Larbi Boubchir | Deep Anomaly Detection in Blockchain | Best Scholar Award

Prof. Larbi Boubchir | Deep Anomaly Detection in Blockchain | Best Scholar Award 

Professor | University of Paris 8 | France

Based on the detailed profile of Prof. Dr. Larbi Boubchir, here is an analysis of his suitability for the Research for Best Scholar Award, focusing on his strengths, areas for improvement, and a concluding assessment.

Strengths

  1. Extensive Research Contributions: Prof. Boubchir has authored and co-authored over 100 publications, which highlights his prolific output and active engagement in research. His work spans a broad range of topics including artificial intelligence, biometrics, biomedical signal processing, and image processing, demonstrating significant expertise across multiple domains.
  2. Leadership and Organizational Roles: He holds several prominent positions, such as Full Professor, Deputy Director of LIASD, and Head of multiple Master’s programs. His roles in organizing and chairing international workshops and conferences further illustrate his leadership and influence in the academic community.
  3. Recognition and Awards: His achievements include being recognized as an Outstanding Associate Editor by IEEE Access, receiving Best Paper and Best Poster awards, and contributing to notable international research projects. These accolades reflect the high quality and impact of his research.
  4. Diverse Teaching Experience: Prof. Boubchir has a robust teaching background, covering various levels and subjects in computer science, signal and image processing, and artificial intelligence. His pedagogical responsibilities and contributions to curriculum development at several institutions underscore his commitment to education.
  5. Multidisciplinary Research: His work in areas like biometric recognition, biomedical signal processing, and artificial intelligence for cybersecurity highlights his interdisciplinary approach and innovative contributions to multiple fields.

Areas for Improvement

  1. Interdisciplinary Collaboration: While Prof. Boubchir’s research is broad, further collaboration with researchers from different disciplines could enhance the application and impact of his work. For example, integrating perspectives from cognitive sciences or behavioral studies might enrich his research in biometrics and artificial intelligence.
  2. Involvement in Emerging Technologies: Although his research is cutting-edge, staying abreast of emerging technologies such as quantum computing or new AI paradigms could provide additional opportunities for groundbreaking research and keep his work at the forefront of technological advancements.
  3. Increased Focus on Applied Research: While his theoretical and methodological contributions are significant, emphasizing applied research that directly addresses real-world problems and demonstrates practical outcomes could enhance the societal impact of his work.

Short Biography

Prof. Dr. Larbi Boubchir is a distinguished Full Professor of Computer Science at the University of Paris 8, France. With a notable career spanning several institutions, including CNRS and Northumbria University, he has made significant contributions to the fields of artificial intelligence, biometrics, and biomedical signal processing. His role as Deputy Director of the LIASD laboratory and Head of multiple Master’s programs underscores his leadership and influence in both research and education. Prof. Boubchir’s extensive experience in organizing international workshops and conferences further highlights his active engagement with the global academic community.

Profile

ORCID

Education

Prof. Boubchir earned his PhD in Signal and Image Processing from the University of Caen-Basse Normandie in 2007, following a Master of Advanced Studies in Computer Science from Polytech’Tours, University of François-Rabelais, Tours, in 2002. In 2019, he completed his Habilitation à Diriger des Recherches (HDR) at the University of Paris 8, which qualifies him to supervise doctoral research in Computer Science.

Experience

Prof. Boubchir has held numerous academic positions throughout his career. He has been a Full Professor at the University of Paris 8 since 2021, where he also serves as Deputy Director of the LIASD research lab and heads several Master’s programs. Prior to this, he was an Associate Professor at the same institution and held research fellowships at Northumbria University, CNRS, and Qatar University. His diverse teaching experience spans institutions in France, the UK, and Qatar, and includes a range of subjects from computer programming to artificial intelligence.

Research Interests

Prof. Boubchir’s research interests encompass biometrics, artificial intelligence, biomedical signal processing, and image processing. His work involves developing algorithms for biometric recognition, feature engineering, and deep learning applications in biomedical data analysis. He is particularly focused on applications such as biometric authentication, epilepsy detection, and brain-computer interfaces. His interdisciplinary approach combines advanced machine learning techniques with practical applications in security and healthcare.

Awards

Prof. Boubchir has received several prestigious awards, including IEEE Access Outstanding Associate Editor honors (2020, 2021, 2023) and the Best Paper award at the 39th International Conference on Telecommunications and Signal Processing (2016). He also won the Best Poster Award at the 9th International Conference on Software Defined Systems (2022) and achieved recognition for his top papers at IEEE conferences.

Publications

Here are some of Prof. Boubchir’s notable publications:

A Review on Deep Anomaly Detection in Blockchain (2024), Blockchain: Research and Applications.

Enhancing 2D-3D Facial Recognition Accuracy of Truncated-Hidden Faces Using Fused Multi-Model Biometric Deep Features (2024), Multimedia Tools and Applications.

Deep Speech Recognition System Based on AutoEncoder-GAN for Biometric Access Control (2023), International Journal of Advanced Computer Science and Applications (IJACSA).

Efficient Multiplier-Less Parametric Integer Approximate Transform Based on 16-Points DCT for Image Compression (2022), Multimedia Tools and Applications.

Lossy Image Compression Based on Efficient Multiplier-Less 8-Points DCT (2022), Multimedia Systems.

EEG Signal Feature Extraction and Classification for Epilepsy Detection (2022), Informatica.

Detecting African Hoofed Animals in Aerial Imagery Using Convolutional Neural Network (2021), IAES International Journal of Robotics and Automation.

Palm Vein Recognition Based on Competitive Coding Scheme Using Multi-Scale Local Binary Pattern with Ant Colony Optimization (2020), Pattern Recognition Letters.

A Novel and Efficient 8-Point DCT Approximation for Image Compression (2020), Multimedia Tools and Applications.

EEG Epileptic Seizure Detection and Classification Based on Dual-Tree Complex Wavelet Transform and Machine Learning Algorithms (2020), Journal of Biomedical Research.

Conclusion

Prof. Dr. Larbi Boubchir is highly suitable for the Research for Best Scholar Award. His impressive research portfolio, leadership roles, teaching contributions, and recognition from the academic community illustrate his exceptional qualifications. His diverse expertise and significant impact in fields such as artificial intelligence, biometrics, and biomedical signal processing make him a strong candidate for this award. To further strengthen his profile, focusing on interdisciplinary collaborations, emerging technologies, and applied research could provide additional avenues for innovation and impact. Overall, Prof. Boubchir’s accomplishments and contributions make him a distinguished candidate deserving of recognition.

Jawad Khan | Software Engineering | Best Researcher Award

Prof Dr.Jawad Khan | Software Engineering | Best Researcher Award

Assistant Professor Gachon University  South Korea

Dr. Jawad Khan is an Assistant Professor at Gachon University, South Korea. With extensive experience in data science and artificial intelligence, Dr. Khan’s work focuses on semantic learning, machine learning, and natural language processing. He has made significant contributions to the fields of text mining, software engineering, and cloud computing. Dr. Khan is dedicated to advancing research in these areas through innovative approaches and interdisciplinary collaboration.

Profile

Scopus

Education

🎓 Dr. Khan completed his Ph.D. in Computer Science and Engineering from Kyung Hee University, South Korea, in 2020. His thesis, “An Ensemble-based Effective Features Engineering and Intelligent Unified Framework for Sentiment Analysis,” was supervised by Prof. Young-Koo Lee. He holds an M.Sc. in Computer Science from Kohat University of Science and Technology, Pakistan, where he developed a computerized hotel management system under the guidance of Mr. Qadeem Khan. He earned his B.Sc. in Computer Science from the University of Malakand, Pakistan.

Experience

💼 From October 2020 to February 2023, Dr. Khan served as a Postdoctoral Fellow in the Department of Applied Artificial Intelligence at Hanyang University, South Korea, focusing on big data mining and sentiment analysis. Prior to this, he was a Research Associate at Kyung Hee University, South Korea, from March 2014 to September 2020, where he worked on social media analytics and sentiment classification.

Research Interests

🔍 Dr. Khan’s research interests encompass data science, artificial intelligence, natural language processing, semantic learning, machine learning, deep learning, text mining, data mining, software engineering, computer vision, cloud computing, and IoT. He is particularly focused on developing intelligent frameworks for sentiment analysis and enhancing feature engineering techniques.

Awards & Honors

🏆 Dr. Khan has been honored with a fully funded Ph.D. scholarship from Kyung Hee University, which he held from March 2014 to September 2020. This prestigious award supported his doctoral studies and research in computer science and engineering.

Publications

📝 Dr. Khan has numerous publications in esteemed journals and conferences. Here are some of his notable works:

  1. Factors influencing vendor organizations in the selection of DevOps for global software development: an exploratory study using a systematic literature review. Cognition, Technology & Work (2023).
  2. Investigating the dynamic relationship between stigma of fear, discrimination and employees performance among healthcare workers during Covid-19 pandemic. Cognition, Technology & Work (2023).
  3. An empirical study for prioritizing issue of software project management team. Cognition, Technology & Work (2023).
  4. Human Activity Recognition Based on Deep-Temporal Learning Using Convolution Neural Networks Features and Bidirectional Gated Recurrent Unit With Features Selection. IEEE Access 11 (2023).
  5. Monkeypox detection using CNN with transfer learning. Sensors 23, no. 4 (2023).