Dr. Kais Iben Nassar | Machine Learning | Best Researcher Award

Dr. Kais Iben Nassar | Machine Learning | Best Researcher Award

Doctorate at University of Aveiro , Portugal

Profiles

Scopus

Orcid

Google Scholar

Academic Background

Dr. Kais Iben Nassar is a researcher with a focus on Condensed Matter Physics and Computational Chemistry. He completed his PhD in Physics of Condensed Materials in 2022 through a joint program between the University of Aveiro, Portugal, and the University of Sfax, Tunisia. Dr. Nassar is renowned for his work in materials science, particularly in the study of 2D materials like MXenes and their applications in energy storage and catalysis.

Education

  • PhD in Physics of Condensed Materials
    Université de Sfax & Universidade de Aveiro (2022)
    Achieved with highest honors.
  • Master’s in Condensed Matter Physics
    Université de Sfax (2018)
    Graduated with distinction.
  • Fundamental License in Physics-Chemistry
    Université de Sfax (2016)
    Graduated with distinction.

Professional Experience

  • Postdoctoral Researcher
    Universidade de Aveiro, CICECO (2023 – Present)
    Focus on MXenes catalysts and computational chemistry.
  • Researcher
    Université de Sfax & Universidade de Aveiro (2018 – 2021)
    Conducted research on perovskites and materials science.
  • Invited Assistant Professor
    Université de Sfax (2021 – 2022)
    Taught and mentored students in physics and chemistry.

🔬 Research Interests

Dr. Nassar’s research interests encompass Condensed Materials Physics, nano-materials, computational chemistry, and machine learning. His work includes investigating the properties of 2D materials such as MXene, exploring their potential in energy storage, catalysis, and electronics. He is actively engaged in the preparation and characterization of new perovskite ceramics and the study of their structural, electrical, and magnetic properties. Dr. Nassar is also a member of the European Materials Acceleration Center for Energy (EU-MACE) under the COST Action CA22123.

 Publications

Tailoring of structural, morphological, electrical, and magnetic properties of LaMn1−xFexO3 ceramics
  • Authors: Thakur, P., Nassar, K.I., Kumar, D., Essid, M., Lal, M.
  • Journal: RSC Advances
  • Year: 2024
Structural, electrical properties of bismuth and niobium-doped LaNiO3 perovskite obtained by sol–gel route for future electronic device applications
  • Authors: Nassar, K.I., Benamara, M., Kechiche, L., Teixeira, S.S., Graça, M.P.F.
  • Journal: Indian Journal of Physics
  • Year: 2024
Investigating Fe-doped Ba0.67Ni0.33Mn1−xFexO3 (x = 0, 0.2) ceramics: insights into electrical and dielectric behaviors
  • Authors: Tayari, F., Iben Nassar, K., Algessair, S., Hjiri, M., Benamara, M.
  • Journal: RSC Advances
  • Year: 2024
Sol–gel synthesized (Bi0.5Ba0.5Ag)0.5 (NiMn)0.5O3 perovskite ceramic: An exploration of its structural characteristics, dielectric properties and electrical conductivity
  • Authors: Tayari, F., Iben Nassar, K., Benamara, M., Soreto Teixeira, S., Graça, M.P.F.
  • Journal: Ceramics International
  • Year: 2024
Study of Electrical and Dielectric Behaviors of Copper-Doped Zinc Oxide Ceramic Prepared by Spark Plasma Sintering for Electronic Device Applications
  • Authors: Benamara, M., Iben Nassar, K., Rivero-Antúnez, P., Serrà, A., Esquivias, L.
  • Journal: Nanomaterials
  • Year: 2024

Ms. Linjing Wei | Deep Learning | Best Researcher Award

Ms. Linjing Wei | Deep Learning | Best Researcher Award

Linjing Wei at Gansu Agricultural University, China

Profile

Scopus

Academic Background:

Ms. Linjing Wei is a distinguished female professor at Gansu Agricultural University, specializing in Grassland Science with a research focus on Grassland Informatics. Born in July 1977, she has made significant contributions to her field through her extensive research, academic guidance, and numerous publications.

Education:

Ms. Wei earned her PhD in Grassland Science from Gansu Agricultural University in June 2015. Her educational background has provided a strong foundation for her academic and research pursuits.

Professional Experience:

Ms. Wei teaches several courses for master’s students, including Introduction to Cloud Computing, Case Analysis of Software Engineering, Information Systems and Information Resource Management, and Distributed Systems and Cloud Computing Technology. As the first supervisor, she has guided numerous master’s students in various majors, particularly in Agricultural Engineering and Information Technology.

Research Interests:

Ms.Wei's research interests lie in Grassland Informatics. Over the past five years, she has led several key research projects with significant funding, focusing on areas such as data resource integration, intelligent cloud platforms for agricultural logistics, ecosystem restoration and monitoring, sustainable development planning, and trustworthy traceability systems for agricultural products. Her published works include papers in prestigious journals like Sensors and the Canadian Journal of Remote Sensing, as well as contributions to national-level textbooks and academic monographs.

📝 Academic Achievements:

Ms. Wei has an impressive list of published papers, including "Fine Segmentation of Chinese Character Strokes Based on Co-ordinate Awareness and Enhanced BiFPN" in Sensors (2024), "Enhanced Wheat Head Detection in Images Using Fourier Domain Adaptation and Random Guided Filter" in Canadian Journal of Remote Sensing (2024), and "Feature Selection in High Dimensional Biomedical Data Based on BF-SFLA" in Neurogenetics (2022).

 Publications:

Fine Segmentation of Chinese Character Strokes Based on Coordinate Awareness and Enhanced BiFPN
  • Authors:Mo, H., Wei, L.
  • Journal: Sensors
  • Year: 2024
A Smart Chicken Farming Platform for Chicken Behavior Identification and Feed Residual Estimation
  • Authors: Yang, J., Gao, J., Li, Y., Lu, Q., Zheng, H.
  • Journal: Proceedings - 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
  • Year: 2023
Feature Selection in High Dimensional Biomedical Data Based on BF-SFLA
  • Authors: Dai, Y., Niu, L., Wei, L., Tang, J.
  • Journal: Frontiers in Neuroscience
  • Year: 2022
Jointly Learning Topics in Sentence Embedding for Document Summarization
  • Authors: Gao, Y., Xu, Y., Huang, H., Wei, L., Liu, L.
  • Journal: IEEE Transactions on Knowledge and Data Engineering
  • Year: 2020
Study on the Matching Algorithm of Turf Grass Introduction Features Based on Big Data Analysis
  • Authors: Wei, L., Dong, W., Gan, S., Wang, Y.
  • Year: 2019

Dr. Seyed Hamed Godasiaei | Deep Learning | Best Researcher Award

Dr. Seyed Hamed Godasiaei, Deep Learning, Best Researcher Award

Doctorate at Xi’an Jiaotong University, China

Professional Profile

Summary:

Dr. Seyed Hamed Godasiaei is a versatile professional with a rich background in chemical engineering, research, and development. His career spans various disciplines, showcasing expertise in computational fluid dynamics (CFD), machine learning applications, environmental experiments, and heat transfer analysis.

🎓 Education:

  • Ph.D. in Chemical Engineering: Xi’an Jiaotong University
  • M.S. in Chemical Engineering: Islamic Azad University of Shahrood
  • Bachelor’s in Chemical Engineering: Islamic Azad University of Birjand

💼 Professional Experience

  • Welding and Mapping GIS: Dr. Godasiaei has applied his skills in welding techniques and Geographic Information System (GIS) mapping to various projects.
  • Lab Researcher: His research includes extensive work in environmental experiments and heat transfer studies.
  • Python for Machine Learning: He leverages Python programming for advanced applications in machine learning.
  • C++ Programming: Proficient in C++ for developing computational models and simulations.

🏆 Achievements & Awards:

  • elected as a top researcher by the Iranian National Standards Organization.
  • Recognized for environmental research contributions at KhatamToos Co, Iran.

Skills and Expertise:

Dr. Godasiaei is proficient in a wide array of software and tools essential for his research and professional endeavors, including Ansys Fluent, Ansys CFX, CFD-Post, ICEM CFD, Space Claim, Gambit, STAR-CCM+, AutoCAD, Photoshop, CorelDRAW, SolidWorks, Comsol, openLB, and Python programming.

 

Publications Top Noted:

Paper Title: Water jet angle prediction in supersonic crossflows: Euler–Lagrange and machine learning approaches
  • Authors: S.H. Godasiaei, H. Kamali
  • Journal: European Physical Journal Plus
  • Volume: 139
  • Issue: 3
  • Pages: 251
  • Year: 2024
  • Citations: 3
Paper Title: Exploring novel heat transfer correlations: Machine learning insights for molten salt heat exchangers
  • Authors: S.H. Godasiaei, A.J. Chamkha
  • Journal: Numerical Heat Transfer; Part A: Applications
  • Year: 2024
  • Citations: 2
Paper Title: Ballistic limit evolution of field-aged flexible multi-ply UHMWPE-based composite armour inserts
  • Authors: S.H. Godasiaei
  • Journal: Numerical Heat Transfer; Part A: Applications
  • Year: 2024
Paper Title: Saturated/subcooled flow boiling heat transfer inside micro/mini-channels: A new prediction correlation and experiment evaluation
  • Authors: X. Ma, X. Ji, C. Hu, J. Wei, S.H. Godasiaei
  • Journal: International Journal of Heat and Mass Transfer
  • Volume: 210
  • Pages: 124184
  • Year: 2023
  • Citations: 5
Paper Title: Advancing heat transfer modeling through machine learning: A focus on forced convection with nanoparticles
  • Authors: S.H. Godasiaei, A.J. Chamkha
  • Journal: Numerical Heat Transfer; Part A: Applications
  • Year: 2023

Xueping-Wang-Generative Models for Computer Vision-Best Researcher Award

Dr. Xueping-Wang-Generative Models for Computer Vision-Best Researcher Award 

Beijing University of Civil Engineering and Architecture-China

Author Profile

Early Academic Pursuits

Dr. Wang Xueping's journey into the field of electrical and information engineering began with her undergraduate studies at Yanshan University, where she pursued a Bachelor of Science in Information Science and Engineering from 2007 to 2011. During this period, she developed a foundational understanding of the principles of information science, honing her analytical and technical skills. Her academic prowess and keen interest in the intricacies of information systems laid a solid groundwork for her future endeavors in computer vision and machine learning.

Following her bachelor's degree, Wang continued her studies at Yanshan University, earning a Master of Science in Information Science and Engineering between 2012 and 2015. Her master's education allowed her to delve deeper into advanced topics within the field, expanding her knowledge and research capabilities. This phase of her academic career was marked by a growing fascination with the potential of machine learning to solve complex problems, setting the stage for her subsequent research focus.

Professional Endeavors

Dr. Wang Xueping's professional career took a significant leap forward when she joined Beihang University for her doctoral studies in the School of Computer Science and Engineering. From September 2015 to November 2021, she pursued her Ph.D., concentrating on facial expression generation methods. Her thesis on this subject underscores her commitment to advancing the field of affective computing, a branch of artificial intelligence focused on understanding and simulating human emotions.

In December 2021, Wang transitioned into a lecturer role at the School of Electrical and Information Engineering at Beijing University of Civil Engineering and Architecture (BUCEA). This position has allowed her to blend her passion for teaching with her research interests, shaping the next generation of engineers and researchers in her field.

Contributions and Research Focus

Dr. Wang Xueping's research primarily revolves around computer vision, machine learning, and affective computing. Her doctoral thesis on facial expression generation methods represents a significant contribution to the field, addressing the challenges of accurately simulating human facial expressions in digital environments. This work is crucial for applications ranging from enhanced human-computer interaction to improved diagnostic tools in healthcare.

In her role at BUCEA, Wang has continued to explore the intersections of these disciplines. Her research projects often focus on developing novel algorithms and models that improve the accuracy and efficiency of computer vision systems. By leveraging machine learning techniques, she aims to enhance the ability of machines to interpret and respond to visual data in a manner that mimics human perception.

Accolades and Recognition

While specific awards and recognitions are not detailed in the provided information, Dr. Wang Xueping's academic and professional trajectory suggests a career marked by significant achievements and contributions. Her progression from a bachelor's degree to a Ph.D. at prestigious institutions, followed by a lecturer position at BUCEA, indicates a recognition of her expertise and dedication to her field.

In recognition of her outstanding contributions to the field of computer vision, Xueping Wang has been honored with the Generative Models for Computer Vision Award.

Impact and Influence

Dr. Wang Xueping's work has a profound impact on several key areas within electrical and information engineering. In the realm of computer vision, her research enhances the capability of systems to process and interpret visual information, which is crucial for advancements in robotics, autonomous vehicles, and surveillance systems. Her focus on affective computing contributes to the development of technologies that can understand and respond to human emotions, leading to more intuitive and empathetic human-computer interactions.

In the academic sphere, Wang's role as a lecturer enables her to influence and mentor future engineers and researchers. Her teaching not only imparts technical knowledge but also inspires students to explore innovative solutions to complex problems, fostering a culture of research and development.

Legacy and Future Contributions

Looking ahead, Dr. Wang Xueping's legacy in the field of electrical and information engineering is likely to be characterized by her contributions to machine learning and affective computing. Her ongoing research will continue to push the boundaries of what machines can achieve in terms of visual and emotional intelligence. Additionally, her influence as an educator will resonate through the accomplishments of her students and the advancements they bring to the field.

Notable Publication