The electrical activity of the heart and the electrocardiogram (ECG) signal are fundamentally related. In the study that has been published, the ECG signal has been examined and used for a number of applications. The monitoring of heart rate and the analysis of heart rhythm patterns, the detection and diagnosis of cardiac diseases, the identification of emotional states, and the use of biometric identification methods are a few examples of applications in the field. Several various phases may be involved in the analysis of electrocardiogram (ECG) data, depending on the type of study being done. Preprocessing, feature extraction, feature selection, feature modification, and classification are frequently included in these stages. Ever

The process of discharging the quantities of dyes resulting from industrial processes with wastewater leads to the occurrence of a serious environmental problem that threatens the environmental health security of humans. Therefore, a number of studies have been addressed that include presenting many physical and chemical treatment methods to get rid or reduce the proportion of pigments such as biological decomposition, ion exchange, and sedimentation. Chemotherapy, reverse osmosis, coagulation, Toxic sludge generation, flocculation. In addition to the above, this review deals with a number of studies that present activated carbon of plant origin, methods of obtaining it, types and advantages of it being cheap and environmentally friendly. A

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

The removal of boron from aqueous solution was carried out by electrocoagulation (EC) using magnesium electrodes as anode and stainless steel electrodes as cathode. Several operating parameters on the removal efficiency of boron were investigated, such as initial pH, current density, initial boron ion concentration, NaCl concentration, spacing between electrodes, electrode material, and presence of carbonate concentration. The optimum removal efficiency of 91. 5 % was achieved at a current density of 3 mA/cm² and  pH = 7 using (Mg/St. St. ) electrodes, within 45 min of operating time. The concentration of NaCl was o. 1 g/l with a 0.5cm spacing between the electrodes. First and second order rate equation were applied to study adsorp

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

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

       Scientists are investigating the efficacy of different biosorbents for promoting economic and environmental viability in purifying contaminants. Among the primary by-products of biodiesel production is waste microalgae biomass, which has the potential to be used as a cheap biosorbent for the treatment of pollution. In the present study, the biomass left over after extracting the chlorella vulgaris was used to test the potential biosorption of CIP from simulated aqueous solutions. Bisorbent's ability was characterized using Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDX). Analysis with a Fourier Transform Infrared Spectrometer revealed that C

This study depicts the removal of Manganese ions (Mn²⁺) from simulated wastewater by combined electrocoagulation/ electroflotation technologies. The effects of initial Mn concentration, current density (C.D.), electrolysis time, and different mesh numbers of stainless steel screen electrodes were investigated in a batch cell by adopting Taguchi experimental design to explore the optimum conditions for maximum removal efficiency of Mn. The results of multiple regression and signal to noise ratio (S/N) showed that the optimum conditions were Mn initial concentration of 100 ppm, C.D. of 4 mA/cm², time of 120 min, and mesh no. of 30 (wire/inch). Also, the relative significance of each factor was attained by the analysis