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All Journal Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Ibrahim Ibrahim
Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria
Computer Science & IT Engineering Mechanical Engineering Transportation

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Mathematical Modelling of Truck Platoon Formation Based on a Dynamic String Stability Ore Ofe Ajayi; Abubakar Umar; Ibrahim Ibrahim; Lawal Abdulwahab Olugbenga; Ajikanle Abdulbasit Abiola
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, developinga Fuzzy Logic Cooperative Adaptive Cruise Control (FCACC) scheme significantly enhanced truck platooning string stability by ensuring rapid stabilization and robustness against disturbances. The mathematical model designed and implemented in SUMO/OMNeT++ simulated various scenarios, demonstrating the superiority of the FCACC over conventional CACC, PATH CACC, and Ploeg CACC controllers. Quantitatively, the FCACC achieved velocity and spacing stability within an average of 7.33 seconds and 4.39 seconds using the triangular-centroid method, outperforming the CACC, PATH CACC, and Ploeg CACC by 28.09%, 25.21%, and 22.26% for velocity stability and 31.69%, 29.96%, and 28.01% for spacing stability, respectively. Additionally, the FCACC reduced the Expected Arrival Time (EAT) deviation by 4.62% compared to the CACC, demonstrating its efficiency in handling disturbances such as truck breakdowns. The FCACC's rapid stabilization, even in the presence of impulse signal disturbances, was evident in its ability to recover within 2.3 seconds for speed and 3.6 seconds for distance, compared to 27.5 seconds and 10.1 seconds for CACC. The fuzzy-PLEXE framework further emphasized the FCACC’s advantage by inducing more minordistance errors and faster stability times than other models, achieving stability in 53 seconds versus 60 seconds for Ploeg CACC. These results underline the FCACC’s efficacy in mitigating unexpected disruptions and maintaining optimal string stability. However, limitations such as dependency on precise sensor data, susceptibility to communication delays, and challenges with scalability for larger platoons were observed, suggesting areas for future optimization.
Early Heart Disease Prediction Using Data Mining Techniques Dugguh Sylvester Aondonenge; Ajayi Ore-Ofe; Kamorudeen Hassan Taiwo; Abubakar Umar; Isa Abdulrazaq Imam; Dako Daniel Emmanuel; Ibrahim Ibrahim
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.v2i2.36735

Abstract

Heart disease is a leading cause of mortality worldwide, characterized by the buildup of plaque in the arteries, which can lead to severe cardiovascular complications. Predicting heart disease is complex due to the need to analyze multiple risk factors, such as age, cholesterol, and blood pressure. This study develops a predictive model for earlyheart disease detection using data mining techniques to enhance timely and accurate diagnosis. The model combines multiple machine learning timely and accurate diagnosis. The model combines multiple machine learning algorithms, including Random Forest, Support Vector Machine, and a hybrid ensemble approach to improve prediction accuracy and reliability. The methodology follows five phases: data collection, data pre-processing, feature extraction, model construction, and model evaluation. Data was gathered from publicly available health repositories, preprocessed to remove missing values and irrelevant information, and subjected to feature extraction techniques to identify influential predictors. The hybrid model was trained and tested using an 80:20 data split and evaluated against various classification algorithms. It achieved an accuracy of 97.56%, precision of 98.04%, and recall of 97.09%, outperforming individual models. These results highlight the effectiveness of the hybrid approach in supporting early interventionfor heart disease, particularly in healthcare settings with limited diagnostic resources. This study demonstrates that advanced data mining techniques provide a viable solution for improving patient outcomes through the early detection of heart disease.
Data-Driven Modelling and Predictive Analysis of Internal Migration and Displacement Trends in Nigeria for Enhanced Humanitarian Planning Ajayi Ore-Ofe Ajayi Ore-Ofe; Muhammad Bashir Yahya; Shehu Mohammed Yusuf; Abubakar Umar; Hassan Maharazu; Ibrahim Ibrahim; Murtala Abduljalil Ahmad
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 3 (2026): (In Progress)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Internal displacement in Nigeria remains a major humanitarian challenge, driven by conflict, insurgency, communal clashes, and climate-related disasters, yet existing responses are often reactive and inadequate for proactive planning. This study, titled Data-Driven Modelling and Predictive Analysis of Internal Migration and Displacement Trends in Nigeria for Enhanced Humanitarian Planning, applies a machine learning framework to displacement data to forecast trends and support timely humanitarian interventions. Using datasets from the International Organization for Migration’s (IOM) Displacement Tracking Matrix, particularly the Baseline Assessment and Needs Monitoring surveys, the research processed over 114,000 records across 19 variables, including demographic indicators, site accessibility, household size, and priority needs. Data preprocessing involved handling missing values, categorical standardization, one-hot encoding, and normalization of numeric features, with attention to balancing the skewed distribution of displacement reasons where insurgency accounted for 95% of cases, communal clashes 4%, and natural disasters 1%. Exploratory analysis revealed that Borno, Adamawa, and Yobe states remain most affected, with insurgency consistently dominating and September 2022 marking the highest peak in displacement. Three algorithms: Logistic Regression, Random Forest, and XGBoost were evaluated, with XGBoost outperforming others by achieving a macro F1-score of 0.96, and balanced accuracy of 0.968 across classes. The model correctly classified nearly all insurgency cases and demonstrated strong performance for communal clashes (F1-score = 0.99) and natural disasters (F1-score = 0.90). These findings show that displacement follows identifiable socio-economic and geographic patterns, and predictive modeling can capture them effectively.
Co-Authors Abubakar Umar Ajayi Ore-Ofe Ajayi Ore-Ofe Ajayi Ore-Ofe Ajikanle Abdulbasit Abiola Dako Daniel Emmanuel Dugguh Sylvester Aondonenge Hassan Maharazu Isa Abdulrazaq Imam Kamorudeen Hassan Taiwo Lawal Abdulwahab Olugbenga Muhammad Bashir Yahya Murtala Abduljalil Ahmad Ore Ofe Ajayi Shehu Mohammed Yusuf