Browsing by Author "Jumoke Falilat AJAO"

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    Comparative Analysis of Selected Classification Algorithms on Weather Data
    (Faculty of Information and Communication Technology, Kwara State University, Malete., 2023) Shakirat Ronke YUSUFF; Ibrahim ODETUNDE; Jumoke Falilat AJAO
    Numerous human daily activities, such as transportation, farming, and commerce, are weather-dependent. Scientists have used a range of techniques to carry out weather forecasting, some more accurate than others, to predict environmental variables. Critical information about the weather is provided through weather forecasts. Accurate weather forecasting would assist the government in planning to prepare its citizens for the worst, since severe weather annually damages life, property, and many government projects that are typically substantially funded. In this study, a comparative analysis is carried out on three classification algorithms: random forest, decision tree and extra tree classifiers on weather data. Two sets of predictions were performed on weather data: the precipitation type and weather summary. With the prediction of precipitation type, the accuracy scores for both random forest and decision tree were 99.71%, while extra tree classifiers achieved 99.44% accuracy. The confusion matrix shows better prediction of records by random forest and decision tree classifiers, with a score of 99.71% each, while the extra tree classifier performed the least, predicting 99.69% records correctly. Also, the Classification report revealed that random forest had a precision of 99.84%, recall of 98.69% and F1-score of 99.26%. The decision tree classifier had a precision of 99.84%, recall of 98.69% and F1-score of 99.26%, while extra tree classifier had a precision of 99.18%, recall of 99.24% and F1-score of 99.21%. With the second set of predictions using weather summary, random forest had an accuracy score of 66.05%, decision tree had 53.9%, and the extra tree had 64.8% accuracy scores. As for the confusion matrix, random forest performed best with 66.17% records correctly predicted, extra tree classifier with 64.8% correct records and decision tree with 53.6% records. The Classification report showed random forest had a precision of 69.6%, recall of 47.8% and F1-score of 52.2%; decision tree achieved a precision of 17.39%, recall of 39.13% and F1-score of 30.43% while extra tree had a precision of 52.17%, recall of 30.43% and F1-score of 21.74%. In conclusion, the random forest classifier has proven to consistently outperform the other classifiers in this study. Further studies could focus on predicting future weather occurrences.
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    Development of a Language Translator Model for Yoruba Language using Bidirectional Long Short-Term Memory Encoder
    (Faculty of Information and Communication Technology, Kwara State University, Malete., 2024) Jumoke Falilat AJAO; Abdulazeez Olorundare Ajao; Rafiu Mope ISIAKA; Shakirat Ronke YUSUFF; Abdullahi YAHAYA
    Nigeria is a nation of vast ethnic diversity, encompassing a multitude of cultures, languages, and values. These differences often present significant communication barriers among its various ethnic groups. To bring the language barrier, Google has integrated several Nigerian languages into its translation model. This study focuses on the development of an automatic speech recognition (ASR) system for Yoruba, one of Nigeria’s indigenous languages. Speech dataset was collected from native Yoruba speakers and subjected to extensive preprocessing eliminate background noise introduced during the recording process. The preprocessed data was then transformed into a speech spectrogram, serving as input to a bidirectional long short-term memory (BiLSTM) model. The hyperparameters of the BiLSTM model were optimised for improved performance. The translation model was evaluated using metrics such as BLEU, METEOR, and ROUGUE. Results demonstrated significant improvements in the ASR model’s ability to accurately transcribe and translate Yoruba language speech. This advancement translation accuracy highlights the potential for better cross-linguistic communication among Nigeria’s ethnic groups, fostering greater inclusivity and understanding. This study contributes to ongoing efforts to incorporate underrepresented languages in global translation models, addressing the challenge of language diversity in multilingual societies.