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All Journal IJISCS (International Journal of Information System and Computer Science) Methods in Science and Technology Studies
Ayobami Emmanuel Mesioye
McPherson University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Comparative Analysis of Machine Learning Algorithms for Predictive Maintenance Odugbesan Olusegun Abayomi; Akinola Emmanuel Kayode; Oyedele Oluwasanya; Ayobami Emmanuel Mesioye
Methods in Science and Technology Studies Vol. 2 No. 2 (2026): December Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v2i2.2026.505

Abstract

The transition toward Industry 4.0 has established predictive maintenance (PdM) as a critical strategy for optimizing operational efficiency and reducing unexpected downtime through real-time sensor data analytics. However, the practical implementation of PdM is frequently hindered by the extreme class imbalance inherent in industrial datasets, where equipment failure events are significantly rarer than normal operating hours. This study presents a comprehensive comparative evaluation of five machine learning algorithms—Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression (LR)—utilizing the AI4I 2020 Predictive Maintenance Dataset. By implementing a rigorous preprocessing pipeline that employs Z-score normalization for feature scaling and the Synthetic Minority Over-sampling Technique (SMOTE) to mitigate majority-class bias within the training partition. The models were evaluated on a hold-out test set of 2,000 instances using metrics including accuracy, precision, recall, and the F1-score. Results indicate a pronounced “Accuracy Paradox” in linear and distance-based models; while KNN and Logistic Regression achieved deceptively high accuracies exceeding 96%, they failed to reliably detect actual failure signatures. In contrast, Random Forest emerged as the superior architecture, achieving an F1-score of 97.10% and a recall of 98.53%, correctly identifying 67 out of 68 failure instances. This research concludes that F1-score and Recall are more vital indicators of industrial reliability than simple accuracy. The findings provide a standardized framework for selecting ensemble-based classifiers to support scalable, data-driven maintenance strategies in modern smart manufacturing environments.
DIAGNOSTIC ACCURACY OF DEEP NEURAL NETWORKS FOR PNEUMONIA AND COVID-19 DETECTION ON MEDICAL IMAGING: A SYSTEMATIC REVIEW AND META-ANALYSIS Johnson Bisi Oluwagbemi; Racheal Shade Akinbo; Ayobami Emmanuel Mesioye
IJISCS (International Journal of Information System and Computer Science) Vol 9, No 3 (2025): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v9i3.1857

Abstract

Pneumonia and COVID-19 remain leading causes of universal morbidity and mortality, with timely and precise diagnosis essential for effective patient management. This systematic review and meta-analysis assessed the diagnostic accuracy of deep neural networks in detecting pneumonia and COVID-19 across main medical imaging modalities. Comprehensive searches of PubMed, Scopus, Web of Science, IEEE Xplore and Cochrane Library identified 80 eligible studies published between 2017 and 2025. Included studies used chest X-ray (CXR), computed tomography (CT) and lung ultrasound (LUS) images analyzed through convolutional neural networks, transformer-based and hybrid deep models. Pooled diagnostic performance was synthesized using a bivariate random-effects model and hierarchical summary receiver operating characteristic analysis. Overall pooled sensitivity and specificity were 0.88 (95% CI: 0.84-0.91) and 0.90 (95% CI: 0.86-0.92), respectively, with an area under the curve of 0.93, indicating high discriminative capability. Subgroup analyses revealed CT-based models outperformed CXR and LUS, while transformer architectures marginally exceeded CNNs. In addition, external validation studies steadily reported lower accuracy than internal validations, reflecting limited model generalizability. Risk of bias assessment using QUADAS-2 emphasized concerns related to patient selection, data leakage and non-standardized reference criteria. Despite moderate heterogeneity (I² = 39-52%) and potential publication bias, findings confirm the substantial potential of DNNs as decision-support tools for fast, scalable and reliable respiratory disease diagnosis. However, broader clinical adoption demands multicenter validation, transparency and adherence to ethical AI standards. This study provides evidence-based insights into the current performance and translational readiness of AI-driven diagnostic imaging for pneumonia and COVID-19.
Co-Authors Akinola Emmanuel Kayode Johnson Bisi Oluwagbemi Odugbesan Olusegun Abayomi Oyedele Oluwasanya Racheal Shade Akinbo