Muhammad Imam | FOG computing | Best Researcher Award

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Assist Prof Dr. Muhammad Imam | FOG computing | Best Researcher Award

Assistant Professor, King Fahd University of Petroleum & Minerals, Saudi Arabia

Dr. Muhammad Y. Imam is a distinguished Cybersecurity Leader and Consultant with over 20 years of experience in the fields of cybersecurity, cryptography, and blockchain. He has a proven track record of combining entrepreneurship with technical expertise, excelling in problem-solving and innovative solutions. Currently an Assistant Professor at KFUPM, Dr. Imam is committed to enhancing cybersecurity education and practice in the region. 🌐🔐

Publication Profile

ORCID

 

Strengths for the Award

  1. Extensive Expertise in Cybersecurity: Dr. Imam has over 20 years of experience in cybersecurity, with a strong background in areas such as cryptography, blockchain, and malware detection. This extensive knowledge positions him as a leader in the field.
  2. Innovative Research Contributions: His PhD research focused on botnet mitigation techniques, showcasing his ability to develop novel solutions for complex problems. This work is crucial in addressing emerging threats in cybersecurity.
  3. Academic and Administrative Leadership: As an Assistant Professor at KFUPM and former Director of the Business Incubator, Dr. Imam demonstrates strong leadership skills. He has been actively involved in various committees, contributing to policy-making and curriculum development.
  4. Impactful Publications: With a range of publications in reputable journals, including works on secure PIN-entry methods and malware classification, Dr. Imam has made significant contributions to academic literature in cybersecurity.
  5. Strong Network and Collaboration: His involvement with various organizations, such as ARAMCO and Saudi Airlines, highlights his ability to bridge academia and industry, fostering collaborations that enhance research impact.
  6. Commitment to Education: Dr. Imam’s experience in teaching, professional training, and mentoring underscores his dedication to educating the next generation of cybersecurity professionals.

Areas for Improvement

  1. Broader Research Focus: While Dr. Imam has a strong background in cybersecurity, expanding his research to include emerging fields like artificial intelligence and machine learning in security applications could further enhance his profile.
  2. Enhanced Public Engagement: Increasing participation in public forums or conferences to share his research findings could amplify his impact and visibility within the global cybersecurity community.
  3. Collaboration with Diverse Disciplines: Engaging with researchers from different fields, such as sociology or behavioral science, could provide a more holistic approach to understanding cybersecurity issues, particularly in user behavior and security practices.
  4. Grant Acquisition: Actively pursuing more research grants and funding opportunities could help elevate his projects and provide resources for broader research initiatives.

Education

Dr. Imam earned his Ph.D. in Electrical and Computer Engineering from Carleton University in Ottawa, Canada, in 2013, focusing on cybersecurity, particularly in developing techniques for botnet mitigation. He also holds a Master’s degree from KFUPM, where he graduated in June 2004, and a Bachelor’s degree from the same institution, completed in May 2000. 🎓📚

Experience

Since September 2013, Dr. Imam has served as an Assistant Professor in the Computer Engineering Department at KFUPM, where he is involved in teaching, professional training, and research projects with industry partners. He previously directed the Business Incubator at KFUPM’s Entrepreneurship Institute, managing incubation and acceleration programs to support new startups. His leadership extends to various committees, including chairing the Cybersecurity Committee at KFUPM since January 2023. 👨‍🏫💼

Research Focus

Dr. Imam’s research interests are centered around cybersecurity, focusing on cryptography, network security, and malware detection. His innovative work includes developing advanced solutions for data privacy and risk management, addressing contemporary challenges in information security. 🔍💻

Awards and Honors

Throughout his career, Dr. Imam has been recognized for his contributions to cybersecurity education and practice, receiving accolades for his research and leadership in various academic and professional capacities. He has also been involved in multiple initiatives to improve cybersecurity awareness and education in Saudi Arabia and beyond. 🏅👏

Publications

F. Binbeshr, L. Y. Por, M. L. M. Kiah, A. A. Zaidan, and M. Imam, “Secure PIN-Entry Method Using One-Time PIN (OTP),” IEEE Access, vol. 11, pp. 18121-18133, 2023.

Al Mousa, M. Al Qomri, and M. Imam, “The Predicament of Privacy and Side-Channel Attacks,” International Journal of Development and Conflict, vol. 12, no. 2, pp. 182–191, 2022.

L. Ghouti and M. Imam, “Malware Classification Using Compact Image Features and Multiclass Support Vector Machines,” IET Information Security, vol. 14, no. 4, pp. 419–429, 2020.

M. Mahmoud, M. Nir, and A. Matrawy, “A Survey on Botnet Architectures, Detection and Defences,” International Journal of Network Security, vol. 17, no. 3, pp. 272–289, 2015.

M. Mahmoud, S. Chiasson, and A. Matrawy, “Does Context Influence Responses to Firewall Warnings?,” 2012 eCrime Researchers Summit, Las Croabas, PR, USA, 2012, pp. 1-10.

Conclusion

Dr. Muhammad Y. Imam exemplifies the qualities of a strong candidate for the Best Researcher Award. His extensive expertise in cybersecurity, innovative research contributions, leadership roles, and commitment to education make him a standout figure in the field. Addressing areas for improvement, such as expanding his research focus and enhancing public engagement, could further strengthen his contributions and influence in the cybersecurity landscape. Given these strengths and opportunities, Dr. Imam is well-positioned to receive recognition for his impactful work and leadership in the realm of cybersecurity.

Bhargavi Krishnamurthy | Internet of Things | Best Researcher Award

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Dr. Bhargavi Krishnamurthy | Internet of Things | Best Researcher Award

Associate Professor, Siddaganga Institute of Technology, India

Bhargavi Krishnamurthy is a dedicated Computer Science researcher specializing in machine learning, high-performance computing, and computer security. With a strong academic foundation and international research experience, she has made significant contributions to the field through her innovative projects and publications.

Publication Profile

Scopus

Strengths for the Award

  1. Strong Academic Background: Bhargavi Krishnamurthy has an impressive academic history, including a Ph.D. in Computer Science and Engineering (CSE) with a focus on the application of machine learning in improving HPC performance. Her postdoctoral research in Software Engineering of Machine Learning systems at the University of Memphis adds to her credibility.
  2. Relevant Research Experience: Bhargavi’s research is in a highly relevant and impactful area, combining machine learning, software engineering, and high-performance computing (HPC). This multidisciplinary approach is crucial in today’s research landscape.
  3. Publications and Conferences: She has presented her research at various reputable international conferences, showcasing her work in areas like remote health monitoring, smart wearables, cloud solutions, and predictive analysis in e-commerce. This indicates a consistent contribution to her field.
  4. Global Exposure: Her postdoctoral experience at an international university (University of Memphis) reflects her exposure to global research standards and collaboration, which is a significant asset for any researcher.

Areas for Improvement

  1. Broader Publication Record: While Bhargavi has presented at conferences, it would be beneficial to see more peer-reviewed journal publications, which typically have a more rigorous review process and greater impact in the academic community.
  2. Focused Research Direction: Bhargavi’s research spans multiple topics within computer science, which is commendable. However, a more focused research trajectory with deeper contributions in one specific area might enhance her profile as an expert in that domain.
  3. Collaboration and Grants: Evidence of successful collaboration with other researchers, securing research grants, and contributing to large-scale projects could further bolster her candidacy for the award.

🎓 Education:

Bhargavi earned her Ph.D. in Computer Science and Engineering from Visveswaraya Institute of Technology in December 2020, focusing her thesis on enhancing HPC performance using machine learning. She holds an M.Tech in Computer Science and Engineering from Siddaganga Institute of Technology, Tumakuru (2012) with a CGPA of 9.02 and first-class distinction, and a B.E. in Computer Science and Engineering from Visveswaraya Institute of Technology (2009) with first-class honors. She also completed her Pre-University and SSLC education from Karnataka boards, achieving first-class grades in both.

💼 Experience:

Bhargavi served as a Postdoctoral Research Scholar at the Game Theory and Computer Security laboratory (GTCS) in the Department of Computer Science at the University of Memphis, USA, from August 2021 to February 2022. During this tenure, she conducted research on the software engineering aspects of machine learning systems. Her academic journey includes extensive research and project work during her Ph.D. and M.Tech studies, contributing to advancements in computer science.

🔬 Research Focus:

Her research primarily explores the application of machine learning to improve the performance of high-performance computing systems. Additionally, Bhargavi has delved into areas such as quality of service in wireless medical sensor networks, query translation between SQL and XPath, context-aware computing, secure data sharing using attribute-based encryption, cloud-based demographic management solutions, and predictive analysis in e-commerce.

🏆 Awards and Honours:

Throughout her academic career, Bhargavi has consistently achieved first-class distinctions, including a CGPA of 9.02 in her M.Tech and first-class honors in her B.E., reflecting her dedication and excellence in her studies.

📝 Publications:

Bhargavi Krishnamurthy has authored and co-authored several research papers presented at international conferences and published in reputable journals. Notable publications include:

“CAs-based QoS Scheme for Remote Health Monitoring over WMSN” – Presented at the International Conference on Advanced Computing, Networking and Security, NITK Surathkal, 2012. Link (Published Year: 2012, Conference Proceedings) – Cited by X articles.

“Join Queries Translation from SQL to XPath” – Published in IEEE proceedings, Tirunelveli, India, 2013. Link (Published Year: 2013, IEEE Conference) – Cited by Y articles.

“Context Aware Smart Watch” – Presented at the International Conference on Emerging Computation and Technologies (ICECIT), Elsevier Procedia, SIT, Tumkur, 2013. Link (Published Year: 2013, Elsevier Procedia) – Cited by Z articles.

“Secure Sharing of Car Using ABE” – Published in Proceedings of IRF International Conference, Mysore, 2014. Link (Published Year: 2014, Conference Proceedings) – Cited by A articles.

“Cloud based Solution to Manage Demographic Demand and Supply of Skills” – Presented at the Indian Technology Congress, NIMANS Convention Hall, Bangalore, 2014. Link (Published Year: 2014, Conference Proceedings) – Cited by B articles.

“Predictive Analysis of E-Commerce Products” – Presented at the International Conference on Intelligent Computing and Communication, Springer, MIT College of Engineering, Pune, 2017. Link (Published Year: 2017, Springer Conference Proceedings) – Cited by C articles.

Conclusion

Bhargavi Krishnamurthy is a strong candidate for the Research for Best Researcher Award, given her solid academic foundation, relevant research experience, and contributions to significant areas in computer science. To further strengthen her case, focusing on a specific research niche, expanding her publication record in high-impact journals, and demonstrating leadership in collaborative projects or grant acquisition would be beneficial.

 

Shaghaf Kaukab | Technology | Young Scientist Award

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Dr. Shaghaf Kaukab | Technology | Young Scientist Award

scientist, ICAR-CIPHET, India

Shaghaf Kaukab is a dedicated Scientist at ICAR-Central Institute of Post-Harvest Engineering & Technology (ICAR-CIPHET), Ludhiana, specializing in Agricultural Structure and Process Engineering. With over 11 years of combined experience in scientific research and academic exploration within the food engineering and technology platform, Shaghaf has made significant contributions to the domain of extrusion processing, storage technology, drying techniques, and functional food product development. His work emphasizes the application of AI, machine learning, and deep learning techniques in agriculture, leading to innovative solutions that improve post-harvest management and food processing.

Publication Profile

Scopus

Strengths for the Award

  1. Research Contributions: Shaghaf Kaukab has made significant contributions to agricultural structure and process engineering, particularly in post-harvest technology. Her work on projects such as IoT-based monitoring systems and AI-enabled robotic harvesters demonstrates her innovative approach and alignment with modern agricultural challenges.
  2. Academic Excellence: With a Ph.D. in Post Harvest Technology and multiple prestigious academic awards, she has a strong academic background. Her high CGPA scores and ICAR merit medals underscore her academic diligence.
  3. Interdisciplinary Expertise: Shaghaf has expertise in various domains, including AI, machine learning, image processing, and food process engineering, making her research impactful and versatile.
  4. Publications and Impact: She has published extensively in refereed journals and contributed to book chapters, highlighting her active involvement in advancing her field of research. The inclusion of her work in high-impact journals reflects her research’s quality and relevance.
  5. Leadership and Collaboration: Shaghaf has demonstrated leadership by managing several projects, mentoring students, and coordinating training programs. Her collaborative efforts with organizations like CDAC and international exposure (e.g., Purdue University) enhance her profile.

Areas for Improvement

  1. Broader Outreach: While Shaghaf has conducted training and outreach activities, expanding these efforts to reach a more diverse audience, including more international platforms, could enhance her influence and recognition.
  2. Grant Acquisition: Although involved in several projects, focusing on securing more independent research grants could further validate her capabilities and drive her research agenda.
  3. Networking and Professional Development: Increased participation in international conferences, workshops, and collaborations outside of India could further her exposure and contribute to professional growth.

 

🎓 Education

Shaghaf Kaukab earned his Ph.D. in Post-Harvest Technology from the Indian Agricultural Research Institute (IARI), New Delhi, with a stellar CGPA of 9.1/10 in 2019. Prior to this, he completed his M.Tech. in Post-Harvest Engineering & Technology from IARI, New Delhi, with a CGPA of 8.97/10 in 2016. His academic journey has been marked by excellence, laying a strong foundation for his research and scientific endeavors.

💼 Experience

Currently, Shaghaf is a Scientist in Agricultural Structures & Process Engineering at ICAR-CIPHET, Ludhiana, where he has been instrumental in the development of technologies such as the stereo-depth based detection and localization module for apples. He has successfully led and contributed to several ongoing projects, including IoT-based modular systems for cold storage and AI-enabled robotic apple harvesters. His role extends to technical writing, project implementation, and collaboration with academic and industrial partners.

🔍 Research Focus

Shaghaf’s research interests lie in the application of new-age technologies like AI, machine learning, and deep learning in the post-harvest agriculture sector. He focuses on image processing techniques (such as Biospeckle, RGB, X-ray, Hyperspectral imaging) and the analysis of food properties (physical, thermal, mechanical, and micro-structural). His work in food process engineering aims to enhance the efficiency and quality of post-harvest processes.

🏆 Awards and Honors

Shaghaf Kaukab’s work has earned him recognition within the scientific community, including membership in prestigious organizations such as the Indian Society of Agricultural Engineers (ISAE) and the American Society of Agricultural and Biological Engineers (ASABE). He serves as a regular reviewer for scientific journals and has been an external examiner for graduate students at Dr. Rajendra Prasad Central Agricultural University, Bihar.

📚 Publication Top Notes

Shaghaf has published numerous articles in refereed journals and contributed to book chapters and training manuals. His notable works include:

Improving Real-time Apple Fruit Detection: Depth and Multi-modal Information Fusions with Non-targeted Background Removal – Published in Ecological Informatics.

Chickpea Temperature Profile Development and its Implication under Microwave Treatment – Published in Biological Forum – An International Journal.

Osmotic Dehydration of Aloe-vera Gel Discs – Published in Journal of AgriSearch.

Engineering Properties, Processing, and Value Addition of Tamarind: A Review – Published in IJBSM.

Study of Engineering Properties of Selected Vegetable Seeds – Published in Indian Journal of Agricultural Sciences.

 

Conclusion

Shaghaf Kaukab is a strong candidate for the Research for Young Scientist Award. Her innovative research, interdisciplinary expertise, and significant contributions to agricultural engineering, particularly in post-harvest technology, make her a standout. While expanding her outreach and securing more independent funding could strengthen her profile further, her accomplishments thus far demonstrate her potential as a leader in her 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.

 

Juxian Zhao | Computer Science | Best Researcher Award

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Dr. Juxian Zhao | Computer Science | Best Researcher Award

PhD candidate, China University of Mining and Technology School of Mechatronic Engineering, China

📚 Juxian Zhao is a PhD candidate at the China University of Mining and Technology, specializing in robotics, computer vision, and deep learning. He focuses on developing innovative technologies for intelligent firefighting equipment and autonomous operations. Currently leading R&D for a key provincial project, Juxian has made significant contributions to the field through his research and innovations.

Profile

Scopus

 

Education

🎓 Juxian Zhao is pursuing a PhD at the China University of Mining and Technology in the School of Mechatronic Engineering. His academic journey has been marked by a strong focus on robotics, computer vision, and deep learning technologies, which he integrates into his research on intelligent firefighting equipment.

Experience

💼 Juxian Zhao has extensive experience in the research and development of intelligent firefighting equipment, multi-agent collaboration, and autonomous firefighting operations. He is currently leading a key provincial-level R&D project and actively collaborating with XCMG Fire Fighting Equipment Co., Ltd., and Xuzhou XCMG Daojin Special Robot Technology Co., Ltd.

Research Interests

🔬 Juxian Zhao’s research interests include robotics, computer vision, and deep learning technologies. He is particularly focused on applying these technologies to intelligent firefighting equipment and autonomous firefighting operations, aiming to enhance efficiency and effectiveness in emergency response scenarios.

Awards

🏆 Juxian Zhao has been recognized for his contributions to the field of robotics and firefighting technology through various accolades. His work on the CG-DALNet model for autonomous firefighting has garnered attention for its innovative approach and significant performance improvements.

Publications

Accurate and Fast Fire Alignment Method Based on a Mono-binocular Vision System

Visual predictive control of fire monitor with time delay model of fire extinguishing jet

An efficient firefighting method for robotics: A novel convolution-based lightweight network model guided by contextual features with dual attention

HaiTian Chen | Computer Science | Best Researcher Award

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Ms. HaiTian Chen | Computer Science | Best Researcher Award

College of Science, North China University of Science and Technology, China

Chen HaiTian is a dedicated researcher in the field of Cyberspace Security from China. Born in December 1998, Chen has made significant strides in federated learning, privacy preservation, and cybersecurity. His contributions span multiple peer-reviewed journals and patents, showcasing his commitment to advancing technology and safeguarding digital spaces.

Profile

ORCID

 

Education

Chen HaiTian holds a major in Cyberspace Security, demonstrating his expertise and focus in this critical area of study. His academic background has equipped him with the skills and knowledge necessary to tackle complex cybersecurity challenges and contribute to innovative solutions in the field. 🎓

Research Interests

Chen HaiTian’s research interests focus on federated learning, privacy preservation, and cybersecurity. He is particularly interested in developing robust aggregation techniques to defend against poisoning attacks in federated learning and exploring personalized fair split learning for resource-constrained Internet of Things (IoT). 🔍

Awards

Chen HaiTian has received recognition for his contributions to software development, including the Huali Academy Backstage Management System V1.0 and the DC Early Warning System V1.0. His work has been registered with computer software registration numbers, showcasing his achievements in developing innovative solutions for network management and security. 🏆

Publications

Chen, H.; Chen, X.; Peng, L. (2023). FLRAM: Robust Aggregation Technique for Defense Against Byzantine Poisoning Attacks in Federated Learning. Electronics. Cited by Electronics.

Chen, H.; Chen, X.; Peng, L. (2024). Personalized Fair Split Learning for Resource-Constrained Internet of Things. Sensors, 24, 88. Cited by Sensors.

Chen, H., Chen, X., Ma R., et al. (2024). A federated learning privacy preserving approach for remote sensing data. Computer Applications. Cited by Computer Applications.

Chen, H., Chen, X. (2023). A Robust Aggregation Technique for Poisoning Attack Defense in Federated Learning. Cited by Journal.

Xu C., Zhang S., Chen H., et al. (2024). A federated learning approach based on adaptive differential privacy and customer selection optimization. Computer Applications. Cited by Computer Applications.

Peng L., Zhang S., Chen H., et al. (2023). Clustered federated learning based on improved CFSFDP algorithm. Journal of North China University of Science and Technology (Natural Science Edition). Cited by NCUST.

Qiang Li | Computer Science | Best Researcher Award

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Mr. Qiang Li | Computer Science | Best Researcher Award

Lecturer, Qingdao University, China

Dr. Li Qiang is an experienced lecturer in computer science with a PhD in Engineering. He specializes in high-performance computing and has a strong background in both teaching and research. Committed to fostering academic excellence and technological innovation, Dr. Li has been a dedicated educator and researcher at Qingdao University since 2015.

Profile

ORCID

 

Education 🎓

PhD in Engineering: University of the Chinese Academy of Sciences, Computer Network Information Center (2010-2014), Advisor: Lu Zhonghua. Master’s in Information Science and Engineering: Shandong University of Science and Technology (2007-2010), Advisor: Zhao Maoxian. Bachelor’s in Education: Qingdao University (2003-2007).

Experience 👨‍🏫

Lecturer at Qingdao University, School of Computer Science and Technology (January 2015-Present). Teaching undergraduate and graduate courses in computer science. Supervising student research projects and theses. Conducting research in high-performance computing. Published 12 research papers in journals and conferences. Granted 2 patents.

Research Interests 🔬

Dr. Li Qiang’s research interests lie in high-performance computing, particularly in the optimization and parallel implementation of numerical simulations and the development of new computational frameworks. His work focuses on enhancing computational efficiency and scalability in large-scale scientific computations.

Awards 🏆

Dr. Li Qiang has been recognized for his contributions to the field of high-performance computing through multiple publications and patents. His innovative work has led to advancements in computational methods and has garnered attention in the academic community.

Publications 📄

Optimization Research of Heterogeneous 2D-Parallel Lattice Boltzmann Method Based on Deep Computing Unit. Appl. Sci. 2024, 14, 6078.

Heterogeneous Parallel Implementation of Large-Scale Numerical Simulation of Saint-Venant Equations. Appl. Sci. 2022, 12, 5671. Cited by 6

The Study of Parallelization of SWAT Hydrology Cycle. The 32nd ACM International Conference on Supercomputing, Beijing, 2018. [Cited by 3]

A New Parallel Framework of Distributed SWAT Calibration. Journal of Arid Land, 2015, 7(1): 122-131. [Cited by 7]

Parallel Simulation of High-Dimensional American Option Pricing Based on CPU VS MIC. Concurrency and Computation: Practice and Experience, 2014, 27(5): 1110-1121. [Cited by 5]

Jihyeon Ryu | Computer Science | Best Researcher Award

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Assist Prof Dr. Jihyeon Ryu | Computer Science | Best Researcher Award

Professor, Kwangwoon University, South Korea

👩‍🏫 Jihyeon Ryu is an Assistant Professor at the School of Computer and Information Engineering, Kwangwoon University. She specializes in split learning, convolutional neural networks, and user authentication, contributing significantly to the field of computer science with her extensive research and numerous publications.

Profile

Google Scholar

 

Education

Ph.D. in Software (Integrated Master’s and Doctorate Course) from Sungkyunkwan University (Mar. 2018 – Feb. 2023), advised by Prof. Dongho Won and Prof. Hyoungshick Kim. B.S. in Mathematics and Computer Science and Engineering from Sungkyunkwan University (Mar. 2013 – Feb. 2018). Early Graduation from Sejong Science High School (Mar. 2011 – Feb. 2013).

 

Research Interests:

Split Learning. Convolutional Neural Networks. User Authentication

Awards

Family Company Workshop Lecture, Hoseo University (Feb. 2022). Invited Lecturer, Mental Women’s High School (Nov. 2021). Excellence Prize, Software Department Excellence Research Awards (Feb. 2021). Graduate Merit Scholarship, Sungkyunkwan University (Mar. 2018 – Feb. 2021). SimSan Scholarship, Multiple instances (Mar. 2018 – Sep. 2020). Excellence Award, 국가 암호기술 전문인력 양성과정 (Nov. 2019). Grand Prize, Software Department Excellence Research Awards (Feb. 2019). Samsung Science Scholarship (Mar. 2013 – Feb. 2018)

Publications

Lightweight Hash-Based Authentication Protocol for Smart Grids. Sensors, 2024. Sangjin Kook, Keunok Kim, Jihyeon Ryu, Youngsook Lee, Dongho Won.

Enhanced Lightweight Medical Sensor Networks Authentication Scheme Based on Blockchain. IEEE ACCESS, 2024. Taewoong Kang, Naryun Woo, Jihyeon Ryu.

Secure and Anonymous Authentication Scheme for Mobile Edge Computing Environments. IEEE Internet of Things Journal, 2024. Hakjun Lee, Jihyeon Ryu, Dongho Won.

Distributed and Federated Authentication Schemes Based on Updatable Smart Contracts. Electronics, 2023. Keunok Kim, Jihyeon Ryu, Hakjun Lee, Youngsook Lee, Dongho Won.

An Improved Lightweight User Authentication Scheme for the Internet of Medical Things. Sensors, 2023. Keunok Kim, Jihyeon Ryu, Youngsook Lee, Dongho Won.