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Research Works

TACL 2021

MasakhaNER: Named Entity Recognition for African Languages

This addressed the under-representation of the African continent in NLP research by bringing together different stakeholders to create the first large, publicly available, high-quality dataset for named entity recognition (NER) in ten African languages. We detail the characteristics of these languages to help researchers and practitioners better understand the challenges they pose for NER tasks. We analyze our datasets and conduct an extensive empirical evaluation of state- of-the-art methods across both supervised and transfer learning settings.

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Interspeech 2022

BibleTTS

BibleTTS is a large, high-quality, open speech dataset for ten languages spoken in Sub-Saharan Africa. The corpus contains up to 86 hours of aligned, studio quality 48kHz single speaker recordings per language, enabling the development of high-quality text-to-speech models. The ten languages represented are: Akuapem Twi, Asante Twi, Chichewa, Ewe, Hausa, Kikuyu, Lingala, Luganda, Luo, and Yoruba. This corpus is a derivative work of Bible recordings made and released by the Open.Bible project from Biblica.

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EKSU Journal of Engineering, Technology and Innovation 2023

Development of Text-to-Speech Synthesis for Yoruba Language usig Deep Learning

The study employs the BibleTTS corpus, consisting of audio recordings and text transcripts, to develop a TTS synthesis method that accommodates diverse rhythms and pitches, resulting in a natural one-to-many relationship between text input and speech output. The focal point of this study is the Yoruba language, a tonal language with distinct linguistic features.

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Abuad Journal of Engneerig, Research and Development 2024

Prediction of Urban House Rental Prices in Lagos - Nigeria: A Machine Learning Approach

This work aims to predict house rental prices in Lagos, Nigeria, using machine learning by examining the relationship between the rental price and features such as the number of bedrooms, bathrooms, toilets, location and house status(newly built, furnished, and/or serviced). Five machine learning models were trained and evaluated using mean absolute error (MAE), root mean squared error (RMSE) and r-square (R2); the random forest regression model outperformed the other four models with the lowest MAE, RMSE and the highest R2. This study also revealed that the number of bedrooms and the apartment's location are the most significant predictors, confirmed using the feature importance analysis. The developed model can be used to estimate the rental price of a property in Lagos, Nigeria.

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Dutse Journal of Pure and Applied Sciences 2024

VocalTweets: Investigating Social Media Offensive Language Among Nigerian Musicians

In this study, we introduce VocalTweets, a code-switched and multilingual dataset comprising tweets from 12 prominent Nigerian musicians, labeled with a binary classification method as Normal or Offensive. We trained a model using HuggingFace's base-Twitter-RoBERTa, achieving an F1 score of 74.5. Additionally, we conducted cross-corpus experiments with the OLID dataset to evaluate the generalizability of our dataset.

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Medium 2024

Exploring ChatGPT’s Bias: Can AI Pass West Africa’s WAEC Exams the Way It Passed US.-Based Exams?

In this article, I put ChatGPT to the test with a dataset of 1000 questions and answers — 200 randomly selected across five subjects (Government, Civic Education, Agricultural Science, Economics, Commerce and Geography), drawn from exams administered between 2015 and 2023. I selected those subjects because they are the available ones African contexts I could get not like mathematics that can be the same worldwide, or English Language that ChatGPT is very familiar with. My goal is to examine how well ChatGPT can tackle these questions and identify any potential biases in the AI’s performance on exams specific to Nigeria and West Africa.

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About Me

Victor Akinode is a techpreneur with expertise and certifications in artificial intelligence (AI), cybersecurity, and software & data engineering. As Co-Founder of Telneting, a subsidiary of Finaflex Technologies Limited, he leads the creation of solutions that deliver actionable financial and security insights to businesses.

Aside from being a first-class graduate of Computer Engineering, Victor has worked on groundbreaking AI projects, including advancing Natural Language Processing (NLP) for African languages at Masakhane. At KPMG Nigeria, he designed AI-driven Threat Intelligence Platform and at Veenode, over 15 software solutions addressing challenges across multiple industries globally.

Passionate about mentorship, Victor has trained over 800 students and 300 professionals in AI, cybersecurity, software engineering and fintech. As a speaker at tech events, he inspires innovation and empowers others to leverage technology for transformative impact.

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My Services

Software Engineering

I provide software engineering services focused on building reliable, scalable, and high-performance solutions tailored to meet your unique needs. From ideation to deployment, I specialize in creating robust applications, seamless integrations, and user-friendly experiences.

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Artificial Intelligence

I offer AI-driven solutions that empower businesses to optimize operations, enhance decision-making, and deliver innovative experiences. My expertise includes building intelligent systems, developing machine learning models, and implementing AI tools tailored to your specific needs.

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Cybersecurity

I provide comprehensive cybersecurity services to help businesses protect their digital assets, ensure compliance, and mitigate risks. My expertise includes vulnerability assessments, penetration testing, threat intelligence, and implementing robust security frameworks tailored to your needs.

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Get In Touch

Get In Touch

My Contact Details

  • Email victor@telneting.com
    victorakinode@gmail.com
  • Phone +234 901 137 9509
  • Address Lagos Nigeria