فراکتال

Open Access Article

1 - Comparison of Fractal Geometry and Kriging Methods to Estimate the Effect of Length Scale on Dispersivity of Reactive Elements in Soil
Yasser Hosseini Behrouz Mehdi nejadiani

10.22034/jest.2017.10624

Background and Objectives: Hydrodynamic dispersion rate of solutes in soil is considered as the major parameter for pollution and solutes transport in soil, which is related to pollutant transport distance. As fractal geometry theory and geostatistical theory are capabl More

Background and Objectives: Hydrodynamic dispersion rate of solutes in soil is considered as the major parameter for pollution and solutes transport in soil, which is related to pollutant transport distance. As fractal geometry theory and geostatistical theory are capable of explaining and predicting the distance-related phenomena, this research used fractal geometry and geostatistics method for determining dispersivity. Methods: Solutes transport experiment was carried out at 16 points of soil vertical column with a diameter of 10 centimeters and a length of 1 meter and BTCs were extracted at the depth of 6, 12, 18, 24, 30, 36, 42, 54, 48, 60, 66, 72, 78, 90, 84, 96 centimeters from the model bottom. CDE equation was then fitted with the BTCs with respect to the fractal assumptions on dispersivity coefficients. Findings: With respect to phosphorus absorption experiments in soil, phosphorus adsorption isotherm had the best fitting at 4, 12, 25, 50, 70 mg/l of phosphorus concentrations. The results showed that both methods are capable of predicting changes and increase of dispersivity coefficient in soil column after performing a mean-comparison test. However, fractal geometry method estimated values at a higher accuracy.  Discussion and Conclusion: Result showed that, dispersivity along the sample followed the exponential relation. The regression coefficients of the fractal and geostatistical models in predicting dispersivity values were 0.97 and 0.84, respectively. Manuscript profile

Open Access Article

2 - 11
Nahid Mohajeri

11

11 Manuscript profile

Open Access Article

3 - The difference between dimensions fractal and Fractal random walks of return index and future fall risk and systematic risk in Tehran Stock Exchange
Amir hosein Abdolmaleki mohsen hamidian ali baghani

Financial markets can be evaluated as dynamic nonlinear systems that consider the interactions of factors in the process of immediate information analysis. Investors with different time horizons in the market may use this information differently. Thus, the financial mar More

Financial markets can be evaluated as dynamic nonlinear systems that consider the interactions of factors in the process of immediate information analysis. Investors with different time horizons in the market may use this information differently. Thus, the financial market has a fractal structure in relation to investment time horizons. This research is of applied type and of post-event type; the method research is applicable and run based on past information. The statistical population of the study includes all companies listed in the Iranian capital market during the period 2008-2018. In this study, after calculating the fractal dimension of the experimental group using ARFIMA model and the fractal dimension and simulated Fractal random walks group using RUN test, the difference between these two dimensions in price index, return, future fall risk and systematic risk is investigated. Data analysis was performed in both 5-year and 10-year intervals using EVIEWS and SPSS software and the results indicate that the difference between dimensions fractal and f simulated Fractal random walks of the return index and the risk of future and systematic stock falls in short-term intervals means and is not significant in the long-term Manuscript profile

Open Access Article

4 - Empirical analysis of fractal dimensions on cash return and price indices of listed companies of Tehran Stock Exchange
Shokrollah Khajavi Hadi Abdi Taleb Beigi

The goal of this research is to empirical analyzing fractal dimensions on cash return and price indices of listed companies of Tehran Stock Exchange. To have access to this goal, cash return and price indices of Tehran Stock Exchange were studied. Statistical sample of More

The goal of this research is to empirical analyzing fractal dimensions on cash return and price indices of listed companies of Tehran Stock Exchange. To have access to this goal, cash return and price indices of Tehran Stock Exchange were studied. Statistical sample of research includes cash return and price indices during period 1382 to 1391. Using R/S analysis and Hurst exponent, this research surveys the cash return and price time series being stochastic. To study the stochastic time series and differentiating from time series are not stochastic, R/S analysis is used as an efficient nonlinear method. Distribution type disrelation is the most important advantage of R/S analysis. Results of research show that, cash return and price indices time series are not stochastic and have a long-memory. Manuscript profile

Open Access Article

5 - Wave Scattering from Fractal Surfaces with Nanometer Roughness
M. Salami S.M. Fazeli

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Open Access Article

6 - Design a fractal innovation model with a sustainable approach in a chaotic environment
Amir Mehrbanpajooh Ghanbar Abbaspour Asghar Moshabaki Esfahani Gholamreza Hashemzadeh Khorasgani

Today,‌sustainable innovation has become-one-of the requirements of-the-world,‌so that most managers and production-activists-who are concerned about the environment,‌use this type of innovation in order to be sustainable in a turbulent and turbulent economic environmen More

Today,‌sustainable innovation has become-one-of the requirements of-the-world,‌so that most managers and production-activists-who are concerned about the environment,‌use this type of innovation in order to be sustainable in a turbulent and turbulent economic environment,‌as well as to adapt to it.‌Due to its unique characteristics,‌fractal innovation is closely related to the concepts of sustainability‌(social and ecological) because this view of innovation seeks to minimize resources‌(raw materials,‌production resources,‌energy, water,‌waste-and-financial resources).The purpose of this study is to provide a model that can be used to consider the dynamics of innovation and business sustainability factors.‌Also,‌the present study is quantitative and-exploratory.‌In this-study,‌the-components of the fractal innovation model of sustainability were classified using Heidegger method and their originality was measured using the Iranian matrix method.‌In addition,‌in order to prioritize the components over the indicators,-the rapid impact assessment matrix method was used, then the four-level focal model was used to present the model.‌The statistical population of this research is science and technology parks active in the field of innovation in Iran,‌ten of which were selected as a sample‌(expert)‌and with the help of interviews and distribution of questionnaires,‌the data obtained with the help of Excel software were analyzed.As a result,30‌components of the fractal sustainable innovation were founded,‌the most important components included components of circular economy,‌large scale change,‌external capability,‌resource efficiency,‌stakeholder co-creation,‌competitive evaluation,‌economical innovation is the most important and prioritized. Manuscript profile

Open Access Article

7 - Investigation of the scheme police to coping with disturbance by chaos theory
sajad namour mohammad javad zahedi homa zanjani zadeh

In this study, we intend to make sociology with chaos theory. Then we intend to Investigation of the scheme police to coping with disturbance from 2006 until 2010 in north khorasan by chaos theory. Our designing research is the secondary analysis of police information. More

In this study, we intend to make sociology with chaos theory. Then we intend to Investigation of the scheme police to coping with disturbance from 2006 until 2010 in north khorasan by chaos theory. Our designing research is the secondary analysis of police information. There are a lot of fractals in our chaotic world. We said a lot of fractals are present in our behavior, therefore in a major view we have fractals in relations of different societies. If we know positive social fractals and expand them in our society we will have progressive social chaos in future. If we accept negative fractals, we will be lead towards regressive social chaos non-consciously. All sociologists should intend to find beautiful social fractals and inform people about it and manage to keep them in order to have progressive social chaos in future. This is the real goal of sociology and sociologists task. The result showed the police design to coping with disturbance from 2006 until 2010 is lead towards progressive social chaos in north khorasan Manuscript profile

Open Access Article

8 - 2D-DOA Estimation of LFM Signal Wideband Using Low Snapshots Dechirping Algorithm in a Two-Dimensional Circular Array
Abbas Partovi Sangi Jasem Jamali Mohammad Hossein Fatehi Mohammad Mehdi Ghanbarian

Wideband linear frequency modulation (LFM) signals are widely used in systems such as radar, sonar, and mobile. 2D-DOA algorithms for LFM signals are relying on a large number of snapshots. For this reason, they are not suitable for low-power applications. In this paper More

Wideband linear frequency modulation (LFM) signals are widely used in systems such as radar, sonar, and mobile. 2D-DOA algorithms for LFM signals are relying on a large number of snapshots. For this reason, they are not suitable for low-power applications. In this paper, we present an algorithm-centered estimation method with low estimation of signal parameters via rotational invariance technique (ESPRIT) calculations based a 2D circular array using a fractal Fourier transform (FrFT). Furthermore, the utilization of a circular array facilitates the two-dimensional DOA calculation. Therefore, the procedure is that firstly, we develop the Dechirping process for LFM signals using the FrFT; secondly, we extend the ESPRIT algorithm- as used for linear arrays (ULA) - for 2D circular arrays (UCA). Finally, DOA calculations are made for a low number of snapshots with low computational volume. The simulation results of the proposed MESPRIT (i.e. modified ESPRIT) algorithm show that this algorithm outperforms compared to other algorithms like MUSIC and TSFDOA. We also have shown that the proposed method has an acceptable accuracy in low SNRs and creates less error in high SNRs. It was also demonstrated that for all algorithms, accuracy of azimuth angle is better than the elevation angle’s.   Manuscript profile

Open Access Article

9 - A Triangular Patch Antenna with a Trapezoidal Fractal with Two Sublayers with Complementary Layers
Mohammadreza Sepehri Mohammad Amin Honarvar

In this paper, the improvement of the radiation pattern and the properties of multi-band trapezoidal fractal antenna with self-complementary layers have been investigated. The antenna is excited by a microstrip feed-line with two sub-layers to increase bandwidth and imp More

In this paper, the improvement of the radiation pattern and the properties of multi-band trapezoidal fractal antenna with self-complementary layers have been investigated. The antenna is excited by a microstrip feed-line with two sub-layers to increase bandwidth and improve the radiation pattern. The dual-layered complementary arrangement has had positive effects on the resonance frequencies and improved the properties of the radiation patterns. This antenna offers a good efficiency, suitable bandwidth, and radiated pattern in a designed resonance frequency. Six bands (S11<-15 dB), with center frequencies of f1=0.9 GHz, f2=1.57 GHz, f3=1.85 GHz, f4=2.15 GHz, f5=2.5 GHz and f6=3.5 GHz are obtained within the band of (0.5-4) GHz. This antenna offers good efficiency which changes from 70% to 95%. The measurement results clearly confirm the simulation results Manuscript profile

Open Access Article

10 - Fire Detection Based on Extraction of Spatio-Temporal Features by Convolutional Neural Networks and Fractal Analysis
Monir Torabian Hossein Pourghassem Homayoun Mahdavi-Nasab Payam Sanaee

Fire is one of the dangers that can endanger human health in a short time and if it is not controlled in time, it will cause a lot of damage. Therefore, timely and accurate identification of the location of the fire can prevent the consequences of its expansion. In this More

Fire is one of the dangers that can endanger human health in a short time and if it is not controlled in time, it will cause a lot of damage. Therefore, timely and accurate identification of the location of the fire can prevent the consequences of its expansion. In this research, a new method for fire detection is proposed based on the extraction of its temporal-spatial features in video frames. In the proposed algorithm, a multiscale convolutional neural network along with a YOLO (you only look once) network is used to extract spatial features and identify fire candidate regions. Then, fractal analysis based on the temporal blanket method is then used to remove non-moving textures similar to fire and to examine the temporal features of the candidate region. Finally, the fire region is separated from the other parts of the image by fusion the results of the two steps. The evaluation results of the proposed method on three data sets show that the accuracy of fire detection is about 96.1%, while the precision and recall values are 92% and 96.9%, respectively. Experimental results show that the proposed method performs better than existing algorithms and thus confirms the ability of this method for efficient use in the real world. Manuscript profile

Open Access Article

11 - A comparative study of deep learning model with binary and multiple classification to predict stock market trends by detecting fractal patterns based on Elliott wave theory.
Masoud Nadem Yahya Kamyabi Esfandiar Malekian

10.30495/fed.2024.709342

Abstract One of the popular but complicated methods in technical analysis is the Elliott wave method. In this method, the most important part is to recognize the main trend patterns of the market, which is a difficult task due to the fractal structure of the market. Bu More

Abstract One of the popular but complicated methods in technical analysis is the Elliott wave method. In this method, the most important part is to recognize the main trend patterns of the market, which is a difficult task due to the fractal structure of the market. But like other fields, the use of artificial intelligence in the field of financial forecasts has also become widespread. Therefore, it seems that the use of artificial intelligence in Elliott wave analysis is attractive. Therefore, in the current research, by introducing a deep learning model to predict the market through the detection of Elliott wave patterns, it has been investigated and compared the power of the model in two modes of binary and multiple classification. In this research, for 15 considered patterns, 1002 examples of stock price charts of companies present in the Iranian stock market in the 11-year period from 1390 to 1400 were collected and labeled, and finally for recognition as input to the deep learning algorithm with Recurrent neural network model was used in binary and multiple classification modes. In this research, RapidMiner 9.9 software was used to design and implement the model, and accuracy criteria were used to determine the power of the model. The results show 18% accuracy in pattern recognition in multiple classification mode and 61% accuracy in binary classification mode. Therefore, the power of the deep learning model in detecting the fractal patterns of Elliott waves and as a result predicting the market trend is significantly higher in the binary classification mode than in the multiple classification mode. Therefore, the present study recommends the use of deep learning model with binary classification to detect fractal patterns of Elliott waves. Manuscript profile

Open Access Article

12 - Investigation of Fractal Property Price and Stock Returns of Tehran Stock Exchange Companies Using Nonlinear ARIFMA Model
amirhosein abdolmaleki mohsen hamidian ali baghani

Much evidence suggests that time series such as stock market prices are complex and random, which makes their changes unpredictable. However, these time series are likely to be a nonlinear dynamic or, in other words, a chaotic process and can therefore be predictable. T More

Much evidence suggests that time series such as stock market prices are complex and random, which makes their changes unpredictable. However, these time series are likely to be a nonlinear dynamic or, in other words, a chaotic process and can therefore be predictable. Therefore, in this study, stock prices and stock returns of Tehran Stock Exchange companies during the period 2014-2018 and monthly intervals were tested to determine whether these variables have fractal properties in their behavior. To achieve the above objective, our model estimation is used to explain the mass fraction of moving average. The findings of the above tests indicate that stock prices and stock returns experience a turbulent and definite process. This implies that the capital market is inefficient, and because of its long-term memory, it can be useful in predicting long-term performance and may have a guide to better understanding market failure factors such as the lack of transparency of information flow and action to address it. Manuscript profile

Open Access Article

13 - Presenting a Model for Predicting Stock Market Trends by Detecting Fractal Patterns Based on Elliott Wave Theory Using Deep Learning Method
Masoud Nadem Yahya Kamyabi esfandiar malekian

Today, artificial intelligence has made a big change in the recognition of chart patterns in technical analysis. Although, the emergence of new and complex analytical methods in technical analysis has provided a new challenge for artificial intelligence methods. One of More

Today, artificial intelligence has made a big change in the recognition of chart patterns in technical analysis. Although, the emergence of new and complex analytical methods in technical analysis has provided a new challenge for artificial intelligence methods. One of the popular and complex technical analysis methods is Elliott Wave Theory. On the other hand, the speed of progress of artificial intelligence methods is such that a more powerful method is introduced every time. One of the new and powerful artificial intelligence methods is the deep learning method. Therefore, in this research, a model has been presented to predict the trend of the stock market through the detection of fractal patterns based on Elliott wave theory using deep learning method. In this research, 15 Elliott wave patterns were considered, and then 1002 samples of stock price charts of companies listed on Tehran Stock Exchange were collected and labeled for patterns, and finally entered as input into deep learning algorithm using recurrent neural network model for recognition. In this research, RapidMiner 9.9 software was used and accuracy criteria were used to determine the power of the model. Based on the results, the accuracy of developed model in recognizing patterns is 61%. Manuscript profile

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