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.

Souhail Dhouib | Artificial Intelligence | Best Researcher Award

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Prof. Souhail Dhouib | Artificial Intelligence | Best Researcher Award

Full Professor, Higher Institute of Industrial Management, University of Sfax, Tunisia

Prof. Souhail Dhouib 🌟 is a distinguished academic and industry expert specializing in Artificial Intelligence and Operations Research. He holds the position of Full Professor at the Higher Institute of Industrial Management, University of Sfax, Tunisia, where he has taught for over twenty years. Prof. Dhouib is renowned for his pioneering work in matrix optimization concepts and has made significant contributions to decision-making and planning through his innovative algorithms and methodologies.

ProfileArtificial Intelligence 

ORCID

 

Education

Prof. Dhouib completed his Ph.D. in Quantitative Methods from the Faculty of Management and Economics Sciences, Sfax University, Tunisia (2004-2009). He also holds a Master’s degree in Operations Research and Production Management (2001-2003) and a Bachelor’s degree in Management Information Systems (1992-1996), all from the same institution. 📚🎓

Experience

Prof. Dhouib has over twenty years of extensive experience in both academia and industry. He has served as a General Manager and an Analyst Programmer, where he was involved in software development and implementation. His academic roles include positions as a Full Professor, Associate Professor, and Assistant Professor, contributing significantly to teaching and research. 🏢💻

Research Interests

Prof. Dhouib’s research focuses on Artificial Intelligence, Operations Research, and Optimization algorithms. His work spans various domains, including Logistics, Supply Chain Management, Business Intelligence Systems, and ERP. He is particularly known for his Dhouib-Matrix methods and their applications in cognitive robotics, multi-objective optimization, and path planning. 🔍📈

Awards

Prof. Dhouib’s contributions to the field have been recognized through numerous journal publications, although specific awards are not listed in the provided information. His innovative research and development efforts continue to impact the industry and academia. 🏆👏

Publications

Intelligent Path Planning for Cognitive Mobile Robot Based on Dhouib-Matrix-SPP Method (2024) – Cognitive Robotics

Multi-Start Constructive Heuristic through Descriptive Statistical Metrics: The Dhouib-Matrix-4 Metaheuristic (2024) – International Journal of Operational Research

Innovative Method to Solve the Minimum Spanning Tree Problem: The Dhouib-Matrix-MSTP (DM-MSTP) (2024) – Results in Control and Optimization

Enhancing the Dhouib-Matrix-4 Metaheuristic to Generate the Pareto Non-Dominated Set Solutions for Multi-objective Travelling Salesman Problem: The DM4-PMO Method (2024) – Results in Control and Optimization

Faster than Dijkstra and A* Methods for the Mobile Robot Path Planning Problem Using Four Movement Directions: The Dhouib-Matrix-SPP-4 (2024) – Advances in Transdisciplinary Engineering, Mechatronics and Automation Technology

Nafis Uddin Khan | Artificial Intelligence | Best Researcher Award

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Dr Nafis Uddin Khan | Artificial Intelligence | Best Researcher Award 

Dr Nafis Uddin Khan, SR University Warangal India, India

Dr. Nafis Uddin Khan is a distinguished academic and researcher at SR University in Warangal, India. His expertise spans a variety of fields, contributing significantly to both the academic community and industry advancements. Dr. Khan’s work is characterized by a strong focus on innovative solutions and sustainable practices, reflecting his commitment to addressing contemporary challenges through research and education. At SR University, he plays a pivotal role in mentoring students and leading research initiatives that aim to drive technological progress and societal impact.

Profile

Orcid

Education

       Bachelor of Engineering (B.E.) in Electronics and Telecommunication Engineering from Amravati University  Maharashtra, in 2003.

       Master of Technology (M.Tech.) in Software Systems from S.A.T.I. Vidisha under Rajiv Gandhi Technological University, Bhopal, in 2008.

        Doctor of Philosophy (Ph.D.) in Signal and Image Processing from the Atal Bihari Vajpayee – Indian Institute of Information Technology & Management, Gwalior

Professional experience

  1. Workshop Coordinator: Coordinated a two-day workshop titled “Synthesis of Wisdom: Crafting AI Tutor Assistants, Navigating Future-Ready Digital Libraries and Elevating Pedagogy in the Age of Skill Enhancement and AI Mastery” on February 02–03, 2024, at the School of CS & AI, SR University, Warangal, India.
  2. Faculty Development Program Coordinator: Coordinated “Utkarsh – Take Flight,” a two-day Faculty Development Program on Leadership and Excellence in Quality Education organized by Jaypee University of Information Technology, Solan (H.P.), on December 22–23, 2022.
  3. Chief Coordinator: Served as Chief Coordinator in a student outreach activity organized by the Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Solan (H.P.), on December 20, 2022.
  4. Invited Session Chair: Served as an Invited Session Chair in the Congress on Intelligent Systems (CIS 2020), World Conference in virtual format, on September 05–06, 2020.
  5. Organizing Committee Member and Reviewer: Participated as an Organizing Committee Member and Reviewer in the Congress on Intelligent Systems (CIS 2020), World Conference in virtual format, on September 05–06, 2020.
  6. Convener: Organized a one-week online short-term course on “Recent Advances in Computational Intelligence for Signal Processing” (RACISP – 2020) at Jaypee University of Information Technology, Solan (H.P.), from August 10–15, 2020.
  7. Coordinator: Coordinated a five-day short-term course on “Recent Advances in Signal and Image Processing” (RASIP – 2019) at Jaypee University of Information Technology, Solan (H.P.), from June 24–28, 2019.
  8. Organizing Committee Member: Served in the organizing committee for the 5th IEEE International Conference on “Signal Processing, Computing and Control” (ICSPC 2019) organized by the Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Solan (H.P.), from October 10–12, 2019.
  9. Invited Session Chair: Chaired an invited session in the 2019 IEEE International Conference on “Image Information Processing” (ICIIP 2019) organized by the Department of Computer Science and Engineering, Jaypee University of Information Technology, Solan (H.P.), from November 15–17, 2019.
  10. Organizing Committee Member: Participated as an organizing committee member in the 4th IEEE International Conference on “Signal Processing, Computing and Control” (ICSPC 2017) at Jaypee University of Information Technology, Solan (H.P.), from September 21–23, 2017.
  11. Invited Session Chair: Served as an invited session chair at the 7th IEEE International Conference on “Computational Intelligence and Communication Networks” (CICN 2015) at Gyan Ganga Institute of Technology and Science, Jabalpur (M.P.), on December 12, 2015.
  12. National Advisory Committee Member: Served on the National Advisory Committee for the National Conference on Contemporary Computing organized by the Department of Computer Science and Information Technology, Chameli Devi Group of Institutions, Indore (M.P.), from October 21–22, 2016.

Research Focus

      Fuzzy Logic and Optimization: Utilizing fuzzy logic for applications in signal and image processing, including the development of fuzzy-based diffusion coefficient functions for selective noise smoothing.

      Medical Image Processing: Enhancing medical imaging techniques, such as de-speckling in ultrasound and X-ray images, and improving image de-noising using soft optimization techniques.

      Pattern Analysis in Machine Intelligence: Investigating pattern analysis and its applications within machine intelligence to improve the accuracy and efficiency of image processing algorithms.

Awards and Honors 

Zhidong CAO | Data Science | Best Researcher Award

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Mr. Zhidong CAO | Data Science |  Best Researcher Award

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

Zhidong CAO is a renowned professor and principal investigator at the National Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences. With a Doctor of Science degree, he has made significant contributions to the field of artificial intelligence and has been recognized for his work in various national and international platforms.

Profile

Orcid

Education

Zhidong CAO earned his Ph.D. from the Institute of Geographic Sciences and Natural Resources Research at the Chinese Academy of Sciences in 2008. He also holds a Master’s degree and a Bachelor’s degree from Changsha University of Science and Technology, completed in 2005 and 2001 respectively.

Research Focus

His research interests lie primarily in the areas of multimodal artificial intelligence systems, social computing, and geographic information analysis. He has been instrumental in several key national scientific and technological projects, including the National Medium- and Long-term Scientific and Technological Development Plan (2021-2035) and the New Generation Artificial Intelligence Strategic Plan.

Professional Journey

Zhidong CAO began his professional journey as a Postdoctoral Fellow at the Institute of Automation, Chinese Academy of Sciences, in 2008. He progressed to become an Assistant Researcher in 2010, then an Associate Researcher in 2011, and has been serving as a Researcher since 2020. His roles have seen him engage deeply with various research projects and contribute significantly to the field of automation and artificial intelligence.

Honors & Awards

Throughout his career, Zhidong CAO has received numerous prestigious awards. Notable among these are the Beijing Science and Technology Progress Award (Second Prize, 2022), the China Surveying and Mapping Society Science and Technology Award (Grand Prize, 2021), and the Chinese Society of Simulation Natural Science First Prize (2018). His contributions have also been recognized by the Chinese Association of Automation and the Chinese Preventive Medicine Association.

Publications Noted & Contributions

Zhidong CAO has an impressive portfolio of over 120 research papers published in leading domestic and international journals and conferences. He has also authored three books, further establishing his expertise in his field. His research has earned him six scientific and technological awards, underscoring his significant contributions to the advancement of artificial intelligence and related domains.

  1. Coordinated Cyber Security Enhancement for Grid-Transportation Systems With Social Engagement
    • Journal: IEEE Transactions on Emerging Topics in Computational Intelligence
    • DOI: 10.1109/TETCI.2022.3209306
    • Contributors: Pengfei Zhao, Shuangqi Li, Paul Jen-Hwa Hu, Zhidong Cao, Chenghong Gu, Da Xie, Daniel Dajun Zeng
    • Summary: This article discusses methods for enhancing cybersecurity in grid-transportation systems through coordinated efforts and social engagement. It emphasizes the importance of integrating social factors and community involvement in cybersecurity strategies.
  2. Energy-Social Manufacturing for Social Computing
    • Journal: IEEE Transactions on Computational Social Systems
    • DOI: 10.1109/TCSS.2024.3379254
    • Contributors: Alexis Pengfei Zhao, Shuangqi Li, Yanjia Wang, Paul Jen-Hwa Hu, Chenye Wu, Zhidong Cao, Faith Xue Fei
    • Summary: This article explores the concept of energy-social manufacturing, which integrates energy systems with social computing to enhance efficiency and sustainability. The research highlights the role of social computing in optimizing energy production and consumption.
  3. Modeling the Coupling Propagation of Information, Behavior, and Disease in Multilayer Heterogeneous Networks
    • Journal: IEEE Transactions on Computational Social Systems
    • DOI: 10.1109/TCSS.2023.3306014
    • Contributors: Tianyi Luo, Duo Xu, Zhidong Cao, Pengfei Zhao, Jiaojiao Wang, Qingpeng Zhang
    • Summary: This study models the interactions and propagation dynamics of information, behavior, and disease within multilayer heterogeneous networks. It provides insights into how these elements influence each other and spread across different network layers.
  4. Socially Governed Energy Hub Trading Enabled by Blockchain-Based Transactions
    • Journal: IEEE Transactions on Computational Social Systems
    • DOI: 10.1109/TCSS.2023.3308608
    • Contributors: Pengfei Zhao, Shuangqi Li, Zhidong Cao, Paul Jen-Hwa Hu, Chenghong Gu, Xiaohe Yan, Da Huo, Tianyi Luo, Zikang Wang
    • Summary: This article examines how blockchain technology can facilitate socially governed energy hub trading. It discusses the implementation of blockchain-based transactions to enhance transparency, security, and efficiency in energy markets.
  5. A Cross-Lingual Transfer Learning Method for Online COVID-19-Related Hate Speech Detection
    • Journal: Expert Systems with Applications
    • DOI: 10.1016/j.eswa.2023.121031
    • Contributors: Lin Liu, Duo Xu, Pengfei Zhao, Daniel Dajun Zeng, Paul Jen-Hwa Hu, Qingpeng Zhang, Yin Luo, Zhidong Cao
    • Summary: This research presents a method for detecting COVID-19-related hate speech online using cross-lingual transfer learning. The study demonstrates the effectiveness of the proposed method in identifying hate speech across different languages, aiding in the fight against online misinformation and discrimination.

 

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.

 

 

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.