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:

Dynhora Danheyda Ramirez Ochoa | Inteligencia de enjambre | Best Researcher Award

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Dr. Dynhora Danheyda Ramirez Ochoa | Inteligencia de enjambre | Best Researcher Award

Universidad Tecnológica de Chihuahua | Mexico

Dynhora Danheyda Ramírez Ochoa is a distinguished full-time professor at Universidad Tecnológica de Chihuahua, specializing in automation, data storage and analysis, project management, and digital innovation. With a Doctorate in Technology, a Master’s in Computer Systems Engineering, and a Bachelor’s in Computer Systems with a Hardware specialization, she has cultivated extensive expertise in multidisciplinary academic and technological environments. Since beginning her tenure at the university, she has advised over seventy students, facilitated seventeen graduations, and coordinated the development and monitoring of curricula for the Information Technologies and Digital Innovation program. Her leadership spans chairing and serving on committees for ethics, research, software, and tutoring, and she actively oversees STEM mentorship initiatives through the MC3T project. Dynhora has contributed to national and international conferences, focusing on swarm intelligence, decision-making processes, and emerging technologies, while publishing in specialized journals. Her dedication to fostering collaborative research teams, integrating immersive technologies, and promoting educational innovation reflects her commitment to developing both academic excellence and applied technological solutions, positioning her as an exemplary candidate for the Best Researcher Award.

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.

Haichen Zhou | Artificial Intelligence | Best Researcher Award

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Dr. Haichen Zhou | Artificial Intelligence | Best Researcher Award

Senior Engineer | Automation Research and Design Institute of Metallurgical Industry | China

Dr. Haichen Zhou is a distinguished metallurgical researcher and Senior Quality Engineer at the Automation Research and Design Institute of Metallurgical Industry Co., Ltd., under the China Iron & Steel Research Institute Group Co., Ltd. He received his Ph.D. from the University of Science and Technology Beijing (USTB), a leading institution renowned for metallurgy and materials science. Over the course of his career, Dr. Zhou has established himself as an expert in steelmaking and metallurgical process optimization, with a strong focus on inclusions control in liquid steel and slab quality improvement. His professional expertise spans physical simulation, numerical modeling, and the integration of artificial intelligence into metallurgical research and industrial practice. Dr. Zhou has authored 14 papers published in highly regarded journals such as Metallurgical and Materials Transactions B (MMTB), ISIJ International, Steel Research International, Ironmaking and Steelmaking, and Metallurgical Research & Technology (MRT). His research contributions have not only advanced theoretical understanding but also delivered practical solutions to improve steel quality and process reliability. Combining academic depth with industrial experience, he continues to play a key role in bridging science, engineering, and innovation in modern steel manufacturing.

Professional Profile

Orcid

Education

Dr. Haichen Zhou earned his doctoral degree in metallurgical engineering from the University of Science and Technology Beijing (USTB), a globally recognized institution for research in materials science, metallurgy, and engineering. During his Ph.D. studies, he specialized in steelmaking processes with a particular focus on inclusions control technology, steel slab quality assessment, and advanced metallurgical process simulations. His academic training combined theoretical knowledge with experimental and computational methods, allowing him to address both fundamental and applied aspects of metallurgical phenomena. At USTB, Dr. Zhou carried out extensive research on the thermodynamics and kinetics of inclusions formation, the influence of microstructural defects on steel properties, and the use of physical simulation for understanding process behavior. In addition, he explored the potential of numerical simulation and artificial intelligence to predict, optimize, and control complex metallurgical processes, thereby merging traditional metallurgy with emerging computational approaches. His Ph.D. thesis provided valuable insights into steel quality improvement, combining laboratory-scale investigations with industrial applications. This solid academic foundation not only prepared him for his current research and engineering responsibilities but also positioned him as a specialist capable of leading interdisciplinary advancements in metallurgical science and steelmaking technology.

Experience

Dr. Haichen Zhou has accumulated extensive professional experience as a metallurgical engineer and researcher. He currently serves as a Senior Quality Engineer at the Automation Research and Design Institute of Metallurgical Industry Co., Ltd., part of the China Iron & Steel Research Institute Group Co., Ltd. In this capacity, he is responsible for developing and implementing advanced technologies for steel quality improvement, defect prevention, and metallurgical process optimization. His work encompasses inclusions control in liquid steel, continuous casting process refinement, and slab defect mitigation, with the overarching goal of producing high-performance steels for industrial applications. Dr. Zhou’s expertise also extends to physical simulation, which he uses to replicate and study metallurgical phenomena under controlled conditions, as well as numerical simulation for predictive modeling of steelmaking processes. More recently, he has contributed to applying artificial intelligence in metallurgy, utilizing machine learning for process monitoring, quality prediction, and optimization. Prior to his current role, his academic research and collaborative projects provided him with strong exposure to both laboratory studies and industrial challenges. His career demonstrates a seamless integration of academic knowledge with industrial practice, ensuring impactful contributions to both scientific progress and steel industry advancements.

Awards and Honors

Throughout his career, Dr. Haichen Zhou has earned recognition for his research contributions, publications, and industrial innovations in metallurgical engineering. While completing his Ph.D. at the University of Science and Technology Beijing (USTB), he was commended for his doctoral research on steel quality improvement and inclusions control technology. His published works in high-impact journals, including Metallurgical and Materials Transactions B, ISIJ International, and Steel Research International, have attracted attention from the global metallurgy community, highlighting his role as a rising expert in his field. At the China Iron & Steel Research Institute Group, Dr. Zhou has been involved in major research and development projects, earning professional acknowledgment for his role in advancing inclusions control methods and integrating artificial intelligence into steel manufacturing practices. His ability to merge classical metallurgical knowledge with modern computational technologies positions him as an innovative thinker in steel engineering. Although specific awards are not listed, his 14 peer-reviewed publications, professional designations, and continued contributions to steel process optimization represent significant milestones of achievement. These accomplishments reflect both his scientific rigor and his dedication to advancing the steel industry’s pursuit of higher quality, efficiency, and sustainability.

Research Focus

Dr. Haichen Zhou’s research focuses on advancing steelmaking and metallurgical science through a combination of experimental, computational, and data-driven approaches. His primary expertise lies in inclusions control technology in liquid steel, which is crucial for improving the purity, mechanical properties, and performance of final steel products. He has extensively studied steel slab quality, analyzing the causes of defects during solidification and developing strategies to minimize flaws, thereby enhancing steel consistency and reliability. His research also integrates physical simulation techniques to reproduce metallurgical processes under controlled laboratory conditions, providing critical insights into inclusions behavior and slab defect evolution. Complementing these experimental approaches, Dr. Zhou applies numerical simulation to predict and optimize complex steelmaking phenomena, offering accurate process models for industrial use. In recent years, he has expanded his work to include artificial intelligence applications in steel manufacturing. By using machine learning and data analytics, he has developed predictive models for defect formation, real-time monitoring systems, and process optimization frameworks. His interdisciplinary approach, combining metallurgy with computational intelligence, contributes to both fundamental metallurgical knowledge and industrial innovation. Ultimately, his research seeks to enhance steel quality, improve production efficiency, and support the sustainable development of advanced steel technologies.

Publication Top Notes 

Mathematical Simulation and Industrial Implications of Swirling Gas-Solid Distributor in the Bottom-Blowing O2–CaO Steelmaking Converter Process
Year: 2025

Development of Ca‐Containing Ferrosilicon Instead of Ca Treatment in High Silicon Steels during Ladle Refining
Year: 2025

Mathematical modeling of the effect of SEN outport shape on the bubble size distribution in a wide slab caster mold
Year: 2025

Optimization of Vortex Slag Entrainment during Ladle Teeming Process in the Continuous Casting of Automobile Outer Panel
Year: 2025

Conclusion

Overall, Dr. Haichen Zhou is a strong candidate for recognition as a Best Researcher, particularly in metallurgical process engineering and steel quality control. His track record of publications, technical expertise, and innovative integration of artificial intelligence into steelmaking research represent clear strengths. With further expansion of international visibility, leadership roles, and demonstration of broader impact, he has the potential to stand out as an exceptional awardee. At this stage, he is certainly a worthy nominee, and with continued contributions, he could establish himself as a leading figure in the global metallurgy research community.

Prof. Rita Santos Inácio | Data Science and Deep Learning | Best Researcher Award

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Prof. Rita Santos Inácio | Data Science and Deep Learning | Best Researcher Award

Professor, at Instituto Politécnico de Beja, Portugal.

Ana Rita Santos Inácio is a Quality Manager and Invited Adjunct Professor at the Polytechnic Institute of Beja. She holds a PhD in Food Science and Nutrition and has research experience in high-pressure technology applied to milk and cheese.

Professional Profile

Scopus

orcid

🎓 Education

– *PhD in Food Science and Nutrition*, Portuguese Catholic University of Porto – School of Biotechnology (2020)- *Master’s in Biotechnology – Food*, University of Aveiro (2013)- *Bachelor’s in Biotechnology*, University of Aveiro (2011)

💼 Experience

– *Quality Manager*, Sensory Laboratory, Polytechnic Institute of Beja (2023-present)- *Invited Adjunct Professor*, Department of Applied Technologies and Sciences, Polytechnic Institute of Beja (2020-present)- *Research Fellow*, University of Aveiro /QOPNA (2019-2020)

🔬 Research Interests

– *Food Science and Nutrition*: high-pressure technology, milk and cheese safety and quality- *Sensory Analysis*: sensory test sheets, sensory session planning and execution, data analysis- *Food Technology*: meat and fish technology, food safety and quality

🏆 Awards

– *”Summa Laude”*, PhD thesis (2020)- *FCT grant*, SFRH/BD/96576/2013 (2014-2019)

📚 Top Noted Publications

– Effect of high-pressure as a non-thermal pasteurisation technology for raw ewes’ milk and cheese safety and quality 🥛
– PhD thesis
– Effect of high-pressure on Serra da Estrela cheese 🧀
– Master’s thesis
– Second-generation bioethanol production: fermentation of acid sulphite liquor by free and immobilised Pichia stipitis 💡

Conclusion

Rita Santos Inácio’s research excellence, teaching experience, and professional activity make her a strong candidate for the Best Researcher Award. With further interdisciplinary collaboration and internationalization, she could further enhance the impact of her research and contribute to advancements in food science and nutrition.

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.