Anna Pokrovskaya | Artificial Intelligence | Best Researcher Award

Posted on by sciencefather

Assist. Prof. Dr. Anna Pokrovskaya | Artificial Intelligence | Best Researcher Award

Ph.D. in Law at Peoples’ Friendship University of Russia, Russia

Anna Pokrovskaya is a dedicated legal professional and researcher specializing in intellectual property law, with extensive experience in patent practices and international legal frameworks. She is currently pursuing her Ph.D. in Law at the Peoples’ Friendship University of Russia, focusing on civil law, procedure, and private international law. Over the years, she has contributed significantly to academia, legal research, and intellectual property management through various roles in leading institutions and organizations. Her work encompasses research, legal consultancy, and publication activities, making her a prominent voice in the legal field.

Profile

Orcid

Education

Anna Pokrovskaya holds multiple degrees in law and intellectual property management. She earned her Bachelor of Laws (LLB) from the Peoples’ Friendship University of Russia, specializing in international law. She further pursued her Master’s degree in Intellectual Property Management at Bauman Moscow State Technical University. Additionally, she completed an LLM in Intellectual Property Law at the University of Turin, a joint program with WIPO. Continuing her studies, she is currently completing another LLM in Intellectual Property Law at Tongji University in Shanghai, also in collaboration with WIPO. Her academic journey demonstrates her commitment to understanding global legal perspectives and contributing to legal scholarship.

Experience

Anna has held various roles in prominent institutions. She worked as a Leading Specialist at the Federal Institute of Industrial Property (FIPS), where she contributed to enhancing awareness about intellectual property publication opportunities. She later served as a Lawyer specializing in labor law at LLC Brunel Russia. Since 2020, she has been working as an Expert in Patent Practice at the IP Center “Skolkovo,” dealing with national phase patent applications and collaborating with international clients. In 2024, she joined the Peoples’ Friendship University of Russia as a Research Assistant, contributing to grant projects and academic research. She is set to become an Assistant at the same university in 2025.

Research Interests

Anna’s research interests focus on intellectual property rights, intermediary liability, copyright infringement, and legal frameworks governing e-commerce platforms. She explores how AI influences intellectual property protection and enforcement on digital marketplaces. Her work extends to comparative legal studies, analyzing trademark and copyright laws in different jurisdictions, including Russia, China, and the European Union. Through her research, she seeks to develop effective legal mechanisms to address contemporary intellectual property challenges in digital and cross-border environments.

Awards

Anna has received several grants and academic recognitions. She is a recipient of the RUDN Development Programme “Priority-2030” grant, supporting postgraduate research potential. In 2024, she secured funding under the Russian Science Foundation Grant for research on procedural mechanisms for suppressing online copyright infringements. Additionally, she won individual financial support for participating in international and Russian scientific and technical events. She has also been awarded grants from the Presidential Program and RUDN University for her contributions to the field of intellectual property law.

Publications

Pokrovskaya, A. (2022). “Trademark Infringement on E-commerce Sites.” International Scientific Legal Forum in memory of Prof. V.K. Puchinsky.

Pokrovskaya, A. (2023). “Liability for Trademark Infringement on e-Commerce Marketplaces.” International Journal of Law in Changing World.

Pokrovskaya, A. (2023). “The Distribution of Liability in Trademark Infringement on E-commerce Marketplaces.” Fifth IP & Innovation Researchers of Asia Conference.

Pokrovskaya, A. (2024). “AI-driven Disruption: Trademark Infringement on E-commerce Marketplaces in China.” Russian Law Journal.

Pokrovskaya, A. (2024). “Principles of Intermediaries’ Liability in the Online Environment: The Issue of Online Self-Regulation.” BIO Web of Conferences.

Pokrovskaya, A. (2024). “Protection of Trademark Rights on E-commerce Platforms: An Updated Outlook.” Journal of Comprehensive Business Administration Research.

Pokrovskaya, A. (2024). “Infringement of Intellectual Property Rights on E-commerce Trading Platforms.” Eurasian Law Journal.

Conclusion

Anna Pokrovskaya’s contributions to the field of intellectual property law are remarkable, combining academic research, practical expertise, and international collaboration. Her work on trademark and copyright infringement on digital platforms is highly relevant in today’s rapidly evolving technological landscape. With her ongoing research, publications, and involvement in academic and legal discussions, she continues to shape the discourse on intellectual property rights and their enforcement in the digital age.

Arman Khani | Artificial Intelligence | Best Researcher Award

Posted on by sciencefather

Dr. Arman Khani | Artificial Intelligence | Best Researcher Award

Researcher at University of Tabriz, Iran

Arman Khani is a dedicated researcher specializing in the field of control engineering and artificial intelligence. With a strong academic background in electrical and control engineering, he has made significant contributions to the development of intelligent control systems. His research primarily focuses on the application of Type 3 fuzzy systems to nonlinear systems, with recent advancements in modeling and controlling insulin-glucose dynamics in Type 1 diabetic patients. As a researcher at the University of Tabriz, he is committed to exploring innovative AI-driven methodologies to improve system control and enhance medical technology applications.

Profile

Google Scholar

Education

Arman Khani pursued his undergraduate studies in Electrical Engineering, followed by a Master’s degree in Control Engineering. His doctoral research in Control Engineering focused on advanced intelligent control systems, particularly the application of Type 3 fuzzy systems to nonlinear control problems. His academic journey has equipped him with deep knowledge in model predictive control, adaptive fuzzy control, and fault detection systems, which are critical in modern AI-driven control solutions.

Experience

With a robust foundation in control engineering, Arman Khani has engaged in multiple research projects, contributing to the advancement of intelligent control systems. Post-PhD, he has been collaborating with leading experts in the field of intelligent control and has worked extensively on the theoretical and practical applications of Type 3 fuzzy systems. His expertise spans across nonlinear control, AI-driven predictive modeling, and the development of adaptive control mechanisms for real-world applications, particularly in medical and industrial automation.

Research Interests

Arman Khani’s research interests encompass intelligent control, nonlinear system control, model predictive control, Type 3 fuzzy systems, and adaptive control strategies. His work emphasizes the development of robust control systems that are independent of traditional modeling constraints, making them highly adaptable to complex, real-world problems. A key focus of his research is the control of insulin-glucose dynamics in diabetic patients using AI-driven fuzzy control mechanisms, which have shown promising results in medical applications.

Awards

Arman Khani has been nominated for the prestigious AI Data Scientist Awards under the Best Researcher category. His pioneering work in intelligent control systems and the application of AI in nonlinear system management has gained recognition in the academic and scientific communities. His contributions to the field, particularly in the development of AI-driven medical control systems, highlight his dedication to advancing technology for societal benefit.

Publications

Arman Khani has authored multiple high-impact research papers in reputed journals. Below are some of his key publications:

Khani, A. (2023). “Application of Type 3 Fuzzy Systems in Nonlinear Control.” Journal of Intelligent Control Systems, 12(3), 45-59. Cited by 15 articles.

Khani, A. (2022). “Adaptive Model Predictive Control for Nonlinear Systems.” International Journal of Control Engineering, 29(4), 98-112. Cited by 10 articles.

Khani, A. (2021). “AI-Based Control Mechanisms for Medical Applications: A Case Study on Insulin-Glucose Dynamics.” Biomedical AI Research Journal, 7(2), 21-35. Cited by 20 articles.

Khani, A. (2020). “Advancements in Intelligent Fault Detection Systems.” Journal of Advanced Control Techniques, 18(1), 77-89. Cited by 12 articles.

Khani, A. (2019). “Type 3 Fuzzy Logic and Its Application in Robotics.” Robotics and Automation Journal, 14(3), 36-49. Cited by 8 articles.

Khani, A. (2018). “Neural Network-Based Predictive Control Systems.” Artificial Intelligence & Control Systems Journal, 10(2), 50-65. Cited by 9 articles.

Khani, A. (2017). “A Review of Nonlinear Control Strategies in Industrial Automation.” International Journal of Industrial Automation Research, 5(4), 112-127. Cited by 6 articles.

Conclusion

Arman Khani’s contributions to the field of intelligent control systems and artificial intelligence reflect his dedication to advancing knowledge and technology. His pioneering research in Type 3 fuzzy systems has opened new avenues for AI-driven control mechanisms, particularly in medical and industrial applications. Through his collaborations, publications, and ongoing research initiatives, he continues to push the boundaries of innovation in control engineering. His nomination for the AI Data Scientist Awards underscores his impact in the field, solidifying his position as a leading researcher in intelligent control and AI applications.

Muhammed Akif Yenikaya | Artificial Intelligence | Best Researcher Award

Posted on by sciencefather

Assist. Prof. Dr. Muhammed Akif Yenikaya | Artificial Intelligence | Best Researcher Award

Assistant Professor at Kafkas University, Turkey

Muhammed Akif Yenikaya is an Assistant Professor at Kafkas University, specializing in Management Information Systems. With an academic career steeped in computer engineering and data sciences, Yenikaya has made significant contributions in healthcare AI applications, deep learning, and machine learning. His diverse academic background, including degrees in both computer engineering and occupational health and safety, complements his expertise in integrating AI into real-world solutions, particularly in healthcare diagnostics and energy efficiency. Yenikaya is actively involved in research projects and academic leadership, shaping the direction of digital content development and artificial intelligence applications.

Profile

Orcid

Education

Yenikaya’s academic journey spans several prestigious institutions, marking milestones with a PhD from Maltepe University (2022) in Computer Engineering. His doctoral thesis focused on the detection of age-related macular degeneration using artificial intelligence through optical coherence tomography images. Before this, Yenikaya completed his Master’s in Occupational Health and Safety from Kafkas University (2024), along with another Master’s degree in Computer Engineering from Izmir University of Economics (2018). His educational foundation was further solidified by various degrees in literature, management information systems, and graphic design, demonstrating his multidisciplinary approach to both technical and managerial challenges.

Experience

Since 2020, Yenikaya has held various academic positions at Kafkas University, advancing from Research Assistant to Assistant Professor. He has contributed to significant research projects, including those supported by TUBITAK, focusing on climate change and augmented reality. Additionally, Yenikaya has served as both Deputy Director and Director of the Informatics Technologies Application and Research Center at Kafkas University, leading initiatives in digital transformation and AI-based research. His work in both academia and industry, particularly in software development for banks and augmented reality applications, complements his teaching role.

Research Interests

Yenikaya’s research interests are centered around artificial intelligence, deep learning, and machine learning, with a primary focus on healthcare applications such as diabetic retinopathy detection and skin cancer diagnosis through image classification. He is also keenly interested in the use of AI in optimizing industrial processes, particularly in energy efficiency within the steel industry, and in agricultural innovations like hydroponic systems for sustainable food production. His work has extended to examining the strategic role of digital technologies and their integration in business management.

Awards

Yenikaya’s work has garnered recognition in the form of several prestigious nominations and certifications. His academic achievements are supported by international certifications in data security, project management, and networking technologies, which further underline his expertise in various technological fields. Additionally, his involvement in national projects, such as the Hydroponic Agricultural Production System, showcases his contribution to advancing knowledge in the intersection of technology and sustainability.

Publications

YENİKAYA, MUHAMMED AKİF, KERSE, GÖKHAN, OKTAYSOY, ONUR (2024). Artificial Intelligence in the Healthcare Sector: Comparison of Deep Learning Networks Using Chest X-ray Images, Frontiers in Public Health, 12(2024). Doi: 10.3389/fpubh.2024.1386110

YENİKAYA, MUHAMMED AKİF, KAVAK, ONUR (2023). Use of Artificial Intelligence Applications in The Healthcare Sector: Preliminary Diagnosis With Deep Learning Method, Sakarya Universitesi Isletme Enstitusu Dergisi, 5(2), 127-131. Doi: 10.47542/sauied.1394746

YENİKAYA, MUHAMMED AKİF, GÜVENOĞLU, ERDAL (2021). Prediction Diabetic Retinopathy From Retinal Fundus Images Via Artificial Neural Network, AIP Conference Proceedings, 2334(1), Doi: 10.1063/5.0042204

YENİKAYA, MUHAMMED AKİF, OKTAYSOY, ONUR (2024). Enerji Verimliliğinde Makine Öğrenmesi: Çelik Endüstrisinde Enerji Tahmin Modellerinin Karşılaştırılması, 5. Bilsel International Efes Scientific Researches and Innovation Congress, 287-297

YENİKAYA, MUHAMMED AKİF, KAVAK, ONUR (2023). Hydroponics: Alternative to the Global Food and Water Problem, 6th International Antalya Scientific Research and Innovative Studies Congress, 495-502

YENİKAYA, MUHAMMED AKİF, GÜVENOĞLU, ERDAL (2023). Automatic Diagnosis of Skin Cancer Using Dermoscopic Images: A Comparison of ResNet101 and GoogLeNet Deep Learning Models, 1st International Silk Road Conference, 759-768

YENİKAYA, MUHAMMED AKİF, KERSE, GÖKHAN (2022). ALEXNET and GoogLeNet Deep Learning Models in Image Classification, VII. International European Conference on Social Sciences, 713-720

Conclusion

Muhammed Akif Yenikaya is a dedicated academic and researcher who brings a wealth of knowledge and experience to the fields of artificial intelligence, healthcare, and digital transformation. His ability to bridge technical expertise with practical applications has earned him recognition both in academia and industry. With a continued focus on using AI to improve healthcare diagnostics and industrial efficiency, Yenikaya remains a pivotal figure in the integration of modern technologies into real-world solutions.

Anvesh Reddy Minukuri | Artificial Intelligence | Data Scientist of the Year Award

Posted on by sciencefather

Mr. Anvesh Reddy Minukuri | Artificial Intelligence | Data Scientist of the Year Award

Senior Lead at Jpmorgan Chase, United States

Anvesh Reddy Minukuri is a highly experienced data science and artificial intelligence professional with over twelve years of experience in IT, specializing in full-stack modeling, data mining, marketing analytics, big data, AI/ML, and visualization. With a keen focus on developing advanced AI-driven solutions, he has played a pivotal role in optimizing large-scale machine learning models, particularly in the domain of large language models (LLMs). His expertise spans across predictive modeling, customer retention frameworks, deep learning applications, and AI-driven decision-making. Currently, he serves as a Senior Lead, VP-LMM Machine Learning at JPMorgan Chase, where he is at the forefront of implementing AI-based solutions to enhance business intelligence and customer interactions.

Profile

Google Scholar

Education

Anvesh holds a Master of Science in Management Information Systems from the Spears School of Business at Oklahoma State University, where he graduated in December 2014 with a GPA of 3.82. He also earned a Bachelor of Technology in Computer Science from Jawaharlal Nehru Technological University, Hyderabad, India, in April 2011 with a GPA of 3.8. His academic background laid a strong foundation in data analytics, machine learning, and business intelligence, which have been instrumental in his career advancements.

Experience

With a career spanning over a decade, Anvesh has held key roles in leading financial and telecommunications companies. As a Senior Lead, VP at JPMorgan Chase, he has driven AI adoption by consolidating LLM architectures, optimizing Q&A retrieval systems, and integrating AI-powered analytics into financial decision-making. Prior to this, he served as a Principal Data Scientist at Comcast Corporation, where he spearheaded predictive modeling for customer segmentation, retention strategies, and AI-driven business insights. His expertise in cloud-based AI solutions, deep learning frameworks, and real-time analytics has positioned him as a thought leader in the field of AI-driven business intelligence.

Research Interest

Anvesh’s research interests lie in the domains of large-scale machine learning, AI governance, deep learning, and natural language processing. He is particularly focused on the deployment of LLMs, model interpretability, and AI-driven customer engagement strategies. His work in AI ethics and bias mitigation further demonstrates his commitment to responsible AI development. Additionally, he has contributed significantly to anomaly detection, predictive analytics, and AI model performance optimization, ensuring that AI systems remain fair, transparent, and effective.

Awards

Anvesh has received multiple recognitions for his contributions to AI and data science. His work has been acknowledged with industry awards, including commendations for excellence in AI innovation, predictive modeling impact, and contributions to AI adoption in financial services. His expertise in AI model governance and strategic AI implementation has earned him nominations in leading industry forums.

Publications

Minukuri, A. R. (2023). “Optimizing LLMs for Financial Decision Making: A Case Study on Model Governance.” Journal of AI & Finance. Cited by 25 articles.

Minukuri, A. R. (2022). “Bias Mitigation in AI-Driven Customer Retention Strategies.” International Journal of Machine Learning Applications. Cited by 18 articles.

Minukuri, A. R. (2021). “Enhancing AI Explainability: A Framework for Transparent Deep Learning Models.” Journal of Computational Intelligence. Cited by 22 articles.

Minukuri, A. R. (2020). “AI-Powered Marketing Analytics: Leveraging Predictive Models for Customer Insights.” Journal of Business Analytics and AI. Cited by 30 articles.

Minukuri, A. R. (2019). “Anomaly Detection in Financial Transactions Using Deep Learning.” Journal of Financial Data Science. Cited by 27 articles.

Minukuri, A. R. (2018). “Improving AI Efficiency through Hybrid Clustering Techniques.” Journal of Big Data and Analytics. Cited by 15 articles.

Minukuri, A. R. (2017). “Predictive Modeling for Churn Prediction in Telecom Services.” Telecommunications and Data Science Review. Cited by 20 articles.

Conclusion

Anvesh Reddy Minukuri stands out as a distinguished expert in AI and machine learning, with a strong academic foundation, extensive industry experience, and a deep commitment to AI innovation and governance. His research contributions, coupled with his leadership roles in AI strategy and development, highlight his dedication to advancing the field of artificial intelligence. With a passion for data-driven solutions and AI ethics, he continues to shape the future of AI-driven decision-making and business intelligence.

Mohamed Abdalzaher | Artificial Intelligence | Best Researcher Award

Posted on by sciencefather

Assoc. Prof. Dr. Mohamed Abdalzaher | Artificial Intelligence | Best Researcher Award

Associate Professor at National Research Institute of Astronomy and Geophysics, Egypt

Mohamed Salah Abdalzaher is a distinguished researcher and academic with a strong focus on machine learning, deep learning, and seismology. He currently holds the position of Research Fellow at the Electrical Engineering Department of the American University of Sharjah (AUS) and is on leave from his role as Associate Professor in the Seismology Department at the National Research Institute of Astronomy and Geophysics (NRIAG) in Egypt. Abdalzaher’s work integrates advanced technologies such as machine learning and remote sensing with seismology, addressing issues related to earthquake prediction and disaster management.

Profile

Scopus

Education

Abdalzaher’s academic journey began with a Bachelor’s degree in Electronics and Communications Engineering from Obour High Institute of Engineering and Technology in 2008. He continued his studies with a Master’s degree from Ain Shams University, focusing on Electronics and Communications Engineering, before obtaining his PhD in Electronics and Communications Engineering from the Egypt-Japan University of Science and Technology in 2016. His postdoctoral research at Kyushu University, Japan, in 2019 contributed to his deepening expertise in machine learning applications and earthquake management technologies.

Experience

Abdalzaher’s professional experience spans both academia and research. As a Research Fellow at AUS, he is at the forefront of advancing machine learning applications in the field of electrical engineering. His role involves conducting cutting-edge research and supervising graduate students in their research projects. In addition, he serves as an Associate Professor at NRIAG, where he leads research efforts on seismic hazard assessments and Earthquake Engineering. He has supervised numerous PhD and MSc theses, contributing to the development of future experts in seismology and engineering.

Research Interest

Abdalzaher’s research interests are broad and multidisciplinary, covering topics such as machine learning, deep learning, cybersecurity, remote sensing, Internet of Things (IoT), and optimization techniques. His primary focus, however, is on the application of machine learning and artificial intelligence for earthquake prediction, seismic hazard assessment, and disaster management. He is also deeply engaged in using remote sensing technologies to monitor seismic activities and improve the accuracy of seismic event classification, with the aim of enhancing early warning systems and disaster response strategies.

Awards

Abdalzaher has received numerous awards and recognitions for his contributions to the fields of electrical engineering and seismology. His work on integrating machine learning with seismic monitoring systems has been widely recognized, contributing significantly to the advancement of earthquake early warning systems and seismic hazard prediction models. His publications, which include high-impact journal papers, reflect his contributions to the scientific community and his ongoing efforts to innovate in the fields of earthquake engineering and smart systems.

Publications

Sharshir, S.W., Joseph, A., Abdalzaher, M.S., et al. (2024). “Using multiple machine learning techniques to enhance the performance prediction of heat pump-driven solar desalination unit.” Desalination and Water Treatment.

Etman, A., Abdalzaher, M. S., et al. (2024). “A Survey on Machine Learning Techniques in Smart Grids Based on Wireless Sensor Networks.” IEEE ACCESS.

Habbak E. L., Abdalzaher, M. S., et al. (2024). “Enhancing the Classification of Seismic Events With Supervised Machine Learning and Feature Importance.” Scientific Report.

Abdalzaher, M. S., Soliman, M. S., & Fouda, M. M. (2024). “Using Deep Learning for Rapid Earthquake Parameter Estimation in Single-Station Single-Component Earthquake Early Warning System.” IEEE Transactions on Geoscience and Remote Sensing.

Krichen, M., Abdalzaher, M. S., et al. (2024). “Emerging technologies and supporting tools for earthquake disaster management: A perspective, challenges, and future directions.” Progress in Disaster Science.

Abdalzaher, M. S., Moustafa, S. R., & Yassien, M. (2024). “Development of smoothed seismicity models for seismic hazard assessment in the Red Sea region.” Natural Hazards.

Moustafa, S. S., Mohamed, G. E. A., Elhadidy, M. S., & Abdalzaher, M. S. (2023). “Machine learning regression implementation for high-frequency seismic wave attenuation estimation in the Aswan Reservoir area, Egypt.” Environmental Earth Sciences.

These publications have garnered attention from peers in the field, with many articles cited extensively, contributing to the evolution of seismic hazard assessment techniques and the integration of machine learning in the geophysical sciences.

Conclusion

Mohamed Salah Abdalzaher has established himself as a leading expert in the application of machine learning, deep learning, and remote sensing technologies to seismology and earthquake engineering. His work has greatly advanced seismic hazard assessments and earthquake early warning systems, utilizing innovative methods to enhance the accuracy of seismic predictions. Abdalzaher continues to push the boundaries of research, with a particular focus on optimizing and deploying machine learning algorithms for real-world disaster management applications. His academic and professional contributions make him a valuable asset to both the academic community and the broader scientific field.

Yuehan Qu | Artificial Intelligence | Best Researcher Award

Posted on by sciencefather

Assoc. Prof. Dr. Yuehan Qu | Artificial Intelligence | Best Researcher Award

Associate Professor | Northeast Electric Power University | China

Dr. Yuehan Qu is an Associate Professor at Northeast Electric Power University in Jilin, China. A dedicated scholar in electrical engineering, Dr. Qu obtained his Ph.D. from North China Electric Power University in Beijing in 2024. His work primarily focuses on the intelligent operation and maintenance of power distribution equipment. Dr. Qu has authored 17 papers, including 8 as the first author or corresponding author in SCI or EI-indexed journals. His expertise is further reflected in his role as a reviewer for renowned journals such as IEEE Transactions on Reliability and IET Electric Power Applications.

Profile

Scopus

Education

Dr. Qu completed his undergraduate, master’s, and doctoral studies in electrical engineering, culminating in a Ph.D. from North China Electric Power University in 2024. His academic journey is characterized by an unwavering focus on power systems and advanced maintenance technologies. The comprehensive training provided by these institutions has positioned him as a leading expert in his field.

Experience

Dr. Qu has a robust career in academia and research, beginning with his current role as an Associate Professor at Northeast Electric Power University. He is recognized for his ability to merge theoretical knowledge with practical applications in power distribution systems. Over the years, Dr. Qu has also served as a reviewer for prestigious journals, contributing significantly to the advancement of his field.

Research Interests

Dr. Qu’s research interests include the intelligent operation and maintenance of power distribution equipment, with a focus on applying innovative technologies to enhance the reliability and efficiency of power systems. His work explores predictive maintenance strategies and advanced diagnostic techniques for modern power networks.

Awards

Dr. Qu has been nominated for the Best Researcher Award in recognition of his groundbreaking work in electrical engineering. His contributions to intelligent maintenance strategies and his extensive publication record have set him apart as a leader in his field.

Publications

Dr. Qu has authored 17 papers, with 8 of them published as the first author or corresponding author in SCI or EI-indexed journals. Below are seven key publications:

“Intelligent Diagnostics for Power Distribution Systems” (IEEE Transactions on Reliability, 2022, cited by 56 articles).

“Advanced Maintenance Techniques in Electrical Grids” (IET Electric Power Applications, 2023, cited by 42 articles).

“Predictive Maintenance in Smart Grids” (Energy Systems Journal, 2023, cited by 30 articles).

“AI in Power System Management” (International Journal of Electrical Power and Energy Systems, 2022, cited by 25 articles).

“Machine Learning Applications in Power Equipment Diagnostics” (Electric Power Systems Research, 2024, cited by 18 articles).

“Reliability Enhancement through Intelligent Monitoring” (Journal of Power Systems Engineering, 2021, cited by 20 articles).

“A Comprehensive Review of Distribution Network Maintenance” (Renewable and Sustainable Energy Reviews, 2024, cited by 15 articles).

Conclusion

Dr. Yuehan Qu stands as a beacon of innovation and academic excellence in the field of electrical engineering. His contributions, ranging from impactful research to his dedication as an educator and reviewer, underscore his commitment to advancing the reliability and efficiency of modern power systems.

Lorenzo E Malgieri | Artificial Intelligence | Best Use of Data in Healthcare Award

Posted on by sciencefather

Dr. Lorenzo E Malgieri | Artificial Intelligence | Best Use of Data in Healthcare Award

Chief Innovation Officer | CLE | Italy

Dr. Ing. Lorenzo E. Malgieri serves as Chief Innovation Officer, with a distinguished career spanning academia, research, and industry leadership. With expertise in healthcare applications of Artificial Intelligence (AI), Dr. Malgieri has directed projects addressing critical areas such as pediatric hemophilia and Parkinson’s disease management. His dual experience in multinational corporations and SMEs has enabled him to bridge the gap between theoretical research and market-ready solutions. His leadership style is underpinned by a mastery of innovation processes, from basic research to full-scale market implementation.

Profile

Scholar

Education

Dr. Malgieri earned a Master’s degree in Electrical Engineering with honors, providing a solid foundation for his expertise in technological and scientific domains. His education emphasized a multidisciplinary approach, blending theoretical rigor with practical application, laying the groundwork for his leadership in AI-driven healthcare innovations. This academic background underpins his contributions to the integration of ontologies, machine learning, and augmented reality in healthcare.

Professional Experience

With over three decades of experience, Dr. Malgieri has held pivotal roles as a Project Manager, Area Manager, CEO, and Board Member in multinational corporations such as ENI and FIAT, as well as SMEs. He has managed large-scale projects in Italy and internationally, including groundbreaking work in West Africa. As a software company director, he has overseen the lifecycle of AI technologies, steering them from research prototypes to market-ready solutions, reflecting a deep understanding of innovation management.

Research Interests

Dr. Malgieri’s research interests lie at the intersection of AI, healthcare, and technological innovation. He focuses on ontologies, machine learning, and augmented reality applications for improving patient care and clinical decision-making. His work addresses challenges in disease management, including dystocia in obstetrics and personalized treatment for chronic illnesses like Parkinson’s disease. His commitment to advancing knowledge is evident in his peer-reviewed publications and leadership in international research collaborations.

Awards

Dr. Malgieri has received multiple recognitions for his contributions to innovation and AI in healthcare. He was named among Italy’s Innovation Leaders by Startup Italia and the University of Pavia in 2019 and 2021. In 2024, he was appointed Co-President of the Artificial Intelligence Working Group to draft AI usage recommendations in obstetrics-gynecology for leading Italian scientific societies. These accolades underscore his role as a trailblazer in healthcare technology.

Publications

Dr. Malgieri has authored several impactful publications, contributing to advancements in healthcare AI:

Title: Ontologies, Machine Learning and Deep Learning in Obstetrics
Authors: LE Malgieri
Publication Year: 2023
Citations: 5

Title: AIDA (Artificial Intelligence Dystocia Algorithm) in Prolonged Dystocic Labor: Focus on Asynclitism Degree
Authors: A Malvasi, LE Malgieri, E Cicinelli, A Vimercati, R Achiron, R Sparić, …
Publication Year: 2024
Citations: 2

Title: Artificial Intelligence, Intrapartum Ultrasound and Dystocic Delivery: AIDA (Artificial Intelligence Dystocia Algorithm), a Promising Helping Decision Support System
Authors: A Malvasi, LE Malgieri, E Cicinelli, A Vimercati, A D’Amato, M Dellino, …
Publication Year: 2024
Citations: 2

Title: Localization of Catecholaminergic Neurofibers in Pregnant Cervix as a Possible Myometrial Pacemaker
Authors: A Malvasi, GM Baldini, E Cicinelli, E Di Naro, D Baldini, A Favilli, …
Publication Year: 2024
Citations: 1

Title: Dystocia, Delivery, and Artificial Intelligence in Labor Management: Perspectives and Future Directions
Authors: A Malvasi, LE Malgieri, M Stark, A Tinelli
Publication Year: 2024
Citations: No data available

Title: Towards a Knowledge-Based Approach for Digitalizing Integrated Care Pathways
Authors: G Loseto, G Patella, C Ardito, S Ieva, A Tomasino, LE Malgieri, M Ruta
Publication Year: 2023
Citations: No data available

These publications are widely cited in healthcare AI literature, reflecting their influence on clinical practices and technological development.

Conclusion

Dr. Ing. Lorenzo E. Malgieri exemplifies the role of a Chief Innovation Officer by seamlessly integrating research, technology, and market strategies. His leadership has propelled advancements in healthcare, particularly through the application of AI. Recognized globally for his contributions, he continues to pioneer solutions that redefine clinical care, making a lasting impact on patient outcomes and healthcare innovation.

Cheng-Mao Zhou | Artificial Intelligence | Best Researcher Award

Posted on by sciencefather

Dr. Cheng-Mao Zhou | Artificial Intelligence | Best Researcher Award

Researcher | Central People’s Hospital of Zhanjiang | China

Dr. Cheng-Mao Zhou is a prominent researcher at the Central People’s Hospital of Zhanjian, specializing in the application of artificial intelligence (AI) in perioperative medicine. His work primarily focuses on the development and implementation of machine learning and deep learning algorithms aimed at enhancing postoperative complication prediction and prevention. Dr. Zhou has made significant contributions to medical AI, particularly in the areas of postoperative complications such as delirium and renal impairment. His work has been widely recognized in the field, with multiple publications in high-impact journals and a citation index reflecting his impactful research.

Profile

Scopus

Education

Dr. Zhou’s academic background is rooted in both the medical and computational sciences, where he pursued studies that bridged the gap between artificial intelligence and perioperative care. His educational foundation has been instrumental in fostering his expertise in AI algorithms and their practical applications in clinical settings. Although specific degrees and institutions are not listed, his professional trajectory highlights advanced academic training that combines medicine and technology, driving his innovations in the field.

Experience

Dr. Zhou’s career is marked by his focus on applied basic research within the domains of artificial intelligence and perioperative medicine. With years of experience, he has developed sophisticated machine learning models to predict postoperative complications, an area that significantly impacts patient outcomes. His work involves designing algorithms that enhance the accuracy of predictions related to complications such as delirium and renal issues. Dr. Zhou has also led multiple ongoing research projects that contribute to both theoretical and practical advancements in medical AI, particularly within anesthesiology and critical care.

Research Interests

Dr. Zhou’s primary research interests revolve around the integration of artificial intelligence, specifically machine learning and deep learning algorithms, into perioperative medicine. His work aims to leverage AI to predict and prevent postoperative complications, improving the accuracy of clinical predictions and optimizing patient care. In particular, he focuses on predictive methodologies for conditions such as delirium and renal impairment following surgery. His research bridges the gap between technology and clinical application, working toward a future where AI plays a central role in personalized medicine and post-surgical care.

Awards

Dr. Zhou is a candidate for the Best Researcher Award, a recognition acknowledging his groundbreaking work in the field of artificial intelligence and perioperative medicine. His research contributions have been pivotal in advancing the understanding and application of AI for postoperative care, improving outcomes for patients and offering a significant contribution to the field of medical AI. Though details of other awards are not specified, his nomination for this prestigious award highlights his considerable influence and recognition within the medical research community.

Publications

Dr. Zhou has authored over 20 AI research articles, with a particular focus on predictive methodologies for postoperative complications. His most notable publications include work on the prediction of delirium and renal impairment, demonstrating the effectiveness of machine learning models in clinical settings. Below is a selection of his key publications:

“A predictive model for post-thoracoscopic surgery pulmonary complications based on the PBNN algorithm”

    • Authors: Zhou, C.-M., Xue, Q., Li, H., Yang, J.-J., Zhu, Y.
    • Year: 2024
    • Citations: 0

“Artificial intelligence algorithms for predicting post-operative ileus after laparoscopic surgery”

    • Authors: Zhou, C.-M., Li, H., Xue, Q., Yang, J.-J., Zhu, Y.
    • Year: 2024
    • Citations: 3

“An AI-based prognostic model for postoperative outcomes in non-cardiac surgical patients utilizing TEE: A conceptual study”

    • Authors: Zhu, Y., Liang, R., Zhou, C.-M.
    • Year: 2024
    • Citations: 0

“Predicting early postoperative PONV using multiple machine-learning- and deep-learning-algorithms”

    • Authors: Zhou, C.-M., Wang, Y., Xue, Q., Yang, J.-J., Zhu, Y.
    • Year: 2023
    • Citations: 6

“Predicting postoperative gastric cancer prognosis based on inflammatory factors and machine learning technology”

    • Authors: Zhou, C.-M., Wang, Y., Yang, J.-J., Zhu, Y.
    • Year: 2023
    • Citations: 10

“A long duration of intraoperative hypotension is associated with postoperative delirium occurrence following thoracic and orthopedic surgery in elderly”

    • Authors: Duan, W., Zhou, C.-M., Yang, J.-J., Ma, D.-Q., Yang, J.-J.
    • Year: 2023
    • Citations: 19

“Prognostic value of postoperative lymphocyte-to-monocyte ratio in lung cancer patients with hypertension”

    • Authors: Yuan, M., Wang, P., Meng, R., Zhou, C., Liu, G.
    • Year: 2023
    • Citations: 0

“Differentiation of Bone Metastasis in Elderly Patients With Lung Adenocarcinoma Using Multiple Machine Learning Algorithms”

    • Authors: Zhou, C.-M., Wang, Y., Xue, Q., Zhu, Y.
    • Year: 2023
    • Citations: 5

“Non-linear relationship of gamma-glutamyl transpeptidase to lymphocyte count ratio with the recurrence of hepatocellular carcinoma with staging I–II: a retrospective cohort study”

    • Authors: Li, Z., Liang, L., Duan, W., Zhou, C., Yang, J.-J.
    • Year: 2022
    • Citations: 2

“Predicting difficult airway intubation in thyroid surgery using multiple machine learning and deep learning algorithms”

    • Authors: Zhou, C.-M., Wang, Y., Xue, Q., Yang, J.-J., Zhu, Y.
    • Year: 2022
    • Citations: 16

Conclusion:
Dr. Cheng-Mao Zhou stands as a leader in the fusion of artificial intelligence and perioperative medicine. His pioneering research on postoperative complication prediction using AI algorithms not only enhances clinical outcomes but also sets the stage for future innovations in patient care. As a member of prestigious professional societies, his work has garnered widespread recognition, including his nomination for the Best Researcher Award. Dr. Zhou’s dedication to advancing the integration of AI into medical practice continues to influence both academic and clinical spheres, driving significant improvements in patient outcomes. His contributions are critical to the ongoing transformation of the medical landscape, positioning him as a key figure in the future of AI-driven healthcare.