Mohamed Zakaria | Engineering | Best Researcher Award

Posted on by ScienceFather

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

Genfeng Liu | Engineering | Best Researcher Award

Posted on by ScienceFather

Dr. Genfeng Liu | Engineering | Best Researcher Award

Research Scholar at Henan University of Technology, China

Genfeng Liu is a highly qualified candidate for the Best Researcher Award, with a strong background in control science and engineering, specializing in data-driven control, adaptive control, and fault-tolerant systems. His research spans intelligent transportation, multiagent systems, and nonlinear systems, contributing to high-impact IEEE journals such as IEEE Transactions on Cybernetics (IF: 19.118) and IEEE Transactions on Neural Networks and Learning Systems (IF: 14.255). As a reviewer for leading journals, he holds strong academic credibility. His work on model-free adaptive control and cybersecurity applications demonstrates real-world relevance. To enhance his profile, he could expand international collaborations, increase industry applications, and lead large-scale research projects. While his contributions are highly significant, further engagement in technology transfer and interdisciplinary research would strengthen his impact. Overall, his extensive publication record and research influence make him a strong contender for the award, with potential for even greater contributions in the future.

Professional Profile

Education

Genfeng Liu received his Ph.D. in Control Science and Engineering from Beijing Jiaotong University, China, in 2021. His doctoral research focused on advanced control methodologies, including data-driven control, iterative learning control, and fault-tolerant control, which have significant applications in intelligent transportation and nonlinear systems. Throughout his academic journey, he developed expertise in adaptive control and multiagent systems, contributing to cutting-edge research in automation and cybernetics. His education provided a strong foundation in both theoretical and applied control engineering, enabling him to publish in prestigious IEEE journals. Additionally, his academic background equipped him with the analytical and problem-solving skills necessary to address complex challenges in system automation and intelligent control. His commitment to continuous learning and research excellence is evident in his contributions to high-impact scientific literature and his role as a reviewer for renowned international journals, solidifying his reputation as an expert in his field.

Professional Experience

Genfeng Liu is currently a Lecturer at the College of Electrical Engineering, Henan University of Technology, Zhengzhou, China. His professional experience revolves around advanced control engineering, with a focus on data-driven control, adaptive control, and fault-tolerant systems. As a researcher, he has made significant contributions to intelligent transportation systems, multiagent systems, and nonlinear control, publishing extensively in high-impact IEEE journals. Beyond his research, he actively participates in academic peer review for prestigious journals such as IEEE Transactions on Cybernetics and IEEE Transactions on Intelligent Vehicles, reinforcing his role as a respected scholar in the field. His expertise extends to supervising students and collaborating on interdisciplinary projects, bridging the gap between theoretical advancements and practical applications. His ongoing work in model-free adaptive control and cybersecurity-related control mechanisms further strengthens his impact in academia and industry, positioning him as a leader in modern control systems and intelligent automation research.

Research Interest

Genfeng Liu’s research interests lie in advanced control engineering, with a strong focus on data-driven control, adaptive control, and fault-tolerant control. His work explores iterative learning control and model-free adaptive control techniques, particularly in applications related to intelligent transportation systems, nonlinear systems, and multiagent systems. He is also interested in cybersecurity aspects of control systems, such as defense mechanisms against false data injection attacks. His research aims to enhance the efficiency, safety, and reliability of automation in modern transportation and industrial systems. By integrating artificial intelligence with control theory, he seeks to develop innovative solutions for complex, real-world engineering challenges. His studies have been published in top-tier journals, reflecting his commitment to advancing theoretical and applied knowledge in control science. Additionally, his expertise in intelligent transportation and system optimization continues to drive impactful contributions to the fields of automation, cybernetics, and industrial informatics.

Award and Honor

Genfeng Liu has received several accolades and recognition for his outstanding contributions to the field of control science and engineering. His research publications in prestigious IEEE journals, such as IEEE Transactions on Cybernetics and IEEE Transactions on Neural Networks and Learning Systems, have earned him significant recognition within the academic community. As an active reviewer for renowned international journals, he has been acknowledged for his critical evaluations and contributions to the peer-review process. His innovative work in data-driven control, adaptive control, and fault-tolerant systems has positioned him as a leading researcher in intelligent transportation and nonlinear systems. Additionally, his participation in high-profile conferences and collaborations with esteemed researchers further highlight his impact in the field. While his research achievements are commendable, pursuing national and international research grants and awards would further enhance his recognition and establish him as a distinguished leader in control engineering and automation.

Research Skill

Genfeng Liu possesses strong research skills in advanced control engineering, specializing in data-driven control, adaptive control, and fault-tolerant control. He is proficient in developing and implementing iterative learning control and model-free adaptive control strategies for complex nonlinear and multiagent systems. His expertise extends to intelligent transportation systems, where he applies innovative control techniques to enhance automation and safety. He is highly skilled in mathematical modeling, algorithm development, and system optimization, enabling him to solve real-world engineering challenges effectively. His ability to conduct in-depth theoretical analysis and translate findings into practical applications is evident in his numerous high-impact publications in top-tier IEEE journals. Additionally, his experience as a reviewer for prestigious academic journals demonstrates his critical thinking and analytical skills. His research capabilities, combined with his ability to collaborate on interdisciplinary projects, make him a valuable contributor to the fields of cybernetics, automation, and industrial informatics.

Conclusion

Genfeng Liu is a highly suitable candidate for the Best Researcher Award due to his exceptional research output, high-impact publications, and contributions to control engineering and intelligent transportation systems. To further strengthen his candidacy, increasing international collaborations, practical industry applications, and leadership roles in large-scale projects would make his research even more impactful.

Publications Top Noted

  • Title: Improved Model-Free Adaptive Predictive Control for Nonlinear Systems with Quantization Under Denial of Service Attacks
    Authors: Genfeng Liu, Jinbao Zhu, Yule Wang, Yangyang Wang
    Year: 2025
    Citation: DOI: 10.3390/sym17030471

  • Title: Adaptive Iterative Learning Fault-Tolerant Control for State Constrained Nonlinear Systems With Randomly Varying Iteration Lengths
    Authors: Genfeng Liu, Zhongsheng Hou
    Year: 2024
    Citation: DOI: 10.1109/TNNLS.2022.3185080

  • Title: Cooperative Adaptive Iterative Learning Fault-Tolerant Control Scheme for Multiple Subway Trains
    Authors: Genfeng Liu, Zhongsheng Hou
    Year: 2022
    Citation: DOI: 10.1109/TCYB.2020.2986006

  • Title: RBFNN-Based Adaptive Iterative Learning Fault-Tolerant Control for Subway Trains With Actuator Faults and Speed Constraint
    Authors: Genfeng Liu, Zhongsheng Hou
    Year: 2021
    Citation: DOI: 10.1109/TSMC.2019.2957299

  • Title: Adaptive Iterative Learning Control for Subway Trains Using Multiple-Point-Mass Dynamic Model Under Speed Constraint
    Authors: Genfeng Liu, Zhongsheng Hou
    Year: 2021
    Citation: DOI: 10.1109/TITS.2020.2970000

  • Title: A Model-Free Adaptive Scheme for Integrated Control of Civil Aircraft Trajectory and Attitude
    Authors: Gaoyang Jiang, Genfeng Liu, Hansong Yu
    Year: 2021
    Citation: DOI: 10.3390/sym13020347

  • Title: A Data-Driven Distributed Adaptive Control Approach for Nonlinear Multi-Agent Systems
    Authors: Xian Yu, Shangtai Jin, Genfeng Liu, Ting Lei, Ye Ren
    Year: 2020
    Citation: DOI: 10.1109/ACCESS.2020.3038629

  • Title: Model-Free Adaptive Direct Torque Control for the Speed Regulation of Asynchronous Motors
    Authors: Ziwei Zhang, Shangtai Jin, Genfeng Liu, Zhongsheng Hou, Jianmin Zheng
    Year: 2020
    Citation: DOI: 10.3390/pr8030333

Najeeb ur rehman Malik | Engineering | Best Researcher Award

Posted on by ScienceFather

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