Aleeza Adeel | Computer Science | Research Excellence Award

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Mrs. Aleeza Adeel | Computer Science | Research Excellence Award

The University of Waikato | New Zealand

Mrs. Aleeza Adeel is a Ph.D. student at the School of Computing and Mathematical Sciences, University of Waikato, New Zealand, specializing in digital twin frameworks, sustainable energy systems, and user-centered computing solutions. Her research focuses on developing interoperable and scalable digital twin technologies to optimize energy system management, enhance operational efficiency, and support sustainable resource utilization. She has contributed to peer-reviewed publications, including a recent article in Energies on an interoperable user-centered digital twin framework, demonstrating her commitment to integrating advanced computational models with real-world energy systems. Aleeza collaborates with interdisciplinary researchers, including experts in energy management and computational modeling, to ensure her work addresses both technical rigor and societal relevance. Her research contributes to sustainable energy transitions by providing data-driven, user-centric solutions that improve system performance, reduce environmental impact, and support informed decision-making in complex energy infrastructures.

Profile: View ORCID Profile 

Featured Publication


An Interoperable User‑Centred Digital Twin Framework for Sustainable Energy System Management

– Adeel, A., Apperley, M., & Walmsley, T. G., Energies, 2026, 19(2), Article 333

Miroslaw Kozielski | Computer Science | Best Researcher Award

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Mr. Miroslaw Kozielski | Computer Science | Best Researcher Award

Kazimierz Wielki University | Poland

Mr. Mirosław Kozielski is a researcher at Kazimierz Wielki University in Bydgoszcz, Poland, specializing in computer science, with a strong focus on natural language processing (NLP), industrial informatics, and Industry 4.0/5.0 technologies. His research addresses the use of intelligent language-based systems for automated industrial documentation, knowledge representation, and digital transformation in modern manufacturing environments. He has authored 7 peer-reviewed publications, which have accumulated 35 citations, and holds an h-index of 3, reflecting a focused and emerging academic impact. Dr. Kozielski collaborates with interdisciplinary teams, contributing to the integration of artificial intelligence with industrial and organizational processes. His work supports the development of efficient, human-centric, and sustainable industrial systems, with societal impact through improved documentation quality, enhanced knowledge accessibility, and the practical adoption of advanced AI-driven solutions in contemporary industrial ecosystems.

Citation Metrics (Scopus)

35
25
15
5
0

Citations

35

Documents

7

h-index

3

Citations

Documents

h-index

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Featured Publications

Christian Schachtner | Computer Science | Research Excellence Award

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Prof. Dr. Christian Schachtner | Computer Science | Research Excellence Award

Professor of Business Informatics | RheinMain University of Applied Sciences | Germany

Dr. Christian Schachtner is a researcher at Fachhochschule Wiesbaden, Germany, specializing in safety culture, social impact assessment, and sustainable development in technical and organizational systems. His work bridges corporate social responsibility, environmental management, and smart district development, emphasizing practical solutions to complex societal challenges. According to Scopus, he has authored 23 scholarly publications, received 9 citations, and holds an h-index of 2. His recent research includes open-access work on the determinants of social impact through safety culture in technical organizations and scholarly contributions to book chapters on smart regional and district development initiatives. Dr. Schachtner actively collaborates with international researchers, supporting interdisciplinary perspectives and knowledge exchange. His research contributes to improving organizational governance, enhancing safety performance, and promoting socially responsible and sustainable practices across technical and socio-economic domains.

Citation Metrics (Scopus)

23
15
10
5
0

Citations

9

Documents

23

h-index

2

Citations

Documents

h-index

View Google Scholar Profile
 View Scopus Profile
 View ORCID Profile

Featured Publications

Zhaozhen Jiang | Computer Science | Best Research Article Award

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Dr. Zhaozhen Jiang | Computer Science | Best Research Article Award

Assistant Researcher | Naval Submarine Academy | China

Dr. Zhaozhen Jiang is a distinguished researcher at the Navy Submarine Academy in Qingdao, China, specializing in intelligent systems, maritime navigation, and dynamic target search. His research focuses on the development of advanced path-planning algorithms and neural network–based optimization techniques for complex maritime environments. He has published extensively and collaborated widely with researchers across multiple disciplines, reflecting a strong commitment to interdisciplinary innovation. His recent work on GBNN-based maritime dynamic target search demonstrates a focus on enhancing operational decision-making and situational awareness in challenging naval contexts. Through his research, he aims to advance autonomous maritime systems and contribute to safer, more efficient naval operations, while fostering technological progress with meaningful societal impact.

Citation Metrics (Scopus)

40
30
20
10
0

Citations

37

Documents

15

h-index

4

Citations

Documents

h-index

View Scopus Profile

Featured Publications

Sarbajit Paul Bappy | Computer Science | Research Excellence Award

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Mr. Sarbajit Paul Bappy | Computer Science | Research Excellence Award

Teaching Assistant | Daffodil International University | Bangladesh

Sarbajit Paul Bappy is an emerging researcher in computer science with a growing focus on applied machine learning, medical image analysis, and agricultural informatics. He is currently serving as a Teaching Assistant in the Department of Computer Science and Engineering at Daffodil International University, Bangladesh, where he has been contributing to academic instruction and research support since 2025. Alongside his professional role, he is pursuing his undergraduate degree in Computer Science and Engineering at the same institution, demonstrating a strong integration of academic excellence and early-career research productivity. His scholarly work includes peer-reviewed publications and openly accessible datasets that address critical challenges in healthcare diagnostics and smart agriculture. Notably, he co-authored SkinVisualNet: A Hybrid Deep Learning Approach Leveraging Explainable Models for Identifying Lyme Disease from Skin Rash Images (MAKE, 2025), which combines deep learning with explainable AI techniques to enhance early disease detection. He also contributed significantly to the dataset Jackfruit AgroVision, a comprehensive benchmark for disease detection in jackfruit and its leaves, supporting advancements in precision agriculture and food-security research. His collaborations span multidisciplinary teams involving experts such as Amir Sohel, Rittik Chandra Das Turjy, Md Assaduzzaman, Ahmed Al Marouf, Jon George Rokne, and Reda Alhajj, illustrating his ability to contribute within diverse international research groups. Through his ongoing work in AI-driven health diagnostics, dataset development, and sustainable agricultural technology, Bappy aims to advance research that supports societal well-being, improves disease detection accuracy, and contributes to innovation within global machine learning communities.

Profiles: Google Scholar | ORCID | LinkedIn

Featured Publications

1. Sohel, A., Turjy, R. C. D., Bappy, S. P., Assaduzzaman, M., Marouf, A. A., Rokne, J. G., & Alhajj, R. (2025). SkinVisualNet: A Hybrid Deep Learning Approach Leveraging Explainable Models for Identifying Lyme Disease from Skin Rash Images. Machine Learning and Knowledge Extraction, 7(4), 157. https://doi.org/10.3390/make7040157  MDPI+1

2. Sohel, A., Bijoy, M. H. I., Turjy, R. C. D., & Bappy, S. P. (2025). Jackfruit AgroVision: A Extensive Dataset for Jackfruit Disease and Leaf Disease Detection using Machine Learning. Mendeley Data. https://doi.org/10.17632/pt647jfn52.1

Jia Jinlong | Engineering | Research Excellence Award

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Assoc. Prof. Dr. Jia Jinlong | Engineering | Research Excellence Award

Head of the Mining Department | Wuhan Institute of Technology | China

Dr. Jinlong Jia is a researcher at the Lanzhou Institute of Technology, China, specializing in coal engineering, gas extraction technologies, and energy-related geomechanics with a focus on improving safety, efficiency, and sustainability in coal mining operations. With 24 scientific publications, 434 citations, and an h-index of 12, he has established a strong research profile in the fields of coal pore structure evolution, borehole optimization, and fluid–rock interactions under complex geological conditions. His recent work includes developing numerical simulation models to quantitatively evaluate effect factors in multi-branch pinnate borehole gas extraction in high-gas thick coal seams, and investigating the influence of CO₂–H₂O interaction time on coal pore morphology and water migration, published in Energy and already earning citations for its contributions to clean energy and mine safety. Dr. Jia’s research integrates computational modeling, experimental coal chemistry, and engineering applications to address critical challenges in methane extraction, gas-solid coupling mechanisms, and geological hazard prevention. Over his career, he has collaborated with more than 67 co-authors, demonstrating extensive engagement in multidisciplinary and multi-institutional research teams working across geology, mining engineering, and energy science. His findings contribute to national and global efforts toward safer mining environments, enhanced gas utilization, reduced greenhouse gas emissions, and improved resource recovery efficiency. Through advancing both theoretical understanding and practical solutions in coalbed methane extraction and pore-scale mechanisms, Dr. Jia continues to play a significant role in supporting sustainable energy development and improving engineering practices within the mining and geoscience sectors.

Profile: Scopus 

Featured Publications

Zhu, X., Jia, J., Zhang, L., Ma, Z., Qin, Z., Zhang, H., & Liu, Z. (2025). Study on the numerical simulation model for quantitative evaluation on effect factors of multi‑branch pinnate borehole gas extraction in high‑gas thick coal seams. Himalayan Geology, 46(2), 125–135.

Xu, H., Hu, J., Liu, H., Ding, H., Zhang, K., Jia, J., Fang, H., & Gou, B. (2024). Effect of the interaction time of CO₂–H₂O on the alterations of coal pore morphologies and water migration during wetting. Energy, 294, Article 130944. https://doi.org/10.1016/j.energy.2024.130944

Mohammed Alenazi | Computer Engineering | Best Researcher Award

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Mr. Mohammed Alenazi | Computer Engineering | Best Researcher Award

Assistant Professor | University of Tabuk | Saudi Arabia

Mr. Mohammed M. Alenazi is an accomplished academic and researcher with expertise in electrical and electronics engineering, computer engineering, and artificial intelligence applications in energy-efficient networks. He earned his Ph.D. in Electrical and Electronics Engineering from the University of Leeds, UK (2018–2022), focusing on energy efficiency in AI-powered communication systems. Prior to this, he completed his M.Eng. in Computer Engineering at Florida Institute of Technology, USA (2016–2017), and a B.Eng. in Computer Engineering from University Sultan Bin Fahad (2007–2011), along with an Associate’s degree in Electrical/Electronics Equipment Installation and Repair from Tabuk College of Technology (2002–2004). Professionally, Mr. Alenazi began his career as a Senior Engineer at Saudi Telecom Company (2006–2011), where he gained practical experience in optical fiber networks, before transitioning to academia as a Teaching Assistant at Northern Border University (2012–2013) and later at the University of Tabuk, where he continues to serve since 2013, eventually advancing into an assistant professorship. His research interests include machine learning, IoT networks, energy optimization, and intelligent systems, with key contributions in developing models for energy-efficient ML-based service placement, neural network embedding in IoT, and intelligent sterilization systems, reflected in several IEEE and Scopus-indexed publications. In addition to publications, he has contributed innovative patents, such as systems for vehicle communication during accidents. His research skills encompass advanced AI modeling, simulation of communication networks, and interdisciplinary problem-solving in sustainable technologies. Mr. Alenazi is an active member of IEEE, AAAI (USA), AISB (UK), PMI, and the Saudi Council of Engineers, and he holds prestigious certifications including CCNA, CompTIA Security+ CE, and PMP. He has consistently demonstrated leadership in academia and professional communities, bridging industry and research while mentoring students. With a growing academic profile of 28 citations, 7 documents, and an h-index of 3, he is well-positioned for continued impact and recognition in his field.

Profiles: Google Scholar | Scopus | ORCID  | ResearchGate

Featured Publications

  1. Alenazi, M. M., Yosuf, B. A., El-Gorashi, T., & Elmirghani, J. M. H. (2020). Energy efficient neural network embedding in IoT over passive optical networks. 2020 22nd International Conference on Transparent Optical Networks (ICTON), 1–6. Cited by: 13

  2. Yosuf, B. A., Mohamed, S. H., Alenazi, M. M., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2021). Energy-efficient AI over a virtualized cloud fog network. Proceedings of the Twelfth ACM International Conference on Future Energy Systems. Cited by: 11

  3. Alenazi, M. M., Yosuf, B. A., Mohamed, S. H., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2021). Energy-efficient distributed machine learning in cloud fog networks. 2021 IEEE 7th World Forum on Internet of Things (WF-IoT), 935–941. Cited by: 9

  4. Banga, A. S., Alenazi, M. M., Innab, N., Alohali, M., Alhomayani, F. M., Algarni, M. H., & others. (2024). Remote cardiac system monitoring using 6G-IoT communication and deep learning. Wireless Personal Communications, 136(1), 123–142. Cited by: 4

  5. Alenazi, M. M., Yosuf, B. A., Mohamed, S. H., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2022). Energy efficient placement of ML-based services in IoT networks. 2022 IEEE International Mediterranean Conference on Communications and Networking (MeditCom). Cited by: 4

Mohamed Zakaria | Engineering | Best Researcher Award

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Dr. Mohamed Zakaria | Engineering | Best Researcher Award

Kafrelsheikh University Faculty of Engineering, Egypt

Dr. Mohamed H. Zakaria, an Assistant Professor in Civil Engineering at Kafrelsheikh University, Egypt, is a dedicated researcher specializing in Soil Mechanics, Foundation Engineering, Highway Engineering, and Reinforced Concrete. With a Ph.D. from Menoufia University and a consistent academic trajectory, he has published extensively in reputable international journals, contributing innovative research on structural behavior, excavation systems, and the integration of advanced techniques such as machine learning and finite element modeling. His recent work addresses critical infrastructure challenges, reflecting both technical depth and practical relevance. Dr. Zakaria maintains active profiles on ORCID, Scopus, and ResearchGate, demonstrating his engagement with the global research community. His research reflects strong potential for collaboration and societal impact. While he could further enhance his profile through increased citations, international projects, and mentorship roles, his achievements and commitment make him a highly suitable candidate for the Best Researcher Award, with significant promise for future contributions.

Professional Profile 

Education🎓

Dr. Mohamed H. Zakaria has pursued a robust and progressive academic path in the field of Civil Engineering. He earned his Ph.D. in Civil Engineering from Menoufia University, Egypt, where he focused on advanced geotechnical and structural engineering concepts. Prior to this, he obtained a Master of Science degree in Civil Engineering from Kafrelsheikh University, further deepening his expertise in soil mechanics and foundation engineering. His academic journey began at Kafrelsheikh University, where he laid a strong foundation in engineering principles. Throughout his educational career, Dr. Zakaria demonstrated academic excellence, dedication to research, and a commitment to innovation. His studies have equipped him with both theoretical knowledge and practical problem-solving skills, which are evident in his applied research and numerous publications. His educational background not only reflects a high level of specialization in his chosen field but also positions him well for continued contributions to civil engineering education and research.

Professional Experience📝

Dr. Mohamed H. Zakaria has amassed extensive professional experience in the field of Civil Engineering, primarily through his longstanding association with Kafrelsheikh University in Egypt. He began his academic career as a Demonstrator in 2014, steadily progressing to the position of Assistant Lecturer in 2019, and currently serves as an Assistant Professor in the Civil Engineering Department. His roles have encompassed teaching, mentoring, and conducting impactful research in soil mechanics, foundation engineering, and highway engineering. Dr. Zakaria has contributed significantly to the academic community through his involvement in experimental investigations, numerical modeling, and structural analysis. His research has been published in numerous high-impact journals, reflecting both academic rigor and practical relevance. Through his professional journey, he has demonstrated a strong commitment to advancing civil engineering knowledge and fostering innovation. His experience positions him as a capable educator, active researcher, and a valuable contributor to both academic and applied engineering projects.

Research Interest🔎

Dr. Mohamed H. Zakaria’s research interests are rooted in the core areas of Civil Engineering, with a particular focus on Soil Mechanics, Foundation Engineering, Highway Engineering, and Reinforced Concrete. He is especially passionate about understanding and improving the behavior of structural systems under various loading and environmental conditions. His work explores critical challenges such as settlement mitigation, bearing capacity enhancement, and the structural performance of pile walls and reinforced concrete elements. Dr. Zakaria is also interested in the application of advanced techniques like finite element modeling, machine learning, and experimental methods to optimize design and construction practices. His interdisciplinary approach combines theoretical modeling with practical experimentation, aiming to develop innovative and sustainable engineering solutions. Through his research, he seeks to enhance the safety, durability, and efficiency of infrastructure systems, making a tangible impact on both academic knowledge and engineering practice. His work invites collaboration and has strong potential for global relevance.

Award and Honor🏆

Dr. Mohamed H. Zakaria has earned recognition for his dedication to research and academic excellence in Civil Engineering. While specific named awards and honors are not extensively listed in public records, his consistent publication of high-quality research in reputable, peer-reviewed international journals reflects his scholarly impact and recognition within the academic community. His achievements in developing innovative solutions for geotechnical and structural engineering challenges, such as enhancing the performance of secant pile walls and utilizing machine learning in structural prediction, demonstrate both technical expertise and thought leadership. His rising citation metrics and growing international research collaborations also highlight his influence and professional standing. Dr. Zakaria’s academic progression—from Demonstrator to Assistant Professor at Kafrelsheikh University—illustrates his merit and recognition by peers and institutions. As he continues to contribute significantly to his field, he is well-positioned to receive further honors and awards in acknowledgment of his impactful research and academic leadership.

Research Skill🔬

Dr. Mohamed H. Zakaria possesses a diverse and well-developed set of research skills that span both theoretical and practical aspects of Civil Engineering. He is highly proficient in experimental design and laboratory testing, particularly in the areas of soil mechanics, foundation behavior, and reinforced concrete structures. His ability to conduct complex analyses is complemented by his expertise in numerical modeling, including the use of finite element methods for simulating structural and geotechnical behavior. Additionally, Dr. Zakaria has demonstrated skill in applying advanced technologies such as machine learning to predict structural performance, showcasing his adaptability and innovation in solving engineering problems. He is also adept at conducting comprehensive literature reviews, synthesizing technical data, and publishing findings in high-impact journals. His collaborative approach and strong communication skills enhance his ability to work across multidisciplinary teams. Overall, his research skillset makes him a valuable contributor to academic advancements and practical engineering solutions.

Conclusion💡

Dr. Mohamed H. Zakaria is a highly promising and dedicated researcher with a strong and focused track record in civil engineering. His steady academic career, continuous publication record, and exploration of advanced methods like machine learning and FE modeling in civil applications showcase technical excellence and innovative thinking.

Publications Top Noted✍️

  1. Title: Mitigating Settlement and Enhancing Bearing Capacity of Adjacent Strip Footings Using Sheet Pile Walls: An Experimental Approach
    Authors: Ali Basha, Ahmed Yousry Akal, Mohamed H. Zakaria
    Year: 2025
    Citation: Infrastructures, 2025, DOI: 10.3390/infrastructures10040083

  2. Title: A Comparative Study of Terrestrial Laser Scanning and Photogrammetry: Accuracy and Applications
    Authors: Mohamed H. Zakaria, Hossam Fawzy, Mohammed El-Beshbeshy, Magda Farhan
    Year: 2025
    Citation: Civil Engineering Journal, March 2025, DOI: 10.28991/cej-2025-011-03-021

  3. Title: Cantilever Piled-Wall Design Criteria in Cohesionless Soil: A Review
    Authors: Mohamed Hamed Zakaria, Ali Basha
    Year: 2024
    Citation: World Journal of Engineering, 2024, DOI: 10.1108/WJE-01-2024-0038

  4. Title: Prediction of RC T-Beams Shear Strength Based on Machine Learning
    Authors: Saad A. Yehia, Sabry Fayed, Mohamed H. Zakaria, Ramy I. Shahin
    Year: 2024
    Citation: International Journal of Concrete Structures and Materials, 2024, DOI: 10.1186/S40069-024-00690-Z

  5. Title: Effect of Insufficient Tension Lap Splices on the Deformability and Crack Resistance of Reinforced Concrete Beams: A Comparative Study Techniques and Experimental Study
    Authors: Roba Osman, Boshra El-taly, Ahmed Fahmy, Mohamed Zakaria
    Year: 2024
    Citation: Engineering Research Journal, Nov 2024, DOI: 10.21608/erjm.2024.296635.1337

  6. Title: Predicting the Maximum Axial Capacity of Secant Pile Walls Embedded in Sandy Soil
    Authors: Ali M. Basha, Mohamed H. Zakaria, Maher T. El-Nimr, Mohamed M. Abo-Raya
    Year: 2024
    Citation: Geotechnical and Geological Engineering, July 2024, DOI: 10.1007/s10706-023-02734-9

  7. Title: Two-Dimensional Numerical Approaches of Excavation Support Systems: A Comprehensive Review of Key Considerations and Modelling Techniques
    Authors: Mohamed Hamed Zakaria, Ali Basha
    Year: 2024
    Citation: Journal of Contemporary Technology and Applied Engineering, July 2024, DOI: 10.21608/jctae.2024.299692.1030

  8. Title: Interfacial Shear Behavior of Composite Concrete Substrate to High-Performance Concrete Overly After Exposure to Elevated Temperature
    Authors: Nagat M. Zalhaf, Sabry Fayed, Mohamed H. Zakaria
    Year: 2024
    Citation: International Journal of Concrete Structures and Materials, March 2024, DOI: 10.1186/s40069-023-00654-9

Fengyu Liu | Computer Science | Best Researcher Award

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Dr. Fengyu Liu | Computer Science | Best Researcher Award

PhD candidate at Southeast University, China

Fengyu Liu is a dedicated researcher specializing in deep learning, integrated navigation, intelligent unmanned systems, multi-sensor fusion, and SLAM (Simultaneous Localization and Mapping). He has authored 10 academic papers, including 5 SCI-indexed Q1 journal articles, and has contributed significantly to the fields of robotics and sensor technology. With 5 domestic invention patents and 1 PCT patent, his work demonstrates a strong focus on innovation. He has received numerous awards, including the National Scholarship and the Southeast University ‘Zhishan’ Scholarship, and has won four national and provincial-level first prizes in student competitions. He actively participates in academic conferences and serves as a reviewer for IEEE TIM, IEEE Sensor Journal, and MST journals. His research contributions and leadership in the academic community make him a promising figure in the field of intelligent navigation and robotics.

Professional Profile

Education

Fengyu Liu earned his B.S. degree in Electronic Science and Technology from the School of Instrument and Electronics, North University of China, in 2020. Currently, he is pursuing a Ph.D. in Instrument Science and Technology at the School of Instrument Science and Engineering, Southeast University, Nanjing, China. His doctoral research focuses on deep learning-driven navigation, SLAM, and multi-sensor fusion for intelligent unmanned systems. Throughout his academic journey, he has been recognized for his outstanding performance, receiving prestigious scholarships and awards for academic excellence and research contributions.

Professional Experience

During his undergraduate studies, Fengyu Liu served as the Chair of the Embedded Laboratory at the Innovation Elite Research Institute, where he led multiple student research projects. He has been actively involved in presenting at international conferences, including the 2023 International Conference on Robotics, Control, and Vision Engineering (Tokyo) and the China-Russia “Navigation and Motion Control” Youth Forum (2024, Nanjing). His research findings have been published in top-tier journals, and he has contributed as a reviewer for leading IEEE journals. His expertise in SLAM, sensor fusion, and AI-driven navigation technologies has led to patents and real-world applications, making him a key contributor to the advancement of autonomous systems and intelligent robotics.

Research Interests

Fengyu Liu’s research focuses on deep learning, integrated navigation, intelligent unmanned systems, multi-sensor fusion, and simultaneous localization and mapping (SLAM). His work explores advanced sensor fusion techniques, including the integration of LiDAR, cameras, inertial measurement units (IMUs), and deep learning models to enhance navigation accuracy and autonomy in complex environments. He is particularly interested in developing robust localization algorithms for dynamic and unstructured environments, with applications in robotics, autonomous vehicles, and aerospace navigation. His contributions to AI-driven SLAM and vision-based perception systems aim to improve real-time mapping, object recognition, and motion estimation for next-generation autonomous systems.

Awards and Honors

Fengyu Liu has received multiple prestigious awards, including the National Scholarship and the Southeast University ‘Zhishan’ Scholarship, recognizing his academic excellence. He has won four first prizes at national and provincial-level university student competitions, demonstrating his problem-solving skills and technical expertise. His research has also been recognized at academic conferences, earning him the Outstanding Paper Award at the 2022 Science and Technology Workers Seminar of the Chinese Society of Inertial Technology. His participation in international research forums, such as the China-Russia “Navigation and Motion Control” Youth Forum (2024, Nanjing), further highlights his growing impact in the field.

Research Skills

Fengyu Liu possesses a diverse skill set in deep learning, computer vision, and multi-sensor data fusion, particularly for robotics and autonomous navigation. He is proficient in developing AI-based SLAM algorithms, sensor calibration techniques, and real-time embedded system implementations. His expertise extends to software tools and programming languages, including Python, MATLAB, C++, TensorFlow, and PyTorch, which he utilizes for machine learning and signal processing applications. He has hands-on experience with robotic perception systems, LiDAR-based mapping, and inertial navigation technologies, contributing to multiple high-impact research projects. Additionally, his role as a peer reviewer for IEEE TIM, IEEE Sensor Journal, and MST journals reflects his strong analytical and critical evaluation skills in cutting-edge research.

Conclusion

Fengyu Liu is a highly promising young researcher with strong academic contributions, patents, and international recognition. His research in SLAM, deep learning, and multi-sensor fusion aligns with cutting-edge advancements in robotics and AI. His leadership roles, awards, and editorial responsibilities further strengthen his profile.

For the Best Researcher Award, he is a strong candidate, but additional international collaborations, funded research projects, and industry partnerships could further enhance his competitiveness for top-tier global research awards.

Publications Top Noted

  • Confidence Factor Based Robust Localization Algorithm with Visual-Inertial-LiDAR Fusion in Underground Space

  • LiDAR-Aided Visual-Inertial Odometry Using Line and Plane Features for Ground Vehicles

    • Authors: Jianfeng Wu, Xianghong Cheng, Fengyu Liu, Xingbang Tang, Wengdong Gu
    • Year: 2025
    • DOI: 10.1109/TVT.2025.3527472

  • Spatial Feature Recognition and Layout Method Based on Improved CenterNet and LSTM Frameworks

  • Transformer-Based Local-to-Global LiDAR-Camera Targetless Calibration With Multiple Constraints

  • Spacecraft-DS: A Spacecraft Dataset for Key Components Detection and Segmentation via Hardware-in-the-Loop Capture

  • A Visual SLAM Method Assisted by IMU and Deep Learning in Indoor Dynamic Blurred Scenes

  • A Spatial Layout Method Based on Feature Encoding and GA-BiLSTM

  • Combination of Iterated Cubature Kalman Filter and Neural Networks for GPS/INS During GPS Outages

    • Authors: Fengyu Liu, Xiaohong Sun, Yufeng Xiong, Huang Haoqian, Xiao-ting Guo, Yu Zhang, Chong Shen
    • Year: 2019
    • DOI: 10.1063/1.5094559

Najeeb ur rehman Malik | Engineering | Best Researcher Award

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Dr. Najeeb ur rehman Malik | Engineering | Best Researcher Award

Assistant Professor at DHA Suffa University, Pakistan

Dr. Najeeb Ur Rehman Malik is a dedicated researcher and electronics engineer specializing in computer vision, deep learning, and image processing. He holds a Ph.D. from Universiti Teknologi Malaysia (UTM), where his research focused on multi-view human action recognition using convolutional neural networks (CNNs) and pose features. His expertise spans artificial intelligence, embedded systems, and digital signal processing. With multiple peer-reviewed publications, including work on COVID-19 detection using X-ray images and AI-driven healthcare solutions, he has significantly contributed to applied AI research. He has industry experience as an Assistant Manager at PTCL and has led technical events at the university and national levels. His proficiency in MATLAB, Python, and embedded systems complements his research acumen. While he has made impactful contributions, further global collaborations, research funding, and high-impact citations would enhance his academic influence. Dr. Malik continues to innovate in AI and computer vision, driving advancements in intelligent systems.

Professional Profile 

Education

Dr. Najeeb Ur Rehman Malik has a strong academic background in electronics engineering and communication systems. He is currently pursuing a Ph.D. at Universiti Teknologi Malaysia (UTM), where his research focuses on multi-view human action recognition using deep learning and convolutional neural networks (CNNs). He earned his Master of Engineering (M.E.) in Communication Systems and Networks from Mehran University of Engineering and Technology (MUET), Jamshoro, Pakistan, graduating with a CGPA of 3.40. His master’s research explored speeded-up robust features (SURF) for image retrieval systems. Prior to that, he completed his Bachelor of Engineering (B.E.) in Electronics Engineering from MUET with a CGPA of 3.45, gaining expertise in power electronics, automation, digital signal processing, and embedded systems. His academic journey reflects a strong foundation in artificial intelligence, image processing, and computer vision, positioning him as a key contributor to advancements in intelligent systems and AI-driven technologies.

Professional Experience

Dr. Najeeb Ur Rehman Malik has diverse professional experience in both academia and industry, specializing in electronics engineering, communication systems, and artificial intelligence. He served as an Assistant Manager at PTCL in Hyderabad, Sindh, Pakistan, from February 2017 to June 2018, where he gained hands-on experience in telecommunications, networking, and system management. Prior to that, he completed an internship at the National Telecommunication Corporation (NTC) in Karachi during June-July 2010, where he worked on networking infrastructure and telecommunication protocols. In addition to his industry experience, he has been actively engaged in research at Universiti Teknologi Malaysia (UTM), focusing on deep learning applications for multi-view human action recognition. His technical expertise spans MATLAB, Python, embedded systems, and digital signal processing, making him a well-rounded professional. With a strong blend of research and industry exposure, Dr. Malik continues to contribute to advancements in AI, image processing, and communication technologies.

Research Interest

Dr. Najeeb Ur Rehman Malik’s research interests lie at the intersection of computer vision, deep learning, image processing, and artificial intelligence. His primary focus is on multi-view human action recognition, where he integrates convolutional neural networks (CNNs) and pose estimation techniques to enhance accuracy in real-world scenarios. He has also explored content-based image retrieval, developing robust techniques using Speeded-Up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT). His work extends to healthcare applications, including AI-driven COVID-19 detection from chest X-ray images and the role of wearable technology in pandemic management. Additionally, he is interested in embedded systems, automation, and signal processing, particularly in developing intelligent and efficient computing solutions. His expertise in MATLAB, Python, and FPGA-based system design enables him to innovate in these areas. Dr. Malik aims to contribute to the advancement of AI-driven technologies for healthcare, surveillance, and human-computer interaction.

Award and Honor

Dr. Najeeb Ur Rehman Malik has been recognized for his contributions to computer vision, deep learning, and artificial intelligence through various academic and professional honors. His research in multi-view human action recognition and AI-driven healthcare solutions has been published in reputed journals, highlighting his impact in the field. During his academic career, he actively participated in technical events, conferences, and research forums, further solidifying his reputation as a dedicated scholar. He has also played a key role in organizing and volunteering at national and university-level exhibitions and competitions, showcasing his leadership and commitment to knowledge dissemination. His work on COVID-19 detection using AI and image processing techniques has received significant attention, demonstrating real-world applications of his research. While he has made commendable contributions, further recognition in the form of best paper awards, patents, and international research grants would enhance his standing in the global research community.

Research Skill

Dr. Najeeb Ur Rehman Malik possesses advanced research skills in computer vision, deep learning, and image processing, making significant contributions to AI-driven solutions. He is proficient in MATLAB and Python, leveraging machine learning frameworks like TensorFlow and PyTorch to develop multi-view human action recognition systems using convolutional neural networks (CNNs) and pose estimation techniques. His expertise extends to content-based image retrieval, feature extraction (SURF & SIFT), and embedded system design, enabling efficient AI model deployment. He is skilled in handling large datasets, performing statistical analysis, and optimizing deep learning architectures for real-world applications, including COVID-19 detection from chest X-ray images. Additionally, he has experience in academic writing, research methodology, and experimental design, ensuring high-quality publications. His ability to analyze complex problems, design innovative solutions, and collaborate on interdisciplinary research projects positions him as a strong contributor to advancements in AI, healthcare, and intelligent automation.

Conclusion

Najeeb Ur Rehman Malik is a strong candidate for the Best Researcher Award due to his technical expertise, interdisciplinary research contributions, and published works in computer vision and AI. However, improving citation metrics, securing research funding, and enhancing global collaboration would further strengthen his profile. If he has additional awards, patents, or high-impact projects, those should be highlighted in the application to maximize competitiveness.

Publications Top Noted

  • Cascading pose features with CNN-LSTM for multiview human action recognition

    • Authors: NR Malik, SAR Abu-Bakar, UU Sheikh, A Channa, N Popescu
    • Year: 2023
    • Citations: 23

  • Robust Technique to Detect COVID-19 using Chest X-ray Images

    • Authors: A Channa, N Popescu, NUR Malik
    • Year: 2020
    • Citations: 23

  • Multi-view human action recognition using skeleton based-FineKNN with extraneous frame scrapping technique

    • Authors: NUR Malik, UU Sheikh, SAR Abu-Bakar, A Channa
    • Year: 2023
    • Citations: 18

  • Managing COVID-19 Global Pandemic With High-Tech Consumer Wearables: A Comprehensive Review

    • Authors: A Channa, N Popescu, NUR Malik
    • Year: 2020
    • Citations: 17

  • Salp swarm algorithm–based optimal vector control scheme for dynamic response enhancement of brushless double‐fed induction generator in a wind energy conversion system

    • Authors: A Memon, MWB Mustafa, TA Jumani, M Olatunji Obalowu, NR Malik
    • Year: 2021
    • Citations: 10

  • Performance comparison between SURF and SIFT for content-based image retrieval

    • Authors: NUR Malik, AG Airij, SA Memon, YN Panhwar, SAR Abu-Bakar
    • Year: 2019
    • Citations: 8

  • Multiview human action recognition system based on OpenPose and KNN classifier

    • Authors: NUR Malik, SAR Abu Bakar, UU Sheikh
    • Year: 2022
    • Citations: 5

  • Association of stride rate variability and altered fractal dynamics with ageing and neurological functioning

    • Authors: A Channa, N Popescu
    • Year: 2021
    • Citations: 3

  • Localized Background Subtraction Feature-Based Approach for Vehicle Counting

    • Authors: MA El-Khoreby, SAR Abu-Bakar, MM Mokji, SN Omar, NUR Malik
    • Year: 2019
    • Citations: 3