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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Applied Sciences and Smart Technologies
Aworinde, Halleluyah Oluwatobi
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Computer Science & IT Energy Engineering Industrial & Manufacturing Engineering

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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.
A Deep Learning Model for Identical National Flag Recognition in Selected African Countries Aworinde, Halleluyah Oluwatobi; Oladimeji, Oladosu; Adebayo, Segun; Akinwunmi, Akinwale; Sakpere, Aderonke Busayo; Oladimeji, Olayanju
International Journal of Applied Sciences and Smart Technologies Volume 05, Issue 02, December 2023
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v5i2.6452

Abstract

The national flags are among the symbolic representations of a country. They make us understand the country of interest in a particular issue. Therefore, they are commonly used in both private and government organizations. It has been discovered in recent times that the younger generation mostly and idly and spend its time online; hence, knowing little about national flags. Additionally, some national flags (particularly in West Africa) are identical in nature. The likeness is in terms of layout, colours, shapes and objects on the national flags. Hence, there is a need to have a model for flag recognition. In this paper, national flag images of some West African countries were gathered to form a dataset. After this, the images were preprocessed by cropping out the irrelevant parts of the images. VGG-16 was used to extract necessary features and to develop the deep learning model. This contrasted with the existing handcrafted feature extraction and traditional machine learning techniques used on this subject matter. It was observed from this study that the proposed approach performed excellently well in predicting national flags; with an Accuracy of 98.20%, and an F1 score of 98.16%. In the future, it would be interesting to incorporate the national flag recognition into Human-Computer Interaction System. For instance, it could be used as flag recognition in some mobile and web applications for individuals with colour blindness. This research work presents a robust model because of nature of the dataset used in this work compared to previous works.
Co-Authors Adebayo, Segun Adeniyi, Abidemi Emmanuel Adeniyi, Jide Kehinde Adigun, Matthew Olusegun Ajagbe, Sunday Adeola Akinwunmi, Akinwale Falola, Peace Busola Oladimeji, Oladosu Oladimeji, Olayanju Sakpere, Aderonke Busayo