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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Adeniyi, Abidemi Emmanuel
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Computer Science & IT 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.
Comparative analysis of selected optimization algorithms for mobile agents’ migration pattern Oyediran, Mayowa O.; Ajagbe, Sunday Adeola; Ojo, Olufemi S.; Elegbede, Adedayo Wasiat; Adio, Michael Olumuyiwa; Adeniyi, Abidemi Emmanuel; Adebayo, Isaiah O.; Obuzor, Princewill Chima; Adigun, Matthew Olusegun
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp685-693

Abstract

Mobile agents are agents that can migrate from host-to-host to work in a heterogeneous network environment. A mobile agent can migrate from host-to-host in its plan with the statistics generated on each host through a route known as migration pattern. Migration pattern therefore is the route the agents use to travel within the plan from the first host to the last host. However, there is a need for a comparison between the commonly used optimization algorithms in developing migration patterns for mobile agents with respect to some evaluation metrics. In this paper, the three techniques firefly algorithm (FFA), honeybee optimization (HBO) and particle swarm optimization (PSO) were used for developing migration patterns for mobile agents and their comparison was done based on migration time, time complexity and network load as metrics. PSO is discovered to perform better in terms of network load with an average of 242.3905 bits per second (bps), time complexity with an average of 41.2688 number of nodes (n), and migration/transmission time with an average of 4.203462 seconds (s).
Virtual learning environment on satisfaction and academic performance of students in institutions of higher learning Olanloye, Odunayo Dauda; Idowu, Peter Adebayo; Adeniyi, Abidemi Emmanuel; Badmus, Afolake Afusat; Aroba, Oluwasegun Julius
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp258-271

Abstract

As a result of the COVID-19 outbreak in 2020, education institutions across the world had to come to a functional standstill since they had to protect their students from viral exposures thereby affecting academic activities. However, several institutions had to adopt online virtual learning environments (VLE) using basic information and communication technology tools to provide platforms for teaching and learning thereby mitigating the effects of the pandemic on the students. This study was focused on the identification of the various types of VLE tools that were adopted alongside the impact that these tools had on learning satisfaction and the academic performance of students of higher learning in Nigeria. This study adopted a purposive simple random selection of undergraduate students of the department of computer science who had adopted the use of VLE to learn during the period of the pandemic. The results of the study showed that the most popular VLE tools were Zoom, Google Classroom, WhatsApp, Telegram, Coursera, Google Forms and learning management systems (LMS) while the least popular VLE tools were Microsoft Teams, Moodle/Edmondo, and Google Meet. The results showed that the students agreed to their behavioral intention to use VLE, the impact of VLE on learning satisfaction, and the impact of VLE on academic performance alongside the existence of a positive correlation among the research variables.
Co-Authors Adebayo, Isaiah O. Adeniyi, Jide Kehinde Adigun, Matthew Olusegun Adio, Michael Olumuyiwa Ajagbe, Sunday Adeola Aroba, Oluwasegun Julius Aworinde, Halleluyah Oluwatobi Badmus, Afolake Afusat Elegbede, Adedayo Wasiat Falola, Peace Busola Idowu, Peter Adebayo Obuzor, Princewill Chima Ojo, Olufemi S. Olanloye, Odunayo Dauda Oyediran, Mayowa O.