Ronny Mabokela | Natural Language Processing | Best Researcher Award

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Mr. Ronny Mabokela | Natural Language Processing | Best Researcher Award

Lecturer, University of Johannesburg, South Africa

KR Mabokela is a South African PhD candidate in Computer Science at the University of the Witwatersrand (2020–2024). With a background in Speech Technology, he holds a Master of Science in Computer Science (2012–2014) and a Bachelor of Science in Computer Science and Mathematics (2008–2010), both from the University of Limpopo. Currently, he serves as Acting Deputy Head of Department for Continuing Education Programs (CEPs) and Online Learning at the University of Johannesburg, where he contributes to academic leadership and strategic planning. His research interests focus on multilingual sentiment analysis, language identification for under-resourced languages, and speech technology. 🧑‍💻📚

Publication Profile

Google scholar

Education

KR Mabokela’s academic journey began at the University of Limpopo, where he earned his Bachelor of Science (Computer Science and Mathematics, 2008–2010), followed by a Bachelor of Science Honours in Computer Science (2011). He continued his studies with a Master of Science in Computer Science, specializing in Speech Technology (2012–2014). He is now pursuing a Doctor of Philosophy (PhD) in Computer Science at the University of the Witwatersrand (2020–2024), focusing on advancing research in speech technology and multilingual systems. 🎓🔬

Experience

Mabokela has built a strong academic career, currently serving as the Acting Deputy Head of the Department of Applied Information Systems at the University of Johannesburg, where he leads the Continuing Education Programs (CEPs) and Online initiatives. His responsibilities include academic leadership, strategic direction, coordinating training for staff and students, and ensuring the quality of postgraduate teaching in online settings. He has also played a pivotal role in the development of online education and the integration of emerging technologies into curriculum delivery. 💼👨‍🏫

Research Interests

KR Mabokela’s research revolves around multilingual sentiment analysis, language identification, and speech technology, with a specific focus on under-resourced languages. He investigates how to build efficient systems for multilingual sentiment analysis and address challenges posed by code-switching in speech. His goal is to create tools that improve the processing and understanding of languages that lack sufficient resources and support technological growth in African languages. 🌍💡

Awards

Mabokela’s work has been recognized for its innovation and contribution to speech technology, especially in the context of under-resourced languages. His research has been cited in numerous papers and has received acknowledgment in the academic community for its practical applications in multilingual sentiment analysis and language identification. 🏅🔍

Publications

Multilingual Sentiment Analysis for Under-Resourced Languages: A Systematic Review of the Landscape
Published in IEEE Access (2023)
Cited by: 34
Read here

Modeling code-Switching speech on under-resourced languages for language identification
Published in SLTU (2014)
Cited by: 30
Read here

An integrated language identification for code-switched speech using decoded-phonemes and support vector machine
Published in Speech Technology and Human-Computer Dialogue (SpeD), 7th Conference (2013)
Cited by: 13
Read here

A sentiment corpus for South African under-resourced languages in a multilingual context
Published in TBD Journal
Cited by: TBD
Read here

Fida Ullah | Natural language Processing | Data Science Contribution Award

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Mr.Fida Ullah | Natural language Processing | Data Science Contribution Award

PhD Student, Institute of politechnical National, Mexico

🎓 Fida Ullah is a dedicated PhD student in Computer Science at Instituto Politécnico Nacional, Mexico, specializing in Named Entity Recognition (NER) and machine learning, with a deep passion for advancing Natural Language Processing (NLP) for low-resource languages. His expertise spans deep learning and transformer models, and he is skilled in applying these techniques to various text analysis challenges. Fida has published extensively in reputable journals and actively engages in the latest NLP developments, making him a promising researcher in this field.

Publication Profile

Google Scholar

Education

📘 PhD in Computer Science – Instituto Politécnico Nacional, Mexico (2022-Present), Thesis: Urdu Named Entity Recognition with Deep Learning
Advisor: Dr. Alexander Gelbukh. M.Sc. in Computer Science – Beijing University of Chemical Technology, China (2018-2021)

Experience

💻 Fida has hands-on experience with Python and essential machine learning libraries like TensorFlow, PyTorch, and Keras. He has worked extensively with deep learning frameworks, focusing on Named Entity Recognition, sentiment analysis, and hate speech detection in low-resource languages. His work has been showcased at international conferences, and he has collaborated with global researchers on NLP projects.

Research Interests

🔍 Fida’s research interests are centered around Natural Language Processing and Named Entity Recognition for low-resource languages, utilizing deep learning, transformer models, and data augmentation techniques. He is also intrigued by advancing explainable machine learning applications for smart city innovations.

Awards and Achievements

🏆 Awards include the CONACYT Scholarship (Mexico) and the Chinese Government Scholarship for his academic excellence and contributions to NLP research.

Publications

Ullah, Fida, Ihsan Ullah, and Olga Kolesnikova. “Urdu Named Entity Recognition with Attention Bi-LSTM-CRF Model.” Mexican International Conference on Artificial Intelligence (2022). Springer Nature Switzerland.

Fida Ullah, Alexander Gelbukh, MT Zamir, EM Felipe Revoron, and Grigori Sidorov. “Enhancement of Named Entity Recognition in Low-Resource Languages with Data Augmentation and BERT Models: A Case Study on Urdu.” Computers, MDPI (2023). https://doi.org/10.3390/computers13100258.

Muhammad Arif, MS Tash, Ainaz Jamshidi, Fida Ullah, et al. “Analyzing Hope Speech from Psycholinguistic and Emotional Perspectives.” Scientific Reports (2024). https://doi.org/10.1038/s41598-024-74630-y.

Fida Ullah, M.Ahmed, MT. Zamir, et al. “Optimal Scheduling for the Performance Optimization of SpMV Computation using Machine Learning Techniques.” IEEE Xplore (2024). https://doi.org/10.1109/ICICT62343.2024.00022.

Alberto Martínez Castro, Jesús, et al. “Suppressor of Cytokine Signaling Members in Lung Adenocarcinoma: Unveiling Expression Patterns, Posttranslational Modifications, and Clinical Significance.” Journal of Population Therapeutics and Clinical Pharmacology 30, no. 18 (2023): 2077-2091.