Yonghong Song | Deep Learning | Best Researcher Award

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Prof. Yonghong Song | Deep Learning | Best Researcher Award

Professor at Xi’an Jiaotong University, China

Professor Song Yonghong is a distinguished academic and researcher at the School of Software Engineering, Xi’an Jiaotong University. As a recognized IEEE member and an active participant in several professional societies including the China Society of Image and Graphics (CSIG) and the China Computer Federation (CCF), she has significantly contributed to advancing the fields of computer vision and intelligent systems. She is also a certified Project Management Professional (PMP) by the American Project Management Institute, combining her academic insight with applied project management expertise. Her contributions to the field include a prolific output of over 100 high-quality publications and more than 20 authorized invention patents, which reflect her sustained impact in theoretical and applied research.

Profile

Scopus

Education

Professor Song’s educational background reflects a strong foundation in computer science and engineering. She pursued rigorous academic training in computer vision, pattern recognition, and artificial intelligence, which laid the groundwork for her subsequent contributions to academia and industry. Her academic preparation, combined with interdisciplinary training, equipped her to approach complex problems with a balance of theoretical depth and practical applicability. This educational trajectory enabled her to engage in and lead high-impact research projects both nationally and internationally, and to cultivate a strong research team within her institution.

Experience

Throughout her career, Professor Song has demonstrated consistent leadership in cutting-edge research and technological development. She has taken the lead on numerous international collaboration projects, national key R&D initiatives, and enterprise partnerships. Her work extends deeply into the real-world challenges associated with object detection and recognition in images and video, providing actionable insights and technological innovations for enterprises. In these roles, she has not only pushed forward the boundaries of academic research but has also ensured that the outcomes are translated into scalable, industry-grade solutions. Her experience spans applications such as intelligent copiers, automated steel surface inspection, and smart appliance systems, showcasing her commitment to cross-disciplinary impact and societal benefit.

Research Interests

Professor Song’s research interests primarily focus on computer vision, pattern recognition, and intelligent systems. She is particularly passionate about designing and refining methodologies for object detection and recognition, especially in real-time industrial environments. Her research addresses complex visual processing problems and develops intelligent solutions that are responsive to the demands of modern industrial applications. She has worked extensively on integrating deep learning algorithms into visual systems for improved performance and automation. Her work is characterized by a high degree of innovation, especially in translating theoretical frameworks into deployable systems.

Awards

Professor Song has been recognized for her excellence through several prestigious awards and honors. While many of her accolades are project-specific and rooted in collaborative successes, her standout achievement includes the development of the “Hot High-Speed Wire Surface Defect Online Detection System,” which was successfully implemented at Baoshan Iron and Steel Co., LTD. This system has proven to be stable, efficient, and internationally competitive in automating quality inspections. The industrial relevance and global recognition of this project exemplify the strength of her applied research. She has also received commendations for leadership in engineering practice and for promoting the industrialization of academic research outputs.

Publications

Professor Song has published over 100 articles in high-impact journals and conferences, with a focus on visual computing and intelligent systems. Selected publications include:

Song Y. et al., “Multi-Scale Feature Fusion for Surface Defect Detection,” IEEE Transactions on Industrial Informatics, 2021 – cited by 56 articles.

Song Y. et al., “Real-Time Target Detection in Complex Industrial Environments,” Pattern Recognition Letters, 2020 – cited by 47 articles.

Song Y. et al., “Deep Learning-based Anomaly Detection in Steel Production,” Journal of Visual Communication and Image Representation, 2019 – cited by 62 articles.

Song Y. et al., “Intelligent Vision System for Smart Appliances,” Sensors, 2022 – cited by 33 articles.

Song Y. et al., “CNN Architectures for Surface Quality Analysis,” Computer Vision and Image Understanding, 2020 – cited by 45 articles.

Song Y. et al., “Efficient Video Object Recognition using Hybrid Networks,” Neurocomputing, 2018 – cited by 50 articles.

Song Y. et al., “Robust Industrial Vision with Deep Supervision,” Machine Vision and Applications, 2021 – cited by 38 articles.

Conclusion

In summary, Professor Song Yonghong exemplifies the integration of academic excellence with industrial relevance. Her work in computer vision and intelligent systems is not only scientifically rigorous but also deeply practical, influencing both research and real-world systems. Her leadership in national and international collaborations, along with her commitment to solving critical industrial challenges, places her at the forefront of applied visual computing research. With an extensive portfolio of publications, patents, and successful enterprise collaborations, Professor Song continues to push the envelope in making intelligent technologies smarter, more robust, and more responsive to contemporary demands.

Maura Mengoni | Extended Reality | Best Researcher Award

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Prof. Maura Mengoni | Extended Reality | Best Researcher Award

Associate Professor at Polytechnic University of Marche, Italy

Maura Mengoni is an Associate Professor at the Polytechnic University of Marche, where she has been a faculty member since 2012. With a strong background in mechanical engineering and artificial intelligence applications, she has played a significant role in bridging engineering design with digital innovation. She is actively involved in research collaborations across Europe, focusing on AI-driven technologies for industrial and cultural heritage applications. Mengoni has also served on various academic boards and commissions, reflecting her dedication to both research and institutional development.

Profile

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Education

Maura Mengoni obtained her Master’s degree in Building and Architecture Engineering, followed by a Ph.D. in Mechanical Engineering. Her academic journey has been characterized by a strong emphasis on interdisciplinary research, integrating AI, virtual prototyping, and smart manufacturing solutions. In 2016, she was qualified as a Full Professor, further cementing her role as a leader in her field. Her educational background has provided her with the technical and analytical skills necessary to contribute to cutting-edge research and innovation projects.

Experience

Mengoni has extensive experience in both academic and industrial research. She has served as a consultant for Indesit Company S.p.A. and has been involved in multiple European and national projects, working closely with businesses and research centers. She was a managing board member of Hyperlean and the president of EMOJ Spin-offs, a pioneering AI tech company in Europe. Additionally, she has been instrumental in various leadership roles, including serving as a coordinator and delegate for numerous academic initiatives related to engineering and digital transformation.

Research Interests

Her research primarily focuses on artificial intelligence, virtual prototyping, and human-centric design. She has contributed significantly to projects exploring AI-driven manufacturing systems, digital twin applications, and interactive virtual reality environments. Mengoni is particularly interested in the integration of smart perception sensors and distributed intelligence for applications in healthcare, cultural heritage, and automotive industries. Her work aims to enhance the efficiency and adaptability of industrial processes while improving user experience through innovative digital solutions.

Awards

Mengoni has received several awards and recognitions for her contributions to engineering research and AI applications. She has been acknowledged for her work in AI-driven manufacturing solutions and has played a crucial role in advancing gender equality initiatives within her institution. Her involvement in high-impact research projects has also earned her recognition at both national and international levels, solidifying her reputation as a thought leader in her field.

Publications

Mengoni, M., et al. (2022). “AI-based Digital Twin for Smart Manufacturing.” International Journal of Advanced Manufacturing Technology. Cited by 75 articles.

Mengoni, M., et al. (2021). “Human-Centered Virtual Prototyping for Industrial Applications.” Computers & Industrial Engineering. Cited by 60 articles.

Mengoni, M., et al. (2020). “Integration of AI and IoT for Smart Factories.” Journal of Manufacturing Systems. Cited by 55 articles.

Mengoni, M., et al. (2019). “Augmented Reality in Cultural Heritage Preservation.” Journal of Cultural Heritage Management. Cited by 40 articles.

Mengoni, M., et al. (2018). “Adaptive Human-Machine Interfaces in Industrial Automation.” Robotics and Computer-Integrated Manufacturing. Cited by 35 articles.

Mengoni, M., et al. (2017). “A Multi-sensor Approach for Proactive Monitoring in Healthcare.” Sensors Journal. Cited by 30 articles.

Mengoni, M., et al. (2016). “Digital Prototyping for Sustainable Product Development.” Journal of Engineering Design. Cited by 25 articles.

Conclusion

Maura Mengoni has established herself as a prominent researcher and innovator in the fields of AI, virtual prototyping, and digital manufacturing. Her extensive academic and industrial collaborations, coupled with her leadership roles in research projects, highlight her commitment to advancing technological solutions for industrial and societal challenges. As an advocate for interdisciplinary research and gender equality in STEM, Mengoni continues to influence the future of engineering and AI applications.

Jaya Raju G | Machine Learning | Best Researcher Award

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Mr. Jaya Raju G | Machine Learning | Best Researcher Award

Assistant Professor at Aditya University, India

G. Jaya Raju is an accomplished academician and researcher with extensive experience in computer science and engineering. With a strong passion for education and research, he has dedicated his career to mentoring students, contributing to academic administration, and advancing knowledge in various fields such as data mining, machine learning, and database management. His expertise spans programming languages, software testing, and artificial intelligence. Throughout his career, he has actively participated in faculty development programs, workshops, and research conferences, contributing to the academic community through publications and professional activities.

Profile

Scopus

Education

G. Jaya Raju is currently pursuing a Ph.D. from Jawaharlal Nehru Technological University, Kakinada (JNTUK), having successfully completed his Pre-PhD requirements. He obtained his M.Tech in Computer Science and Engineering from Aditya Engineering College, Surampalem, under JNTUK, with a commendable academic performance. Additionally, he holds an M.Sc in Computer Science from Andhra University College of Engineering, Visakhapatnam. His strong educational foundation has played a pivotal role in shaping his expertise and research contributions in the field of computer science.

Experience

With over a decade of experience in academia, G. Jaya Raju has served as an Assistant Professor at several esteemed institutions. Currently, he holds the position of Senior Assistant Professor at Aditya College of Engineering and Technology. Previously, he has contributed to institutions such as Sri Vasavi Engineering College, Rajahmahendri Institute of Engineering and Technology, Sri Venkateswara Institute of Science & Information Technology, and Lenora College of Engineering. His responsibilities have encompassed teaching, academic administration, mentoring students, and guiding research projects at both undergraduate and postgraduate levels. Additionally, he has actively participated in university external examinations and accreditation processes.

Research Interests

His research interests include Data Warehousing and Data Mining, Machine Learning, Compiler Design, Formal Languages and Automata Theory, Database Management Systems, and Web Technologies. He is particularly focused on developing innovative solutions in sentiment analysis, data categorization, and optimization techniques for artificial intelligence applications. His research contributions have led to several publications in reputed international and national journals, reflecting his commitment to advancing knowledge in his areas of expertise.

Awards and Recognitions

G. Jaya Raju has received multiple accolades for his academic and professional achievements. He has qualified for APSET-2024 and GATE-2023, demonstrating his proficiency in computer science and engineering. He was also recognized as an Associate Member of the Institution of Engineers (AMIE) in 2016. Additionally, he has been awarded “Elite Certificates” from SWAYAM NPTEL for excelling in courses such as Compiler Design, Database Management Systems, and Data Mining, offered by the Indian Institute of Technology (IIT), Kharagpur. These accomplishments highlight his dedication to continuous learning and professional development.

Publications

“Deep Belief Neural Network based Categorization of Uncertain Data Streams,” International Journal of Software Innovation, DOI: https://doi.org/10.4018/IJSI.312262, cited by multiple research articles.

“Classical Software Testing Using Semi-Proving,” IJCST Vol. 3, Issue 3, July-Sept 2012, ISSN: 0976-8491 (Online), 2229-4333 (Print), cited in numerous studies related to software testing methodologies.

“Implementation of Skyline Sweeping Algorithm,” International Journal of Computer Science and Technology (IJCST) Vol. 3, Issue 3, July-Sept 2012, ISSN: 0976-8491 (Online), 2229-4333 (Print), referenced in data structure optimization research.

“Perturbation Approach for Protecting Data Server Used for Decision Tree Mining,” IJCST Vol. 3, Issue 4, Oct-Dec 2012, ISSN: 0976-8491 (Online), 2229-4333 (Print), widely cited in data security studies.

Conclusion

G. Jaya Raju’s career is marked by a strong commitment to education, research, and professional growth. His extensive teaching experience, active participation in research, and dedication to mentoring students highlight his contributions to academia. With expertise in data mining, machine learning, and programming, he continues to make significant advancements in computer science. His awards, certifications, and publications demonstrate his dedication to academic excellence and research innovation. As an educator and researcher, he remains committed to fostering knowledge and inspiring future generations of computer science professionals.

Ameni Chetouane | Computer Science | Best Researcher Award

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Dr. Ameni Chetouane | Computer Science | Best Researcher Award

Contractual assistant at Higher Institute of Computer Science – Tunisia (ISI), Tunisia

Ameni Chetouane is a dedicated doctoral student specializing in computer science, currently pursuing her PhD at the Ecole Nationale des Sciences de l’Informatique (ENSI) at the University of Manouba, Tunisia. Her academic journey began with a Bachelor’s in Applied Computer Networks followed by a Master’s degree, where she concentrated on network technologies and video analysis for traffic congestion detection. She is deeply involved in research aimed at securing Software Defined Networking (SDN) systems against cyber-attacks using Artificial Intelligence (AI) methods.

Profile

Orcid

Education

Ameni’s education spans several years, starting with a Bachelor’s degree in Applied Computer Networks from the Institut Supérieur d’Informatique de Mahdia (ISIMA) in 2014. She pursued two Master’s degrees, one focusing on network technologies and telecommunications, and the other on research in computer science, both from the University of Carthage’s Faculté des Sciences de Bizerte (FSB). Her doctoral studies, commenced in 2021, are focused on the application of AI for intrusion detection systems (IDS) in SDN environments, with a goal to combat cyber-attacks.

Experience

Ameni has gained practical teaching experience as a part-time instructor at the Institut Supérieur des Etudes Technologiques de Bizerte and the Faculté des Sciences de Bizerte, where she taught subjects such as database engineering and object-oriented programming. Her internships, including research at LaBRI, University of Bordeaux, and her professional project at Millénia Engineering, have allowed her to apply theoretical knowledge in real-world network and software development projects.

Research Interests

Ameni’s research is primarily focused on the security of SDN environments, particularly in utilizing AI for effective threat detection and mitigation. Her doctoral thesis specifically explores AI-driven solutions for securing SDN systems against Distributed Denial of Service (DDoS) attacks. She aims to improve the performance of IDSs by incorporating machine learning (ML) and continual learning methods into SDN security architectures, ensuring adaptive and real-time defenses against evolving threats.

Awards

Ameni has earned recognition for her academic and research excellence, notably her significant contributions to the field of SDN and AI. Her work has been presented at various international conferences, contributing to advancements in network security research. While specific awards are not listed, her impact within the academic community, through her publications and conference participations, is considerable.

Publications

Ameni Chetouane, Sabra Mabrouk, Imen Jemili, and Mohamed Mosbah. “A comparative study of vehicle detection methods in a video sequence.” International Workshop on Distributed Computing for Emerging Smart Networks, Springer, 2019.

Ameni Chetouane, Sabra Mabrouk, Imen Jemili, and Mohamed Mosbah. “Vision-based vehicle detection for road traffic congestion classification.” Concurrency and Computation: Practice and Experience, 2022.

Ameni Chetouane, Sabra Mabrouk, and Mohamed Mosbah. “Traffic congestion detection: Solutions, open issues, and challenges.” International Workshop on Distributed Computing for Emerging Smart Networks, Springer, 2020.

Ameni Chetouane and Kamel Karoui. “A survey of machine learning methods for DDoS threats detection against SDN.” International Workshop on Distributed Computing for Emerging Smart Networks, Springer, 2022.

Ameni Chetouane, Kamel Karoui, and Ghayth Nemri. “An intelligent ML-based IDS framework for DDoS detection in the SDN environment.” International Conference on Advances in Mobile Computing and Multimedia Intelligence, Springer, 2022.

Ameni Chetouane and Kamel Karoui. “DDoS detection approach based on continual learning in the SDN environment.” International Conference on Hybrid Intelligent Systems, Springer, 2022.

Ameni Chetouane and Kamel Karoui. “Risk-based intrusion detection system in Software Defined Networking.” Concurrency and Computation: Practice and Experience, 2023.

Conclusion

Ameni Chetouane stands out in her field with a robust educational background, strong professional experiences, and an ongoing commitment to researching the intersection of AI and SDN security. Through her published works, she has made significant contributions to securing networks using intelligent methods, focusing on solving complex cyber threats in modern network infrastructures. As she continues her research, her work promises to shape the future of AI-driven cybersecurity in SDN environments.

Luigi Bibbo’ | Artificial Intelligence | AI & Machine Learning Award

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Dr. Luigi Bibbo’ | Artificial Intelligence | AI & Machine Learning Award

Research Fellow | Mediterranea University of Reggio Calabria | Italy

Dr. Luigi Bibbò is a distinguished researcher and academician specializing in electronic and computer engineering. With a strong foundation in biomedical engineering, he has contributed significantly to the fields of sensors, photonics, artificial intelligence, and nanotechnology. His extensive research experience spans multiple institutions across Italy, China, and the United States, where he has worked on cutting-edge technologies for biomedical applications, environmental monitoring, and robotics. Dr. Bibbò is actively involved in research projects focusing on big data analysis, forecasting systems, and healthcare-related AI applications.

Profile

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Education

Dr. Bibbò holds a PhD in Electronic and Computer Engineering from the Second University of Naples, awarded in 2015. His doctoral research focused on the development of sensors based on plasmon resonance in polymer optical fibers and photonic crystals. Prior to his PhD, he obtained a Master’s degree in Biomedical Engineering from Federico II University of Naples in 2009, where he specialized in organic semiconductor-based OFET for biomedical applications. His academic journey began with a Bachelor’s degree in Biomedical Engineering from the same institution in 2006, focusing on innovative cardiac diagnostic technologies using multislice computed tomography. He later qualified as a professional engineer in 2010.

Experience

Dr. Bibbò has held various research positions at prestigious institutions. Since April 2024, he has been a Research Fellow at the Mediterranean University of Reggio Calabria, working on big data analysis and forecasting systems for climate change adaptation under the TECH4YOU project. From March 2023 to March 2024, he was a Research Fellow at the University of Florence, contributing to the Pharaon Project, which focuses on robotic technologies, IoT, and artificial intelligence for biomedical applications. Prior to this, he served as an Assistant Professor (RTDA) at the Mediterranean University of Reggio Calabria from 2019 to 2022, leading projects on elderly monitoring and localization systems. His international experience includes research fellowships at Shenzhen University, China (2016-2019), where he developed metasurfaces for OAM beam generation, and a visiting scientist role at Tufts University, USA (2013-2014), working on plasmonic-photonic hybrid crystal sensors.

Research Interests

Dr. Bibbò’s research interests encompass a wide range of interdisciplinary fields, including sensors, photonics, fiber optics, MEMS, metamaterials, nanotechnology, artificial intelligence, neural networks, virtual reality, and augmented reality. He has led multiple projects involving CNN-based image classification, predictive modeling using Random Forest Regressor, and AI-driven motion analysis in healthcare. His work integrates fundamental engineering principles with advanced computational techniques to develop innovative solutions for biomedical and environmental challenges.

Awards

Dr. Bibbò has been recognized for his outstanding contributions to research and technology development. He was the winner of the Technologist I° competition at the Mediterranean University of Reggio Calabria. Additionally, he has been a fellow of the Engineering Research Council (FERC) and an active member of Frontiers in Neuroscience. His research has earned him invitations to prestigious international conferences and collaborations with leading scientific journals as a guest editor and reviewer.

Publications

Dr. Bibbò has authored several influential publications in high-impact journals.

Bibbò, L., et al. (2023). “Human Activity Recognition in Healthcare: A Machine Learning Approach.” MDPI Applied Sciences. Cited by 45 articles.

Bibbò, L., et al. (2022). “Development of AI-driven Motion Analysis for Biomedical Applications.” IEEE Access. Cited by 38 articles.

Bibbò, L., et al. (2021). “Nanophotonic Metasurfaces for Orbital Angular Momentum Beam Generation.” Journal of Optics. Cited by 56 articles.

Bibbò, L., et al. (2020). “Plasmonic Nanoparticles and Tunable Dielectric Matrix for Optical Sensing.” Journal of Physics D: Applied Physics. Cited by 72 articles.

Bibbò, L., et al. (2019). “Indoor Navigation System for Dementia Patients Using Augmented Reality.” Frontiers in Neuroscience. Cited by 33 articles.

Bibbò, L., et al. (2018). “Integration of MEMS Sensors for Real-Time Tracking in Smart Environments.” Nanotechnology. Cited by 41 articles.

Bibbò, L., et al. (2017). “Plasmonic-Photonic Hybrid Crystal Sensors for Biochemical Detection.” Journal of Optical Society of America B. Cited by 60 articles.

Conclusion

Dr. Luigi Bibbò’s career is marked by a dedication to advancing electronic and computer engineering through interdisciplinary research. His contributions to biomedical applications, nanotechnology, and artificial intelligence have positioned him as a leading researcher in his field. Through his extensive publication record, international collaborations, and innovative projects, he continues to push the boundaries of technology to improve healthcare, environmental monitoring, and human-computer interaction. His ongoing work at the Mediterranean University of Reggio Calabria and other institutions highlights his commitment to cutting-edge research and knowledge dissemination in engineering and applied sciences.