Federico D’ Asaro | Artificial intelligence | Best Researcher Award

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Mr. Federico D’ Asaro | Artificial intelligence | Best Researcher Award

Mr. Federico D’ Asaro, Politecnico di Torino, Italy

Based on Mr. Federico D’Asaro’s background and achievements, he appears to be a strong candidate for the Research for Best Researcher Award. Here’s an evaluation of his suitability:

Publication profile

Education 🎓

  • Polytechnic University of Turin: M.Sc. in Data Science and Engineering with a thesis on algorithm discrimination. Achieved a final grade of 110/110.
  • University of Palermo: B.Sc. in Engineering and Management, graduating cum laude with a final grade of 110/110.
  • Scientific Lyceum “Galileo Galilei”: High School Diploma with a grade of 83/100.

Work Experience 💼

  • Ph.D. Student at Polytechnic University of Turin: Conducting research on Modality-Gap in multimodal feature space and submitting articles to prominent conferences.
  • AI Applied Researcher at LINKS Foundation: Developed advanced applications in business analytics, sentiment analysis, retrieval systems, and speech emotion recognition. Engaged in proposal writing and reviewing conference papers.
  • Intern at Technology Reply: Gained experience in NLP, sentiment analysis, and textual data modelization.

Skills and Competencies 🛠️

  • Proficient in multiple programming languages and tools including Python, Java, TensorFlow, PyTorch, and SQL.
  • Experienced in data visualization, machine learning, and deep learning.
  • Competent in using various IT tools and frameworks like Apache Spark and Hadoop.

Other Information 🌍

  • Languages: Fluent in Italian and proficient in English (B2 level).
  • Interests: Diverse interests including sports, literature, and technology.
  • Certifications: B2 First (FCE) and driving license.

Publication Top Notes

Zero-Shot Content-Based Crossmodal Recommendation System

Sensitive attributes disproportion as a risk indicator of algorithmic unfairness

Conclusion🏆

Mr. Federico D’Asaro demonstrates a solid academic background, relevant work experience, and a diverse skill set, aligning well with the criteria for the Research for Best Researcher Award. His ongoing research and contributions to AI applications show a strong potential for impactful research and innovation.

Rainer Knauf | Evolutionary Algorithms | Lifetime achievement Award

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Prof Dr Rainer Knauf |  Evolutionary Algorithms |  Lifetime achievement Award

Fachgebietsleiter für KI at  Technische Universität Ilmenau, Germany

Rainer Knauf is an apl. Prof. Dr.-Ing. habil., currently serving as the Chair of Artificial Intelligence at the Faculty of Computer Science and Automation, Technical University Ilmenau, Germany. He earned his Diploma Engineer (Dipl.-Ing.) in Electrical and Computer Engineering in 1987, followed by a Doctor of Engineering (Dr.-Ing.) in Computer Engineering in 1990, and a Doctor of Engineering habilitatus (Dr.-Ing. habil.) in Computer Science in 2000, all from Technical University Ilmenau. His research focuses on knowledge acquisition, validation, and refinement of intelligent systems, inductive inference, and machine learning.

 

profile

🎓 Education:

  • Dipl.-Ing. in Electrical and Computer Engineering
    Technical University Ilmenau, Germany
    📅 February 5, 1987
  • Dr.-Ing. in Computer Engineering
    Technical University Ilmenau, Germany
    📅 September 25, 1990
    Dissertation: “Applying Logic Programming to Design Knowledge Based Systems for Diagnostic Problems”
  • Dr.-Ing. habil. in Computer Science
    Technical University Ilmenau, Germany
    📅 November 15, 2000
    Habilitation: “Validating Rule Based Systems: A Complete Methodology”

💼 Professional Experience:

  • Full Professor (apl. Prof.)
    Chair of Artificial Intelligence, Technical University Ilmenau
    📅 March 2010 – Present
  • Associate Professor (Privatdozent)
    Chair of Artificial Intelligence, Technical University Ilmenau
    📅 April 2004 – February 2010
  • Assistant Professor (Privatdozent)
    Technical University Ilmenau
    📅 December 2000 – March 2004
  • Scientific Assistant
    Technical University Ilmenau
    📅 September 1991 – November 2000
  • Scientific Associate
    Ilmenau Institute of Technology
    📅 March 1987 – August 1991

🏅 Awards & Recognitions

  • Fellowship Awards from the Japan Society for the Promotion of Science 📜 (2008, 2011, 2015)
  • Graduate Faculty Scholar at the University of Central Florida 🎓 (2010)

Research Focus: Evolutionary Algorithms 🧬💡

Research Interests:

  • Optimization and Search Algorithms: Rainer Knauf’s work in evolutionary algorithms involves developing and improving algorithms for optimization and search problems. These algorithms are inspired by the principles of natural selection and genetics.
  • Artificial Intelligence Applications: He applies evolutionary algorithms to various AI challenges, including machine learning, robotics, and automated reasoning.
  • Knowledge Acquisition and Refinement: His research integrates evolutionary algorithms with knowledge-based systems to enhance the processes of knowledge acquisition, validation, and refinement.
  • Data Mining: Knauf explores the use of evolutionary algorithms in data mining, particularly in extracting meaningful patterns and insights from large datasets.
  • Inductive Inference: His work also includes using evolutionary algorithms for inductive inference, aiming to generalize from specific data to broader rules or patterns.

Citation:

Cited by:

  • All: 1082 citations
  • Since 2019: 253 citations

h-index:

  • Overall: 16
  • Since 2019: 7

i10-index:

  • Overall: 33
  • Since 2019: 4

Publication Top Notes:

  • “Didactic design through storyboarding: Standard concepts for standard tools”
    • Authors: KP Jantke, R Knauf
    • Publication: Proceedings of the 4th International Symposium on Information and Communication Technologies
    • Citations: 122 (2005)
    • Summary: This paper explores the use of storyboarding as a method for didactic design, emphasizing standard concepts to standardize tools for educational purposes.
  • “A framework for validation of rule-based systems”
    • Authors: R Knauf, AJ Gonzalez, T Abel
    • Publication: IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
    • Citations: 80 (2002)
    • Summary: This paper presents a comprehensive framework for validating rule-based systems, addressing the need for systematic validation processes in artificial intelligence.
  • “Validation of human behavior representation”
    • Authors: SY Harmon, VB Barr, AJ Gonzalez, DC Hoffmann, R Knauf
    • Publication: University Library
    • Citations: 45 (2006)
    • Summary: The authors discuss methodologies for validating models of human behavior representation, crucial for developing reliable AI systems that simulate human actions.
  • “Modeling didactic knowledge by storyboarding”
    • Authors: R Knauf, Y Sakurai, S Tsuruta, KP Jantke
    • Publication: Journal of Educational Computing Research
    • Citations: 39 (2010)
    • Summary: This research focuses on the use of storyboarding to model didactic knowledge, enhancing the design and delivery of educational content through structured visual methods.
  • “Toward reducing human involvement in validation of knowledge-based systems”
    • Authors: R Knauf, S Tsuruta, AJ Gonzalez
    • Publication: IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans
    • Citations: 25 (2006)
    • Summary: This paper proposes methods to minimize human intervention in the validation process of knowledge-based systems, aiming for more autonomous and efficient validation techniques.
  • “Tweet credibility analysis evaluation by improving sentiment dictionary”
    • Authors: T Kawabe, Y Namihira, K Suzuki, M Nara, Y Sakurai, S Tsuruta, R Knauf
    • Publication: 2015 IEEE Congress on Evolutionary Computation (CEC)
    • Citations: 24 (2015)
    • Summary: This work evaluates the credibility of tweets by enhancing sentiment dictionaries, leveraging evolutionary computation techniques to improve the accuracy of sentiment analysis.
  • “A simple optimization method based on backtrack and GA for delivery schedule”
    • Authors: Y Sakurai, K Takada, N Tsukamoto, T Onoyama, R Knauf, S Tsuruta
    • Publication: 2011 IEEE Congress of Evolutionary Computation (CEC)
    • Citations: 22 (2011)
    • Summary: The authors present an optimization method combining backtracking and genetic algorithms (GA) to improve delivery scheduling, demonstrating the application of evolutionary algorithms in logistics.
  • “Generation of a minimal set of test cases that is functionally equivalent to an exhaustive set, for use in knowledge-based system validation”
    • Authors: T Abel, R Knauf, AJ Gonzalez
    • Publication: Proceedings of the 9th FLAIRS Conference
    • Citations: 22 (1996)
    • Summary: This paper discusses a method for generating a minimal set of test cases that maintains functional equivalence to an exhaustive set, enhancing the efficiency of knowledge-based system validation.
  • “Modeling academic education processes by dynamic storyboarding”
    • Authors: Y Sakurai, S Dohi, S Tsuruta, R Knauf
    • Publication: Journal of Educational Technology & Society
    • Citations: 21 (2009)
    • Summary: The study models academic education processes through dynamic storyboarding, offering a structured approach to designing and implementing educational curricula.
  • “Validating Rule-Based Systems: A Complete Methodology”
    • Author: R Knauf
    • Publication: Shaker
    • Citations: 21 (2000)
    • Summary: This book provides a comprehensive methodology for the validation of rule-based systems, detailing systematic approaches to ensure the reliability and accuracy of these systems.

 

 

Zhidong CAO | Artificial Intelligence Award | Best Researcher Award

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Mr Zhidong CAO | Artificial Intelligence Award | Best Researcher Award

Mr Zhidong CAO, Institute of Automation, Chinese Academy of Sciences, China

Zhidong Cao 🎓 is a prominent Professor and Principal Investigator at the National Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences. He holds a Doctor of Science degree from the Institute of Geographic Sciences and Natural Resources Research, CAS. With over a decade of experience at CAS, Cao has contributed significantly to AI and automation research, leading over 20 national-level projects. His scholarly impact includes 120+ papers in prestigious journals and international conferences, along with authoring 3 books. Cao has been honored with multiple awards, highlighting his substantial contributions to Chinese scientific advancement.

Publication profile

Scopus

Education

Zhidong Cao 📚 pursued a rigorous academic journey that culminated in a Ph.D. from the Institute of Geographic Sciences and Natural Resources Research at the Chinese Academy of Sciences, completed from September 2005 to July 2008. Prior to his doctoral studies, he earned a Master’s degree from Changsha University of Science and Technology, spanning from September 2002 to July 2005. Cao’s educational foundation began with a Bachelor’s degree, also from Changsha University of Science and Technology, covering the period from September 1997 to July 2001. These academic milestones provided him with a comprehensive background for his subsequent influential research career in artificial intelligence and automation.

Experience

Sivayazi Kappagantula | Autonomous intelligent systems | Best Researcher Award

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Mr. Sivayazi Kappagantula | Autonomous intelligent systems | Best Researcher Award

Mr. Sivayazi Kappagantula, MIT Manipal, India

Dr. Sivayazi Kappagantula is an accomplished Roboticist, experienced in Software, Defense, and Aerospace industries. With expertise in ROS, Python, and SolidWorks, he has excelled in teaching and research roles, notably at Manipal Institute of Technology and Defence Institute of Advanced Technology. His doctoral focus at Vellore Institute of Technology delves into autonomous robotics and reinforcement learning. Dr. Kappagantula’s contributions include designing biomimetic robots and pioneering motion planning algorithms for unmanned vehicles. Recognized for his patents and publications, he continues to shape the field through workshops, conferences, and guided student projects. 🤖📚

 

Publication Profile

Google Scholar

📚 Education

Undertaking doctoral studies at Vellore Institute of Technology, Sivayazi delves into the complexities of motion planning in autonomous robotics, focusing on reinforcement learning and path planning. His master’s thesis explored the control algorithm of a biomimetic robot fish for underwater exploration.

Professional Experience

Sivayazi Kappagantula boasts a rich background in Robotics, having contributed significantly across various sectors including academia, defense, and software industries. Currently serving as an Assistant Professor at Manipal Institute of Technology, Udupi, he supervises academic projects, publishes papers, and conducts robotics lab sessions.

Research Focus

Sivayazi Kappagantula’s research primarily centers around robotics and autonomous systems, with a particular emphasis on developing innovative solutions for obstacle avoidance, navigation, and control in various domains. His work spans diverse applications, including unmanned aerial vehicles (UAVs), biomimetic robot fish, and sea surface vehicles. By leveraging advanced techniques such as fuzzy logic algorithms and reinforcement learning, he aims to enhance the autonomy, efficiency, and adaptability of robotic systems. Through his contributions, Sivayazi endeavors to advance the field of robotics and address real-world challenges in areas like defense, agriculture, and environmental monitoring. 🌐

 

Publication Top Notes

Essam Al Hroob | Artificial Intelligence Award | Best Researcher Award

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Assist Prof Dr. Essam Al Hroob | Artificial Intelligence Award | Best Researcher Award

Assist Prof Dr. Essam Al Hroob, Isra University, Jordan

Dr. Fadhl Mohammed Omar Hujainah, a postdoctoral researcher in Software Engineering at Chalmers and University of Gothenburg, Sweden 🇸🇪. With a focus on enhancing software systems, his expertise lies in Artificial Intelligence, particularly in pattern classification. He actively contributes to academia with numerous publications, including collaborations on Fuzzy Min-Max Neural Networks. Dr. Hujainah is recognized for his excellence, evidenced by his contributions receiving awards and grants. His dedication to research extends globally, reflecting his commitment to advancing knowledge in Software Engineering.

Publication Profile

Google Scholar

Academic Qualification

Dr. Essam Alhroob has pursued an extensive academic journey, culminating in a Doctor of Philosophy in Computer Science/Artificial Intelligence from Universiti Malaysia Pahang (UMP), Malaysia 🎓. Prior to this, he earned a Master of Science in Software Engineering from Limkokwing University of Creative Technology (LUCT), Malaysia, and a Bachelor’s Degree in Computer Science (Computer Information Systems) from Al-Zaytoonah University of Jordan 🌍. With his diverse educational background spanning across Malaysia and Jordan, Essam has fortified his expertise in computer science, artificial intelligence, and software engineering, positioning him as a seasoned academic and researcher in the field.

 

Experiences

Throughout his career journey, Essam Alhroob has navigated various roles, each contributing to his expertise and leadership in the field of cybersecurity and academia 🌐. He has served as the Assistant Professor and head of the cybersecurity department at Isra University and Khawarizmi University Technical College, demonstrating his commitment to advancing education in this critical domain. Prior to this, Essam held positions as a part-time Assistant Professor at Al-Zaytoonah University of Jordan and engaged in impactful research as a PhD researcher and graduate assistant at University Malaysia Pahang. His multifaceted experience also includes roles as an Assistant Lecturer and Laboratory Teaching Assistant, emphasizing his dedication to both teaching and practical application. Additionally, he leveraged his communication and problem-solving skills as a Sales Senior Representative at Umniah Mobile Communications Company, further enriching his professional repertoire.

 

Awards

Essam Alhroob’s dedication to academic excellence and research innovation has been recognized through prestigious awards and grants 🏅. Notably, he received the Best Paper Award at the 8th IEEE International Conference on Control Systems, Computing, and Engineering in 2018, highlighting the impact of his contributions to the field. Additionally, Essam was honored with the Excellent Publication Award from the Faculty of Computing at Universiti Malaysia Pahang in 2020, acknowledging his outstanding scholarly output. He furthered his research endeavors through grants such as the Postgraduate Research Grants Scheme, Doctoral Research Scheme Scholarship, and Fundamental Research Grant Scheme, all from Universiti Malaysia Pahang, enabling him to pursue cutting-edge research initiatives.

 

Research Focus

Essam Alhroob’s research focus lies primarily in the domain of pattern classification and artificial intelligence 🧠. Through critical reviews and innovative solutions, he explores the intricacies of fuzzy min-max neural networks, addressing their significance and challenges in pattern classification. His work delves into the development of refined neural network models with novel learning procedures, enhancing the accuracy and efficiency of pattern classification systems. Additionally, Essam contributes to the exploration of artificial intelligence’s role in higher education institutions, elucidating its promises and requirements. With a blend of theoretical analysis and practical application, his research endeavors advance the understanding and application of artificial intelligence in various domains.

 

Publication Top Notes
  1. A Critical Review on Selected Fuzzy Min-Max Neural Networks and Their Significance and Challenges in Pattern Classification📝 Cited by 24, 2019.
  2. Fuzzy Min-Max Classifier Based on New Membership Function for Pattern Classification: A Conceptual Solution 📝 Cited by 16, 2018.
  3. A Refined Fuzzy Min–Max Neural Network With New Learning Procedures for Pattern Classification📝 Cited by 13, 2019.
  4. Higher Education Institutions with Artificial Intelligence: Roles, Promises, and Requirements📝 Cited by 7, 2021.
  5. Analysis on Misclassification in Existing Contraction of Fuzzy Min–Max Models 📝 Cited by 5, 2020.
  6. Choosing the right MFA method for online systems: A comparative analysis📝 Cited by 3, 2024.
  7. Interrelated Elements in Formulating an Efficient Requirements Prioritization Technique📝 Cited by 2, 2020.
  8. Investigation of contraction process issue in fuzzy min-max models📝 Cited by 1, 2022.