Many consumers of electric power have excesses in their electric power consumptions that exceed the permissible limit by the electrical power distribution stations, and then we proposed a validation approach that works intelligently by applying machine learning (ML) technology to teach electrical consumers how to properly consume without wasting energy expended. The validation approach is one of a large combination of intelligent processes related to energy consumption which is called the efficient energy consumption management (EECM) approaches, and it connected with the internet of things (IoT) technology to be linked to Google Firebase Cloud where a utility center used to check whether the consumption of the efficient energy is satisfied. It divides the measured data for actual power (A_p ) of the electrical model into two portions: the training portion is selected for different maximum actual powers, and the validation portion is determined based on the minimum output power consumption and then used for comparison with the actual required input power. Simulation results show the energy expenditure problem can be solved with good accuracy in energy consumption by reducing the maximum rate (A_p ) in a given time (24) hours for a single house, as well as electricity’s bill cost, is reduced.
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
... Show MoreThe research aims to measure the relationship and the impact of knowledge management processes to achieve the performance of insurance service, as well as analysis of the reality of the National Insurance Company to identify the level of overall performance, and to achieve this goal, it has been the selection of knowledge management processes according to the survey prepared a supplement to the study (Qubaisi, 2002), and of the four operations (knowledge generation, and storage of knowledge, and the distribution of knowledge, and application of knowledge), which represented the independent variable, and the performance has been the use of quantitative and qualitative measures, (sales growth, customer satisfaction), which represented the
... Show MoreBackground: During acrylic resin processing, the mold must be separated from the surface of the gypsum to prevent liquid resin from penetrating into the gypsum, and water from the gypsum seeping into the acrylic resin. For many years, tin foil was the most acceptable separating medium, and because it's difficult to apply, a tin-foil substitute is used. In this study, olive oil is used as an alternative to tin foil separating medium for first time, and evaluating its effect as a separating medium on some mechanical properties such as (indentation hardness and transverse strength) of acrylic resins denture base comparing it with those processed using tin-foil and tin foil substitute such as (cold mold seal) separating medium. Materials and M
... Show MoreThis article aims to establish and evaluate standards for critical equipment and materials in highway projects in Iraq. Delphi technique has been used to analyze, explore, and discover the main criteria and sub-criteria that affect equipment and materials in highway construction projects in Iraq. To determine the correct response to the criteria presented in this study, a program (IBM, SPSS/V25) was used to assess the main criteria and sub-criteria using the mean score (MS) and standard deviation (SD) technique, as well as to check reliability using Cronbach's alpha factor (α). The experts' qualifications and the extent to which the person is ready to commit are both important factors in panel selection. The design of a
... Show MorePresent study was conducted to evaluate the different levels of energy to protein ratios (EPR) using food waste and black soldier fly larvae meal (FWBSFL) on growth performance and nutrient digestibility of broilers. A total of 160 one-day old broiler chicks were divided randomly to four groups and each group had 8 replicates with 5 chicks per replicate. The control diet was formulated using conventional feed ingredients with EPR of 154 for the starter period and 167 for the finisher period. The other treatments were diets with normal, low, and high EPR (154,143, and 166 for the starter period; 167, 155, and 177 for the finisher period) using FWBSFL. Feed consumption and body weight gain as well as digestibility of crude protein, cr
... Show MoreBased on nonlinear self- diffraction technique, the nonlinear optical properties of thin slice of matter can be obtained. Here, nonlinear characterization of nano-fluids consist of hybrid Single Wall Carbon Nanotubes and Silver Nanoparticles (SWCNTs/Ag-NPs) dispersed in acetone at volume fraction of 6x10-6, 9x10-6, 18x10-6 have been investigated experimentally. Therefore, CW DPSS laser at 473 nm focused into a quartz cuvette contains the previous nano-fluid was utilized. The number of diffraction ring patterns (N) has been counted using Charge - Coupled- Device (CCD) camera and Pc with a certain software, in order to find the maximum change of refractive index ( of fluids. Our result show that the fraction volume of 18x10-6 is more nonli
... Show MoreSupport vector machine (SVM) is a popular supervised learning algorithm based on margin maximization. It has a high training cost and does not scale well to a large number of data points. We propose a multiresolution algorithm MRH-SVM that trains SVM on a hierarchical data aggregation structure, which also serves as a common data input to other learning algorithms. The proposed algorithm learns SVM models using high-level data aggregates and only visits data aggregates at more detailed levels where support vectors reside. In addition to performance improvements, the algorithm has advantages such as the ability to handle data streams and datasets with imbalanced classes. Experimental results show significant performance improvements in compa
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