Nowadays, people's expression on the Internet is no longer limited to text, especially with the rise of the short video boom, leading to the emergence of a large number of modal data such as text, pictures, audio, and video. Compared to single mode data ,the multi-modal data always contains massive information. The mining process of multi-modal information can help computers to better understand human emotional characteristics. However, because the multi-modal data show obvious dynamic time series features, it is necessary to solve the dynamic correlation problem within a single mode and between different modes in the same application scene during the fusion process. To solve this problem, in this paper, a feature extraction framework of

Historical concepts are among the concepts that are difficult to present to the pre-school child except by using modern techniques, as well as the difficulty of making visits to all historical monuments and going back to antiquity and the lack of studies in this area, therefore, we need an attractive medium that children love which is able to convey some abstract concepts that are difficult to teach to children using traditional methods, and among these activities that the kindergarten provides to children are the stories through which they develop their linguistic wealth, consolidate their religious and spiritual inclinations, refine their correct morals, and form their proper attitudes, knowledge and life concepts.

The

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet

Background: The timing of eruption of permanent teeth is of considerable importance to the dental health planning for diagnostic, preventive and therapeutic measures for children and teenagers. The purposes of this study were to determine timing of maxillary and mandibular permanent teeth emergence (except third molars) and to evaluate the effect nutritional status by anthropometric measures on the eruption time of permanent teeth, investigations had been done according to jaw and gender variations. Materials and Methods: This study was conducted among four to fifteen years old children and teenagers from kindergarten and schools in Basrah city in the south region of Iraq. The total sample composed of 1807 children and teenagers that were

Internal control system is a safety valve that preserves economic units assets and ensure the accuracy of financial data, as well as to obligation in the laws, regulations, administrative policies ,and improve the efficiency, effectiveness and economic of operation, so it has become imperative for these units attention to internal and developed control system The research problem in exposure the economic units when the exercise of their business to many of the risks to growth or hinder the achievement of its objectives and the risks (financial, operational, strategy, risk) and not it rely on risk Assessment according to modern scientific methods, as in Brown's risk Classification, Which led to the weakness of the internal control identif

The study aimed to design a test of pre-writing skills for public kindergartens in Baghdad city. The test consisted of (25) items applied on a sample of (150) kindergarteners to identify these skills as well as to identify the significant difference between male and female children and if there is a difference between pre-school children and kindergarteners. The results showed the presence of pre-writing skills with a high degree in kindergarten children. The differences were clear in these skills between male and female children and those in pre-school than those in kindergartens. The researcher suggested a number of recommendations and proposals.

In this paper, the Active Suspension System (ASS) of road vehicles was investigated. In addition to the conventional stiffness and damper, the proposed ASS includes a fuzzy controller, a hydraulic actuator, and an LVDT position sensor. Furthermore, this paper presents a nonlinear model describing the operation of the hydraulic actuator as a part of the suspension system. Additionally, the detailed steps of the fuzzy controller design for such a system are introduced. A MATLAB/Simulink model was constructed to study the proposed ASS at different profiles of road irregularities. The results have shown that the proposed ASS has superior performance compared to the conventional Passive Suspension System (PSS), where the body displacemen

The blade pitch angle (BPA) in wind turbine (WT) is controlled to maximize output power generation above the rated wind speed (WS). In this paper, four types of controllers are suggested and compared for BPA controller in WT: PID controller (PIDC), type-1 fuzzy logic controller (T1-FLC), type-2 fuzzy logic controller (T2-FLC), and hybrid fuzzy-PID controller (FPIDC). The Mamdani and Sugeno fuzzy inference systems (FIS) have been compared to find the best inference system used in FLC. Genetic algorithm (GA) and Particle swarm optimization algorithm (PSO) are used to find the optimal tuning of the PID parameter. The results of500-kw horizontal-axis wind turbine show that PIDC based on PSO can reduced 2.81% in summation error of power

        Urban land price is the primary indicator of land development in urban areas. Land prices in holly cities have rapidly increased due to tourism and religious activities. Public agencies are usually facing challenges in managing land prices in religious areas. Therefore, they require developed models or tools to understand land prices within religious cities. Predicting land prices can efficiently retain future management and develop urban lands within religious cities. This study proposed a new methodology to predict urban land prices within holy cities. The methodology is based on two models, Linear Regression (LR) and Support Vector Regression (SVR), and nine variables (land price, land area,