Nafiz Fahad | Computer Science | Best Researcher Award

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Mr. Nafiz Fahad | Computer Science | Best Researcher Award

Multimedia University | Malaysia

Mr. Nafiz Fahad is an emerging AI researcher at Multimedia University, Cyberjaya, Malaysia, recognized for his growing contributions to artificial intelligence in healthcare, computer vision, and natural language processing. His research focuses extensively on explainable AI, clinical decision support systems, and data-driven healthcare intelligence. According to Scopus, he has 19 indexed publications, 122 citations, and an h-index of 6, reflecting the influence and visibility of his scholarly work within the global research community. His scientific output spans chronic disease prediction, dementia analytics, lung disease classification, hypertension ontology development, wound-image segmentation, obesity prediction, and precision public health. These studies incorporate techniques such as deep learning, transfer learning, ensemble learning, hybrid architectures, and explainable machine learning to advance diagnostic accuracy and interpretability in medical AI systems. Beyond health-focused research, Fahad has also contributed high-impact work in fake news detection, generative AI, machine learning security, student performance prediction, agricultural disease detection, vision transformers for physics data, and federated learning enhanced with homomorphic encryption. His ongoing research extends to mental health analytics, EEG decoding models, diabetic retinopathy detection, and agentic AI solutions for healthcare innovation. Fahad’s growing academic recognition includes research awards, best paper achievements, and contributions to high-impact journals and conferences. His multidisciplinary scholarship positions him as a promising young researcher advancing applied AI at the intersection of healthcare, societal well-being, and intelligent systems.

Profiles: Scopus | Google Scholar | LinkedIn

Featured Publications

1. Ahmed, Z., Shanto, S. S., Rime, M. H. K., Morol, M. K., Fahad, N., Hossen, M. J., … (2024). The generative AI landscape in education: Mapping the terrain of opportunities, challenges and student perception. IEEE Access.

2. Mahamud, E., Fahad, N., Assaduzzaman, M., Zain, S. M., Goh, K. O. M., & Morol, M. K. (2024). An explainable artificial intelligence model for multiple lung diseases classification from chest X-ray images using fine-tuned transfer learning. Decision Analytics Journal, 12, 100499.

3. Ahmed, R., Fahad, N., Miah, M. S. U., Hossen, M. J., Morol, M. K., Mahmud, M., … (2024). A novel integrated logistic regression model enhanced with recursive feature elimination and explainable artificial intelligence for dementia prediction. Healthcare Analytics, 6, 100362.

4. Fahad, N., Goh, K. M., Hossen, M. I., Shopnil, K. M. S., Mitu, I. J., Alif, M. A. H., & Tee, C. (2023). Stand up against bad intended news: An approach to detect fake news using machine learning. Emerging Science Journal, 7(4), 1247–1259.

5. Hossain, M. N., Fahad, N., Ahmed, R., Sen, A., Al Huda, M. S., & Hossen, M. I. (2024). Preventing student’s mental health problems with the help of data mining. International Journal of Computing, 23(1), 101–108.

Mehnaz Tabassum | Computer Science and Artificial Intelligence | Women Researcher Award

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Dr. Mehnaz Tabassum | Computer Science and Artificial Intelligence | Women Researcher Award

University of Sydney | Australia

Dr. Mehnaz Tabassum is an accomplished researcher in Computational Neurosurgery and Health Innovation, with core expertise in medical image analysis, artificial intelligence, and brain tumor diagnostics. Her research integrates deep learning, radiomics, and neuroimaging to enhance the precision of tumor segmentation, classification, and recurrence prediction in neuro-oncology. Her scholarly contributions include 14 Scopus-indexed publications, with a total of 87 citations and an h-index of 4 (Scopus metrics). She has published in prestigious journals such as Cancers, European Radiology, and Neuro-Oncology Advances, and has presented her findings at leading international conferences including IEEE EMBC and IEEE ISBI. Dr. Mehnaz Tabassum’s recent research explores cross-modality medical image synthesis, MRI-to-PET generation using diffusion and GAN-based models, and meta transfer learning for brain tumor segmentation. Her innovative work advances computational solutions for precision medicine and AI-assisted neuroimaging. She has received multiple distinctions, including the Pro-Vice Chancellor’s Research Excellence Scholarship and the Henry Sutton Postgraduate Research Scholarship, alongside a Best Paper Award for excellence in scientific contribution. Her interdisciplinary research continues to impact the fields of AI-driven diagnostics, eye-tracking in medical imaging, and computational modeling for neurosurgical innovation, reflecting her commitment to advancing data-driven healthcare and translational neuroscience.

Profiles: Scopus | Google Scholar | ORCID | ResearchGate | Staff Page

Featured Publications

  • Tabassum, M., Suman, A. A., Suero Molina, E., Pan, E., Di Ieva, A., & Liu, S. (2023). Radiomics and machine learning in brain tumors and their habitat: A systematic review. Cancers, 15(8), Article 2034. https://doi.org/10.3390/cancers15082034

  • Ghose, P., Alavi, M., Tabassum, M., Ashraf Uddin, M., Biswas, M., Mahbub, K., … & Hassan, M. (2022). Detecting COVID-19 infection status from chest X-ray and CT scan via single transfer learning-driven approach. Frontiers in Genetics, 13, 980338. https://doi.org/10.3389/fgene.2022.980338

  • Moradizeyveh, S., Tabassum, M., Liu, S., Newport, R. A., Beheshti, A., & Di Ieva, A. (2024). When eye-tracking meets machine learning: A systematic review on applications in medical image analysis. arXiv preprint arXiv:2403.07834. https://arxiv.org/abs/2403.07834

  • Tabassum, M., Suman, A. A., Russo, C., Di Ieva, A., & Liu, S. (2023). A deep learning framework for skull stripping in brain MRI. Neurocomputing (Under review).

  • Afrin, F., Al-Amin, M., & Tabassum, M. (2015). Comparative performance of using PCA with K-means and fuzzy C means clustering for customer segmentation. International Journal of Scientific and Technology Research, 4(8), 70–74.

Mahmoud Abd-Ellah | Computer Science | Best Researcher Award

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Assoc. Prof. Dr. Mahmoud Abd-Ellah | Computer Science | Best Researcher Award

Egyptian Russian University | Egypt

Dr. Mahmoud Khaled Abd-Ellah is an accomplished Assistant Professor at the Faculty of Artificial Intelligence, Egyptian Russian University, Badr, Egypt, widely recognized for his pioneering research at the intersection of artificial intelligence, medical imaging, and deep learning. Holding a Ph.D. in Electrical Engineering from Minia University, his doctoral research focused on brain tumor diagnosis through MRI using advanced machine learning techniques. His impressive publication portfolio includes 22 Scopus-indexed papers, collectively cited 830 times by 774 documents, with an h-index of 12 demonstrating substantial scientific impact and research excellence. His scholarly work has been featured in leading journals such as Scientific Reports, Neural Computing and Applications, and Ecological Informatics, advancing AI-driven approaches for medical image analysis, automated brain tumor detection, COVID-19 classification, and environmental data modeling. Beyond research, Dr. Abd-Ellah actively contributes to academic governance and quality enhancement as a member of the Egyptian International Ranking Committee, head of the Quality Management Unit, and ranking official at the Egyptian Russian University. He is also an active member of multiple IEEE councils, engaging in the development and application of AI across engineering and biomedical domains. His ORCID profile (0000-0002-6840-2503) and Scopus ID (57191265348) further reflect his consistent record of impactful scholarship and international collaboration. With his interdisciplinary expertise, editorial service, and mentorship of Ph.D. and master’s students, Dr. Mahmoud Khaled Abd-Ellah exemplifies academic leadership, innovation, and a transformative approach to research that advances both science and society.

Profile: Scopus | Google Scholar | ORCID | ResearchGate

Featured Publications

  • Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A. M., & Hamed, H. F. A. (2019). A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned. Magnetic Resonance Imaging, 61, 300–318.

  • Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A. M., & Hamed, H. F. A. (2018). Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks. EURASIP Journal on Image and Video Processing, 2018(1), 1–10.

  • El-Rawy, M., Abd-Ellah, M. K., Fathi, H., & Ahmed, A. K. A. (2021). Forecasting effluent and performance of wastewater treatment plant using different machine learning techniques. Journal of Water Process Engineering, 44, 102380.

  • Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A. M., & Hamed, H. F. A. (2016). Design and implementation of a computer-aided diagnosis system for brain tumor classification. In 2016 28th International Conference on Microelectronics (ICM) (pp. 73–76).

  • MostafaShokry, A. A. M. K., Awad, A. I., & Abd-Ellah, M. K. (2022). Systematic survey of advanced metering infrastructure security: Vulnerabilities, attacks, countermeasures, and future vision. Future Generation Computer Systems, 1–21.

Wael Badawy | Computer Science | Best Researcher Award

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Prof. Dr. Wael Badawy | Computer Science | Best Researcher Award

Egyptian Russian University | Egypt

Prof. Wael Badawy, is a distinguished engineer, researcher, and academic leader with over 28 years of experience in higher education, research, technology commercialization, and innovation management. He earned his Ph.D. in Computer Engineering from the University of Louisiana at Lafayette, USA, and an equivalent Ph.D. in Electrical Engineering recognized by the Egyptian Higher Council of Universities, complemented by M.Sc. and B.Sc. degrees in Computer Science and Automatic Control Engineering from Alexandria University, Egypt. Prof. Wael Badawy has held senior academic and leadership positions, including Executive Director of ABM College, Canada, Program Head of Data Science and Cybersecurity at the Egyptian Russian University, and professorships at Nile University, Badr University, and the American University in Cairo, where he has taught and supervised students in Artificial Intelligence, Deep Learning, Multimedia Engineering, Cybersecurity, and Information Technology Management. His research contributions encompass over 400 publications in high-impact journals and conferences, 56 books and proceedings, and 34 co-invented patents, with highly cited work including the IEEE Transactions on Circuits and Systems for Video Technology (2018). Prof. Badawy has received more than 90 prestigious awards and honors, including the QS Reimagine Education Awards (2023, shortlisted), Silicon Review “30 Innovative Brands of the Year” (2022), and multiple distinctions in STEM, business innovation, and leadership. He actively serves on international standardization committees, professional organizations such as IEEE and ACM, and national research councils, contributing to curriculum development, program design, and strategic planning in higher education. Prof. Wael Badawy’s extensive contributions to research, innovation, and education demonstrate his sustained impact on technology, society, and the global academic community, making him an exemplary candidate for the Best Researcher Award.

Profile: Google Scholar | ORCID | LinkedIn | Staff Page

Featured Publications

  • Du, S., Ibrahim, M., Shehata, M., & Badawy, W. (2012). Automatic license plate recognition (ALPR): A state-of-the-art review. IEEE Transactions on Circuits and Systems for Video Technology, 23(2), 311–325.

  • Rahman, C. A., Badawy, W., & Radmanesh, A. (2003). A real-time vehicle’s license plate recognition system. In Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance.

  • Shehata, M. S., Cai, J., Badawy, W. M., Burr, T. W., Pervez, M. S., Johannesson, R. J., … (2008). Video-based automatic incident detection for smart roads: The outdoor environmental challenges regarding false alarms. IEEE Transactions on Intelligent Transportation Systems, 9(2), 349–360.

  • Ghallab, Y. H., Badawy, W., Kaler, K. V. I. S., & Maundy, B. J. (2005). A novel current-mode instrumentation amplifier based on operational floating current conveyor. IEEE Transactions on Instrumentation and Measurement, 54(5), 1941–1949.

  • Du, S., Shehata, M., & Badawy, W. (2011). Hard hat detection in video sequences based on face features, motion and color information. In 2011 3rd International Conference on Computer Research and Development, 4, 25–29.

  • Ghallab, Y., & Badawy, W. (2004). Sensing methods for dielectrophoresis phenomenon: From bulky instruments to lab-on-a-chip. IEEE Circuits and Systems Magazine, 4(3), 5–15.

  • Badawy, W., & Gomaa, H. (2015). Analyzing a segment of video. U.S. Patent No. 9,014,429.

  • Ghallab, Y. H., & Badawy, W. (2010). Lab-on-a-chip: Techniques, circuits, and biomedical applications. Artech House.

  • Badawy, W. (2009). Mesh based frame processing and applications. U.S. Patent No. 7,616,782.

  • Badawy, W. (2009). Video based monitoring system. U.S. Patent No. 7,612,666.

Ruo Hu | Computer Science | Best Keynote Speaker

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Prof. Ruo Hu | Computer Science | Best Keynote Speaker

Guangdong Polytechnic Normal University | China

Professor Hu Ruo is a distinguished academician with a prolific background in artificial intelligence (AI) and medical image recognition. For over a decade, he has been leading graduate research in AI-driven medical diagnostics, with an emphasis on image-based disease classification and predictive systems. As a seasoned professor at Guangdong Polytechnic Normal University, his groundbreaking contributions have helped bridge clinical practice with intelligent systems, yielding innovations that benefit both academia and healthcare industries.

Professional profile👤

Scopus

Strengths for the Awards✨

Professor Hu Ruo brings a compelling blend of academic depth, technological innovation, and real-world collaboration that makes him exceptionally suitable for the “Best Keynote Speaker” award. With over a decade of leadership in AI-driven medical image recognition, his research is not only pioneering but also highly relevant to global health and AI advancements.

He holds three advanced degrees in computer science and information systems, complemented by long-standing teaching and research roles at esteemed Chinese institutions. His work spans key domains like deep learning, neuro-fuzzy systems, medical big data, and Internet of Things (IoT), with numerous peer-reviewed publications in top journals like Medical Physics and IEEE Transactions on Geoscience and Remote Sensing.

Prof. Hu has been PI of several government-funded high-impact projects, such as the Guangzhou Industry-University-Research Science & Tech project, with funding up to 2 million RMB, and has contributed to cross-sector collaborations with tech companies and hospitals. These collaborations have led to clinical applications, including tumor prediction systems and medical IoT platforms, underscoring his applied vision.

🎓 Education

Prof. Hu Ruo’s academic journey began with a Master of Science in Computer Science and Technology at Shaanxi Normal University (1987–1991). He then pursued a Master of Engineering in Computer Application at East China University of Science and Technology (1998–2002). Culminating his academic pursuits, he earned a Doctor of Management in Information System Management from the University of Shanghai for Science and Technology (2003–2006). His diverse interdisciplinary background forms a strong foundation for his research in AI applications.

🏢 Experience

Starting his career as a faculty member at the School of Mathematics and Computer Science, Ningxia University (1992–2007), Dr. Hu transitioned to the School of Computer Science, Guangdong Polytechnic Normal University in 2007. He was promoted to Associate Professor in 2006 and then to Full Professor in 2013. With over 30 years of teaching and research experience, he has played a pivotal role in shaping AI education and research methodologies in China.

🔬 Research Interests On Computer Science

Professor Hu’s research centers on AI in medical image recognition, big data semantic conflict resolution, neuro-fuzzy systems, and deep learning applications in disease diagnosis. He is particularly invested in the development of smart systems for tumor classification, sleep disorder diagnosis, and colorectal cancer detection. His work reflects a robust integration of knowledge-driven and data-driven models, contributing significantly to intelligent healthcare technologies.

🏅 Awards & Honors

Professor Hu Ruo has been the recipient of multiple prestigious research grants, including:

  • 2017 Guangzhou Industry-University-Research Major Science and Technology Project (¥2 million)

  • 2015 Guangdong Natural Science Foundation Project on big data and semantic conflict modeling (¥100,000)
    He has also received acclaim for his collaboration with high-tech companies and top medical institutions, cementing his reputation as a leader in interdisciplinary research.

📚 Selected Publications

Here are some of Professor Hu Ruo’s significant publications with citation details:

  1. Vision Transformer-based recognition of diabetic retinopathy grade, Medical Physics, 2021 — cited by numerous works in AI diagnostics.

  2. Diagnosis of sleep disorders in traditional Chinese medicine based on adaptive neuro-fuzzy inference system, Biomedical Signal Processing and Control, 2021 — contributed to fuzzy modeling in TCM.

  3. Research on data classification and feature fusion method of cancer nuclei image, International Journal of Imaging Systems and Technology, 2021 — applied deep learning in oncology.

  4. Locality Regularized Robust-PCRC framework for hyperspectral images, IEEE Transactions on Geoscience and Remote Sensing, 2020 — innovative feature extraction method.

  5. Key technologies for medical image knowledge discovery, International Journal of Pattern Recognition and Artificial Intelligence, 2020 — on intelligent knowledge systems.

  6. “A Mechanism for Healthy Big Data System Confliction Detection”, Basic & Clinical Pharmacology & Toxicology, 2016.

  7. “A New Efficiency Judging Method for Healthy Big Data”, Basic & Clinical Pharmacology & Toxicology, 2016.

  8. “Sensor Network Component Searching Method”, Journal of Investigative Medicine, 2015.

  9. “Semantic Data Network Analysis System”, Journal of Computer, 2013.

  10. “Stability Analysis of IoT Service via Data Stream Methods”, Applied Mathematics & Information Sciences, 2012.

🤝 Collaborations

Prof. Hu collaborated with Guangzhou Jinglian Information Technology Co., Ltd. to develop an AI-powered cloud platform for tumor prediction and postoperative classification. This was based on his earlier Guangdong-funded big data project. He also engaged in clinical-level partnerships with Sun Yat-sen University Cancer Prevention Center and Sixth Affiliated Hospital, providing valuable datasets and expertise for AI training and medical validation.

🎓 Supervision & Mentorship

Professor Hu has mentored numerous graduate theses, including work on deep learning for colorectal cancer, fuzzy systems for sleep disorders, and image-based diagnostics for lung and brain tumors. His students have published in top-tier journals and contributed to a total of 10 high-impact papers and 5 patents under his leadership.

🏁 Conclusion

In conclusion, Professor Hu Ruo exemplifies the spirit of innovation and interdisciplinary excellence. With a solid academic background, pioneering research in medical AI, and extensive industry and clinical collaborations, he stands out as a prime candidate for the Best Keynote Speaker. His work has not only advanced academic knowledge but also translated into tangible tools that enhance modern healthcare.

Sam Clarke | Computer Science | Best Researcher Award

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Mr. Sam Clarke | Computer Science | Best Researcher Award

Canterbury Christ Church University | United Kingdom

Sam Clarke is a dynamic and forward-thinking educator with over eight years of experience as a class teacher across Key Stages 1 and 2, and a key contributor to senior leadership teams. With a career rooted in both classroom practice and strategic educational leadership, Sam has transitioned seamlessly into higher education as a Lecturer of Primary Education at Canterbury Christ Church University. His expertise spans teaching, mentoring, research, and academic innovation, particularly in the realm of artificial intelligence (AI) in education. A passionate advocate for equity and ethical innovation, Sam combines classroom experience with pioneering research, curriculum design, and community outreach. His professional philosophy echoes Nelson Mandela’s powerful belief that “Education is the most powerful weapon to change the world.”

Professional profile👤

ORCID

Strengths for the Awards✨

  1. Innovative Research in AI and Education
    Sam Clarke’s work sits at the intersection of two rapidly evolving fields: education and artificial intelligence. His research addresses critical questions about GenAI’s impact on pedagogy, curriculum design, and interdisciplinary learning. His publication “Education in the Age of GenAI” and his co-edited journal BQIL demonstrate a commitment to pioneering research.

  2. Leadership in Academic Initiatives
    Sam is not just a participant but a leader in several academic and professional settings. As Founding Co-Editor of BQIL, Peer Reviewer for research funding, and Lecturer at CCCU, he has shown initiative and scholarly leadership.

  3. Policy-Relevant Contributions
    His guest lectures at UCL, Cambridge, and the Association of Citizenship Teaching suggest his work resonates beyond academia. He connects research to practice, influencing national discourse on AI in education.

  4. Community Engagement and Equity
    Through outreach in underprivileged schools and staff CPD on AI literacy, Sam applies his research to reduce educational inequities, making a tangible social impact.

  5. Collaborative and Interdisciplinary Work
    Clarke’s collaboration with prestigious institutions like the University of Oxford and his emphasis on interdisciplinary knowledge building demonstrate a wide-ranging and cooperative research ethos.

  6. Strong Publication Record with Open Access & Accessibility
    His commitment to knowledge dissemination is evident in his open-access publications and engagement in practitioner journals, ensuring that research reaches a diverse audience.

🎓 Education

Sam’s educational journey reflects a consistent trajectory of academic excellence and professional growth. He holds a Bachelor of Arts in Education Studies and Geography (First Class, 2014), a Postgraduate Certificate in Education with QTS (Distinction, 2015), and a Master of Arts in Education (Merit, 2024)—all from Canterbury Christ Church University. Most recently, he achieved a University Certificate of Advanced Practice (Distinction, 2025), further cementing his commitment to lifelong learning and pedagogical excellence.

👨‍🏫 Experience

Sam began his teaching career in 2015, serving in various roles including KS1 and KS2 Teacher, Mathematics Lead, and Computing Lead. His leadership capabilities emerged early, culminating in a place on the Senior Leadership Team at Sussex Road Primary School, where he managed appraisals, led curriculum initiatives, and coached newly qualified teachers. From 2017 to 2019, Sam also contributed as a School Improvement Facilitator for the Education Development Trust. Transitioning into higher education in 2024, he now lectures at Canterbury Christ Church University, shaping future educators with research-informed teaching and innovation-driven curriculum design.

🔬 Research Interests On Computer Science

Sam’s primary research interests lie in the ethical and educational implications of Generative AI within primary and higher education settings. He is especially passionate about exploring how AI can foster interdisciplinary learning, democratize access to education, and promote epistemic insight. His involvement with the Professoriate Group on AI and as a board member of the AI Ethics Committee at UCL underscores his commitment to ethical innovation. Sam also co-founded the academic journal Big Questions and Interdisciplinary Learning, promoting complex thinking and boundary-crossing knowledge creation.

🏆 Awards

Sam has not only received academic distinctions across all his university qualifications, but has also been repeatedly entrusted with leadership and mentoring roles that reflect his excellence. His invitation to prestigious conferences and involvement in ethics committees, editorial boards, and university strategic roles stands as a testament to the recognition of his thought leadership within the educational community.

📄 Publications

Sam Clarke has authored and co-authored several impactful publications exploring the intersections of education and AI:

  • Clarke, S. and Billingsley, B. (2024). Education in the Age of GenAI. Cambridge Generative AI in Education Conference Booklet of Abstracts.
    Published in 2024 | Cited by 3 articles

  • Clarke, S. (2024). A General Election and My Classroom in the Age of AI. Teaching Citizenship, Issue 59, pp. 8–9.
    Published in 2024 | Cited by 1 article

  • Clarke, S., Billingsley, B., & Heath, L. (Eds.) (2024). Big Questions and Interdisciplinary Learning. Zenodo.
    Published in 2024 | Cited by 2 articles

  • Clarke, S., Billingsley, B. and O’Leary, S. (2024). Using Generative Artificial Intelligence to Catalyse Further Interdisciplinarity Across Higher Education. Graduate College Working Papers, CCCU. Read paper
    Published in 2024 | Cited by 4 articles

✅ Conclusion

Sam Clarke embodies the ideal fusion of practitioner expertise and academic innovation. His contributions to AI in education—through teaching, research, publication, and community outreach—are both timely and transformative. With a consistent record of academic achievement, leadership, and ethical vision, Sam is poised to continue shaping the future of education at the intersection of pedagogy and technology. His work reflects a profound dedication to empowering educators and learners alike in a rapidly evolving digital world. 🌟

Miqdad Hussain | Engineering | Best Researcher Award

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Mr. Miqdad Hussain | Engineering | Best Researcher Award

University of Shanghai for Science and Technology | Pakistan

Miqdad Hussain is a dedicated structural engineer with a keen interest in numerical simulation and the optimization of masonry and concrete structures. Currently based in Shanghai, China, he is pursuing a Master’s degree in Civil Engineering at the University of Shanghai for Science and Technology (USST). With strong technical skills, multilingual proficiency, and hands-on experience in both academia and industry, Miqdad aims to contribute to cutting-edge research in structural health monitoring and retrofitting.

Professional profile👤

Google Scholar

ORCID

Strengths for the Awards✨

  • Strong Academic Foundation & Research Focus:

    • Miqdad is pursuing a Master’s in Civil Engineering with a solid CGPA of 3.6/4 from a reputable Chinese university (USST).

    • His research focus on numerical simulation, UHPC (Ultra High-Performance Concrete), and structural optimization reflects current trends and demands in structural engineering.

  • Published Research Output:

    • He has already published in reputable journals such as IJRES and has an accepted paper in the UCAS Indexed Journal of Umm Al-Qura University.

    • Topics are diverse and impactful, including masonry wall performance, material behavior at elevated temperatures, and fiber modeling using Python.

  • Technical Proficiency:

    • Proficient in industry-standard tools such as SAP2000, ETABS, STAAD.Pro, AutoCAD, and ABAQUS.

    • He demonstrates multi-language programming ability (Python, Maple) and technical writing skills—crucial for research dissemination.

  • Scholarships and Awards:

    • Recipient of the SGS Full Scholarship and Academic Excellence Award—showcasing academic merit and competitiveness.

  • Teaching and Leadership:

    • He has taught at school level, indicating communication and mentorship ability.

    • Participation in international conferences in both Pakistan and China reflects global exposure and research communication skills.

🎓 Education

Miqdad holds a Bachelor’s degree in Civil Engineering from Iqra National University, Peshawar, Pakistan (2015–2019), where he focused on fatigue behavior in reinforced concrete. He is now completing his Master’s in Civil Engineering at USST, China (Expected June 2025), with a CGPA of 3.6/4. His thesis involves Numerical Simulation to Investigate the Performance of Existing Masonry Walls Strengthened by Ultra High-Performance Concrete (UHPC) Layer. His academic portfolio includes rigorous coursework in nonlinear analysis, FEM, and advanced concrete/steel structures.

🧠 Experience

Miqdad’s experience spans academia, research, and fieldwork. As a Graduate Researcher at USST (2022–Present), he led simulation-based investigations into UHPC-strengthened masonry. Earlier, he worked as a Site Engineer for contractors in Pakistan, gaining practical insight into construction. Additionally, he served as a Lecturer in Math & Physics, demonstrating leadership in education. His blend of field experience and research makes him well-rounded in structural engineering.

🔬 Research Interest On Engineering

Miqdad’s research interests lie in structural health monitoring, retrofitting techniques, numerical modeling, and sustainable building materials. He is particularly focused on how ultra-high-performance concrete can enhance the strength and durability of aging or unreinforced masonry walls. His work emphasizes performance optimization and fire resistance analysis under elevated temperatures. 🔎🧪

🏅 Awards & Honors

Miqdad has received several accolades, including the prestigious SGS Full Scholarship for his Master’s program at USST (2022–2025). He also earned First Position in Intermediate Studies and the Academic Excellence Award at USST in 2024. His academic and research achievements reflect his commitment to excellence and innovation in civil engineering. 🏆🎖️

📚 Publications

  1. Miqdad Hussain, Bin Peng. “Simulating Influence of Different Mortar types on Performance of Masonry Wall” International Journal of Research in Engineering and Science (IJRES), Vol. 12(05), 2024, pp. 303–315.
    📝 Cited by 3 articles

  2. Xiakun Lin, Surendra Kumar Mahato, Miqdad Hussain. “Enhancing Teaching of Robotics through Computational Modelling” IJRES, Vol. 12(06), 2014, pp. 16–20.
    📝 Cited by 4 articles

  3. Miqdad Hussain, Bin Peng. “Numerical Simulation to Investigate the Performance of Existing Masonry Walls Strengthened by Ultra High-Performance Concrete (UHPC) Layer.” Journal of Umm Al-Qura University for Engineering and Architecture (Accepted, UCAS Indexed).
    📝 Cited by: In review

  4. Upcoming: “Parametric Study of UHPC as a Strengthening Material for Unreinforced Masonry Walls using Detailed Micro-Modeling Approach.” (Ready for Submission)
    📝 Expected to submit in Q2 2025

🔚 Conclusion

Miqdad Hussain is a motivated and talented structural engineer whose blend of technical expertise, research acumen, and cross-cultural experience equips him for advanced studies and impactful innovation. With a vision to strengthen global infrastructure through smart materials and simulations, Miqdad is well-positioned to contribute to structural resilience in the face of modern engineering challenges.

Xiaogang Song | Computer Science | Best Researcher Award

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Prof. Xiaogang Song | Computer Science | Best Researcher Award

School of Computer Science and Engineering | Xi ‘an University of Technology | China

Dr. Xiaogang Song is an Associate Professor at the School of Computer Science and Engineering, Xi’an University of Technology. He earned his Ph.D. from Northwestern Polytechnical University and is a member of IEEE. His research focuses on computer vision and the autonomous navigation of unmanned systems. Throughout his career, Dr. Song has led several significant projects and has an extensive publication record in esteemed journals and conferences.

Profile

Scopus

Orcid

Strengths for the Awards

  • Strong Academic Background – Dr. Xiaogang Song holds a Ph.D. from Northwestern Polytechnical University and serves as an Associate Professor and Associate Dean at the School of Computer Science and Engineering, Xi’an University of Technology.
  • Significant Research Contributions – His expertise in computer vision and autonomous navigation is demonstrated through extensive research, including national and provincial-level funded projects.
  • Publications in High-Impact Journals – He has authored over 30 papers in prestigious IEEE journals and other well-known international conferences, which reflect the quality and impact of his research.
  • Innovative Research Work – The development of the Spatial and Channel Enhanced Self-Attention Network (SCESN) and the Global Self-Attention Module (GSM) shows his contributions to advancing AI and machine learning.

Education 🎓

Dr. Song completed his doctoral studies at Northwestern Polytechnical University, where he specialized in areas that laid the foundation for his future research in computer vision and autonomous systems. His academic journey equipped him with the expertise to contribute significantly to these fields.

Experience 🏫

Currently serving as an Associate Professor at Xi’an University of Technology, Dr. Song has been instrumental in advancing research in computer science and engineering. His role involves both teaching and leading cutting-edge research projects, fostering innovation and knowledge dissemination within the academic community.

Research Interests On Computer Science🔍

Dr. Song’s research interests encompass:

  • Machine Learning
  • Multimodal Learning
  • Computer Vision

He is particularly focused on developing advanced algorithms and models that enhance the capabilities of autonomous systems and improve image processing techniques.

Awards 🏆

Dr. Song has been recognized for his contributions to the field, including:

  • Leading projects funded by the National Natural Science Foundation of China.
  • Securing grants from the National Key Research and Development Program of China.
  • Receiving support from the Key Research and Development Program of Shaanxi Province.

These accolades underscore his commitment to advancing research and innovation in computer science.

Publications 📚

  1. “Spatial and Channel Enhanced Self-Attention Network for Efficient Single Image Super-Resolution”
    • Authors: Song, X.; Tan, Y.; Pang, X.; Lu, X.; Hei, X.
    • Publication Year: 2025
    • Citations: 0
  2. “Single Image Super-Resolution with Lightweight Multi-Scale Dilated Attention Network”
    • Authors: Song, X.; Pang, X.; Zhang, L.; Lu, X.; Hei, X.
    • Publication Year: 2025
    • Citations: 0
  3. “Local Motion Feature Extraction and Spatiotemporal Attention Mechanism for Action Recognition”
    • Authors: Song, X.; Zhang, D.; Liang, L.; He, M.; Hei, X.
    • Publication Year: 2024
    • Citations: 0
  4. “Salient Object Detection With Dual-Branch Stepwise Feature Fusion and Edge Refinement”
    • Authors: Song, X.; Guo, F.; Zhang, L.; Lu, X.; Hei, X.
    • Publication Year: 2024
    • Citations: 4
  5. “TransBoNet: Learning Camera Localization with Transformer Bottleneck and Attention”
    • Authors: Song, X.; Li, H.; Liang, L.; Lu, X.; Hei, X.
    • Publication Year: 2024
    • Citations: 5
  6. “A Universal Multi-View Guided Network for Salient Object and Camouflaged Object Detection”
    • Authors: Song, X.; Zhang, P.; Lu, X.; Hei, X.; Liu, R.
    • Publication Year: 2024
    • Citations: 0
  7. “Self-Supervised Monocular Depth Estimation Method for Joint Semantic Segmentation”
    • Authors: Song, X.; Hu, H.; Ning, J.; Lu, X.; Hei, X.
    • Publication Year: 2024
    • Citations: 0
  8. “PSNS-SSD: Pixel-Level Suppressed Nonsalient Semantic and Multicoupled Channel Enhancement Attention for 3D Object Detection”
    • Authors: Song, X.; Zhou, Z.; Zhang, L.; Lu, X.; Hei, X.
    • Publication Year: 2024
    • Citations: 1
  9. “Unsupervised Monocular Estimation of Depth and Visual Odometry Using Attention and Depth-Pose Consistency Loss”
    • Authors: Song, X.; Hu, H.; Liang, L.; Lu, X.; Hei, X.
    • Publication Year: 2024
    • Citations: 4
  10. “Image Super-Resolution with Multi-Scale Fractal Residual Attention Network”
  • Authors: Song, X.; Liu, W.; Liang, L.; Lu, X.; Hei, X.
  • Publication Year: 2023
  • Citations: 5

Conclusion 📝

Dr. Xiaogang Song is a distinguished scholar in computer science, with a focus on machine learning, multimodal learning, and computer vision. His extensive research, numerous publications, and leadership in significant projects highlight his dedication to advancing technology and contributing to the academic community.