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All Journal Journal of Computing Theories and Applications Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Ajayi, Ore-Ofe
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mechanical Engineering Transportation

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Mathematical Modelling of Truck Platoon Formation Based on a Dynamic String Stability Ajayi, Ore-Ofe; Umar, Abubakar; Ibrahim, Ibrahim; Olugbenga, Lawal Abdulwahab; Abiola , Ajikanle Abdulbasit
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 2 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i2.34941

Abstract

In this research, the development of a fuzzy logic-based cooperative adaptive cruise control scheme for truck platooning string stability was developed. String stability, which is critical to the operation of truck platooning in the area of enhancing traffic flow and reducing fuel consumption can be affected by unknown uncertainties such as truck incapacitation, delay of platoons and inability to maintain a constant inter-vehicular gap. A commonly reported approach in addressing truck platooning string stability is the Cooperative Adaptive Cruise Control (CACC) scheme. The CACC scheme consists of Adaptive Cruise Control (ACC) and vehicle-to-vehicle (V2V) communication. However, the CACC lacks the requisite flexibility in dealing with unexpected disturbances that can result in the inability to maintain a constant speed and inter-vehicular gap.
A Lightweight Maize Leaf Disease Recognition Using PCA-Compressed MobileNetV2 Features and RBF-SVM Abubakar, Mustapha; Ibrahim, Yusuf; Ajayi, Ore-Ofe; Saminu, Sani Saleh
Journal of Computing Theories and Applications Vol. 3 No. 3 (2026): JCTA 3(3) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.15675

Abstract

The integration of Artificial Intelligence (AI) into precision agriculture has significantly improved plant disease recognition; however, many existing deep learning models remain computationally expensive and feature-redundant, limiting their deployment on low-power and edge devices. To address these limitations, this study proposes a lightweight framework for maize leaf disease recognition based on serial deep feature extraction, dimensionality reduction, and machine-learning–based classification. A pre-trained MobileNetV2 network is employed as a fixed feature extractor to obtain discriminative visual representations, while Principal Component Analysis (PCA) is applied to reduce feature dimensionality by approximately 76%, retaining 95% of the original variance and improving computational efficiency. The compressed features are subsequently classified using a Radial Basis Function Support Vector Machine (RBF-SVM), optimized via grid search and cross-validation. Experiments conducted on a four-class maize leaf disease dataset (Northern Leaf Blight, Common Rust, Gray Leaf Spot, and Healthy), with class imbalance handled during training, demonstrate that the proposed MobileNetV2–PCA–SVM pipeline achieves 97.58% accuracy, 96.60% precision, 96.59% recall, and 96.59% F1-score, outperforming the DenseNet201 + Bayesian-optimized SVM baseline (94.60%, 94.40%, 94.40%, and 94.40%, respectively). This improvement corresponds to a 2.98% accuracy gain, a 55% reduction in error rate, an 86% reduction in model parameters (20.31M to 2.75M), and an 85% reduction in model size (81 MB to 12 MB). These results indicate that the proposed framework provides a compact and efficient solution with strong potential for deployment in resource-constrained agricultural environments.
Co-Authors Abiola , Ajikanle Abdulbasit Abubakar, Mustapha Ibrahim Ibrahim Olugbenga, Lawal Abdulwahab Saminu, Sani Saleh Umar, Abubakar Yusuf Ibrahim