Application of the Data-Driven Educational Decision-Making System to Curriculum Optimization of Higher Education

Advancement in information technology has given a tremendous change in the education system. The traditional classroom education system is slowly getting transferred to the modernized system. In this conversion, the students choose to select the courses to learn in their higher education. The selection will aid the student in learning advanced technologies through theoretical and practical methods. In this research work, a data-driven educational decision-making system with the support of a course curriculum is analyzed with student’s response after the course. The educational decision-making is implemented with the help of the mobile learning technology designed and maintained by the colleges and universities. For performing the analysis, the student response dataset is given as input to the fuzzy logic system to perform the analysis. The research shows that mobile learning technology with the fuzzy logic system has provided better decision-making analysis to curriculum optimization for the student and teachers.

Membership Function Optimization of Fuzzy Inference System Using Cuckoo Search Algorithm for Peak Load Forecasting in National Holiday

This study aims to analysis peak load prediction of Indonesian national holidays for Jawa-Bali electricity system. Forecasting applied using the Fuzzy Logic System (FLS) method combined with the Cuckoo Search Algorithm (CSA). CSA is used to determine the optimal membership function in fuzzy logic. Cuckoo search algorithm has a very good performance in terms of optimization. This method is applied for short-term load estimates on holidays/special days on the Jawa-Bali electricity system, Indonesia. The study used data from daily peak loads during Indonesian national holidays in 2014 on the Jawa-Bali electricity system. The data analyzed is the daily peak load documentation data for 4 days before national holidays and during national holidays in 2014. Testing the simulation results, it was found that the Fuzzy Logic System - Cuckoo Search Algorithm (FLS-CSA) method gives good forecasting results, this is evidenced by using the mean absolute percentage error (MAPE). Forecasting results using the Cuckoo Search Algorithm (CSA) optimization method on fuzzy logic membership functions for peak loads on national holidays on the Java-Bali 500kV electrical system give satisfactory results with an average forecasting error of 1.511314562%.

Fuzzy-Based Adaptive Higher-Order Sliding Mode Control for Uncertain Steer-by-Wire System

This paper proposes an adaptive higher-order sliding mode (AHOSM) control method based on the adaptive fuzzy logic system for steer-by-wire (SbW) system to achieve the tracking control of the front wheels steering angle. First, an adaptive fuzzy logic system is adopted to estimate the unknown dynamics of the SbW system. Then, the AHOSM control is constructed to overcome the lumped uncertainties including unknown external perturbation and fuzzy logic system approximation error, and has the advantage of attenuating the chattering caused by the discontinuous control signal. Finally, the adaptation scheme is designed for the dynamic gain of the proposed AHOSM controller without a priori knowledge of the bounds of the uncertainties. In contrast to the existing controllers applied in the SbW system, this controller has a better control performance in practical application. By means of Lyapunov stability analysis, it is theoretically proved that the system trajectory converges to an adjustable neighborhood of the origin in finite time. Simulations and vehicle experiments are carried out to verify the effectiveness of the proposed approach.

Assessment of Quality in Saudi Arabian Roadways

A new assessment model was developed in this work. The developed model introduced a new approach and reference for assessment factors control the quality in Saudi Arabian roads using the fuzzy logic system. Fuzzy logic system was chosen to be used in this research due to its suitability for uncertain and approximate reasoning that involves human intuitive thinking as well as, it requires a little data to achieve the aim of the proposed model. Furthermore, the fuzzy logic has the ability for combining many effects such as the probability of occurrence and the impact of factors on the quality of roadways construction. The methodology of this research included conducting a field survey with construction practitioners in the field of roadways projects across Saudi Arabia. The proposed model was then evaluated and applied using the collected data from the field survey. The study results proved that the proposed model can be successfully used in the assessment of factors affecting quality in roadways. The major factors that found to be significantly affecting the quality in construction of roadways projects were identified according to their importance as; 1) Change order control; 2) Fluctuation default of Subcontractor; 3) Third party delay 4) Contract dispute results from disagreement. The proposed model is considered as a general model that can be easily adapted and applied to other types of projects.