Muhammad Asfand Hafeez | Information Security | Best Researcher Award

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Mr. Muhammad Asfand Hafeez | Information Security | Best Researcher Award

Research Assistant, Florida Atlantic University, United States

Muhammad holds a B.Sc. in Electrical Engineering from the University of Management and Technology, Lahore, Pakistan, with a CGPA of 3.89/4.00. He completed his M.Sc. in IT Convergence Engineering at Gachon University, South Korea, with a CGPA of 4.38/4.50, where he focused on GPU-based PQC implementations. He is now pursuing his Ph.D. at Florida Atlantic University with a perfect CGPA of 4.0/4.0. 🎓📚

Publication Profile

Strengths for the Award:

  1. Outstanding Academic Record: Muhammad Asfand Hafeez has demonstrated exceptional academic performance, with a CGPA of 4.0/4.0 in his PhD program and a CGPA of 4.38/4.50 in his Master’s program, showcasing his dedication and excellence in his studies.
  2. Innovative Research Contributions: His research in GPU-based implementations of Post-Quantum Cryptography (PQC) algorithms for IoT applications and side-channel analysis exhibits a strong focus on cutting-edge technologies and practical applications. This includes significant contributions to improving security protocols in emerging technologies.
  3. High-Impact Publications: Hafeez has a robust publication record in reputable journals and conferences, including IEEE Internet of Things Journal and IEEE Access. His work on GPU acceleration and cryptographic methods is relevant to current and future research in security and optimization.
  4. Awards and Recognition: He has received multiple awards such as the Rector Innovation Award, Patron’s Medal, and Best Paper Award, indicating recognition from academic and industry peers for his innovative work and contributions.
  5. Diverse Experience: His experience spans research assistant roles in various prestigious institutions and internships, providing him with a broad perspective and expertise in different aspects of electrical engineering and computer science.

Areas for Improvement:

  1. Broader Research Impact: While his research is highly specialized, expanding his work to address a wider range of practical problems and applications could further enhance its impact and relevance to diverse fields.
  2. Collaborative and Interdisciplinary Work: Increasing collaboration with researchers from other disciplines or institutions could lead to more comprehensive research outcomes and foster interdisciplinary innovations.
  3. Public Engagement and Dissemination: Greater emphasis on public outreach and dissemination of his research findings through non-academic channels could raise awareness and highlight the societal impacts of his work.

 

Experience

Muhammad has gained substantial research experience through his roles as a Research Assistant at various esteemed institutions, including ISCAAS Lab at Florida Atlantic University, Kansas State University, and Information Security & Machine Learning Lab at Gachon University. His internships and assistant roles have provided him with practical insights into electrical engineering and information security. 🧪💼

Research Focus

Muhammad’s research interests include GPU computing, Post-Quantum Cryptography (PQC), cryptographic protocols, and secure multi-party computation. He is dedicated to enhancing the efficiency and security of cryptographic systems and optimizing deep learning models. His work also encompasses side-channel analysis and applications of PQC in IoT. 💻🔒

Awards and Honors

Muhammad has been honored with several prestigious awards, including the Rector and Dean Merit Awards, the Rector Innovation Award, and the Patron’s (Gold) Medal Award. He has also achieved notable positions in competitions such as IEEE Xtreme Programming and Mechnofest. His recognition includes the Best Paper Award by BK21 FAST Intelligence Convergence Center and accolades from the Pakistan International Auto Show. 🏅🎖️

Publications Top Notes

Efficient TMVP-Based Polynomial Convolution on GPU for Post-Quantum Cryptography Targeting IoT Applications (2024) – IEEE Internet of Things Journal

GPU-Accelerated Deep Learning-based Correlation Attack on Tor Networks (2023) – IEEE Access

High Throughput Acceleration of Scabbard Key Exchange and Key Encapsulation Mechanism Using Tensor Core on GPU for IoT Applications (2023) – IEEE Internet of Things Journal

H-QNN: A Hybrid Quantum–Classical Neural Network for Improved Binary Image Classification (2024) – AI

A Low-Overhead Countermeasure Against Differential Power Analysis for AES Block Cipher (2021) – Applied Sciences

Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm (2021) – Applied Sciences

A Hybrid Linear Quadratic Regulator Controller for Unmanned Free-Swimming Submersible (2021) – Applied Sciences

Conclusion:

Muhammad Asfand Hafeez is a highly promising candidate for the Best Researcher Award due to his exemplary academic achievements, innovative research contributions, and significant awards and recognitions. His work in GPU-based implementations of Post-Quantum Cryptography and other advanced areas reflects a deep understanding of and commitment to his field. Addressing areas for improvement, such as broadening the scope of his research impact and increasing public engagement, could further enhance his candidacy and contributions to the field.

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

 

Obsa Gilo Wakuma | Computer Science | Best Researcher Award

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Dr. Obsa Gilo Wakuma | Computer Science | Best Researcher Award

Ass. Prof, Wallaga University, Ethiopia

Dr. Obsa Gilo Wakuma is a dedicated computer scientist specializing in deep learning and domain adaptation. With extensive experience in academia, he has contributed significantly to the field through his research and teaching. Dr. Obsa has held various positions at Wallaga University, Ethiopia, and currently serves as a research scholar at the Indian Institute of Technology Patna, India. His expertise spans multiple programming languages and database management systems, making him a versatile and valuable contributor to the field of computer science.

Profile

Strengths for the Award:

  1. Extensive Research Background: Dr. Obsa Gilo has a robust academic background, culminating in a Ph.D. in Computer Science and Engineering with a focus on deep learning approaches for efficient domain adaptation. His research in domain adaptation, particularly in sensor data and image classification, showcases his innovative contributions to the field.
  2. Publications: Dr. Gilo has a significant number of publications in reputed journals and conference proceedings. Notable among them are:
    • “Kernel bures metric for domain adaptation in sensor data” in Expert System with Applications (2024)
    • “Subdomain adaptation via correlation alignment with entropy minimization for unsupervised domain adaptation” in Pattern Analysis and Applications (2024)
    • “Rdaot: Robust unsupervised deep sub-domain adaptation through optimal transport for image classification” in IEEE Access (2023)
  3. Teaching and Mentoring: His employment history includes roles such as Lecturer and Graduate Assistant at Wallaga University, demonstrating his commitment to education and mentoring the next generation of scholars.
  4. Skills and Competencies: Dr. Gilo possesses strong technical skills in various programming languages, databases, and web development technologies, along with proficiency in English, Afaan Oromoo, and Amharic. This multilingual ability enhances his capacity to engage with diverse communities.
  5. Community Service: His experience includes academic research, teaching, training, consultation, and community service, reflecting a well-rounded professional dedicated to both academic and societal contributions.

Areas for Improvement:

  1. Practical Community Impact: While Dr. Gilo has an impressive academic and research portfolio, there could be more emphasis on the practical application of his research directly benefiting local communities. Highlighting specific projects or initiatives where his work has directly impacted community development would strengthen his case.
  2. Collaboration with Local Institutions: Greater collaboration with local institutions and involvement in projects addressing community-specific issues could further enhance his profile. Establishing partnerships with local universities, NGOs, or governmental bodies to implement his research findings in real-world settings would be beneficial.
  3. Visibility and Outreach: Increasing the visibility of his work through public lectures, community workshops, or outreach programs can help in demonstrating the broader societal impact of his research. Engaging with the community through these platforms can showcase the practical benefits of his research.

 

Education: 🎓

Dr. Obsa Gilo Wakuma holds a Ph.D. in Computer Science and Engineering from the Indian Institute of Technology Patna, with a thesis on “Deep Learning Approaches for Efficient Domain Adaptation.” He earned his M.Sc. in Computer Science from Wallaga University, where he developed an Information Extraction Model for Afaan Oromo news texts. Dr. Obsa also holds a B.Sc. in Computer Science from Wallaga University and has a strong foundation from Sibu Sire Preparatory and High School.

Experience: 💼

Dr. Obsa’s professional journey includes roles such as Recorder and Laboratory Technician at Wallaga University. He progressed to become a Graduate Assistant, Lecturer, and finally a Research Scholar at IIT Patna. His career reflects a blend of administrative, technical, and academic responsibilities, showcasing his diverse skill set and commitment to the field.

Research Interests: 🔍

Dr. Obsa’s research interests lie in deep learning, domain adaptation, and unsupervised learning. He has focused on developing efficient methods for domain adaptation in sensor data and image classification, contributing to several high-impact publications. His work aims to enhance the applicability and robustness of machine learning models in diverse environments.

Awards: 🏆

Dr. Obsa has been recognized for his academic excellence and research contributions throughout his career. He has received accolades for his innovative work in domain adaptation and deep learning, highlighting his role as a prominent researcher in the field of computer science.

Publications

  1. Unsupervised Sub-Domain Adaptation Using Optimal Transport. Journal of Visual Communication and Image Representation, 94, 103857. Cited by: 1 article.
  2. Kernel Bures Metric for Domain Adaptation in Sensor Data. Expert System with Applications, 255(Part C), 124725. Cited by: 1 article.
  3. Subdomain Adaptation via Correlation Alignment with Entropy Minimization for Unsupervised Domain Adaptation. Pattern Analysis and Applications, 27(1), 13. Cited by: 1 article.
  4. RDAOT: Robust Unsupervised Deep Sub-Domain Adaptation through Optimal Transport for Image Classification. IEEE Access. Cited by: 1 article.
  5. Integration of Discriminate Features and Similarity Preserving for Unsupervised Domain Adaptation. In 2022 IEEE 19th India Council International Conference (INDICON), pp. 1–6. Cited by: 1 article.

 

GAN XU | Computer Science | Best Researcher Award

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Mr. GAN XU | Computer Science | Best Researcher Award

PhD. candidate, capital university of economics and business, China

Xu Gan is a Doctoral student at Capital University of Economics and Business, Beijing, specializing in Rural Finance, Financial Theory and Policy, and Supply Chain Finance. With a solid academic background and practical experience, Xu has contributed significantly to research in financial services and blockchain technology. 🏛️📚

Profile

Scopus

 

Education

Xu Gan completed a Master of Finance from Capital University of Economics and Business in Beijing (2018-2021) and a Bachelor of Biotechnology from Beijing Union University (2006-2010). Xu also has an extensive academic background in these fields, providing a strong foundation for research and analysis. 🎓📈

Experience

Xu has actively participated in several high-profile research projects, including the National Social Science Foundation of China’s research on rural financial services and the China Mobile Communication Federation’s blockchain technology applications in finance. Xu was responsible for designing research sub-topics and writing research reports. 📊🔬

Research Interests

Xu Gan’s research interests include Rural Finance, Financial Theory and Policy, and Supply Chain Finance. Xu is focused on improving rural financial services and exploring innovative financial technologies such as blockchain. 🌾💡

Awards

Xu has been recognized for academic excellence with several honors: Beijing Outstanding Graduates (2020), Outstanding Graduate of Beijing Union University (2020), and several awards for excellent papers at various financial and rural finance forums. 🏅📜

Publications

Yang, GZ., Xu, G., Zhang, Y., et al. Financial density of village banks and income growth of rural residents. Economic Issues, 2021, 504(08): 89-94.

Zhang, F., Xu, G., Zhang, XY, Cheng, X. Knowledge mapping analysis of seven decades of rural finance research in China. Rural Finance Research, 2020(01): 10-20.

Zhang, F., Xu, G., Cheng, X. A review of blockchain applications in the financial sector. Technology for Development, 2019, 15(08): 865-871.

Hafiza Sadia Nawaz | Computer Science | Women Researcher Award

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Dr. Hafiza Sadia Nawaz | Computer Science | Women Researcher Award

Shenzhen University, China

🎓 Dr. H. Sadia Nawaz is a distinguished researcher and academic in Computer Vision, with a Ph.D. from Ocean University of China. Her work focuses on Temporal Activity Detection using Natural Language. With a background in both teaching and research, she has contributed significantly to the field of computer vision and continues to pursue a career as a postdoctoral researcher. Her extensive knowledge and dedication to the subject make her a valuable asset to any research group.

Profile

Scopus

 

Education

📚 Dr. H. Sadia Nawaz holds a Ph.D. in Computer Science from Ocean University of China, specializing in Temporal Moment Localization using Natural Language. She also completed her Master’s in Information Science and Technology at the University of Sargodha and an MS in Computer Science and Technology with a focus on Cyber Security at the University of Lahore. Her academic background provides a strong foundation for her research and teaching roles.

Experience

💼 Dr. Nawaz has a robust teaching background, having served as a Programming Teaching Assistant at Heriot-Watt University and Ocean University of China, and as a Computer Science Lecturer at Superior University of Lahore and the University of Sargodha. Her roles in academia have honed her skills in teaching, mentoring, and research, contributing to her expertise in computer vision and machine learning.

Research Interests

🔍 Dr. Nawaz’s research interests include Activity Localization in Videos via Language, Temporal Moment Detection, and Natural Language Processing. She is particularly focused on moment detection in complex scenarios and the integration of machine learning methods with computer vision techniques to enhance video understanding and activity localization.

Awards

🏅 Dr. H. Sadia Nawaz has been recognized for her research contributions in the field of computer vision, particularly for her work on Temporal Moment Localization using Natural Language. Her research has been published in high-impact journals, reflecting her expertise and commitment to advancing the field.

Publications

Temporal Moment Localization via Natural Language by Utilizing Video Question Answers as a Special Variant and Bypassing NLP for Corpora

IVTEN – Integration of Visual-Textual Entities for Temporal Activity Localization via Language in Video

Revelation of Novel Paradigm in Temporal Moment Localization by Using Natural Language in Video

Multi-level Edges and Context Arranger Architecture Using Referencing Expressions for Temporal Moment Localization via Natural Language

A Frame-Based Feature Model for Violence Detection from Surveillance Cameras Using ConvLSTM Network

Zongbao Jiang | Cybersecurity | Best Researcher Award

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Mr. Zongbao Jiang | Cybersecurity | Best Researcher Award

Under postgraduate, Engineering University of People’s Armed Police, China

📘 Zongbao Jiang is an emerging researcher specializing in computer technology at the Engineering University of People’s Armed Police. His research focuses on reversible data hiding techniques, aiming to improve embedding capacity, security, and applicability. Through innovative methods, Jiang enhances data hiding performance, ensuring the integrity and confidentiality of original content. Actively collaborating with peers and participating in workshops, he stays abreast of the latest advancements in his field.

Profile

Scopus

 

🎓 Education:

Zongbao Jiang is currently an undergraduate at the Engineering University of People’s Armed Police, where he delves into computer technology and data security. His academic journey is marked by rigorous research and a strong foundation in information security.

💼 Experience:

Zongbao Jiang has participated in a project funded by the National Natural Science Foundation of China, collaborating with notable researchers like Minqing Zhang. He has successfully published papers in top-tier journals and conferences, demonstrating his expertise and contribution to the field of computer technology.

🔬 Research Interests:

Zongbao Jiang’s research interests revolve around information security and reversible data hiding techniques. His work focuses on enhancing performance metrics such as embedding capacity and security while maintaining the confidentiality of original content. Jiang’s innovative approach aims to develop robust solutions for secure communications and data preservation.

🏆 Awards:

Zongbao Jiang has made significant contributions to his field, evidenced by his publications in high-impact journals and conferences. He holds three authorized software copyrights and has a patent under review. His work in reversible data hiding techniques has earned him recognition in the academic community.

Publications

Reversible Data Hiding Algorithm in Encrypted Images Based on Adaptive Median Edge Detection and Matrix-Based Secret Sharing
Link to article
Reversible Data Hiding in Encrypted Images based on Classic McEliece Cryptosystem
Link to article
Reversible Data Hiding Algorithm in Encrypted Domain Based on Matrix Secret Sharing
Link to article