In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the well known algorithms used in data mining classification are Backpropagation Neural Network (BNN) and Naïve Bayesian (NB). This paper investigates the performance of these two classification methods using the Car Evaluation dataset. Two models were built for both algorithms and the results were compared. Our experimental results indicated that the BNN classifier yield higher accuracy as compared to the NB classifier but it is less efficient because it is time-consuming and difficult to analyze due to its black-box implementation.

This paper is devoted to investigate the effect of internal curing technique on the properties of self-compacting concrete. In this study, self-compacting concrete is produced by using limestone powder as partial replacement by weight of cement with percentage of (5%), sand is partially replaced by volume with saturated fine lightweight aggregate which is thermostone aggregate as internal curing material in three percentages of (5%, 10%, 15%) for self-compacting concrete, and the use of two external curing conditions which are water and air. The experimental work was divided into three parts: in the first part, the workability tests of fresh self-compacting concrete were conducted. The second part included conducting compressive str

Ad-Hoc Networks are a generation of networks that are truly wireless, and can be easily constructed without any operator. There are protocols for management of these networks, in which the effectiveness and the important elements in these networks are the Quality of Service (QoS). In this work the evaluation of QoS performance of MANETs is done by comparing the results of using AODV, DSR, OLSR and TORA routing protocols using the Op-Net Modeler, then conduct an extensive set of performance experiments for these protocols with a wide variety of settings. The results show that the best protocol depends on QoS using two types of applications (+ve and –ve QoS in the FIS evaluation). QoS of the protocol varies from one prot

The study's objective is to produce Nano Graphene Oxide (GO) before using it for batch adsorption to remove heavy metals (Cadmium Cd⁺², Nickel Ni⁺², and Vanadium V⁺⁵) ions from industrial wastewater. The temperature effect (20-50) °C and initial concentration effect (100-800) mg L^-1 on the adsorption process were studied. A simulation aqueous solution of the ions was used to identify the adsorption isotherms, and after the experimental data was collected, the sorption process was studied kinetically and thermodynamically. The Langmuir, Freundlich, and Temkin isotherm models were used to fit the data. The results showed that Cd, Ni, and V ions on the GO adsorbing surface matched the Langmuir mo

The study's objective is to produce Nano Graphene Oxide (GO) before using it for batch adsorption to remove heavy metals (Cadmium Cd+2, Nickel Ni+2, and Vanadium V+5) ions from industrial wastewater. The temperature effect (20-50) °C and initial concentration effect (100-800) mg L-1 on the adsorption process were studied. A simulation aqueous solution of the ions was used to identify the adsorption isotherms, and after the experimental data was collected, the sorption process was studied kinetically and thermodynamically. The Langmuir, Freundlich, and Temkin isotherm models were used to fit the data. The results showed that Cd, Ni, and V ions on the GO adsorbing surface matched the Langmuir model with correlation coefficients (R2)

In this paper we present a method to analyze five types with fifteen wavelet families for eighteen different EMG signals. A comparison study is also given to show performance of various families after modifying the results with back propagation Neural Network. This is actually will help the researchers with the first step of EMG analysis. Huge sets of results (more than 100 sets) are proposed and then classified to be discussed and reach the final.

Specialized hardware implementations of Artificial Neural Networks (ANNs) can offer faster execution than general-purpose microprocessors by taking advantage of reusable modules, parallel processes and specialized computational components. Modern high-density Field Programmable Gate Arrays (FPGAs) offer the required flexibility and fast design-to-implementation time with the possibility of exploiting highly parallel computations like those required by ANNs in hardware. The bounded width of the data in FPGA ANNs will add an additional error to the result of the output. This paper derives the equations of the additional error value that generate from bounded width of the data and proposed a method to reduce the effect of the error to give

An Intelligent Internet of Things network based on an Artificial Intelligent System, can substantially control and reduce the congestion effects in the network. In this paper, an artificial intelligent system is proposed for eliminating the congestion effects in traffic load in an Intelligent Internet of Things network based on a deep learning Convolutional Recurrent Neural Network with a modified Element-wise Attention Gate. The invisible layer of the modified Element-wise Attention Gate structure has self-feedback to increase its long short-term memory. The artificial intelligent system is implemented for next step ahead traffic estimation and clustering the network. In the proposed architecture, each sensing node is adaptive and able to