Faisal Alamri | Object Detection for Security and Surveillance | Best Researcher Award

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Dr. Faisal Alamri | Object Detection for Security and Surveillance | Best Researcher Award

Chairperson of the Department of Computer Science and Information Technology | Jubail Industrial College (JIC) | Saudi Arabia

Dr. Faisal Alamri is an accomplished artificial intelligence researcher specializing in computer vision, machine learning, object detection, classification, segmentation, similarity search, adversarial perturbation, and zero-shot learning. He holds a Ph.D. in Computer Science with a focus on computer vision and machine learning from the University of Exeter, and completed his undergraduate and master’s degrees in computer systems engineering and networking. He currently serves as the Computer Science Department Chairperson at Jubail Industrial College, where he oversees academic and administrative activities and leads departmental initiatives. Previously, he worked as a machine learning engineer developing practical AI solutions, a postdoctoral research fellow, and a teaching assistant, and has also contributed as an online tutor and teaching volunteer. His research interests include developing innovative approaches for object detection, image analysis, and real-world AI applications. Dr. Alamri has been recognized for his achievements through multiple certifications and active participation in international conferences, workshops, and professional communities such as IEEE, Kaggle, NVIDIA, and MATLAB. He possesses strong technical skills in Python, MATLAB, C#, SPSS, AWS, Google Cloud ML Engine, and other platforms, and has completed various professional courses in deep learning, AI, cybersecurity, and digital analytics. His dedication to research, education, and community engagement reflects his commitment to advancing both science and society. He has a total of 49 citations, 7 documents, and an h-index of 5.

Profiles: Google Scholar | Scopus | ORCID | LinkedIn

Featured Publications

  1. Alamri, F., & Dutta, A. (2021). Multi-head self-attention via vision transformer for zero-shot learning. arXiv preprint arXiv:2108.00045.

  2. Alamri, F., & Pugeault, N. (2020). Improving object detection performance using scene contextual constraints. IEEE Transactions on Cognitive and Developmental Systems, 14(4), 1320–1330.

  3. Alamri, F., & Dutta, A. (2021). Implicit and explicit attention for zero-shot learning. In DAGM German Conference on Pattern Recognition (pp. 467–483).

  4. Alamri, F., & Dutta, A. (2023). Implicit and explicit attention mechanisms for zero-shot learning. Neurocomputing, 534, 55–66.

  5. Alamri, F., Kalkan, S., & Pugeault, N. (2021). Transformer-encoder detector module: Using context to improve robustness to adversarial attacks on object detection. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 9577–9584). IEEE.

Xinrong Hu | Object Detection and Recognition | Women Researcher Award

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Prof. Xinrong Hu | Object Detection and Recognition | Women Researcher Award

Dean of Computer Science and Artificial Intelligence | Wuhan Textile University | China

Prof. Xinrong Hu is a distinguished researcher and academic leader in computer vision, natural language processing, virtual reality, and machine learning. She serves as Dean of the School of Computer and Artificial Intelligence at Wuhan Textile University and is a doctoral supervisor, leading an innovative research team at the Hubei Provincial Engineering Technology Research Center for Garment Informatization. She holds a Ph.D. and has extensive experience in guiding research projects, including over 30 funded initiatives, some with national and international significance. Her research interests focus on advancing artificial intelligence applications in real-world scenarios, combining theoretical innovation with practical solutions. She has authored more than 100 academic papers, edited six textbooks, translated a book, and holds 26 invention patents, demonstrating her strong research skills and contribution to knowledge dissemination. Prof. Hu has been recognized with multiple awards and honors, including provincial and ministerial-level scientific research awards, teaching achievement awards, and prestigious titles such as Hubei Provincial Distinguished Teacher and recipient of the Special Government Allowance from the State Council. Her professional engagement includes leadership in academic communities, mentorship of young researchers, and active participation in advancing the field of AI through both education and research initiatives. Her comprehensive expertise, innovative contributions, and dedication to fostering academic excellence make her a leading figure in her field. Her research impact is reflected in 1,044 citations, 209 documents, and an h-index of 16.

Profiles: Scopus | ResearchGate 

Featured Publications

  1. Hu, X., et al. (2025). CDPMF-DDA: Contrastive deep probabilistic matrix factorization for drug-disease association prediction. BMC Bioinformatics.

  2. Hu, X., et al. (2025). Source-free cross-modality medical image synthesis with diffusion priors. Journal of King Saud University – Computer and Information Sciences.

  3. Hu, X., et al. (2025). TADUFMA: Transformer-based adaptive denoising and unified feature modeling for multi-condition anomaly detection in computerized flat knitting machines. Measurement Science and Technology.

  4. Hu, X., et al. (2025). ViT-BF: Vision transformer with border-aware features for visual tracking. Visual Computer.

  5. Hu, X., et al. (2025). Adaptive debiasing learning for drug repositioning. Journal of Biomedical Informatics.

Madhuri Rao | Machine Learning | Best Researcher Award

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Dr. Madhuri Rao | Machine Learning | Best Researcher Award

Senior Assistant Professor | MIT World Peace University | India

Dr. Madhuri Rao is a dedicated researcher and academic in computer science with expertise in wireless sensor networks, Internet of Things, artificial intelligence, blockchain, and cybersecurity, with her current work focusing on deep learning, cloud security, and healthcare applications. She earned her Ph.D. in Computer Science and Engineering from Biju Patnaik University of Technology, where her research emphasized energy-efficient object tracking in wireless sensor networks. Over her career, she has gained extensive professional experience as a faculty member, academic coordinator, research supervisor, and editorial board member, contributing significantly to both teaching and research. She has authored and co-authored numerous publications in reputed journals and conferences, including IEEE, Springer, Elsevier, and Scopus-indexed platforms, along with patents and book chapters that highlight her innovative approach. Her research interests span interdisciplinary applications of advanced technologies to address challenges in security, healthcare, and sustainability, with ongoing involvement in collaborative projects and international initiatives. She has received recognition through awards such as best paper honors and a best research scholar award, underscoring her contributions to the academic community. Her research skills include problem-solving, experimental design, data analysis, and guiding students at undergraduate, postgraduate, and doctoral levels, coupled with active roles as session chair, track chair, and guest lecturer in international conferences. She is also a life member of professional societies and holds certifications that strengthen her academic profile. Her impactful contributions are reflected in 116 citations and an h-index of 7.

Profile: Google Scholar | ORCID | ResearchGate | LinkedIn

Featured Publications

  1. Rao, M., & Kamila, N. K. (2021). Cat swarm optimization based autonomous recovery from network partitioning in heterogeneous underwater wireless sensor network. International Journal of System Assurance Engineering and Management, 1–15.

  2. Rao, M., Kamila, N. K., & Kumar, K. V. (2016). Underwater wireless sensor network for tracking ships approaching harbor. 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), 1098–1102. IEEE.

  3. Rao, M., & Kamila, N. K. (2018). Spider monkey optimisation based energy efficient clustering in heterogeneous underwater wireless sensor networks. International Journal of Ad Hoc and Ubiquitous Computing, 29(1–2), 50–63.

  4. Chaudhury, P., Rao, M., & Kumar, K. V. (2009). Symbol based concatenation approach for text to speech system for Hindi using vowel classification technique. 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 1393–1396. IEEE.

  5. Kumar, K. V., Kumari, P., Rao, M., & Mohapatra, D. P. (2022). Metaheuristic feature selection for software fault prediction. Journal of Information and Optimization Sciences, 43(5), 1013–1020.

Ahmet Kayabaşı| Artificial Intelligence | Best Researcher Award

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Prof. Dr. Ahmet Kayabaşı | Artificial Intelligence | Best Researcher Award

Professor | Karamanoglu Mehmetbey University | Turkey

Prof. Dr. Ahmet Kayabaşı is a distinguished academic in electrical-electronics engineering with expertise in artificial intelligence, antennas, biomedical signal processing, image processing, fuzzy logic, and power electronics. He earned his PhD in Electrical-Electronics Engineering from Selcuk University and has since built a strong academic career combining teaching, research, and leadership. His professional experience includes serving as Head of Department, Director of the Institute of Graduate Studies, and Senate Member, along with mentoring numerous MSc and PhD students. His research interests span interdisciplinary fields, applying advanced AI techniques in UAV swarm algorithms, smart agriculture, biomedical diagnostics, and energy-efficient power systems. He has been actively involved in TÜBİTAK and institutional projects, contributing to impactful solutions for both academia and industry. Recognized for his excellence, he has received awards such as Best Presenter Award at ICAT and has played vital roles in academic conferences and scientific communities. His research skills include developing intelligent systems, applying machine learning to engineering challenges, and designing novel antenna and biomedical applications. He has published widely in leading international journals indexed in IEEE, Scopus, and Web of Science, with notable contributions in Applied Thermal Engineering, Swarm and Evolutionary Computation, and Computers and Electronics in Agriculture. His academic excellence is reflected in 609 citations by 522 documents, 47 publications, and an h-index of 13.

Profile: Google Scholar | Scopus | ORCID

Featured Publications

  1. Sabanci, K., Kayabasi, A., & Toktas, A. (2017). Computer vision‐based method for classification of wheat grains using artificial neural network. Journal of the Science of Food and Agriculture, 97(8), 2588–2593.

  2. Yigit, E., Sabanci, K., Toktas, A., & Kayabasi, A. (2019). A study on visual features of leaves in plant identification using artificial intelligence techniques. Computers and Electronics in Agriculture, 156, 369–377.

  3. Kayabasi, A., Toktas, A., Yigit, E., & Sabanci, K. (2018). Triangular quad-port multi-polarized UWB MIMO antenna with enhanced isolation using neutralization ring. AEU-International Journal of Electronics and Communications, 85, 47–53.

  4. Sabanci, K., Toktas, A., & Kayabasi, A. (2017). Grain classifier with computer vision using adaptive neuro‐fuzzy inference system. Journal of the Science of Food and Agriculture, 97(12), 3994–4000.

  5. Yildiz, B., Aslan, M. F., Durdu, A., & Kayabasi, A. (2024). Consensus-based virtual leader tracking swarm algorithm with GDRRT*-PSO for path-planning of multiple-UAVs. Swarm and Evolutionary Computation, 88, 101612.

Fatma Zahra Sayadi | Deep Learning | Best Innovation Award

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Prof. Fatma Zahra Sayadi | Deep Learning | Best Innovation Award

Associate Professor | University of Sousse | Tunisia

Fatma Elzahra Sayadi is a highly accomplished researcher and academic specializing in electronics and microelectronics, with current research focused on video surveillance systems, real-time processing, and signal compression. She earned her PhD in electronics for real-time systems from the University of Bretagne Sud in collaboration with the University of Monastir and has also completed her engineering and master’s studies in electrical and electronic systems. She has extensive professional experience as a maître de conférences and previously as a maître assistante and assistant technologist, teaching courses in microprocessors, multiprocessors, programming, circuit testing, and industrial electronics. Her research interests include signal processing, parallel architectures, microelectronics, real-time systems, and communication networks. She has actively participated in national and international research projects and collaborations with institutions in France, Italy, Germany, and Morocco. Her work has been published in over 37 journal articles, 40 conference papers, and six book chapters, and she has supervised several doctoral and master’s theses. She has been recognized with awards such as the first prize at the Women in Research Forum at the University of Sharjah and contributes to professional communities as a reviewer, evaluator, and organizer of academic events. She is skilled in research methodologies, signal and data analysis, electronic system design, and digital education innovation. Her academic contributions have been cited by 395 documents, with 69 documents contributing to her citations, and she has an h-index of 13.

Profile: Google Scholar | Scopus | ORCID 

Featured Publications

  1. Basly, H., Ouarda, W., Sayadi, F. E., Ouni, B., & Alimi, A. M. (2020). CNN-SVM learning approach based human activity recognition. In International Conference on Image and Signal Processing (pp. 271–281). 77 citations.

  2. Bouaafia, S., Khemiri, R., Sayadi, F. E., & Atri, M. (2020). Fast CU partition-based machine learning approach for reducing HEVC complexity. Journal of Real-Time Image Processing, 17(1), 185–196. 53 citations.

  3. Haggui, O., Tadonki, C., Lacassagne, L., Sayadi, F., & Ouni, B. (2018). Harris corner detection on a NUMA manycore. Future Generation Computer Systems, 88, 442–452. 48 citations.

  4. Basly, H., Ouarda, W., Sayadi, F. E., Ouni, B., & Alimi, A. M. (2022). DTR-HAR: Deep temporal residual representation for human activity recognition. The Visual Computer, 38(3), 993–1013. 40 citations.

  5. Bouaafia, S., Khemiri, R., Messaoud, S., Ben Ahmed, O., & Sayadi, F. E. (2022). Deep learning-based video quality enhancement for the new versatile video coding. Neural Computing and Applications, 34(17), 14135–14149. 35 citations.

Minh-Son Dao | Deep Learning | Best Researcher Award

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Dr. Minh-Son Dao | Deep Learning | Best Researcher Award

Researcher at The National Institute of Information and Communications Technology (NICT), Japan.

Dr. Minh-Son DAO is a distinguished Senior Researcher and Research Manager at the Big Data Integration Research Center, National Institute of Information and Communications Technology (NICT), Japan. With over two decades of research and leadership experience across academia and government, he leads cutting-edge initiatives in artificial intelligence, big data analytics, and smart IoT systems. He has played a pivotal role in Japan’s Society 5.0 vision through projects like MMCRAI and collaborative smart-city platforms. Dr. DAO is also a committed educator, serving as a thesis supervisor and adjunct lecturer across multiple international universities. His work has earned him numerous accolades, including multiple Best Challenge Awards, national recognitions, and research excellence honors. With over 100 peer-reviewed publications and international partnerships spanning Europe and Asia, he continues to bridge academic rigor with real-world impact. His current focus lies in multimodal AI frameworks and data-driven societal innovation.

Professional Profile

Suitability For Best Researcher Award – Dr. Minh-Son Dao

Dr. Minh-Son DAO exemplifies the qualities of an outstanding researcher through his sustained, interdisciplinary contributions to artificial intelligence, big data analytics, and smart IoT systems. With over 20 years of research leadership, a strong publication record (100+ peer-reviewed papers), and international collaboration across Europe and Asia, he has significantly influenced both theoretical advancements and real-world applications. His active role in Japan’s Society 5.0 vision and the development of the MMCRAI framework further underscore his commitment to data-driven societal innovation. Dr. DAO also demonstrates excellence in mentoring, editorial roles, and academic service, enriching the broader research ecosystem.

Education

Dr. Minh-Son DAO holds a Ph.D. in Information and Communications Technology from Trento University, Italy, where his research focused on similarity measures and shape matching using genetic algorithms. His doctoral dissertation introduced the Edge Potential Function (EPF), a novel contribution to shape-based image retrieval. Prior to that, he earned a Master’s degree in Computer Science from Vietnam National University, specializing in handwritten character recognition using Convolutional Neural Networks—an early demonstration of his interest in deep learning. His Bachelor’s degree, also in Computer Science from the University of HCM City, Vietnam, emphasized image processing and hypertext applications. These academic milestones laid a strong foundation in AI, machine learning, and multimedia processing, enabling him to merge theoretical knowledge with practical innovation throughout his career. His educational journey reflects a continuous pursuit of excellence across diverse computational and applied domains.

Experience

Dr. Minh-Son DAO brings over 20 years of extensive research and leadership experience across Asia and Europe. Currently, he serves as Research Manager and Senior Researcher at NICT Japan, spearheading national AI and Smart IoT initiatives. His prior roles include Deputy Director and Senior Assistant Professor at Universiti Teknologi Brunei, where he also founded the ELEDIA@UTB lab focused on smart farming and wireless technologies. He has held prestigious research roles at Trento University, Osaka University (as a JSPS Fellow), and GraphiTech Italy. He has supervised more than 40 postgraduate students, co-authored over 100 publications, and led multi-institutional projects in smart cities, multimedia analytics, and health informatics. His teaching portfolio spans creative multimedia, data science, and database systems. Known for building strong global research networks, Dr. DAO has established successful collaborations with institutions in Norway, Ireland, Vietnam, and Switzerland, playing a vital role in cross-disciplinary and cross-cultural scientific advancements.

Professional Development

Dr. Minh-Son DAO has consistently invested in professional development to enhance his academic and leadership capabilities. He completed the UTB Faculty Development Program and the Foundations of University Learning and Teaching at Universiti Teknologi Brunei, gaining proficiency in teaching pedagogy, assessment strategies, and flipped classroom techniques. He also holds Oracle certifications in SQL, PL/SQL, and web application development. His involvement as a guest editor for high-impact journals such as IEEE ACCESS, ACM TOMM, and Frontiers in Big Data, along with his participation as program committee member for numerous international conferences, highlights his role as a thought leader in multimedia, AI, and big data. Dr. DAO frequently chairs and organizes conferences and workshops, including ICMLSC, ICCRD, and MediaEval. His holistic development in research, teaching, industry consulting, and international collaboration exemplifies a well-rounded professional commitment to lifelong learning and knowledge dissemination in cutting-edge computing technologies.

Research Focus

Dr. Minh-Son DAO’s research primarily focuses on multidisciplinary applications of Artificial Intelligence, Big Data Analytics, and Smart IoT systems, aligning closely with the vision of a data-driven, intelligent society (Society 5.0). His most notable initiative, the Multimodal and Cross-modal AI Framework (MMCRAI), demonstrates his commitment to converting raw multimodal data into actionable insights across domains like environmental monitoring, health informatics, multimedia forensics, and smart cities. He has applied his research to real-world challenges such as air pollution prediction, disaster management, and cheapfake detection. His work spans from foundational AI techniques to practical societal applications, including the integration of sensor networks, robotics, and citizen-driven data platforms. Through collaborative international projects, he explores the intersections between cyber-physical-social systems, smart urban planning, and sustainable development. This focus enables him to address complex problems with scalable, intelligent solutions that impact public health, education, urban resilience, and digital media integrity.

Research Skills

Dr. Minh-Son DAO possesses a comprehensive suite of research skills that bridge theoretical and applied domains. He is proficient in machine learning, deep learning, multimedia retrieval, and big data analytics, often applying these in cross-modal and multimodal AI frameworks. His technical abilities include programming in C++, R, SQL, HTML/JavaScript, and Python, and working with AI tools such as TensorFlow and Keras. Dr. DAO’s expertise spans data fusion, smart sensor integration, pattern recognition, event detection, and AI-based forecasting models, enabling him to tackle large-scale and heterogeneous data sources. Additionally, he has extensive experience in research project management, proposal writing, international collaboration, and supervising graduate students. His editorial and peer-review roles in IEEE, Springer, and Elsevier journals further reflect his analytical and evaluative skill set. These capabilities have allowed him to lead multi-disciplinary teams and create impactful AI-driven solutions for urban management, environmental monitoring, and personalized health analytics.

Awards and Honors

Dr. Minh-Son DAO has received numerous national and international awards recognizing his research excellence and innovation. Notably, he won the Best Challenge Awards at ICMR 2023 and ACM MM 2022 for his groundbreaking work in cheap fake detection. He was honored with the Excellent Performance Award by Japan’s NICT in 2022, reflecting his leadership in national projects. Earlier, he earned first-place awards at prestigious competitions such as image CLEF 2018 and Media Eval 2017 for his contributions to multimedia understanding and disaster response. He received the Research Excellence Mid-Career Academic Award from University Technology Brunei in 2017. His early career was marked by competitive international fellowships, including the JSPS International Fellowship (Japan) and ERCIM Fellowship (Europe), and he was awarded Vietnam’s highest youth scientific honor, the Creative Youth Medal. These accolades affirm his sustained contributions to AI, data science, and societal innovation across multiple countries and disciplines.

Conclusion

Dr. Minh-Son DAO’s profile aligns exceptionally well with the criteria for a Best Researcher Award. His work bridges high-impact research, global collaboration, and societal benefit. His innovations in AI and multimodal systems, combined with his leadership in international research initiatives and dedication to mentorship, make him a deserving candidate. His recognition through prestigious awards and fellowships across continents further validates his global research excellence.

Publication Top Notes

1. Deep learning for mobile multimedia: A survey

  • Authors: K Ota, MS Dao, V Mezaris, FGBD Natale

  • Journal: ACM Transactions on Multimedia Computing, Communications, and Applications

  • Cited by: 188

  • Year: 2017

Summary:
This comprehensive survey explores how deep learning techniques have been adapted and optimized for mobile multimedia applications. It covers both theoretical advancements and practical implementation challenges. The paper also discusses energy efficiency and processing limitations of mobile devices. It has become a foundational reference in mobile multimedia research.

2. Exploring convolutional neural network architectures for EEG feature extraction

  • Authors: I Rakhmatulin, MS Dao, A Nassibi, D Mandic

  • Journal: Sensors, Vol. 24(3), Article 877

  • Cited by: 62

  • Year: 2024

Summary:
This paper investigates CNN-based methods for extracting features from EEG signals, a key step in brain-computer interface development. Multiple CNN architectures are compared for performance and accuracy. The study demonstrates significant improvement in signal interpretation. It contributes to the emerging field of AI-powered neuro technology.

3. Daily human activities recognition using heterogeneous sensors from smartphones

  • Authors: MS Dao, TA Nguyen-Gia, VC Mai

  • Journal: Procedia Computer Science, Vol. 111, pp. 323–328

  • Cited by: 34

  • Year: 2017

Summary:
The paper presents a method for recognizing daily human activities using various smartphone sensors. It highlights sensor fusion techniques to improve detection accuracy. The approach is lightweight and suitable for real-time implementation. It holds potential for fitness, health, and smart environment applications.

4. A real-time complex event discovery platform for cyber-physical-social systems

  • Authors: MS Dao, S Pongpaichet, L Jalali, K Kim, R Jain, K Zettsu

  • Conference: International Conference on Multimedia Retrieval

  • Cited by: 34

  • Year: 2014

Summary:
This work proposes a real-time platform for discovering complex events from integrated cyber, physical, and social sources. It focuses on fusing multi-modal data streams for event detection. The platform is designed for smart city and situational awareness applications. It bridges the gap between social sensing and real-time analytics.

5. Edge potential functions (EPF) and genetic algorithms (GA) for edge-based matching of visual objects

  • Authors: MS Dao, FGB De Natale, A Massa

  • Journal: IEEE Transactions on Multimedia, Vol. 9(1), pp. 120–135

  • Cited by: 33

  • Year: 2006

Summary:
This paper introduces edge potential functions (EPF) combined with genetic algorithms for visual object matching. It enhances robustness in noisy or occluded conditions. The method shows improvements in object recognition performance. It contributes foundational techniques for multimedia and computer vision systems.

Prof. Dr. Shaofeng Shao | AI-driven Material Discovery | Best Researcher Award

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Prof. Dr. Shaofeng Shao | AI-driven Material Discovery | Best Researcher Award

Nanjing University of Information Science and Technology, China

👨‍🎓 Profiles

Scopus

Publications

Harnessing Transfer Deep Learning Framework for the Investigation of Transition Metal Perovskite Oxides with Advanced p-n Transformation Sensing Performance

  • Authors: S. Shao, Shaofeng; L. Yan, Liangwei; J. Li, Jiale; H.W. Kim, Hyoun Woo; S.S. Kim, Sang Sub
    Journal: ACS Sensors
    Year: 2025

Language Model-Assisted Machine Learning, Photoelectrochemical, and First-Principles Investigation of Compatible Solvents for a CH3NH3PbI3 Film in Water

  • Authors: Y. Huang, Yiru; S. Li, Shenyue; W. Hu, Wenguang; Q. Li, Qingfang; L. Zhang, Lei
    Journal: ACS Applied Materials and Interfaces
    Year: 2024

Data-driven exploration of terbium-doped tungsten oxide for Ultra-Precise detection of 3H-2B: Implications for gas sensor applications

  • Authors: S. Shao, Shaofeng; L. Yan, Liangwei; L. Zhang, Lei; H.W. Kim, Hyoun Woo; S.S. Kim, Sang Sub
    Journal: Chemical Engineering Journal
    Year: 2024

Ionic liquid-assisted preparation of Ag–Zn–In–S quaternary quantum dot thin films and luminescence performance optimized by machine learning

  • Authors: S. Wei, Song; X. Luo, Xiang; S. Shao, Shaofeng; L. Zhang, Lei
    Journal: Dyes and Pigments
    Year: 2024

Mr. Andrews Tang | Deep Learning | Best Researcher Award

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Mr. Andrews Tang | Deep Learning | Best Researcher Award

Andrews Tang at Kwame Nkrumah University of Science and Technology, Ghana

👨‍🎓 Profiles

Scopus

Google Scholar

Publications

Assessing blockchain and IoT technologies for agricultural food supply chains in Africa: A feasibility analysis

  • Authors: Andrews Tang, Eric Tutu Tchao, Andrew Selasi Agbemenu, Eliel Keelson, Griffith Selorm Klogo, Jerry John Kponyo
  • Journal: Heliyon
  • Year: 2024

An Open and Fully Decentralised Platform for Safe Food Traceability

  • Authors: Eric Tutu Tchao, Elton Modestus Gyabeng, Andrews Tang, Joseph Barnes Nana Benyin, Eliel Keelson, John Jerry Kponyo
  • Year: 2022

Prof. Ling Yang | Deep Learning | Women Researcher Award

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Prof. Ling Yang | Deep Learning | Women Researcher Award

Professor at Kunming University of Science and Technology, China

👨‍🎓 Profiles

Scopus

Orcid

Publications

Enhancing Panax notoginseng Leaf Disease Classification with Inception-SSNet and Image Generation via Improved Diffusion Model

  • Authors: Wang, R., Zhang, X., Yang, Q., Liang, J., Yang, L.
  • Journal: Agronomy
  • Year: 2024

Deep learning implementation of image segmentation in agricultural applications: a comprehensive review

  • Authors: Lei, L., Yang, Q., Yang, L., Wang, R., Fu, C.
  • Journal: Artificial Intelligence Review
  • Year: 2024

Alternate micro-sprinkler irrigation and organic fertilization decreases root rot and promotes root growth of Panax notoginseng by improving soil environment and microbial structure in rhizosphere soil

  • Authors: Zang, Z., Yang, Q., Liang, J., Guo, J., Yang, L.
  • Journal: Industrial Crops and Products
  • Year: 2023

A BlendMask-VoVNetV2 method for quantifying fish school feeding behavior in industrial aquaculture

  • Authors: Yang, L., Chen, Y., Shen, T., Yu, H., Li, D.
  • Journal: Computers and Electronics in Agriculture
  • Year: 2023

An FSFS-Net Method for Occluded and Aggregated Fish Segmentation from Fish School Feeding Images

  • Authors: Yang, L., Chen, Y., Shen, T., Li, D.
  • Journal: Applied Sciences (Switzerland)
  • Year: 2023

Prof. Igor Belenichev | Machine Learning | Excellence in Innovation

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Prof. Igor Belenichev | Machine Learning | Excellence in Innovation

Professor at Zaporizhzhia State Medical University, Ukraine

Profiles

Scopus

Orcid

Google Scholar

📚 Summary

Prof. Igor Fedorovich Belenichev is a distinguished Full Professor and Head of the Department of Pharmacology and Medical Formulation at Zaporizhzhia State Medical University. Renowned for his innovative research in neuroprotection and pharmacology, he is a laureate of the Cabinet of Ministers of Ukraine Prize for the development and implementation of groundbreaking technologies.

Education

  • Zaporizhzhia State Medical Institute (1988): Graduated with a degree in medicine.
  • Postgraduate studies (1988), professor assistant (1991), senior teacher (1999), associate professor (2004), and full professor (2006).

💼 Professional Experience

  • Zaporizhzhia State Medical University: Head of the Department of Pharmacology and Medical Formulation since 2005.
  • Main Scientific Researcher at «Pharmatrone» (since 1993).
  • Head of the regional branch of the Association of Pharmacologists of Ukraine.
  • Co-worker of the regional group of the National Expert Centre of the Ministry of Health of Ukraine.

🔬 Research Interests

Prof. Belenichev’s research focuses on the molecular and biochemical mechanisms of ischemic brain damage and the development of effective neuroprotectors. His work explores the roles of reactive oxygen and nitrogen species, thiol-disulfide systems, pro-/anti-apoptotic proteins, estrogen receptors, and endogenous neuroprotection factors. He also investigates drugs for CNS pathologies and effective neuro- or cardioprotectors from derivatives of 1,2,4-triazole, chinazoline, and xanthine.

🏆 Achievements

  • Scientific Works: Authored and co-authored 715 scientific publications.
  • Patents: Holder of 182 patents in Ukraine and the Russian Federation.
  • Theses: Supervised 3 Dr. Habs and 7 Ph.D. theses.
  • Drug Development: Contributed to the creation of drugs like Thiotriazoline, Thiocetam, and Thiodarone.
  • Awards: Token of the Bibliographical Society of America (2003), Regional Program “Zoryaniy Shlyakh” Prize (2000), and Cabinet of Ministers of Ukraine Prize (2017).

 

Publications

5+1-Heterocyclization as preparative approach for carboxy-containing triazolo[1,5-c]quinazolines with anti-inflammatory activity

  • Authors: Krasovska, Natalya; Berest, Galina; Belenichev, Igor; Severina, Hanna; Nosulenko, Inna; Voskoboinik, Oleksii; Okovytyy, Sergiy; Kovalenko, Serhii
  • Journal: European Journal of Medicinal Chemistry
  • Year: 2024

Beta-Blockers of Different Generations: Features of Influence on the Disturbances of Myocardial Energy Metabolism in Doxorubicin-Induced Chronic Heart Failure in Rats

  • Authors: Igor Belenichev; Olexiy Goncharov; Nina Bukhtiyarova; Oleh Kuchkovskyi; Victor Ryzhenko; Lyudmyla Makyeyeva; Valentyn Oksenych; Oleksandr Kamyshnyi
  • Journal: Biomedicines
  • Year: 2024

Characteristics of HIF-1α and HSP70 mRNA Expression, Level, and Interleukins in Experimental Chronic Generalized Periodontitis

  • Authors: Parkhomenko Daria; Igor Belenichev; Kuchkovskyi Oleh; Ryzhenko Victor
  • Journal: MicroRNA
  • Year: 2024

Comparative Analysis of the Effect of Beta Blockers of Different Generations on the Parameters of Myocardial Energy Metabolism in Experimental Doxorubicin-Induced Chronic Heart Failure

  • Authors: Igor Belenichev; Olexiy Goncharov; Nina Bukhtiyarova; Oleh Kuchkovskyi; Victor Ryzhenko; Lyudmyla Makyeyeva; Valentyn Oksenych; Oleksandr Kamyshnyi
  • Journal: Preprint
  • Year: 2024

Development and Optimization of Nasal Composition of a Neuroprotective Agent for Use in Neonatology after Prenatal Hypoxia

  • Authors: Igor Belenichev; Olena Aliyeva; Bogdan Burlaka; Kristina Burlaka; Oleh Kuchkovskyi; Dmytro Savchenko; Valentyn Oksenych; Oleksandr Kamyshnyi
  • Journal: Pharmaceuticals
  • Year: 2024