Abdulrahman Alojail |Information Systems | Best Researcher Award

Assist. Prof. Dr. Abdulrahman Alojail |Information Systems | Best Researcher Award

Assistant Professor at King Faisal University Saudi, Arabia

Abdulrahman Alojail, from Al Ahsa, Saudi Arabia, is an academic specializing in Information Systems. With a strong focus on enhancing his skills, he leverages his academic background and professional experience to contribute effectively to both education and the industry. He is currently an Assistant Professor at King Faisal University, where he also leads the Alumni Committee. With a career spanning over a decade, Abdulrahman has consistently demonstrated a passion for education, research, and professional development. He has also worked as an Application Development Analyst at Saudi Electricity Company, showcasing his practical IT expertise.

Profile

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🎓 Education

Abdulrahman completed his Bachelor of Computer Information Systems at King Faisal University, Saudi Arabia (2010). He later earned a Master of Science in Information Systems from Central Michigan University, USA (2016). Furthering his academic journey, he obtained a PhD in Business Information Systems from RMIT University, Australia (2023). His educational background reflects a deep commitment to mastering IT and business systems, positioning him as an expert in the field.

💼 Experience

Abdulrahman’s professional experience spans academia and industry. He served as a Lecturer at King Faisal University from 2011 to 2023, where he taught undergraduate courses in Information Systems and contributed to curriculum development. Since 2023, he has been an Assistant Professor, focusing on alumni relations and creating opportunities for graduates. Prior to his academic career, he worked as an Application Development Analyst at Saudi Electricity Company (2010-2011), where he was responsible for developing and optimizing database applications and maintaining server structures.

🏆 Awards and Honors

Abdulrahman has received numerous accolades, including Ethical Leadership and Money Management Certificates from Central Michigan University (2015). He also completed a Project Management course (2019) in line with PMI’s PMBOK standards. His expertise in digital transformation earned him certifications in Agile Management and Digital Transformation Project Management (2022). He received recognition for his academic research with the publication of his work in the Mathematics journal, MDPI (2024).

🔬 Research Focus

Abdulrahman’s research interests include the application of deep learning in enhancing sentiment classification and consistency analysis, particularly in customer reviews. His work also explores digital transformation, business information systems, and the use of data analysis tools like Python for informed decision-making. His PhD research focused on the development of innovative technologies to improve system performance and efficiency in business contexts. He is passionate about the intersection of technology and business practices, aiming to bridge gaps in both fields.

💡 Skills

Abdulrahman is proficient in communication, SQL, MS Access, System Development Methodologies (DFD, ERD, Use Case), Microsoft Power BI, and Python programming. He possesses strong teamwork and collaboration skills, along with a passion for lifelong learning and public speaking. His academic experience and industry background allow him to engage effectively in both teaching and practical application of business information systems.

🌟 Conclusion

Abdulrahman Alojail is an exceptional candidate for the Best Researcher Award due to his impressive academic credentials, impactful research, and professional expertise. His ability to merge academic theory with industry practice, along with his ongoing commitment to professional development, makes him a valuable asset to the field of Information Systems. With continued growth in interdisciplinary research and emerging technologies, Alojail is poised to make significant contributions to both academia and industry in the years to come.

📚Publications 

A Hybrid Deep Learning Approach for Enhanced Sentiment Classification and Consistency Analysis in Customer Reviews
  • Author: Dr. Abdulrahman Alojail
  • Journal: Mathematics
  • Date of Publication: December 7, 2024
  • Type: Journal article
  • Focus: The paper presents a hybrid deep learning approach aimed at improving sentiment classification and analyzing the consistency of customer reviews.
  • Contributors: Dr. Abdulrahman Alojail

Li Xia | Plant biotechnology |Best Researcher Award

Prof. ‌Li Xia |Plant biotechnology | Best Researcher Award

Prof at Qingdao University of Science and Technology China

Li Xia is a distinguished professor at the College of Chemical Engineering, Qingdao University of Science and Technology. With a PhD in Chemical Engineering, his research expertise spans process system engineering, chemical thermodynamics, dynamic simulation, process optimization, and energy-saving technologies. He has contributed extensively to the fields of chemical process simulation and intelligent chemical industry through numerous high-impact publications and patents. Li Xia has led and collaborated on several national research projects, focusing on innovative energy-saving strategies and the development of new chemical processes. His work has significantly advanced the application of thermodynamic models and optimization techniques in industrial processes.

Profile

 

📚Education

PhD in Chemical Engineering Qingdao University of Science and Technology, China [2010-2016]

 🏢 Professional Appointments

  • Professor College of Chemical Engineering, Qingdao University of Science and Technology [2006-present]

Research Projects 📝

Project Title: Research on energy saving strategies and methods for chemical processes based on fire accumulation Duration: January 2015 – December 2017 Funding Source: National Natural Science Foundation of China Grant Number: 21406124 Role: Project Leader Outcome: Concluded Project Title: Intelligent method and application research of low-temperature waste heat extraction and utilization Duration: January 2022 – December 2025 Funding Source: National Natural Science Foundation of China Grant Number: 22178190 Role: Second completer Status: Amidst Research Project Title: A new method and its application research for vapor-liquid equilibrium prediction based on elements and chemical bonds Duration: January 2012 – December 2015 Funding Source: National Natural Science Foundation of China Grant Number: 21176127 Role: Third completer Outcome: Concluded

Publications Top Notes 📝

  1. Benzoic Acid Catalyzed Production of Xylose and Xylooligosaccharides from Poplar
    • Authors: Li, L., Wan, Q., Lu, Y., Xu, J., Gou, J.
    • Journal: Industrial Crops and Products
    • Year: 2024
    • Volume: 213
    • Pages: 118460
    • Abstract: This study explores the catalytic production of xylose and xylooligosaccharides from poplar using benzoic acid, focusing on optimizing yield and process efficiency.

 

  1. A Matrix Completion Method for Imputing Missing Values of Process Data
    • Authors: Zhang, X., Sun, X., Xia, L., Tao, S., Xiang, S.
    • Journal: Processes
    • Year: 2024
    • Volume: 12
    • Issue: 4
    • Pages: 659
    • Abstract: This open-access article presents a matrix completion method aimed at imputing missing values in process data, potentially improving data integrity and analysis in various industrial applications.

 

  1. Design Method of Extractant for Liquid–Liquid Extraction Based on Elements and Chemical Bonds
    • Authors: Wei, Y., Zhang, C., Zhang, Y., Sun, X., Xiang, S.
    • Journal: Chinese Journal of Chemical Engineering
    • Year: 2024
    • Volume: 68
    • Pages: 193–202
    • Abstract: The study proposes a novel design method for extractants used in liquid-liquid extraction, focusing on the role of elements and chemical bonds to enhance extraction efficiency.

 

  1. A Projected Newton Algorithm Based on Chemically Allowed Interval for Chemical Equilibrium Computations
    • Authors: Lu, H., Tao, S., Sun, X., Xia, L., Xiang, S.
    • Journal: Frontiers of Chemical Science and Engineering
    • Year: 2024
    • Volume: 18
    • Issue: 3
    • Pages: 27
    • Abstract: This article introduces a projected Newton algorithm designed to improve chemical equilibrium computations by using chemically allowed intervals, aiming to enhance accuracy and computational efficiency.

 

  1. Enhancing Working Fluid Selection for Novel Cogeneration Systems by Integrating Predictive Modeling: From Molecular Simulation to Process Evaluation
    • Authors: Wang, L., Zong, F., Liu, Z., Sun, X., Xiang, S.
    • Journal: Process Safety and Environmental Protection
    • Year: 2024
    • Volume: 183
    • Pages: 587–601
    • Abstract: This research focuses on the selection of working fluids for cogeneration systems, integrating predictive modeling from molecular simulation to process evaluation to optimize performance and safety.

 

  1. Development of Special Process Simulation System for Sulfuric Acid Alkylation Process
    • Authors: Zhao, T., Xia, L., Sun, X., Chen, Y., Xiang, S.
    • Journal: Gao Xiao Hua Xue Gong Cheng Xue Bao/Journal of Chemical Engineering of Chinese Universities
    • Year: 2024
    • Volume: 38
    • Issue: 1
    • Pages: 121–127
    • Abstract: This study details the development of a specialized process simulation system for the sulfuric acid alkylation process, aiming to improve efficiency and reliability in industrial applications.