Dr. Yirga Yayeh Munaye | security | Best Researcher Award

Dr. Yirga Yayeh Munaye | secuirty | Best Researcher Award

PhD, Director of e-learning management unit, Injibara University, Ethiopia.

Dr. Yirga Yayeh Munaye is an Ethiopian academic and researcher with expertise in Electrical Engineering, Computer Science, and Information Technology. He currently serves as an Assistant Professor and Director of the E-learning Management Unit at Injibara University, Ethiopia. With a Ph.D. from National Taipei University of Technology, Taiwan, Dr. Munaye is known for his significant contributions in wireless communication, AI, and UAV-assisted resource management. His leadership in academia spans various universities, reflecting his passion for teaching, research, and community service.

Publication Profile

Google Scholar

πŸŽ“ Education Background

Dr. Munaye earned his Ph.D. in Electrical Engineering and Computer Science from National Taipei University of Technology (NTUT), Taiwan, in 2021 with a dissertation graded Excellent (91.4/100). Prior to that, he obtained an M.Sc. in Information Science from Addis Ababa University, Ethiopia, in 2014 and a B.Sc. in Information Technology from Bahir Dar University in 2009. His academic training reflects consistent excellence and specialization in advanced communication and AI applications.

πŸ‘¨β€πŸ« Professional Experience

Dr. Munaye has served in various academic roles, including as Assistant Professor and Researcher at Injibara University since 2022, where he also coordinated postgraduate and community research services. Previously, he held teaching and research positions at Bahir Dar Institute of Technology and Assosa University. He has mentored Master’s and Ph.D. students, led network and internet chair units, and participated in proposal writing and journal editing, contributing significantly to Ethiopia’s higher education landscape.

πŸ† Awards and Honors

Dr. Munaye has received numerous certificates and awards recognizing his academic contributions. These include participation in the Foundations for Excellence in Teaching Online masterclass (2023), the Science and Engineering Research training by AWB (2022), and international ICT training at XIDIAN University, China (2017). He has also earned honors for research writing, project proposal development, and higher diploma program achievements, underlining his commitment to continuous academic development.

πŸ”¬ Research Focus

Dr. Munaye’s research focuses on AI and wireless communication systems, UAV deployment strategies, mobile communications, and cybersecurity. He is especially passionate about the intersection of deep learning with resource management in next-generation networks. His work spans across emerging technologies including IoT security, biomedical sensors, and machine learning applications, reflecting a strong interdisciplinary and future-oriented research profile.

βœ… Conclusion

With a career rooted in excellence, leadership, and innovation, Dr. Yirga Yayeh Munaye exemplifies the qualities of a modern researcher and educator. His contributions to teaching, mentoring, and groundbreaking research continue to make a lasting impact on Ethiopian academia and global knowledge systems.

πŸ“š Top Publications Notes

  1. Cyber security: State of the art, challenges and future directions
    Cyber Security and Applications, 2024
    Cited by: 184

  2. UAV positioning for throughput maximization using deep learning approaches
    Sensors, 2019
    Cited by: 60

  3. An indoor and outdoor positioning using a hybrid of support vector machine and deep neural network algorithms
    Journal of Sensors, 2018
    Cited by: 58

  4. Applying Deep Neural Network (DNN) for large-scale indoor localization using feed-forward neural network (FFNN) algorithm
    IEEE ICASI Conference Proceedings, 2018
    Cited by: 38

  5. Big data: security issues, challenges and future scope
    International Journal of Computer Engineering & Technology, 2016
    Cited by: 37

  6. Deep-reinforcement-learning-based drone base station deployment for wireless communication services
    IEEE Internet of Things Journal, 2022
    Cited by: 33

  7. Indoor localization using K-nearest neighbor and artificial neural network back propagation algorithms
    WOCC Conference Proceedings, 2018
    Cited by: 33

  8. Convolutional neural networks and histogram-oriented gradients: a hybrid approach for automatic mango disease detection and classification
    International Journal of Information Technology, 2024
    Cited by: 32

 

Muhammad Bilal Mahmood | Security | Best Researcher Award

Mr. Muhammad Bilal Mahmood | Security | Best Researcher Award

PhD Scholar, Dalian University of Technology, China

Muhammad Bilal Mahmood is a dedicated researcher and educator in Software Engineering, currently pursuing his Ph.D. at Dalian University of Technology, China πŸŽ“. With a strong foundation in Computer Engineering and extensive expertise in machine learning, natural language processing, and cybersecurity, he has contributed significantly to academia and research. As a lecturer at The University of Lahore, Pakpattan Campus, since 2017, he has been instrumental in shaping young minds and fostering innovation πŸ’‘. His research interests include deep learning, facial expression recognition, and AI-driven cybersecurity solutions.

Publication Profile

ORCID

πŸ“š Education

Muhammad Bilal Mahmood’s academic journey reflects his passion for cutting-edge technology and innovation πŸš€. He is currently a Ph.D. candidate in Software Engineering (2021–2025) at Dalian University of Technology, China. Prior to this, he earned his MS in Computer Engineering from NUST College of Electrical & Mechanical Engineering, Rawalpindi, Pakistan (2015) πŸŽ“. His undergraduate studies in Computer System Engineering were completed at NFC Institute of Engineering & Technological Training, Multan, Pakistan (2011), laying the groundwork for his expertise in programming and research.

πŸ’Ό Experience

With over six years of academic and research experience, Muhammad Bilal Mahmood has been serving as a Lecturer at The University of Lahore, Pakpattan Campus, since 2017 🏫. His responsibilities include developing system requirement specifications, designing databases, programming under strict coding guidelines, and ensuring seamless project management πŸ“Š. His ability to mentor students and collaborate with fellow researchers has made him a valuable asset in the field of software engineering.

πŸ† Awards and Honors

Throughout his career, Muhammad Bilal Mahmood has been recognized for his contributions to research and academia πŸ…. His innovative work in deep learning, NLP, and cybersecurity has earned him appreciation within the research community. His expertise in artificial intelligence-driven security solutions has led to impactful publications, making significant contributions to advancing technological frontiers.

πŸ”¬ Research Focus

Muhammad Bilal Mahmood’s research primarily revolves around artificial intelligence, deep learning, and cybersecurity πŸ”. His work includes speech emotion recognition, malicious package detection in PyPI, and facial expression analysis using convolutional neural networks πŸ€–. He has also contributed to medical image processing, topic discovery in health data, and chatbot development for university applications. His interdisciplinary research bridges the gap between AI and real-world applications, improving human-computer interactions.

πŸ“ Conclusion

Muhammad Bilal Mahmood is a passionate researcher, educator, and software engineer dedicated to advancing AI-driven solutions and cybersecurity methodologies πŸ’‘. His commitment to academia, combined with his innovative research projects, makes him a valuable contributor to the field. Through his teaching and research, he continues to inspire the next generation of engineers and scientists, pushing the boundaries of technological advancements. πŸš€

πŸ“„ Publications

Recognizing Semi-Natural and Spontaneous Speech Emotions Using Deep Neural Networks – This research explores deep learning-based speech emotion recognition to enhance human-computer interaction. [Published in: Journal Name, Year] πŸ“œπŸ”—

PypiGuard: A novel meta-learning approach for enhanced malicious package detection in PyPI through static-dynamic feature fusion – This paper introduces a cutting-edge security framework for detecting malicious packages in Python’s package repository. [Published in: Journal Name, Year] πŸ“œπŸ”—