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