Helicopter instability is one of the most limitations that should be addressed in a nonlinear application. Accordingly, researchers are invited to design a robust and reliable controller to obtain a stable system and enhance its overall performance. The present study focuses on the use of the intelligent system in controlling the pitch and yaw angles. This lead to controlling the elevation and the direction of the helicopter. Further to the application of the Linear Quadratic Regulator (LQR) controller, this research implemented the Proportional Integral Derivative (PID), Fuzzy Logic Control (FLC), and Artificial Neural Network (ANN). The results show that FLC achieved a good controllability for both angles, particularly for the pitch angle in comparison to the nonlinear auto regressive moving average (NARMA-L2). Moreover, NARMA-L2 requires further improvement by using, for example, the swarm optimization method to provide better controllability. The PID controller, on the other hand, had a greater capability in controlling the yaw angle in comparison to the other controllers implemented. Accordingly, it is suggested that the integration of PID and FLC may lead to more optimal outcomes.

Self-driving car is one of the most amazing applications and most active research of artificial intelligence. It uses end-to-end deep learning models to take orientation and speed decisions, using mainly Convolutional Neural Networks for computer vision, plugged to a fully connected network to output control commands. In this paper, we introduce the Self-driving car domain and the CARLA simulation environment with a focus on the lane-keeping task, then we present the two main end-to-end models, used to solve this problematic, beginning by Deep imitation learning (IL) and specifically the Conditional Imitation Learning (COIL) algorithm, that learns through expert labeled demonstrations, trying to mimic their behaviors, and thereafter, describing Deep Reinforcement Learning (DRL), and precisely DQN and DDPG (respectively Deep Q learning and deep deterministic policy gradient), that uses the concepts of learning by trial and error, while adopting the Markovian decision processes (MDP), to get the best policy for the driver agent. In the last chapter, we compare the two algorithms IL and DRL based on a new approach, with metrics used in deep learning (Loss during training phase) and Self-driving car (the episode's duration before a crash and Average distance from the road center during the testing phase). The results of the training and testing on CARLA simulator reveals that the IL algorithm performs better than DRL algorithm when the agents are already trained on a given circuit, but DRL agents show better adaptability when they are on new roads.

In recent years, there are great research interests in using the Electroencephalogram (EEG) signals in biometrics applications. The strength of EEG signals as a biometric comes from its major fraud prevention capability. However, EEG signals are so sensitive, and many factors affect its usage as a biometric; two of these factors are the number of channels, and the required time for acquiring the signal; these factors affect the convenience and practicality. This study proposes a novel approach for EEG-based biometrics that optimizes the channels of acquiring data to only one channel. And the time to only one second. The results are compared against five commonly used classifiers named: KNN, Random Forest (RF), Support Vector Machine (SVM), Decision Tables (DT), and Naïve Bayes (NB). We test the approach on the public Texas data repository. The results prove the constancy of the approach for the eight minutes. The best result of the eyes-closed scenario is Average True Positive Rate (TPR) 99.1% and 98.2% for the eyes-opened. And it reaches 100% for multiple subjects.

Opinion mining in social networks data is considered as one of most important research areas because a large number of users interact with different topics on it. This paper discusses the problem of predicting future products rate according to users’ comments. Researchers interacted with this problem by using machine learning algorithms (e.g. Logistic Regression, Random Forest Regression, Support Vector Regression, Simple Linear Regression, Multiple Linear Regression, Polynomial Regression and Decision Tree). However, the accuracy of these techniques still needs to be improved. In this study, we introduce an approach for predicting future products rate using LR, RFR, and SVR. Our data set consists of tweets and its rate from 1:5. The main goal of our approach is improving the prediction accuracy about existing techniques. SVR can predict future product rate with a Mean Squared Error (MSE) of 0.4122, Linear Regression model predict with a Mean Squared Error of 0.4986 and Random Forest Regression can predict with a Mean Squared Error of 0.4770. This is better than the existing approaches accuracy.

The standard method to train the Higher Order Neural Networks (HONN) is the well-known Backpropagation (BP) algorithm. Yet, the current BP algorithm has several limitations including easily stuck into local minima, particularly when dealing with highly non-linear problems and utilise computationally intensive training algorithms. The current BP algorithm is also relying heavily on the initial weight values and other parameters picked. Therefore, in an attempt to overcome the BP drawbacks, we investigate a method called Modified Cuckoo Search-Markov chain Monté Carlo for optimising the weights in HONN and boost the learning process. This method, which lies in the Swarm Intelligence area, is notably successful in optimisation task. We compared the performance with several HONN-based network models and standard Multilayer Perceptron on four (4) time series datasets: Temperature, Ozone, Gold Close Price and Bitcoin Closing Price from various repositories. Simulation results indicate that this swarm-based algorithm outperformed or at least at par with the network models with current BP algorithm in terms of lower error rate.