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All Journal Bulletin of Social Informatics Theory and Application Journal of Computing Theories and Applications Journal of Future Artificial Intelligence and Technologies
Oladipo, Francisca
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Computer Science & IT Decision Sciences, Operations Research & Management Social Sciences

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A Review of Sentiment Analysis Approaches for Quality Assurance in Teaching and Learning (RETRACTED) Oghu, Emughedi; Ogbuju, Emeka; Abiodun , Taiwo; Oladipo, Francisca
Bulletin of Social Informatics Theory and Application Vol. 6 No. 2 (2022)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v6i2.581

Abstract

The education industry considers quality to be a crucial factor in its development. Nevertheless, the quality of many institutions is far from perfect, as there is a high rate of systemic failure and low performance among students. Consequently, the application of digital computing plays an increasingly important role in assuring the overall quality of an educational institution. However, the literature lacks a reasonable number of systematic reviews that classify research that applied natural language processing and machine learning solutions for students’ sentiment analysis and quality assurance feedback. Thus, this paper presents a systematic literature review that structure available published papers between 2014 and 2023 in a high-impact journal-indexed database. The work extracted 59 relevant papers from the 3392 initially found using exclusion and inclusion criteria. The result identified five (5) prevalent techniques that are majorly researched for sentiment analysis in education and the prevalent supervised machine learning algorithms, lexicon-based approaches, and evaluation metrics in assessing feedback in the education domain.
A Technical Review of the State-of-the-Art Methods in Aspect-Based Sentiment Analysis Yusuf, Kabir Kasum; Ogbuju, Emeka; Abiodun, Taiwo; Oladipo, Francisca
Journal of Computing Theories and Applications Vol. 1 No. 3 (2024): JCTA 1(3) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.9999

Abstract

With the advent and rapid advancement of text mining technology, a computer-based approach used to capture sentiment standpoints from data in textual form is increasingly becoming a promising field. Detailed information about sentiment can be provided using aspect-based sentiment analysis, which can be used in better decision-making. This study aims to study, observe, and classify previous methods used in aspect-based sentiment analysis. A systematic review is adopted as the method used to collect and review papers to achieve this research's aim. Papers focused on sentiment analysis, aspect extraction, and aspect aggregation from different academic databases such as Scopus, ScienceDirect, IEEE Explore, and Web of Science were gathered based on the inclusion and exclusion criteria of the study. The gathered papers were further reviewed to answer the stated research questions. The findings from the research show the most used methods for aspect extraction, sentiment analysis, and aspect aggregation in aspect-based sentiment analysis. This research offers a robust synthesis of evidence to guide further academic exploration in sentiment analysis.
Development of a Model to Classify Skin Diseases using Stacking Ensemble Machine Learning Techniques Jaiyeoba, Oluwayemisi; Ogbuju, Emeka; Yomi, Owolabi Temitope; Oladipo, Francisca
Journal of Computing Theories and Applications Vol. 2 No. 1 (2024): JCTA 2(1) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.10488

Abstract

Skin diseases are highly prevalent and transmissible. It has been one of the major health problems that most people face. The diseases are dangerous to the skin and tend to spread over time. A patient can be cured of these skin diseases if they are detected on time and treated early. However, it is difficult to identify these diseases and provide the right medications. This study's research objectives involve developing an ensemble machine learning based model for classifying Erythemato-Squamous Diseases (ESD). The ensemble techniques combine five different classifiers, Naïve Bayes, Support Vector Classifier, Decision Tree, Random Forest, and Gradient Boosting, by merging their predictions and utilizing them as input features for a meta-classifier during training. We tested and validated the ensemble model using the dataset from the University of California, Irvine (UCI) repository to assess its effectiveness. The Individual classifiers achieved different accuracies: Naïve Bayes (85.41%), Support Vector Machine (98.61%), Random Forest (97.91%), Decision Tree (95.13%), Gradient Boosting (95.83%). The stacking method yielded a higher accuracy of 99.30% and a precision of 1.00, recall of 0.96, F1 score of 0.97, and specificity of 1.00 compared to the base models. The study confirms the effectiveness of ensemble learning techniques in classifying ESD.
AI-Based Detection Techniques for Skin Diseases: A Review of Recent Methods, Datasets, Metrics, and Challenges Jaiyeoba, Oluwayemisi; Jaiyeoba, Oluwaseyi; Ogbuju, Emeka; Oladipo, Francisca
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 3 (2024): December 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.3048-3719-46

Abstract

The identification and early treatment of skin diseases are crucial to mitigate serious health risks. The growing attention on researching skin disease analysis stems from the transformative impact of artificial intelligence (AI) in dermatology. In this systematic review, we adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to comprehensively assess recent approaches for skin disease detection. Our study addressed four key research questions exploring the methods for skin disease detection, the evaluation techniques employed to measure the effectiveness of skin disease detection models, the datasets utilized, and the challenges encountered in applying machine learning and deep learning techniques for skin disease detection. We screened studies from 2019 to 2023 from reputable databases, including IEEE Explore, Science Direct, and Google Scholar. Our findings revealed that the CNN model outperformed other deep learning models. Additionally, our analysis identified the ISIC public dataset as the most frequently used dataset. The studies reviewed employed evaluation metrics such as accuracy, recall, precision, sensitivity, and F1 score to evaluate model performance. We identified several limitations in the studies we reviewed, including the use of limited datasets, challenges in distinguishing between diseases with similar features, and other related limitations. Overall, we provided a comprehensive overview of the current state-of-the-art techniques in skin disease detection and highlighted the future directions.
Co-Authors Abiodun , Taiwo Abiodun, Taiwo Jaiyeoba, Oluwaseyi Jaiyeoba, Oluwayemisi Ogbuju, Emeka Oghu, Emughedi Yomi, Owolabi Temitope Yusuf, Kabir Kasum