Saraswathy Shamini Gunasekaran | Computer Science | Research Excellence Award

Published on by Book of Award

Assoc. Prof. Dr. Saraswathy Shamini Gunasekaran | Computer Science | Research Excellence Award

Taylor's University | Malaysia

Assoc. Prof. Dr. Saraswathy Shamini Gunasekaran is an accomplished researcher and academic specializing in Artificial Intelligence, with a strong focus on agent-based systems, intelligent autonomous systems, machine learning applications, smart energy systems, and climate change–related digital intelligence. Her scholarly impact is reflected in an h-index of 16, with 70 research documents generating 899 citations across international indexing platforms, demonstrating sustained influence in AI-driven and interdisciplinary research domains. Her work spans collective intelligence, knowledge transfer models, data mining, educational technologies, and intelligent digitalization, with publications appearing in IEEE conferences, international journals, and Springer book chapters. In addition to academic publishing, she has led significant intellectual property initiatives, including a granted patent on cooperative control systems for unmanned aerial platforms, utility innovations in autonomous multi-UAV task allocation, and copyrighted micro-credential programs. Her research excellence has been recognized through multiple prestigious awards, including international science communication accolades, industry research honors, and selection for global digital leadership programs. With over two decades of academic engagement and active research contributions, her profile reflects a strong integration of theoretical innovation, applied intelligence systems, and impactful scholarly dissemination across AI, energy, education, and digital transformation domains.

Citation Metrics (Scopus)

1000

800

600

400

200

0

Citations
899

Documents
70

h-index
16

 

Citations

 

Documents

 

h-index


View Scopus Profile

Featured Publications

Exploring the Roles of Agents and Multi-Agent in Improving Mobile Ad Hoc Networks
– International Symposium on Agents, Multi-Agent Systems and Robotics, ISAMSR, 2021

 

Haiwei Wu | Engineering | Best Researcher Award

Published on by Book of Award

Prof. Dr. Haiwei Wu | Engineering | Best Researcher Award

Jilin Agricultural University | China

Prof. Dr. Haiwei Wu is an emerging multidisciplinary researcher whose contributions span energy systems, machine learning, spectroscopy, and intelligent diagnostics. His recent research focuses on advanced computational methods applied to energy storage and electric vehicle systems, including the development of an attention-based multi-feature fusion physics-informed neural network for accurate state-of-health estimation of lithium-ion batteries and the application of queuing-theoretic models for sustainable EV charging infrastructure planning. Beyond energy research, he has contributed significantly to the use of mid-infrared spectroscopy combined with machine learning and support vector machines for the authentication and identification of biological and agricultural products, reflecting strong capabilities in analytical modeling and pattern recognition. His publications from 2022 to 2025 highlight expertise in spectral analysis, counterfeit detection, and quality assessment. In addition, he has explored applications of improved YOLOv8 for mechanical part inspection and contributed to research on task-driven cooperative inquiry learning in education. His innovative work is supported by several patents related to electric vehicle charging technologies, demonstrating a commitment to advancing practical, technology-driven solutions across sectors.

Profile : Scopus | Orcid

Featured Publications

Wu, H., Liu, J., Wang, Z., & Li, X. (2025). An attention-based multi-feature fusion physics-informed neural network for state-of-health estimation of lithium-ion batteries. Energies.

Wang, Z., Zou, J., Tu, J., Li, X., Liu, J., & Wu, H. (2025). Towards sustainable EV infrastructure: Site selection and capacity planning with charger type differentiation and queuing-theoretic modeling. World Electric Vehicle Journal.

He, T., Kaimin, W., & Wu, H. (2025). Research on the construction and implementation of a task-driven cooperative inquiry learning model for postgraduate students majoring in music education. Chinese Music Education, (05), 47–53.

Yang, C.-E., Wu, H., Yuan, Y., et al. (2025). Efficient recognition of plum blossom antler hats and red deer antler hats based on support vector machine and mid-infrared spectroscopy. Journal of Jilin Agricultural University, 1–7.

Yang, C.-E., Su, L., Feng, W.-Z., Zhou, J.-Y., Wu, H.-W., Yuan, Y.-M., & Wang, Q. (2023). Identification of Pleurotus ostreatus from different producing areas based on mid-infrared spectroscopy and machine learning. Spectroscopy and Spectral Analysis.

Yang, C.-E., Su, L., Feng, W., et al. (2023). Identification of Pleurotus ostreatus from different origins by mid-infrared spectroscopy combined with machine learning. Spectroscopy and Spectral Analysis, 43(02), 577–582.

Yang, C.-E., Wu, H.-W., Yang, Y., Su, L., Yuan, Y.-M., Liu, H., Zhang, A.-W., & Song, Z.-Y. (2022). A model for the identification of counterfeited and adulterated Sika deer antler cap powder based on mid-infrared spectroscopy and support vector machines. Spectroscopy and Spectral Analysis.

Yang, C.-E., Wu, H., Yang, Y., et al. (2022). Identification model of counterfeiting and adulteration of plum blossom antler cap powder based on mid-infrared spectroscopy and support vector machine. Spectroscopy and Spectral Analysis, 42(08), 2359–2365.

Bo Zhang | Computer Science | Research Excellence Award

Published on by Book of Award

Assoc. Prof. Dr. Bo Zhang | Computer Science | Research Excellence Award

Northwest Polytechnic University | China

Assoc. Prof. Dr. Bo Zhang is an accomplished researcher whose work spans remote sensing, geospatial intelligence, environmental monitoring, and machine learning–driven Earth observation analytics. With 252 citations,  an h-index of 7, and 5, i10-index publications, his scholarly contributions demonstrate a growing and impactful presence in environmental data science. His research advances high-resolution satellite image processing, atmospheric pollutant estimation, digital elevation model reconstruction, and intelligent geospatial mapping. He has produced notable work on transfer learning–enhanced remote sensing, sparse-sample super-resolution mapping, neural-network–based PMx estimation, land surface temperature retrieval, and ozone concentration modeling. His publications in leading journals such as IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Science Bulletin, Remote Sensing, and Indoor and Built Environment highlight his expertise in integrating artificial intelligence with satellite observations to address environmental challenges. His research also contributes to epidemiological spatial analysis and geospatial data fusion, offering multidisciplinary value in Earth system science. Through continuous work on novel algorithms and high-fidelity environmental datasets, he has strengthened the scientific foundation for climate monitoring, pollution assessment, and large-scale geospatial modeling, positioning him as a significant contributor to advanced remote sensing and environmental informatics.

Profile : Scopus | Orcid | Google Scholar

Featured Publications

Yang, C., Zhang, B., Zhang, M., Wang, Q., & Zhu, P. (2025). Research on decision-making strategies for multi-agent UAVs in island missions based on Rainbow Fusion MADDPG algorithm. Drones, 9(10), 673.

Zhang, B., Shi, Z., Hong, D., Wang, Q., Yang, J., Ren, H., & Zhang, M. (2025). Super-resolution reconstruction of the 1 arc-second Australian coastal DEM dataset. Geo-Spatial Information Science, 1–21.


Zhang, B., Xiong, W., Ma, M., Wang, M., Wang, D., Huang, X., Yu, L., Zhang, Q., & others. (2022). Super-resolution reconstruction of a 3 arc-second global DEM dataset. Science Bulletin, 67(24), 2526–2530.


Pan, D., Zhang, M., & Zhang, B. (2021). A generic FCN-based approach for road-network extraction from VHR remote sensing images using OpenStreetMap as benchmarks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.


Zhang, B., Zhang, M., Kang, J., Hong, D., Xu, J., & Zhu, X. (2019). Estimation of PMx concentrations from Landsat 8 OLI images based on a multilayer perceptron neural network. Remote Sensing, 11(6), 646.


Zhu, B., Liu, J., Fu, Y., Zhang, B., & Mao, Y. (2018). Spatio-temporal epidemiology of viral hepatitis in China (2003–2015): Implications for prevention and control policies. International Journal of Environmental Research and Public Health, 15(4), 661.

Chao Wang | Computer Science | Research Excellence Award

Published on by Book of Award

Mr. Chao Wang | Computer Science | Research Excellence Award

North China University of Technology | China

Mr. Chao Wang is an accomplished researcher whose work spans vehicular networks, IoT security, blockchain mechanisms, and food engineering applications, reflecting a multidisciplinary impact. With 809 citations, an h-index of 12, and 16 i10-index publications, he has established a strong scholarly presence supported by numerous high-impact journal articles and competitive conference papers. His research contributions include advanced blockchain-based frameworks for secure communication, innovative privacy-preserving data-sharing models, anomaly detection algorithms for intelligent vehicles, and distributed system security. He has also co-authored influential studies on anti-glycation mechanisms, food bioactive compounds, and cellular protection. His publications from 2021 to 2025 demonstrate consistent output across IEEE Transactions, Future Generation Computer Systems, Food Biomacromolecules, and other reputable venues. His work on collaborative quality control, CAN bus anomaly detection, distributed GAN attack resistance, and multi-party payment channels represents notable advancements in secure systems. He has also contributed to reviews on AGEs inhibition, IoV security, NGS applicability, and blockchain-enabled vehicular applications. Beyond technical innovation, his research extends to biologically focused studies that explore glycation inhibition, fermentation mechanisms, and cellular oxidative protection. Across domains, his scholarly contributions continue to advance secure intelligent systems, data integrity solutions, and interdisciplinary applications, reinforcing his role as a productive and influential researcher.

Profiles : Orcid | Google Scholar

Featured Publications

Bao, C., Niu, Z., He, B., Li, Y., Han, S., Feng, N., Huang, H., Wang, C., Wang, J., & others. (2025). A novel high‐protein composite rice with anti‐glycation properties prepared with crushed rice flour, whey protein and lotus seed proanthocyanidins. Food Biomacromolecules, 2(1), 23–34.

He, Y., Zhou, Z., Wu, B., Xiao, K., Wang, C., & Cheng, X. (2024). Game-theoretic incentive mechanism for collaborative quality control in blockchain-enhanced carbon emissions verification. IEEE Transactions on Network Science and Engineering.

Li, Q., Xiao, K., Yi, C., Yu, F., Wang, W., Rao, J., Liu, M., Zhang, L., Mu, Y., Wang, C., & others. (2024). Inhibition and mechanism of protein nonenzymatic glycation by Lactobacillus fermentum. Foods, 13(8), 1183.

Wang, C., Xu, X., Xiao, K., He, Y., & Yang, G. (2024). Traffic anomaly detection algorithm for CAN bus using similarity analysis. High-Confidence Computing, 4(3), 100207.

Xiao, K., Li, J., He, Y., Wang, X., & Wang, C. (2024). A secure multi-party payment channel on-chain and off-chain supervisable scheme. Future Generation Computer Systems, 154, 330–343.

Feng, N., Feng, Y., Tan, J., Zhou, C., Xu, J., Chen, Y., Xiao, J., He, Y., Wang, C., & others. (2023). Inhibition of advance glycation end products formation, gastrointestinal digestion, absorption and toxicity: A comprehensive review. International Journal of Biological Macromolecules, 249, 125814.

Wu, Q., Kong, Y., Liang, Y., Niu, M., Feng, N., Zhang, C., Qi, Y., Guo, Z., Xiao, J., & others. (2023). Protective mechanism of fruit vinegar polyphenols against AGEs-induced Caco-2 cell damage. Food Chemistry: X, 19, 100736.

Wang, C., Liu, X., He, Y., Xiao, K., & Li, W. (2023). Poisoning the competition: Fake gradient attacks on distributed generative adversarial networks. In Proceedings of the IEEE International Conference on Mobile Ad Hoc and Smart Systems.

Xu, X., Wang, L., Wang, C., Zhu, H., Zhao, L., Yang, S., & Xu, C. (2023). Intelligent connected vehicle security: Threats, attacks and defenses. Journal of Information Science & Engineering, 39(6).

Wang, C., Jiang, L., He, Y., Yang, G., & Xiao, K. (2023). Age of information-based channel scheduling policy in IoT networks under dynamic channel conditions. In China Conference on Wireless Sensor Networks (pp. 88–98).

Zhou, J., Wang, C., Luo, M., Liu, X., Xu, X., & Chen, S. (2023). Spatial-temporal based multi-head self-attention for in-vehicle network intrusion detection system. SSRN 4581213.

Wang, C., Wang, S., Cheng, X., He, Y., Xiao, K., & Fan, S. (2022). A privacy and efficiency-oriented data sharing mechanism for IoTs. IEEE Transactions on Big Data, 9(1), 174–185.

Li, Q., Li, L., Zhu, H., Yang, F., Xiao, K., Zhang, L., Zhang, M., Peng, Y., Wang, C., & others. (2022). Lactobacillus fermentum as a new inhibitor to control advanced glycation end-product formation during vinegar fermentation. Food Science and Human Wellness, 11(5), 1409–1418.

Wu, Q., Liang, Y., Kong, Y., Zhang, F., Feng, Y., Ouyang, Y., Wang, C., Guo, Z., & others. (2022). Role of glycated proteins in vivo: Enzymatic glycated proteins and non-enzymatic glycated proteins. Food Research International, 155, 111099.

Wang, C., Cheng, X., Li, J., He, Y., & Xiao, K. (2021). A survey: Applications of blockchain in the Internet of Vehicles. EURASIP Journal on Wireless Communications and Networking, 2021(1), 77.

Xu, S., Chen, X., Wang, C., He, Y., Xiao, K., & Cao, Y. (2021). A lattice-based ring signature scheme to secure automated valet parking. In Wireless Algorithms, Systems, and Applications.

Leema Nelson | Computer Science | Research Excellence Award

Published on by Book of Award

Dr. Leema Nelson | Computer Science | Research Excellence Award

Chitkara University | India 

Dr. Leema Nelson is an accomplished researcher whose scholarly contributions span machine learning, clinical decision support systems, composite materials, signal processing, and intelligent diagnostic frameworks. With a total of 1206 citations, an h-index of 17, and 29 i10-index publications, her research demonstrates both depth and sustained impact across interdisciplinary domains. She has produced numerous high-quality peer-reviewed articles, many in leading Elsevier journals such as Applied Soft Computing and Materials & Design, focusing on neural network optimization, characterization of metal-matrix composites, wear modelling, and advanced computational methods. Her work in clinical data classification, including diabetes and PCOS diagnosis, highlights the integration of artificial intelligence into healthcare decision-making. In recent years, she expanded her research into video smoke detection, cyber-security–based email filtering, audio source separation, and welding parameter optimization using intelligent algorithms. Her studies in deepfake detection, text recognition, and clinical support systems reflect her continuing advancements in data-driven AI models. She has also contributed extensively to IEEE conferences, presenting innovations in masked face detection, ultrasound image analysis, mobile app frameworks, and disease prediction models. Overall, her scientific output reflects strong productivity, interdisciplinary expertise, and meaningful contributions to both computational intelligence and applied engineering research.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Jibinsingh, B. R., & Nelson, L. (2025). FL-WOSP: Federated learning with Walrus Optimization for sepsis prediction using MIMIC-III physiological and clinical data. Pattern Recognition. Advance online publication.

Batra, H., & Nelson, L. (2024). ESD: E-mail spam detection using cybersecurity-driven header analysis and machine learning-based content analysis. International Journal of Performability Engineering, 20(4).

Nelson, L. (2024). Data-driven clinical decision support system using neural network topology optimization for PCOS diagnosis. Journal of Soft Computing and Data Mining.

Batra, H., & Nelson, L. (2024). A three-stage deepfake detection framework using deep learning models with multimedia data. International Journal of Intelligent Systems and Applications.

Shanmuga Priya, M., Pavithra, A., & Leema, N. (2024). Character/word modelling: A two-step framework for text recognition in natural scene images. Computer Science.

Batra, H., & Nelson, L. (2023). DCADS: Data-driven computer aided diagnostic system using machine learning techniques for polycystic ovary syndrome. International Journal of Performability Engineering, 19(3).

Kumar, V. A., Rao, C. V. R., & Leema, N. (2023). Audio source separation by estimating the mixing matrix in underdetermined condition using successive projection and volume minimization. International Journal of Information Technology, 15(4), 1831–1844.

Ramesh, A., Sivapragash, M., Ajith Kumar, K. K., & Leema, N. (2023). Investigating the quality of TIG-welded aluminium alloy 5086 using the online acoustic emission and optimization of welding parameters using global best-based modified artificial bee colony algorithm. Transactions of the Indian Institute of Metals, 1–14.

Pranshu Kumar Soni, & Leema, N. (2023). PCP: Profit-driven churn prediction using machine learning techniques in banking sector. International Journal of Performability Engineering, 19(5), 303–311.

Vettum Perumal, S., Suyamburajan, V., Chidambaranathan, V. S., & Nelson, L. (2023). Characterization of microstructure and mechanical behaviour in activated tungsten inert gas welded dissimilar AA joint of AA 5083 and AA 6061 alloys. Journal of the Institution of Engineers (India): Series D, 1–9.

Zhang Zhenqian | Neuroscience | Best Researcher Award

Published on by Book of Award

Mr. Zhang Zhenqian | Neuroscience | Best Researcher Award

University of Toyama | Japan

Mr. Zhang Zhenqian is a dedicated researcher whose work bridges artificial intelligence, machine learning, and meteorology, with an emphasis on developing advanced neural network models for predictive analytics. His recent publication, “RD2: Reconstructing the Residual Sequence via Under Decomposing and Dendritic Learning for Generalized Time Series Predictions,” featured in Neurocomputing (October 2025), showcases his innovative approach to enhancing time series forecasting accuracy through the integration of dendritic learning mechanisms and residual sequence reconstruction. Collaborating with Houtian He, Zhenyu Lei, Zihang Zhang, and Shangce Gao, Mr. Zhang contributes to advancing the computational intelligence field by addressing challenges in dynamic data modeling and predictive reliability. His research explores the intersection of data-driven modeling and environmental systems, offering valuable insights for improving real-world forecasting, particularly in meteorological and environmental applications. With a growing scholarly presence and contributions recognized through peer-reviewed international publications, Mr. Zhang exemplifies a new generation of researchers committed to interdisciplinary innovation. His work not only strengthens the theoretical foundations of artificial intelligence but also demonstrates its transformative potential in understanding and managing complex natural and engineered systems.

Profile : Orcid

Featured Publication

Zhang, Z., He, H., Lei, Z., Zhang, Z., & Gao, S. (2025). RD2: Reconstructing the residual sequence via under decomposing and dendritic learning for generalized time series predictions. Neurocomputing, 131867.

Simy Baby | Engineering | Best Researcher Award

Published on by Book of Award

Mrs. Simy Baby | Engineering | Best Researcher Award

National Institute of Technology | India

Mrs. Simy Baby is an emerging researcher whose scholarly contributions center on semantic communications, machine learning, and computer vision, with a strong emphasis on communication-efficient feature extraction for edge inference tasks. She has authored 2 documents, received 2 citations, and holds an h-index of 1, reflecting the growing impact of her research in advanced communication technologies. Her publications in SCI-indexed journals, including Elsevier’s Computers & Electrical Engineering and IEEE Transactions on Cognitive Communications and Networking, demonstrate her commitment to innovation and excellence. Her study, “Complex Chromatic Imaging for Enhanced Radar Face Recognition”, introduced a novel complex-valued representation preserving amplitude and phase information of mmWave radar signals, achieving 99.7% recognition accuracy. Another major contribution, “Complex-Valued Linear Discriminant Analysis on mmWave Radar Face Signatures for Task-Oriented Semantic Communication”, proposed a CLDA-based encoding framework that improved feature interpretability and robustness under varying channel conditions. Her ongoing projects explore Data Fusion Discriminant Analysis (DFDA) for multi-view activity recognition and Semantic Gaussian Process Regression (GPR) for vehicular pose estimation, advancing the integration of semantic communication and computer vision. Mrs. Simy Baby’s research represents a vital step toward the development of intelligent, efficient, and adaptive communication systems for next-generation technologies.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Baby, S. M., & Gopi, E. S. (2025). Complex valued linear discriminant analysis on mmWave radar face signatures for task-oriented semantic communication. IEEE Transactions on Cognitive Communications and Networking.

Baby, S. M., & Gopi, E. S. (2025, April). Complex chromatic imaging for enhanced radar face recognition. Computers and Electrical Engineering.

Yaqin Wu | Computer Science | Excellence in Research Award

Published on by Book of Award

Ms. Yaqin Wu | Computer Science | Excellence in Research Award

Shanxi Agricultural University | China

Ms. Yaqin Wu is an accomplished researcher and educator specializing in acoustic signal analysis, deep learning, and multimodal information fusion, with a research record reflecting 80 citations across 78 documents, 9 publications, and an h-index of 3. She holds a Master of Engineering in Electronic and Communication Engineering from Tianjin University and a Bachelor’s degree in Communication Engineering from Dalian Maritime University. Currently serving as a full-time faculty member at the School of Software, Shanxi Agricultural University, she teaches courses such as Speech Signal Processing, Natural Language Processing, and Human-Computer Interaction. Ms. Wu has led and contributed to several cutting-edge research projects, including pathological voice restoration, multimodal animal behavior monitoring, and AVS audio codec development. She has authored multiple SCI-indexed papers and holds several patents and software copyrights related to voice signal processing. Her technical proficiency spans Python, MATLAB, Linux systems, and MySQL databases. Notably, her master’s thesis earned the Outstanding Achievement Award of Engineering Master’s Practice from Tianjin University. Through her innovative contributions in signal processing and intelligent systems, Ms. Wu continues to advance the intersection of engineering and artificial intelligence research.

Profiles : Scopus | Orcid

Featured Publications

Zhang, J., Wu, Y., & Zhang, T. (2025). Fusing time-frequency heterogeneous features with cross-attention mechanism for pathological voice detection. Journal of Voice. Advance online publication.

Li, X., Wang, K., Chang, Y., Wu, Y., & Liu, J. (2025). Combining Kronecker-basis-representation tensor decomposition and total variational constraint for spectral computed tomography reconstruction. Photonics, 12(5), 492.

Hanlin Liu | Engineering | Research for community Impact Award

Published on by Book of Award

Mr. Hanlin Liu | Engineering | Research for community Impact Award

Jilin Jianzhu University | China

Author profile

Google Scholar

Early Academic Pursuits

Mr. Hanlin Liu began his academic journey in the field of surveying and mapping engineering during his undergraduate studies at Jilin Jianzhu University. His dedication to precision and technical learning laid a strong foundation in geospatial sciences and civil engineering. With consistent performance and research-oriented thinking, he advanced to pursue a master’s degree in architecture and civil engineering at the same university. Under the guidance of his academic mentor, he cultivated a deep interest in remote sensing, machine learning, and environmental studies, setting the stage for his future research career.

Professional Endeavors

During his postgraduate years, Mr. Liu devoted himself to intensive laboratory work and field research. His professional endeavors included collaborative projects on soil analysis, wetland dynamics, mineral exploration, and fault diagnosis in mechanical systems. He demonstrated strong proficiency in scientific software, programming languages, and experimental design, which allowed him to develop advanced computational models and analytical frameworks. His role as an academic leader, serving as a class representative and editorial head, reflects his ability to balance research with organizational responsibilities.

Contributions and Research Focus

Mr. Liu’s research contributions span across environmental monitoring, mechanical fault diagnosis, and hyperspectral remote sensing. He explored the spatiotemporal dynamics of natural wetlands in Northeast China by integrating machine learning methods with optimization algorithms, offering new insights into ecological change drivers. His work on offshore wind turbine gearbox fault diagnosis proposed an interpretable, knowledge-driven framework that enriched mechanical reliability studies. Additionally, he advanced hyperspectral techniques for mineral alteration information extraction and developed innovative models to estimate soil heavy metal contents. These studies highlight his interdisciplinary focus combining artificial intelligence, geoscience, and environmental engineering.

Accolades and Recognition

Throughout his academic journey, Mr. Liu received multiple honors that reflect his excellence in research and innovation. He was awarded the National Scholarship and university-level first-class academic scholarships during his master’s program. His innovative projects earned recognition in provincial competitions, including awards in the “Internet+” Innovation and Entrepreneurship Contest, the “Challenge Cup,” and the Aerospace Knowledge Contest. During his undergraduate studies, he also won several distinctions in provincial surveying skill competitions, affirming his technical expertise and problem-solving ability.

Impact and Influence

Mr. Liu’s scholarly output includes multiple first-author and co-authored publications in high-impact journals indexed in SCI and EI. His research on wetlands, hyperspectral analysis, and mechanical fault diagnosis has been acknowledged in leading platforms, showcasing his ability to address both environmental and industrial challenges. Beyond publications, his patents for soil sampling and laser scanning devices demonstrate his commitment to translating research into practical technological solutions. His work not only contributes to scientific literature but also provides valuable methodologies for sustainable resource management and engineering applications.

Legacy and Future Contributions

Driven by a spirit of perseverance and innovation, Mr. Liu aspires to further his academic path through doctoral studies. His long-term vision is to refine computational methods for solving pressing environmental and engineering challenges. By integrating artificial intelligence with remote sensing and fault diagnosis systems, he seeks to contribute solutions with real-world impact. His dedication to teamwork, resilience under pressure, and scientific curiosity positions him as a researcher capable of leaving a lasting legacy in the interdisciplinary fields of environmental monitoring and intelligent engineering systems.

Publications

Article: Research on Abrasive Particle Target Detection and Feature Extraction for Marine Lubricating Oil
Authors: Chenzhao Bai, Jiaqi Ding, Hongpeng Zhang, Zhiwei Xu, Hanlin Liu, Wei Li, Guobin Li, Yi Wei, Jizhe Wang
Journal: Journal of Marine Science and Engineering
Year: 2024

Article: An axiomatic fuzzy set theory-based fault diagnosis approach for rolling bearings
Authors: Xin Wang, Hanlin Liu, Wankang Zhai, Hongpeng Zhang, Shuyao Zhang
Journal: Engineering Applications of Artificial Intelligence
Year: 2024

Article: An adversarial single-domain generalization network for fault diagnosis of wind turbine gearboxes
Authors: Xinran Wang, Chenyong Wang, Hanlin Liu, Cunyou Zhang, Zhenqiang Fu, Lin Ding, Chenzhao Bai, Hongpeng Zhang, Yi Wei
Journal: Journal of Marine Science and Engineering
Year: 2023

Article: Driving force analysis of natural wetland in Northeast plain based on SSA-XGBoost model
Authors: Hanlin Liu, Nan Lin, Honghong Zhang, Yongji Liu, Chenzhao Bai, Duo Sun, Jiali Feng
Journal: Sensors
Year: 2023

Article: Extraction of mineralized indicator minerals using ensemble learning model optimized by SSA based on hyperspectral image
Authors: Nan Lin, Hanlin Liu, Genjun Li, Menghong Wu, Delin Li, Ranzhe Jiang, Xuesong Yang
Journal: Open Geosciences
Year: 2022

Conclusion

Mr. Hanlin Liu is an emerging researcher whose academic pursuits blend civil engineering, remote sensing, and machine learning. His contributions span from ecological studies of wetlands to industrial fault diagnostics and soil heavy metal analysis, underpinned by strong technical skills and innovative methodologies. Recognized with scholarships, competition awards, and impactful publications, he has already established himself as a promising scholar. His future vision is centered on advancing scientific understanding and delivering practical solutions through rigorous doctoral research. With his blend of academic excellence, technical expertise, and research dedication, Mr. Liu represents the new generation of scholars poised to make meaningful contributions to science and society.

Hafiz Mohammad Hasan Babu | Computer Science | Lifetime Achievement in Books Award

Published on by Book of Award

Prof. Hafiz Mohammad Hasan Babu | Computer Science | Lifetime Achievement in Books Award

University of Dhaka | Bangladesh

Author Profile

Scopus

Orcid

Google Scholar

Early Academic Pursuits

Prof. Hafiz Mohammad Hasan Babu began his academic journey in the realm of computer science and engineering with a strong foundation from the Brno University of Technology, Czech Republic, where he completed his M.Sc. with a focus on logic network automation. His curiosity for advanced computational systems took him to the Kyushu Institute of Technology in Japan, where he earned his Ph.D. in Computer Science and Electronics. His doctoral work concentrated on data structures for multiple-output functions and their applications in VLSI CAD, under the guidance of Prof. Dr. Tsutomu Sasao. These formative years laid the groundwork for his future innovations in quantum computing, reversible logic, and nanotechnology.

Professional Endeavors

Prof. Hasan Babu's academic career spans several decades and institutions, notably the University of Dhaka, where he served in various capacities, including as professor in the departments of Computer Science and Engineering, and Robotics and Mechatronics Engineering. His early academic roles also included positions at Khulna University. He has been deeply involved in curriculum development, student mentorship, and departmental leadership. Beyond teaching, he also contributed significantly as a research supervisor and played a critical role in developing the academic and research culture of computer science in Bangladesh.

Contributions and Research Focus

A prolific researcher, Prof. Hasan Babu has made groundbreaking contributions in the fields of quantum computing, reversible logic design, DNA computing, and machine learning applications in healthcare and agriculture. His interdisciplinary research integrates electronics, artificial intelligence, and biological systems. His most recent works delve into quantum biocomputing and nanotechnology, as evidenced by his multi-volume publications with Springer Nature and CRC Press. He has also authored numerous peer-reviewed articles on topics such as cardiovascular disease detection using mobile AI, air quality forecasting, and toxic substance identification in fruits through deep learning.

Accolades and Recognition

Prof. Hasan Babu has received numerous prestigious awards recognizing his excellence in research and scholarly contributions. These include the Dhaka University Research Excellence Recognition, the UGC Gold Medal, and the Dr. M. O. Ghani Memorial Gold Medal from the Bangladesh Academy of Sciences. His biography has been featured in “Who's Who in the World, USA.” He has also received international fellowships such as the Japanese Government Scholarship, the DAAD Fellowship from Germany, and a Czechoslovakian Government Scholarship, marking his global academic influence.

Impact and Influence

Throughout his academic life, Prof. Hasan Babu has significantly influenced the fields of computer science, electronics, and artificial intelligence. His innovations in reversible logic and DNA computing have shaped research methodologies and applications in both academia and industry. He has been instrumental in advancing computational methods that address real-world problems, particularly in environmental monitoring, biomedical diagnostics, and agricultural automation. His role as a mentor to doctoral and master’s students further amplifies his impact on the next generation of scholars.

Legacy and Future Contributions

Prof. Hasan Babu’s extensive scholarly contributions, particularly in the emerging domains of quantum AI and biocomputing, position him as a thought leader in futuristic technologies. His upcoming publications promise to offer new paradigms in nanotechnology and molecular-level computing. As he continues to mentor new researchers and expand the boundaries of interdisciplinary science, his legacy will be defined by his relentless pursuit of innovation and his dedication to fostering a globally relevant research ecosystem.

List of Book Publications

Books Published in 2025:

1. Quantum AI Emerging Technologies: Driving Innovation and Shaping the Future of Nanotechnology, Volume I, Springer Nature, Singapore.

2. Quantum AI Emerging Technologies: Driving Innovation and Shaping the Future of Nanotechnology, Volume II, Springer Nature, Singapore.

3. Quantum Biocomputing in Quantum Biology, Volume I, Springer Nature, Singapore.

4. Quantum Biocomputing in Quantum Biology, Volume II, Springer Nature, Singapore.

Book Published in 2024:
5. DNA Logic Design: Computing with DNA, World Scientific Publishing Co Pte Ltd., Singapore.

Books Published in 2023:
6. Multiple-Valued Computing in Quantum Molecular Biology, Volume I, CRC Press, USA.
7. Multiple-Valued Computing in Quantum Molecular Biology, Volume II, CRC Press, USA.

Books Published in 2022:
8. VLSI Circuits and Embedded Systems, CRC Press, USA.
9. Control Engineering Theory and Applications (Co-authored with Md. Jahangir Alam, Guoqing Hu, and Huazhong Xu), CRC Press, USA.

Books Published in 2020:
10. Quantum Computing: A Pathway to Quantum Logic Design, 2nd Edition, IOP Publishers, Bristol, UK.
11. Reversible and DNA Computing, Wiley Publishers, UK.

Journal Publications

Analyzing infant cry to detect birth asphyxia using a hybrid CNN and feature extraction approach
Authors: Samrat Kumar Dey, Khandaker Mohammad Mohi Uddin, Arpita Howlader, Md Mahbubur Rahman, Hafiz Md Hasan Babu, Nitish Biswas, Umme Raihan Siddiqi, Badhan Mazumder
Journal: Neuroscience Informatics (Elsevier)
Year: 2025

Empowering early detection: A web-based machine learning approach for PCOS prediction
Authors: Md. Mahbubur Rahman, Ashikul Islam, Forhadul Islam, Mashruba Zaman, Md Rafiul Islam, Md Shahriar Alam Sakib, Hafiz Md Hasan Babu
Journal: Journal of Informatics in Medicine (Elsevier)
Year: 2024

Computer vision based deep learning approach for toxic and harmful substances detection in fruits
Authors: Abdus Sattar, Md. Asif Mahmud Ridoy, Aloke Kumar Saha, Hafiz Md. Hasan Babu, Mohammad Nurul Huda
Journal: Heliyon (Cell Press)
Year: 2024

A Comprehensive Approach to Detecting Chemical Adulteration in Fruits Using Computer Vision, Deep Learning, and Chemical Sensors
Authors: Abdus Sattar, Md. Asif Mahmud Ridoy, Aloke Kumar Saha, Hafiz Md. Hasan Babu, Mohammad Nurul Huda
Journal: Journal of Intelligent Systems with Applications (Elsevier)
Year: 2024

A Voice assistive mobile application tool to detect cardiovascular disease using machine learning approach
Authors: Khandaker Mohammad Mohi Uddin, Samrat Kumar Dey, Hafiz Md Hasan Babu
Journal: Biomedical Materials & Devices (Springer US)
Year: 2024

Conclusion

Prof. Hafiz Mohammad Hasan Babu embodies the spirit of academic excellence and innovation in computer science. With a career rich in scholarly output, international collaborations, and student mentorship, he has become a beacon of transformative research and a visionary in integrating quantum theory with computational systems. His work continues to influence the scientific community both in Bangladesh and globally, promising continued advancements in technology and applied sciences.