CHAOTIC SEISMIC SIGNAL MODELING BASED ON NOISE AND EARTHQUAKE ANOMALY DETECTION

Since ancient times, people have tried to predict earthquakes using simple perceptions such as animal behavior. The prediction of the time and strength of an earthquake is of primary concern. In this study chaotic signal modeling is used based on noise and detecting anomalies before an earthquake using artificial neural networks (ANNs). Artificial neural networks are efficient tools for solving complex problems such as prediction and identification. In this study, the effective features of chaotic signal model is obtained considering noise and detection of anomalies five minutes before an earthquake occurrence. Neuro-fuzzy classifier and MLP neural network approaches showed acceptable accuracy of 84.6491% and 82.8947%, respectively. Results demonstrate that the proposed method is an effective seismic signal model based on noise and anomaly detection before an earthquake

Toward Quaternary QCA : Novel Majority and XOR Fuzzy Gates

As an emerging nanotechnology, quantum-dot cellular automata (QCA) has been considered an alternative to CMOS technology that suffers from problems such as leakage current. Moreover, QCA is suitable for multi-valued logic due to the simplicity of implementing fuzzy logic in a way much easier than CMOS technology. In this paper, a quaternary cell is proposed with two isolated layers because of requiring three particles to design this quaternary cell. Moreover, due to the instability of the basic gates, the three particles cannot be placed in one layer. The first layer of the proposed two-layer cell includes a ternary cell and the second one includes a binary cell. It is assumed that the overall polarization of the quaternary QCA (QQCA) cell is determined as the combined polarization of the two layers. The proposed QQCA cell can also be implemented in one layer. Simulations of the QQCA cell are performed based on analytical calculations. Moreover, a majority fuzzy gate, an XOR fuzzy gate, and a crossbar structure are simulated.peerReviewe

Evaluation of effective features in the diagnosis of Covid‐19 infection from routine blood tests with multilayer perceptron neural network: A cross‐sectional study

Abstract Background and Aim Coronavirus is an infectious disease that is now known as an epidemic, early and accurate diagnosis helps the patient receive more care. The aim of this study is to investigate Covid‐19 using blood tests and multilayer perceptron neural network and affective factors in improving and preventing Covid‐19. Methods This cross‐sectional study was performed on 200 patients referred to Sina Hospital, Tehran, Iran, who were confirmed cases of Covid‐19 by computerized tomography‐scan analysis between 2 March 2020 to 5 April 2020. After verification of lung involvement, blood sampling was done to separate the sera for C‐reactive protein (CRP), magnesium (Mg), lymphocyte percentage, and vitamin D analysis in healthy and unhealthy people. Blood samples from healthy and sick people were applied to the multilayer perceptron network for 70% of the data for training and 30% for testing. Result By examining the features, it was found that in patients with Covid‐19, there was a significant relationship between increased CRP and decreased lymphocyte levels, and increased Mg (p < 0.01). In these patients, the amount of CRP and Mg in women and the number of lymphocytes and vitamin D in men were significantly higher (p < 0.01). Conclusion The important advantage of using a multilayer perceptron neural network is to speed up the diagnosis and treatment

Resolving Spectra Overlapping Based on Net Analyte Signal for Simultaneous Spectrophotometric Determination of Fluoxetine and Sertraline

The net analyte signal standard addition method was used for simultaneous spectrophotometric determination of sertraline and fluoxetine in pharmaceutical preparations. The method combines the advantages of the standard addition method with the net analyte signal concept to enable the extraction of information about an analyte from the spectra of multi-component mixtures. This method uses full spectrum realization and does not require calibration and prediction steps. Determination requires only a few measurements. The limit of detection for fluoxetine was 0.31 µg ml-1 and for sertraline was 0.20 µg ml-1. The root mean square error for fluoxetine was 0.45 and for sertraline was 0.39

Adaptive Neuro-Fuzzy Inference System (ANFIS) Applied for Spectrophotometric Determination of Fluoxetine and Sertraline in Pharmaceutical Formulations and Biological Fluid: Determination of fluoxetine and sertraline in pharmaceutical formulations and biological fluid

The UV-spectrophotometric method of analysis was proposed for simultaneous determination of fluoxetine (FLX) and sertraline (SRT). Considering the strong spectral overlap between UV-Vis spectra of these compounds, a previous separation should be carried out in order to determine them by conventional spectrophotometric techniques. Here, full-spectrum multivariate calibrations adaptive neuro-fuzzy inference system (ANFIS) method is developed.Adaptive neuro-fuzzyinference system (ANFIS) is a neuro fuzzy technique where the fusion is made between the neural network and the fuzzy inference system that is a computational method. The experimental calibration matrix was constructed with 30 samples. The concentration ranges considered were 5-120μg.mL−1fluoxetine and 10-120μg.mL−1sertraline .Absorbance data of the calibration standards were taken between 200-300nm with UV-Vis spectrophotometer. The method was applied to accurately and simultaneously determine the content of pharmaceutical in several synthetic mixtures and real samples. Assaying various synthetic mixtures of the components validated the presented methods. Mean recovery values were found to be 101.26% and 100.24%, respectively for determination of FLX and SRT