Ioannis Chatzilygeroudis | Computer Science and Artificial Intelligence | Best Researcher Award

Ioannis Chatzilygeroudis | Computer Science and Artificial Intelligence | Best Researcher Award

Prof Ioannis Chatzilygeroudis, University of Patras, Greece

Prof. Emeritus at the University of Patras, Greece, with a rich educational background in Mechanical and Electrical Engineering (NTUA), Theology (University of Athens), MSc in Information Technology, and a PhD in Artificial Intelligence (University of Nottingham). Fluent in Greek and English, he specializes in AI, KR&R, knowledge-based systems, theorem proving, intelligent tutoring, e-learning, machine learning, natural language generation, sentiment analysis, semantic web, and educational robotics. His prolific research includes a PhD thesis, 18 edited volumes, 21 book chapters, 46 journal papers, 115 conference papers, 8 national conference papers, and 14 workshop papers. πŸ“šπŸ€–πŸ’»πŸŒ

Publication profile

Orcid

Education

πŸ“š From September 1968 to June 1974, completed secondary education, earning a Certificate of High School Graduation in Science. πŸŽ“ Pursued a Diploma in Mechanical and Electrical Engineering with a specialization in Electronics at the National Technical University of Athens from October 1974 to July 1979. πŸ“œ From February to June 1983, obtained a Certificate of Educational Studies from PATES of SELETE, Greece. πŸ“– Achieved a Bachelor in Theology from the University of Athens, completed between October 1979 and December 1987. πŸŽ“ Earned an MSc in Information Technology from the University of Nottingham in 1989, followed by a PhD in Artificial Intelligence from the same university in 1992. 🧠 Thesis: “Integrating Logic and Objects for Knowledge Representation and Reasoning.”

Experience

πŸ“˜ From Feb. 1982 to June 1982, I served as a part-time lab professor at PALMER Higher School of Electronics in Greece, teaching Electronics Lab. My full-time teaching journey began at TEI of Athens (1982-84), where I covered courses like Electrotechnics and Circuit Theory. My secondary education tenure (1984-92) focused on electrical engineering subjects. I then transitioned to higher education, teaching at TEI of Kozani and Chalkida, and later at the University of Nottingham (1990-92). From 1995-2006, I was a senior researcher and lecturer at the University of Patras, ultimately becoming a professor (2009-2023). Now, I am a Professor Emeritus. πŸŽ“πŸ”¬

Projects

From June 1993 to November 1995, I managed the CTI team for the DELTA-CIME project, developing a knowledge-based production control system. I led several initiatives, including the MEDFORM project for multimedia education and the national project for educational software in chemistry. As a senior researcher, I contributed to intelligent systems for tele-education and hybrid knowledge representation. I led multiple European projects like MENUET, AVARES, and TESLA, focusing on innovative education through virtual reality. My work aims to enhance learning experiences across disciplines, involving collaboration with various international partners. πŸŒπŸ“šπŸ’»πŸŽ“

Research focus

Ioannis Hatzilygeroudis specializes in artificial intelligence and its applications in various domains, particularly in agriculture and healthcare. His research includes intelligent systems for diagnosing farmed fish diseases, employing deep learning techniques for image analysis, and exploring natural language processing methods. He has contributed significantly to the development of expert systems and reinforcement learning approaches to improve disease prediction in aquaculture. Additionally, his work in sentiment analysis and e-learning demonstrates a commitment to advancing educational technologies and user experience. Hatzilygeroudis’s interdisciplinary approach combines computer science with practical applications, making significant strides in health and environmental management. πŸŒ±πŸŸπŸ’»πŸ“Š

Publication focus

Using Level-Based Multiple Reasoning in a Web-Based Intelligent System for the Diagnosis of Farmed Fish Diseases

An Integrated GIS-Based Reinforcement Learning Approach for Efficient Prediction of Disease Transmission in Aquaculture

Speech Emotion Recognition Using Convolutional Neural Networks with Attention Mechanism

Expert Systems for Farmed Fish Disease Diagnosis: An Overview and a Proposal

Expert Systems for Farmed Fish Disease Diagnosis: An Overview and a Proposal

A Convolutional Autoencoder Approach for Boosting the Specificity of Retinal Blood Vessels Segmentation

Evaluating Deep Learning Techniques for Natural Language Inference

Ming-Yen Wei | Engineering and Technology | Best Researcher Award

Ming-Yen Wei | Engineering and Technology | Best Researcher Award

Assist Prof Dr Ming-Yen, Wei National Formosa University, Department of Electrical Engineering, Taiwan

Assist. Prof. Dr. Ming-Yen was born in Taichung City, Taiwan πŸ‡ΉπŸ‡Ό on April 20, 1983. He earned his Bachelor’s and Master’s degrees in Electrical Engineering from National Formosa University in 2005 and 2007, and his Ph.D. from National Taiwan University of Science and Technology in 2012 πŸŽ“. After a decade in industrial technical roles, he joined National Formosa University as an Assistant Professor in early 2023 πŸ‘¨β€πŸ«. His research interests include motor drive control, embedded systems, control theory applications, mechatronics, and robotics πŸ€–βš™οΈ.

Publication profile

Scopus

Research focus

Dr. Ming-Yen Wei’s research primarily focuses on the design, control, and implementation of advanced motion control systems and platforms. His work encompasses the development of servo control systems, multi-axis motion chairs, and flight simulators, utilizing CAN bus and microcontroller technologies. Additionally, he has contributed to the creation of motion-cueing algorithms and inverse kinematics for six degrees of freedom (6DoF) platforms. His research has significant applications in robotics, aerospace, and virtual reality simulations, showcasing innovations in precision motion control and system integration.

Publication top notes

Design and Control of a Three-Axis Motion Servo Control System Based on a CAN Bus

Design and Implementation of a New Training Flight Simulator System

Design and Control of a Multi-Axis Servo Motion Chair System Based on a Microcontroller

Design of a DSP-Based Motion-Cueing Algorithm Using the Kinematic Solution for the 6-DoF Motion Platform

Design and implementation of inverse kinematics and motion monitoring system for 6dof platform

Optimal Control-based Motion Cueing Algorithm Design for 6DOF Motion Platform

Design and Implementation of the Inverse Kinematics and Monitoring Module for Six-axis Crank Arm Platform

Design, Analysis, and Implementation of a Four-DoF Chair Motion Mechanism

Tech Advancement Award

Tech Advancement Award

Introduction:

Welcome to the Tech Advancement Award, celebrating innovation and excellence in technology. This award recognizes individuals or teams who have made significant contributions to advancing technology and pushing the boundaries of what's possible.

About the Award:

The Tech Advancement Award honors pioneers who have demonstrated exceptional creativity, ingenuity, and impact in the realm of technology. From groundbreaking inventions to transformative software solutions, this award celebrates those who drive progress and innovation in the tech industry.

Eligibility:

  • Open to individuals, teams, and organizations worldwide
  • No age limits
  • Candidates must have developed or contributed to innovative technological advancements
  • Qualifications may include relevant degrees, certifications, or professional experience
  • Publications, patents, or other documentation showcasing technological achievements are encouraged

Recurrence:

Annual

Evaluation Criteria:

  • Degree of innovation and originality
  • Impact on industry or society
  • Technological advancement and scalability
  • Demonstrated effectiveness or applicability of the technology

Submission Guidelines:

  • Submit a detailed description of the technological advancement or innovation
  • Provide evidence of impact, such as case studies, testimonials, or adoption rates
  • Include any relevant publications, patents, or technical documentation
  • Follow the specified format and submission deadlines

Recognition:

Recipients of the Tech Advancement Award receive a prestigious certificate, public recognition through various channels, and opportunities for further collaboration and support from industry partners.

Community Impact:

The Tech Advancement Award aims to inspire and empower individuals and organizations to push the boundaries of technology for the betterment of society. By recognizing and promoting technological innovation, this award fosters a culture of creativity, collaboration, and progress in the tech industry.

Biography:

Applicants are invited to provide a brief biography highlighting their contributions to technological advancements, relevant experience, and notable achievements in the field.

Abstract and Supporting Files:

Include a comprehensive abstract summarizing the technological advancement's objectives, methodology, and impact. Supporting files such as technical specifications, demonstrations, or user testimonials can enhance the submission and demonstrate the innovation's significance.