Adedoyin Adeyemi | Machine Learning | Innovative Research Award

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Innovative Research Award

Adedoyin Adeyemi
Federal University of Technology, Akure, Nigeria

Adedoyin Adeyemi
Affiliation Federal University of Technology, Akure
Country Nigeria
Scopus ID 59368133300
Documents 3
Citations 31
h-index 2
Subject Area Machine Learning
Event Computer Scientists Awards
ORCID 0009-0007-4701-1259

Adedoyin Adeyemi is a researcher affiliated with the Federal University of Technology, Akure, Nigeria, whose documented research activity includes work related to environmental modelling, hydrological analysis, land-use change and computational approaches relevant to machine learning. Available bibliographic information records three documents, 31 citations and an h-index of 2. These indicators provide a quantitative overview of the researcher’s indexed scholarly output and citation activity.

Abstract

The research profile of Adedoyin Adeyemi includes scholarly contributions addressing environmental and hydrological systems, with particular relevance to computational modelling of land-use change and flood-related processes. Recent collaborative research examines urbanization-driven land-cover change and floodplain transformation in the Ogun River Basin using HAND and CA-Markov models, as well as hydrological responses and flood hazards under projected climate and land-use change. These studies demonstrate an interdisciplinary connection between environmental science, spatial modelling, hydrology and computational analysis. [1] [2]

Keywords

  • Machine Learning
  • Hydrological Modelling
  • Land-Use Change
  • Flood Hazard Mapping
  • Ogun River Basin

Introduction

Research on flood risk increasingly combines hydrological modelling with spatial representations of land-use and environmental change. The Ogun River Basin provides an important setting for examining how urbanization and changing land cover can influence hydrological behaviour. Adeyemi’s collaborative publications contribute to this research area through computational modelling and assessment of projected environmental conditions. The work connects empirical geographical information with models capable of representing terrain, land-cover transitions and hydrological response. [1] [2]

Research Profile

Bibliographic records identify Adeyemi with three indexed documents and 31 citations, with a reported h-index of 2. The available publication evidence places his research within multidisciplinary environmental and computational investigations. Such work involves collaboration across hydrology, environmental modelling, geography and data-driven analytical methods.

Research Contributions

A notable contribution is the study of urbanization-driven land-cover change and floodplain transformation in the Ogun River Basin using Height Above Nearest Drainage (HAND) and Cellular Automata–Markov (CA-Markov) models. The approach provides a framework for examining relationships between terrain, projected land-cover transitions and floodplain characteristics. [1]

A second contribution concerns hydrological response and flood hazard mapping under projected climate and land-use change. Published in Water Science & Technology in 2026, the study examines how changing environmental conditions may influence hydrological behaviour and flood hazards in the basin. [2]

Publications

  1. “Urbanization driven land cover change and floodplain transformation in the Ogun River Basin using HAND and CA-Markov models.
  2. “Hydrological response and flood hazard mapping of the Ogun River Basin under projected climate and land-use change.” Water Science & Technology, 2026-05-15.

Research Impact

The documented citation record indicates that Adeyemi’s indexed publications have received scholarly attention. The research also has practical relevance to understanding flood hazards, land-use transformation and environmental planning. By combining spatial and hydrological modelling approaches, the reported studies contribute evidence that can support further research into basin-scale flood assessment and climate-sensitive land-use planning. [2]

Award Suitability

For an Innovative Research Award assessment, the documented profile presents relevant evidence of interdisciplinary research involving computational models, environmental change and flood-risk analysis. The use of HAND and CA-Markov approaches, together with hydrological hazard assessment under projected scenarios, provides a substantive basis for considering the research within an innovation-oriented academic recognition framework. Award decisions, however, should be based on the complete nomination record and independently verified evidence.

Conclusion

Adedoyin Adeyemi’s documented research profile reflects participation in multidisciplinary studies of hydrology, land-use change and flood hazards. His publications concerning the Ogun River Basin demonstrate the application of computational and spatial modelling to contemporary environmental questions. The available bibliographic indicators and publication record provide a concise scholarly basis for evaluating his research contributions in the context of the Innovative Research Award.

References

  1. Springer Nature. (2026). Urbanization driven land cover change and floodplain transformation in the Ogun River Basin using HAND and CA-Markov models.
    https://doi.org/10.1007/s44288-026-00529-y
  2. IWA Publishing. (2026). Hydrological response and flood hazard mapping of the Ogun River Basin under projected climate and land-use change. Water Science & Technology.
    https://doi.org/10.2166/wst.2026.274
  3. Elsevier. (n.d.). Scopus author details: Adedoyin Adeyemi, Author ID 59368133300. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=59368133300
  4. ORCID. (n.d.). Adedoyin Adeyemi, ORCID iD 0009-0007-4701-1259.
    https://orcid.org/0009-0007-4701-1259
  5. Computer Scientists Awards. (n.d.). Award and nomination information.
    https://computerscientists.net/

Mr. Vivek Dwivedi | Machine learning | Research Excellence Award

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Mr. Vivek Dwivedi | Machine learning | Research Excellence Award

Slovak University of Technology in Bratislava | Slovakia

Mr. Vivek Dwivedi is an emerging researcher specializing in computer vision, adaptive camera systems, robotics, and intelligent imaging technologies, with a strong focus on real-time object detection and virtual teleportation systems. His work integrates machine learning, OpenCV, and embedded systems to develop computationally efficient solutions for dynamic visual environments. He has also contributed to research in mechatronics, haptic systems, and origami-inspired robotics. His academic output demonstrates growing impact, with Scopus indexing 12 documents, 25 citations, and an h-index of 3, while Google Scholar reports 37 citations and an h-index of 4, reflecting consistent scholarly advancement.

Citation Metrics ( Scopus )

40

30

20

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Citations 25

Documents
12

h-index
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                    ■ Citations          ■ i10-index           ■ h-index


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Assist. Prof. Dr. HalitErdem Çolakoğlu | Computer Science | Research Excellence Award

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Assist. Prof. Dr. HalitErdem Çolakoğlu | Computer Science | Research Excellence Award

Giresun University | Turkey

Assist. Prof. Dr. Halit Erdem Çolakoğlu is a civil engineering researcher specializing in structural behavior of reinforced concrete systems, with emphasis on high-temperature effects, cyclic loading, seismic performance, and finite element modeling. His work contributes to understanding durability, safety, and performance of structural elements under extreme conditions, including corrosion and material degradation. He has published in recognized engineering journals and conferences, focusing on advanced numerical analysis and experimental validation. According to available metrics, his research impact includes approximately 10 Scopus-indexed citations across 4 documents with an h-index of 2, and 24 Google Scholar citations with an h-index of 4, reflecting growing academic influence and research consistency.

Citation Metrics (Scopus)

10

8

6

4

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10

Documents
4

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                    🟦 Citations    🟥 Documents    🟩 h-index


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Featured Publications

Investigation of the Change in Mechanical Properties of Concrete Subjected After High-Temperature Effect to Cyclic Lateral Load – Arabian Journal for Science and Engineering, 2025

The behavior of reinforced concrete frames exposed to high temperature under cyclic load effect
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Investigation of cyclic load behavior of reinforced concrete frames exposed to high temperatures using FEM
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Research focus: Reinforced Concrete, Earthquake Engineering, High Temperature Effects, Structural Analysis

Dr. Xianchen Liu | Machine Learning | Best Researcher Award

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Dr. Xianchen Liu | Machine Learning | Best Researcher Award

Researcher | Florida International University | United States

Dr. Xianchen Liu is a computer scientist specializing in machine learning, natural language processing, recommender systems, predictive analytics, and data-driven optimization. His research integrates deep learning architectures such as BERT, LSTM, attention mechanisms, and swarm intelligence to address challenges in sentiment analysis, financial risk prediction, dynamic pricing, and energy systems modeling. He has contributed to peer-reviewed journals including Systems and the Journal of Software Engineering and Applications, and presented work at international conferences. According to Scopus, he has 2 indexed documents with 3 citations and an h-index of 1; Google Scholar reports 17 citations with an h-index of 2.

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Mr. Zeeshan Rasheed | Machine Learning | Research Excellence Award

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Mr. Zeeshan Rasheed | Machine Learning | Research Excellence Award

Lecturer Computer Science | Mir Chakar Khan Rind University Sibi Balochistan | Pakistan

Mr. Zeeshan Rasheed is a computer science researcher whose work spans machine learning, data intelligence, wireless networks, and AI-driven decision systems. His research focuses on optimizing network cooperation, developing neural models for sustainable wireless resource management, improving early disease prediction, and analyzing AI’s role in media and social systems. He has contributed to studies on sentiment analysis, intelligent network strategies, pandemic modelling, and crowdsourced data reliability. His scholarly output reflects a continuous commitment to advancing practical and socially relevant AI applications, supported by publications across multidisciplinary journals. His work also demonstrates growing academic impact with ongoing contributions to emerging technological challenges.

Citation Metrics (Google Scholar)

20

15

10

5

0

Citations
17

Documents
17

h-index
3

Citations
Documents
h-index


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Mr. John de Britto Chinnappan | Computer Science | Editorial Board Member

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Mr. John de Britto Chinnappan | Computer Science | Editorial Board Member

Saveetha Engineering College| India

Dr. C. John De Britto is a dedicated researcher in Electrical and Electronics Engineering with a strong focus on power electronics, renewable energy systems, electric drives, optimization algorithms, and intelligent control strategies. His research work explores innovative solutions for improving power quality, enhancing the efficiency of renewable energy integration, and advancing smart energy systems. With contributions spanning image enhancement techniques, hybrid renewable systems, DC–DC converter architectures, electric vehicle impact mitigation, and intelligent control for photovoltaic systems, he brings a multidisciplinary approach bridging conventional power engineering with modern computational intelligence. His scholarly output includes 14 Scopus-indexed documents that have collectively received 40 citations with an h-index of 4 on Scopus. Additionally, his Google Scholar profile reflects 50 citations, an h-index of 4, and an i10-index of 1, highlighting the growing influence and visibility of his work. His publications demonstrate a strong commitment to developing sustainable engineering solutions, especially in areas such as quasi Z-source converters, hybrid renewable energy design, embedded platforms, fault recognition in industrial motors, and bio-inspired optimization for control systems. Dr. De Britto’s research impact is evident across peer-reviewed journals, international conferences, and interdisciplinary collaborations, with several studies addressing modern challenges such as electric vehicle charging impacts, microgrid performance, and automation for safety-critical applications. His continuous contributions to energy systems, computational approaches, and power conversion technologies position him as an emerging academic voice in renewable and intelligent power engineering research.

Profile

Scopus | ORCID | Google Scholar

Featured Publications

Venkatesh, S., De Britto, C. J., Subhashini, P., & Somasundaram, K. (2022). Image enhancement and implementation of CLAHE algorithm and bilinear interpolation. Cybernetics and Systems, 1–13.

Pradeep, M., Sathishkumar, S., & Subramanian, A. T. S. (2019). Recognition of fault and security of three phase induction motor by means of programmable logic controller. IOP Conference Series: Materials Science and Engineering, 623, 012017.

Yuvaraj, T., Prabaharan, N., De Britto, C. J., Thirumalai, M., Salem, M., & others. (2024). Dynamic optimization and placement of renewable generators and compensators to mitigate electric vehicle charging station impacts using the spotted hyena optimization algorithm. Sustainability, 16(19), 8458.

De Britto, C. J., Nagarajan, S., & Kumar, R. S. (2023). Effective design and implementation of hybrid renewable system using convex programming. International Journal of Green Energy, 20(13), 1473–1487.

De Britto, C. J., & Nagarajan, S. (2018). High performance quasi Z-source resonant converter with hybrid energy resources for rural electrification. International Journal of Engineering and Advanced Technology, 8(2C2), 132–135.

Prof. Zhiguo Zhao | Machine Learning | Best Researcher Award

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Prof. Zhiguo Zhao | Machine Learning | Best Researcher Award

Professor | Huaiyin Institute of Technology | China

Prof. Zhiguo Zhao is a distinguished academic and researcher in automotive engineering, currently serving as Dean at the School of Traffic Engineering, Huaiyin Institute of Technology. His research primarily focuses on automotive system dynamics and control, intelligent connected vehicles, new energy vehicle technology, and energy equipment fault diagnosis. He has made significant contributions to battery State of Health (SOH) estimation, vehicle safety, and energy management systems, developing advanced models integrating artificial intelligence and optimization algorithms. Professor Zhao has authored over 20 high-impact publications in leading SCI and EI journals, alongside securing 10 invention patents. His research outputs have received provincial and national recognition, particularly for their practical applications in intelligent transportation and energy-efficient vehicle systems. He has successfully led multiple national and provincial research projects and has cultivated innovative industry-university collaboration models for talent development. According to Scopus, his academic record includes 36 indexed documents with 147 citations and an h-index of 7, while Google Scholar reports higher citation metrics, reflecting his growing international academic influence. His interdisciplinary expertise bridges theoretical modeling and industrial applications, fostering advancements in intelligent mobility, new energy systems, and vehicular safety technology.

Profile

Scopus

Featured Publications

Zhao, Z. (2025). Estimation of lithium battery state of health using hybrid deep learning with multi-step feature engineering and optimization algorithm integration. Energies, 18(21), 5849.

Zhao, Z. (2019). Construction and verification of equivalent mechanical model for liquid sloshing in hazardous material tankers. Journal of Huaiyin Institute of Technology, 5, 1–10.

Zhao, Z. (2023). Integrated energy management strategy for hybrid electric vehicles based on adaptive control and machine learning. Journal of Energy Storage, 59, 106781.

Zhao, Z. (2022). Fault diagnosis of power equipment using hybrid neural network and sensor fusion techniques. IEEE Transactions on Industrial Electronics, 69(8), 8123–8134.

Zhao, Z. (2021). Dynamic modeling and control optimization for intelligent connected vehicles in complex traffic environments. Vehicle System Dynamics, 59(4), 613–631.

Avraham Lalum | Machine Learning | Best Researcher Award

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Mr. Avraham Lalum | Machine Learning | Best Researcher Award

PhD | University of Córdoba | Israel

Avraham (Avi) Lalum is a distinguished legal scholar and researcher specializing in the intersection of real estate law, artificial intelligence, and conflict resolution. His research explores advanced AI-driven models for risk management in real estate transactions, integrating decision-oriented mediation (DOM), behavioral analytics, and deep learning to enhance investment decision frameworks. Lalum’s scholarly contributions bridge the gap between legal regulation and computational modeling, offering innovative methodologies for explainable AI in property law, negotiation, and human–machine interaction. His studies emphasize how artificial intelligence can simulate human reasoning to mitigate financial risk and promote fairness in high-stakes negotiations. His works are widely recognized in Scopus and Web of Science-indexed journals, contributing significantly to the fields of law, data science, and behavioral AI. With a growing academic impact reflected in over 300 citations and an h-index of 6 on Scopus (and 9 on Google Scholar), Lalum’s publications demonstrate both theoretical depth and practical application in LegalTech and AI ethics.

Profile

ORCID

Featured Publications 

Lalum, A., López del Río, L. C., & Villamandos, N. C. (2024). Synthetic reality mapping of real estate using deep learning-based object recognition algorithms. SN Business & Economics, Springer.
Lalum, A., Caridad López del Río, L., & Ceular Villamandos, N. (2025). Multi-dimensional AI-based modeling of real estate investment risk: A regulatory and explainable framework for investment decisions. Mathematics, MDPI.

 

Mr. Zhenduo Meng | Machine Learning | Best Researcher Award

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Zhenduo Meng | Machine Learning | Best Researcher Award

Inner Mongolia University, China

Zhenduo Meng is a graduate student pursuing his M.Sc. in Electronic Information Engineering at the School of Electronic Information Engineering, Inner Mongolia University, with a strong academic foundation built during his B.Eng. studies in Automation at Guangxi University. His research primarily focuses on multi-agent reinforcement learning (MARL), deep reinforcement learning, cooperative control of multi-agent systems, and the broader applications of artificial intelligence in intelligent decision-making. He has actively participated in several research projects, where he contributed to the development of algorithms integrating attention mechanisms and value decomposition methods to improve collaboration efficiency in MARL environments. Recently, his research work, “DDWCN: A Dual-Stream Dynamic Strategy Modeling Network for Multi-Agent Elastic Collaboration,” was accepted for publication in Applied Sciences (2025), highlighting his innovative contributions in the field. Despite being at the early stage of his academic journey, his scholarly output includes 2 documents, and his current citation count stands at zero, reflecting the fresh and emerging nature of his research profile. His h-index is also recorded as zero, consistent with his recent entry into the publication landscape. Proficient in Python, MATLAB, PyTorch, and TensorFlow, along with strong command of both Chinese and English, Meng demonstrates promising potential for impactful contributions in intelligent systems research.

Profile: Scopus

Featured Publications

Meng, Z., Na, X., Wang, T., Liu, J., & Wang, W. (2025). DDWCN: A dual-stream dynamic strategy modeling network for multi-agent elastic collaboration.

Wang, T., Na, X., Nie, Y., Liu, J., Wang, W., & Meng, Z. (2025). Parallel task offloading and trajectory optimization for UAV-assisted mobile edge computing via hierarchical reinforcement learning. Drones, 9(2),

Changhyoun Park | Machine Learning | Best Researcher Award

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Dr. Changhyoun Park | Machine Learning | Best Researcher Award

Research Scientist | Pusan National University | South Korea

Changhyoun Park is a South Korean atmospheric scientist and research scholar currently serving as a Research Scientist at the Institute of Environmental Studies and a Lecturer in the Department of Atmospheric Environmental Sciences at Pusan National University (PNU), South Korea. With extensive international academic and research experience, including postdoctoral positions in the USA, Dr. Park has focused on the intersection of atmospheric modeling, greenhouse gas fluxes, and artificial intelligence. His work bridges theoretical research and practical applications, contributing to the advancement of climate and environmental science through teaching, mentorship, and high-impact scholarly publications.

Publication Profile

Scopus

ORCID

Google Scholar

Education Background

Dr. Changhyoun Park holds a Ph.D. in Atmospheric Sciences from Texas A&M University in the United States, where he conducted advanced research in greenhouse gas fluxes and atmospheric modeling. Prior to this, he earned both his Master’s and Bachelor’s degrees in Atmospheric Sciences from Pusan National University (PNU), South Korea. His academic path reflects a strong commitment to environmental and climate research, enhanced by international collaborations and exposure to multidisciplinary approaches in atmospheric science, machine learning, and mesoscale modeling.

Professional Experience

Dr. Park currently holds dual positions as a Research Scientist at the Institute of Environmental Studies and a Lecturer in the Department of Atmospheric Environmental Sciences at PNU. His prior appointments include postdoctoral research roles at Texas A&M University, the University of California, Los Angeles (JIFRESSE), and PNU. He also has industry experience as a Project Manager at YhKim Co. Ltd. His work includes developing AI-based prediction models, conducting mesoscale simulations, managing national-level carbon modeling projects, and mentoring gifted science students through national science education programs in Korea.

Awards and Honors

Throughout his academic and professional journey, Dr. Changhyoun Park has received multiple awards recognizing his contributions to research and science education. These include the Best Researcher of the Year Award from the Institute of Environmental Studies at PNU, an Outstanding Presentation Award by the Korean Society for Atmospheric Environment, and a Regent’s Graduate Fellowship at Texas A&M University. He was also a session winner at Texas A&M’s Student Research Week and received an Encouragement Award from Korea’s Director’s Council of Gifted Science Education.

Research Focus

Dr. Park’s research centers on micrometeorology, atmospheric carbon modeling, greenhouse gas (GHG) dynamics, and the application of artificial intelligence to environmental prediction systems. His expertise includes mesoscale numerical modeling of GHGs, machine learning-based fog and flux prediction, and eddy covariance data analysis. He has led significant projects on CO₂ radiative forcing, VOC fluxes, and vegetation uptake across East Asia and Korea. His interdisciplinary approach integrates atmospheric science with cutting-edge computational techniques to address pressing climate and environmental challenges.

Publications

Significance of Time-Series Consistency in Evaluating Machine Learning Models for Gap-Filling Multi-Level Very Tall Tower Data
Published Year: 2025
Cited by: 5

Environmental factors contributing to variations in CO2 flux over a barley–rice double‑cropping paddy field in the Korean Peninsula
Published Year: 2022
Cited by: 12

Numerical simulation of atmospheric CO2 concentration and flux over the Korean Peninsula using WRF-VPRM model during Korus-AQ 2016 campaign
Published Year: 2020
Cited by: 20

CO2 transport, variability, and budget over the southern California air basin using the high-resolution WRF-VPRM model during the CalNex 2010 campaign
Published Year: 2018
Cited by: 30

Anthropogenic and biogenic features of long-term measured CO2 flux in north downtown Houston, Texas
Published Year: 2016
Cited by: 24

Conclusion

Dr. Changhyoun Park’s academic and research journey reflects a robust commitment to advancing atmospheric and environmental sciences. His diverse roles across academia, research, and education have positioned him as a leader in micrometeorological modeling and AI applications in climate science. With numerous peer-reviewed publications and funded research projects, he continues to contribute significantly to understanding biosphere-atmosphere interactions, offering scientific insights that support sustainable environmental policy and technological innovation in atmospheric monitoring.