Sukumar Letchmunan | Computer Science | Best Researcher Award

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Dr. Sukumar Letchmunan | Computer Science | Best Researcher Award

University Sains Malaysia | Malaysia

Dr. Sukumar Letchmunan is an accomplished researcher and academic at Universiti Sains Malaysia, specializing in Software Engineering, Machine Learning, Software Metrics, and Service-Oriented Software Engineering. His research primarily focuses on developing intelligent computational models and data-driven frameworks that address complex real-world problems in domains such as crime prediction, healthcare analytics, uncertainty modeling, and sustainable digital systems. Over the years, Dr. Sukumar Letchmunan has made significant contributions to artificial intelligence applications through the integration of fuzzy logic, evidential theory, and deep learning, as reflected in his high-impact publications in leading journals including IEEE Access, ACM Transactions on Knowledge Discovery from Data, Knowledge-Based Systems, and Applied Soft Computing. His collaborations have produced novel methods for multi-view evidential clustering, belief function-based uncertainty representation, and medical decision-making systems, demonstrating his expertise in handling imprecision in data-centric environments. Notably, his works on AI-driven crime forecasting, machine learning-based diabetes prediction, and deep neural network analysis for human activity recognition have been widely cited, influencing interdisciplinary research across computer science and healthcare informatics. With a strong record of international collaboration, particularly with researchers from China, Turkey, and Saudi Arabia, Dr. Sukumar Letchmunan continues to advance the frontier of trustworthy and interpretable AI systems. His recent works in fuzzy similarity measures, transformer-based spatiotemporal modeling, and green decision-making frameworks exemplify his commitment to enhancing computational intelligence for sustainable and socially relevant applications. With 1,298 citations, an h-index of 19, and an i10-index of 22, Dr. Sukumar Letchmunan research impact underscores his influential role in advancing next-generation AI methodologies and their meaningful application to global scientific and societal challenges.

Profiles: Google Scholar | Scopus | Orcid

Featured Publications

1. Butt, U. M., Letchmunan, S., Ali, M., Hassan, F. H., Baqir, A., & Sherazi, H. H. R. (2021). Machine learning based diabetes classification and prediction for healthcare applications. Journal of Healthcare Engineering, 2021(1), 9930985.

2. Khaw, T. Y., Teoh, A. P., Abdul Khalid, S. N., & Letchmunan, S. (2022). The impact of digital leadership on sustainable performance: A systematic literature review. Journal of Management Development, 41(9–10), 514–534.

3. Butt, U. M., Letchmunan, S., Hassan, F. H., Ali, M., Baqir, A., & Sherazi, H. H. R. (2020). Spatio-temporal crime hotspot detection and prediction: A systematic literature review. IEEE Access, 8, 166553–166574.

4. Liu, Z., & Letchmunan, S. (2024). Enhanced fuzzy clustering for incomplete instance with evidence combination. ACM Transactions on Knowledge Discovery from Data, 18(3), 1–20.

5. Liu, Z., Huang, H., Letchmunan, S., & Deveci, M. (2024). Adaptive weighted multi-view evidential clustering with feature preference. Knowledge-Based Systems, 294, 111770.

Ivica Kopriva | Pattern Recognition | Best Researcher Award

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Dr. Ivica Kopriva | Pattern Recognition | Best Researcher Award

Rudjer Boskovich Institute | Croatia

Dr. Ivica Kopriva is a distinguished Senior Scientist at the Ruđer Bošković Institute, Croatia, internationally recognized for his pioneering contributions to signal processing, machine learning, and blind source separation. Dr. Ivica Kopriva’s interdisciplinary research integrates statistical learning, low-rank sparse modeling, and nonlinear decomposition methods to address complex challenges in biomedical imaging, remote sensing, and hyperspectral data analysis. His highly influential publications, including “Multi-view Low-Rank Sparse Subspace Clustering” (Pattern Recognition, 2018) and “l₀-Motivated Low-Rank Sparse Subspace Clustering” (IEEE Transactions on Cybernetics, 2018), have significantly advanced subspace clustering and unsupervised learning, with the former ranked among the top 1% of highly cited papers in Engineering. Dr. Ivica Kopriva’s research further encompasses image co-segmentation, tumor detection, and signal demixing, contributing to innovations in medical imaging and AI-based diagnostics. With over 2,546 citations, an h-index of 21, and an i10-index of 50, Dr. Ivica Kopriva’s scholarly impact extends across multiple disciplines of computational and biomedical sciences. His exceptional achievements have been recognized through numerous awards, including the State Award of the Republic of Croatia for Scientific Achievement (2009), multiple Director’s Awards for Scientific Excellence (2010–2021), and MICCAI Outstanding Reviewer Awards. As a Senior Member of IEEE and OSA and an affiliated faculty member at Virginia Commonwealth University, Dr. Ivica Kopriva continues to contribute extensively to international scientific communities, shaping the global research landscape in computational imaging, data-driven signal analysis, and intelligent biomedical systems.

Profiles: Google Scholar | Scopus | Orcid

Featured Publications

  • Brbić, M., & Kopriva, I. (2018). Multi-view low-rank sparse subspace clustering. Pattern Recognition, 73, 247–258.

  • Huang, T. M., Kecman, V., & Kopriva, I. (2006). Kernel based algorithms for mining huge data sets: Supervised, semi-supervised, and unsupervised learning. Springer Berlin Heidelberg.

  • Ju, W., Xiang, D., Zhang, B., Wang, L., Kopriva, I., & Chen, X. (2015). Random walk and graph cut for co-segmentation of lung tumor on PET-CT images. IEEE Transactions on Image Processing, 24(12), 5854–5867.

  • Brbić, M., & Kopriva, I. (2020). l₀-Motivated low-rank sparse subspace clustering. IEEE Transactions on Cybernetics, 50(4), 1711–1725.

  • Tolić, D., Antulov-Fantulin, N., & Kopriva, I. (2018). A nonlinear orthogonal non-negative matrix factorization approach to subspace clustering. Pattern Recognition, 82, 40–55.

Di Fan | AI for Education | Best Researcher Award

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Dr. Di Fan | AI for Education | Best Researcher Award

Northeastern University | China

Dr. Di Fan, a prominent researcher at Northeastern University, Shenyang, specializes in big data mining and recommendation systems, with a strong focus on artificial intelligence applications in education. Her research advances data-driven behavior modeling, interpretability analysis, and the construction of personalized learning environments through AI and large language models (LLMs). As a core member of several National Natural Science Foundation of China key projects, she has contributed to studies on learning portrait technology and interactive educational systems. Dr. Fan leads the 2025 Youth Artificial Intelligence Education Project, developing dynamic assessment methods for digital literacy using generative AI. She holds multiple invention patents in knowledge tracking and AI-driven recommendation methods and has played a significant role in formulating local standards for industrial software and cloud computing skills training. Her publications span reputed venues such as JCST, IEEE ISPA, APWeb, and Bench2024, reflecting expertise in explainable AI and multitask learning. Recognized through numerous national awards, including the CCF Outstanding Communicator and National Scholarship honors, Dr. Fan also serves on several executive committees of the China Computer Federation and international editorial boards, contributing actively to advancing AI education, data science, and public technology outreach.

Profile: Orcid

Featured Publications

  • Fan, D., Zhang, T., Li, F., Wu, W., Yu, Y., & Yu, G. (2025). Research on multidimensional evaluation technology of teachers’ digital literacy for LLM as a judge. In Proceedings of Bench2024 (Book chapter). Springer. https://doi.org/10.1007/978-981-96-6310-1_7

  • Fan, D., & Li, F. (2024). A survey of static and temporal explainable methods and their applications in knowledge tracing. Journal of Computer Science and Technology (JCST), CCF-B category.

  • Fan, D., Zhang, T., & Yu, G. (2024). An LLM augmentation and multitask learning-based recommendation model for MOOCs. In Proceedings of the IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA), CCF-C category. IEEE.

  • Fan, D., Wu, W., Yu, Y., & Zhang, T. (2024). ProteinMM: Adaptive multi-view and task-grouped evolutionary learning for prediction of protein structural features. In Proceedings of the Asia-Pacific Web Conference (APWeb), CCF-C category.

Gustavo Arroyo Figueroa | Big Data Architecture | Best Researcher Award

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Dr. Gustavo Arroyo Figueroa | Big Data Architecture | Best Researcher Award

National Institute of Electricity and Clean Energy | Mexico

Dr. Gustavo Arroyo-Figueroa is a distinguished Mexican computer scientist and applied artificial intelligence researcher whose work bridges the domains of intelligent systems, data analytics, and smart grid technologies. He earned his Ph.D. in Computer Science from the Monterrey Institute of Technology and currently serves as Head of Information Technologies Research at the Instituto Nacional de Electricidad y Energías Limpias (INEEL) in Cuernavaca, Mexico. Over his career, he has contributed significantly to the application of machine learning, data science, and big data analytics in power systems, focusing on automation, intelligent control, diagnostics, prediction, and forecasting within energy infrastructures. His research explores Bayesian networks, temporal reasoning, and artificial intelligence methods for fault detection and predictive maintenance in complex industrial systems. Dr. Arroyo-Figueroa has authored influential publications such as A Temporal Bayesian Network for Diagnosis and Prediction, Virtual Reality Training System for Maintenance and Operation of High-Voltage Overhead Power Lines, and Advanced Control Algorithms for Steam Temperature Regulation of Thermal Power Plants, which demonstrate his interdisciplinary expertise combining AI, virtual reality, and control engineering. His recent work also investigates renewable energy integration and the role of data-driven analytics in smart grid optimization. Recognized as a National Researcher by Mexico’s National System of Researchers (SNI), he is a member of the Mexican Society of Artificial Intelligence (SMIA), Academia Mexicana de Computación (AMEXCOMP), and the international CIGRE Study Committee D2, where he actively contributes to research on information systems and telecommunications in the power sector. In 2022, he was honored with the CIGRE Technical Council Award for his outstanding contributions to artificial intelligence applications in the energy industry, underscoring his leadership and commitment to advancing intelligent technologies for sustainable and resilient power systems. His research impact is reflected in over 1,712 citations, an h-index of 24, and an i10-index of 38, highlighting his sustained influence in the fields of artificial intelligence and energy informatics.

Profile: Google Scholar | Orcid | Scopus

Featured Publications

  • García, A. A., Bobadilla, I. G., Figueroa, G. A., Ramírez, M. P., & Román, J. M. (2016). Virtual reality training system for maintenance and operation of high-voltage overhead power lines. Virtual Reality, 20(1), 27–40.

  • Arroyo-Figueroa, G., & Sucar, L. E. (2013). A temporal Bayesian network for diagnosis and prediction. arXiv preprint arXiv:1301.6675.

  • Sánchez-López, A., Arroyo-Figueroa, G., & Villavicencio-Ramírez, A. (2004). Advanced control algorithms for steam temperature regulation of thermal power plants. International Journal of Electrical Power & Energy Systems, 26(10), 779–785.

  • Pérez-Ramírez, M., Arroyo-Figueroa, G., & Ayala, A. (2021). The use of a virtual reality training system to improve technical skill in the maintenance of live-line power distribution networks. Interactive Learning Environments, 29(4), 527–544.

  • Arroyo-Figueroa, G., Ruiz-Aguilar, G. M. L., Cuevas-Rodríguez, G., & others. (2011). Cotton fabric dyeing with cochineal extract: Influence of mordant concentration. Coloration Technology, 127(1), 39–46.

Teodoro Cassola | Spatial Databases | Best Researcher Award

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Dr. Teodoro Cassola | Spatial Databases | Best Researcher Award

Schlumberger | Netherlands

Dr. Teodoro Cassola is a distinguished geoscientist and petroleum systems expert currently affiliated with Schlumberger, specializing in structural geology, basin modeling, and petroleum geology. With 68 citations, an h-index of 5, and an i10-index of 2, his research contributions demonstrate growing influence in the fields of basin evolution, geothermal exploration, and subsurface energy systems. Trained at ETH Zurich, Dr. Cassola has built a strong academic and professional foundation that bridges advanced geological modeling with practical energy applications. His research focuses on understanding the interplay between tectonics, sedimentation, and thermal processes to predict the distribution and behavior of hydrocarbons and geothermal resources. Among his most cited works are “A Basin Thermal Modelling Approach to Mitigate Geothermal Energy Exploration Risks: The St. Gallen Case Study” (Geothermics, 2020) and “Structural Restoration and Basin Modelling of the Central Apennine Orogen/Foredeep/Foreland System” (Marine and Petroleum Geology, 2021), both of which significantly advanced the understanding of subsurface processes affecting exploration risks. His basin-scale modeling across Central Italy and the Adriatic Sea has provided critical insights into petroleum system development, hydrocarbon migration, and structural restoration. Recent works, including studies on CO₂ storage in the Baltic Sea (Applied Sciences, 2025) and geothermal drilling risk mitigation in hydrocarbon-bearing zones (International Journal of Coal Geology, 2025), illustrate his commitment to leveraging petroleum geology expertise for energy transition and carbon management. Earlier investigations into forearc basin mechanics and Neogene vertical tectonics in Anatolia and the Mediterranean have further enriched the geoscientific understanding of crustal deformation and basin dynamics. Overall, Dr. Cassola’s interdisciplinary research integrates structural, thermal, and geochemical modeling to enhance exploration efficiency and sustainability, reflecting a career devoted to advancing geothermal and petroleum system science for a low-carbon energy future.

Profile: Google Scholar

Featured Publications

  1. Omodeo-Salé, S., Eruteya, O. E., Cassola, T., Baniasad, A., & Moscariello, A. (2020). A basin thermal modelling approach to mitigate geothermal energy exploration risks: The St. Gallen case study (eastern Switzerland). Geothermics, 87, 101876.

  2. D’Ambrosio, A., Lipparini, L., Bigi, S., Cassola, T., Bambridge, V. R., Derks, J. F., & Moscariello, A. (2021). Structural restoration and basin modelling of the Central Apennine orogen/foredeep/foreland system: New insights on the regional petroleum system. Marine and Petroleum Geology, 127, 104948.

  3. Ruggieri, R., Trippetta, F., Cassola, T., & Petracchini, L. (2022). Basin modeling constrains source rock position and dimension in the Burano-Bolognano petroleum system (Central Italy). Journal of Asian Earth Sciences, 240, 105436.

  4. Lipparini, L., D’Ambrosio, A., Trippetta, F., Bigi, S., Derks, J. F., Bambridge, V. R., & Cassola, T. (2021). A new regional petroleum systems model for Central Italy and the central Adriatic Sea supported by basin modelling and an analysis of hydrocarbon occurrences. Journal of Petroleum Geology, 44(4), 461–485.

  5. Fernández-Blanco, D., Mannu, U., Cassola, T., Bertotti, G., & Willett, S. D. (2021). Sedimentation and viscosity controls on forearc high growth. Basin Research, 33(2), 1384–1406.

Javier Alcover | Data Provenance | Best Researcher Award

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Dr. Javier Alcover | Data Provenance | Best Researcher Award

Laboratorios Diater | Spain

Dr. Javier Alcover is a distinguished Spanish biomedical researcher and Director of the Laboratorio de Aplicaciones at Diater (Spain), where he has been serving since 2000. His research primarily focuses on immunotherapy, allergology, and infectious disease prevention, emphasizing the development of innovative therapeutic approaches that integrate clinical and molecular insights. Over the years, Dr. Alcover has contributed extensively to translational research aimed at improving patient outcomes through safer and more effective immunological treatments. His 2023 study in Vaccines provided real-world clinical evidence on the safety and efficacy of an enhanced allergen-specific immunotherapy for bee venom allergy, underscoring his expertise in clinical immunology. His earlier works, such as the Urologia Internationalis (2019) publication, examined bacterial immune prophylaxis in preventing recurrent urinary tract infections, while his Future Microbiology (2017) study highlighted the antimicrobial potential of natural compounds like xyloglucan, hibiscus, and propolis. In Dermatology and Therapy (2018), Dr. Alcover explored novel nonsteroidal formulations targeting inflammatory and pruritic mediators in allergic contact dermatitis, demonstrating his commitment to advancing topical immunotherapies. His work in Allergy, Asthma & Immunology Research (2016) contributed to the understanding of orthologous allergens and the diagnostic relevance of the major allergen Alt a 1, reflecting his impact on diagnostic innovation. Furthermore, his involvement in hepatitis C virus antibody detection research, published in Transfusion (2016), showcases his broader contributions to infectious disease diagnostics. Dr. Alcover’s interdisciplinary research portfolio reflects a strong dedication to bridging laboratory innovation with clinical practice, advancing immune-based therapeutics, and enhancing diagnostic precision in allergy and infection-related diseases.

Profile: Orcid

Featured Publications

  • González Guzmán, L. A., García Robaina, J. C., Barrios Recio, J., Escudero Arias, E., Liñares Mata, T., Cervera Aznar, R., De La Roca Pinzón, F., Miguel Polo, L. del C., Arenas Villarroel, L., López Couso, V. P., et al., & Alcover, J. (2023). Real-world safety and efficacy clinical data of an improved allergen-specific immunotherapy product for the treatment of bee venom allergy. Vaccines, 11(5), 979.

  • López-Martín, L., Alcover-Díaz, J., Charry-Gónima, P., González-López, R., Rodríguez-Gil, D., Palacios-Peláez, R., & González-Enguita, C. (2019). Prospective observational cohort study of the efficacy of bacterial immune prophylaxis in the prevention of uncomplicated, recurrent urinary tract infections. Urologia Internationalis, 103(4), 456–462.*

  • Gordon, W. C., García López, V., Bhattacharjee, S., Rodríguez Gil, D., Alcover Díaz, J., Pineda de la Losa, F., Palacios Peláez, R., Tiana Ferrer, C., Bacchini, G. S., Jun, B., et al. (2018). A nonsteroidal novel formulation targeting inflammatory and pruritus-related mediators modulates experimental allergic contact dermatitis. Dermatology and Therapy, 8(1), 111–126.*

  • Fraile, B., Alcover, J., Royuela, M., Rodríguez, D., Chaves, C., Palacios, R., & Piqué, N. (2017). Xyloglucan, Hibiscus and Propolis for the prevention of urinary tract infections: Results of in vitro studies. Future Microbiology, 12(6), 533–541.*

  • Moreno, A., Pineda, F., Alcover, J., Rodríguez, D., Palacios, R., & Martínez-Naves, E. (2016). Orthologous allergens and diagnostic utility of major allergen Alt a 1. Allergy, Asthma & Immunology Research, 8(5), 428–437.*

Gokalp Oner | Reproductive endocrinology | Best Academic Researcher Award

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Prof. Dr. Gokalp Oner | Reproductive endocrinology | Best Academic Researcher Award

Istanbul Aydin University | Turkey

Prof. Dr. Gökalp Öner is a distinguished Turkish obstetrician, gynecologist, and reproductive endocrinologist recognized for his pioneering contributions to assisted reproductive technologies, artificial intelligence in medicine, and women’s health research. Born in Çorum in 1981, he demonstrated academic excellence from an early age, ranking among the top students nationally and graduating at the top of his class from Hacettepe Faculty of Medicine in 2005. He specialized in Obstetrics and Gynecology at Erciyes University, where his research began focusing on reproductive endocrinology, polycystic ovary syndrome (PCOS), endometriosis, and in vitro fertilization (IVF). Prof. Öner has significantly advanced the integration of artificial intelligence into reproductive medicine, being the first in Turkey—and among the first globally—to develop AI-assisted embryo selection and uterine evaluation technologies, revolutionizing IVF success prediction and clinical decision-making. With over 1,089 citations, an h-index of 17, and an i10-index of 28, Prof. Öner’s scholarly impact is widely recognized. His prolific academic output includes over 100 publications and multiple high-impact studies on topics such as the efficacy of omega-3 in PCOS, the comparative effects of metformin and letrozole on endometriosis, and hormonal influences on ovarian reserve and fertility outcomes. He has received seven national scientific awards, authored two books, and serves as editor for 11 medical journals while directing the IVF Center at Kayseri System Hospital and serving as a professor at Istanbul Aydın University Faculty of Medicine. Prof. Öner’s interdisciplinary expertise bridges reproductive endocrinology, clinical gynecology, and AI-driven diagnostics, positioning him at the forefront of innovation in fertility science. His work continues to shape modern reproductive medicine through the application of intelligent technologies to enhance personalized treatment, improve pregnancy outcomes, and expand scientific understanding of female reproductive health.

Profile: Google Scholar | Orcid | Scopus

Featured Publications

  • Öner, G., & Müderris, İ. İ. (2013). Efficacy of omega-3 in the treatment of polycystic ovary syndrome. Journal of Obstetrics and Gynaecology, 33(3), 289–291.

  • Öner, G., Özçelik, B., Özgun, M. T., Serin, İ. S., Öztürk, F., & Başbuğ, M. (2010). The effects of metformin and letrozole on endometriosis and comparison of the two treatment agents in a rat model. Human Reproduction, 25(4), 932–937.

  • Müderris, İ. İ., Boztosun, A., Öner, G., & Bayram, F. (2011). Effect of thyroid hormone replacement therapy on ovarian volume and androgen hormones in patients with untreated primary hypothyroidism. Annals of Saudi Medicine, 31(2), 145–151.

  • Öner, G., & Müderris, İ. İ. (2011). Clinical, endocrine and metabolic effects of metformin vs N-acetyl-cysteine in women with polycystic ovary syndrome. European Journal of Obstetrics & Gynecology and Reproductive Biology, 159(1), 127–131.

  • Cabıoğlu, N., Karanlık, H., Kangal, D., Özkurt, E., Öner, G., Sezen, F., Yılmaz, R., et al. (2018). Improved false-negative rates with intraoperative identification of clipped nodes in patients undergoing sentinel lymph node biopsy after neoadjuvant chemotherapy. Annals of Surgical Oncology, 25(10), 3030–3036.

Arman Gheysari | Consensus Algorithms | Best Researcher Award

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Mr. Arman Gheysari | Consensus Algorithms | Best Researcher Award

Amirkabir University of Technology | Iran

Mr. Arman Gheysari is a researcher in Computer Engineering at Amirkabir University of Technology, Tehran, Iran, with expertise in blockchain technology, distributed systems, fault tolerance, dependability, and reinforcement learning. His research is centered on improving the reliability, performance, and security of computing architectures and networked systems through advanced optimization and intelligent algorithms. Mr. Gheysari’s notable contribution includes the security-aware optimization of Proof-of-Work (PoW) blockchain performance using a Genetic Algorithm, published in Sustainable Computing: Informatics and Systems (2025), which introduces a systematic method to enhance blockchain efficiency without compromising its resilience to attacks. He has also worked on simultaneous optimization of network-on-chip (NoC) architectures using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to achieve optimal trade-offs between latency, reliability, and buffer size constraints, significantly advancing scalable on-chip communication networks. In addition to his academic research, Mr. Gheysari holds patents on fault-tolerant and Byzantine-resilient system-on-chip (SoC) designs, where he integrates blockchain-based consensus mechanisms and Merkle tree analysis to detect and isolate faulty processing elements, ensuring robust and energy-efficient system performance. His interdisciplinary work effectively bridges blockchain systems, artificial intelligence, and hardware reliability engineering, contributing to the development of secure, adaptive, and high-performance distributed computing infrastructures. Through his innovative research, Mr. Gheysari continues to advance the fields of dependable computing and blockchain-based fault-tolerant systems.

Profile: Google Scholar

Featured Publications

Gheysari, A., & Zarandi, H. R. (2025). Security-aware optimization of PoW-based blockchain performance using a genetic algorithm approach. Sustainable Computing: Informatics and Systems, 101232.

Abdolhosseini, H., Zarandi, H. R., & Gheysari, A. (2025). Simultaneous optimization of network-on-chip to improve reliability and reduce average packet latency considering buffer size constraints. The Journal of Supercomputing, 81(13), 1260.

Gheysari, A., Almahdawi, M., & Zarandi, H. R. (2025, March). Optimizing Proof-of-Work blockchain network communications using a genetic algorithm. In Proceedings of the 29th International Computer Conference, Computer Society of Iran (CSICC 2025).

Gang Qin | Sports Science | Best Researcher Award

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Dr. Gang Qin | Sports Science | Best Researcher Award

Hanyang University | China

Dr. Gang Qin is an emerging researcher in the interdisciplinary field of Sports Science and Artificial Intelligence, currently pursuing his Ph.D. in Sport Science at Hanyang University, Seoul, South Korea (2021–2026). His academic and research pursuits bridge Sports Training, Exercise Physiology, Sports Statistics, and Sports Medicine with cutting-edge AI-driven network technologies, particularly focusing on enhancing athletic performance through digital and intelligent systems. Dr. Qin’s recent work, featured in the IEEE Transactions on Consumer Electronics (2025), presents a groundbreaking study titled “AI-driven 6G network slicing for Distance Collaborative Sports Training: Edge Cloud Resource Allocation Strategy.” This research explores the integration of 6G communication networks and deep learning algorithms to optimize remote and collaborative sports training environments, especially in basketball. By introducing an Adaptive Pelican Optimized–Elman Spike Neural Network (APO-ESNN), his study demonstrates an innovative resource allocation framework achieving 92% efficiency and 85% session productivity, marking a significant advancement in real-time sports analytics and network utilization. Dr. Qin’s model effectively leverages edge cloud computing to deliver ultra-low latency, high connectivity, and personalized coaching through AI-powered data processing, enabling interactive and efficient athlete–coach engagement. His research not only contributes to the future of intelligent sports ecosystems but also establishes a technological foundation for 6G-enabled virtual coaching systems that can transform training methodologies across disciplines. Through his pioneering approach, Dr. Gang Qin exemplifies the potential of interdisciplinary innovation—merging sports science with emerging AI and communication technologies to redefine how performance optimization, data-driven coaching, and immersive training are achieved in the era of intelligent sports networks.

Profile: Orcid

Featured Publication

Hou, Y., Wang, Z., Qin, G., & Zhong, H. (2025). AI-driven 6G network slicing for distance collaborative sports training: Edge cloud resource allocation strategy. IEEE Transactions on Consumer Electronics.

Jian Nong | Computer Vision and Image Processing | Best Researcher Award

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Prof. Dr. Jian Nong | Computer Vision and Image Processing | Best Researcher Award

Associate Professor at Wuzhou University | China

Prof. Dr. Jian Nong is a distinguished Associate Professor at the School of Artificial Intelligence, Wuzhou University, with expertise spanning computer vision, deep learning, and high-performance computing. He earned his Ph.D. in Computer Technology and Its Applications from the Macau University of Science and Technology, where he cultivated a strong research foundation in intelligent computing and visual information analysis. His academic pursuits center on visual object tracking, multi-modal data fusion, sentiment analysis, and GPU-based parallel processing. Prof. Dr. Nong has authored several influential papers in reputed international journals and conferences, including “Robust Tracking via Rethinking Prediction Head” (Image and Vision Computing, 2025), “Dual-stream Multi-modal Interactive Vision-language Tracking” (ACM, 2024), “SentiRank: A Novel Approach to Sentiment Leader Identification in Social Networks Based on the D-TFRank Model” (Electronics, 2025), and “Efficient Parallel Processing of R-Tree on GPUs” (Mathematics, 2024). His research outcomes contribute substantially to advancing intelligent vision systems, data-driven decision-making, and high-efficiency computing architectures. As the head of a research and teaching team supporting the Digital Xijiang River Project, he integrates academic research with applied innovation to address regional and industrial digitalization challenges. His ongoing research projects include the development of deep reinforcement learning-based recommendation methods for multi-objective optimization in complex shipping environments and object tracking algorithms leveraging multi-cue information. With over a decade of experience and more than ten scholarly publications, Prof. Dr. Jian Nong continues to play a pivotal role in bridging artificial intelligence theory and practical application, fostering the next generation of intelligent computing systems and contributing to the growth of AI-driven technologies on a global scale.

Profile: Orcid

Featured Publications

  1. Nong, J., Qi, Y., Mo, Z., Wang, J., & Liang, Y. (2025). Robust tracking via rethinking prediction head. Image and Vision Computing, 152, 105780.

  2. Huang, J., Lan, B., Nong, J., Pang, G., & Hao, F. (2025). SentiRank: A novel approach to sentiment leader identification in social networks based on the D-TFRank model. Electronics, 14(14), 2751.

  3. Mo, Z., Zhang, G., Nong, J., Zhong, B., & Li, Z. (2024, December 3). Dual-stream multi-modal interactive vision-language tracking. In Proceedings of the ACM Conference.

  4. Nong, J., He, X., Chen, J., & Liang, Y. (2024). Efficient parallel processing of R-Tree on GPUs. Mathematics, 12(13), 2115.