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.

profile

scopus

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.

Tushar Kafare | Artificial Intelligence | Best Researcher Award

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Dr. Tushar Kafare | Artificial Intelligence | Best Researcher Award

Assistant Professor at Sinhgad College of Engineering, India

Dr. Tushar Vaman Kafare is an Assistant Professor in the Department of Electronics and Telecommunication (E&TC) at the Sinhgad Technical Education Society (STES). With over 14 years of experience in teaching, he has made a significant impact in the field of Electronics and Telecommunication. His research and expertise span across machine learning, deep learning, computer vision, embedded systems, and various programming languages like Python, MATLAB, C, and Embedded C. Dr. Kafare is known for his dedication to teaching and research, having guided numerous student projects and published research work, focusing particularly on machine learning applications in plant disease analysis.

Profile

Google Scholar

Education

Dr. Kafare holds an M.E. degree in Electronics and Telecommunication, as well as a B.E. in Electronics. His strong academic background has been further reinforced by his ranking 6th in his graduation. His academic qualifications, combined with extensive practical and theoretical knowledge, make him a highly skilled educator and researcher. His ongoing Ph.D. research focuses on plant disease analysis using machine learning models, showcasing his commitment to advancing technological applications in agriculture.

Experience

Having joined STES on September 7, 2022, Dr. Kafare brings with him a wealth of experience in academia and industry. His teaching career spans over 14 years, during which he has mentored undergraduate and postgraduate students. He has contributed significantly to course development and the enhancement of educational experiences for students, incorporating advanced techniques in machine learning and embedded systems. Additionally, Dr. Kafare has served as a resource person for numerous workshops and faculty development programs, further demonstrating his expertise and commitment to professional growth.

Research Interests

Dr. Kafare’s primary research interest lies in the application of machine learning and image processing for agricultural advancements. His Ph.D. research focuses on using machine learning models to analyze plant diseases, particularly in grape and apple plants, through advanced image processing techniques. He is also interested in deep learning, computer vision, and embedded systems, areas that allow for the development of innovative solutions for real-world problems. Through his research, he aims to contribute to the growing field of agri-tech by leveraging modern computational techniques to assist in plant disease diagnostics and management.

Awards

Dr. Kafare has been recognized for his outstanding contributions in teaching and research. He received the prestigious Digital Teacher Award from ICT Academy, highlighting his exceptional use of technology in education. Additionally, his academic excellence is reflected in his university ranking, securing 6th place in his graduation. In 2024, he was honored with the Best Paper Award at the International Conference on Machine Learning in Jaipur, India, acknowledging the high impact and relevance of his research in the machine learning community.

Publications

Dr. Kafare has made significant contributions to the field of machine learning and telecommunication through his publications. His work has been widely cited, demonstrating the importance of his research. Below is a list of selected publications:

Kafare, T.V. et al., “Analysis on Plant Disease Diagnosis Using Convolution Neural Networks,” International Journal of Machine Learning, 2023, Scopus/SCI.

Kafare, T.V. et al., “Segmentation Techniques for Plant Disease Detection,” Journal of Image Processing, 2022, Scopus.

Kafare, T.V., “Double Convolution in CNN for Improved Plant Disease Classification,” International Conference on Machine Learning, 2024, Conference paper.

Kafare, T.V., et al., “Fungal Disease Detection in Grapes Using Machine Learning,” Journal of Agricultural Technology, 2021, Scopus.

Conclusion

Dr. Tushar Vaman Kafare’s career is marked by his dedication to both teaching and research, with a clear focus on applying machine learning and image processing to solve practical problems in agriculture. With over 14 years of teaching experience, he has proven himself as a skilled educator and researcher. His ongoing Ph.D. research, along with his numerous publications and awards, highlights his expertise in his field. As an active participant in academic and professional activities, he continues to contribute to the development of students and the academic community at large, particularly in the domains of machine learning and embedded systems.

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.

Profile

Orcid

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.

Muhammed Akif Yenikaya | Artificial Intelligence | Best Researcher Award

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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.

Mamoona Humayun | Artificial intelligence | Best Researcher Award

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Dr. Mamoona Humayun | Artificial intelligence | Best Researcher Award

Senior Lecturer at University of Roehampton, United Kingdom

Dr. Mamoona Humayun is a distinguished academician and researcher with over 15 years of experience in teaching and administrative roles across international institutions. She holds a Ph.D. in Computer Sciences from Harbin Institute of Technology, China. Her expertise encompasses artificial intelligence, cybersecurity, predictive analytics, and IoT integration in healthcare. She has authored over 200 publications and secured more than 20 funded research grants, reflecting her commitment to advancing innovation and technology-driven solutions in various domains.

Profile

Google Scholar

Education

Dr. Humayun has an impressive educational background. She earned her Ph.D. in Computer Science from Harbin Institute of Technology, China, in 2014. She holds two master’s degrees: one in Software Engineering from International Islamic University, Islamabad (2011), and another in Computer Science from the same institution (2005). Her academic journey began with a Bachelor of Science in Mathematics from F.G. College for Women, Islamabad, where she graduated with honors in 2002.

Experience

Dr. Humayun has held significant positions throughout her career. She currently serves as a Senior Lecturer at the University of Roehampton, London, UK. Previously, she was an Assistant Professor at Jouf University, Saudi Arabia, where she also coordinated research and accreditation programs. She has served in various roles at PMAS-Arid Agriculture University, Pakistan, and other institutions, contributing extensively to curriculum development, research supervision, and administrative operations.

Research Interests

Dr. Humayun’s research interests lie in artificial intelligence, cybersecurity, healthcare informatics, and IoT systems. She focuses on AI-driven chronic disease management, secure software development, and IoT integration for remote patient monitoring. Her innovative work extends to disability advocacy through AI and predictive analytics for improving healthcare outcomes.

Awards

Dr. Humayun’s accolades include being named a distinguished researcher at Jouf University for 2021-2022. She received the second-best researcher award at the College of Computer and Information Sciences. Additionally, her innovative projects and contributions have garnered recognition across academic and professional platforms.

Publications

“Cyber security threats and vulnerabilities: a systematic mapping study”

  • Year: 2020
  • Citations: 395

“Emerging smart logistics and transportation using IoT and blockchain”

  • Year: 2020
  • Citations: 278

“Internet of things and ransomware: Evolution, mitigation and prevention”

  • Year: 2021
  • Citations: 254

“Detection of skin cancer based on skin lesion images using deep learning”

  • Year: 2022
  • Citations: 208

“Secure healthcare data aggregation and transmission in IoT—A survey”

  • Year: 2021
  • Citations: 204

“Analysis of software development methodologies”

  • Year: 2019
  • Citations: 150

“Blockchain for Internet of Things (IoT) research issues challenges & future directions: A review”

  • Year: 2019
  • Citations: 132

“Energy optimization for smart cities using IoT”

  • Year: 2022
  • Citations: 121

“Cyber security issues and challenges for smart cities: A survey”

  • Year: 2019
  • Citations: 119

“Hybrid smart grid with sustainable energy efficient resources for smart cities”

  • Year: 2021
  • Citations: 117

“Privacy protection and energy optimization for 5G-aided industrial Internet of Things”

  • Year: 2020
  • Citations: 116

Conclusion

Dr. Mamoona Humayun’s exceptional achievements in research, innovation, and academic leadership make her an outstanding candidate for the “Research for Best Researcher Award.” Her contributions have not only advanced her field but also inspired students, peers, and the global research community.

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.

Diana Morales | Deep Learning | Best Researcher Award

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Dr. Diana Morales | Deep Learning | Best Researcher Award

Critical Care Fellow | University of Toronto | Canada

Dr. Diana Morales Castro, MD, MSc, is a renowned Costa Rican physician specializing in critical care medicine, echocardiography, and perioperative medicine. Currently serving as an Adult Critical Care Senior International Fellow at Toronto General Hospital, University Health Network, and University of Toronto, Dr. Morales Castro has an extensive academic and clinical background. With advanced training in critical care, anesthesiology, and echocardiography, her expertise has been shaped by prestigious fellowships and master’s programs in various global institutions, including the University of Toronto and University College London. She has contributed significantly to research in pharmacokinetics, critical care, and echocardiography, publishing in esteemed medical journals. Her dedication to education is evidenced by her role as a mentor for the European Diploma in Advanced Critical Care Echocardiography.

Profile

Scholar

Education

Dr. Morales Castro’s educational background is rooted in excellence and dedication to advancing medical knowledge. She graduated with a Licentiate in Medicine and Surgery from the University of Costa Rica in 2011, followed by a Specialty in Anesthesiology and Recovery in 2015 from the same institution. Seeking to deepen her knowledge in critical care, she completed a Master in Perioperative Medicine at University College London in 2018. Her journey continued with a series of fellowships, including the Adult Critical Care Medicine Fellowship and Adult Critical Care Echocardiography Fellowship at the University of Toronto in 2018 and 2020, respectively. Dr. Morales Castro further expanded her expertise by pursuing a Master in Pharmaceutical Sciences at the University of Toronto, which she is expected to complete in 2024.

Experience

Dr. Morales Castro’s clinical experience spans across several high-profile institutions in Costa Rica and Canada. She began her career as a General Physician at the El Caoba EBAIS in Costa Rica, where she served in mandatory social service. She then advanced to become an Attending Anesthesiologist at Trauma Hospital and Hospital Calderón Guardia, before further specializing in adult critical care at the University of Toronto. Her role as an Attending Intensivist at the National Transplant and ECMO Center in Costa Rica was a significant milestone, where she provided critical care to patients undergoing complex treatments like ECMO. Currently, she balances her work as an attending physician with her position as a mentor for advanced critical care echocardiography at the European Society of Intensive Care Medicine.

Research Interests

Dr. Morales Castro’s research primarily focuses on pharmacokinetics and pharmacodynamics in critically ill patients, particularly those undergoing extracorporeal membrane oxygenation (ECMO). Her work delves into optimizing sedative and anesthetic pharmacokinetics during critical illness and exploring the role of therapeutic drug monitoring for drugs like propofol and fentanyl in patients on ECMO. She also investigates the impact of echocardiography and ultrasound techniques in the management of critically ill patients, with a special interest in COVID-19-related complications. Her work not only contributes to improving clinical outcomes but also advances the education of healthcare providers through innovative teaching methods like self-learning videos in transthoracic echocardiography.

Awards

Dr. Morales Castro has received numerous accolades throughout her career, recognizing her excellence in research, education, and clinical care. She was awarded the 2023 Allan Spanier Award for the best education study on simulator-based echocardiography training. In 2022, she received the MD Program Teaching Award of Excellence from the Temerty Faculty of Medicine at the University of Toronto. Her dedication during the COVID-19 pandemic was recognized with a certificate from the Costa Rican Social Security. Further demonstrating her academic prowess, she received honors for her master’s degree in perioperative medicine from University College London in 2019 and honors for her specialty in anesthesiology from the University of Costa Rica in 2015.

Publications

Dr. Morales Castro has authored several impactful publications in leading medical journals, reflecting her research contributions in critical care and pharmacokinetics. Key publications include:

Morales Castro D, Wong I, Panisko D, Najeeb U, Douflé G. Self-Learning Videos in Focused Transthoracic Echocardiography Training. Clin Teach. 2025 Feb;22(1):e70014.

Morales Castro D, Balzani E, Abdul-Aziz MH, et al. Propofol and Fentanyl Pharmacokinetics and Pharmacodynamics in Extracorporeal Membrane Oxygenation. Annals of the American Thoracic Society. 2025;22(1):121-9.

Morales Castro D, Granton J, Fan E. Ceftobiprole and Cefiderocol for Patients on Extracorporeal Membrane Oxygenation: The Role of Therapeutic Drug Monitoring. Current Drug Metabolism. 2024;25:1-5.

Morales Castro D, Ferreyro B.L., McAlpine D, et al. Echocardiographic Findings in Critically Ill COVID-19 Patients Treated with and Without ECMO. J Cardiothorac Vasc Anesth. 2024.

Douflé G, Dragoi L, Morales Castro D, et al. Head-to-Toe Bedside Ultrasound for ECMO Patients. Intensive Care Med. 2024.

Morales Castro D, Dresser L, Granton J, Fan E. Pharmacokinetic Alterations in Critical Illness. Clin Pharmacokinet. 2023; 62(2):209-220.

Morales Castro D, Abdelnour-Berchtold E, Urner M, et al. Transesophageal Echocardiography-Guided ECMO Cannulation in COVID-19. J Cardiothorac Vasc Anesth. 2022;36(12):4296-4304.

Conclusion

Dr. Diana Morales Castro stands out as a dedicated physician, educator, and researcher with a profound impact on the fields of critical care medicine and pharmacokinetics. Through her academic achievements, clinical experience, and innovative research, she has contributed to improving the quality of care in critical settings, especially for patients undergoing complex treatments like ECMO. Her commitment to education and mentorship further elevates the standards of healthcare. As she continues to explore the intersections of critical care, pharmacokinetics, and echocardiography, Dr. Morales Castro’s work promises to shape the future of intensive care and pharmacological management in critically ill patients.

Amir veisi | Artificial Intelligence | Best Researcher Award

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Dr. Amir veisi | Artificial Intelligence | Best Researcher Award

PhD | Bu-Ali Sina University | Iran

Amir Veisi is a dedicated PhD student specializing in Control Engineering at Bu-Ali Sina University, Hamedan, Iran, under the guidance of Dr. Hadi Delavari. With a strong academic foundation, he has cultivated expertise in nonlinear fractional-order systems, renewable energy, and artificial intelligence. His research primarily revolves around advanced control methods, such as data-driven and fault-tolerant controls, applied to renewable energy and biomedical systems. Amir is also an award-winning researcher with a notable record of publications in esteemed journals, reflecting his commitment to innovation and knowledge dissemination in control engineering.

Profile

Scholar

Education

Amir began his academic journey with a Bachelor of Science in Electronic Engineering at Islamic Azad University, Zahedan, graduating in 2017. He pursued a Master of Science in Control Engineering at Hamedan University of Technology, completing his thesis on fractional-order sliding mode control for wind turbines in 2021. Currently, he is pursuing a PhD in Control Engineering at Bu-Ali Sina University. His doctoral research focuses on developing nonlinear fractional-order data-driven controllers for complex nonlinear systems.

Experience

Amir’s academic and professional experiences highlight his deep involvement in control systems and engineering education. As a teaching assistant at Hamedan University of Technology, he contributed to courses on linear control systems, providing valuable insights to students. Additionally, Amir worked as an electronic board repair instructor at Pishtaz Electronic Company from 2013 to 2018, bridging theoretical concepts with practical applications. His work demonstrates a seamless integration of academic knowledge and hands-on expertise.

Research Interests

Amir’s research interests span a range of cutting-edge topics in control engineering and related fields. He is deeply invested in renewable energy systems, artificial intelligence, machine learning, reinforcement learning, and data-driven control. His expertise extends to fractional-order nonlinear control, fault-tolerant control, and real-time systems. Amir’s commitment to advancing knowledge in estimation and control of nonlinear dynamic systems reflects his vision for a sustainable and technologically advanced future.

Awards

Amir has received several prestigious accolades throughout his career. He was honored as the best researcher of the year at Hamedan University in 2021 and at Bu-Ali Sina University in 2022. His work on fractional-order nonlinear controllers earned him the best paper award at the 2023 International Conference on Technology and Energy Management (ICTEM). Amir also serves as a reviewer for reputed journals, including Springer Nature, Elsevier, and others, contributing significantly to the academic community.

Publications

Amir Veisi has authored several impactful papers in renowned journals and conferences:

Robust control of a permanent magnet synchronous generators based wind energy conversion
Authors: H Delavari, A Veisi
Year: 2021
Citations: 14

Adaptive fractional order control of photovoltaic power generation system with disturbance observer
Authors: A Veisi, H Delavari
Year: 2021
Citations: 11

A new robust nonlinear controller for fractional model of wind turbine based DFIG with a novel disturbance observer
Authors: H Delavari, A Veisi
Year: 2024
Citations: 10

Adaptive optimized fractional order control of doubly‐fed induction generator (DFIG) based wind turbine using disturbance observer
Authors: A Veisi, H Delavari
Year: 2024
Citations: 10

Fractional‐order backstepping strategy for fractional‐order model of COVID‐19 outbreak
Authors: A Veisi, H Delavari
Year: 2022
Citations: 8

Adaptive fractional backstepping intelligent controller for maximum power extraction of a wind turbine system
Authors: A Veisi, H Delavari
Year: 2023
Citations: 5

Maximum power point tracking in a photovoltaic system by optimized fractional nonlinear controller
Authors: A Veisi, H Delavari, F Shanaghi
Year: 2023
Citations: 5

Power Maximization of Wind Turbine Based on DFIG using Fractional Order Variable Structure Controller
Authors: H Delavari, A Veisi
Year: 2021
Citations: 5

Fuzzy-type 2 fractional fault tolerant adaptive controller for wind turbine based on adaptive RBF neural network observer
Authors: A Veisi, H Delavari
Year: 2024
Citations: 4

Fuzzy fractional-order sliding mode control of COVID-19 virus variants
Authors: H Delavari, A Veisi
Year: 2023
Citations: 4

Conclusion

Amir Veisi’s journey in control engineering exemplifies his dedication to solving complex challenges through innovative research and application-driven solutions. His contributions to renewable energy systems, artificial intelligence, and control systems reflect his commitment to addressing pressing global issues. As a scholar and practitioner, Amir continues to push boundaries, inspiring both academic and industrial advancements in his field.

Cheng-Mao Zhou | Artificial Intelligence | Best Researcher Award

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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.

Jalel Euchi | AI in Healthcare | Best Researcher Award

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Assist. Prof. Dr. Jalel Euchi | AI in Healthcare | Best Researcher Award

Assistant professor | University of Sfax | Tunisia

Dr. Jalel Euchi is an accomplished academic and researcher specializing in operations research, optimization, and transportation systems. He currently serves as a faculty member at ISGI, Sfax University’s Department of Operations Management, and ISAE, Gafsa University’s Department of Economic Quantitative Methods and Informatics in Tunisia. With a Ph.D. in quantitative methods jointly awarded by Sfax University in Tunisia and Le Havre University in France in 2011, Dr. Euchi has built an illustrious career in academia and research. His work addresses critical challenges in transportation, logistics, and operational efficiency, contributing significantly to the scientific community through publications in high-impact journals and active involvement as a referee and editorial board member.

Profile

Scopus

Education

Dr. Euchi’s academic journey showcases his strong foundation in quantitative methods and operations research. He completed his Ph.D. in 2011, focusing on optimization and transportation problems. He earned his Master’s degree in Production Management and Operational Research in 2007 and a Bachelor’s degree in Operational Research in 2005, both from Sfax University. In 2017, he received an HDR (Habilitation) degree, qualifying him as an associate research professor, further underscoring his expertise in his field.

Experience

Dr. Euchi’s professional experience spans over 15 years in academia and research. He has held teaching positions at various prestigious institutions, including ISGI, Sfax University, and Qassim University in Saudi Arabia. His courses have covered diverse subjects such as optimization, data analysis, operations management, and statistics. In addition to his teaching responsibilities, he has been deeply involved in research, mentoring, and administrative roles, making significant contributions to his departments and institutions.

Research Interests

Dr. Euchi’s research focuses on operations research, optimization, logistics, and transportation. His studies delve into stochastic and distributed optimization, the environmental impacts of transport, and advanced logistics solutions such as routing and scheduling. Recently, he has expanded his research interests to include machine learning and its applications in transportation, exploring innovative solutions for challenges like electric vehicle routing and drone logistics.

Awards

Dr. Euchi has been recognized for his contributions to the field through several awards and nominations. His innovative research and dedication to academic excellence have earned him invitations to international conferences, editorial roles in reputed journals, and accolades for his impactful publications.

Publications

Dr. Euchi has authored numerous high-impact articles in journals and conferences. Here are seven selected works:

Belkhamsa, M., Euchi, J., Siarry, P. (2024). Optimizing Elective Surgery Scheduling Amidst the COVID-19 Pandemic Using Artificial Intelligence Strategies. Swarm and Evolutionary Computation, 90, 101690.

Masmoudi, M., Euchi, J., Siarry, P. (2024). Home healthcare routing and scheduling: Operations research approaches and contemporary challenges. Annals of Operations Research, 1-51.

Sadok, A., Euchi, J., Siarry, P. (2024). Vehicle routing with multiple UAVs for last-mile logistics distribution problem: Hybrid distributed optimization. Annals of Operations Research.

Euchi, J., Sadok, A. (2023). Optimising the travel of home health carers using a hybrid ant colony algorithm. Proceedings of the Institution of Civil Engineers-Transport, 176(6), 325-336.

Hamdi, F., Euchi, J., Messaoudi, L. (2023). A fuzzy stochastic goal programming for selecting suppliers in case of potential disruption. Journal of Industrial and Production Engineering, 40(8), 677-691.

Euchi, J., Zidi, S., Laouamer, L. (2021). A new distributed optimization approach for home healthcare routing and scheduling problem. Decision Science Letters, 10(3), 217-230.

Euchi, J., Sadok, A. (2020). Hybrid genetic-sweep algorithm to solve the vehicle routing problem with drones. Physical Communication, 44, 101236.

Conclusion

Dr. Jalel Euchi exemplifies excellence in academia and research, combining extensive experience, a robust educational background, and pioneering research interests. His contributions to optimization and logistics have practical applications in addressing modern transportation and environmental challenges. Through his publications and professional activities, Dr. Euchi continues to inspire and influence the field of operations research globally.