ABDULFATTAH AHMED QASEM ALWAH | Engineering and Technology | Best Researcher Award

ABDULFATTAH AHMED QASEM ALWAH | Engineering and Technology | Best Researcher Award

Assist Prof Dr ABDULFATTAH AHMED QASEM ALWAH, Ibb University, Yemen

Based on Mr. Tailong Lv’s educational background, research experience, and publication, here’s an assessment of whether he is a suitable candidate for the Best Researcher Award.

Publication profile

google scholar

Education

Mr. Tailong Lv holds a Bachelor’s degree in Automation from Henan University of Urban Construction and is currently pursuing a Master’s degree in Mechanical Engineering at Xi’an University of Posts & Telecommunications. His academic foundation is strong, particularly in technical fields relevant to his research focus.

Research Project

Mr. Lv’s main research project, “Deep Learning Based Human Activity Recognition,” showcases his proficiency in applying deep learning techniques to real-world problems. His work focuses on optimizing neural networks for better recognition of complex human activities. This is a cutting-edge area in artificial intelligence and has significant potential for applications in areas such as surveillance, healthcare, and human-computer interaction. His contribution to this field is commendable, given the complexity and real-world relevance of the project.

Awards & Recognition

Mr. Lv has received consecutive scholarships from Xi’an University of Posts and Telecommunications in 2022 and 2023, demonstrating academic excellence and a consistent track record of high achievement.

Research focus

The research focus of this person primarily revolves around urban planning, environmental sustainability, and public space management. Their work involves evaluating the disparity between supply and demand for green spaces 🌳, analyzing visual pollution in historical cities 🏙️, and predicting urban waterlogging risks 🌧️. Additionally, they contribute to developing tools to measure public space efficiency and studying the relationship between built environments and social sustainability 🏞️. Their research emphasizes creating better urban environments by addressing ecological concerns, enhancing public spaces, and promoting social well-being through thoughtful urban design.

Publication

His recent publication, “Multihead-Res-SE Residual Network with Attention for Human Activity Recognition” in the journal Electronics, reflects his engagement in research at an advanced level. Co-authoring this paper with established researchers like Hongbo Kang and Chunjie Yang, and contributing to a field as impactful as human activity recognition, highlights his research capabilities.

Conclusion

Mr. Tailong Lv’s solid educational background, innovative research in deep learning, continuous academic excellence, and publication record make him a strong candidate for the Best Researcher Award. His work contributes significantly to the field of AI and human activity recognition, aligning with the qualities expected of an award-winning researcher.

Publication top notes

Evaluating the disparity between supply and demand of park green space using a multi-dimensional spatial equity evaluation framework

Predicting urban waterlogging risks by regression models and internet open-data sources

Developing a quantitative tool to measure the extent to which public spaces meet user needs

Difficulty and complexity in dealing with visual pollution in historical cities: The historical city of Ibb, Yemen as a case study

Relationship between physical elements and density of use of public spaces in Sana’a City

Research of urban suitable ecological land based on the minimum cumulative resistance model: A Case Study from Hanoi, Vietnam

Analysis of visual pollution of the urban environment in the old city of Ibb

Characteristics of visiting urban open spaces in Sana’a city in Yemen

Relationship between the perceived characteristics of the built environment and social sustainability: Sana’a City, Yemen use case..

Predicting urban waterlogging risks by regression models and internet open-data sources. Water 12 (3): 879