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

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. 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),

Farzaneh Zareian | Machine Learning | Best Researcher Award

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Ms. Farzaneh Zareian | Machine Learning | Best Researcher Award

Ms. Farzaneh Zareian – Graduate Student, Amirkabir University of Technology, Iran.

Farzaneh Zareian is a dynamic civil engineering researcher with a specialization in earthquake engineering and machine learning applications in structural analysis. Holding a master’s degree from the prestigious Amirkabir University of Technology and a bachelor’s from the University of Tehran, she has consistently demonstrated academic excellence and innovation. Farzaneh has contributed significantly through teaching, research, and scholarly publications in seismic assessment and structural resilience. With experience in AI-powered modeling, fragility curve generation, and passive control systems, she stands at the intersection of engineering and intelligent computation, contributing to safer, more resilient infrastructure in seismic-prone regions.

Publication Profile

Google Scholar

🎓 Education Background

Farzaneh Zareian earned her M.Sc. in Civil Engineering (Earthquake Engineering) from Amirkabir University of Technology, Tehran (2020–2023) with an excellent-rated thesis supervised by Dr. Mehdi Banazadeh. Her research focused on nonlinear dynamic response estimation using machine learning. Prior to that, she completed her B.Sc. in Civil Engineering at the University of Tehran (2016–2020), with coursework emphasizing earthquake engineering, bridge design, and hydraulic structures. Her academic journey highlights a deep commitment to blending structural theory with advanced computational methods, maintaining strong GPAs and securing top ranks in national entrance exams at both undergraduate and postgraduate levels.

💼 Professional Experience

Farzaneh Zareian has accumulated valuable academic experience through teaching and research roles. She worked as a sessional instructor for the “Soft Computing” course at Shahab Danesh University during 2023–2024 and currently serves as a Teaching Assistant in “Theory of Structural Analysis” at Amirkabir University of Technology. Her practical engagements also include academic projects involving seismic hazard analysis, vulnerability assessment, and AI-driven structural modeling. These roles reflect her dual strength as both an educator and practitioner in earthquake-resistant design and computational engineering, making her a well-rounded and impactful civil engineering professional.

🏅 Awards and Honors

Farzaneh’s academic excellence has been widely recognized through several honors. In 2024, she was selected as a distinguished Ph.D. candidate by Amirkabir University’s Committee of Exceptional Talents. She ranked 1st among her peers in the Earthquake Engineering master’s program in 2022 and was among the top 0.2% in both bachelor’s and master’s national entrance exams in 2016 and 2020, respectively. Additionally, she was the top high school student at NODET. These accolades reflect her exceptional dedication, intelligence, and potential as a future leader in structural and earthquake engineering research.

🔬 Research Focus

Farzaneh’s research focuses on AI-enabled structural design and optimization, particularly in seismic contexts. She specializes in applying machine learning and physics-informed models to estimate structural responses, assess risk and reliability, and enhance infrastructure resilience. Her projects include probabilistic seismic hazard analysis, fragility curve generation, and the use of deep learning for crack detection in masonry. She is deeply committed to integrating data-driven approaches with classical civil engineering practices to improve safety, sustainability, and performance of critical infrastructure under seismic hazards.

🧾 Conclusion

Farzaneh Zareian exemplifies the emerging generation of civil engineers who are leveraging artificial intelligence to redefine structural safety and resilience. Her academic accomplishments, hands-on project experiences, teaching engagements, and scholarly contributions highlight a well-rounded professional profile. As she progresses toward doctoral research, her innovative mindset and strong foundation in both theory and practice make her a prime candidate for research excellence in AI-integrated earthquake engineering. With her interdisciplinary approach, she is poised to make impactful contributions to the global civil and seismic engineering community.

📚 Publication Top Notes

 Prediction of nonlinear dynamic responses and generation of seismic fragility curves for steel moment frames using boosting machine learning techniques
📅 Year: 2024 (Nov.)
📘 Journal: Computers & Structures
🔢 Cited by: 1

 Machine learning-based seismic risk assessment of steel moment structures: a reliability analysis framework
📅 Year: In Preparation (Expected 2025)
📘 Journal: Engineering Structures
🔢 Cited by: –

Wai Yie Leong | Data Science | Best Researcher Award

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Prof. Dr. Wai Yie Leong | Data Science | Best Researcher Award

Senior Professor at INTI International University, Malaysia

IR. Prof. Dr. Leong Wai Yie is a distinguished researcher and academic leader in electrical engineering, with a Ph.D. from The University of Queensland, Australia. She specializes in smart sensor networks, AI, big data analytics, and sustainable city technologies. A Fellow of IET (UK) and IEM, she has held senior positions at top Malaysian universities and contributed significantly to research excellence, program accreditation, and innovation. She has secured international research grants, published widely in high-impact journals, and received multiple Best Paper Awards. Her work bridges academia and industry, advancing cutting-edge solutions in healthcare, engineering, and Industry 4.0 systems.

📚Professional Profile

Orcid

Scopus

Google Scholar

🎓Academic Background

IR. Prof. Dr. Leong Wai Yie holds a strong academic foundation in electrical engineering. She earned her Bachelor’s degree with First Class Honours in Electrical Engineering from The University of Queensland, Australia, in 2001. Continuing her academic excellence, she completed her Ph.D. in Electrical Engineering at the same institution in 2005. Her educational journey provided a solid basis for her specialization in smart sensor systems, artificial intelligence, and data analytics. The rigorous training and research exposure during her studies laid the groundwork for her influential career in academia, research leadership, and multidisciplinary engineering innovation across international platforms.

💼Professional Experience

IR. Prof. Dr. Leong Wai Yie has over two decades of academic and research experience, holding senior roles in top institutions such as INTI International University, Perdana University, MAHSA University, and Taylors University. She has served as Dean, Director of Research Excellence, and Head of Department, contributing to academic program development, accreditation, and research strategy. Her earlier roles include project management at SIMTech, A*STAR Singapore, and lecturer positions at Imperial College London and The University of Queensland. Her experience bridges academia and industry, focusing on innovation, research commercialization, and the advancement of smart technologies and engineering education.

🏅Awards and Honors

IR. Prof. Dr. Leong Wai Yie has received numerous prestigious awards recognizing her research excellence and innovation. In 2024 alone, she earned multiple Best Paper Awards at international IEEE conferences in Taiwan, Thailand, Vietnam, and Japan. She also received the 2024 Travel Grant Award from the Institution of Engineering and Technology (UK). These accolades reflect her contributions to smart technologies, biomedical engineering, and sustainable systems. Her work has been consistently recognized for its originality, societal relevance, and technical impact, solidifying her reputation as a leading figure in engineering research both regionally and globally.

🔬Research Focus

IR. Prof. Dr. Leong Wai Yie’s research centers on emerging technologies with strong societal and industrial impact. Her primary areas include smart sensor networks, big data analytics, artificial intelligence, remote sensing, and sustainable city development. She is actively involved in advancing Industry 4.0 applications and international standards for engineering systems. Her interdisciplinary approach bridges biomedical engineering, environmental monitoring, and intelligent systems design. Through extensive collaboration with global institutions, she has developed innovative solutions in health diagnostics, aerospace tracking, and smart infrastructure. Her research aims to enhance quality of life through data-driven, intelligent, and sustainable technological advancements.

Citations:

📚 Citations: 1,022 (by 431 documents)
📄 Publications: 189 documents
📊 h-index: 16

📖Publication Top Notes

Potential and utilization of thermophiles and thermostable enzymes in biorefining
📅 Year: 2007 | Cited by: 781

Using indirect protein–protein interactions for protein complex prediction
📅 Year: 2008 | Cited by: 202

Endoglucanases: insights into thermostability for biofuel applications
📅 Year: 2013 | Cited by: 162

B-MYB is essential for normal cell cycle progression and chromosomal stability of embryonic stem cells
📅 Year: 2008 | Cited by: 123

Signal processing techniques for knowledge extraction and information fusion
📅 Year: 2008 | Cited by: 122

Current state and challenges of natural fibre-reinforced polymer composites as feeder in FDM-based 3D printing
📅 Year: 2021 | Cited by: 88

Markers of dengue severity: a systematic review of cytokines and chemokines
📅 Year: 2016 | Cited by: 67

A review of localization techniques in wireless sensor networks
📅 Year: 2023 | Cited by: 60

Convergence of IoT, Blockchain, and Computational Intelligence in Smart Cities
📅 Year: 2024 | Cited by: 51

The nine pillars of technologies for Industry 4.0
📅 Year: 2020 | Cited by: 50

✨Conclusion

Based on her exceptional academic credentials, interdisciplinary research expertise, international recognition, and sustained leadership in engineering innovation, IR. Prof. Dr. Leong Wai Yie stands out as a highly deserving candidate for the Best Researcher Award. With a Ph.D. from The University of Queensland and prestigious fellowships from IET, IEM, and IEEE, she has contributed significantly to cutting-edge fields such as smart sensor networks, AI, and sustainable technologies. Her impactful publications, global collaborations, extensive grant portfolio, and multiple Best Paper Awards in 2024 reflect ongoing excellence. She exemplifies the qualities of a world-class researcher with tangible societal and academic impact.

 

 

Mrs. Edna Rocio Bernal Monroy | Machine Learning | Best Researcher Award

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Mrs. Edna Rocio Bernal Monroy | Machine Learning | Best Researcher Award

UNAD, Colombia

Dr. Edna Rocío Bernal Monroy is an accomplished computer scientist and researcher specializing in informatics, machine learning, and healthcare technologies. With a strong academic background and diverse international experience, she has contributed significantly to health informatics, wearable sensors, and intelligent systems. Dr. Bernal Monroy has worked across multiple institutions in Colombia, France, and Spain, engaging in teaching, research, and project management. Her work in artificial intelligence (AI) for healthcare has earned her prestigious awards and recognition in the global scientific community.

Publication Profile

🎓 Education

Dr. Bernal Monroy holds a Ph.D. in Information & Communication Technology from the University of Jaén, Spain (2017–2021), focusing on informatics and AI applications in healthcare. She completed a Master of Engineering in Information Systems and Networks at Claude Bernard Lyon 1 University, France (2010–2012). Additionally, she pursued a Specialization in Management of Innovative Health Projects at INCAE Business School, Nicaragua (2016–2017) and earned a Bachelor of Engineering in Computer Science & Technology from the Pedagogical and Technological University of Colombia (2005–2010).

💼 Experience

Dr. Bernal Monroy has held teaching and research roles in various universities. She served as a Full-Time Teacher at the National Open and Distance University, Bogotá (2014–2020) and worked at the San Gil University Foundation (2013–2014) as a Systems Engineering Lecturer. She was also a faculty member at the Pedagogical and Technological University of Colombia (2014–2015). Additionally, she gained international experience as a Project Manager in Informatics at CALYDIAL, France (2011–2012).

🏆 Awards and Honors

Dr. Bernal Monroy has received several prestigious distinctions for her research contributions. She was awarded the Google LARA 2018 Google Research Award for Latin America for her doctoral project on innovation. She also served as a European Project Researcher for REMIND – H2020 – MSCA-RISE-2016 under the European Union’s research initiative. Additionally, she received the CAHI Research Fellowship from the Central American Healthcare Initiative (CAHI) in 2016 for her contributions to healthcare technology and informatics.

🔬 Research Focus

Dr. Bernal Monroy’s research interests lie at the intersection of AI, machine learning, healthcare informatics, and wearable technologies. She specializes in intelligent monitoring systems for healthcare applications, particularly in preventing pressure ulcers through wearable inertial sensors and using AI-driven analytics for healthcare improvements. Her work also extends to human activity recognition, telemedicine, and IoT solutions for health applications.

🏁 Conclusion

Dr. Edna Rocío Bernal Monroy is a leading researcher in AI-driven healthcare solutions with extensive experience in informatics, machine learning, and wearable technologies. Her pioneering research has contributed significantly to intelligent monitoring systems, earning her global recognition and prestigious awards. Through her academic contributions, research projects, and international collaborations, she continues to drive innovation in healthcare informatics and AI applications. 🚀

📚 Publications

Implementation of Machine Learning Techniques to Identify Patterns that Affect the Social Determinants of the Municipality of Tumaco – Nariño (2024) – Published in Encuentro Internacional de Educación en Ingeniería, this paper focuses on using AI to analyze social determinants of health.

Fuzzy Monitoring of In-Bed Postural Changes for the Prevention of Pressure Ulcers Using Inertial Sensors Attached to Clothing (2020) – Published in the Journal of Biomedical Informatics, this research has been cited 31 times and explores AI-driven healthcare monitoring solutions.

Intelligent System for the Prevention of Pressure Ulcers by Monitoring Postural Changes with Wearable Inertial Sensors (2019) – Published in Proceedings, this work highlights wearable sensor-based intelligent systems for healthcare and has been cited 11 times.

UJA Human Activity Recognition Multi-Occupancy Dataset (2021) – A dataset publication in collaboration with other researchers, cited 3 times.

Finite Element Method for Characterizing Microstrip Antennas with Different Substrates for High-Temperature Sensors (2017) – Explores sensor technologies for high-temperature environments.

Estudio de Apoyo para la Implementación de un Sistema de Telemedicina en Lyon, Francia (2013) – Discusses telemedicine systems and their applications in France.