Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

That internal control is particularly important in improvingperformance and tax reform، they play an important role in the regularity ofwork and development and the anti corruption and activating the decisionsand tax legislation، as contained in the organizational plan and the means،procedures and components designed from which to ensure a policy andimplementation plans The research aims to review the reality of the internalcontrol in the General Commission for Taxes and stand on the deficiencies init, with the strengthening of the role of internal control in the GeneralAuthority for taxes based on the laws and regulations and by using modernmeans to work as well as developing the performance of employees in thebody، including helpin

This study identified intellectual and applied research in key variables (Strategic Control and Strategic Pitfalls) through internal dimensions then founded the (Federal Board of Supreme Audit) is the good area for analyzing the relationship and effect between variables by (125) questionnaire selected from (148) was distributed to the top and middle management as a seniors in controlling action. Appropriate methods, statistical tools, and programs were used to enhance the basic outputs, represented as a set of Conclusions and recommendations from the real control work with a specificity of potential Pitfalls in the effectiveness impact by Strategic Control in other dimensions which Studied.

Magneto-rheological (MR) Valve is one of the devices generally used to control the speed of Hydraulic actuator using MR fluid. The performance of valve depends on the magnetic circuit design. Present study deals with a new design of MR valve. The finite element analysis is carried out on this valve to optimize its design. The design of the magnetic circuit is accomplished by magnetic finite element software such as Finite Element Method Magnetic (FEMM). The Model dimensions of MR valve, material properties and the circuit properties of valve coil are taken into account. The results of analysis are presented in terms of magnetic strength and magnetic flux density. The valve can be operated with variable flow rate by varying the current. It i

Prediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence (AI) algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure (SLP) which is physically related to rainfall on land and thus able to predict unseen rainfall events. Daily rainfall of east coast of Peninsular Malaysia (PM) was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine (ELM), Bay

Abstract. In this paper, a high order extended state observer (HOESO) based a sliding mode control (SMC) is proposed for a flexible joint robot (FJR) system in the presence of time varying external disturbance. A composite controller is integrated the merits of both HOESO and SMC to enhance the tracking performance of FJR system under the time varying and fast lumped disturbance. First, the HOESO estimator is constructed based on only one measured state to precisely estimate unknown system states and lumped disturbance with its high order derivatives in the FJR system. Second, the SMC scheme is designed based on such accurate estimations to govern the nominal FJR system by well compensating the estimation errors in the states and the lumped

This work is concerned with the vibration attenuation of a smart beam interacting with fluid using proportional-derivative PD control and adaptive approximation compensator AAC. The role of the AAC is to improve the PD performance by compensating for unmodelled dynamics using the concept of function approximation technique FAT. The key idea is to represent the unknown parameters using the weighting coefficient and basis function matrices/vectors. The weighting coefficient vector is updated using Lyapunov theory. This controller is applied to a flexible beam provided with surface bonded piezo-patches while the vibrating beam system is submerged in a fluid. Two main effects are considered: 1) axial stretching of the vibrating beam that leads

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In this study, the effect of carboxylic methyl cellulose (CMC), and sodium dodcyl benzene sulfonate (SDBS) as an aqueous solution on the drag reduction was investigated. Different concentrations of (CMC) and (SDBS) such as (50, 100, 150, 200, 250, 300, 350, 400, 450, and 500 ppm) were used to analyze the aqueous solution properties, including surface tension, conductivity, and shear viscosity. The optimum four concentrations (i.e., 50, 100, 200, and 300 ppm) of fluid properties were utilized to find their effect on the drag reduction. Two different PVC pipe diameters (i.e., 1" and 3/4") were used in this work. The  results showed that blending CMC with SDBS gives

This search has introduced the techniques of multi-wavelet transform and neural network for recognition 3-D object from 2-D image using patches. The proposed techniques were tested on database of different patches features and the high energy subband of discrete multi-wavelet transform DMWT (gp) of the patches. The test set has two groups, group (1) which contains images, their (gp) patches and patches features of the same images as a part of that in the data set beside other images, (gp) patches and features, and group (2) which contains the (gp) patches and patches features the same as a part of that in the database but after modification such as rotation, scaling and translation. Recognition by back propagation (BP) neural network as