A modified version of the artificial Bee Colony Algorithm (ABC) was suggested namely Adaptive Dimension Limit- Artificial Bee Colony Algorithm (ADL-ABC). To determine the optimum global path for mobile robot that satisfies the chosen criteria for shortest distance and collision–free with circular shaped static obstacles on robot environment. The cubic polynomial connects the start point to the end point through three via points used, so the generated paths are smooth and achievable by the robot. Two case studies (or scenarios) are presented in this task and comparative research (or study) is adopted between two algorithm’s results in order to evaluate the performance of the suggested algorithm. The results of the simulation showed that modified parameter (dynamic control limit) is avoiding static number of limit which excludes unnecessary Iteration, so it can find solution with minimum number of iterations and less computational time. From tables of result if there is an equal distance along the path such as in case A (14.490, 14.459) unit, there will be a reduction in time approximately to halve at percentage 5%.

In this paper a robust fractional order PID (FOPID) controller is proposed to control the automatic voltage regulator (AVR) system, the tuning of the controller gains are done using whale optimization algorithm (WOA) and integral time absolute error (ITAE) cost function is adopted to achieve an efficient performance. The transient analysis was done and compared with conventional PID in terms of overshoot, settling time, rise time, and peak time to explain the superiority of the proposed controller. Finally, a robustness analysis is done by adding external disturbances to the system and changing the system parameters by ±20% from its original value, the controller overcomes the disturbances signals with less than 0.25 s and faces the changes of the system values and returning the response within (0.7-1) sec and led the system to the desired response efficiently. The numerical simulations showed that the smart WOA offers satisfying results and faster response reflected clearly on the robust and stable performance of the proposed controller in improving the transient analysis of AVR system response.

In this study, an end-to-end human iris recognition system is presented to automatically identify individuals for high level of security purposes. The deep learning technology based new 2D convolutional neural network (CNN) model is introduced for extracting the features and classifying the iris patterns. Firstly, the iris dataset is collected, preprocessed and augmented. The dataset are expanded and enhanced using data augmentation, histogram equalization (HE) and contrast-limited adaptive histogram equalization (CLAHE) techniques. Secondly, the features of the iris patterns were extracted and classified using CNN. The structure of CNN comprises of convolutional layers and ReLu layers for extracting the features, pooling layers for reducing the parameters, fully connected layer and Softmax layer for classifying the extracted features into N classes. For the training process and updating the weights, the backpropagation algorithm and adaptive moment estimation Adam optimizer are used. The experimental results carried out based on a graphics processing unit (GPU) and using Matlab. The overall training accuracy of the introduced system was 95.33% with a consumption time of 17.59 minutes for training set. While the testing accuracy 100% with a consumption time of 12 seconds. The introduced iris recognition system has been successfully applied.

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