Artificial Neural Network (ANN

Open Access Article

1 - A new method based on texture analysis for the classification of automatic detection of breast microcalcifications of mammography images
Zahra Maghsoodzadeh Sarvestani Jasem Jamali mhdi taghizadeh Mohammad h Fatehi

Mammography is a diagnostic technology used in screening programs to find breast cancer early. By using two techniques for image enhancement and highlighting breast tissue microcalcifications for the desired areas by regional ROI based on fuzzy system and also Gabor fil More

Mammography is a diagnostic technology used in screening programs to find breast cancer early. By using two techniques for image enhancement and highlighting breast tissue microcalcifications for the desired areas by regional ROI based on fuzzy system and also Gabor filtering method, the study's objective was to assess the viability of automatic separation of images of breast tissue microcalcifications and to assess its accuracy. The decision tree classification algorithm is used to categorize the clusters of breast tissue microcalcifications after the clusters have been identified. The samples that are thought to have microcalcification are next highlighted and masked for segmentation, and in the last step, tissue properties are extracted. Then, it was possible to distinguish between benign and malignant forms of segmented ROI clusters with the aid of an artificial neural network (ANN). The results of this work show a high accuracy of 93% and an improvement of sensitivity of 95%, which shows that the presented solution can be reliably applied to detect breast cancer.. Manuscript profile

Open Access Article

2 - Water Quality Index Estimation Model for Aquaculture System Using Artificial Neural Network
Taliha Folorunso Musa Aibinu Jonathan Kolo Suleiman Sadiku Abdullahi Orire

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Open Access Article

3 - پیش بینی نرخ ارز در بازار سرمایه با استفاده از مدل های میانگین متحرک خود رگرسیون انباشته و شبکه عصبی )مطالعه موردی: دلار استرالیا، دلار کانادا، ین ژاپن و پوند انگلستان(
Mohammad Ehsanifar Reza Ehtesham Rasi

Monetary policy in order to prevent losses arising from changes in exchange rates of disruptive are Always trying to find a suitable method to predict exchange rates. However, multi-dimensional characteristics of the converter makes it is complicated and nonlinear behav More

Monetary policy in order to prevent losses arising from changes in exchange rates of disruptive are Always trying to find a suitable method to predict exchange rates. However, multi-dimensional characteristics of the converter makes it is complicated and nonlinear behavior. One of the traditional methods of forecasting, time series analysis, which is based on two as sumptions static linearity. Some doubts about the performance of these traditional models have been created One of the alternative methods, artificial neural networks that In some cases are shown a good potential for time series prediction. In this Article , After reviewing the research conducted to clarify the predictive ability of mass moving average models and Artificial Neural Networks to compare The two methods for the prediction of the daily exchange rate has been made in the period from 01.01.1990 till 01.01.2012. The results showed that the neural network approach estimates the Autoregressive Integrated Moving Average (ARIMA) method provides better responses. In this study, MATLAB software and computational tools and data STATGRAPHICS economies of Australia, Canada, Japan and the United Kingdom, and the dollar exchange rate in those countries than in America is using. Manuscript profile

Open Access Article

4 - Comparison of artificial neural network and multivariate linear regression (MLR) models to predict cover percentage Artemisia aucheri from some soil properties
Mansoreh Kargar Zeynab Jafarian

Soil ecosystems management for different purposes requires accurate and quantitative understanding of the soil characteristics and their processes. This study was aimed to predict Artemisia aucheri cover though some soil physical and chemical properties in Vavsar rangel More

Soil ecosystems management for different purposes requires accurate and quantitative understanding of the soil characteristics and their processes. This study was aimed to predict Artemisia aucheri cover though some soil physical and chemical properties in Vavsar rangeland, Kiasar, Mazandaran province. Random systematic sampling was used. Five transects with 100 m length and 10 plots 4 m2 on each transect were established. Then cover (%) of A. aucheri and 50 soil sample from 0-15 cm depth was estimated in each plot. Soil properties including soil organic carbon, total nitrogen, EC, water percentage, CaCo3 percentage, soil texture, and pH were measured. Data were divided in two series: a series for analysis including 70% of the data for and 30% for evaluation of customized models. Result showed that soil water, silt and sand percentages were the most important soil properties for prediction A. aucheri cover in the study area. Prediction of the statistical models in the study area resulted in mean error and root mean square error values of 0.25, 0.06 for ANN equation and 0.43, 0.12 for MLR, respectively. Therefore, the ANN model could provide superior predictive performance when was compared with MLR model. Manuscript profile

Open Access Article

5 - Optimizing the exploitation of the Shahryar plain aquifer by simulating the groundwater flow using the combined modeling method
Nima Salehi Shafa Hossein Babazadeh Fayaz Aghayari Ali Saremi MohammadReza Ghafouri Masoud Safavi Ali Panahdar

10.30495/jrnr.2022.70366.10272

In this study, a multi-objective simulation model was presented and six scenarios were defined based on the groundwater budget for optimal exploitation of Shahryar plain aquifer. For this purpose, first, using the GIS based models and using fuzzy and weight methods, the More

In this study, a multi-objective simulation model was presented and six scenarios were defined based on the groundwater budget for optimal exploitation of Shahryar plain aquifer. For this purpose, first, using the GIS based models and using fuzzy and weight methods, the regions with high water and low water in the study area were identified in order to prioritize resources and uses. Then the desired scenarios were simulated and evaluated by the (GMS) model. Finally, in order to increase the accuracy of the research results, the (TDS) concentration and the groundwater budget were simulated using the artificial neural network model (ANN). According to the results of fuzzy and weighted methods, the areas of Rabat Karim, then Islamshahr and finally Shahryar were identified as low water areas. Based on the obtained results, the unsteady state budget and verification were calculated as -344.68 and -109.98 (MCM), respectively. The third scenario with a budget of 203.33 (MCM) was chosen as the best scenario and the budget resulting from that, ratio to the budget resulting from the model (GMS) and the neural network for the year 95, has increased by 284.87 percent and 284.83 percent respectively. Also, the concentration of total dissolved solids in the groundwater obtained from the desired models in the entire study period was estimated by 655 and 651 mg/liter on average. The criteria of correlation coefficient and determination coefficient obtained from neural network models for groundwater budget and total data were estimated to be equal to one and for the total dissolved solids concentration of groundwater were estimated to be 0.997 and 0.994, respectively. In the present research, the multi-objective simulation pattern as a comprehensive and practical method by providing new simulation methods has the ability to support several effective scenarios and leads to increase the stability of the groundwater system. Manuscript profile

Open Access Article

6 - Prediction of Carbon Monoxide Concentration in Tehran using Artificial Neural Networks
Hamid Reza Jeddi Rahim Ali Abbaspour Mina Khalesian Seyed Kazem Alavipanah

Background and Objective: Nowadays, air pollution is one of the most important problems almost all over the world. There are many strategies to control and reduce air pollution, one of which is prediction of this event and getting ready to deal with the negative effects More

Background and Objective: Nowadays, air pollution is one of the most important problems almost all over the world. There are many strategies to control and reduce air pollution, one of which is prediction of this event and getting ready to deal with the negative effects of it. The aim of this study is to provide a multi-layer structure of artificial neural networks (ANN) for predicting of carbon monoxide pollution at subsequent 24 hours in Tehran metropolis. Method: To predict the amount of CO emissions in near future (subsequent 24 hours), wind speed and direction, temperature, relative humidity, and barometric pressure characteristics are used as meteorological data, and concentration of carbon monoxide is considered as a pollution parameter. To eliminate the noise of data, wavelets transform method and determining the threshold with normal distribution are used before training the ANN. Finally, two neural networks as two general models are proposed and used for modelling. Findings: The results show that the correlation coefficient, index of agreement, accuracy of prediction, and root mean square error for model no. 1 with duplicate data are 0.9012, 0.915, 0.848, and 0.1012 and for model no. 2 with duplicate data are 0.9572, 0.978, 0.963, and 0.0385 respectively. Moreover, the results of listed parameters for model no. 1 with new data are 0.9086, 0.89, 0.885, and 0.0825 and for model No. 2 with new data are 0.8678, 0.928, 0.932, and 0.1163 respectively. Conclusion: Results showed that there is a good agreement between predicted and observed values, hence the proposed models have a high potential for air pollution prediction. Manuscript profile

Open Access Article

7 - Predicting bankruptcy of companies listed on the Stock Exchange using the artificial neural network
Mohsen Vaez-Ghasemi Saeid Ramezanpour Chardeh

Being informed of capital market’s companies financial situation is one of the shareholders and economic analysts’ perturbation. Thus, financial market analysts and researchers were looking for methods to predict capital market’s company’s future More

Being informed of capital market’s companies financial situation is one of the shareholders and economic analysts’ perturbation. Thus, financial market analysts and researchers were looking for methods to predict capital market’s company’s future conditions. This research is finding a model to predict bankruptcy of stock exchanges market’s companies with using the artificial neural network. In this research we used Zemijewski financial ratios with one macro – economic variable to predict companies’ bankruptcy. Population of study was selected from the accepted companies in Iran’s stock and exchanges organization. Financial ratios have been extracted from companies’ financial statement in a five years’ period between 2010 and 2014, finally we choose 84 companies that divided to salubrious and bankrupt equal number in each. We used multi-layer perceptron (MLP) with back propagation algorithm to create predictor model and data analysis. The network has been trained once with financial ratios and again with additional macro – economic variable to confirm that the accuracy of network model will increase by additional macro – economic variable. Ultimately the designed model in total mode has 92.95 percent of accuracy and 85 percent correct prediction of bankrupted companies for one year earlier of bankruptcy.   Manuscript profile

Open Access Article

8 - Comparative Study and Robustness Analysis of Quadrotor Control in Presence of Wind Disturbances
Reham Mohammed

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Open Access Article

9 - Assessment of efficiency of artificial neural network in predicting the trend of desertification processes by using GIS (Case study: Dehloran plain, Ilam)
Soraya Yaghoobi Marzban Faramarzi Haji Karimi Javad Sarvarian

Desertification is recognized as a main problem in the arid and semi-arid areas. Therefore, identification and prediction of the effective factors in development of desertification are very important for better management of these areas. The main purpose of this study w More

Desertification is recognized as a main problem in the arid and semi-arid areas. Therefore, identification and prediction of the effective factors in development of desertification are very important for better management of these areas. The main purpose of this study was evaluating the accuracy of an artificial neural network model for predicting the desertification process and selects the most effective criteria on desertification in the Dehloran plain by using the Iranian model for desertification potential assessment (IMDPA). In IMDPA model, water and climatic were selected as effective factors in desertification. In this model, three indicators for climate criteria; annual precipitation, drought index (Standardized precipitation index; SPI and continued drought and for water criteria; ground water table depletion, sodium absorption ratio, Cl, electrical conductivity (EC) and total dissolved solids were evaluated. Each index was rated using of IMDPA model. Then desertification intensity and criteria maps were prepared using a geometric average for predicting period in ArcGIS®9.3. Final data were entered into neural network to predict. The results showed that the neural network model has a high efficiency for predicting the desertification process in the study area. The accuracy of the model was about 80% and mean square error (MSe) was less than one. In addition, the climate factor and the index of EC were found the most effective variables for predicting the desertification process. In 2015-2016 predicted the most important probable criteria affecting the intensity of desertification were  climate  and water with weighted average 2 (moderate in sub-class1, 2 and 3), 1.84 (moderate in sub-class 1and 2), respectively. Manuscript profile

Open Access Article

10 - Spectral discrimination of important orchard species using hyperspectral indices and artificial intelligence approaches
Mohsen Mirzaie Mozhgan Abbasi Safar Marofi Eisa Solgi Roohollah Karimi

Study spectral reflectance through spectral indices allows the optimal use of the wide range of spectral wavelengths in hyperspectral data. The purpose of this study was to introduce and evaluate the performance of spectral indices to discriminate dominant orchard speci More

Study spectral reflectance through spectral indices allows the optimal use of the wide range of spectral wavelengths in hyperspectral data. The purpose of this study was to introduce and evaluate the performance of spectral indices to discriminate dominant orchard species in Chaharmahal Bakhtiari province. In this study, 150 spectral curves were measured in the range of 350 to 2500 mm, from grapes, walnuts and almond trees. After the initial correction, 30 of the most important spectral indices were extracted. Analysis of variance and comparisons of meanings was applied to identify the optimal indices for species discrimination at a 99% confidence level. Then, an artificial neural network (ANN) and support vector machine (SVM) approaches were used to evaluate the performance of indices in species discrimination. ANOVA results indicated that the Moisture Stress Index (MSI), Band ratio at 1,200 nm, normalized phaepophytiniz index (NPQI) and cellulose absorption index (CAI) indices are optimal for discrimination of the studied species. The performance evaluation of the introduced indicators in some of the ANN and SVM enhancement structures has been associated with 100% accuracy in both education and testing, which shows the effectiveness of these studies in distinguishing orchard species. The performance evaluation of the introduced indicators has been validated at 100% in both training and testing stages. This result emphasizes the necessity of performing spectroscopic studies to separate the orchard species before analyzing the hyperspectral images due to their large data volume, high cost and huge data analysis. Manuscript profile

Open Access Article

11 - Modeling and zoning water quality parameters using Sentinel-2 satellite images and computational intelligence (Case study: Karun river)
Kazem Rangzan Mostafa Kabolizade Mohsen Rahshidian Hossein Delfan

Considering the progress made in remote sensing technology, collecting information on the quality of surface water resources by this technology, while reducing the cost and time of traditional sampling, can monitor all surface water zones. In this study, the Sentinel-2 More

Considering the progress made in remote sensing technology, collecting information on the quality of surface water resources by this technology, while reducing the cost and time of traditional sampling, can monitor all surface water zones. In this study, the Sentinel-2 satellite images were used to estimate the concentration of acidity, bicarbonate and sulfate parameters. Initially, Sentinel-2 satellite images were pre-processing and then bands and spectral indexes were determined to identify the significant relationship between the parameter values of water quality and images using the multivariate regression method. In the next stage, using Artificial neural network (ANN) and Adaptive Neuro fuzzy inference system (ANFIS) models, the relationship between Sentinel-2 satellite images and water quality parameters were modeled and then their accuracy was calculated for real values. The results showed that in the modeling of sulfate parameter using Sentinel-2 satellite, ANFIS model with relative error equal to 0.0773 and RMSe equal to 0.8014 has a higher accuracy compared to ANN models with relative error equal to 0.1581 and RMSe equal to 1.2477. While, the relative error of the results of the ANN model are obtained 0.0064 and 0.0556 for acidity and bicarbonate parameter, respectively, and RMSe is equal to 0.0702 and 0.2691, respectively.  The ANFIS model has a relative error of 0.0165 and 0.0722, and RMSe is 0.1975 and 0.3037 for acidity and bicarbonate parameter, respectively. Finally, using satellite images, the mentioned models were applied to prepare a qualitative map of each parameter along the part of the Karun river. Manuscript profile

Open Access Article

12 - Landslide hazard zonation using artificial neural network (Case study: Sepiddasht-Lorestan, Iran)
Syamak Bharvand Salman Soori

This study was carried out to determine the relative hazard zonation of the slope instabilities and landslide occurrence in Sepiddasht, Iran. The method of Artificial Neural Network with the multiple-layer percepteron structure and the back propagation learning algorith More

This study was carried out to determine the relative hazard zonation of the slope instabilities and landslide occurrence in Sepiddasht, Iran. The method of Artificial Neural Network with the multiple-layer percepteron structure and the back propagation learning algorithm were used. In order to study the stability of the slopes, the landslides of the region were initially identified and recorded using satellite images of TM and ETM+, aerial images of 1:50,000, and field surveys (year, 2014). The impact of each factor including slope, aspect, land use, elevation, lithology, precipitation, the distance from the fault road and drainage on the slope instabilities was estimated using the ArcGIS®10.1 software via combining the map of the factors influencing the landslide with the landslide distribution map. Then a proper structure (1-13-9) for the landslide hazard zonation of Sepiddasht region was obtained through training the artificial neural network by MATLAB software. Based on the results of the landslide hazard zonation, 0.18, 12.41, 14.09, 29.85, and 43.52 percent of the region were located in very low, low, medium, high, and very high risk classes respectively. Manuscript profile

Open Access Article

13 - Forecasting the Profitability in the Firms Listed in Tehran Stock Exchange Using Data Envelopment Analysis and Artificial Neural Network
Maryam Saberi Mohammad Reza Rostami Mohsen Hamidian Nafiseh Aghami

10.22034/amfa.2016.527823

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Open Access Article

14 - Using Neural Network to Control STATCOM
Mozhgan Balavar

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Open Access Article

15 - Evaluate the performance of the organization using two integrated approaches the DEA-BSC and ANN-DEA
Reza Ehtesham Rasi Eisa Naji

Considering the importance of efficiency in advancing communities and the place that today's organizations have embraced, the use of performance evaluation has become an inevitable necessity. Therefore, we are trying to investigate the effectiveness of organizations by More

Considering the importance of efficiency in advancing communities and the place that today's organizations have embraced, the use of performance evaluation has become an inevitable necessity. Therefore, we are trying to investigate the effectiveness of organizations by combining the two DEA-BSC systems and neural networks in this study. In this regard, first, the performance indicators were determined by using the Balanced Scorecard technique in four dimensions of customer, internal processes, learning and growth and financial, then using the data envelopment analysis method and non-radial SBM model and GAMS software, the organization's efficiency we calculated. In the next step, with the help of the indicators derived from the DEA-BSC method and using the combination of two systems of data envelopment analysis and neural network and using the MATLAB software, the efficiency was re-calculated. In the last step, we compared the two methods of DEA-BSC and ANN-DEA. The results of the comparison of the two methods indicated the compatibility of the two models in discussing the efficiency and superiority of the ANN-DEA method in terms of the short response time and the determination of the efficiency and the possibility of using Its trained algorithm to measure the performance of organizational units in the future. Manuscript profile

Open Access Article

16 - Prediction of Corrosion Rate for Carbon Steel in Soil Environment by Artificial Neural Network and Genetic Algorithm
Amir Akhtari-Goshayeshi Moslem Ghobadi Ehsan Saebnoori Alireza Zarezadeh Mohammad Rostami Mohammad Nematollahi

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17 - Reduction of Sub-synchronous Resonances with D-FACTS Devices using intelligent Control ,
Zahra Amini Abbas Kargar

When a turbine–generator set connect to a long transmission line, may results side effects such as Sub-Synchronous Resonances (SSR). The capabilities of the Distributed Static Series Compensator (DSSC) as a member of the family of D-FACTS can be used to reduce the More

When a turbine–generator set connect to a long transmission line, may results side effects such as Sub-Synchronous Resonances (SSR). The capabilities of the Distributed Static Series Compensator (DSSC) as a member of the family of D-FACTS can be used to reduce these SSR. To achieve this desired goal, the fuzzy controller, Particle Swarm Optimization (PSO) and artificial neural network is used to control of the DSSC. Particle swarm optimization is designed Based on the Conventional Damping Controller (CDC) and fuzzy logic is designed based on damping controller (FLBDC) and damping control based on artificial neural network trained using the fast pace of changes has been designed. Stability of the system is analysed by simulations in the time domain with performance index (PI). All simulations are done using Matlab / Simulink software. Case studies show that proposed algorithms can reduce SSR in the system.All simulations are done using Matlab / Simulink software. Case studies show that proposed algorithms can reduce SSR in the system. Manuscript profile

Open Access Article

18 - Partial Discharge Analysis in Generator Stator Winding Using Artificial Neural Network
Seyed Meisam Ezzati Faramarz Faghihi Ali Moarefian poor

This paper analyses the Partial Discharge (PD) on the stator terminals of synchronous generator. This is necessary to collect experimental data for the analysis. First, exploiting the measurement devices, special signals that describe the partial discharges are repeated More

This paper analyses the Partial Discharge (PD) on the stator terminals of synchronous generator. This is necessary to collect experimental data for the analysis. First, exploiting the measurement devices, special signals that describe the partial discharges are repeatedly collected. Then, based on the current standards, the collected empirical data are subjected to interpretation. To ease the interpretation process, an Artificial Neural Network is trained and validated. We have used a double layers forward perceptron neural network which is trained by Levenberg–Marquardt algorithm that utilizes least square method as the performance index. As the case study, three gas turbine-generators located in Shahre-Rey power plant (Rey Power Generation Management Company) have been subjected to repeatedly data collection. The mentioned generators are manufactured by Mitsubishi with 85 MW of nominal power. Generally, partial discharge analysis has the following practical implication about the probable defects: lamination of the internal terminal, mobility within the main insulation and discharge into the groove in stator of synchronous generator. Manuscript profile

Open Access Article

19 - مدل ترکیبی شبکه‌ی‌ عصبی و تحلیل پوششی داده ها برای ارزیابی کارایی عملکرد واحدها
صادق حیدری احسان زنبوری حمید پروین

کایی و ارزیابی یکی از اصلی‌ترین و مهم‌ترین نیاز های سازمان‌ها، شرکت‌ها و موسسات می‌باشد و این سازمان ها چون با حجم زیادی از داده سر و کار دارند. تحلیل پوششی داده‌ها روشی مناسب برای کارایی و ارزیابی عملکرد سازمان‌ها می‌باشد. این تحقیق برای ارزیابی عملکرد و کارایی واحدهای More

کایی و ارزیابی یکی از اصلی‌ترین و مهم‌ترین نیاز های سازمان‌ها، شرکت‌ها و موسسات می‌باشد و این سازمان ها چون با حجم زیادی از داده سر و کار دارند. تحلیل پوششی داده‌ها روشی مناسب برای کارایی و ارزیابی عملکرد سازمان‌ها می‌باشد. این تحقیق برای ارزیابی عملکرد و کارایی واحدهای تصمیم گیرنده انجام گرفته است، ابتدا رویکردی با مدل BCC خروجی محور رتبه‌بندی واحدهای کارا در قالب مدل‌های تحلیل پوششی داده‌ها مورد بررسی قرار گرفت و ضعف مدل، از نظر محاسبه و تفکیک کارایی مشخص گردید سپس برای از بین رفتن این مشکلات از روش ترکیبی تحلیل پوششی داده‌ها مدل BCC خروجی محور و شبکه عصبی مصنوعی به منظور ارزیابی کارایی این واحدها استفاده گردید تا بتوان این مشکل را بر طرف نمود. در پایان نیز مقایسه‌ای بین نتایج حاصل از دو مدل انجام گرفته است. با توجه به مقدار کارایی بدست آمده با روش bcc خروجی محور، مشاهده می گردد تعدادی از واحدها مقدار کارایی آنها برابر با یک است که این باعث می‌گردد نتوانیم این واحدها رتبه بندی نماییم. اما با استفاده از روش پیشنهادی Neuro-DEA هیچ دو شعبه ای دارای مقدار کارایی برابر نبوده و با توجه به کارایی بدست آمده به راحتی می توان این واحد ها را ارزیابی و رتبه بندی نمود. Manuscript profile

Open Access Article

20 - Development of a Wavelet Hybrid Models for Estimating Regional Droughts in Siminehroud Basin
Erfan Rostam Zade alireza parvishi

In the present study, the drought of Siminehroud basin was investigated by intelligent Support Vector Machine (SVM) models, Artificial Neural Network (ANN) and Wavelet theory (W). Data from six rain gauge stations in the region were used and drought index was calculated More

In the present study, the drought of Siminehroud basin was investigated by intelligent Support Vector Machine (SVM) models, Artificial Neural Network (ANN) and Wavelet theory (W). Data from six rain gauge stations in the region were used and drought index was calculated in four time scales. The first-order autocorrelation was also selected as the optimal delay. Then the appropriate structure of the Artificial Neural Network was determined using Trial and Error Method and the three coefficients of the SVM model were determined and modeled. The results of evaluating individual models showed that there is no significant difference between two methods in predicting droughts. Then WANN and WSVM hybrid models were prepared. The results showed that the application of Wavelet theory greatly improved the performance of individual models and the amount of RMSE and MAE indices decreased by 19% and 21% and the correlation coefficient increased by 30%, respectively. Manuscript profile

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21 - Expectation of Chamomile Fundamental Oil Abdicate by Using the Artificial Neural Network System
Nazanin Khakipour Mahtab Payandeh

The 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 More

The 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

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22 - Town trip forecasting based on data mining techniques
Mohammad Fili Majid Khedmati

10.30495/jiei.2020.678774

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23 - Long-term Streamflow Forecasting by Adaptive Neuro-Fuzzy Inference System Using K-fold Cross-validation: (Case Study: Taleghan Basin, Iran)
Reza Esmaeelzadeh Alireza Borhani Dariane

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24 - Hourly Wind Speed Prediction using ARMA Model and Artificial Neural Networks
Farzaneh Tatari Majid Mazouchi

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25 - On the dynamic stability of a flying vehicle under the follower and transversal forces
Omid Kavianipour Majid Sohrabian

This paper deals with the problem of the instability regions of a free-free uniform Bernoulli beam consisting of two concentrated masses at the two free ends under the follower and transversal forces as a model for a space structure. The follower force is the model for More

This paper deals with the problem of the instability regions of a free-free uniform Bernoulli beam consisting of two concentrated masses at the two free ends under the follower and transversal forces as a model for a space structure. The follower force is the model for the propulsion force and the transversal force is the controller force. The main aim of this study is to analyze the effects of the concentrated masses on the beam instability. It is determined that the transverse and rotary inertia of the concentrated masses cause a change in the critical follower force. This paper also offers an approximation method as a design tool to find the optimal masses at the two tips using an artificial neural network (ANN) and genetic algorithm (GA). The results show that an increase in the follower and transversal forces leads to an increase of the vibrational motion of the beam which is not desirable for any control system and hence it must be removed by proper approaches. Manuscript profile

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