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All Journal Bulletin of Social Informatics Theory and Application Journal of Future Artificial Intelligence and Technologies Spektrum Industri
Ogbuju, Emeka
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology 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.
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.
Emotion Detection for Health and Well-being in Short Messaging Systems Ogbuju, Emeka
Spektrum Industri Vol. 21 No. 2 (2023): Spektrum Industri - October 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/si.v21i2.139

Abstract

The exposure to unpleasant emotions or content in messages can lead to health complications, including high blood pressure and several heart-related disorders. Hence, the identification of unpleasant emotions in written content can serve as a beneficial instrument in addressing certain health-related issues. Emotions can be communicated through diverse modalities, including written text, spoken language, and facial gestures. The objective of this work is to create a Text-based emotion detection system that possesses the capability to accurately identify emotions within text messages. The use of message filtering mechanisms that detect and block content containing negative emotions can serve as a preventive measure to shield users from accessing messages that have the potential to adversely impact their well-being. Conversely, messages that convey positive or neutral emotions remain accessible for comprehension. In order to accomplish this objective, a combination of three machine learning algorithms, namely Naive Bayes, Support Vector Machine, and Logistic Regression, were employed, adhering to the CRISP-DM approach. The Logistic Regression technique achieved the greatest accuracy rate of 98.4% and was employed in the construction of the detection system. The Graphical User Interface (GUI) of the system was developed utilizing HTML and CSS, with the integration of diverse components to establish a comprehensive and operational interface for the user.
Co-Authors Abiodun , Taiwo Jaiyeoba, Oluwaseyi Jaiyeoba, Oluwayemisi Oghu, Emughedi Oladipo, Francisca