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Open Access Article
1 - Short-term prediction of carbon monoxide gas concentration in the air of Ahvaz city using artificial neural network analysis
Maryam Kavosi سیما سبزعلی پور hossein fathianIntroduction: Air pollution in cities is one of the most critical environmental problems, representing a constant and severe threat to both the health and hygiene of society and the environment. The primary air pollutants include nitrogen oxides, with a particular empha MoreIntroduction: Air pollution in cities is one of the most critical environmental problems, representing a constant and severe threat to both the health and hygiene of society and the environment. The primary air pollutants include nitrogen oxides, with a particular emphasis on nitrogen dioxide, sulfur oxides, especially sulfur dioxide, hydrocarbons, carbon monoxide (CO), carbon dioxide, and suspended particles. Ahvaz, a metropolis in Iran, stands out as one of the most polluted cities. Effective environmental management, particularly in addressing air pollution, is of paramount importance. This research aims to predict the concentration of CO pollutants in Ahvaz city for the first seven days of 2015. Materials and Methods: Based on previous studies, meteorological variables including weather, air temperature and wind speed were selected as gas input titles in the network for gas prediction. CO gas was procured in 2014 through the Environmental Protection Organization of Ahvaz city. In order to develop the Multilayer Perceptron (MLP) neural network, Neuro Solution5 software was used to create the neural network, 70% of the data was used for training (validation), 15% for testing, and the remaining 15% for validating the results of the network. is used. was used. Results and Discussion: In order to determine the best MLP network structure for short-term prediction of CO gas concentration, different structures were considered in terms of the number of intermediate layers, the type of network training algorithm, the type of transfer function, the number of intermediate layer neurons and the number of repetitions (Epoch) of training. The results showed that the MLP network with a structure of 1-5-3 (that is, 3 input neurons, 5 neurons in the middle layer and one neuron for the output layer) with 1500 repetitions of training per Tansig transfer function (Tansant Sigmoid) and Traingdm training algorithm (reduction gradient with momentum), is the best MLP network. In addition, the values of NSE, RMSE and MAE statistical indices for the network training stage are equal to 0.72, 0.22 and 0.15 respectively. Conclusion: Air pollution, the primary environmental challenge in Ahvaz, arises from the intersection of traffic and the oil industry. Its impacts on health and the environment necessitate comprehensive investigation. In this study, an MLP network was employed to predict CO gas concentration values in the air of Ahvaz city. The findings demonstrate that the network's accuracy and performance in forecasting CO gas concentration are at an optimal level. As this research progresses, it is recommended to extend the prediction to other gaseous pollutants and to employ optimization algorithms for determining the optimal structure of the artificial neural network Manuscript profile -
Open Access Article
2 - Monte Carlo simulations of the magnetic properties of a site-disordered Blume Capel multilayer spin-1 system
Amel Benmansour Nabil Brahmi Smaine Bekhechi Abdeljalil Rachadi Hamid Ez-Zahraouy -
Open Access Article
3 - Developing A Fault Diagnosis Approach Based On Artificial Neural Network And Self Organization Map For Occurred ADSL Faults
Vahid Golmah Mina Tashakori -
Open Access Article
4 - Developing a Stock Technical Trading System Integrating MLP Neural Network with Evolutionary Algorithms
Alireza Saranj Ahmadreza Ghasemi Asghar Eram Reza TehraniStock trading systems development using evolutionary algorithms over the past few years has become a hot topic in financial fields. In this paper, an intelligent technical trading system was proposed using a combination of MLP neural network and evolutionary algorithms MoreStock trading systems development using evolutionary algorithms over the past few years has become a hot topic in financial fields. In this paper, an intelligent technical trading system was proposed using a combination of MLP neural network and evolutionary algorithms (i.e., GA, ACOR, and PSO). In order to select the final variables as the selected features, a return comparison of each indicator ratings was used based on tradings. Finally, the performance of each model is tested in comparison with the buy and hold strategy. The results show that the evolutionary learning algorithms significantly outperform the benchmark models in terms of the average return and the hybrid MLP_PSO model outperforms others. Manuscript profile -
Open Access Article
5 - Numerical simulation of multilayer cellular scaffolds with 3D and 1D elements
Hamid Reza khanaki Sadegh Rahmati Mohammad Nikkhoo Mohammad Haghpanahi Javad Akbari -
Open Access Article
6 - Multilayer Perceptron Approach in Breast Cancer Diagnosis
Emine Avşar Aydin Gözde Saribaş -
Open Access Article
7 - Investigation of the land potential of Kermanshah province for rainfed wheat cultivation using artificial neural network
Milad Bagheri Mohammadreza Jelokhani Noaryki Kayvan BagheriWith increasing population growth and the need for food, wheat as the crop with the largest cultivated area and annual production on a global scale has been especially important. Therefore, identifying and recommending suitable areas for cultivation in each area is esse MoreWith increasing population growth and the need for food, wheat as the crop with the largest cultivated area and annual production on a global scale has been especially important. Therefore, identifying and recommending suitable areas for cultivation in each area is essential. Kermanshah province as the study area is one of the areas that most wheat crops are from among. Therefore, in this study Multilayer Perceptron Neural Network (MLP) with Levenberg-Marquardt algorithm was used to identify the potential of rainfed wheat cultivation. The input layer network consists of 12 layers: land use, average annual rainfall, average rainfall in the autumn, the average spring rainfall, the average annual temperature, average temperatures in spring, average temperatures in autumn, slope, aspect, elevation, humidity the relative and degree of days. The rainfall and temperature layers were prepared using the data from the stations of adventurous and synoptic and the interpolation operation in the ArcGIS environment, respectively. The altitude-related layer was extracted using with a DEM 30×30 meter IRS. To determine the search space of the neural network algorithm, the uncultivated areas are determined and removed from the entire input layers. 210 points of The right place to cultivate were prepared as network training points. Finally, the class of uncultivated areas which 15% and The results of the model consists of five classes: very suitable, suitable, somewhat suitable, poor or very poor, respectively, 5.4, 14.8, 24, 22.5 and 18.3 percent of the total area of the province is allocated. Regression analysis of all data on the network is 91% of the network of the company, effective for the MLP neural network is in these zoning. Manuscript profile -
Open Access Article
8 - Deforestation modeling using artificial neural network and GIS (Case study: forests of Khorramabad environs)
Hassan Mahmoudzadeh Majid AzizmoradiIn this research, occurred changes in the forests around Khorramabad between 1986 and 2018 using TM and OLI Landsat images were investigated. For this purpose, after making the necessary atmospheric and geometric corrections, the images were classified by the maximum li MoreIn this research, occurred changes in the forests around Khorramabad between 1986 and 2018 using TM and OLI Landsat images were investigated. For this purpose, after making the necessary atmospheric and geometric corrections, the images were classified by the maximum likelihood algorithm in five classes with a total accuracy of 95% and a kappa coefficient of 0.94. By overlaying the images, the amount of lost forest (34 km2) was determined and as a dependent variable was imported into the multilayer perceptron (MLP) model. In the GIS environment, were prepared the effective factors in the process of deforestation (independent variables); then by using MLP, the deforestation process in the years under review was determined. It was also land use changes was extracted that the results show the highest changes belonged to the forest to barren land changes and finally the deforestation forecast for 10, 20 and 30 years displays a decrease of 4.6% for the year 1407, 7.5% for the year 1417 and 9.3 for 1427. The results of the network training involving all variables with mean squared error (RMS) of 0.13 indicate that the MLP-based modeling is accurate and also, using Receiver Operating Characteristic (ROC) index, the real amount of deforestation was compared to the result of the MLP model; which showed the high accuracy of the MLP model with 0.88 of the ROC. Manuscript profile -
Open Access Article
9 - Drought prediction and modeling by hybrid wavelet method and neural network algorithms
Jahanbakhsh Mohammadi Alireza Vafaeinezhad Saeed Behzadi Hossein Aghamohammadi Amirhooman HemmasiBackground and Objective A drought crisis is a dry period of climate that can occur anywhere globally and with any climate. Although this crisis starts slowly, it can have a serious impact on health, agricultural products, the economy, energy, and the environment for a MoreBackground and Objective A drought crisis is a dry period of climate that can occur anywhere globally and with any climate. Although this crisis starts slowly, it can have a serious impact on health, agricultural products, the economy, energy, and the environment for a long time to come. Drought severely threatens human livelihood and health and increases the risk of various diseases. Therefore, modeling and predicting drought is one of the most important and serious issues in the scientific community. In the past, mathematical and statistical models such as simple regression, Auto-regression (AR), moving average (MA), and ARIMA were used to model the drought. In recent years, machine learning methods and computational intelligence to model and predict drought have been of great interest to scientists. Computational intelligence algorithms that have been previously considered by scientists to model drought include multilayer perceptron neural network, RBF neural network, support vector machine, fuzzy, and ANFIS methods. In this research, the purpose of modeling and predicting drought is by using three neural network algorithms, including multilayer perceptron, RBF neural network, and generalized regression neural. The drought index used in this research is the standardized precipitation index (SPI). In this research, the wavelet technique in combination with artificial neural network algorithms for modeling and predicting drought in 10 synoptic stations in Iran (Abadan, Babolsar, Bandar Abbas, Kerman, Mashhad, Rasht, Saqez, Tehran, Tabriz, and Zahedan) have been used in different climates and with suitable spatial distribution throughout Iran.Materials and Methods This study, initially using monthly precipitation data between 1961 and 2017, SPI drought index in time scales of 3, 6, 12, 18, 24, and 48 months through programming in soft environment MATLAB software implemented. The results of this step were validated using the available scientific software MDM and Drinc. Then, prediction models were designed using the Markov chain. In this study, a total of six computational intelligence models, including three single models of multilayer perceptron neural network (MLP), radial basis function neural network (RBF), and generalized regression neural network (GRNN), and three hybrids wavelet models with these three models (WMLP-WRBF-WGRNN) have been used to model and predict the SPI index in 10 stations of this research. In implementing all these six models, the MATLAB software programming environment has been used. In this study, four types of discrete wavelets were used, including Daubechies, Symlets, Coiflets, and Biorthogonal. Due to the better performance of the Dobbies wavelet, this type of wavelet was used as a final option in the research. In the Daubechies wavelet used between levels 1 to 45, level 3 showed the best performance among different SPI time scales; therefore, the Daubechies level 3 wavelet was used in all hybrid models of this study. After training all six algorithms used, the evaluation criteria of coefficient of determination (R2) and root mean square error (RMSE) was used to measure the difference between actual and estimated values.Results and Discussion The results of this study showed that computational intelligence methods have high accuracy in modeling and predicting the SPI drought index. In the first stage, the results showed that the individual MLP, RBF, and GRNN models, if properly trained, have close results in modeling and predicting the SPI drought index. In the next step, it was observed that the wavelet technique would improve the modeling results. In using the wavelet technique in combination with three single models MLP, RBF, and GRNN, the choice of wavelet type is also more effective in modeling, so in this research, the first of the four types of discrete wavelets Daubechies, Symlet, Qoiflet, and Biorthogonal in combination with Three single models of this research were used and the results of these four types of wavelets showed the relative superiority of the Daubechies wavelet over the other three wavelets. In using the Daubechies wavelet, since this wavelet has 45 times and the choice of order was also effective in modeling, it was observed by testing the wavelet 45 times that the 3rd wavelet, in general, has higher accuracy in all time scales of SPI index, 3, 6, 12, 18, 24 and 48 months and also in all three algorithms MLP, RBF, and GRNN. Therefore, in this research, the third-order Daubechies wavelet was used in all three algorithms of this research, as well as in all time scales. The results showed that combining the wavelet technique with all three models MLP, RBF, and GRNN will improve the results. The research graphs showed that for the quarterly time scale, the values obtained from the single model prediction in MLP and RBF modeling have a somewhat one-month phase difference compared to the hybrid model, while in the GRNN model, this prediction difference is negligible. The modeling results for both single and hybrid modeling modes indicate that there is no phase difference between the single and hybrid modeling methods in time scales of 6, 12, 18, 24, and 48. For the 12- and 24-month time scales, the single GRNN model had more fluctuations and errors in SPI monthly modeling and forecasting, while the hybrid model in these two-time scales had much better behavior in monthly modeling and forecasting. Distribution diagrams of data related to observational SPI of Abadan station showed that the modeling results for single and hybrid modes in 3 and 6-month time scales are less accurate than other time scales and fit line separation, and its uncertainty is higher than others. However, in all neural network models and in all time scales, the hybrid method has shown more accuracy. The numerical results of the study indicate that in all SPIs and stations under study, the differential values of R2 are positive, which indicates higher values of R2 in the hybrid model than in single neural network modeling, which indicates an improvement in hybrid modeling compared to individual models. Also, the differential values of RMSE are negative in all studied models and stations, which indicates that the amount of RMSE in predicting hybrid models is lower than individual neural network models. In the research graphs, it can be seen that the amount of differences in RMSE and R2 indicates a greater difference in time scales 3 and 6 than the time scales 12, 18, 24, and 48, which somehow goes back to the nature of the data of these time scales. The most significant improvement in R2 and RMSE is from the 3-month low to the 48-month high, respectively.Conclusion From the findings of this study, it can be concluded that artificial neural network algorithms are efficient methods for modeling and predicting the SPI drought index. The use of wavelets in all three models of artificial neural networks will also improve the results. It can also be concluded that for better modeling of the SPI drought index, it is necessary to select the optimal wavelet type and order. From the results of this study, it can be concluded that the wavelet technique has a greater impact on the lower time scales, i.e., 3 and 6 months, than the higher scales, i.e., 24 and 48 months. Manuscript profile -
Open Access Article
10 - Thermoelastic Behaviour in a Multilayer Composite Hollow Sphere with Heat Source
S.P Pawar J.J Bikram G.D Kedar -
Open Access Article
11 - Effect of Thermosensitivity on Heat Conduction and Stresses of a Multilayered Annular Disk
G.D. Kedar V.B. Srinivas V.R Manthena -
Open Access Article
12 - Bending Analysis of Multi-Layered Graphene Sheets Under Combined Non-Uniform Shear and Normal Tractions
M.M Alipour M Shaban -
Open Access Article
13 - Delamination of Two-Dimensional Functionally Graded Multilayered Non-Linear Elastic Beam - an Analytical Approach
V Rizov -
Open Access Article
14 - The Comparison of Applying a Designed Model to Measure Credit Risk Between Melli and Mellat Banks
Ardeshir Salari Hamidreza Vakilifard Ghodrat-Allah Talebnia -
Open Access Article
15 - Optimization of drilling fluid effectiveness based on fluid mechanics in well drilling process of one of the squares in the southwest of Iran
maryam sadat ghavami kuros nekufar Seyed Arash Seyed Shams Taleghani maryam sadat ghavamiThe speed of drilling operations has a direct effect on drilling costs and various parameters such as drilling fluid properties and hydraulic drilling affect it. Therefore, it is very important to use models with different parameters that have high accuracy. Because the MoreThe speed of drilling operations has a direct effect on drilling costs and various parameters such as drilling fluid properties and hydraulic drilling affect it. Therefore, it is very important to use models with different parameters that have high accuracy. Because the relationship between these parameters is complex, a computational method is needed. Artificial neural network is a new computational method for learning that is used to predict the output responses of complex systems. In this paper, the neural network is used to predict the drill penetration rate by considering the parameters of drilling fluid and multilayer artificial intelligence models and radial base are used to detect and predict drilling speed as output parameters. Manuscript profile -
Open Access Article
16 - Defects Detection of Rotating Machine Using Vibration Analysis and Neural Network
Seyed Majid Ataei ArdestaniThe base of diagnosing the possible defects of a machine is comparing the frequency ‎spectra of the vibrations at different points with the existing reference spectra. Due to the ‎needless stoping of machine for investigation of its various parts, use of this &l MoreThe base of diagnosing the possible defects of a machine is comparing the frequency ‎spectra of the vibrations at different points with the existing reference spectra. Due to the ‎needless stoping of machine for investigation of its various parts, use of this ‎troubleshooting method is affordable; Also, regarding to progress of possible ‎defectes, the machine can be rapaired in any required times. In this study , using ‎Neural Network (MLP and FNN), firstly common defects in rotating machines were created ‎separately, then the produced vibrational frequency were measured by ADASH 4400 ‎analyzer. Introducing four vibrational characteristics including angular misalignment, ‎clearance, failure and unbalance of bearing as input data of artificial neural network ,the ‎results were compared to the reference frequency signals. The results show that neural ‎networks MLP and FNN increase the defects detection ability by 73% and 78%, ‎respectively. So, FNN method is proposed for useful life prediction and detection of rotating ‎parts.‎ Manuscript profile -
Open Access Article
17 - Entrepreneurship policy and innovative indicators of industrial companies: Evaluation by MCDM and ANN Methods
mehdi karimi farshid namamian farhad vafaei Alireza Moradi -
Open Access Article
18 - Land Use Mapping of Sabzevar using Maximum Likelihood and Artificial Multilayer Perceptron Neural Network
Elahe Akbari Majid Ebrahimi Abolghasem AmirAhmadiAmong 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 re MoreAmong 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. Manuscript profile -
Open Access Article
19 - Evaluation of Starting Current of Induction Motors Using Artificial Neural Network
Iman Sadeghkhani Ali Reza SadoughiInduction motors (IMs) are widely used in industry including it be an electrical or not. However during starting period, their starting currents are so large that can damage equipment. Therefore, this current should be estimated accurately to prevent hazards caused by i MoreInduction motors (IMs) are widely used in industry including it be an electrical or not. However during starting period, their starting currents are so large that can damage equipment. Therefore, this current should be estimated accurately to prevent hazards caused by it. In this paper, the artificial neural network (ANN) as an intelligent tool is used to evaluate starting current peak of IMs. Both Multilayer Perceptron (MLP) and Radial Basis Function (RBF) structures have been analyzed. Six learning algorithms, backpropagation (BP), delta-bar-delta (DBD), extended delta-bar-delta (EDBD), directed random search (DRS), quick propagation (QP), and levenberg marquardt (LM) were used to train the MLP. The simulation results using MATLAB show that most developed ANNs can estimate the starting current peak of IMs with good accuracy. However, it is proven that LM and EDBD algorithms present better performance for starting current evaluation based on average of relative and absolute errors. Manuscript profile -
Open Access Article
20 - Common Spatial Pattern for Human Identification Based on Finger Vein Images in Radon space
Akram Gholami Hamid Hassanpour -
Open Access Article
21 - Expectation of Chamomile Fundamental Oil Abdicate by Using the Artificial Neural Network System
Nazanin Khakipour Mahtab PayandehThe aim of this research was to forecast the proportion and production of chamomile essential oils by employing an artificial neural network system reliant on specific soil physicochemical characteristics. Various chamomile cultivation sites were explored, and 100 soil MoreThe aim of this research was to forecast the proportion and production of chamomile essential oils by employing an artificial neural network system reliant on specific soil physicochemical characteristics. Various chamomile cultivation sites were explored, and 100 soil samples were transported to the greenhouse. The pH, EC, K, OM (organic matter), CCE (calcium carbonate equivalent), and clay content in the soils ranged from 8.75 to 7.94, 1.6 to 1.0, 381 to 135, 2.30 to 0.22, 69 to 16, and 55.6 to 32.0, respectively. Growth parameters, essential oil percentage, and yield were measured. The artificial neural network modeling aimed to predict essential oil concentration and yield using three sets of soil properties as predictors: Nitrogen (N), phosphorus (P), potassium (K), and clay; pH, EC, organic matter (OM), and clay; CCE, clay, silt, sand, N, P, K, OM, pH, and EC. Consequently, three pedotransfer functions (PTFs) were formulated using the multi-layer perceptron (MLP) with the Levenberg-Marquardt training algorithm to estimate chamomile essential oil content. The evaluation of results indicated that the third PTF (PTF3), developed using all independent variables, exhibited the highest accuracy and reliability. Furthermore, the findings suggested the feasibility of predicting chamomile essential oil concentration and yield based on soil physicochemical properties. This has significant implications for land suitability assessments, identifying areas conducive to chamomile cultivation, and planning for essential oil yields. Manuscript profile -
Open Access Article
22 - MLP learning-based landslide susceptibility assessment for Kurdistan province, Iran
Mohammad Vand Jalili -
Open Access Article
23 - Step change point estimation in the multivariate-attribute process variability using artificial neural networks and maximum likelihood estimation
Mohammad Reza Maleki Amirhossein Amiri Seyed Meysam Mousavi -
Open Access Article
24 - مطالعه عددی و تجربی پدیده گوشوارهای شدن در کشش عمیق فنجان مربعی از ورق دولایهی Al-Br
محمدحسین محمودیانیکی از مهمترین روشهای شکلدهی ورق، کشش عمیق ورقهای فلزی میباشد. کشش عمیق بطور گستردهای در تغییر شکل ورقهای فلزی و تبدیل آن به ورقهای تو خالی به کار میرود. در این پژوهش کشش عمیق ورقهای ساخته شده از دو جنس برنج و آلومینیم ابتدا شبیه سازی میشود و با استفاده Moreیکی از مهمترین روشهای شکلدهی ورق، کشش عمیق ورقهای فلزی میباشد. کشش عمیق بطور گستردهای در تغییر شکل ورقهای فلزی و تبدیل آن به ورقهای تو خالی به کار میرود. در این پژوهش کشش عمیق ورقهای ساخته شده از دو جنس برنج و آلومینیم ابتدا شبیه سازی میشود و با استفاده از آزمایشهای عملی مورد تحقیق و بررسی قرار گرفته است.ساختار کلی رساله به این شکل است که ابتدا سوابق و پژوهشهایی که در داخل و خارج کشور در زمینه کشش عمیق ورقهای دو لایه تا به حال انجام شده را معرفی کرده و نتایج بدست آمده مرور شده است. در ادامه آزمایشهای عملی و بررسی تجربی کشش عمیق ورقهای دو جداره و پارامترهای موثر در این فرآیند پرداخته شده است. جهت تایید نتایج شبیه سازی تعدادی آزمایش که به دو بخش آزمایشهای مقدماتی و اصلی تقسیم شده انجام میشود. همچنین، نحوه انجام شبیه سازی توسط نرم افزار آباکوس به صورت مرحله به مرحله بیان شده است.نتایج حاصل از پژوهشهای انجام شده توسط نرم افزار آباکوس و آزمایشها عملی نشان میدهد که نحوه چیدن لایهها در قالب بر روی چروکیدگی ورقها اثر میگذارد. همچنین، مشخص شده که مقدار نیروی رابطه عکس با میزان چین خوردگی دارد. در انتها پیشنهاداتی برای ادامه کار بر روی این موضوع ارائه شده است. Manuscript profile -
Open Access Article
25 - .Application of Meta-Heuristic Algorithms in Predicting Financial Distress using intra-corporate (Financial and non-financial) and Economic Variables (Grasshopper Optimization and Ant Colony Algorithms)
فریدون مرادی احمد یعقوب نژاد آزیتا جهانشاد. Abstract The purpose of this study is investigating the capability of Grasshopper Optimization Algorithm (GOA) in more accurately predicting the financial distress by-using intra-corporate (financial and non-financial) and economic variables. The method of this rese More. Abstract The purpose of this study is investigating the capability of Grasshopper Optimization Algorithm (GOA) in more accurately predicting the financial distress by-using intra-corporate (financial and non-financial) and economic variables. The method of this research is improving the performance of the basic model of Multilayer Perceptron Artificial Neural Network (ANN-MLP) by-using a hybrid model with GOA (MLP-GOA) and Ant Colony Optimization Algorithm (MLP-ACO). The statistical research population of companies active in Tehran Stock Exchange during a 7-year period (from 1391 to 1397) included 476 companies, and finally, after systematic elimination, there were 289 qualified companies (including 2023 observation year-company). Checked and screened. The results showed the ability of ANN-MLP model to predict financial distress by-using financial and non-financial variables, and in addition the hybrid models (MLP-GOA and MLP-ACO) had been improved this ability. The accuracy of the MLP-GOA model for the year t, year t-1and year t-2 (before financial distress occurs), respectively are 97.30%, 94.53% and 91.30% that higher than the accuracy of the basic model and the hybrid MLP-ACO model. Although, entering the economic variables has increased the capability of all models significantly but the results showed that the financial distress is more affected by intra-corporate variables and the effect of economic variables has already been considered through the effect on financial events recorded in the accounting system. The results of this study can be used by company managers, banks and rating and credit institutions, insurance companies, financial analysts, investors and investment companies in assessing the risk of financial distress to make appropriate decisions and actions. Manuscript profile -
Open Access Article
26 - Robot control system using SMR signals detection
faeze asadi -
Open Access Article
27 - New full adders using multi-layer perceptron network
Reza Sabbaghi Leila Dehbozorgi Reza Akbari-Hasanjani -
Open Access Article
28 - Hourly Wind Speed Prediction using ARMA Model and Artificial Neural Networks
Farzaneh Tatari Majid Mazouchi -
Open Access Article
29 - Credit risk optimization model for crowdfunding process by using Neural Network(MLP)
ALI MALEKI Ali Zare Hashem NiKoumaram Shadi ShahverdianiThe purpose of this study is predict and design Credit risk model for debut crowdfunding .According, the complexity of the risk assessment the best neural network architecture with Customize hidden layer neurons selected Multilayer perceptron algorithm for simulation. T MoreThe purpose of this study is predict and design Credit risk model for debut crowdfunding .According, the complexity of the risk assessment the best neural network architecture with Customize hidden layer neurons selected Multilayer perceptron algorithm for simulation. The statistical population of this study is the financial information of credit / loan file of all customer (506 cases) one of the banks of the country for the year 1997-98. In order to show the significant relationship the extracted indices of the sample and the model output variables (non-default and default), the sample member tested by regression.Thus, thirteen indices entered to the model neural network input vector with three hidden layers in non-default and default groups. In the simulation results, the proposed model was able to optimize the weights of each of the inputs to the network with lower prediction error and 94.1% efficiency .also the average error absolute value obtained for training data (0.88), test data (0.94) and evaluation data (0.84) indicating high capability of the proposed model. According to the research Results, among the indices, income, 0.163 weight, Current Account weight 0.123 are more important, but “degree of education of education” 0.053 are less important in the non-defaulted group. Manuscript profile -
Open Access Article
30 - Providing a Model for Selecting the Optimal Stock Portfolio Using Salp Swarm Algorithm and Multilayer Perceptron Neural Networks
Seyed Ali Hoseini zahra pourzamani Aَzita JahanshadThe most important courses are the ones that are taught and the one that is taught and the ones that are taught are the ones that work for each other, in order to make the most profit.In our research, it can be seen that all sorts of solutions are one of the solutions, MoreThe most important courses are the ones that are taught and the one that is taught and the ones that are taught are the ones that work for each other, in order to make the most profit.In our research, it can be seen that all sorts of solutions are one of the solutions, but the concept of skewness should be considered in the future as well. In the first twenty-first of the first fifty years of 2019, the stock market is given as an example..Evolution is also a model in which the future potential of stocks is predicted by the multilayer perceptron neural network with several scenarios, including the prediction of the stock price time series method itself or the prediction of the impact of factors influencing stock price changes. The results show that the models presented in this article, compared to traditional methods, provide investors with and achieve the optimal formation of the portfolio by selecting the appropriate shares of companies. Manuscript profile -
Open Access Article
31 - Providing a model for predicting stock prices using ultra-innovative neural networks
Seyyed Hosein Miralavi zahra pourzamaniDue to the complexity of the stock market and the high volume of processable information, often using a simple system to predict cannot release appropriate results. Therefore, researchers have been trying to provide a system with less complexity and more efficiency and MoreDue to the complexity of the stock market and the high volume of processable information, often using a simple system to predict cannot release appropriate results. Therefore, researchers have been trying to provide a system with less complexity and more efficiency and accuracy using hybrid models. nowadays various patters are used including statistical technique (discriminate analysis , logistic , analysis factors) and artificial intelligent techniques ( neural networks(NN) , decision trees , case based reasoning , genetic algorithm , rough sets , support vector machine , fuzzy logic ) and the combination of these two technique for predicating stock prices. For most predictive models, the system uses only one indicator to predict, but in the proposed model in this study, a two-level system of multilayered perceptron neural networks is presented which uses several indicators to predict. To do this, required information of Tehran Stock Exchange price indicators, for fiscal years 2012 - 2017 was collected. We also used the Grasshopper Optimization Algorithm to select the best samples for better nerve network training and thus to improve the results. The results show that the proposed model can operate with lower prediction error than other models. Manuscript profile -
Open Access Article
32 - تغییر مکان استاتیکی تیرهای پیزوالکتریک چندلایه دو سر لولا تحت بارگذاریهای مختلف
افشین منوچهریفر علیرضا جلیلیدر این مقاله ابتدا معادلات ساختاری پیزوالکتریکها بیان شده و با بکارگیری این معادلات، انرژی داخلی تیر چند لایه پیزوالکتریک محاسبه شده است. سپس با ترکیب اصل حداقل انرژی پتانسیل و روش رایلی- ریتز، رابطهای برای جابهجایی تیر پیزوالکتریک دو سر لولا، تحت ممان متمرکز، نیروی Moreدر این مقاله ابتدا معادلات ساختاری پیزوالکتریکها بیان شده و با بکارگیری این معادلات، انرژی داخلی تیر چند لایه پیزوالکتریک محاسبه شده است. سپس با ترکیب اصل حداقل انرژی پتانسیل و روش رایلی- ریتز، رابطهای برای جابهجایی تیر پیزوالکتریک دو سر لولا، تحت ممان متمرکز، نیروی متمرکز، بار گسترده و ولتاژ خارجی، به طوریکه شرایط مرزی را ارضا کند حدس زده شده است و ضرایب مجهول با حداقل کردن انرژی پتانسیل بهدست آمده است. در ادامه روابط بهدست آمده برای تیرهای بایمورف و یونیمورف دو سر لولا ساده شده و بار الکتریکی و همچنین ولتاژ ایجاد شده در آنها در حالت حسگری به دست آمده است. جهت اطمینان از صحت و دقت روابط تحلیلی بهدست آمده، نتایج تحلیلی با نتایج نرمافزار ANSYS 10 در قالب مثالهای عددی مقایسه شده است. Manuscript profile -
Open Access Article
33 - ارزیابی عملکرد ابزار با پوشش های چند لایه نانوکریستال در ماشین کاری سوپر آلیاژ اینکونل 718
رسول مختاری همامی بهروز موحدی ایرج لیرابی مهدی بازرگان حقیقیدر این مقاله، عملکرد ابزار با پوشش­های چند لایه نانوکریستال با ترکیب TiN+TiAlN برای ماشین­کاری سوپر آلیاژ پایه نیکل اینکونل 718 در شرایط برشکاری خشک و مرطوب مورد مطالعه قرار گرفته است. برای این منظور پوشش چند لایه TiN و TiAlN با ساختار نانوکریستال توسط فرایند رس Moreدر این مقاله، عملکرد ابزار با پوشش­های چند لایه نانوکریستال با ترکیب TiN+TiAlN برای ماشین­کاری سوپر آلیاژ پایه نیکل اینکونل 718 در شرایط برشکاری خشک و مرطوب مورد مطالعه قرار گرفته است. برای این منظور پوشش چند لایه TiN و TiAlN با ساختار نانوکریستال توسط فرایند رسوب فیزیکی بخار و با روش قوس تبخیری بر اینسرتهایی با ترکیب کاربید تنگستن- کبالت اعمال شد. نتایج حاصل از آزمون سایشی بال بر دیسک و همچنین ماشین­کاری سوپر آلیاژ پایه نیکل اینکونل718 در شرایط برشکاری خشک و مرطوب نشان داد که نانوکریستال بودن لایه­ای، عملکرد بسیار عالی را برای ابزارها در حین ماشین­کاری بهوجود میآورد. وجود کریستال­ها یا دانه­هایی در ابعاد40-15 نانومتر همراه با درصد بهینه­ای از آلومینیوم در پوشش TiAlN، مقاومت سایشی بالا ایجاد میکند و ترکیب مناسبی از مقاومت به سایش خراشان و چسبندگی به زیر لایه به همراه چقرمگی و پایداری حرارتی مطلوب را فراهم مینماید. Manuscript profile -
Open Access Article
34 - Comparison of Tribological Behavior of Single-Layer and Multilayer Electroless Nickel-Phosphorus Coatings in the Presence of Al2O3 and SiC Reinforcing Particles
A.I. Abduljaleel Al Rabeeah M. Razazi BoroujeniDue to their morphology, chemical composition, and phase structure, electroless nickel-phosphorus coatings are used on various substrates, including st37 steel, with the aim of improving working life in various industries. The latest generation of these coatings is the MoreDue to their morphology, chemical composition, and phase structure, electroless nickel-phosphorus coatings are used on various substrates, including st37 steel, with the aim of improving working life in various industries. The latest generation of these coatings is the multilayer or hybrid type of nickel-phosphorus electroless coatings. In this research, for the first time, a three-layer Ni-P/Ni-P-Al2O3/Ni-P-SiC coating was produced and its tribological properties were investigated. X-ray diffraction test, energy beam spectrometer, optical and electron microscope images, hardness measurement, roughness measurement, adhesion test (according to ASTM B571 standard), and pin-on-disk wear (according to ASTM-G99 standard) were used for characterization. In the X-ray diffraction pattern related to the multilayer coating, in addition to the amorphous nickel-phosphorus phase, SiC and Al2O3 phases were also seen. The hardness of multilayer coating was 126 Vickers more than that of single-layer coating. The adhesion of all the coatings was very good, so after performing the bending test, no galling was observed in the coatings. In general, it was found that the use of multi-layer coating compared to single-layer coating (with the same thickness) leads to increased hardness, better adhesion, and superior wear behavior. The wear mechanism of the coatings was also evaluated with the help of electron microscope images and energy-dispersive X-ray spectroscopy. The wear mechanism of the electroless nickel-phosphorus coating was delamination and Abrasive, while the hybrid coating changed the mechanism to adhesive by creating a gradient of mechanical properties and lubrication. Manuscript profile
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