VANI VATHSALA ATLURI | Machine Learning | Best Researcher Award

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Dr. VANI VATHSALA ATLURI | Machine Learning | Best Researcher Award

Professor and HOD | CVR College of Engineering | India

Dr. A. Vani Vathsala is a distinguished academician and researcher in the field of Computer Science and Engineering, currently serving as a Professor and Head of the Department of Computer Science and Engineering at CVR College of Engineering, Hyderabad. With a rich background spanning both academia and industry, including a foundational professional stint at Tata Consultancy Services, Dr. Vathsala has built a career marked by innovation, leadership, and impactful research. She holds a Ph.D. in Computer Science from the University of Hyderabad, preceded by a Master of Technology in Computer Science from the same university and a Bachelor’s degree in Computer Science and Engineering from Nagarjuna University. Her research portfolio reflects expertise in artificial intelligence, cloud computing, machine learning, data security, and sustainable computing systems. She has published extensively in Scopus and SCI-indexed journals, contributing to emerging technologies that bridge healthcare, security, and IoT-driven solutions. Her notable publications include Early Diagnosis for the Bacterial Infections for the Patients under the Medical Intervention of Automated Peritoneal Dialysis Using Machine Learning Techniques, A Cloud Integrity Verification and Validation Model Using Double Token Key Distribution Model, Energy-Efficient and Sustainable Cluster-Based Routing in IoT-Based WSNs Using Metaheuristic Optimization, and Enhanced Real-Time Surveillance and Suspect Identification Using CNN-LSTM Based Body Language Analysis.

Profile: Google Scholar 

Featured Publications:

Yin Fei Xu | Deep Learning | Excellence in Research Award

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Assoc. Prof. Dr. Yin Fei Xu | Deep Learning | Excellence in Research Award 

Associate Professor | Southeast University  | China

Yinfei Xu is an Associate Researcher in the Department of Signal Processing, School of Information Science and Engineering at Southeast University, a master’s and doctoral supervisor and a Zhishan Young Scholar of Southeast University. He received his PhD in signal and information processing from Southeast University and carried out research as a research assistant and postdoctoral fellow at the Chinese University of Hong Kong and as a visiting PhD scholar at McMaster University in Canada. His research is deeply rooted in statistical signal processing, information theory, machine-learning-driven algorithmic design, optimization for real-world scenarios, statistical data analysis, and the development of artificial-intelligence models for image, speech, and multimodal applications. He has led or participated in more than ten national, provincial, industrial, and laboratory research projects and has published over seventy academic papers in high-impact international journals. As first author he has contributed to a number of influential publications including New Proofs of Gaussian Extremal Inequalities With Applications in IEEE Transactions on Information Theory, Information Embedding With Stegotext Reconstruction in IEEE Transactions on Information Forensics and Security, Secret Key Generation From Vector Gaussian Sources With Public and Private Communications in IEEE Transactions on Information Theory, Vector Gaussian Successive Refinement With Degraded Side Information in IEEE Transactions on Information Theory, Asymptotical Optimality of Change Point Detection With Unknown Discrete Post-Change Distributions in IEEE Signal Processing Letters, The Sum Rate of Vector Gaussian Multiple Description Coding with Tree-Structured Covariance Distortion Constraints in IEEE Transactions on Information Theory.

Profile: Orcid

Featured Publications:

Zhang, J., Xu, H., Zheng, A., Cao, D., Xu, Y., & Lin, C. (2025). Transmitting status updates on infinite capacity systems with eavesdropper: Freshness advantage of legitimate receiver. Entropy.

Zhang, J., & Xu, Y. (2022). Age analysis of status updating system with probabilistic packet preemption. Entropy.

Xu, Y., Zu, Y., & Zhang, H. (2021). Optimal inter-organization control of collaborative advertising with myopic and far-sighted behaviors. Entropy.

Zhang, J., & Xu, Y. Age analysis of status updating system with probabilistic packet preemption.

Zinah Saeed | Deep Learning | Best Researcher Award

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Ms. Zinah Saeed | Deep Learning | Best Researcher Award

Universiti Sains Malaysia | Iraq

Saeed ZR is a dedicated researcher and academic with a strong background in computer science, networking technology, and innovative applications of artificial intelligence, currently pursuing his doctoral studies in computer science at the School of Computer Sciences, Universiti Sains Malaysia, after completing a master’s degree in networking technology at Universiti Teknikal Malaysia Melaka and a bachelor’s degree in computer science at Mustansiriyah University in Baghdad, building his academic journey on a foundation of technical expertise and analytical thinking, his research interests cover metaheuristic algorithms, artificial intelligence, deep learning, gesture recognition, assistive technologies, human–computer interaction, and networking security, he has contributed to the academic community with impactful publications including a hybrid improved IRSO–CNN algorithm for accurate recognition of dynamic gestures in Malaysian sign language, a systematic review on systems-based sensory gloves for sign language pattern recognition, and research on improving cloud storage security using three layers of cryptography algorithms, his professional journey includes significant teaching experience as a lecturer at the Iraqi Police Academy where he worked to advance education and training, and his ongoing research and doctoral studies have strengthened his ability to design, implement, and test intelligent systems addressing real-world challenges, his technical skills encompass proficiency in computer software, Microsoft Office applications, and operating systems across Windows and Mac environments, alongside practical programming expertise in Python for scripting and data processing, he is also experienced with widely used research and software tools such as Jupyter, Colab, Git, SPSS, and basic MATLAB, beyond his professional life he nurtures a passion for reading, research, and continuous learning, qualities that support his growth as a thoughtful academic and innovative researcher, his multidisciplinary focus, combined with a strong commitment to impactful scientific contributions, reflects a future-oriented career in advancing artificial intelligence and human-centered technologies.

Profile: Google Scholar

Featured Publications:

Saeed, Z. R., Ibrahim, N. F., Zainol, Z. B., & Mohammed, K. K. (2025). A hybrid improved IRSO–CNN algorithm for accurate recognition of dynamic gestures in Malaysian sign language. Journal of Electrical and Computer Engineering, 2025(1), 6430675.

Saeed, Z. R., Zainol, Z. B., Zaidan, B. B., & Alamoodi, A. H. (2022). A systematic review on systems-based sensory gloves for sign language pattern recognition: An update from 2017 to 2022. IEEE Access, 10, 123358–123377.

Saeed, Z. R., Zakiah Ayop, N. A., & Baharon, M. R. (2018). Improved cloud storage security using three layers cryptography algorithms. International Journal of Computer Science and Information Security, 16(10), 11–18.

 

Tao Hu | Artificial Intelligence| Best Researcher Award

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Dr. Tao Hu | Artificial Intelligence | Best Researcher Award

The Affiliated Yuyao Yangming Hospital of Medical School of Ningbo University | China

Dr. Tao Hu is a highly accomplished medical professional and researcher from China, serving at The Affiliated Yuyao Yangming Hospital of the Medical School of Ningbo University, with specialization in thyroid surgery, breast surgery, and anorectal surgery. Having completed his doctoral education in health sciences, Dr. Hu has developed an expertise in combining surgical practice with advanced computational methods, particularly artificial intelligence and machine learning applications in clinical diagnostics and predictive modeling. His professional experience includes independently completing over surgical operations and contributing to multiple provincial-level scientific research projects, including support from the Zhejiang Health Information Association Research Program , which highlights his ability to bridge medical practice with innovative research applications. Dr. Hu’s research interests lie primarily in developing predictive tools that integrate clinical information data with artificial intelligence to forecast disease occurrence, progression, and postoperative risks, especially in thyroid carcinoma, where his recent work has introduced novel models for preoperative risk stratification and lymph node metastasis prediction. His research skills are demonstrated through proficiency in clinical data analysis, ultrasound imaging interpretation, radiomics, and the application of machine learning frameworks to enhance diagnostic accuracy and surgical decision-making. In recent years, Dr. Hu has published several impactful articles in high-quality, peer-reviewed journals such as Endocrine, Frontiers in Endocrinology, and the Journal of Clinical Ultrasound, marking him as a significant contributor to evidence-based surgical practices. While his awards and honors primarily reflect academic and clinical achievements, his recognition through this nomination underscores his growing international reputation as a leader in health sciences research. In conclusion, Dr. Hu’s blend of clinical excellence, innovative research in artificial intelligence applications, and dedication to improving surgical outcomes make him a highly deserving recipient of the Best Researcher Award, as his work holds great promise for advancing both scientific knowledge and patient care globally.

Profile:  Orcid

Featured Publications:

Hu, T., Cai, Y., Zhou, T., Zhang, Y., Huang, K., Huang, X., Qian, S., Wang, Q., & Luo, D. (2025). Machine learning‐based prediction of lymph node metastasis and volume using preoperative ultrasound features in papillary thyroid carcinoma. Journal of Clinical Ultrasound. Advance online publication.

Hu, T., Zhou, T., Zhang, Y., Zhou, L., Huang, X., Cai, Y., Qian, S., Huang, K., & Luo, D. (2024). The predictive value of the thyroid nodule benign and malignant based on the ultrasound nodule‐to‐muscle gray‐scale ratio. Journal of Clinical Ultrasound, 52(1).

Zhao, L., Hu, T., Cai, Y., Zhou, T., Zhang, W., Wu, F., Zhang, Y., & Luo, D. (2023). Preoperative risk stratification for patients with ≤ 1 cm papillary thyroid carcinomas based on preoperative blood inflammatory markers: Construction of a dynamic predictive model. Frontiers in Endocrinology, 14, 1254124.

Zhou, T., Xu, L., Shi, J., Zhang, Y., Lin, X., Wang, Y., Hu, T., Xu, R., Xie, L., & Sun, L., et al. (2023). US of thyroid nodules: Can AI-assisted diagnostic system compete with fine needle aspiration? European Radiology. Advance online publication.

Zhou, T., Hu, T., Ni, Z., Yao, C., Xie, Y., Jin, H., Luo, D., & Huang, H. (2023). Comparative analysis of machine learning-based ultrasound radiomics in predicting malignancy of partially cystic thyroid nodules. Endocrine. Advance online publication.

Sheeba Rachel S | Machine Learning | Best Researcher Award

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Mrs. Sheeba Rachel S | Machine Learning| Best Researcher Award

Assistant Professor | Sri Sai Ram Engineering College | India

  S. Sheeba Rachel has contributed extensively to the fields of artificial intelligence, machine learning, deep learning, healthcare technologies, smart devices, image processing, cloud computing, and Internet of Things with publications including Cardiovascular Disease Prediction Using Machine Learning and Deep Learning, Heart Disease Prediction of an Individual Using SVM Algorithm, Automated Driving License Testing System, Real-Time Face Detection and Identification Using Machine Learning Algorithm for Improving the Security in Public Places Using Closed Circuit Television, LEARNAUT – Upgraded Learning Environment and Web Application for Autism Environment Using AR-VR, VATTEN – A Smart Water Monitoring System, Segmentation and Classification of Glaucoma Using U-Net with Deep Learning Model, EDSYS – A Smart Campus Management System, TRACKME – Smart Watch for Women, Women’s Safety with a Smart Foot Device, Mental Health Monitoring Using Sentimental Analysis, Facilitation of Multipurpose Gloves for Impaired People, Extending OVS with Deep Packet Inspection Functionalities, Courier Service Management and Tracking Using Android Application, Detecting the Abandoned Borewell Using Image Processing, Smart Hospitals E-Medico Management System, ADROIT LIMB – Brain Controlled Artificial Limb, Autonomous Movable Packrat for Habitual Chores, Postal Bag Tracking and Alerting System, Applying Social Network Aided Efficient Live Streaming System for Reducing Server Overhead, Image Fusion of MRI Images Using Discrete Wavelet Transform, Probabilistic Flooding Based File Search in Peer to Peer Network, Multi Stage for Informative Gene Selection, Mutual Information in Stages for Informative Gene Selection, Computation of Mutual Information in Stages for Gene Selection from Microarray Data, and several other impactful studies in international journals and conferences indexed in Scopus, IEEE, and UGC; she has further contributed to innovation through consultancy projects such as AI-based pre-examination dental software and non-invasive sugar detection using eye retina, authored books and chapters including Fundamentals of Machine Learning, Management Analytics and Software Engineering, Recent Trends in Engineering and Technology – Edge Computing, and secured patents like Artificial Intelligence Based Heart Rate Monitoring Device for Sports Training, IOT Based Washing Machine for Agricultural Crops, Human Identity Recognition System Using Cloud Machine Learning and Deep Learning Algorithms, Gesture Based Anti-Rape Device, while also holding active memberships with IEEE, ISTE, IEI, UACEE, IAENG, and IACSIT; her academic journey has been marked by mentorship of award-winning projects, reviewer and session chair responsibilities in international conferences, and recognition such as the Best Faculty Advisor Award demonstrating her influence in advancing technology-driven solutions for healthcare, safety, smart systems, and education through research, teaching, patents, and community engagement.

Profile:  Google Scholar

Featured Publications:

Abebaw Agegne | Deep Learning | Best Researcher Award

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Mr. Abebaw Agegne | Deep Learning | Best Researcher Award

Debark University | Ethiopia

Abebaw Agegne Engda is an Ethiopian scholar and academic who has devoted his professional career to the advancement of computer science education and research while fostering strong community engagement and service. He earned his Bachelor of Science degree in Computer Science from Debre Tabor University with high academic distinction, completing his studies with a focus on programming, systems, and applied computing. He later pursued a Master of Science degree in Computer Science at the University of Gondar, where he further deepened his knowledge of computational theory, advanced software systems, and the practical applications of computer science in solving real-world challenges. His academic excellence is demonstrated by his strong cumulative performance in both degrees, which reflect a commitment to rigor and perseverance. Professionally, he began his teaching journey as an Assistant Lecturer at Debark University, where he taught undergraduate computer science courses and contributed to shaping the foundational knowledge of young scholars. Later, he advanced to the position of Lecturer at Debark University, where he continues to teach computer science students across a variety of specializations, delivering core programming, system analysis, and applied computing courses while contributing to other departments with harmonized curriculum approaches. His students have consistently benefited from his structured teaching style, with many advancing to careers in high-level companies and industries, demonstrating the practical effectiveness of his teaching methodologies. He is capable of teaching a wide range of programming languages and has also been recognized for his leadership within his department, guiding academic processes, curriculum harmonization, and student development initiatives. His research works and community service contributions are documented and accessible through his ORCID profile, reflecting his engagement with both scholarly and societal responsibilities. Beyond academics, he is a person of discipline, patience, and strong work habits, qualities that enhance his ability to serve effectively in challenging environments and to maintain positive relationships with colleagues and students. He is fluent in Amharic and English, which allows him to engage in both local and international academic contexts, and his hobbies such as reading, traveling, counseling, and cultural exploration reflect a personality committed to lifelong learning, empathy, and service to others. Overall, his biography presents the portrait of a self-respecting, fair, and hardworking educator who combines academic achievement, teaching excellence, research contributions, and community service, making him a valuable asset in the advancement of computer science education in Ethiopia and beyond.

Profile: Orcid

Featured Publications:

Asnake, N. W., Ayalew, A. M., & Engda, A. A. (2025). Detection of oral squamous cell carcinoma cancer using AlexNet on histopathological images. Discover Applied Sciences.

Ayele, M. K., Baye, G. A., Yesuf, S. H., Engda, A. A., & Mitiku, E. T. (2025). Predicting stunting status among under five children in Ethiopia using ensemble machine learning algorithms. Scientific Reports.

Engda, A. A., Salau, A. O., & Ajala, O. (2025). Classical machine learning approaches for early hypertension risk prediction: A systematic review. Applied AI Letters.

Engda, A. A., Zewale, G. E., Mihret, B. G., & Adane, A. T. (2025). Developing pneumonia detection model using chest X-ray images: Deep learning approach. Preprint.

Engda, A. A. (2025). Detection of oral squamous cell carcinoma cancer using AlexNet on histopathological images. Conference paper.

Engda, A. A. (2025). Development of a case-based reasoning system for onion disease diagnosis and treatment. Proceedings of the IEEE International Conference on Emerging and Sustainable Technologies for Power and ICT in a Developing Society.

Micheal Arowolo | Machine Learning | Best Researcher Award

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Dr. Micheal Arowolo | Machine Learning | Best Researcher Award

Assistant Professor | Xavier University of Louisiana | United States

Dr. Micheal Olaolu Arowolo is an accomplished scholar, researcher, and educator in the field of computer science, with expertise in machine learning, health informatics, and bioinformatics. He currently serves as an Assistant Professor of Health Informatics at Xavier University of Louisiana, where he teaches master’s students in areas such as population health, statistics in health sciences, and healthcare quality. He earned his Ph.D. in Computer Science from Landmark University in Nigeria, building on a Master’s degree in Computer Science from Kwara State University and a Bachelor’s degree from Al-Hikmah University. He later advanced his academic career as a Post-doctoral Research Scholar at the University of Missouri’s Bond Life Sciences Center, where he contributed to the development of deep learning and machine learning models aimed at predicting relevant gene names in pathway figures for health practitioners. Dr. Arowolo’s teaching and research experience spans institutions in both the United States and Nigeria, where he has lectured and supervised students across a broad range of subjects, including artificial intelligence, data communication and networking, object-oriented programming, and computational theory. His research efforts have produced impactful publications in reputable journals indexed by Elsevier, IEEE, ISI, and Web of Science. He has also developed applied solutions for the United Nations Sustainable Development Goals, particularly SDG 11, by applying machine learning models to domains such as healthcare, telecommunications, and banking. His contributions to academic excellence helped Landmark University improve its global ranking significantly. An active member of the global research community, Dr. Arowolo belongs to several professional organizations, including IEEE, ACM, ISCB, and IAENG. He also serves as a reviewer and editorial board member for internationally recognized journals such as Heliyon, IEEE Access, and Journal of Big Data. His dedication to academic mentorship is reflected in his supervision of numerous graduate and undergraduate projects, guiding students to adopt innovative approaches to machine learning and computational methods. Recognized among the top 500 scholars in Nigeria by SciVal-Scopus, Dr. Arowolo has received certifications in SQL, Linux, Oracle, project management, and network administration. Through a blend of research, teaching, and leadership, he continues to contribute to knowledge creation, innovation, and the advancement of computational science and health informatics worldwide.

Profile:  Scopus | ORCID | Google Scholar

Featured Publications:

Arowolo, M. O., & co-authors. (n.d.). Enhancing cyber threat detection with an improved artificial neural network model. Data Science and Management.

Arowolo, M. O., & co-authors. (n.d.). Computational intelligence in big data analytics. In Book chapter.

Arowolo, M. O., & co-authors. (n.d.). A comprehensive evaluation of large language models in mining gene relations and pathway knowledge. Quantitative Biology.

Arowolo, M. O., & co-authors. (n.d.). Internet of things (IoT): Concepts, protocols, and applications. In Book chapter.

Arowolo, M. O., & co-authors. (n.d.). Adsorptive removal of synthetic food dyes using low-cost biochar: Efficiency prediction, kinetics and desorption index evaluation. Bioresource Technology Reports.

Arowolo, M. O., & co-authors. (n.d.). Gene name recognition in gene pathway figures using Siamese networks. In Conference proceedings.

Arowolo, M. O., & co-authors. (n.d.). Enhancing healthcare data security: An intrusion detection system for web applications with SVM and decision tree algorithms.

Dr. Zhiwei Zuo | Machine Learning | Best Researcher Award Lecturer

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Dr. Zhiwei Zuo | Machine Learning | Best Researcher Award

Lecturer | Hunan University | China

Dr. Zhiwei Zuo is a researcher specializing in machine learning, artificial intelligence, and machine unlearning. He earned his Ph.D. in Computer Science from Hunan University, China, under the supervision of Prof. Zhuo Tang, where his research explored machine unlearning, adversarial robustness, and efficient deep learning methods. He also gained international research experience as a visiting student at Nanyang Technological University, Singapore, under the mentorship of Prof. Anwitaman Datta, further expanding his expertise in trustworthy AI. Dr. Zuo is currently a lecturer at the Faculty of Artificial Intelligence in Education, Central China Normal University, where he continues to focus on designing algorithms that address data privacy, security, and robustness challenges in artificial intelligence systems. He has published in prestigious journals and conferences such as IEEE Transactions on Knowledge and Data Engineering, ICASSP, and Information Sciences. His work contributes to advancing trustworthy AI while ensuring ethical and responsible deployment of machine learning technologies.

Professional Profile

Scopus

Education

Dr. Zhiwei Zuo pursued his academic journey across several prestigious institutions. He completed his Ph.D. in Computer Science at Hunan University focusing on machine learning, adversarial robustness, and machine unlearning, under the supervision of Prof. Zhuo Tang. During his doctoral studies, he broadened his international exposure as a visiting student at Nanyang Technological University, Singapore where he collaborated with Prof. Anwitaman Datta at the School of Computer Science and Engineering, working on machine unlearning algorithms and data privacy in AI systems. Prior to his doctoral research, he earned his Bachelor’s degree in Computer Science from Central China Normal University  which laid the foundation for his interest in artificial intelligence and secure computing. Building on these academic milestones, he now serves as a Lecturer at the Faculty of Artificial Intelligence in Education, Central China Normal University where he integrates his strong educational background with active research and teaching.

Experience

Dr. Zuo’s professional and research experience spans academia and international collaboration in computer science. Currently, he is a Lecturer at the Faculty of Artificial Intelligence in Education, Central China Normal University, where he engages in teaching and research on artificial intelligence and its applications in education and security. His doctoral research at Hunan University provided him with extensive experience in algorithm development, adversarial machine learning, and machine unlearning frameworks. As a visiting student at Nanyang Technological University, Singapore, he collaborated with Prof. Anwitaman Datta on advancing fine-grained approaches to machine unlearning, combining theoretical insights with practical applications. Dr. Zuo has also contributed to multiple interdisciplinary projects, focusing on robust classifiers, text adversarial attacks, and efficient algorithms for high-performance computing. His teaching and mentorship roles further reflect his dedication to cultivating the next generation of AI researchers. His career demonstrates a blend of innovative research, teaching excellence, and international collaboration.

Research Focus

Dr. Zuo’s research focuses on machine unlearning, privacy-preserving artificial intelligence, adversarial robustness, and trustworthy machine learning systems. His work seeks to address one of the emerging challenges in AI—how to efficiently remove specific data or knowledge from trained models without retraining them entirely. He has developed fine-grained parameter perturbation methods and incremental learning frameworks to advance machine unlearning. His research also explores adversarial robustness, designing models that can withstand adversarial text and image attacks, and developing generative classifiers resistant to transfer attacks. Additionally, he has contributed to efficient high-performance algorithms for Bayesian text classification in distributed environments. His interdisciplinary approach combines theory, algorithm design, and practical implementation to ensure machine learning models remain reliable, secure, and ethically aligned. Currently, his research bridges AI and education, focusing on the safe deployment of machine learning systems in sensitive domains, while addressing privacy, fairness, and accountability in artificial intelligence.

Awards and Honors

Dr. Zuo has received recognition for his academic excellence, innovative research, and contributions to the field of artificial intelligence. His publications in top-tier venues such as IEEE Transactions on Knowledge and Data Engineering, ICASSP, and Information Sciences have been well received in the research community. As a doctoral student, he earned research scholarships and support for his outstanding performance and contributions at Hunan University. His visiting research tenure at Nanyang Technological University was also supported by competitive funding, reflecting the significance of his work in machine unlearning. Additionally, his contributions to adversarial robustness and parallel algorithms have been acknowledged through conference presentations and collaborative projects. Dr. Zuo has participated in international conferences, where his work received positive recognition for originality and practical relevance. His career highlights include balancing strong theoretical research with applied solutions in secure AI systems, establishing him as a promising researcher in trustworthy and privacy-preserving AI.

Publication Top Notes 

A distributed skewed stream processing system based on scoring high-frequency key perception

Year: 2025

Conclusion

Zhiwei Zuo’s impressive research experience, innovative research, and interdisciplinary collaboration make them a strong candidate for the Best Researcher Award. With further development of their publication record, global impact, and research translation, Zuo could solidify their position as a leading researcher in machine learning.

Wei Zhou | Software Engineering | Best Researcher Award

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Dr. Wei Zhou | Software Engineering | Best Researcher Award 

Project manager, at The 34th Research Institute of China Electronics Technology Group Corporation, China.

Dr. Wei Zhou is a dedicated research manager at the Guangxi Key Laboratory of Optical Network and Optical Information Security, under the 34th Research Institute of China Electronics Technology Group Corporation, based in Guilin, China. With a Ph.D. in Information and Communication Engineering, he brings over 18 years of industry and academic experience in next-generation communication technologies. His primary research revolves around network communication protocols, satellite optical networks, machine learning, and advanced algorithms. 📡🛰️ His work plays a pivotal role in developing secure and intelligent communication systems in China’s evolving technology landscape. He has successfully led and contributed to multiple research initiatives and engineering projects within national-level laboratories. Dr. Zhou’s work bridges theoretical innovations and practical implementations, contributing significantly to scientific literature with high-impact publications. His efforts have garnered academic and industrial recognition, making him a promising candidate for prestigious awards. 🏅

Professional Profile

ORCID

🎓 Education

Dr. Wei Zhou’s academic journey reflects a continuous pursuit of knowledge and innovation in the field of communications. 🎓 He began his studies in Electronic Information Engineering at the Chengdu University of Technology from 2001 to 2005, earning his bachelor’s degree. He then advanced his expertise in Communication and Information Systems at Guilin University of Electronic Technology (GUET), where he received his master’s degree in 2013. His deepening interest in wireless and network systems led him to pursue a Ph.D. in Information and Communication Engineering, again at GUET, which he completed in 2023. 📚 His doctoral research focused on Key Technologies of Long-Distance Wireless Communication, laying a strong theoretical and practical foundation for his future research in satellite optical networking and secure communication protocols. Throughout his educational path, Dr. Zhou has demonstrated a commitment to excellence and innovation that now underpins his extensive contributions to China’s tech research landscape. 🎖️

💼 Experience

Dr. Wei Zhou’s professional experience spans nearly two decades at the 34th Research Institute of China Electronics Technology Group Corporation, where he has held key engineering and leadership positions. 🏢 Starting in 2005 as an Assistant Engineer, he worked in the First Division, gradually advancing to Engineer by 2012. From 2012 to 2017, he served in the Third Division and the Department of Digital Optical Communication and Optoelectronics, earning the rank of Senior Engineer. His work included crucial national projects in network security and digital communications. Between 2017 and 2023, Dr. Zhou worked at the Innovation Center, leading R&D initiatives in quantum-safe communication and satellite-ground integration technologies. Since July 2023, he has held a senior engineering role at the Guangxi Key Laboratory of Optical Network and Optical Information Security. ⚙️ His career reflects consistent technical growth, leadership in innovation, and impact-driven research contributing to China’s digital infrastructure.

🔬 Research Interests

Dr. Zhou’s research interests span several high-impact fields critical to modern communication technology. His focus includes network communication protocols, satellite optical networks, machine learning, and intelligent algorithms. 🧠📡 At the intersection of artificial intelligence and communication, he applies reinforcement learning models to optimize Quality of Service (QoS) in software-defined networks (SDNs). In satellite-terrestrial integration, his work addresses low-latency, high-security routing using quantum-safe technologies and optical inter-satellite links (OISL). 🔒🛰️ His doctoral research on long-distance wireless communication forms the technical bedrock for many of his publications and applied projects. Dr. Zhou is particularly keen on how distributed learning algorithms can enhance the performance, adaptability, and resilience of next-generation networks. He frequently collaborates with interdisciplinary teams to bridge gaps between theoretical research and real-world engineering systems, making significant contributions to China’s information security and intelligent network development. 🧪

🏆 Awards

While specific individual awards are currently under consideration, Dr. Wei Zhou has been a consistent contributor to award-winning research projects at both institutional and national levels. 🥇 His technical leadership in projects involving SDN integration, quantum-safe optical transport, and intelligent routing optimization has earned departmental honors and commendations from his institute. Several of his publications have gained attention for their innovation, and his work has been cited in high-impact journals, reflecting growing recognition in the academic community. His collaborative nature and mentorship also contribute to cultivating research excellence in his teams. Dr. Zhou is actively involved in proposing and implementing cutting-edge solutions that align with China’s strategic development goals in secure and intelligent communication networks. 📘 He is a strong candidate for individual recognition due to his demonstrated leadership, technical depth, and substantial contribution to advancing the field of optical communication and network protocol innovation.

📚Top Noted Publications

Dr. Wei Zhou has published extensively on topics related to SDN, optical satellite networks, and quantum-safe transport protocols. His contributions are listed below, with hyperlinks to each publication:

1. AQROM: A Quality of Service Aware Routing Optimization Mechanism

  • Authors: Wei Zhou, Xing Jiang, Qingsong Luo, Bingli Guo, Xiang Sun, Fengyuan Sun, Lingyu Meng

  • Journal: Digital Communications and Networks (KeAi Communications), December 2022 (though your query noted 2024, published in 2022)

  • DOI: 10.1016/j.dcan.2022.11.016

  • Access: Available via DOAJ (open access) bohrium.dp.tech+7doaj.org+7bohrium.dp.tech+7

2. Design and Implementation of Semi‑Physical Platform for Label‑Based Frame Switching

  • Authors: Wei Zhou, Xing Jiang*, Qingsong Luo, Shanguo Huang, Bingli Guo, Xiang Sun, Shaobo Li, Xiaochuan Tan, Mingyi Ma, Tianwen Fu

  • Journal: Applied Sciences, Volume 12, Issue 13, June 13, 2022

  • DOI: 10.3390/app12136674

  • Access: PDF available via MDPI mdpi.com

3. PQROM: SDN QoS‑Aware Routing with PPO

  • Authors: (Appears under same team—confirm manually) The ACM listing shows only abstract on ACM; article published by IOS Press.

  • Journal: Journal of Intelligent & Fuzzy Systems, Vol. 42 Iss. 4, January 1 2022

  • Title: To optimize software defined network QoS‑aware routing with proximal policy optimization: PQROM

  • Access: DOI listing on ACM bohrium.dp.tech+9dl.acm.org+9researchgate.net+9

4. Quantum‑Safe Metro‑Optimized Optical Transport Networks

5. Routing Optimization Algorithm for Integrated Computing and Communication Satellite

  • Authors: (Not shown in web results)

  • Journal: Optical Communication Technology, Vol. 48 Issue 5, 2024

  • DOI: 10.13921/j.cnki.issn1002-5561.2024.05.009

  • Access: Listed on Researching.cn link.springer.com

6. M‑OTN Optical Domain Encryption via QKD

  • Journal: Acta Optica Sinica, 2025

  • Note: Specific details not located; likely behind paywall or in Chinese-language site.

7. SDH Remote Maintenance via Telephone Network MODEM

  • Journal: Optical Communication Technology, 2014

  • DOI: Not publicly indexed; likely available via CNKI or institutional library.

8. IP Telephony with Switching Function and Variable Rate

  • Journal: Optical Communication Technology, 2011

  • DOI: Similarly not in open domain; accessible via CNKI/institutional library.

9. Patent: Method for Integrated Networking of SDN and Wireless Network

  • Patent Title: Method for Integrated Networking of SDN and Wireless Network

  • Patent No.: 202010166435.9

  • Year: Registered in 2022

  • Access: Viewable via CNIPA or equivalent patent databases.

Conclusion

Based on the provided CV, Dr. Wei Zhou is a strong candidate for the Best Researcher Award, particularly in the field of next-generation communication technologies. His work is technically significant, aligned with strategic national interests, and shows consistency and growth in publication quality.

However, to be more competitive at an international level, he should:

Enhance global academic visibility (via platforms like Google Scholar, ORCID, ResearchGate).

Highlight measurable impact metrics and funded projects.

Pursue more international collaborations.

Prof. Dragan Randelovic | Machine learning | Innovative Research Award

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Prof. Dragan Randelovic | Machine learning | Innovative Research Award

Full professor at Faculty for diplomacy and security, University Uniin Nikola Tesla Belgrade,Serbia,

Prof. Dr. Dragan Randjelovic is a renowned full professor at the University Union Nikola Tesla, Faculty of Diplomacy and Security. With over 45 years of research experience, he has made significant contributions to the field of information technology and computer science.

Professional Profile

scholar

🎓 Education

– *PhD*, Faculty of Science and Mathematics, University of Pristina (1999)- *Master’s Degree*, Faculty of Electronics, University of Niš (1984)- *Bachelor’s Degree*, Faculty of Electronics, University of Niš (1977)

💼 Experience

– *PhD*, Faculty of Science and Mathematics, University of Pristina (1999)- *Master’s Degree*, Faculty of Electronics, University of Niš (1984)- *Bachelor’s Degree*, Faculty of Electronics, University of Niš (1977)

🔬 Research Interests

Prof. Randjelovic’s research focuses on:- *Information Technology*: software engineering, computer science- *Computer Science*: informatics, decision-making

🏆 Awards

– *Published over 15 university textbooks*- *Over 300 references, 40 registered on Web of Science*- *Over 400 citations on Google Scholar, h-index and i-index over 10*

📚Top Noted  Publications

– The impact of the July 2007 heat wave on daily mortality in Belgrade, Serbia ☀️
– Published in Central European Journal of Public Health, 2013
– Handbook of Research on Democratic Strategies and Citizen-Centered E-Government Services 📚
– Published by Information Science Reference, 2015
– A framework for delivering e-government support 🤝
– Published in Acta Polytechnica Hungarica, 2014
– Weight coefficients determination based on parameters in factor analysis 📊
– Published in Metalurgia International, 2013
– Triple modular redundancy optimization for threshold determination in intrusion detection systems 🔒
– Published in Symmetry, 2021
– Determining VLSI array size for one class of nested loop algorithms 🔍
– Published in Advances in Computer and Information Sciences, 1998
– Use of determination of the importance of criteria in business-friendly certification of cities as sustainable local economic development planning tool 🏙️
– Published in Symmetry, 2020
– An advanced quick-answering system intended for the e-Government service in the Republic of Serbia 📱
– Published in Acta Polytechnica Hungarica, 2019
– SOSerbia: Android-Based Software Platform for Sending Emergency Messages 📞
– Published in Complexity, 2018
– A multicriteria decision aid-based model for measuring the efficiency of business-friendly cities 🏙️
– Published in Symmetry, 2020
– The design of the personal enemy-MIMLebot as an intelligent agent in a game-based learning environment 🤖
– Published in Acta Polytechnica Hungarica, 2017
– Intelligent agents and game-based learning modules in a learning management system 🤖
– Published in Agent and Multi-Agent Systems, 2016
– Study program selection by aggregated DEA-AHP measure 📊
– Published in Metalurgia International, 2013
– Prediction of important factors for bleeding in liver cirrhosis disease using ensemble data mining approach 💊
– Published in Mathematics, 2020
– Visokotehnološki kriminal 🔍
– Published in 2013
– Challenging ergonomics risks with smart wearable extension sensors 👕
– Published in Electronics, 2022
– Determination of invariant measures: An approach based on homotopy perturbations 🔍
– Published in University Politehnica of Bucharest Scientific Bulletin, 2018
– Different methods for fingerprint image orientation estimation 🔒
– Published in Telecommunications Forum, 2012
– EnCase forenzički alat 🔍
– Published in Bezbednost, 2009

Conclusion

 

Prof. Dr. Dragan Randjelovic’s extensive research experience, prolific publication record, leadership roles, editorial board membership, and mentorship make him a strong candidate for the Best Researcher Award. By emphasizing interdisciplinary collaboration and international collaboration, he could further strengthen his application and demonstrate his potential for continued excellence in research.