Nearly, in the middle of 1970s the split-brain theory became the only theory that explains human creativity used in all fine art and art education schools. In fact, this theory- which appeared for first time in the middle of 1940s – faced many radical changes including its concepts and structures, and these changes affected both teaching art and art criticism. To update people awareness within art field of study, this paper reviews the split-brain theory and its relationship with teaching art from its appearance to its decay in 2013 and after.

In this paper a dynamic behavior and control of  a jacketed continuous stirred tank reactor (CSTR)  is developed using different control strategies, conventional feedback control (PI and PID), and neural network (NARMA-L2, and NN Predictive) control. The dynamic model for CSTR process is described by a first order lag system with dead time.

The optimum tuning of control parameters are found by two different methods; Frequency Analysis Curve method (Bode diagram) and Process Reaction Curve using the mean of Square Error (MSE) method. It is found that the Process Reaction Curve method is better than the Frequency Analysis Curve method and PID feedback controller is better than PI feedback controller.

The results s

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a

Cyber-attacks keep growing. Because of that, we need stronger ways to protect pictures. This paper talks about DGEN, a Dynamic Generative Encryption Network. It mixes Generative Adversarial Networks with a key system that can change with context. The method may potentially mean it can adjust itself when new threats appear, instead of a fixed lock like AES. It tries to block brute‑force, statistical tricks, or quantum attacks. The design adds randomness, uses learning, and makes keys that depend on each image. That should give very good security, some flexibility, and keep compute cost low. Tests still ran on several public image sets. Results show DGEN beats AES, chaos tricks, and other GAN ideas. Entropy reached 7.99 bits per pix

Background: The roles of AI in the academic community continue to grow, especially in the enhancement of learning outcomes and the improvement of writing quality and efficiency. Objectives: To explore in depth the experience of senior pharmacy students in using artificial intelligence for academic purposes. Methods: This qualitative study included face-to-face individual interviews with senior pharmacy students from March to May 2023 using a pre-planned interview guide of open-ended questions. All interviews were audio-recorded. Thematic analysis was used to analyze the data. Results: The results were obtained from 15 in-depth face-to-face interviews with senior pharmacy students (5th and 4th years). Eight participants were male, an

This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm n

Data generated from modern applications and the internet in healthcare is extensive and rapidly expanding. Therefore, one of the significant success factors for any application is understanding and extracting meaningful information using digital analytics tools. These tools will positively impact the application's performance and handle the challenges that can be faced to create highly consistent, logical, and information-rich summaries. This paper contains three main objectives: First, it provides several analytics methodologies that help to analyze datasets and extract useful information from them as preprocessing steps in any classification model to determine the dataset characteristics. Also, this paper provides a comparative st

The purpose of the current investigation is to distinguish between working memory ( ) in five patients with vascular dementia ( ), fifteen post-stroke patients with mild cognitive impairment ( ), and fifteen healthy control individuals ( ) based on background electroencephalography (EEG) activity. The elimination of EEG artifacts using wavelet (WT) pre-processing denoising is demonstrated in this study. In the current study, spectral entropy ( ), permutation entropy ( ), and approximation entropy ( ) were all explored. To improve the  classification using the k-nearest neighbors ( NN) classifier scheme, a comparative study of using fuzzy neighbourhood preserving analysis with -decomposition ( ) as a dimensionality reduction technique an