Artificial Neural Network

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 - Predicting stock prices using data mining methods.
Mojtaba Hajigholami

This article discusses data mining methods for predicting financial markets and analyzing sustainable development in financial matters. It also examines the impact of using data mining methods in the stock market and their effectiveness in this area. The research introd More

This article discusses data mining methods for predicting financial markets and analyzing sustainable development in financial matters. It also examines the impact of using data mining methods in the stock market and their effectiveness in this area. The research introduces a machine learning approach that generates information using publicly available data and uses this information for accurate prediction. It also explores various data mining methods relevant to financial market analysis, focusing on predicting stock market movements and trends. The study demonstrates that due to the dynamic and variable nature of financial markets influenced by economic, political, and social factors, the use of machine learning and data mining methods can lead to more accurate predictions of stock price movements. Given the extensive and complex data in financial markets, data mining methods have the potential to discover hidden patterns and determine relationships between various variables. Various machine learning algorithms such as artificial neural networks, support vector machines, and random forests, alongside statistical analyses, help improve the analytical capabilities of analysts and investors in making economic decisions. Furthermore, the use of big data and complex analyses has contributed to the development of intelligent trading strategies that can help optimize returns on investments. For example, analysts can enhance the accuracy of their predictions by incorporating sentiment data from social networks into their models. The study emphasizes that sustainable development in financial markets requires a deeper understanding and more precise analysis of data, ultimately leading to stronger data-driven decision-making and trading processes. Manuscript profile

Open Access Article

3 - Future Studies for Presenting Model of Personal Differentiation Role on Guerilla Advertising with Using Perceptron Artificial Neural Network
tara taefi vahidreza Mirabi Ghasemali Bazaee soheil sarmad saeidi

Guerilla Advertising in Contrast of Definition Pattern, Attempt to Use Simplicity and Flexibility Idea For Penetrate and Attract In Customer Heart to Have Maximum Profit With Minimize their Cost. Personal Differentiation Research is so important for result some side of More

Guerilla Advertising in Contrast of Definition Pattern, Attempt to Use Simplicity and Flexibility Idea For Penetrate and Attract In Customer Heart to Have Maximum Profit With Minimize their Cost. Personal Differentiation Research is so important for result some side of this Attitude Made by Attention on personal personality. The goal of this research is Future Studies Presenting for Presenting Model of Personal Differentiation Role on Guerilla Advertising. The Purpose is Exploratory, the Method is Descriptive Survey. Two Indicator Recognize such as Attitude with psychology Motivation and technical dimension, personality With Myers-Briggs Indicator Test. Population is Go sport Customer, use classify method to choose the sample, number of Sample with Cochran Formula 277 person. For data collecting have2 Specific and closed Questioner including standard MBTI Questioner with 70 Question and another one with 25 Closed And Specified Questioner Made by Researcher , with use Perceptron Artificial Neural Network by (MATLAB Software). the result of Neural Network Get Fit Model With 92%Coefficience Confidence to Define 4 clearly strategic of Interdependencies between Guerilla Advertising and personality, Attitude Indicator for Predict and analysis the new input and select the suitable strategy. Manuscript profile

Open Access Article

4 - Developing A Fault Diagnosis Approach Based On Artificial Neural Network And Self Organization Map For Occurred ADSL Faults
Vahid Golmah Mina Tashakori

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Open Access Article

5 - Solving of stochastic Voltaire integral equations by fuzzy artificial neural network method
Hadi Abtahi Hamid Reza Rahimi Maryam mosleh

Voltaire integral equations as the output of problems in basic sciences and engineering have a special application in advancing the solution of complex problems. One of the most widely used types, which consists of a random process under external motion, is the equation More

Voltaire integral equations as the output of problems in basic sciences and engineering have a special application in advancing the solution of complex problems. One of the most widely used types, which consists of a random process under external motion, is the equations of random Volta integral. Solving this type of equation has always been a challenge for researchers. On the other hand, with the development of artificial intelligence and the presentation of fuzzy artificial neural network method as a model inspired by the process of thinking and analysis in the human brain, advanced models of algorithms have been designed. Some of these learning algorithms have been used in fuzzy artificial neural networks to solve equations. In this paper, using this method and designing a learning algorithm, the random equations of random Voltaire type is investigated. The method presented in this article, in addition to being more accurate than the previous methods, posseses more speed for solving problem. This topic provides an acceptable level of confidence for researchers when dealing with such issues. Manuscript profile

Open Access Article

6 - Measurement of Bitcoin Daily and Monthly Price Prediction Error Using Grey Model, Back Propagation Artificial Neural Network and Integrated model of Grey Neural Network
Mahdi Madanchi Zaj Mohammad Ebrahim Samavi Emad Koosha

10.22034/amfa.2020.1881110.1315

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Open Access Article

7 - Option pricing with artificial neural network in a time dependent market
Mehran Araghi Elham Dastranj Abdolmajid Abdolbaghi Ataabadi Hossein Sahebi Fard

In this article, the pricing of option contracts is discussed using the Mikhailov and Nogel model and the artificial neural network method. The purpose of this research is to investigate and compare the performance of various types of activator functions available in ar More

In this article, the pricing of option contracts is discussed using the Mikhailov and Nogel model and the artificial neural network method. The purpose of this research is to investigate and compare the performance of various types of activator functions available in artificial neural networks for the pricing of option contracts. The Mikhailov and Nogel model is the same model that is dependent on time. In the design of the artificial neural network required for this research, the parameters of the Mikhailov and Nogel model have been used as network inputs, as well as 700 data from the daily price of stock options available in the Tehran Stock Exchange market (in 2021) as the net-work output. The first 600 data are considered for learning and the remaining data for comparison and conclusion. At first, the pricing is done with 4 commonly used activator functions, and then the results of each are com-pared with the real prices of the Tehran Stock Exchange to determine which item provides a more accurate forecast. The results obtained from this re-search show that among the activator functions available in this research, the ReLU activator function performs better than other activator functions. Manuscript profile

Open Access Article

8 - Financial Reporting Readability: A new Artificial Neural Network and Multi-Indicator Decision Making Approach
Ali Asghar Khazaei Harivand Arash Naderian Majid Ashrafi Ali Khozin

The desirability of the financial reporting can greatly help the users of finan-cial information in making investment decisions. The purpose of this re-search is to measure the readability of financial reporting using a multi-indicator decision-making model and the arti More

The desirability of the financial reporting can greatly help the users of finan-cial information in making investment decisions. The purpose of this re-search is to measure the readability of financial reporting using a multi-indicator decision-making model and the artificial neural network method and the role of information presentation time in its improvement. In this research, various indicators have been used to measure the readability of financial reporting, and the quality of reporting is obtained through the rank-ing of companies by the stock exchange. In this research, the number of 149 companies admitted to the Tehran Stock Exchange in the period of 2010-2020 was examined, and to measure the financial readability through struc-tural equations and Stata software, and to test the hypothesis of the research, the regression model and Eviews econometrics software were used. In this study, we have tried to Use machine learning techniques and optimization tools as a way to derive adaptive-robust nonlinear models that can reduce the risk of model error as much as possible. The findings of the research show that the time of providing information has an impact on the readability of financial reporting. The obtained outputs from the estimation of the artificial neural networks and results obtained from estimation, using of this method with evaluation scales concerning random amount and comparing it with adjusted R, we found that there is meaningful relation between the associated variables and return. However, such network has the least error than other networks. The results show an overall improvement in forecasting using the neural network as compared to linear regression method. In other words, our proposed system displays an extremely higher profitability potential. The obtained result can be argued that the more the company's information is provided by the managers to the company's shareholders and investors on time and at the right time, the more readable and understandable the financial reports will be. Manuscript profile

Open Access Article

9 - Improving the Performance of Forecasting Models with Classical Statistical and Intelligent Models in Industrial Productions
Maryam Bahrami Mehdi Khashei Atefeh Amindoust

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Open Access Article

10 - Predicting Hook -Shaped and Concrete Steel Fibers Adhesion Parameters Using Artificial Neural Networks
amir ebrahim akbari baghal Amir ebrahim akbari bagal

Given the importance of using steel fibers in reinforcement of concrete, in the present study using artificial neural networks to predict the behavior of hook -shaped steel fibers from concrete. Due to the constraint of comprehensive laboratory data, data obtained from More

Given the importance of using steel fibers in reinforcement of concrete, in the present study using artificial neural networks to predict the behavior of hook -shaped steel fibers from concrete. Due to the constraint of comprehensive laboratory data, data obtained from limited element analysis has been used as neural network input. The fibers are used to simulate the fiber and the Abacus software. In the limited element model, the interactions between fibers and concrete are simulated using the concept of the transitional area of the common surface whose parameters are obtained using the reversed limited element method and the use of the out -of -the -way experimental test results on a fiber sample. After assessment of the numerical model results with the empirical results, the results were extracted for effective parameters of the fibers and based on them using neural networks. Forecasting of the outburst has been carried out by the Multi-Layer Artificial Neural Networks and the Rear Publishing Algorithm, with Marcoradet-Clberg optimization techniques. The results show that the neural network model presented in this study, due to the ability to use more variables in modeling and more accurate results, is an effective way to predict the fiber's extrusion force. Manuscript profile

Open Access Article

11 - An integrated data envelopment analysis–artificial neural network approach for benchmarking of bank branches
Elsa Shokrollahpour Farhad Hosseinzadeh Lotfi Mostafa Zandieh

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Open Access Article

12 - Modeling and forecasting US presidential election using learning algorithms
Mohammad Zolghadr Seyed Armin Akhavan Niaki S. T. A. Niaki

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Open Access Article

13 - .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

10.30495/fed.2023.1962198.2734

. 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

14 - Microgrid Planning Including Renewables Considering Optimum Compressed Air Energy Storage Capacity Determination Using HANN-MDA Method
Seyedamin Saeed Tahere Daemi Zohreh Beheshtipour

Microgrids, with their ability to integrate renewable energy sources, play a crucial role in achieving sustainable and resilient energy systems. Effective planning and optimization of microgrids, particularly considering the inclusion of compressed air energy storage (C More

Microgrids, with their ability to integrate renewable energy sources, play a crucial role in achieving sustainable and resilient energy systems. Effective planning and optimization of microgrids, particularly considering the inclusion of compressed air energy storage (CAES) systems, are essential for maximizing their benefits. This study proposes a novel approach, the Hybrid Artificial Neural Network-Modified Dragonfly Algorithm (HANN-MDA), for determining the optimum capacity of CAES in microgrid planning. The HANN-MDA method combines the learning capabilities of artificial neural networks with the optimization power of the modified dragonfly algorithm. The proposed method aims to minimize the overall cost of microgrid operation while considering the integration of renewable energy sources and the storage capabilities of CAES. Simulation results demonstrate the effectiveness of the HANN-MDA method in accurately determining the optimal CAES capacity, leading to improved microgrid performance and cost savings. The findings highlight the importance of considering CAES in microgrid planning and the potential of the HANN-MDA method for achieving efficient and economically viable microgrid designs. Manuscript profile

Open Access Article

15 - Designing non-linear pattern contagious influence of the Tehran Price Index from the physical assets market (Application of NARX artificial neural network model)
mahdi shaban habibollah nakhaei Ghodrat Alloh Talebnia nazanin bashirimanesh

The present study examines the contagiousness of the Tehran Stock Exchange from the price of parallel assets using the dynamic neural network. To perform calculations, the time series of coin price variables as a representative of the gold market, the average price per More

The present study examines the contagiousness of the Tehran Stock Exchange from the price of parallel assets using the dynamic neural network. To perform calculations, the time series of coin price variables as a representative of the gold market, the average price per square meter of residential building as a representative of the housing market. The price of each barrel of Iranian crude oil and the US dollar exchange rate and their conditional fluctuations as explanatory variables and the total index of Tehran Stock Exchange and its conditional fluctuation as the target variable from 1387 to 1397 are examined daily .The dynamic neural network is evaluated with four input variables and one target variable with different neurons with the MSE criteria, and the models with 20 neurons and 10 neurons have the lowest MSE, .Research results show that the stock exchange has a maximum of two lag from competing markets has become contagious, indicating the poor performance of the Tehran Stock Exchange. The results show that the proposed neural network patterns have a high power in predicting the index of Tehran Stock Exchange and its fluctuations from 1387 to 1397 as in-sample forecast and in 1398 as extra-sample forecast. Manuscript profile

Open Access Article

16 - Optimization of technical indicators’ parameters for intraday data using optics – inspired optimization (OIO): a case study of Tehran stock exchange
Mohammad Ali Rastegar Farah Ashuri

In this paper a stock trading system based on the combination of six technical indicators is designed. The indicators are combined using an artificial neural network and their parameters are optimized using convex combination-based optics-inspired optimization (COIO) al More

In this paper a stock trading system based on the combination of six technical indicators is designed. The indicators are combined using an artificial neural network and their parameters are optimized using convex combination-based optics-inspired optimization (COIO) algorithm. In the proposed model the technical indicators’ optimized parameters are obtained using both COIO and genetic algorithms with the aim of maximization of modified Sharpe ratio. The presented paper uses stock intra-day prices as input data and considers the transaction costs. The designed strategy is compared against several other approaches including: using the indicators’ default parameters, buy and hold strategy and optimization using genetic algorithm, for both daily and intra-day prices and due to a greater modified Sharpe ratio for the proposed model, its superiority is shown in all cases. Moreover, in a comparison based on end- of- period returns, it is shown that without considering the transaction costs the results of the intra-day data beats the results of the daily data while no superiority is observed when considering the transaction costs. So reducing the transaction costs is recommended to motivate traders to trade on an intra-day basis. Manuscript profile

Open Access Article

17 - The Modeling of Exchange Rate Predict in Iran by Using Neural Network Based on Genetic Algorithms and Particle Swarm Algorithm
ali jamali saeed daie karimzadeh

In recent years the use of artificial intelligence techniques in the financial and investment markets instead of customary quantitative methods has been increasing and gives better performance towards classic methods usually. Artificial Neural Network (ANN), has weakn More

In recent years the use of artificial intelligence techniques in the financial and investment markets instead of customary quantitative methods has been increasing and gives better performance towards classic methods usually. Artificial Neural Network (ANN), has weaknesses points despite its enormous benefits also. In this study, in order to overcome the weaknesses of the network consists of combining artificial intelligence methods with Evolutionary algorithms, means of artificial neural network combined with genetic algorithm (GA) and Particle Swarm algorithm (PSO) to model and daily predict of nominal exchange rates or the exchange rate dollar by Rial in Iran in the period 21.03.2013 to 22.12.2019 is used. This combined model with neural networks method as one artificial intelligence model according to the criteria of MSE , RMSE, MAE, U.Theil compared. The results of this research show the superiority of synthetic neural network model -Particle Swarm algorithm compare to other models of investigation. Manuscript profile

Open Access Article

18 - Precipitation-runoff Simulation with Neural Network(Case study: Nasa Bam Plain)
mehdi shahrokhi sardoo mojtaba jafari kermanipour

Short-term runoff forecasting is of particular importance due to its direct relationship with how managers interact with life risks caused by floods. In this research, by using artificial neural networks, simulation of rainfall-runoff process has been done on a daily ba More

Short-term runoff forecasting is of particular importance due to its direct relationship with how managers interact with life risks caused by floods. In this research, by using artificial neural networks, simulation of rainfall-runoff process has been done on a daily basis in the Nasa Bam watershed. In order to predict the future process of using the water resources of the mentioned plain, different combinations of rainfall and temperature data and discharge and discharge difference of two consecutive days were used. The number of hidden layer neurons in the neural network varied between 2 and 10 neurons. The statistical criteria of root mean square error RMSE, mean absolute value of error MAE and correlation coefficient R were used to evaluate and compare the performance of neural networks in runoff forecasting. The results showed that by having 2 inputs and feedforward neural network or 1 input and newrbe network, the best performance was achieved and the rainfall-runoff process was predicted with higher accuracy. Manuscript profile

Open Access Article

19 - Prediction of Adhesion Parameters of Hook-shaped Steel Fibers and Concrete Using Artificial Neural Networks
Amir ebrahim akbari bagal

As steel fibers are important reinforcement materials in concrete, in this study, the behavior of hook-shaped steel fibers from concrete is predicted through the use of artificial neural networks. In the absence of comprehensive laboratory data, data obtained from finit More

As steel fibers are important reinforcement materials in concrete, in this study, the behavior of hook-shaped steel fibers from concrete is predicted through the use of artificial neural networks. In the absence of comprehensive laboratory data, data obtained from finite element analysis was used for modeling. The simulations are carried out using ABAQUS software's finite element method in 3D. Using the concept of the transition zone of the interface, whose parameters were obtained by inverse finite element analysis and experimental tests conducted on a sample of fibers, this model has been developed to simulate the interaction between fibers and concrete. On the basis of the results of the numerical model validated against the experimental results, the effective parameters of the fibers were extracted, and a neural network was then constructed based on the results. A multilayer forward perceptron artificial neural network and back-propagation training algorithm are used to predict pull-out force, with Marquardt-Lonberg optimization applied. The results demonstrate that the neural network model presented in this research is an effective method for predicting the pull-out force of fibers from concrete, in part because it allows the use of more variables in modeling, as well as delivering more accurate results. Manuscript profile

Open Access Article

20 - Prediction of compressive strength of concretes containing micro silica subject to carbonation using neural network
Ali Delnavaz

Concrete materials are exposed to special weather conditions, corrosion and significant damage. For this purpose, the effect of 28-day compressive strength changes on the samples studied in this study was investigated by considering the simultaneous effect of chloride i More

Concrete materials are exposed to special weather conditions, corrosion and significant damage. For this purpose, the effect of 28-day compressive strength changes on the samples studied in this study was investigated by considering the simultaneous effect of chloride ion penetration and carbonation phenomenon. For this reason, in the first case, the samples are exposed to carbon dioxide once and then to chloride ions. In the latter case, only samples under the influence of chloride infiltration are examined. To make the samples, which include 9 mixing designs, three water-to-cement ratios of 0.35, 0.4 and 0.5 and three percent of 0%, 7% and 10% silica fume have been used. Finally, an optimal model is introduced to predict the compressive strength of concrete containing micro silica exposed to carbonation using artificial neural network. Also, a relation for estimating compressive strength based on the ratio of water to cement and the amount of silica is presented. Manuscript profile

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