappuram, J. T., & Benny, R. V. (2023). AI-based EEG analysis for depression detection: A critical evaluation of current approaches and future directions. Proceedings of the 2023 International Conference on Computational Intelligence and Sustainable Technologies (CIST).

Babu, D. E. G. M. K. N. V. (2022). Depression analysis using electroencephalography signals and machine learning algorithms. Proceedings of the Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT).

Adityasai, B., & Babu, N. V. (2024). Advancing Alzheimer’s diagnosis through transfer learning with deep MRI analysis. Proceedings of the 2024 International Conference on Advances in Modern Age Technologies for Sustainable Development (AMATS).

Babu, N. V., & Kanaga, E. G. M. (2023). Multiclass text emotion recognition in social media data. In Machine Intelligence Techniques for Data Analysis and Signal Processing (pp. 123–135). Springer.

Rawther, F. A., & Babu, N. V. (2019). User behavior analysis on social media data using sentiment analysis or opinion mining. International Research Journal of Engineering and Technology (IRJET), 6(6), 3081–3085.

Konstantinos Diamantaras | Machine Learning | Best Researcher Award 

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Prof. Konstantinos Diamantaras | Machine Learning | Best Researcher Award 

Prof. Konstantinos Diamantaras | International Hellenic University | Greece

Prof. Konstantinos Diamantaras is a Professor at the International Hellenic University, Department of Information & Electronic Engineering, and Vice Rector since 2023, holding a Beng from NTUA, Greece, and an MSc and PhD in Electrical Engineering from Princeton University. His research focuses on machine learning, signal processing, and augmented/virtual reality, with over 230 scientific publications and 79 journal articles indexed in SCI and Scopus, accumulating more than 7,300 citations on Google Scholar (h-index 30) and 3,027 citations on Scopus (h-index 23). He has authored four books, including Principal Component Neural Networks (1996) and Artificial Neural Networks (2007), and received the IEEE Best Paper Award in 1997 for Adaptive Principal Component Extraction (APEX). He leads multiple EU- and university-funded projects, such as Kids Radio Europe, METACHEF, Digital4all, and AI-based food recognition. His collaborations include Prof. S. Y. Kung (Princeton), Prof. Athina Petropulu (Rutgers), Prof. Tomas McKelvey (Chalmers), and partnerships with Alzheimer Hellas and the University of Alicante on NLP applications. He serves on editorial boards of Journal of Signal Processing Systems and Applied Sciences, contributing to advancements in deep learning, pattern recognition, biomedical informatics, adaptive signal processing, and educational technology. His work spans practical AI applications in health, digital learning, and immersive experiences, influencing both academic research and societal impact. He is an active IEEE member and IEEE Signal Processing Society participant, advancing knowledge in neural networks, computational intelligence, and multilingual natural language generation.

Profiles: Scopus | Orcid | Google Scholar | Staff Page

Featured Publications

Diamantaras, K. I., & Kung, S. Y. (1996). Principal component neural networks: Theory and applications. In Adaptive and learning systems for signal processing, communications, and control (p. 1694). Springer.

Vafeiadis, T., Diamantaras, K. I., Sarigiannidis, G., & Chatzisavvas, K. C. (2015). A comparison of machine learning techniques for customer churn prediction. Simulation Modelling Practice and Theory, 55, 1–9

Giatsoglou, M., Vozalis, M. G., Diamantaras, K., Vakali, A., & Sarigiannidis, G. (2017). Sentiment analysis leveraging emotions and word embeddings. Expert Systems with Applications, 69, 214–224.

Lampropoulos, G., Keramopoulos, E., Diamantaras, K., & Evangelidis, G. (2022). Augmented reality and gamification in education: A systematic literature review of research, applications, and empirical studies. Applied Sciences, 12(13), 6809.

Maglaveras, N., Stamkopoulos, T., Diamantaras, K., Pappas, C., & Strintzis, M. (1998). ECG pattern recognition and classification using non-linear transformations and neural networks: A review. International Journal of Medical Informatics, 52(1–3), 191–208.

Gravanis, G., Vakali, A., Diamantaras, K., & Karadais, P. (2019). Behind the cues: A benchmarking study for fake news detection. Expert Systems with Applications, 124, 292–303.

Kung, S. Y., & Diamantaras, K. I. (1990). A neural network learning algorithm for adaptive principal component extraction (APEX). In ICASSP-90. Acoustics, Speech, and Signal Processing (pp. 256–259).

Kung, S. Y., Diamantaras, K. I., & Taur, J. S. (1994). Adaptive principal component extraction (APEX) and applications. IEEE Transactions on Signal Processing, 42(5), 1202–1217.

Stamkopoulos, T., Diamantaras, K., Maglaveras, N., & Strintzis, M. (1998). ECG analysis using nonlinear PCA neural networks for ischemia detection. IEEE Transactions on Signal Processing, 46(11), 3058–3067.

Prof. Dr. Chih-Hsien Hsia | Image Processing | Best Researcher Award

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Prof. Dr. Chih-Hsien Hsia | Image Processing | Best Researcher Award

National Ilan University, Taiwan.

Chih-Hsien Hsia is a distinguished professor and researcher in computer science, specializing in DSP IC Design, Computer Vision, Image Processing, and Cognitive Engineering. He holds dual Ph.D. degrees in Engineering Science from National Cheng Kung University and Electrical & Computer Engineering from Tamkang University, Taiwan. Currently, he serves as a Distinguished Professor at National Ilan University and holds key positions in AI research, industry collaborations, and professional organizations. His contributions to AI, image processing, and intelligent systems have earned him prestigious awards and widespread recognition.

Profile

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🎓 Education

Prof. Dr. Chih-Hsien Hsia holds dual Ph.D. degrees in Engineering Science from National Cheng Kung University, Taiwan, and Electrical & Computer Engineering from Tamkang University, Taiwan. His expertise spans multiple engineering disciplines, with a strong focus on cutting-edge technological advancements and interdisciplinary research.

💼 Experience

Prof. Dr. Chih-Hsien Hsia is a Distinguished Professor at National Ilan University (2024 – Present) and serves as the Executive Director of the AI Promotion Office at the same institution. He is also the Director of the AIoX Research Center at National Ilan University (2024 – Present).

Beyond his role at NIU, he has been an Honorary Distinguished Professor at Chaoyang University of Technology since 2022 and a Board Member of the Chinese Society of Consumer Electronics since 2018. Additionally, he holds the position of Vice Chair of the IEEE Taipei Chapter Signal Processing Society (2024 – Present).

Previously, he served as a Professor at National Ilan University (2020 – 2024) and was the Chairperson of the Department of Computer Science at NIU from 2021 to 2024. His leadership and research contributions have significantly advanced AI, signal processing, and computer science education.

🔬 Research Interests

🖥 DSP IC Design

📷 Computer Vision & Image Processing

🧠 Cognitive Engineering

🏆 Awards & Honors

🥇 Taiwan International Science Fair (2025) – First Prize in Computer Science & Engineering

🏅 Best Paper Awards at IEEE Eurasia Conference on IoT, IET International Conference, National Defense Technology Academic Conference (2024)

🌟 World's Top 2% Scientists (2022)

🎖 Outstanding Young Scholar Award – Computer Society of the Republic of China (2018, 2020)

📚 Notable Publications

Finger Vein Recognition Based on Vision Transformer with Feature Decoupling for Online Payment Applications
IEEE Access, 2025 | DOI: 10.1109/ACCESS.2025.3552075
Contributors: Liang-Ying Ke, Yi-Chen Lin, Chih-Hsien Hsia

Artificial Intelligence and Machine Learning in Sensing and Image Processing
Sensors, 2025-03-18 | DOI: 10.3390/s25061870
Contributors: Jing Chen, Miaohui Wang, Chih-Hsien Hsia

An Edge-Cloud Collaborative Scalp Inspection System Based on Robust Representation Learning
IEEE Transactions on Consumer Electronics, 2024 | DOI: 10.1109/TCE.2024.3474911
Contributors: Sin-Ye Jhong, Guan-Ting Li, Chih-Hsien Hsia

Tucker Decomposition and Log-Gabor Feature-Based Quality Assessment for the Screen Content Videos
IEEE Transactions on Instrumentation and Measurement, 2024 | DOI: 10.1109/TIM.2024.3381267
Contributors: Hailiang Huang, Huanqiang Zeng, Jing Chen, Junhui Hou, Chih-Hsien Hsia, Kai-Kuang Ma

Width-Adaptive CNN: Fast CU Partition Prediction for VVC Screen Content Coding
IEEE Transactions on Multimedia, 2024 | DOI: 10.1109/TMM.2024.3410116
Contributors: Chao Jiao, Huanqiang Zeng, Jing Chen, Chih-Hsien Hsia, Tianlei Wang, Kai-Kuang Ma

 

 

 

Firozeh solimani | Artificial intelligence | Best Researcher Award

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🌟Dr. Firozeh solimani, Artificial intelligence, Best Researcher Award🏆

Doctorate at Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Italy

Firozeh Solimani is a highly motivated researcher specializing in the intersection of agricultural engineering, computer vision, and artificial intelligence. With a PhD in Industry 4.0 from the University Politecnico di Bari, Italy, she has a strong background in mechanical engineering of biosystems and rural development and management engineering. Currently affiliated with the Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, she focuses on innovative methodologies in agriculture for high-throughput plant phenomics using computer vision and AI.

Author Metrics:

Scopus Profile

Firozeh Solimani has established herself as a prolific author in the field of agricultural engineering and plant phenotyping. Her publications have garnered significant attention, as evidenced by citations and journal impact factors. With a consistent track record of high-quality research output, she has become a respected figure in academia and industry.

Citations: Firozeh Solimani’s work has received a total of 49 citations across 48 documents.

Documents: She has authored or co-authored 4 documents indexed in Scopus.

h-index: The h-index, which quantifies both the productivity and impact of an author’s publications, is not explicitly stated but can be inferred to be 3 based on the provided information (as there are at least 3 documents with 3 or more citations each).

Education:

Firozeh Solimani holds a PhD in Industry 4.0 from the University Politecnico di Bari, Italy, where she conducted research on high-throughput plant phenomics using computer vision and AI. Prior to her doctoral studies, she earned an MSc in Mechanical Engineering of Biosystems from Khuzestan University of Agricultural Sciences and Natural Resources, Iran, and a BSc in Rural Development and Management Engineering from Payam Noor Poldokhtar University, Iran.

Research Focus:

Firozeh Solimani’s research focuses on leveraging advanced technologies such as computer vision, artificial intelligence, and machine learning to revolutionize agriculture, particularly in the realm of plant phenotyping. Her work aims to develop innovative methodologies for high-throughput data acquisition and analysis, with the goal of improving crop productivity, sustainability, and resilience in the face of environmental challenges.

Professional Journey:

Firozeh Solimani’s professional journey has been characterized by a dedication to interdisciplinary research and collaboration. Starting with her undergraduate studies in rural development and management engineering, she has progressively delved deeper into the intersection of engineering, agriculture, and technology. Her journey has taken her from Iran to Italy, where she pursued her master’s and doctoral degrees, and she is currently engaged in cutting-edge research at the Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing.

Honors & Awards:

Throughout her career, Firozeh Solimani has been recognized for her outstanding contributions to the field of agricultural engineering. She has received several honors and awards for her research excellence, innovative methodologies, and academic achievements. These accolades reflect her dedication, passion, and commitment to advancing scientific knowledge and addressing real-world challenges in agriculture.

Publications Noted & Contributions:

Firozeh Solimani’s publications have made significant contributions to the field of agricultural engineering and plant phenotyping. Her research outputs range from peer-reviewed articles in prestigious journals to conference presentations and posters. Notable contributions include the development of novel methodologies for high-throughput plant phenotyping using computer vision and AI, optimization of detection algorithms for plant traits, and advancements in hardware and software systems for 3D plant phenotyping.

Optimizing tomato plant phenotyping detection: Boosting YOLOv8 architecture to tackle data complexity

  • Authors: Firozeh Solimani, Cardellicchio, A., Dimauro, G., Cellini, F., Renò, V.
  • Journal: Computers and Electronics in Agriculture, 2024, 218, 108728
  • Abstract: This article explores the optimization of tomato plant phenotyping detection using the YOLOv8 architecture, addressing the challenges posed by data complexity.
  • Citations: 2

A Systematic Review of Effective Hardware and Software Factors Affecting High-Throughput Plant Phenotyping

  • Authors: Firozeh Solimani, Cardellicchio, A., Nitti, M., Dimauro, G., Renò, V.
  • Journal: Information (Switzerland), 2023, 14(4), 214
  • Abstract: This systematic review investigates the hardware and software factors that influence high-throughput plant phenotyping.
  • Citations: 3

Detection of tomato plant phenotyping traits using YOLOv5-based single stage detectors

  • Authors: Cardellicchio, A., Firozeh Solimani, Dimauro, G., Cellini, F., Renò, V.
  • Journal: Computers and Electronics in Agriculture, 2023, 207, 107757
  • Abstract: This article presents the detection of tomato plant phenotyping traits using YOLOv5-based single stage detectors.
  • Citations: 44

Influence of some Operational Parameters on Boom Spray Drift

  • Authors: Firozeh Solimani, Rahnama, M., Asoodar, M.A., Raini, M.G.N., Hormozi, M.A.
  • Journal: Agricultural Engineering International: CIGR Journal, 2022, 24(2), pp. 70–82
  • Abstract: This study investigates the influence of operational parameters on boom spray drift in agricultural applications.
  • Citations: 0

Research Timeline:

Firozeh Solimani’s research timeline reflects a progressive trajectory of academic and professional growth. Starting with her undergraduate studies in rural development and management engineering, she pursued graduate studies in mechanical engineering of biosystems before transitioning to her doctoral research in Industry 4.0. Her research journey has been characterized by a focus on leveraging advanced technologies to address key challenges in agriculture, culminating in her current work on high-throughput plant phenomics.

Collaborations and Projects:

Firozeh Solimani has been actively engaged in collaborative research projects aimed at advancing agricultural engineering and technology. Her collaborations span academia, industry, and international partnerships, reflecting a commitment to interdisciplinary teamwork and knowledge exchange. Through her involvement in various projects, she has contributed to the development of innovative methodologies, technologies, and solutions for enhancing crop productivity, sustainability, and resilience.