Gousia Habib | Machine Learning | Research Excellence Award 

Published on by Computer Scientists Awards

Research Excellence Award

Gousia Habib
Bahrain Polytechnic 

Gousia Habib
Affiliation Bahrain Polytechnic
Country Bahrain
Scopus ID 57221589277
Documents 21
Citations 700
h-index 6
Subject Area Machine Learning
Event Computer Scientists Awards
ORCID 0000-0002-4773-3733

Gousia Habib is a researcher affiliated with Bahrain Polytechnic, Bahrain, whose academic profile is associated with Machine Learning. Her reported scholarly record includes 21 documents, 700 citations and an h-index of 6. Her profile is presented in connection with the Research Excellence Award recognition of the Computer Scientists Awards.

Abstract

This article presents the academic profile of Gousia Habib, highlighting her institutional affiliation, research subject area and reported scholarly indicators. Her publication and citation record provides a quantitative overview of her research activity.

Keywords

Gousia Habib; Machine Learning; Computer Science; Artificial Intelligence; Research Publications; Citation Impact; Bahrain Polytechnic; Research Excellence Award.

Introduction

Machine Learning is an important area of computer science concerned with computational methods that enable systems to identify patterns and learn from data. Research in this field contributes to predictive modelling, intelligent systems and data-driven applications. Gousia Habib’s listed subject area connects her academic profile with this evolving discipline.

Research Profile

Habib is affiliated with Bahrain Polytechnic in Bahrain. Her reported Scopus author identifier is 57221589277. The supplied bibliometric information records 21 documents, 700 citations and an h-index of 6. These indicators provide a quantitative overview of publication activity and scholarly visibility.

Research Contributions

Machine Learning is relevant to computational intelligence, predictive analysis and modern data-driven technologies. Research in this discipline can support automated classification, pattern recognition and decision-making. Specific contributions should be evaluated through the researcher’s original publications.

Publications

The supplied Scopus profile reports 21 research documents. These publications form the documented basis for assessing her research activity. Individual publication titles, journal details and DOI identifiers can be consulted through the author profile.

Research Impact

The reported 700 citations indicate that her publications have received scholarly references from other research works. An h-index of 6 indicates at least six publications with six or more citations each. These metrics provide a quantitative perspective on research visibility, but do not independently measure research quality.

Award Suitability

The Research Excellence Award associated with the Computer Scientists Awards recognizes academic research activity and scholarly contributions. Habib’s reported publication and citation indicators provide relevant information for presenting her research profile. Final award assessment should follow the event’s official eligibility and selection criteria.

Conclusion

Gousia Habib’s academic profile highlights her affiliation with Bahrain Polytechnic and her research association with Machine Learning. Her reported 21 documents, 700 citations and h-index of 6 provide a measurable overview of her scholarly record.

References

  1. Elsevier. (n.d.). Scopus author details: Gousia Habib, Author ID 57221589277. Scopus.
    Scopus Author Record
  2. Google Scholar. (n.d.). Gousia Habib – Google Scholar Profile.
    Google Scholar Profile

 

Saadia Humayun | Machine Learning | Innovative Research Award

Published on by Computer Scientists Awards

 

Innovative Research Award

Saadia Humayun
Institute of Business Administration

Saadia Humayun
Affiliation Institute of Business Administration
Country Pakistan
Scopus ID 59717002600
Documents 3
Citations 4
h-index 1
Subject Area Machine Learning
Event Computer Scientists Awards
ORCID 0009-0001-9266-7835

Saadia Humayun is affiliated with the Institute of Business Administration in Pakistan, with machine learning identified as her research area. This profile summarizes the supplied academic information and its relevance to the Innovative Research Award associated with Computer Scientists Awards. Bibliometric figures are reported as provided and may change as research databases update their records. [1]

Abstract

This article presents the available academic profile of Saadia Humayun, including her institutional affiliation, machine learning subject area, and reported research indicators. It provides a concise overview for readers interested in her scholarly profile and potential relevance to research recognition.

Keywords

Saadia Humayun; machine learning; research innovation; academic publications; citation analysis; computational research; Pakistan; Computer Scientists Awards.

Introduction

Machine learning is a field of artificial intelligence focused on computational methods that identify patterns in data and support prediction and decision-making. Evaluating research in this area involves considering methodological rigor, originality, reproducibility, and the practical or scientific value of reported findings.

Research Profile

The supplied information identifies Humayun with the Institute of Business Administration in Pakistan. Her Scopus author identifier and ORCID profile provide reference points for locating scholarly records and distinguishing research outputs.

Research Contributions

The available details identify machine learning as the subject area but do not specify particular projects or technical findings. A comprehensive assessment of contributions would require reviewing verified publications, research methods, and evidence of originality.

Publications

The supplied bibliometric summary lists three documents. Publication titles, journal details, and DOI identifiers were not provided, so individual works are not listed here. Readers can consult the linked Scopus and Google Scholar profiles for available publication records.

Research Impact

The provided profile reports four citations and an h-index of one. These metrics describe aspects of citation activity but do not independently establish research quality or societal impact. Counts may vary across databases and over time. [1]

Award Suitability

The Innovative Research Award provides a context for recognizing research and innovation. Humayun’s stated machine learning specialization is relevant to this broad field. Any formal recognition decision should follow the organizer’s criteria and be supported by verified research outputs and evidence of contribution.

Conclusion

Saadia Humayun’s supplied profile records her affiliation, research area, and bibliometric indicators. Additional verified publication details would help readers evaluate her research contributions and their relevance to innovation in machine learning.

References

  1. Elsevier. (n.d.). Scopus author details: Saadia Humayun, Author ID 59717002600. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=59717002600
  2. ORCID. (n.d.). Researcher identifier record for Saadia Humayun.
    https://orcid.org/0009-0001-9266-7835
  3. Google Scholar. (n.d.). Scholar profile supplied for Saadia Humayun.
    Google Scholar profile

 

 

Adedoyin Adeyemi | Machine Learning | Innovative Research Award

Published on by Computer Scientists Awards

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

Published on by Computer Scientists Awards

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

10

0

Citations 25

Documents
12

h-index
3

                    ■ Citations          ■ i10-index           ■ h-index


View Scopus Profile View Google Scholar Profile

Featured Publications

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

2

0

Citations
10

Documents
4

h-index
2

                    🟦 Citations    🟥 Documents    🟩 h-index


View Scopus Profile
 View Google Scholar Profile

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
– Structures, 2024

Investigation of cyclic load behavior of reinforced concrete frames exposed to high temperatures using FEM
– Engineering Journal, 2025

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.

View Scopus Profile
 View Google Scholar Profile

Featured Publications

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


View Google Scholar Profile

Featured Publications

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.