This work intends to improve the educational process through the development of teachers skills and raise the level of learning by reducing the problems and obstacles facing the teacher and providing many different learning methods. This is done through expert systems, which is one of the important fields of knowledge-based artificial intelligence, where the expert system acquires all the information acquired by the expert in the field of mathematics by transferring all his scientific experience and how the mathematics expert deals with it, so that the knowledge acquired by experts in the field of mathematics is collected. In order to prepare a knowledge base and deal with it through the expert system, as well as the possibility of making the expert system that the teacher deals with, ready to answer any inquiries or the possibility of explaining any part of the curriculum on demand.

this paper presents feature selection for fault diagnosis based on recursive generalized extended least squares algorithm (RGELS). RGELS model is derived and validation of this model is tested utilizing good statistical methods, which, namely best-fit criterion. The system parameters are estimated employing the proposed algorithm. Dimension reduction of the system parameters is done to get best important features using linear discriminant analysis. Finally, the simulation results confirm the effectiveness of the algorithm.

Automobiles of today must constantly change their speeds in reaction to changing road and traffic circumstances as the pace and density of road traffic increases. In sophisticated automobiles, the Anti-lock Braking System (ABS) is a vehicle safety system that enhances the vehicle's stability and steering capabilities by varying the torque to maintain the slip ratio at a safe level. This paper analyzes the performance of classical control, model reference adaptive control (MRAC), and intelligent control for controlling the (ABS). The ABS controller's goal is to keep the wheel slip ratio, which includes nonlinearities, parametric uncertainties, and disturbances as close to an optimal slip value as possible. This will decrease the stopping distance and guarantee safe vehicle operation during braking. A Bang-bang controller, PID, PID based Model Reference Adaptive Control (PID-MRAD), Fuzzy Logic Control (FLC), and Adaptive Neuro-Fuzzy Inference System (ANFIS) controller are used to control the vehicle model. The car was tested on a dry asphalt and ice road with only straight-line braking. Based on slip ratio, vehicle speed, angular velocity, and stopping time, comparisons are performed between all control strategies. To analyze braking characteristics, the simulation changes the road surface condition, vehicle weight, and control methods. The simulation results revealed that our objectives were met. The simulation results clearly show that the ANFIS provides more flexibility and improves system-tracking precision in control action compared to the Bang-bang, PID, PID-MRAC, and FLC.

The spread of ICT in education and learning has changed the way of teaching and learning, and

this raised the issue of getting e-learning systems to adapt/personalize with respect to student's

knowledge achievement. However, the relation between the concepts in the programming domain

are not definitive and they are fuzzily related. In this Paper, we describe a practical experience for

developing adaptable e-learning system using fuzzy logic to model and evaluate student's

knowledge for the purpose of adaptation in the field of Computer Programming. Fuzzy logic

technique modeled as a plugin on MOODLE Learning Management System at the Libyan

Academy for Postgraduate Studies is developed. Conclusions highlighted some important lessons

in increasing learning effectiveness and learner's satisfaction.

In this paper, the data filtering based Recursive Least Squares algorithm (RLS) of linear Box–Jenkins systems is proposed for fault detection. The system is decomposed into two subsystems, one containing the parameters of the system model and the other containing the parameters of the noise model to improve the computational load. In addition, the parameters of the system model and the noise model are estimated and their accuracy are developed. The model validation is tested using two statistical methods, histogram and mean square errors. The residual is generated based on the proposed algorithm to design the threshold and therefore, this designed thresholdis used for fault detection. Simulation results are performed to illustrate the algorithm performance.

For Arabic letters recognition, we achieve three of pattern recognition approaches namely gray level co-occurrence matrix (GLCM), local binary pattern recognition (LBP) and artificial neural network (ANN) and compare between them to result best performance. Two of these methods level co-occurrence matrix and local binary pattern recognition are used for feature extraction whereas in artificial neural network (ANN) we use the intensity values of pixels for input of the neural network. Two classifiers are used, the K-Nearest Neighbor classifier (KNN) for the LBP, GLCM and neural network classifier for (ANN) artificial neural network. Also, we evaluate the results by using leave one person out approach, fold classification and leave one out.

this paper derives extended three-stage recursive identification algorithm of MISO for (CARARMA) systems. Based on The decomposition technique, four subsystems are obtained and the parameters of each subsystem are identified. Some model validation methods are computed to measure the model value and Akaike’s Final Prediction Error Criterion (FPE) is used to verify the selection of system order. The algorithm has a high computational efficiency because the covariance matrices dimensions become small in each subsystem. Finally, this algorithm effectiveness is demonstrated in simulation example.