Soumeya Belabbas | Artificial Intelligence | Best Researcher Award

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Soumeya Belabbas| Artificial Intelligence| Best Researcher Award

Soumeya Belabbas,University of Sciences and Technology Houari Boumediene,United state.

Soumeya has taught telecommunications systems and networks to fourth-year engineering students at the Higher National School of Information and Communication Technologies and Post in Algiers. Her teaching module includes mobile networks.

Publication Profile

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Education 

Soumeya Belabbas is currently pursuing her PhD in Telecommunication and Information Processing at the University of Sciences and Technology Houari Boumediene in Algiers, Algeria, a journey she embarked on in December 2017. Her doctoral research focuses on multi-variable acoustic modeling for assessing pathological speech understanding, showcasing her deep commitment to advancing the field of telecommunications and speech processing. In addition to her ongoing doctoral studies, Soumeya holds a Master’s degree in Telecommunications, Networks, and Multimedia, which she earned in June 2017 from the same university. This advanced degree equipped her with a robust foundation in telecommunications, further honed through her bachelor’s degree in Telecommunications and Electronics, completed in June 2015. Her academic journey began with a Baccalaureate Diploma in Experimental Sciences, awarded in July 2012, which laid the groundwork for her subsequent specialization and research. Soumeya has also actively participated in professional development activities, such as a workshop on FPGA systems in April 2024, reflecting her continuous pursuit of knowledge and expertise in her field.

Soumeya Belabbas has cultivated a solid foundation in both research and teaching throughout her academic career. As a part-time teacher at the Higher National School of Information and Communication Technologies and Post in Algiers, Algeria, she has been responsible for educating fourth-year engineering students in telecommunications systems and networks, with a specific focus on mobile networks. Her teaching role, undertaken in June 2024, has allowed her to share her extensive knowledge and experience with the next generation of engineers. Alongside her teaching duties, Soumeya has made significant contributions to the field of telecommunications and information processing through her research. She has published multiple articles and presented her findings at international conferences, showcasing her expertise in improving mobile speech recognition systems and pathological speech classification. Her role as a researcher and educator demonstrates her commitment to advancing the field and fostering academic growth.

Soumeya Belabbas’s research is centered on the cutting-edge domains of telecommunications and speech processing, with a particular emphasis on addressing challenges in automatic speech recognition and classification. Her work is dedicated to developing innovative solutions for individuals with voice disorders, aiming to enhance the robustness and accuracy of speech recognition systems. Her current PhD research project, titled “Multi-variable Acoustic Modeling for Assessing Pathological Speech Understanding,” exemplifies her focus on improving pathological speech classification systems. This project involves a comprehensive three-stage framework that incorporates advanced techniques such as speech enhancement, multi-stream approaches, and deep machine learning algorithms like convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) networks. Additionally, Soumeya’s interests extend to signal processing, image processing, and audio speech processing, leveraging machine learning and deep learning methodologies to tackle complex problems in these areas. Her dedication to these research areas is reflected in her numerous publications and presentations, which contribute to the advancement of knowledge and technology in telecommunications and speech processing.

Skills

Soumeya Belabbas possesses a diverse and robust skill set that spans various aspects of telecommunications, information processing, and programming. She is proficient in multiple programming languages, including C/C++, Python, Perl, MATLAB, VHDL, PHP, HTML5, and CSS3, which enable her to develop sophisticated algorithms and models for speech processing and recognition. Her technical expertise extends to the use of HTK tools, essential for hidden Markov model training and evaluation in speech recognition systems. Soumeya is also skilled in managing and operating different operating systems, including Windows Server and Linux (Ubuntu), ensuring that she can work in a versatile computing environment. Her strong foundation in signal and audio speech processing is complemented by her experience with machine learning and deep learning techniques, which she applies to enhance the performance of speech recognition systems. Additionally, Soumeya is well-versed in the use of various software tools and platforms essential for research and development in her field, making her a well-rounded and highly capable researcher and education .

Publications 📚📝

Mulugeta Adibaru Kiflie| Deep Learning | Best Researcher Award

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Mulugeta Adibaru Kiflie| Deep Learning| Best Researcher Award

Dr.Mulugeta Adibaru Kiflie, Adama Science and Technology University,Colombia

Dr.Mulugeta Adibaru Kiflie, PhD Cand. is an Assistant Professor specializing in Computer Science and Engineering. With over 14 years of experience in academia and industry, he excels in digital entrepreneurship, IT education, machine learning, and ICT management. He leverages Python for data analysis in agriculture, health, education, and business, aiming to optimize digitalization for sustainable development. Mulugeta is also adept in project management, technical proposal writing, and mentoring. His extensive skill set includes advanced statistical models and proficiency in various software packages. 🌐📊🎓

 

Publication Profile

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Education And Experience

Dr.Mulugeta Adibaru Kiflie is a dedicated professional in digital entrepreneurship, IT education, and machine learning. With over 14 years of experience in academia and industry, he excels in digital marketing analysis, data management, and statistical modeling. Mulugeta is passionate about utilizing Python for agricultural, health, and business data analysis to predict outcomes and uncover trends. As an assistant professor and machine learning research fellow, he mentors students, conducts research, and delivers professional training. Fluent in English and Amharic, he holds multiple certifications in deep learning, data science, and business intelligence. 📊👨‍🏫🚀

 

Professional Development

Dr.Mulugeta Adibaru Kiflie, a Ph.D. candidate and Assistant Professor at Adama Science and Technology University, Ethiopia, is a dedicated computer scientist and engineer with over 14 years of experience in academia and industry. His expertise includes digital entrepreneurship, IT education, machine learning, data analytics, and ICT management. Mulugeta excels in leveraging Python for data analysis across various fields, such as healthcare and agriculture, to predict outcomes and optimize digital practices for sustainable development. He is proficient in statistical software like SPSS, R, and STATA, and is committed to mentoring students and conducting innovative research in AI and digital technology. 💻📊🌿

 

Research Focus

Dr. Mulugeta Adibaru Kiflie’s research focuses on various applications of computer science and engineering in fields such as healthcare, agriculture, and education. His expertise includes digital entrepreneurship, IT education, machine learning, and data analysis. His work emphasizes the use of deep learning and machine learning techniques to address issues like crop disease detection, precision agriculture, electronic health record analysis, and species identification. Additionally, he is skilled in digital marketing analysis, database management, and statistical modeling, making significant contributions to the optimization of digital practices for sustainable development. 🌿📊💻

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