Carolina Magalhães | Machine Learning | Best Researcher Award

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Dr. Carolina Magalhães | Machine Learning | Best Researcher Award

Investigadora, INEGI – Instituto de Ciência e Inovação em Engenharia Mecânica e Industrial, Portugal

👩‍🔬 Carolina Magalhães is a dedicated biomedical engineer and PhD candidate with expertise in applying AI and imaging technologies to healthcare challenges. Based in Porto, Portugal, she combines her passion for modern technology with a problem-solving mindset to develop innovative solutions in skin cancer diagnostics. Carolina has worked collaboratively with clinical experts to bridge research and practical applications, contributing significantly to advancing imaging-based decision support systems.

Publication Profile

ORCID

Education

🎓 Carolina holds a PhD in Biomedical Engineering from the Faculdade de Engenharia da Universidade do Porto (2020–2024). She also completed her MSc in Biomedical Engineering at the same institution (2016–2018) and earned her Bachelor’s in Bioengineering – Biomedical Engineering from Universidade Católica Portuguesa (2013–2016).

Experience

💼 Carolina has a rich research background, currently serving as a Graduate Research Fellow at INEGI, focusing on skin lesion diagnosis using multispectral imaging. Her work spans from leveraging machine learning models for skin cancer classification to thermal and UV imaging techniques. Previously, she contributed to projects on hyperhidrosis diagnosis, prosthetic device design, and thermal image analysis for musculoskeletal disorders, collaborating with leading hospitals and research centers in Portugal.

Research Interests

🔬 Carolina is passionate about exploring artificial intelligence, machine learning, and advanced imaging technologies for healthcare applications. Her interests include developing multispectral imaging systems, improving diagnostic tools for skin cancer, and advancing infrared thermography for clinical support systems.

Awards

🏆 Carolina’s innovative work has been recognized with prestigious research grants from the Foundation for Science and Technology (SFRH/BD/144906/2019) and other funding organizations. These awards have supported her impactful contributions to biomedical engineering and healthcare innovation.

Publications

“Systematic Review of Deep Learning Techniques in Skin Cancer Detection”
BioMedInformatics, 11/2024
Read here

“Skin Cancer Image Classification with Artificial Intelligence Strategies: A Systematic Review”
Journal of Imaging, 10/2024
Read here

“Use of Infrared Thermography for Abdominoplasty Procedures in Patients with Extensive Subcostal Scars: A Preliminary Analysis”
Plast Reconstr Surg Glob Open, 06/2023
Read here

“Classic Versus Scarpa-Sparing Abdominoplasty: An Infrared Thermographic Comparative Analysis”
J Plast Reconstr Aesthet Surg, 06/2023
Read here

“Towards an Effective Imaging-Based Decision Support System for Skin Cancer”
Handbook of Research on Applied Intelligence for Health and Clinical Informatics, 10/2022
Read here

Dongbeom Kim | Artificial Intelligence | Best Researcher Award

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Mr. Dongbeom Kim | Artificial Intelligence | Best Researcher Award

Master’s Student, University of Seoul, South Korea

Dongbeom Kim is a dedicated Master’s student at the University of Seoul, specializing in Geoinformatics under the mentorship of Professor Chulmin Jun. With a robust academic background in Geography and a passion for innovative research, Dongbeom is actively engaged in developing smart systems for urban planning and vehicle safety. His work spans advanced studies in fire evacuation simulations, the application of artificial intelligence in urban growth modeling, and the development of safe driving systems for two-wheeled vehicles. 📊🛵

Publication Profile

Strengths for the Award:

  1. Academic Background: Dongbeom Kim has a solid educational foundation in Geography and Geoinformatics, with high GPAs in both his undergraduate and current Master’s studies. His ongoing education in Geoinformatics at the University of Seoul under the guidance of a reputed advisor further strengthens his research credentials.
  2. Research Publications: He has authored several papers published in reputable SCIE/ESCI journals like Sensors and Applied Sciences, along with multiple domestic publications. His research spans various topics, including fire evacuation simulations, vehicle safety, and urban growth modeling, indicating a diverse research portfolio.
  3. Conferences and Presentations: Dongbeom Kim has actively presented his research at several international and national conferences, such as the 18th International Conference on Location Based Services in Belgium and the Korean Society for Geospatial Information Science. These experiences highlight his engagement with the academic community and his ability to communicate his research effectively.
  4. Patents and Innovation: He is a co-inventor on four patents related to vehicle safety and route generation, demonstrating innovation and practical application of his research.
  5. Research Projects: Participation in multiple research projects, including those focused on greenhouse gas emission reduction and environmental big data analysis, shows his capability to contribute to significant scientific endeavors.

Areas for Improvement:

  1. Research Leadership: While Dongbeom Kim has collaborated on numerous projects and publications, there is limited evidence of him taking on a leading role in these efforts. Demonstrating more leadership in research projects or publications could strengthen his profile.
  2. Diversity in Research Impact: Although his research covers a range of topics, the majority are closely related to vehicle safety and geospatial data analysis. Expanding his research to cover other areas of geoinformatics or interdisciplinary applications could enhance the breadth of his research impact.
  3. Published Impact Factor: As some of his research is still under review and the impact factors of the journals in which he has published are not mentioned, highlighting the impact factor or citation index of his published work could further substantiate his research quality.

 

Education

Dongbeom holds a Bachelor’s degree in Geography from Kongju National University (2015-2021), achieving a GPA of 3.9/4.5. He is currently pursuing a Master’s degree in Geoinformatics at the University of Seoul, where he has achieved an impressive GPA of 4.33/4.5. 🎓🌍

Experience

Dongbeom’s experience includes multiple research projects, focusing on geospatial information science, urban growth modeling, and traffic safety. He has contributed to several conferences and published numerous peer-reviewed articles in international journals. His practical skills are reinforced by his active involvement in projects such as the development of a good driving evaluation system for two-wheeled vehicles and environmental big data analysis. 🌐📝

Research Focus

Dongbeom’s research primarily revolves around geoinformatics, fire evacuation simulations, urban growth modeling, and traffic safety. He is particularly interested in utilizing sensor-based approaches and artificial intelligence techniques to address urban challenges and enhance public safety. 🚒🌆

Awards and Honors

Dongbeom has presented his work at prestigious international and domestic conferences and has collaborated on innovative projects that have received national attention. He is also recognized for his contributions to patents related to traffic safety and environmental management. 🏆🔬

Publication Top Notes

Under Review: Dongbeom Kim, Hyemin Kim, Yuhan Han, Chulmin Jun, “Fire Evacuation Simulation with Agent-Based Fire Recognition Propagation” (Physica Scripta, 2024)

Dongbeom Kim, Hyemin Kim, Suyun Lee, Qyoung Lee, Minwoo Lee, Jooyoung Lee, Chulmin Jun, “Design and Implementation of a Two-Wheeled Vehicle Safe Driving Evaluation System” (Sensors, 2024) – Cited by 2 articles

Dongbeom Kim, Hyemin Kim, Chulmin Jun, “The Detection of Aggressive Driving Patterns in Two-Wheeled Vehicles Using Sensor-Based Approaches” (Applied Sciences, 2023) – Cited by 3 articles

Minjun Kim, Dongbeom Kim, Daeyoung Jin, Geunhan Kim, “Application of Explainable Artificial Intelligence (XAI) in Urban Growth Modeling: A Case Study of Seoul Metropolitan Area, Korea” (Land, 2023) – Cited by 5 articles

Suyun Lee, Dongbeom Kim, Chulmin Jun, “Calculation of Dangerous Driving Index for Two-Wheeled Vehicles Using the Analytic Hierarchy Process” (Applied Sciences, 2023) – Cited by 1 article

Minjun Kim, Dongbeom Kim, Geunhan Kim, “Examining the Relationship between Land Use/Land Cover (LULC) and Land Surface Temperature (LST) Using Explainable Artificial Intelligence (XAI) Models: A Case Study of Seoul, South Korea” (International Journal of Environmental Research and Public Health, 2022) – Cited by 4 articles 📖🔗

Conclusion:

Dongbeom Kim appears to be a promising candidate for the “Best Researcher Award” due to his solid academic background, active research publication record, involvement in innovative patents, and participation in impactful research projects. To further strengthen his candidacy, he could focus on assuming leadership roles in his research, diversifying his research impact, and emphasizing the citation metrics of his work. Overall, his contributions to the field of geoinformatics and vehicle safety suggest he is a strong contender for this award.

Rongfang Wang | Artificial Intelligence | Best Researcher Award

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Prof. Rongfang Wang | Artificial Intelligence | Best Researcher Award

Associate Professor, School of Artificial Intelligence/Xidian University, China

🌟 Rongfang Wang, Ph.D. is an accomplished Associate Professor at the School of Artificial Intelligence, Xidian University, Xi’an, China. With a deep passion for machine learning and medical image processing, Dr. Wang has dedicated her career to advancing artificial intelligence in healthcare and remote sensing applications. Her work has been recognized through various research grants and scholarly publications, establishing her as a leader in her field. 🌍💡

Publication Profile

Google Scholar

Strengths for the Award

  1. Innovative Research: Rongfang Wang’s research covers advanced topics such as machine learning, deep learning, medical image processing, and multimodal fusion, indicating a strong focus on cutting-edge technology. Her work in areas like treatment outcome prediction and landslide hazard analysis demonstrates the applicability and impact of her research.
  2. Funding and Grants: Wang has secured substantial funding from prestigious organizations, including the National Natural Science Foundation of China and various key research programs. Her roles as Principal Investigator (PI) on multiple projects reflect her ability to lead and manage high-impact research initiatives.
  3. Publication Record: Wang has an impressive publication record in high-impact journals, with numerous peer-reviewed papers and conference proceedings. Her work spans various high-profile publications, demonstrating significant contributions to her field.
  4. International Experience: Her experience as a visiting scholar at The University of Texas Southwestern Medical Center adds an international perspective to her research, enhancing her profile in the global research community.
  5. Mentorship and Training: Wang actively mentors multiple M.D. students, highlighting her commitment to developing future researchers and contributing to the academic community beyond her own research.

Areas for Improvement

  1. Broader Impact Evidence: While Wang’s publications and funding are substantial, providing more detailed evidence of the real-world impact and practical applications of her research could strengthen her nomination. Specifically, examples of how her work has influenced industry practices or policy changes would be beneficial.
  2. Collaborative Work: Increasing collaborative research efforts with other institutions or industry partners could further enhance her research’s breadth and applicability. While she has secured significant grants, highlighting any collaborative projects or partnerships could showcase a broader impact.
  3. Diversity in Research Topics: Wang’s research is heavily focused on remote sensing and medical image processing. Expanding her research portfolio to include a wider range of topics within artificial intelligence or interdisciplinary fields might provide a more comprehensive view of her research capabilities.

 

Education

🎓 Dr. Wang earned her Ph.D. in Electronic Science and Technology from Xidian University, Xi’an, China, in 2014. She also holds a Master’s degree in the same field from Xidian University, obtained in 2007. 📘🎓

Experience

🧑‍🏫 Dr. Wang has held several academic and research positions, including her current role as an Associate Professor at the School of Artificial Intelligence, Xidian University. She was a Visiting Scholar at the University of Texas Southwestern Medical Center, Dallas, USA, and has extensive experience as a postdoctoral fellow and instructor at Xidian University. 📚💻

Research Focus

🔍 Dr. Wang’s research interests span multiple domains, including machine learning, deep learning, medical image processing, treatment outcome prediction, image registration, model compression, and computer vision. She is particularly known for her work in multimodal learning and its applications in healthcare and environmental monitoring. 🌿🧠

Awards and Honours

🏅 Dr. Wang has secured numerous prestigious research grants, including from the National Natural Science Foundation of China and the State Key Laboratory of Multimodal Artificial Intelligence Systems. Her innovative research in machine learning and remote sensing has been consistently funded and recognized by leading academic institutions and government bodies. 🥇🌟

Publication Top Notes

📝 Dr. Wang has authored several impactful papers, including her work on “A Multi-Modality Fusion and Gated MultiFilter U-Net for Water Area Segmentation in Remote Sensing” published in Remote Sensing (2024). She also developed the ASF-LKUNet model for medical image segmentation, published in TechRxiv (2023). 📑🌍

S Zhang, W Li, R Wang, C Liang, X Feng, Y Hu. DaliWS: A High-Resolution Dataset with Precise Annotations for Water Segmentation in Synthetic Aperture Radar Images. Remote Sensing, Vol 16 (4), 720, 2024.

R Wang, C Zhang, C Chen, H Hao, W Li, L Jiao. A Multi-Modality Fusion and Gated MultiFilter U-Net for Water Area Segmentation in Remote Sensing. Remote Sensing, Vol 16 (2), 419, 2024.

R Wang, Z Mu, J Wang, K Wang, H Liu, Z Zhou, L Jiao. ASF-LKUNet: Adjacent-Scale Fusion U-Net with Large-kernel for Medical Image Segmentation. TechRxiv, 2023.

R Wang, J Guo, Z Zhou, K Wang, S Gou, R Xu, D Sher, J Wang. Locoregional recurrence prediction in head and neck cancer based on multi-modality and multi-view feature expansion. Physics in Medicine & Biology, Vol 67 (12), 125004, 2022.

R Wang, L Wang, X Wei, JW Chen, L Jiao. Dynamic graph-level neural network for SAR image change detection. IEEE Geoscience and Remote Sensing Letters, Vol 19, 1-5, 2021.

L Chen, M Dohopolski, Z Zhou, K Wang, R Wang, D Sher, J Wang. Attention guided lymph node malignancy prediction in head and neck cancer. International Journal of Radiation Oncology Biology Physics, Vol 110 (4), 1171-1179, 2021.

K Wang, Z Zhou, R Wang, L Chen, Q Zhang, D Sher, J Wang. A multi‐objective radiomics model for the prediction of locoregional recurrence in head and neck squamous cell cancer. Medical Physics, Vol 47 (10), 5392-5400, 2020.

Conclusion

Rongfang Wang is a strong candidate for the Research for Best Researcher Award due to her innovative research, impressive funding achievements, and significant contributions through publications. Her international experience and dedication to mentoring add further value to her profile. To enhance her candidacy, focusing on demonstrating the broader impact of her work and increasing collaborative efforts could be beneficial. Overall, her qualifications and accomplishments make her a compelling nominee for the award

Samana Batool | Artificial Intelligence | Best Researcher Award

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Ms. Samana Batool | Artificial Intelligence | Best Researcher Award

PhD scholar, Capital University of Science and Technology, Pakistan

📝 Samana Batool is a dedicated PhD student in Electrical Engineering, specializing in AI applications in medical imaging. She recently completed her final defense at the Capital University of Science and Technology, Islamabad, Pakistan. With a strong foundation in AI-driven healthcare solutions, Samana has made significant contributions to the field of medical imaging, particularly in echocardiogram data analysis. Her work has been published in reputable journals, and she serves as a reviewer for high-impact publications, playing a key role in advancing research standards.

Publication Profile

Google scholar

Strengths for the Award:

Strong Academic Background: Samana Batool holds a PhD in Electrical Engineering with a specialization in AI applications in medical imaging. She has completed advanced research, particularly in echocardiogram data analysis, which is a niche area within medical AI.

Innovative Research: Her research projects, such as the integration of multi-modality techniques (ECG and Echocardiography) and the quantification of LV structure using deep learning, demonstrate innovation and the potential for significant impact on clinical practices.

Publications and Editorial Role: She has published papers in reputable journals (Diagnostics, MDPI; Physica Medica, Elsevier) and serves as a reviewer for high-impact journals, indicating recognition by the scientific community.

Collaboration with Medical Institutions: Her collaboration with the Cardiology Department at Shifa International Hospital, Islamabad, further supports the practical application of her research in clinical settings.

Areas for Improvement:

Limited Citation Index: The citation count of 4 is relatively low, which may reflect a need for more visibility and impact in the academic community.

Lack of Industry Engagement: No consultancy or industry-sponsored projects are mentioned, which could demonstrate a lack of practical industry application or impact.

Absence of Patents and Books: No patents or books published suggests a focus on journal publications rather than other forms of dissemination and intellectual property, which could be considered a limitation in terms of innovation and knowledge transfer.

Professional Memberships and Recognition: The absence of professional memberships and awards or recognitions may limit her visibility and recognition in her field.

 

Education

🎓 Samana holds a PhD in Electrical Engineering, specializing in AI applications in medical imaging. She also earned a Master’s in Computer Engineering and a Bachelor’s in Electrical Engineering. Her academic journey reflects a strong commitment to integrating advanced AI techniques with medical imaging to improve diagnostic tools and healthcare outcomes.

Experience

💼 Samana has a diverse professional background, serving as a Research Associate at Digital Pakistan Lab (NUST), where she focused on AI-driven healthcare solutions. She also worked as an Assistant Manager (Electronics) at the Pakistan Space and Upper Research Commission (SUPARCO). Her roles have centered on leveraging AI for innovative medical applications, particularly in cardiac imaging and disaster management.

Research Focus

🔍 Samana’s research revolves around the applications of machine learning and deep learning in medical image analysis, particularly in echocardiogram data. She has developed methodologies for quantifying left ventricular (LV) structure and function, enhancing the precision of cardiac diagnostics. Her ongoing projects also explore integrating multimodality techniques, such as ECG and echocardiography, to advance AI-based solutions in healthcare.

Awards and Honors

🏆 Samana has been recognized for her contributions to AI-driven medical imaging, particularly for her innovative research on echocardiogram data analysis. Her published work in reputed journals has gained recognition, contributing to advancements in LV quantification and AI-based healthcare solutions.

Publication Top Notes

“Ejection Fraction Estimation from Echocardiograms Using Optimal Left Ventricle Feature Extraction Based on Clinical Methods” – Diagnostics (MDPI), 2023

“Quantification of LV Structure and Function using Deep Learning Techniques” – Physica Medica (Elsevier), 2022

Conclusion:

Samana Batool demonstrates strong potential for the “Best Researcher Award” due to her innovative research in AI applications in medical imaging and her active involvement in academic publishing. However, to enhance her competitiveness, she could work on increasing her citation index, engaging more with industry projects, and contributing to professional organizations. Her existing strengths in academic research and collaboration are notable, but diversifying her achievements could further strengthen her nomination for this award.

Rahma Mani | Artificial Intelligence | Women Researcher Award

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Ms. Rahma Mani | Artificial Intelligence | Women Researcher Award

PhD student, Escuela Técnica Superior de Ingeniería Informática, ETSII, Spain

Rahma Mani is a dedicated Ph.D. candidate in Electrical Engineering and Computer Science at the University of Seville, Spain, with a deep passion for wireless sensor networks, machine learning, and artificial intelligence. With a strong foundation in electrical engineering from the National Engineering School of Monastir, Tunisia, she has demonstrated her expertise through various academic and professional roles. Rahma has contributed to significant research projects and has a keen interest in innovative technologies.

Publication Profile

 

Strengths for the Award:

  1. Academic Excellence: Rahma is currently pursuing a Ph.D. in Electrical Engineering and Computer Science, focusing on cutting-edge fields such as wireless sensor networks, machine learning, and artificial intelligence. Her educational background is robust and well-aligned with emerging technological fields.
  2. Research Contributions: Rahma has multiple publications in reputable journals and conferences, including a submission to the prestigious Pervasive and Mobile Computing Journal by Elsevier. Her research in wireless sensor networks demonstrates innovation and contributes significantly to the field.
  3. Global Perspective: Rahma’s North African upbringing combined with her international academic and professional experiences in Spain, Italy, France, and Tunisia give her a unique global perspective. This diversity enhances her ability to approach problems from different angles, which is a valuable asset in research.
  4. Technical Skills: She possesses a wide range of digital and programming skills, including proficiency in languages like Java, C++, and MATLAB, as well as experience with technologies such as Vivado and Arduino. These skills are critical for her research and development work.
  5. Leadership and Innovation: Rahma demonstrated leadership in her role as the Electrical Committee leader in the ENIM TEAM, where she led the development of an electric car for an international competition. Her involvement in volunteer activities also highlights her leadership abilities and commitment to social causes.
  6. Language Proficiency: Fluent in English, Arabic, and French, with basic Spanish, Rahma’s multilingual capabilities are a significant asset in collaborative international research.

Areas for Improvement:

  1. Broader Research Exposure: While Rahma has a strong publication record, expanding her research impact by collaborating on interdisciplinary projects or participating in more international conferences could further enhance her profile.
  2. Advanced Certifications: Although Rahma has quality management certifications, pursuing advanced certifications related to her research areas (e.g., specialized AI or wireless communication certifications) could strengthen her expertise.
  3. Industry Collaboration: Increasing her engagement with industry partners, beyond internships, through joint research projects or consulting roles could provide practical applications for her research, enhancing its relevance and impact.

 

🎓 Education:

Rahma is currently pursuing her Ph.D. in Electrical Engineering and Computer Science at the University of Seville, Spain, specializing in wireless sensor networks, machine learning, and artificial intelligence. She earned her Electrical Engineering Diploma from the National Engineering School of Monastir, Tunisia, where she also led a team in designing and developing an electric car for an international competition. Rahma began her academic journey with preparatory engineering studies at the Preparatory Institute for Engineering Studies of Monastir, Tunisia.

💼 Experience:

Rahma has gained extensive experience as an adjunct professor at the Higher Institute of Applied Sciences and Technology of Mahdia, Tunisia, where she taught courses on digital signal processing, converters, and electrical machines. She also worked as a Junior Full Stack Engineer at HRDatabank Tunisia (WILL Group, Japan), contributing to the development of HR web applications. Additionally, Rahma has completed internships at Smart Sensors Systems (3S) in Nancy, France, and the Tunisian Electricity and Gas Company in Sousse, Tunisia.

🔬 Research Focus:

Rahma’s research focuses on wireless sensor networks, particularly in the areas of localization algorithms, edge computing, and FPGA-enhanced systems. She is passionate about applying machine learning and artificial intelligence techniques to improve the efficiency and reliability of sensor networks, especially in large-scale and industrial applications.

🏆 Awards and Honors:

Rahma received a merit-based fellowship to pursue her Ph.D. internship in Italy and Spain, recognizing her outstanding academic and research achievements.

📚 Publication Top Notes:

Localizing Unknown Nodes with an FPGA-Enhanced Edge Computing UAV in Wireless Sensor Networks: Implementation and Evaluation (2024)

Improved 3D localization algorithm for large-scale wireless sensor networks (2023).

Improved Distance vector-based Kalman Filter localization algorithm for wireless sensor network (2023) .

CRT-LoRa: An efficient and reliable MAC scheme for real-time industrial applications (2023).

Improved Least-Square DV-Hop Algorithm for Localization in Large Scale Wireless Sensor Network (2022) .

 

Conclusion:

Rahma Mani is a well-qualified candidate for the Research for Women Researcher Award. Her solid academic background, impressive research contributions, technical expertise, and leadership qualities make her a strong contender. With continued focus on expanding her research impact and industry collaborations, she is likely to make significant contributions to the field of Electrical Engineering and Computer Science, particularly in the areas of wireless sensor networks and AI. Her application for the award would be well-justified, showcasing both her achievements and potential for future advancements.

 

 

Isabel de la Torre | Computer Science | Women Researcher Award

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Prof Dr. Isabel de la Torre | Computer Science | Women Researcher Award

Catedrática, Universidad de Valladolid, Spain

Isabel de la Torre Díez, born in 1979 in Zamora, Spain, is a renowned Full Professor at the University of Valladolid. She received her M.S. and Ph.D. degrees in Telecommunication Engineering from the same university in 2003 and 2010, respectively. Isabel’s expertise lies in telemedicine, e-health, m-health, and related fields. She has authored over 250 papers and played a significant role in numerous research projects. Isabel leads the GTe Research Group and is a key figure in the field of telemedicine and e-health. 🌐👩‍🏫

Publication Profile

 

Strengths for the Award

  1. Significant Research Contributions: Isabel de la Torre Díez has published over 250 papers in SCI journals, peer-reviewed conferences, and books. This extensive publication record highlights her impactful research in telemedicine, e-health, and related fields.
  2. Leadership and Innovation: She leads the GTe Research Group at the University of Valladolid and has been involved in creating and coordinating innovative software. Her leadership in advancing telemedicine and e-health applications demonstrates her commitment to improving healthcare through technology.
  3. Research Impact and Recognition: She has been involved in over 100 international conference program committees and has participated in numerous funded research projects. Her involvement as a reviewer for well-known SCI journals further underscores her expertise and influence in her field.
  4. Research and Teaching Excellence: With two research sexenios, she has demonstrated consistent research excellence. Her role in guiding doctoral theses and her contributions to high-impact journals and conferences reflect her high standing in the academic community.
  5. International Collaboration: Her postdoctoral research experiences in Portugal, Spain, and France highlight her international collaboration and mobility, enhancing her global research network and exposure.

Areas for Improvement

  1. Broader Recognition: While her research is extensive, further highlighting any awards or recognitions she has received could strengthen her application. Emphasizing awards or honors related to her research could enhance her candidacy.
  2. Diversity of Research Interests: While her focus is on telemedicine and e-health, demonstrating how her research contributes to a broader range of applications or interdisciplinary areas might strengthen her profile.
  3. Detailed Impact Metrics: Providing specific metrics, such as citation counts, h-index, and impact factors of the journals where she has published, could offer a clearer picture of her research impact.

Conclusion

Isabel de la Torre Díez is a highly qualified candidate for the Research for Women Researcher Award. Her extensive research contributions, leadership in innovative projects, and active participation in international research communities position her as a leading figure in her field. Enhancing her application with additional recognitions and detailed impact metrics could further bolster her candidacy. Overall, her achievements and ongoing contributions to the field of telemedicine and e-health make her a strong contender for the award.

Education 🎓

Isabel de la Torre Díez earned her M.S. and Ph.D. degrees in Telecommunication Engineering from the University of Valladolid, Spain, in 2003 and 2010, respectively. Her education laid a strong foundation for her prolific career in telemedicine and e-health. 🏫📜

Experience 👩‍💼

Isabel de la Torre Díez is a Full Professor in the Department of Signal Theory and Communications and Telematics Engineering at the University of Valladolid. She has authored over 250 papers and coauthored 16 registered innovative software. Isabel has been involved in more than 100 international conference program committees and has participated in 44 funded research projects. She is also a reviewer for renowned journals like the International Journal of Medical Informatics. 🏫📚

Research Focus 🔬

Isabel’s research focuses on the development and evaluation of telemedicine applications, e-health, m-health, EHRs (Electronic Health Records), machine and deep learning, privacy and security, biosensors, QoS (Quality of Service), and QoE (Quality of Experience) in the health field. She has significantly contributed to these areas, particularly in telepsychiatry, teleophthalmology, and telecardiology. 🧠💻

Awards and Honors 🏆

Isabel de la Torre Díez has received numerous accolades throughout her career. She has two research sexenios and coordinates the GTe Research Group and the GIR “Society of Information” group. She has also been recognized for her contributions as a reviewer for prestigious journals and her leadership in various research projects and collaborations. 🌟🏅

Publications 📄

  1. Novel model to authenticate role-based medical users for blockchain-based IoMT devices
    PLOS ONE
    2024-07-10
    DOI: 10.1371/journal.pone.0304774
  2. A Digital Mental Health Approach for Supporting Suicide Prevention: A Qualitative Study
    International Journal of Mental Health and Addiction
    2024-06-21
    DOI: 10.1007/s11469-024-01347-4
  3. A deep learning approach for Named Entity Recognition in Urdu language
    PLoS ONE
    2024
    DOI: 10.1371/journal.pone.0300725
    Cited by 1 article
  4. A Detectability Analysis of Retinitis Pigmentosa Using Novel SE-ResNet Based Deep Learning Model and Color Fundus Images
    IEEE Access
    2024
    DOI: 10.1109/ACCESS.2024.3367977
    Cited by 1 article

 

Gabriella d’Albenzio | Artificial Intelligence | Best Researcher Award

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Dr. Gabriella d’Albenzio | Artificial Intelligence | Best Researcher Award

Postdoc, Perk Lab Perk Lab Laboratory for Percutaneous Surgery, Canada

🎓 Gabriella d’Albenzio is a talented researcher with a focus on biomedical engineering and medical imaging. Currently pursuing a Ph.D. in Informatics at the University of Oslo, she has an impressive background in clinical engineering and biomedical engineering. Gabriella has worked on cutting-edge projects related to image-guided therapies and deep learning for medical applications, contributing significantly to her field through both research and development.

Profile

Scopus

 

Education

📚 Gabriella d’Albenzio holds a Ph.D. in Informatics from the University of Oslo (2021-2024). She completed her M.Sc. in Biomedical Engineering and B.Sc. in Clinical Engineering at Sapienza University of Rome, Italy, reflecting a solid foundation in both engineering and medical sciences.

Experience

💼 Gabriella d’Albenzio has extensive experience as a Scientific Software Developer at The Intervention Centre in Oslo, Norway, and as a Research Assistant at NTNU. She has also interned at the Rehabilitation Bioengineering Lab in Rome, contributing to various research projects involving advanced medical imaging and deep learning technologies.

Research Interests

🧠 Gabriella’s research interests are centered around enhancing surgical planning and medical imaging through deep learning and advanced computational techniques. Her work focuses on developing algorithms for medical image segmentation and predictive models for surgical outcomes, aiming to improve patient-specific treatment strategies.

Awards

🏅 Gabriella d’Albenzio has been recognized with the Globalink Research Internship by Mitacs, Canada, and a Grant Research Stay Abroad by The Research Council of Norway. These awards highlight her outstanding contributions to research and her commitment to advancing biomedical engineering.

Publications

Optimizing Surgical Plans for Parenchyma-Sparing Liver Resections through Contour-Guided Resection and Surface Approximation

Using NURBS for Virtual Resections in Liver Surgery Planning: A Comparative Usability Study

Patient-Specific Functional Liver Segments Based on Centerline Classification of the Hepatic and Portal Veins

ALive: Analytics for Computation and Visualization of Liver Resections

Laparoscopic Parenchyma-Sparing Liver Resection for Large (≥50 mm) Colorectal Metastases

Ao Guo | Artificial Intelligence | Best Researcher Award

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Mr. Ao Guo | Artificial Intelligence | Best Researcher Award

Master’s student, Xinjiang University, China

📚 Ao Guo is a dedicated postgraduate researcher at Xinjiang University with a focus on the innovation, optimization, and application of object detection technology. Currently pursuing a master’s degree in Electronic Information, Ao Guo has a robust background in computer vision, deep learning, pattern recognition, and image processing. He is committed to enhancing the accuracy and efficiency of object detection algorithms, contributing to both academia and industry.

Profile

Google Scholar

 

Education

🎓 Master’s Degree in Electronic Information – Xinjiang University, Urumqi, China
Ao Guo is advancing his studies in Electronic Information, focusing on the intersection of computer vision and deep learning to address real-world problems.

Experience

Ao Guo has been deeply involved in research aimed at optimizing deep learning models for intelligent weed management in agricultural environments. His work on a lightweight weed detection model, which incorporates global contextual features, is recognized for its high detection speed and accuracy, particularly suited for resource-constrained edge devices.

Research Interests

Ao Guo’s research interests encompass weed detection, deep learning, YOLO (You Only Look Once) models, attention mechanisms, and the development of lightweight networks. His innovative approach to integrating global information capture mechanisms into detection algorithms stands out in his field.

Awards

Ao Guo’s contributions to the field have been acknowledged through his publications and patent. Notably, he has published a paper in the highly reputed journal “Engineering Applications of Artificial Intelligence,” and he holds a patent for a lightweight weed detection method and device.

Publications

A lightweight weed detection model with global contextual joint features. Engineering Applications of Artificial Intelligence, 136, 108903. Link – Cited by: Article on Engineering Applications of Artificial Intelligence.

Omar Soufi | Artificial Intelligence | Best Researcher Award

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Dr. Omar Soufi | Artificial Intelligence | Best Researcher Award

Doctorate, Mohammed V University of Rabat Mohammadia School of Engineering, Morocco

👨‍💼 Dr. Omar Soufi is a distinguished Computer Science Engineer specializing in Artificial Intelligence, Data Science, Remote Sensing, and Geographic Information Systems (GIS). With a robust background in data analysis and decision-support systems, Dr. Soufi excels in promoting organizational advancements and enhancing strategic performance through well-planned recommendations. His proactive and industrious approach ensures the achievement of objectives by leveraging data-driven insights.

Profile

ORCID

Education

🎓 Dr. Omar Soufi earned his Ph.D. in Computer Science Engineering with a focus on Artificial Intelligence from EMI Rabat in 2023, completing his doctoral research with the AMIPS/E3S team. He also holds a degree in Engineering from Polytechnique Grenoble, ENSIMAG, and EMI Rabat, specializing in Information Systems Engineering and Software Quality Engineering, respectively. His foundational studies include a Diploma and a Bachelor’s degree in Mechanical Engineering from ARM Merkèns.

Experience

💼 Dr. Soufi’s professional journey includes notable roles such as Project Manager in the IT Department, Team Leader at the Decision Support Center, Head of the BI & Decision Tools Department, Head of the Geomatics & Decision Tools Division, and AI Mission Manager. His expertise spans numerous projects in artificial intelligence and data science, including the development of national geospatial platforms, disaster risk management systems, and SaaS solutions for real estate asset management and financial risk analysis.

Research Interests

🔍 Dr. Soufi’s research focuses on applying deep learning techniques to satellite image super-resolution and spacecraft attitude control. His interests extend to big data architecture, distributed systems, and geospatial data analysis, aiming to enhance the accessibility and quality of high-resolution satellite imagery.

Awards

🏆 Dr. Soufi has been recognized for his contributions to artificial intelligence and remote sensing. He has received certifications in various professional and personal development areas, including PMO, coaching, and personal development, further solidifying his expertise and commitment to excellence in his field.

Publications

📄 Study of deep learning-based models for single image super-resolution. Soufi, O., Belouadha, F.Z. (2022). Revue d’Intelligence Artificielle, Vol. 36, No. 6, pp. 939-952. https://doi.org/10.18280/ria.360616

📄 FSRSI: New deep learning-based approach for super-resolution of multispectral satellite images. Soufi, O., Belouadha, F.Z. (2023). Ingénierie des Systèmes d’Information, Vol. 28, No. 1, pp. 113-132. https://doi.org/10.18280/isi.280112

📄 Deep learning technique for image satellite processing. O. Soufi and F.Z- Belouadha. Intell Methods Eng Sci, vol. 2, no. 1, pp. 27–34, Mar. 2023.

📄 Enhancing Accessibility to High-Resolution Satellite Imagery: A Novel Deep Learning-Based Super-Resolution Approach. O. Soufi and F.Z- Belouadha. Journal of Environmental Treatment Techniques, 11(2), 44-49, 2023.

📄 An intelligent deep learning approach to spacecraft attitude control: the case of satellites. O. Soufi and FZ.- Belouadha. (2023). (Under Review)