The study aimed to reveal the level of knowledge and tendencies of high- study students specializing in curriculum and teaching methods at King Khalid University towards harmonious strategies with brain-based learning (BBL). And Then, putting a proposed concept to develop knowledge and tendencies of high-study students specializing in curriculum and teaching methods at King Khalid University towards harmonious strategies with Brain-based learning (BBL). For achieving this goal, a cognitive test and a scale of tendency were prepared to apply harmonious strategies with brain-based learning. The descriptive approach was used because it suits the goals of the study. The study sample consisted of (70) male and female students of postgraduate

The purpose of this study is testing the effect of orgnizational learning in orgnizational Effectivness an applied study in Al-hiqma Jordinan Medecine Company . study sosiety 88 manegers sleect 80 of them .study used SPSS to test the hypothesis.study reachs to significant conculctions

The current research aims to reveal the strength of education and the direction of the relationship between the formal thinking and learning methods of Kindergarten department students. To achieve this objective, the researcher developed a scale of formal thinking according to the theory of (Inhelder & Piaget 1958) consisting of (25) items in the form of declarative phrases derived from the analysis of formal thinking skills based on a professional situation that students are expected to interact with in a professional way. The research sample consisted of (100) female students selected randomly who were divided into four groups based on the academic stages, the results revealed that The level of formal thinking of the main sample is

In recent years, the world witnessed a rapid growth in attacks on the internet which resulted in deficiencies in networks performances. The growth was in both quantity and versatility of the attacks. To cope with this, new detection techniques are required especially the ones that use Artificial Intelligence techniques such as machine learning based intrusion detection and prevention systems. Many machine learning models are used to deal with intrusion detection and each has its own pros and cons and this is where this paper falls in, performance analysis of different Machine Learning Models for Intrusion Detection Systems based on supervised machine learning algorithms. Using Python Scikit-Learn library KNN, Support Ve

Self-driving automobiles are prominent in science and technology, which affect social and economic development. Deep learning (DL) is the most common area of study in artificial intelligence (AI). In recent years, deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. Different studies investigated a variety of significant technologies for autonomous vehicles, including car navigation systems, path planning, environmental perception, as well as car control. End-to-end learning control directly converts sensory data into control commands in autonomous driving. This research aims to identify the most accurate pre-trained Deep Neural Network (DNN) for predicting the steerin

This review paper examines the crucial impact of YouTube on learning English as a Foreign Language. Recently, learners’ interaction and development of their skills have been improved due to the integration of digital platforms into language education. YouTube is regarded as one of the most prevalent platforms due to its accessibility, multimodal content, and capacity to simulate real-life communication. This study tackles thirty selected research articles from various cultural and institutional backgrounds to identify the pedagogical benefits and challenges associated with using YouTube in teaching English. Conventional methods of teaching English as a foreign language encounter difficulties in improving students’ engagement and

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F

Advanced strategies for production forecasting, operational optimization, and decision-making enhancement have been employed through reservoir management and machine learning (ML) techniques. A hybrid model is established to predict future gas output in a gas reservoir through historical production data, including reservoir pressure, cumulative gas production, and cumulative water production for 67 months. The procedure starts with data preprocessing and applies seasonal exponential smoothing (SES) to capture seasonality and trends in production data, while an Artificial Neural Network (ANN) captures complicated spatiotemporal connections. The history replication in the models is quantified for accuracy through metric keys such as m

م.د. فاطمة حميد ،أ.م.د وفاء صباح محمد الخفاجي, International Journal of Psychosocial Rehabilitation,, 2020 - Cited by 1