In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database

Abstract

This research aim to overcome the problem of dimensionality by using the methods of non-linear regression, which reduces the root of the average square error (RMSE), and is called the method of projection pursuit regression (PPR), which is one of the methods for reducing dimensions that work to overcome the problem of dimensionality (curse of dimensionality), The (PPR) method is a statistical technique that deals with finding the most important projections in multi-dimensional data , and With each finding projection , the data is reduced by linear compounds overall the projection. The process repeated to produce good projections until the best projections are obtained. The main idea of the PPR is to model

Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model bas

The evolution in the field of Artificial Intelligent (AI) with its training algorithms make AI very important in different aspect of the life. The prediction problem of behavior of dynamical control system is one of the most important issue that the AI can be employed to solve it. In this paper, a Convolutional Multi-Spike Neural Network (CMSNN) is proposed as smart system to predict the response of nonlinear dynamical systems. The proposed structure mixed the advantages of Convolutional Neural Network (CNN) with Multi -Spike Neural Network (MSNN) to generate the smart structure. The CMSNN has the capability of training weights based on a proposed training algorithm. The simulation results demonstrated that the proposed

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to

In this golden age of rapid development surgeons realized that AI could contribute to healthcare in all aspects, especially in surgery. The aim of the study will incorporate the use of Convolutional Neural Network and Constrained Local Models (CNN-CLM) which can make improvement for the assessment of Laparoscopic Cholecystectomy (LC) surgery not only bring opportunities for surgery but also bring challenges on the way forward by using the edge cutting technology. The problem with the current method of surgery is the lack of safety and specific complications and problems associated with safety in each laparoscopic cholecystectomy procedure. When CLM is utilize into CNN models, it is effective at predicting time series tasks like iden

The corrosion behavior of carbon steel at different temperatures 100,120,140 and 160 Cͦ under different pressures 7,10 and 13 bar in pure distilled water and after adding three types of oxygen scavengers Hydroquinone, Ascorbic acid and Monoethanolamine in different concentrations 40,60 and 80 ppm has been investigated using weight loss method. The carbon steel specimens were immersed in water containing 8.2 ppm dissolved oxygen (DO) by using autoclave. It was found that corrosion behavior of carbon steel was greatly influenced by temperature with high pressure. The corrosion rate decreases, when adding any one of oxygen scavengers. The best results were obtained at a concentration of 80 ppm of each scavenger. It was observed that 

The present work provides to treat real oily saline wastewater released from drilling oil sites by the use of electrocoagulation technique. Aluminum tubes were utilized as electrodes in a concentric manner to minimize the concentrations of 113400 mg TDS/L, 65623 mg TSS/L, and the ions of 477 mg HCO3/L, 102000 mg Cl/L and 5600 mg Ca/L presented in real oily wastewater under the effect of the operational parameters (the applied current and reaction time) by making use of the central composite rotatable design. The final concentrations of TDS, TSS, HCO3, Cl, and Ca that obtained were 93555 ppm (17.50%), 11011 ppm (83.22%), 189ppm (60.38%), 80000ppm (22%), and 4200 ppm (25%), respectively, under the optimum values of the operational parameters