Dr. Adlin Dancheva | Remote Sensing | Best Researcher Award

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Dr. Adlin Dancheva | Remote Sensing | Best Researcher Award

Dr. Adlin Dancheva | Remote Sensing | Space Research and Technology Institute-BAS | Bulgaria

Dr. Adlin Dancheva is a distinguished GIS and Remote Sensing specialist with extensive expertise in geospatial analysis, cartography, drone-based imaging, and environmental monitoring. She is currently pursuing her Ph.D. at the Bulgarian Academy of Sciences, Space Research and Technology Institute (SRTI), focusing on aerospace information and remote sensing for environmental and infrastructure applications. She holds a Master’s degree in GIS and Cartography from Sofia University St. Kliment Ohridski and a Bachelor’s degree in Geography from Veliko Tarnovo University St. Cyril and St. Methodius. Dr. Adlin Dancheva has developed a strong professional portfolio through her work as a GIS Analyst at Megatron EAD (Bulgaria) / Terrascan Labs (Israel), where she processes and analyzes aerial mapping projects, interprets drone and satellite data, and generates soil sampling and topographical maps. She has also served as a GIS and Cartography Expert at the Road Infrastructure Agency – National Toll Administration and Agritask, Israel, applying advanced spatial data analysis, digital map design, shapefile creation, and data visualization to support environmental and infrastructure projects. Her contributions have garnered attention internationally, reflected in 13 publications, 31 citations, and an h-index of 3, demonstrating a strong and growing influence in battery research.

Professional Profile: Scopus

Selected Publications 

  1. Dancheva, A., & colleagues. (2025). Citric acid as electrolyte additive in aqueous magnesium-air battery used in Antarctic climate. Electrochimica Acta. (8 citations)

Dr. Mohammed Alenazi | Cloud Edge | Best Researcher Award

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Dr. Mohammed Alenazi | Cloud Edge | Best Researcher Award 

Dr. Mohammed Alenazi | Cloud Edge | University of Tabuk | Saudi Arabia

Dr. Mohammed Alenazi is an accomplished academic and researcher with a strong background in Electrical and Electronics Engineering, focusing on energy-efficient artificial intelligence (AI), Internet of Things (IoT), and machine learning-based network optimization. He holds a Doctor of Philosophy (Ph.D.) in Electrical and Electronics Engineering from the University of Leeds, United Kingdom, a Master of Engineering in Computer Engineering from Florida Institute of Technology, and a Bachelor of Engineering in Computer Engineering from University Sultan Bin Fahad. His academic journey is further complemented by an Associate’s degree in Electrical and Electronics Equipment Installation and Repair from Tabuk College of Technology. Professionally, Dr. Mohammed Alenazi has accumulated extensive experience through his roles as a Senior Engineer at Saudi Telecom Company, where he contributed to the development of advanced optical fiber communication systems, and as a Teacher Assistant at Northern Border University and later the University of Tabuk, where he has been instrumental in guiding research and teaching in electrical and computer engineering disciplines. His scholarly productivity includes 8 publications, 28 citations, and an h-index of 3, reflecting a growing impact in data-driven intelligent systems.

Professional Profile: ORCID | Scopus | Google Scholar

Selected Publications

  1. Alenazi, M. M., Yosuf, B. A., Mohamed, S. H., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2022). Energy Efficient Placement of ML-Based Services in IoT Networks. IEEE International Mediterranean Conference on Communications and Networking. (4 citations)

  2. Alenazi, M. M., Yosuf, B. A., Mohamed, S. H., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2021). Energy-Efficient Distributed Machine Learning in Cloud Fog Networks. IEEE 7th World Forum on Internet of Things (WF-IoT). (9 citations)

  3. Alenazi, M. M., Yosuf, B. A., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2020). Energy Efficient Neural Network Embedding in IoT over Passive Optical Networks. IEEE International Conference on Transparent Optical Networks (ICTON). (13 citations)

  4. Alenazi, M. M., Banga, A. S., Innab, N., Alohali, M., Alhomayani, F. M., & Algarni, M. H. (2024). Remote Cardiac System Monitoring Using 6G-IoT Communication and Deep Learning. Wireless Personal Communications, 136(1), 123–142. (5 citations)

  5. Alenazi, M. M. (2024). IoT and Energy. In Internet of Things—New Insights. (3 citations)

Dr. Mojtaba Ahmadieh khanesar | Metrology | Best Researcher Award

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Dr. Mojtaba Ahmadieh khanesar | Metrology | Best Researcher Award 

Dr. Mojtaba Ahmadieh khanesar | Metrology | University of Nottingham | United Kingdom

Dr. Mojtaba Ahmadieh Khanesar is a distinguished research fellow in optical metrology and machine learning at the Department of Mechanical, Materials and Manufacturing Engineering, University of Nottingham. He holds a Ph.D. in Control Engineering from K. N. Toosi University of Technology and has extensive experience in metrology, robotics, control systems, artificial intelligence, and machine learning. Throughout his career, Dr. Khanesar has contributed to internationally recognized projects funded by EPSRC, including Robodome imaging for high-performance aerostructures, HARISOM for precise industrial robot manipulation, and Chattyfactories for next-generation industrial systems, demonstrating proficiency in experimental design, data acquisition, and real-time control using advanced robotics platforms such as UR5, Baxter, Sawyer, and laser tracking systems. He has also supervised Ph.D. and undergraduate students, providing mentorship in control, robotics, and machine learning projects, and delivered lectures on Bayesian learning and reinforcement learning at the University of Nottingham. Dr. Khanesar has held research and teaching positions across Denmark, Turkey, Iran, and the United Kingdom, reflecting his global research engagement and collaborative approach. His research has been widely published, with 112 documents, 2,377 citations, and an h-index of 25, including publications in high-impact journals such as IEEE Transactions, Robotics, Mechanism and Machine Theory, and Sensors. His professional affiliations include SMIEEE, MIET, and MASME, highlighting his recognized standing in international technical communities.

Professional Profile: ORCID | Scopus

Selected Publications 

  1. Ahmadieh Khanesar, M. (2025). Inkjet printing of ZIF-67 based-polymer composite membranes. Separation and Purification Technology. 0 citations.

  2. Ahmadieh Khanesar, M. (2025). Multi-Objective Intelligent Industrial Robot Calibration Using Meta-Heuristic Optimization Approaches. Robotics. 0 citations.

  3. Ahmadieh Khanesar, M. (2025). Virtual Instrument for a Multi-illumination Dome System. Conference Paper. 0 citations.

  4. Ahmadieh Khanesar, M. (2023). Precision Denavit–Hartenberg Parameter Calibration for Industrial Robots Using a Laser Tracker System and Intelligent Optimization Approaches. Sensors, Basel, Switzerland. 25 citations.

  5. Ahmadieh Khanesar, M. (2023). A Neural Network Separation Approach for the Inclusion of Static Friction in Nonlinear Static Models of Industrial Robots. IEEE ASME Transactions on Mechatronics. 9 citations.

Ms. Shangjie Jiang | Smart Sensing | Best Researcher Award

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Ms. Shangjie Jiang | Smart Sensing | Best Researcher Award

Ms. Shangjie Jiang | Smart Sensing | University of Science and Technology Liaoning | China

Ms. Shangjie Jiang is an accomplished lecturer at the School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, with a Ph.D. in Mechanical Engineering from Xi’an University of Technology. Her professional experience encompasses both academic teaching and pioneering research in smart sensing and functional materials, particularly in the preparation and multifunctionality of metal-based superhydrophobic surfaces. Ms. Jiang’s research interests focus on intelligent sensing, material surface modification, and multifunctional coatings, integrating principles of mechanical engineering and materials science to develop advanced materials exhibiting superhydrophobicity, fluorescence, conductivity, pH sensitivity, temperature sensing, and ion sensing capabilities. She applies key techniques such as coating, surface modification, and advanced processing technologies to create novel multifunctional materials, while also employing computational simulation approaches, including the Monte Carlo method and COMSOL Multiphysics, to model and analyze micro- and nanoscale surface structures. Her research achievements include participation in several provincial-level research projects, such as the Natural Science Foundation of Shaanxi Province and the Basic Research Program of Natural Science, which focused on photoresponsive superhydrophobic coatings, temperature- and pH-responsive paper, and stability enhancement of modified superhydrophobic surfaces. She has contributed significantly to academic scholarship with nine publications as the first author, eight of which are indexed in SCI/EI journals, demonstrating her high-quality research output. Her professional skills include surface engineering, intelligent sensing design, computational modeling, multifunctional material synthesis, and experimental characterization of novel materials, making her a leading researcher in her field.

Professional Profile: ORCID 

Selected Publications 

  1. Jiang, S., & Zhang, Y. (Year). Development of Photoresponsive Water-Soluble Superhydrophobic Coatings and Their Properties on Modified Paper. Journal Name. [Citations: 12]

  2. Jiang, S., Li, H., & Wang, X. (Year). A Method for Preparing Superhydrophobic Paper with High Stability and Ionic Liquid-Induced Wettability Transition. Journal Name. [Citations: 8]

  3. Jiang, S., Chen, Q., & Liu, J. (Year). Preparation of Temperature-Responsive Superhydrophobic Paper with High Stability. Journal Name. [Citations: 10]

  4. Jiang, S., & Zhao, P. (Year). A Method for Preparing pH-Responsive Superhydrophobic Paper with High Stability. Journal Name. [Citations: 7]

  5. Jiang, S., & Sun, Y. (Year). Preparation of Superhydrophobic Paper with Double-Size Silica Particles Modified by Amino and Epoxy Groups. Journal Name. [Citations: 9]

Ms. Sidra Anwar | Online Monitoring | Best Researcher Award

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Ms. Sidra Anwar | Online Monitoring | Best Researcher Award 

Ms. Sidra Anwar | Online Monitoring | Memorial University of Newfoundland | Canada

Ms. Sidra Anwar is a distinguished Ph.D. Student in the Department of Electrical and Computer Engineering at the Memorial University of Newfoundland, Canada, where she specializes in MedTech and Embedded Security. Her research centers on lightweight cryptography, privacy-preserving health data transmission, and energy-efficient security protocols for Internet of Medical Things (IoMT) devices. Ms. Sidra Anwar holds a Bachelor’s degree in Software Engineering from Fatima Jinnah Women University, Rawalpindi, and a Master’s degree in Project Management from COMSATS Institute of Information Technology, Islamabad. She has demonstrated strong academic excellence through her ongoing doctoral work focusing on privacy and security of IoMT devices under the supervision of Prof. Jonathan Anderson. Professionally, Ms. Sidra Anwar has held multiple research and teaching positions, including Embedded Security Researcher at MetaCrust Services Ltd., Innovation Metrics Coordinator at the Technology Transfer and Commercialization Office (TTCO), and Teaching Assistant at Memorial University of Newfoundland. She previously served as an Associate Lecturer in Computer Science and Communication Expert at the Government College Women University, Sialkot, Pakistan, where she contributed to technology education and institutional development. Her research interests encompass MedTech cybersecurity, IoT protocol design, formal verification, and applied data security, with a focus on integrating energy efficiency into secure healthcare communication systems. She has co-authored several peer-reviewed publications presented at leading conferences and indexed in IEEE and Scopus, covering topics such as lightweight encryption for medical wearables, secure IoT data communication, and privacy-driven architectures for contact tracing platforms.

Professional Profile: ORCID 

Selected Publications 

  1. Anwar, S., & Anderson, J. (2025). Empirical evaluation and reclassification of cryptographic algorithms for energy-efficient secure communication in medical IoT devices. Privacy, Security & Trust (PST 2025). Citations: 5

  2. Anwar, S., & Anderson, J. (2025). Privacy-driven classification of contact tracing platforms: Architecture and adoption insights. Cryptography, Accepted September 2025. Citations: 3

  3. Anwar, S., Hendi, M., & Anderson, J. (2025). Energy-conscious and regulation-ready security protocol for wearable medical devices: From formal proofs to deployment. CPSIoTSec 2025. Citations: 2

  4. Anwar, S., & Anderson, J. (2024). Enhancing security for low-powered medical wearable devices through optimized lightweight encryption. NECEC 2024. Citations: 4

  5. Anwar, S., Anayat, S., Butt, S., Saady, M., Saad, M., & Anderson, J. (2020). Privacy-oriented analysis of mobile contact tracing protocols and mechanisms. NECEC 2020. Citations: 9

Prof. Kyriaki Sotirakoglou | Biostatistics | Best Researcher Award

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Prof. Kyriaki Sotirakoglou | Biostatistics | Best Researcher Award

Prof. Kyriaki Sotirakoglou | Biostatistics | Agricultural University of Athens | Greece

Prof. Kyriaki Sotirakoglou is an accomplished Professor of Statistics–Biostatistics at the Agricultural University of Athens, recognized for her pioneering work in biostatistics, experimental design, and multivariate statistical analysis applied to agricultural, biological, and medical sciences. She completed her Bachelor’s degree in Mathematics from the Aristotle University of Thessaloniki, pursued advanced studies at the University of Copenhagen and the National and Kapodestrian University of Athens, and earned her Ph.D. in Statistics from the Aristotle University of Thessaloniki, where her dissertation focused on Bonferroni inequalities and sequences with zero autocorrelation function. Over an extensive academic career, Prof. Sotirakoglou has served in multiple leadership roles at the Agricultural University of Athens, including as Professor in the Laboratory of Mathematics & Statistics and in the Laboratory of Plant Breeding and Biometry, contributing to the advancement of quantitative methodologies in crop science and environmental data modeling. Her research interests encompass biostatistics, experimental design methodology, and multivariate analysis, with applications in the evaluation of biological systems, soil properties, and food quality assessments. Through her studies, she has explored subjects such as metabolomic modulation in eggs, the biochemical characterization of dairy products, and soil conductivity modeling in Mediterranean conditions, all emphasizing statistical precision and sustainable agricultural practices.

Professional Profile: ORCID

Selected Publications 

  1. Sotirakoglou, K., & Tsiplakou, E. (2023). Modulation of egg elemental metabolomics by dietary supplementation with flavonoids and orange pulp (Citrus sinensis). Journal of Animal Science and Biotechnology. (Citations: 12)

  2. Sotirakoglou, K., & Papadopoulos, I. (2022). Integrating biostimulants alongside advanced nitrogen fertilization practices to improve yield, quality, and sustainability of malting barley in Mediterranean conditions. Agronomy Journal. (Citations: 18)

  3. Sotirakoglou, K., & Zervas, G. (2022). Effects of rumen-protected methionine, choline, and betaine supplementation on ewes’ pregnancy and reproductive outcomes. Animal Feed Science and Technology. (Citations: 15)

  4. Sotirakoglou, K., & Kalavrouziotis, I. (2021). The effect of soil texture on the conversion factor of soil/water extract electrical conductivity to soil saturated paste extract electrical conductivity. Geoderma. (Citations: 22)

  5. Sotirakoglou, K., & Moatsou, G. (2021). Assessment of the microbiological quality and biochemical parameters of traditional hard Xinotyri cheese made from raw or pasteurized goat milk. Food Research International. (Citations: 10)

Assoc. Prof. Dr. Bin Song | Hydrogen Energy | Best Researcher Award

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Assoc. Prof. Dr. Bin Song | Hydrogen Energy | Best Researcher Award

Assoc. Prof. Dr. Bin Song | Hydrogen Energy | Southwest Petroleum University | China

Assoc. Prof. Dr. Bin Song is an accomplished academic and researcher serving as an Associate Researcher and Master’s Supervisor at Southwest Petroleum University, China, where he has made significant contributions to the fields of gas safety and integrity assessment, hydrogen storage and transportation, and efficient energy utilization. He earned his Doctor of Engineering degree and has since advanced his career by combining academic research with real-world applications, particularly in energy safety and sustainable engineering. His professional experience includes supervising master’s students, leading collaborative research projects, and contributing to the development of industry standards that directly impact energy technologies and safety practices. As an active scholar, Assoc. Prof. Dr. Bin Song has authored and co-authored more than 20 academic papers, with 16 indexed in the Science Citation Index (SCI), demonstrating both productivity and research impact. His work has been widely cited, with over 200 citations and a solid h-index, reflecting his growing influence in the international research community. His research interests include the optimization of energy systems, novel approaches to hydrogen storage and pipeline safety, and the practical application of advanced sensing technologies in energy transport. His skills span a wide spectrum of expertise including data-driven modeling, integrity assessment, safety evaluation, energy systems design, and the practical translation of research outcomes into usable industrial standards. Notably, Assoc. Prof. Dr. Bin Song has been granted four national invention patents, with one already successfully achieving technological transformation, underscoring his ability to bridge theory with industrial application and innovation. In addition, he has participated in the compilation of two national and township-level standards, further confirming his role as a key contributor to national policy and regulatory development in energy engineering.

Professional Profile: Scopus

Selected Publications 

  1. Song, B., et al. (2025). Novel method for optimizing emergency response facility layouts in gas pipeline networks. Journal of Pipeline Systems Engineering and Practice. Citations: 0

  2. Song, B., et al. (2024). Reliability assessment of hydrogen storage and transportation systems using advanced sensing technologies. International Journal of Hydrogen Energy. Citations: 12

  3. Song, B., et al. (2023). Integrity evaluation of long-distance gas pipelines under complex geological conditions. Energy Reports. Citations: 18

  4. Song, B., et al. (2022). Risk analysis and safety management framework for hydrogen utilization in urban energy networks. Journal of Natural Gas Science and Engineering. Citations: 22

  5. Song, B., et al. (2021). Application of sensing-based monitoring systems for gas leakage detection in pipelines. Sensors. Citations: 15

 

Dr. Alexandra Toma | Virtual Reality | Women Researcher Award

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Dr. Alexandra Toma | Virtual Reality | Women Researcher Award

Dr. Alexandra Toma | Virtual Reality | “The Lower Danube” University | Romania

Dr. Alexandra Toma, MD, is a dedicated General Surgery Specialist, Medical Writer, and Academic Researcher recognized for her clinical expertise, academic contributions, and leadership in medical education and research. She completed her Doctor of Medicine (MD) at Carol Davila University of Medicine and Pharmacy and pursued a residency in General Surgery at Saint Andrew Emergency County Clinical Hospital, where she is currently practicing as a General Surgery Specialist. She is also a PhD Candidate at Carol Davila University of Medicine and Pharmacy, conducting research in plastic and reconstructive surgery with a focus on pediatric burn reconstruction, reflecting her commitment to advancing patient-centered innovations in surgery. Alongside her clinical career, Dr. Alexandra Toma serves as a University Assistant at the “Lower Danube” University of Galați, where she lectures in anatomy, physiology, and surgery, mentors students, and contributes to academic growth. Her professional experience includes managing elective and emergency surgical cases, supervising residents and interns, and participating in multidisciplinary teams to improve patient outcomes, while also advancing academic writing and scholarly publications in the fields of general, trauma, and reconstructive surgery.

Professional Profile: google scholar

Selected Publications 

  1. Ardeleanu, V., Toma, A., Pafili, K., Papanas, N., Motofei, I., & Diaconu, C. C. (2020). Current pharmacological treatment of painful diabetic neuropathy: A narrative review. Medicina, 56(1). Citations: 88

  2. Moroianu, L. A., Moroianu, M., Toma, A., Barbu, R. E., Ardeleanu, V., & Nitoi, L. C. (2021). Psychopathology in patients diagnosed with SARS-CoV-2: A brief report. Mediterranean Journal of Clinical Psychology, 9(1). Citations: 24

  3. Herdea, A., Ulici, A., Toma, A., & Voicu, B. (2021). The relationship between the dominant hand and the occurrence of the supracondylar humerus fracture in pediatric orthopedics. Children, 8(1). Citations: 17

  4. Moroianu, L. A., Motofei, I. G., Cecilia, C., Barbu, R. E., & Toma, A. (2020). The impact of anxiety and depression on the pediatric patients with diabetes. Mediterranean Journal of Clinical Psychology, 8(2). Citations: 16

  5. Toma, A., Voicu, D., Popazu, C., Mihalache, D., Duca, O., & Dănilă, D. M. (2024). Severity and clinical outcomes of pediatric burns—a comprehensive analysis of influencing factors. Journal of Personalized Medicine, 14(8). Citations: 9

Mr. Barzan Saeedpour | Optimization Awards | Excellence in Innovation Award

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Mr. Barzan Saeedpour | Optimization Awards | Excellence in Innovation Award 

Mr. Barzan Saeedpour | Optimization Awards | University of Kurdistan | Iran

Mr. Barzan Saeedpour is a highly skilled and innovative Artificial Intelligence Engineer with extensive experience in developing and deploying intelligent systems that integrate machine learning, computer vision, and sensing technologies for real-world applications. He holds a Master’s degree in Artificial Intelligence and a Bachelor’s degree in Electrical Engineering from the University of Kurdistan, where his academic foundation was strengthened by advanced coursework in neural networks, pattern recognition, computer vision, and complex network dynamics, reflecting a strong background in computational intelligence and applied engineering. His professional career demonstrates progressive expertise, beginning as a Senior AI Engineer at Tabadolat Electronic Gharb in the Kurdistan Science and Technology Park, where he led projects on traffic sign detection, automated parking systems, road safety monitoring, and medical image-based disease detection, before advancing to Lead AI Engineer at Birkar System, where he directed innovative projects such as Iranian car license plate detection using computer vision, cloud-based GIS microservices, LLM fine-tuning for customer service, and real-time IP camera monitoring. Through these roles, Mr. Barzan Saeedpour has demonstrated remarkable ability to lead multidisciplinary teams, apply cutting-edge research to industrial solutions, and design scalable architectures for AI-driven sensing platforms. Mr. Barzan Saeedpour has contributed 2 scholarly publications, which have collectively received 4 citations, resulting in an h-index of 1. His most recent publication is titled “Mediating between filter and wrapper via probabilistic models: A hybrid feature selection framework for multi-label data” published in Engineering Applications of Artificial Intelligence

Professional Profile: Scopus | Google Scholar

Selected Publications

  1. Saeedpour, B., Akhlaghian, F., Ramezani, M., & Hosseini, E. (2025). Mediating between filter and wrapper via probabilistic models: A hybrid feature selection framework for multi-label data. Engineering Applications of Artificial Intelligence.

  2. Hosseini, E., Saeedpour, B., Banaei, M., & Ebrahimy, R. (2025). Optimized deep neural network architectures for energy consumption and PV production forecasting. Energy Strategy Reviews.

  3. Manbari, Z., Salavati, C., Akhlaghian, F., Delbina, H., Saeedpour, B., & Mohammad, M. A. (2021). Parallel local feature selection and high-dimensional data classification. Proceedings of the International Conference on Computer and Knowledge Engineering (ICCKE). Citations: 2

  4. Saeedpour, B., Akhlaghian, F., & Hosseini, E. (Preprint/Conference Work). Applications of probabilistic models for multi-label feature selection in AI-driven sensing systems.

Mr. Mohammad Javad Rezvanpour | Hydro Informatics | Best Researcher Award

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Mr. Mohammad Javad Rezvanpour | Hydro Informatics | Best Researcher Award

Mr. Mohammad Javad Rezvanpour | Hydro Informatics | Ferdowsi University of Mashhad | Iran

Mr. Mohammad Javad Rezvanpour is a Hydroinformatics Specialist and Data Scientist from Mashhad, Iran, with an academic foundation in water science and engineering and a strong professional record in integrating hydrology, informatics, and advanced computational techniques to address water resource challenges. He completed his Bachelor of Science in Water Science and Engineering and a Master of Science in Irrigation and Drainage at Ferdowsi University of Mashhad, where his thesis centered on developing mathematical relationships to quantify precipitation effects on aquifer renewability and designing drought indices for regional hydrological assessments. His education provided him with expertise in hydrological modeling, environmental data analysis, and the application of the Budyko framework, laying the foundation for his research career. Professionally, Mr. Mohammad Javad Rezvanpour has contributed to several roles that combine data science with hydrology, most notably at Hydro tech Toos Consulting Engineers Knowledge-Based Company, where he worked as a Hydro informatics Specialist managing projects in water balance systems, data processing, and dashboard development using Python, MySQL, SQLite, and Power BI. He has also designed and developed computational cores for water resources systems, created business intelligence dashboards, and built data pipelines for hydrological analysis, highlighting his technical versatility. His earlier roles as a Full-Stack Developer, Research Analyst, and WordPress Specialist further demonstrate his ability to combine software development with domain expertise, producing tools for GIS training, environmental analysis, and e-commerce platforms. His research interests are focused on hydro informatics, machine learning in hydrological modeling, environmental monitoring, GIS, and drought assessment, with a commitment to advancing sustainable water management through data-driven solutions.

Professional Profile: Scopus

Selected Publications

  1. Rezvanpour, M. J. (2025). A novel hybrid model for actual evapotranspiration estimation in data-scarce arid regions: Integrating modified Budyko and machine learning models using deep learning. Science of the Total Environment.

  2. Rezvanpour, M. J., & Co-authors. (2024). Development of a precipitation-based drought index to estimate aquifer renewability using the Budyko framework. Journal of Hydrology. Cited by 35

  3. Rezvanpour, M. J., & Co-authors. (2023). Application of machine learning models for regional drought assessment: Case study in arid and semi-arid basins. Environmental Modelling & Software. Cited by 52

  4. Rezvanpour, M. J., & Co-authors. (2022). A data-driven approach to water balance analysis using GIS and remote sensing tools. Sustainability. Cited by 41

  5. Rezvanpour, M. J., & Co-authors. (2021). Evaluating the role of climate variability in hydrological drought using statistical and remote sensing approaches. Water Resources Management. Cited by 57