Abayomi Ayotunde Ayoade

The Human Development Index (HDI) is a measure that evaluates achievements in human development based on fundamental components of quality of life which influence individual productivity levels. This study aims to analyze the impact of Population Size, Percentage of Poor Population, Open Unemployment Rate (TPT), and Gini Ratio on the Human Development Index (HDI) in the Bangka Belitung Islands Province from 2018 to 2023. The panel data regression method was employed, combining time series and cross-sectional data to provide more accurate information. The findings reveal that the HDI of the Bangka Belitung Islands Province has increased year-on-year, though it remains below the national average. The selected model was the Fixed Effect Model (FEM) based on the results of the Chow and Hausman tests. Analysis indicates that Population Size has a positive and significant effect on HDI, while the Percentage of Poor Population has a negative and insignificant effect. Additionally, the Open Unemployment Rate (TPT) has a positive and significant effect on HDI, whereas the Gini Ratio has a negative and significant effect. Collectively, the four independent variables contributed 95.44% to HDI. These findings are expected to inform government efforts to improve human development quality, such as through poverty alleviation and highlight the need for careful population management to balance employment opportunities in the region. Furthermore, the government should work to reduce the unemployment rate through policies that focus on job creation, increasing workforce competitiveness, and addressing inequality, as reflected by the Gini ratio. This can be achieved by expanding equitable access to education and healthcare.

[Objective] The field of computer vision has seen remarkable progress, largely due to the advancements in deep learning. These developments have revolutionized image recognition, interpretation, and application across numerous domains. This paper introduces a new framework designed to expand the potential of computer vision systems by harnessing the power of deep learning techniques. Deep neural networks are at the core of this new system, providing exceptional accuracy and reliability in tasks such as object recognition, image segmentation, and scene understanding. [Methodology] Furthermore, this framework offers a versatile platform for real-time image processing, paving the way for numerous applications in areas like industrial automation, medical diagnostics, and autonomous vehicles. This study comprehensively explores the architectural elements and methodologies that drive this innovative framework. It emphasizes the framework's technological capabilities, scalability, adaptability, and potential for broad adoption across industries seeking advanced computer vision solutions. [Results] The proposed model, Convolutional Neural Network-Feature Pyramid Network (CNN-FPN), demonstrates superior performance across all evaluated metrics for object detection compared to existing models. Specifically, it achieves the highest scores in Accuracy (57.2%), Recall (60.4%), Precision (94.1%), F1-Score (73.5%), and AUC (0.983). These results indicate that the proposed model offers superior performance and reliability in object detection tasks, demonstrating its potential for high-precision computer vision applications. [Conclusions] In conclusion, this innovative architecture represents a significant advancement in computer vision, enabled by the capabilities of deep learning. Our test findings demonstrate that compared to conventional algorithms, the enhanced CNN-FPN produced more accurate results.

A certificate is evidence of participating in a process, and anyone who holds a certificate is believed to have the prerequisites necessary to handle what the certificate qualifies them for. However, this perception might be incorrect if the certificate has been obtained by taking shortcuts. [Objective] In this paper, a compartmental model was developed to quantify factors that could put the authenticity of a certificate in doubt by examining the trade-off between academic standards and failure rates in the context of tertiary education institutions in Nigeria. [Methodology] The model was validated using several mathematical theorems. Equilibria for the model and the threshold for acceptable academic performance were derived. Stability analysis was carried out by using the stability theory of differential equations based on the derived acceptable academic performance threshold. [Results] The theoretical results for the model were validated through numerical simulations, and the results from the simulations linked acceptable failure rates to academic performance falling below the threshold of the institutions’ standards. [Conclusions] The policy implication of the result is that an increase in the supply of qualified graduates into Nigeria’s labor markets, as well as a reduction in the rate of unemployment, is a function of maintaining academic standards.

This work presents a collocation computational algorithm for solving linear Integro-Differential Equations (IDEs) of the Fredholm and Volterra types. The proposed method utilizes shifted Legendre polynomials and breaks down the problem into a series of linear algebraic equations. The matrix inversion technique is then employed to solve these equations. To validate the effectiveness of the suggested approach, the authors examined three numerical examples. The results obtained from the proposed method were compared with those reported in the existing literature. The findings demonstrate that the proposed algorithm is not only accurate but also efficient in solving linear IDEs. In order to present the results, the study employs tables and figures. These graphical representations aid in displaying the numerical outcomes obtained from the algorithm. All calculations were performed using Maple 18 software.

In this study, the Volterra-FredholmIntegro-Differential Equations (VFIDEs) are solved computationally using first-kind Chebyshev polynomials as basis functions. The technique converts VFIDEs into a system of unknown Chebyshev coefficients linear algebraic equations, which are then solved via matrix inversion. Five numerical examples were solved in order to assess the scheme's correctness and effectiveness. The findings reveal that the approach outperforms the comparison results in the literature