Introduction: Acute appendicitis is the most common cause of admittance of patients with abdominal pain to hospital and appendectomy is the most commonly performed emergency surgery. Despite significant advances in the field of diagnosis, a significant number of negative appendectomies are reported. In this study, the design and evaluation of artificial neural networks to help diagnose acute appendicitis was investigated.Methods: In this descriptive study, variables affecting the diagnosis were identified through literature review. Then, these variables were categorized in the form of a checklist, and evaluated and prioritized by general surgery specialists. The sample size was determined as 181 cases to train and evaluate the performance of neural networks. The database was created using records of patients who had undergone appendectomy during 2015 in Modarres Hospital, Tehran, Iran. Then, different architectures of artificial Multilayer perceptron (MLP) neural network were implemented and compared in MATLAB environment to determine the optimal diagnostic performance. Parameters such as specificity, sensitivity, and accuracy were used for network assessment.Results: Comparison of the optimal output of the MLP with pathological results showed that the sensitivity, specificity, and accuracy of the diagnosis network were 68.8%, 82%, and 78.5%, respectively. Based on the existing standards and the general surgeons’ opinions, the MLP network improved diagnostic accuracy for acute appendicitis.Conclusion: The designed MLP can model the performance of an expert with acceptable accuracy. The use of this MLP in clinical decision support systems can be useful in the reduction of negative references to medical centers, timely diagnosis, prevention of negative appendectomy, reduction of the duration of hospitalization, and reduction of medical expenses.

Among the important factors in urban planning and management, particularly in line with the achievement of the sustainable development in the urban areas as well as regarding the optimal use of the land, is on-time access to the data of land cover conditions in these regions. The remote sensing data has a high potential for the preparation of the update urban land cover maps. In order to present on-time and digital satellite data, a variety of shapes and possibility of processing during land cover maps are of high significance. In order to use the satellite photos Landsat/ETM+ and two algorithm of supervised classification including the maximum likelihood and the artificial neural network, land cover maps were prepared. During classification, the neural network algorithm of a perceptron network with a hidden layer and 7 input neurons, nine middle neurons and 4 output neurons were used. The input neurons are the same in number as the bands of the Landsat photos and the number of output neurons are the same as land cover map classes. Eventually, land cover map of the region has been classified into four classes of residential areas, barren lands, plant coverage, and roads. In order to evaluate the correctness of the classification results, many photos have been taken using GPS. Using overall accuracy and Kappa Coefficient the precision evaluation results of these two methods indicate that perceptron neural network has an overall accuracy of 98.24 and Kappa Coefficient 97.03 compared to the algorithm of maximum likelihood with an overall accuracy of 94.23 and Kappa Coefficient 90.34 is of higher precision. The findings of this study also show that the classification method for Multilayer perceptron neural network as compared with the maximum likelihood method is of higher separation and capability for preparing the land cover map in the urban regions.

This paper proposes a novel approach to analyzing and managing electricity consumption using a clustering algorithm and a high-accuracy classifier for smart meter data. The proposed method utilizes a Multilayer perceptron neural network classifier optimized by an Imperialist Competitive Algorithm (ICA) called ICA-optimized MLP, and a CD Index based on Fuzzy c-means to optimally determine representative load curves. A case study involving a real dataset of residential smart meters is conducted to validate the effectiveness of the proposed method, and the results demonstrate that the ICA-optimized MLP method achieves an accuracy of 98.62%, outperforming other classification methods. This approach has the potential to improve energy efficiency and reduce costs in the power system, making it a promising solution for analyzing and managing electricity consumption.

Introduction: Physical development of cities is inevitable and a dynamic process which will change the land cover areas. Urban growth must be led by the most appropriate land use planning. Urban land cover/use maps are used for current and future land use and urban planning. Remote sensing technology and application of satellite data in mapping land cover often will reduce costs, save time, and increase accuracy and speed. There are several methods to classify land cover. If we classify the methods of supervised classification algorithms based on complexity and accuracy, they can be divided into two main methods (the average distance to the minimum, maximum likelihood, etc.) and advanced methods (neural network, fuzzy classification methods and knowledge base methods). In support of image classification, two different methods including, Fuzzy ARTMAP classifier and Multilayer perceptron neural network classifier were used. In this study, in order to produce land cover map of Isfahan city, digital image of LISS-III scanner that was acquired on 8th August 2008 were employed.

Estimation of wave velocities is very important for designing geotechnical structures and modeling deep drillings. The purpose of this study is to estimate shear wave velocity (Vs) using Gaussian process regression (GPR), Multilayer perceptron artificial neural network ((MLP-ANN)) and multivariate linear regression (MVLR) methods. In order to carry out this study, 14 rock blocks were prepared from the northwest of Damavand city and after being transferred to the laboratory, cores were extracted from them. In order to develop a predictive model, point load index, compressional wave velocity (Vp), porosity and density tests were performed on 61 rock core samples. Point load index, Vp, porosity and density were used as input parameters of models to predict Vs. The results of lithological studies showed that the studied sandstones are feldspathic litharnite and litharnite. The results showed that the ratio of Vp to Vs is equal to 1.70 on average. The results of the (MLP-ANN) showed that the highest accuracy of the models was obtained by using the Levenberg-Marquardt training algorithm. The most accurate models were obtained using this algorithm to estimate the Vs in neuron number 2 (optimal neuron). The GPR, (MLP-ANN) and MVLR predicted Vs with correlation coefficients of 0.97, 0.96 and 0.95, respectively. GPR method showed better performance in predicting Vs than other methods.

Concerns about water quality have widely increased in the last three decades; thus, water quality is now as important as its quantity. To study and model the quality of the Gamasiab River, its data, including chemical oxygen demand (COD), biological oxygen demand (BOD), dissolved oxygen (DO), total dissolved solids (TDS), total suspended solids in water, acidity, temperature, turbidity, and cations and anions were measured at four stations. Then, the correlations between these parameters and COD were measured using Pearson’ s correlation coefficient and modeled by Multilayer perceptron artificial neural network. In order to minimize the cost of the experiments performed and to provide the input parameters to the artificial neural network based on the correlations between the data and COD, the number of input parameters was reduced and finally, model No. 3, with the Momentum training function and the TanhAxon activation function with the validation correlation coefficient of 0. 97, mean absolute error of 2. 88, and normalized root mean square error of 0. 11 was identified as the most accurate model with the lowest cost. The results of the present study showed that the Multilayer perceptron neural network has high ability in modeling the COD of the river, and those data correlated with each other have the greatest effect on the model. Moreover, the number of input parameters can be reduced in order to lower the cost of experiments while the performance of the model is not undermined.

Background: Chronic kidney disease is one of the mostcommon diseases. The early diagnosis of this disease will reduce the length of treatment and decrease high medical costs. In recent years, the use of computer techniques in data mining and intelligent algorithms has accelerated the early diagnosis of this disease. One of the intelligent methods to diagnose this disease is artificial intelligence networks. Objectives: This study aimed to investigate the diagnosis of chronic kidney disease using an artificial intelligence network based on the Multilayer perceptron method. Methods: The data of laboratory samples were collected from 140 healthy people and patients with chronic kidney disease. After preprocessing and normalization, the data were given to a Multilayer perceptron and the accuracy of disease diagnosis was evaluated. All analyses were performed using MATLAB software. Results: The simulation showed a 98% accuracy of diagnosis using the proposed model. Conclusions: The results of real data suggested that the proposed system was more effective and faster than other methods in the diagnosis of acute kidney disease and it can be used as a physician assistant tool in clinical practice. In addition, it can be a costeffective method for patients.

The purpose of the current study is to design and optimize Rosuvastatin calcium orally fast disintegrating tablet (OFDT) with the assistance of an artificial neural network (ANN) based Multi-layer perceptron (MLP) model. Rosuvastatin calcium is commonly employed as a cholesterol-lowering agent. In our previous work established literature raw material data of OFDTs were collected from 92 research articles, which contain compositional and evaluation parameters and the data trained with Machine learning techniques (ML) to evaluate the optimal ingredients which helps further to develop and optimize Rosuvastatin calcium OFDTs using ANN based MLP. Rosuvastatin calcium OFDTs were formulated according to a 32-factorial design (randomized Box-Behnken method), and formulations were compressed using the direct compression method with varying compositions of superdisintegrant (Crospovidone) 2-4% binder microcrystalline cellulose (MCC) 5-20%, Mannitol as a diluent, magnesium stearate (Mg st) as a lubricant, and talc (1-3%) as a glidant. The developed formulations were assessed to determine their thickness, hardness, friability, disintegration time, and drug content. ANN was used for optimization, and the MLP model was trained using experimental data until a satisfactory R2 of 0.99 and normalized root mean square error (NRMSE) of 0.024 was reached. The compressed tablets (F19) exceeded the desired criteria in terms of thickness (2.6mm), hardness (2.8 kg), friability (0.6%), drug content (99%), and disintegration time (36 sec). The potential use of ANN in pharmaceutical formulation optimization to achieve desired performance characteristics is demonstrated by this work. This study shows the efficacy of ANN with MLP in the development of Rosuvastatin calcium OFDTs.

Stock market forecasting is a challenging task for investors and researchers in the financial market due to highly noisy, nonparametric, volatile, complex, non-linear, dynamic and chaotic nature of stock price time series. With the development of computationally intelligent method, it is possible to predict stock price time series more accurately. artificial neural networks (ANNs) are one of the most promising biologically inspired techniques. ANNs have been widely used to make predictions in various research. The performance of ANNs is very dependent on the learning technique utilized to train the weight and bias vectors. The proposed study aims to predict daily Tehran Exchange Dividend Price Index (TEDPIX) via the hybrid Multilayer perceptron (MLP) neural networks and metaheuristic algorithms which consist of genetic algorithm (GA), particle swarm optimization (PSO), black hole (BH), grasshopper optimization algorithm (GOA) and grey wolf optimization (GWO). We have extracted 18 technical indicators based on the daily TEDPIX as input parameters. Therefore, the experimental result shows that grey wolf optimization has superior performance to train MLPs for predicting the stock market in metaheuristic-based.

Prediction of the sediment load in water resources engineering projects such as flow diversion projects and dam construction is important factor for determining their service life. In this study, a model for estimation of daily sediment discharge was proposed using Multilayer perceptron artificial neural network (ANN) model with back-propagation learning algorithm. For this purpose, current day’ s discharge (Qt), precipitation, number of day in the year (DOY) and previous day’ s discharge (Qt-1) data of Zoghal Bridge station (located on Chalus River) from 1990 to 2009 were used for training, verification and test. Results of testing different combinations of input data sets showed that effective parameters of the model performance are current discharge parameter, antecedent discharge, precipitation and DOY, respectively. This results has a relatively good agreement with standardized coefficients of regression model. Coefficient of determination (R2) and Root Mean Square Error (RMSE) were used to compare the different structures of ANN. Therefore, best network with 3-5-1 architecture and the amounts of R2=0. 89 and RMSE=0. 02 was obtained by elimination of DOY variable. The performance of ANN model in the prediction of sediment discharge was compared with Sediment Rating Curve (SRC) and Multiple Non-Linear Regression (MNLR) model. The results showed, in the training and test steps, SRC method and ANN model have the best performance, respectively. Furthermore, in the test step, the ANN model performed better results compared to two other methods by increasing R2 about 16%. Generally, the proposed ANN model can be estimated sediment discharge by less calculation time and cost and also with more accuracy.