Introduction: Academic success is primary goal of every student. It is described as the extent to which a student has successfully achieved his or her short and long-term educational goals. Several factors have been established to predict academic performance of students. Machine learning techniques have been employed in predicting students’ performance, but it has not been prevalent in developing countries like Nigeria and most studies did not consider class teachers’ end-of-the-year rating. Aim: The aim of this work is to develop a Radial Basis Function Neural Network (RBFNN) for prediction of secondary school students’ performance. Materials and Methods: We obtained data from school repository containing students’ raw score and classteachers’ rating from year one to year six. The data was labelled into pass or fail given the actual outcome of their examinations. Subjects were categorized into Mathematics, English and major, depending on the student’s specialization. Class-teachers’ ratings were also included in the dataset. The preprocessed dataset was used to train the RBFNN model. The impact of Principal Component Analysis (PCA) was also measured. Results: We set up four experiments in order to achieve our aim. The best result gave the sensitivity of 93.49%, specificity of 75%, accuracy of 86.59% and an AUC score of 94%. Other experiments gave a relatively low performance. Conclusion: This study helps students to get a projection of academic success even before sitting for the examination. This will also help parents and counsellors in knowing the direction of their counseling to each student. Teachers and parents should pay attention to class teacher ratings of the students as this is discovered to affect the prediction accuracy of their examination success.

Accurate measurement of systemic financial risk is critical to maintaining the stability of financial markets. Taking China as the subject of investigation, the Chinese Financial Stress Index (CFSI) indicator system was con-structed by integrating six dimensions and employing Gray Relation Analysis (GRA) for dimensionality reduction of the indicators. The CFSI was derived using the Attribute Hierarchy Model (AHM) method with the Criteria Importance Through the Intercriteria Correlation (CRITIC) method, and an Improved Gravitational Search Algorithm (IGSA) optimized Radial Basis Function Neural Network (RBFNN) was proposed for out-of-sample prediction of CFSI trends from 2024 to 2026. By analyzing the trend of financial pressure indicators, the relationship between financial pressure and economic activity can be effectively identified. The research findings indicate that: (1) The CFSI is capable of accurately reflecting the current financial stress situation in China; (2) The IGSA-RBFNN demonstrates good robustness and generalization capabilities, predicting that the CFSI index will reach a peak value of 0.543 by the end of 2024, and there exists a regular pattern of stress rebound towards the end of each year. The novel methodology benefits policymakers and regulatory authorities to formulate preventive measures and risk management by identifying potential risks and vulnerabilities in advance.

In this article, the research team has systematically developed a method to model the kinematics and dynamics of a 3-wheeled robot subjected to external disturbances and sideways wheel sliding. These models will be used to design control laws that compensate for wheel slippage, model uncertainties, and external disturbances. These are control algorithms developed based on Dynamic Surface Control (DSC). Adaptive trajectory tracking DSC algorithm using fuzzy logic system (AFDSC) and radial neural network (RBFNN) with fuzzy logic system to overcome the disadvantages of DSC and expand the application domain for wheeled Mobile Robots (WMR). However, this Adaptive Fuzzy Neural Network Dynamic Surface Control (AFNNDSC) adaptive controller ensures the closed system is stable and follows the preset trajectory in the presence of wheel slippage model uncertainty and is affected by significant amplitude disturbances. The stability and convergence of the closed-loop system are guaranteed based on the Lyapunov analysis. The AFNNDSC adaptive controller is evaluated by simulation on MATLAB/Simulink software and in a steady state. The maximum position error on the right wheel and left wheel is 0.000572 (m) and 0.000523 (m), and the angular velocity tracking error in the right and left wheels of the control method is 0.000394 (rad/s). The experimental results show the correctness of the theoretical analysis, the effectiveness of the proposed controller, and the possibility of practical application.