mohammad mohsen sadr | Artificial Intelligence | AI & Machine Learning Award

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Mr. mohammad mohsen sadr | Artificial Intelligence | AI & Machine Learning Award

Assistant Professor of Information Technology at payame noor univercity, Iran

Dr. Mohsen Sadr is a distinguished scholar and industry leader specializing in information science, artificial intelligence, and business technology. With extensive experience in academia, corporate leadership, and research, he has made significant contributions to digital transformation, data science, and machine learning applications. Currently serving as the Vice Chairman and CEO of Navaran Boom Gostar Omid (affiliated with Bank Sepah), he is also an Assistant Professor in the Information Technology Department at Payame Noor University. His work spans across AI-based decision-making, network security, and advanced data analysis, making him a key figure in both academic and professional domains.

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Education

Dr. Sadr has an interdisciplinary academic background, holding a Ph.D. in Information Science. He completed his M.Sc. in Information Technology Engineering at Tarbiat Modares University and earned a B.Sc. in Computer Engineering – Software. Additionally, he pursued a second bachelor’s degree in Law and is currently studying for a master’s degree in Financial Management. His foundational education includes an associate degree in Mathematics from Hamedan.

Experience

Dr. Sadr has held numerous executive and managerial positions in both the public and private sectors. He has served as the CEO and board member of various technology and financial institutions, including Navaran Boom Gostar Omid, RighTel Information Services, and the Financial Technology Services Company of Refah Bank. His leadership extends to the steel, pharmaceutical, and telecommunications industries. Furthermore, he has played a pivotal role in governmental organizations such as Payame Noor University, where he managed IT, public relations, and digital transformation initiatives.

Research Interests

His research primarily focuses on artificial intelligence, machine learning, and digital transformation. Specific interests include fake news detection using deep learning, optimization of wireless sensor networks, webometrics, and knowledge management. He is particularly engaged in the application of AI-driven solutions for decision-making in business and governance, including CRM implementation, sentiment analysis, and network security.

Awards & Recognitions

Dr. Sadr has been recognized for his academic and professional excellence, including:

Outstanding Student Award in Associate Mathematics

Best Lecturer Award at Payame Noor University in 2012

National Best Director Award for exceptional management contributions

Publications

Dr. Sadr has authored several books and research papers in leading journals. Below are some of his notable publications:

Sadr, M.M., & Torkashvand, S. (Year). Coverage Optimization of Wireless Sensor Network Using Learning Automata Techniques. Published in Chemical and Process Engineering.

Sadr, M.M., & Dadstani, M. (Year). Webometrics of Payame Noor University of Iran with Emphasis on Provincial Capital Branches’ Websites. Published in Library Philosophy and Practice.

Sadr, M.M., et al. (Year). A Predictive Model Based on Machine Learning Methods to Recognize Fake Persian News on Twitter. Published in Turkish Journal of Computer and Mathematics Education.

Sadr, M.M., & Akhavan Safar, M. (Year). The Use of LSTM Neural Networks to Detect Fake News on Persian Twitter. Published in Applied Research in Sports Management.

Sadr, M.M., & Asgari, P. (Year). Scientometric Analysis of Research Published in the Journal of Applied Research in Sports Management. Published in Organizational Behavior Management Studies in Sports.

Khani, M., & Sadr, M.M. (Year). A Mapping and Visualization of the Role of Artificial Intelligence in the Sports Industry. Published in Concurrency and Computation: Practice and Experience.

Sadr, M.M., et al. (Year). Deep Reinforcement Learning-Based Resource Allocation in Multi-Access Edge Computing. Published in Transactions on Emerging Telecommunications Technologies.

Conclusion

With his strong academic background, extensive research, publications, AI-driven projects, and contributions to education, Dr. Mohammad Mohsen Sadr is a highly deserving candidate for the Research in AI & Machine Learning Award. His work in fake news detection, deep learning, reinforcement learning, and AI applications in various industries aligns perfectly with the objectives of this prestigious award.

Anna Pokrovskaya | Artificial Intelligence | Best Researcher Award

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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.

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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

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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.

Ouafae El Melhaoui | Machine Learning | Best Researcher Award

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Dr. Ouafae El Melhaoui | Machine Learning | Best Researcher Award

Electronic and System Laboratory National School of Applied Sciences, ENSA Mohammed first University, Morocco

Dr. Ouafae El Melhaoui is a distinguished researcher in the field of electronics and artificial intelligence, specializing in data classification through innovative AI approaches. With extensive experience in teaching and research, she has contributed significantly to the development of machine learning algorithms, deep learning models, genetic optimization techniques, and convolutional neural networks. Her expertise spans various domains, including signal processing, data mining, and fuzzy classification. Dr. El Melhaoui’s academic journey and professional career reflect her commitment to advancing AI-driven methodologies for complex data analysis.

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Education

Dr. El Melhaoui earned her Ph.D. in Electronics with a specialization in artificial intelligence from Mohammed Premier University in 2013. Her doctoral research focused on developing new data classification techniques through advanced signal processing methods. Prior to that, she obtained a Diploma of Advanced Studies (D.E.S.A) in Physics and Technology of Microelectronic Devices and Sensors from Cadi Ayyad University in 2007, where she explored the structural and optical properties of boron nitride. She also holds a Bachelor’s degree in Electronics from Mohammed Premier University, solidifying her strong foundation in electronic systems and computational methodologies.

Professional Experience

Dr. El Melhaoui has an extensive teaching and research background, having worked at various academic institutions. She has supervised numerous undergraduate and graduate projects, focusing on machine learning applications, image processing, and signal analysis. Her professional journey includes collaborations with research laboratories such as LETSER and LETAS, where she contributed to projects in electromagnetism, renewable energy, and electronic systems. She has also been involved in industrial collaborations, developing AI-based solutions for quality control, object recognition, and signal denoising in real-world applications.

Research Interests

Dr. El Melhaoui’s research focuses on artificial intelligence applications in electronics and signal processing. She is particularly interested in computer vision, deep learning, convolutional neural networks, data mining, and optimization algorithms. Her work involves developing novel classification methods for complex data structures, integrating evolutionary computing techniques, and enhancing predictive analytics for diverse applications. Her contributions aim to bridge the gap between theoretical advancements in AI and their practical implementations in engineering and medical diagnostics.

Awards and Recognitions

Dr. El Melhaoui has received several accolades for her research contributions. She has been recognized for her innovative approaches in AI-driven signal processing and has participated in multiple national and international scientific conferences. Her work has been instrumental in advancing knowledge in AI-based classification techniques, earning her a reputation as a leading researcher in her field.

Publications

Novel Classification Algorithm for Complex Class Structures, e-Prime – Advances in Electrical Engineering, Electronics and Energy (Under Review, 2024). Scopus Q1, SJR=0.65.

Hybridization Denoising Method for EMG Signals Using EWT and EMD Techniques, International Journal on Engineering Applications (Under Review, 2024). Scopus Q2, SJR=0.28.

A Novel Signature Recognition System Using a Convolutional Neural Network and Fuzzy Classifier, International Journal of Computational Vision and Robotics (2024). Scopus Q4, SJR=0.21.

Improved Signature Recognition System Based on Statistical Features and Fuzzy Logic, e-Prime – Advances in Electrical Engineering, Electronics and Energy (2024). Scopus Q1, SJR=0.65.

Optimized Framework for Signature Recognition Using Genetic Algorithm, Loci Method, and Fuzzy Classifier, Engineered Science Publisher (2024). Scopus Q1, SJR=0.87.

Design of a Patch Antenna for High-Gain Applications Using One-Dimensional Electromagnetic Band Gap Structures, Engineered Science Publisher (2024). Scopus Q1, SJR=0.87.

Enhancing Signature Recognition Performance through Convolutional Neural Network and K-Nearest Neighbors, International Journal of Technical and Physical Problems of Engineering (2023). Scopus Q3, SJR=0.23.

Conclusion

Dr. Ouafae El Melhaoui’s career exemplifies a strong dedication to research and education in the fields of electronics and artificial intelligence. Her contributions to AI-based classification and signal processing have led to significant advancements in the domain. With a solid academic background, extensive teaching experience, and a robust publication record, she continues to drive innovation in machine learning, deep learning, and AI applications. Her work not only enhances theoretical models but also provides practical solutions to complex engineering problems, making a lasting impact in the field.

Busuyi Akeredolu | Machine Learning | Best Researcher Award

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Busuyi Akeredolu | Machine Learning | Best Researcher Award

Lecturer at Lagos State University of Education, Nigeria

Busuyi E. Akeredolu is an accomplished Earth Scientist and Geospatial Data Analyst with over ten years of experience. His expertise spans mineral exploration, environmental assessments, electrification planning, and groundwater investigation. Akeredolu’s experience encompasses both office and field operations, where he has been instrumental in satellite image analysis, geophysical data processing, and spatial decision support. His professional background also includes providing technical support for various multidisciplinary projects, blending his scientific skills with real-world applications in resource management and environmental sustainability.

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Education

Akeredolu’s academic journey is marked by a solid foundation in geophysics. He is currently pursuing a Ph.D. in Exploration Geophysics at the Federal University of Technology, Akure (FUTA), expected in 2024. He holds an M.Tech. in Exploration Geophysics, also from FUTA (2017), and a B.Sc. in Applied Geophysics from Obafemi Awolowo University (OAU), Ile-Ife (2012). Additionally, he has enhanced his technical skills through certifications such as a Post Graduate Diploma in Project Management and a Certificate in Remote Sensing and GIS, further expanding his interdisciplinary knowledge and capabilities.

Experience

Akeredolu has accumulated extensive professional experience in the geophysical field, including his current role as a Field Geophysicist at Mukolak Geoconsult Nigeria Ltd. Since June 2023, he has conducted magnetic and resistivity data acquisition, processing, and interpretation for mineral exploration projects, contributing to mapping and identifying mineralized zones. His previous roles include serving as a Project Planning Specialist at Protergia Energy Nigeria Ltd. (2022-2023), where he supported off-grid mini-grid electrification projects. Earlier, Akeredolu worked as a Technical Assistant at Bluesquare Belgium (2019-2020), aiding in data management and training for health sector projects. His experience in environmental and geospatial analysis has also been instrumental in environmental assessments and community consultations at Sahel Consult, Nigeria.

Research Interest

Akeredolu’s research interests focus on geophysical methods for groundwater exploration and environmental impact assessments. His work includes applying geophysical data to understand groundwater systems, vulnerability, and aquifer characteristics, as well as studying the impact of environmental factors on mineralization and resource potential. Akeredolu has also delved into the integration of geophysical data with remote sensing techniques to enhance the prediction and management of groundwater resources, particularly in mining areas. His current research aims to develop advanced models for groundwater prediction and resource management using clustering and regression techniques.

Awards

Akeredolu has been recognized for his contributions to geophysics and the environment. His award nominations include the prestigious “Geophysicist of the Year” award by the Society of Exploration Geophysicists (SEG), reflecting his consistent excellence and innovative work in the field. He has also been nominated for awards related to his contributions to sustainable development in environmental science, particularly his work in groundwater resource management and environmental impact assessments.

Publications

Akeredolu, B. E., Adiat, K. A. N., Akinlalu, A. A., Olayanju, G. M., & Afolabi, D.O. (2024). Spatial characterisation of groundwater systems using fuzzy c-mean clustering: A multiparameter approach in crystalline aquifers. Resources, Conservation and Recycling, 100051, ISSN 2211-148, https://doi.org/10.1016/j.rines.2024.100051.

Adegbola, R.B., Whetode, J., Adeogun, O., Akeredolu, B., & Lateef, O. (2023). Geophysical Characterization of the subsurface using Electrical Resistivity Method. Journal of Research and Review in Science, 10, 14-20, DOI: 10.36108/jrrslasu/2202.90.0250.

Akeredolu, B. E., Adiat, K. A. N., Akinlalu, A. A., & Olayanju, G. M. (2022). The Relationship Between Morpho-Structural Features and Borehole Yield in Ilesha Schist Belt, Southwestern Nigeria: Results from Geophysical Studies. Earth Sciences, 11(1), 16-28, doi: 10.11648/j.earth.20221101.13.

Adiat, K. A. N., Akeredolu, B. E., Akinlalu, A. A., & Olayanju, G. M. (2020). Application of logistic regression analysis in prediction of groundwater vulnerability in gold mining environment: a case of Ilesa gold mining area, southwestern, Nigeria. Environmental Monitoring and Assessment, 192(9), doi:10.1007/s10661-020-08532-7.

Adiat, K. A. N., Adegoroye, A. A., Akeredolu, B. E., & Akinlalu, A. A. (2019). Comparative assessment of aquifer susceptibilities to contaminant from dumpsites in different geological locations. Heliyon, 5(5), e01499.

Bawallah, M. A., Akeredolu, B. E., et al. (2019). Integrated Geophysical Investigation of Aquifer and its Groundwater Potential in Camic Garden Estate, Ilorin Metropolis North-Central Basement Complex of Nigeria. IOSR Journal of Applied Geology and Geophysics, 7(2), 01-08.

Akinlalu, A. A., Akeredolu, B. E., & Olayanju, G. M. (2018). Aeromagnetic mapping of basement structures and mineralisation characterisation of Ilesa Schist Belt, Southwestern Nigeria. Journal of African Earth Sciences, 138, 383-389.

Conclusion

Busuyi E. Akeredolu stands as a highly skilled and experienced Earth scientist whose expertise spans geophysical data analysis, mineral exploration, and environmental management. His work has not only contributed to the academic field but has also had a direct impact on practical applications in resource management and environmental sustainability. Akeredolu’s research continues to provide valuable insights into groundwater systems, mineral exploration, and environmental impact assessments, marking him as a leader in his field. His continued commitment to scientific innovation and practical applications will undoubtedly shape the future of Earth sciences and geospatial data analysis.

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

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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

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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.

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.

Penghao Wu | Artificial Intelligence | Best Researcher Award

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Mr. Penghao Wu | Artificial Intelligence | Best Researcher Award

postgraduate | Soochow University | China

Penghao Wu is a dedicated postgraduate student specializing in Control Science and Engineering at Suzhou University, where he is transitioning from the first to the second year of his master’s program. His research centers on explainable neural networks, fault diagnosis in large-scale systems, and multidimensional data analysis, leveraging advanced AI and machine learning methodologies. He has a strong foundation in academic research, evidenced by three high-quality publications and extensive experience with state-of-the-art algorithms. His career goal is to contribute to AI-driven solutions in fields such as large model algorithms, autonomous driving, and data analysis, aligning closely with his expertise.

Profile

Scopus

Education

Penghao Wu began his academic journey with a Bachelor’s degree in Automation from Inner Mongolia University of Technology, graduating in 2023. Excelling academically, he ranked 3rd in his major (top 3%), achieved a GPA of 4.2/5.0, and earned an average credit score of 98.94. Continuing his pursuit of excellence, he joined Suzhou University in 2023 to pursue a master’s degree in Control Science and Engineering. Currently maintaining a GPA of 3.5/4.0 and an average credit score of 87, he has undertaken courses like Advanced Mathematics, Matrix Theory, Modern Control Theory, and Mobile Robot Autonomous Navigation, building a robust technical foundation.

Experience

Penghao Wu has been actively involved in research and development throughout his academic career. His undergraduate graduation project on deep learning-based building change detection algorithms using remote sensing imagery was recognized as one of only three “Outstanding Graduation Designs” in his college. He has also participated in several impactful projects, including vehicle battery fault diagnosis using Variational Mode Decomposition and spiking neural networks for lithium-ion battery fault detection. His practical expertise extends to software systems, having developed a multifunctional intelligent control device awarded a computer software copyright.

Research Interests

Penghao’s research interests revolve around explainable artificial intelligence (XAI), deep learning, and large-scale system fault diagnosis. He focuses on designing interpretable neural network algorithms for critical applications such as autonomous vehicles and aerospace systems. By integrating data-driven approaches with domain knowledge, he aims to enhance the transparency and reliability of AI systems. His work also extends to multidimensional data analysis, with applications in remote sensing and industrial fault detection, underlining his commitment to addressing real-world challenges through cutting-edge technologies.

Awards

Penghao Wu has received multiple accolades for his academic and extracurricular achievements. Notable awards include the Graduate First-Class Scholarship (2023), recognition as an “Outstanding Student” for three consecutive years during his undergraduate studies, and a top-four finish in the CIMC China Intelligent Manufacturing Challenge (university level). His graduation project on remote sensing image analysis earned distinction as one of only three outstanding projects in his college. Additionally, he won third place in the North China University Computer Application Competition.

Publications

Exponential Weighted Moving Average-Based Variational Mode Decomposition Method for Fault Diagnosis of Vehicle Batteries
Published in Data-driven Control and Learning Systems Conference (EI Indexed, 2024).
Cited by: 15 articles.

Data-Driven Spiking Neural Networks for Explainable Fault Detection in Vehicle Lithium-Ion Battery Systems
Under major revision in a Tier-2 SCI journal (2024).
Cited by: 10 articles.

Multi-modal Intelligent Fault Diagnosis for Large Aviation Aircraft Based on Mamba-2
Submitted as an invited article to a Tier-1 SCI journal (2024).
Cited by: 8 articles.

Conclusion

Penghao Wu is a driven researcher and engineer, blending academic excellence with practical expertise in artificial intelligence and control systems. His strong background in fault diagnosis, deep learning, and explainability positions him as an ideal candidate for AI algorithm roles. With a proven track record of research, publications, and accolades, he is poised to make significant contributions to advancing technology in areas such as autonomous systems and intelligent data analysis.

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

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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.