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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) IJHCS
Falola, Peace Busola
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Neuroscience

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EASESUM: an online abstractive and extractive text summarizer using deep learning technique Adeniyi, Jide Kehinde; Ajagbe, Sunday Adeola; Adeniyi, Abidemi Emmanuel; Aworinde, Halleluyah Oluwatobi; Falola, Peace Busola; Adigun, Matthew Olusegun
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1888-1899

Abstract

Large volumes of information are generated daily, making it challenging to manage such information. This is due to redundancy and the type of data available, most of which needs to be more structured and increases the amount of search time. Text summarization systems are considered a real solution to this vast amount of data because they are used for document compression and reduction. Text summarization keeps the relevant information and eliminates the text's non-relevant parts. This study uses two types of summarizers: Extractive Text summarizers and Abstractive text summarizers. The Text Rank Algorithm was used to implement the Extractive summarizer, while Bi-directional Recurrent Neural Network (RNN) was used to implement the Abstractive text summarizer. To improve the quality of summaries produced, word embedding was also used. For the evaluation of the summarizers, the ROUGE evaluation system was used. ROUGE contrasts summaries created by hand versus those created automatically. ROUGE examination of the produced summary revealed the superiority of human-produced summaries over those generated automatically. For this paper, a summarizer was implemented as a Web Application. The average ROUGE recall score ranging from 30.00 to 60.00 for abstractive summarizer and 0.75 to 0.82 for extractive text showed an encouraging result.
Music Genre Classification Using 1D Convolution Neural Network Falola, Peace Busola; Akinola, Solomon Olalekan
International Journal of Human Computing Studies Vol. 3 No. 6 (2021): IJHCS
Publisher : Research Parks Publishing LLC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31149/ijhcs.v3i6.2108

Abstract

Music genre classification system is a system that is important to the users for effectiveness in the digital music industry. One of the effective ways of genre classification is in music recommendation and access to users. With accurate classification system built, songs can be readily accessed by the users when the genre of the song is known and recommendation of songs to the users is made easy. Also, automatic classification of genre is important to solve problems such as tracking down related songs, discovering societies that will like specific songs and also for survey purposes. In recent times, deep learning techniques have proven to be effective in several classification tasks including music genre classification. This paper therefore examines the application of 1D Convolutional Neural Network for music genre classification. A new dataset consisting of 1000 Nigerian traditional songs with seven genres was used for this work. As features extraction is crucial to audio analysis, seven low level features also known as content based features were extracted from the songs in the dataset which served as input into the classifier. Our results showed that the accuracy level of the system is 92.5% with a precision of 92.7%, recall of 92.5% and f1 score of 92.5%.
Co-Authors Adeniyi, Abidemi Emmanuel Adeniyi, Jide Kehinde Adigun, Matthew Olusegun Ajagbe, Sunday Adeola Akinola, Solomon Olalekan Aworinde, Halleluyah Oluwatobi