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Open Access Article
1 - Predicting Index of Stock Exchange by Hidden Markov Model and K-Mean Algorithm
Saeid Asgari Naser Yazdani Mohsen Nazem BokaeiStock price prediction is a classic problem that has been analyzed by different tools and models. Stock market trend changes depends on supply and demand rule and other macroeconomic forces in the market circumstance. Non liner and full swing process makes it hard to pr MoreStock price prediction is a classic problem that has been analyzed by different tools and models. Stock market trend changes depends on supply and demand rule and other macroeconomic forces in the market circumstance. Non liner and full swing process makes it hard to predict future stock price. Traditional statistical techniques and models cannot explain seasonal and non-station time series data in stock markets. Hidden markov model has widely used in the way of predicting statistical time series. It extensively has used in such majors as speech recognition and DNA sequencing and also it can be used in order to next stock price prediction. In this study we tried to use discrete hidden markov model to predict next day’s index in Brussels (Euro Next) and answer the question that “which market will get the more accurate prediction by hidden markov model?. Manuscript profile -
Open Access Article
2 - Prediction of meteorological drought conditions in Iran using Markov chain model
Mehdi Ghamghami Javad BazrafshanDrought management is very important for optimal water resources application in arid and semi-arid regions. One strategy to manage drought is to predict drought conditions by probabilistic tools. In this study, total monthly precipitation records related to 33 MoreDrought management is very important for optimal water resources application in arid and semi-arid regions. One strategy to manage drought is to predict drought conditions by probabilistic tools. In this study, total monthly precipitation records related to 33 synoptic stations of Iran during 1976-2005 were used to monitor and predict future drought conditions. Regarding the dry periods greater than six months in the arid regions of the country, the Standardized Precipitation Index (SPI) at 6-month timescale was used for drought monitoring. The first-order Markov chain model was employed to predict drought condition up to 3-step ahead. This model was fitted on the SPI series at all stations of interest, and it was identified that can represent the probabilistic behavior of drought over Iran. The results obtained from drought prediction at 1, 2, and 3-step ahead over Iran showed that the occurrence of the severe drought (9 percent of stations) or normal conditions (87 percent of stations) is most probable in the future months, regardless of drought condition at current month. Also, drought monitoring based on aerial mean of monthly total precipitation time series over country showed that the trend of drought severity has been increasing in recent years. Manuscript profile -
Open Access Article
3 - Developing a model for predicting the Tehran Stock Exchange index using a combination of artificial neural network and Markov hidden model
Leila Talaie Kakolaki Mehdi Madanchi Taghi Torabi Farhad GhaffariThe purpose of this study was to design a new model for predicting the Tehran Stock Exchange index using pattern recognition in a combination of hidden Markov model and artificial intelligence. The present study is an applied type and mathematical analytical method. Its MoreThe purpose of this study was to design a new model for predicting the Tehran Stock Exchange index using pattern recognition in a combination of hidden Markov model and artificial intelligence. The present study is an applied type and mathematical analytical method. Its location is the Tehran Stock Exchange and during the years 2010 to 2020. Findings showed that the prediction error rate with artificial neural network has a higher accuracy than Markov's hidden model. Also, the prediction error of the hybrid model is much lower than the other two models for predicting the total stock index of Tehran Stock Exchange, so it has higher accuracy for forecasting stocks. According to the MAPE index, the hybrid model method could improve the predictive power of the artificial neural network by 0.044% and also improve the predictive power of the hidden Markov model by 0.70%. Manuscript profile -
Open Access Article
4 - Improving the Mean Time to Failure of the System with the New Architecture of the Main Node with the Replacement Node of Industrial Wireless Sensor Networks for Monitoring and Control using Markov Model
Ahmadreza Zamani Mohammad Ali Pourmina Ramin Shaghaghi Kandovan -
Open Access Article
5 - Monitoring and forecasting of land use change by applying Markov chain model and land change modeler (Case study: Dehloran Bartash plains, Ilam)
Seyed Reza Mir Alizadehfard Seyedeh Maryam AlibakhshiNowadays modeling and forecasting of land use changes by application of satellite images can be a very useful tool for describing relations between natural environment and human activities to help planners to make decisions in complicated conditions. There are various m MoreNowadays modeling and forecasting of land use changes by application of satellite images can be a very useful tool for describing relations between natural environment and human activities to help planners to make decisions in complicated conditions. There are various methods for forecasting of land uses and coverage, in which the Markov chain model is one of them. In this research, land use changes in Bartash plain in Dehloran which is located in Ilam province in the area of 135244 hectares in 3 time periods (1988, 2001 and 2013) of landSat satellite images, providing land use map in 6 classes (low density forest, medium-dense grassland, poor grassland, agricultural, alluvium sediments and non-vegetated lands) by application of Kohonens neural network and also Markov anticipation model and Land change modeler (LCM) approach was predicted for the year 2030. The classification results showed the rate of demolition and a reduction of the area of low density forests and medium grassland land uses and increase in area of other land uses. Reduction of low density forest and the medium grassland area and increasing growth of other land uses demonstrated the overall destruction in the region and replaced with poorer land uses. At the end, by application of the Markov chain model and LCM modeling approach, land use changes were a forecasted for the year 2030. The results of changes anticipation matrix based on maps of years 2001 and 2013 showed that it is likely that in the period of 2013-2030, 45% of low density forest, 71% of medium grassland, 96% of poor grassland, 81% of agricultural lands, 93% alluvialvium sediments and 100% of non-vegetated lands remain changeless; non-vegetated lands have the most stability and low density forest have the least stability. Manuscript profile -
Open Access Article
6 - Predicting locational trend of land use changes using CA-Markov model (Case study: Safarod Ramsar watershed)
Nahid Salehi Mohammad Reza Ekhtesasi Ali TalebiPredicting land use changes using satellite imagery is now a useful tool for helping planners in complex situations. The purpose of this study was to detect and predict land use changes during the 28-year period (1986-2014) by CA- Markov model in the Safarood-Ramsar wat MorePredicting land use changes using satellite imagery is now a useful tool for helping planners in complex situations. The purpose of this study was to detect and predict land use changes during the 28-year period (1986-2014) by CA- Markov model in the Safarood-Ramsar watershed of Mazandaran province. In this research, land use and NDVI maps were prepared using Landsat TM (1986), ETM+ (2000) and OLI (2014) satellite images. The accuracy of the CA-Markov model was estimated using the Kappa index of 87%. In order to calibrate the CA-Markov model, the land use map was prepared in 2014, and the Kappa coefficient of the mapping from modeling and user base map (2014) was 82%. The results showed that during the period between 1986 and 2014, the area of forest lands decreased by 10.26% and the total area of residential areas increased by 3.27%. The land use map for the years 2021 and 2028 was predicted by the CA-Markov model. The results showed that during the period 2014-2028, forested lands and rangelands decreased by 4.92% and 1.7%, respectively. Residential areas will increase by 8.04% and the agricultural land will change slightly, indicating the changes in land use to residential land. Manuscript profile -
Open Access Article
7 - Three-dimensional calibration of land use changes using the integrated model of Markov chain automatic cell in Gorgan-rud river basin
Mahboobeh Hajibigloo Vahed berdi Sheikh Hadi Memarian Chooghi Bairam komakiBackground and ObjectiveLand use/cover changes (LU/LC) are considered as one of the most important issues in natural resource management, sustainable development and the environmental changes on a local, national, regional and global scale. Changing uses into each other MoreBackground and ObjectiveLand use/cover changes (LU/LC) are considered as one of the most important issues in natural resource management, sustainable development and the environmental changes on a local, national, regional and global scale. Changing uses into each other and changing permissible uses into impermissible uses such as changing agricultural lands into residential regions or changing rangelands into eroded and low-yielding dry farming lands are always considered as importand issues in natural resources. Detection of the patterns of the land use changes and prediction of the changes in the future to carry out suitable planning for optimal utilization of uses in natural resource management reveal the need for modeling spatial and temporal changes of LU/LC. This study aims to assess the efficiency of the integrated model of Markov chain automatic cell (CA-Markov model) in simulation and prediction of spatial and temporal changes of Land use/Land cover (LU/LC) in Gorgan-rud river basin by applying three-dimensional Pentius-Melinus analysis in calibration of land use changes by using three assessment indices of Quantity Disagreement, Allocation Disagreement and Figure of Merit as new indices in the assessment of the accuracy of CA-Markov model. Materials and Methods In this research, the Earth observing sensor images of Landsat-5 Thematic Mapper (TM) and Landsat-8 Operational Land Imager (OLI) acquired from the U.S. geographical site dependent on the U.S. Geographical Survey (USGS) were used to predict land use changes by using the integrated model of Markov chain automatic cell in Gorgan-rud river basin. Seven land use classes were separated for Gorgan-rud river basin including forest land class with the use code 1, agricultural land class with the use code 2, rangeland class (a mixture of shrubbery,langeland,agriculture) with the use code 3, water bodies class with the use code 4, barren land class (barren, rangeland, agriculture) with the use code 5, residential and industrial region class with the use code 6, streambed class with the use code 7. In this study, object-oriented classification method and Support Vector Machine (SVM) algorithm were used to classify Landsat 5 and 8 satellite images and extract the land use classes of Gorgan-rud river basin. Segmentation scale in this algorithm on a 50 unit scale (SL 50) was selected to classify the satellite images of 1987, 2000, 2009 and 2017. The assessment of the accuracy of Support Vector Machine algorithm in the object-based classification of satellite images was done by representing overall accuracy, Kappa cefficient, user accuracy, producer accuracy, commission error and omission error for four study periods. To understand how the changes in the region were created during the period of the study three decades and which classes had the area expansion and which classes had the area decrease, changes in the limits of the classes were revealed and percent of the changes in each class were obtained by using the classification maps and IDRISI software. CA-Markov model predicts the changes of different groups of LU/LC units based on spatial neighbourhood concept, transition probability matrix. Preparing land suitability maps is necessary to predict land use changes so that spatial changes can be controlled for each use by probability rules via filtering suitability maps. Validation of Markov model was performed by using three-dimensional Pentius-Melinus analysis with three assessment indices of Figure of Merit, Quantity Disagreement and Allocation Disagreement. Results and Discussion Support Vector Machine algorithm in the classification of the land use based on object-oriented showed that the highest rate of commission error and omission error were observed in rangelands and agricultural lands with 19.12 and 18.55 percent respectively in the land use map of the year 2009. The lowest accuracy of the producer with 71.49 percent belongs to the rangeland use class in the land use map of the year 2009 and the lowest use accuracy with 71.45 percent belongs to agricultural land use class in the land use map of the year 2017. In keeping with the obtained results, the highest positive change belongs to the agricultural land use increase and the highest negative changes belong to rangeland and forest land use decrease during the period of three decades from 1987 to 2017. The highest forest land decrease with 4.8 percent, the highest agricultural land increase with 5.3 percent, the highest rangeland decrease with 9 percent, the highest barren land increase with 4.6 percent and the highest residential and industrial land increase with 0.8 happened during the periods of 2000-2017, 1987-2017, 2009-2017, 2009-2017, and 1987-2017 respectively. After validating the predicted land use chnges in CA-Markov model, based on the analysis of the 5 existing states in three-dimensional Pentius-Melinus analysis, the CA-Markov model with the accurate prediction of simulation of 89.92 percent showed the high efficiency of CA-Markov model in simulation process. After the implementation of the CA-Markov model analysis on the obtained land use map from the classification of the satellite images, one transition probability matrix and one transitioned area matrix were created. In predictions made by using CA-Markov model in 2017 to 2033, the most changes relate to barren and forest land expansion decrease to 16966 and 6961 hectare respectively and in contrast to the use decrease, rangeland, residential and agricultural land expansion increase will be observed to 20397, 3913 and 3825 hectare respectively. Conclusion Detecting land use changes by using LCM tool for the period of three decades 1987-2017 in Gorgan-rud river basin showed that the forest, agricultural and residential use has had significant changes in this region. The obtained results of the prediction of the land use changes during the coming eighteen years by using the integrated model of Markov chain automatic cell following the detected changes by LCM tool show that we will face extreme deforestation phenomenon in this area. Investigation of the obtained results from the implementation of the future use network model by using Markov transition estimator showed that the future use changes can be predicted based on the existing environmental conditions showing that the agriculture will extremely increase in Gorgan-rud river basin during the coming eighteen years. Thus we can protect water and soil resources with comprehensive and long-term management and prevent the degradation of these valuable resources. Three indices of Quantity Disagreement, Allocation Disagreement and Figure of Merit in three-dimensional Pentius-Melinus analysis had an important role in representation of the accuracy rate and calibration of the land use classification and the land use prediction corresponding with the obtained results from the carried out studies concerning the accuracy assessment with indices of Quantity Disagreement, Allocation Disagreement and Figure of Merit. The results of the studied land use changes by using LCM tool and the integrated model of Markov chain automatic cell during the period of 1987 to 2035 show the degradation of more than 24309 hectare of the forest lands and agriculture increase in an area about 62421 hectare indicating human interfernces and deforestation we face in this area. Manuscript profile -
Open Access Article
8 - Monitoring and predicting the trend of changing rangelands using Satelite images and CA-Markov model (Case study: Noor-rood basin, Mazandaran proince)
Nematollah Koohestani Shafagh Rastgar Ghodratollah Heidari Shaban Shatai Joybari Hamid AmirnejadPredicting the trend of land use/land cover chenges in natural range ecosystem via remote sensing techniques and evaluating their potentials by modeling, plays an important role in decision making. The goal of this research is monitoring and predicting land use/land cov MorePredicting the trend of land use/land cover chenges in natural range ecosystem via remote sensing techniques and evaluating their potentials by modeling, plays an important role in decision making. The goal of this research is monitoring and predicting land use/land cover changes in Nour-rood basin by CA-Markov in a 60 year periods (1988-2048). Landsat TM (1988, 1998, 2008) and OLI (2018) imagery of similar months (in July) were classified by maximum likelihood method algorithm. Terrestrial reality derived from topographic at scale 1:25000 and aerial photos available in the (GDNR) and (WMM) during 1988-2008 and field visits (2018) were evaluated for accuracy. The accuracy of the production maps calculated with Kappa coefficient. So that the highest and lowest ratio were related to the images of 1998 and 1988, respectively with the values of 0.86 and 0.81. The results were compared with field ground truth to determine the accuracy of results. Random matric used to convert land use classes and the map of land cover of Nour-rud basin predicted, in (2018-2028). The results showed that in (1988-2018), forests and rangelands with excellent and fair cover conditions had decreasing and ranges with good condition, rocks and residential areas had increasing trend. Total area of rangelands decreased from 116206 hectares in 1988 to 106336 hectares in 2018. Moreover, the results of Markov model with more than 85% precision showed the same trend of land use changes from 2018-2048. Excellent rangeland cover conditions, showed decreasing trend, rocky and residential areas will also have an increasing trend until 2048. Markov's prediction model also shows an accuracy of more than 85%. The trend of land use changes during 2018-2048 will be the same as in previous. In whitch case, excellent range condition will have decreasing trend; rocky and residential areas will have an increasing trend until 2048. Manuscript profile -
Open Access Article
9 - Monitoring and prediction of spatial and temporal changes of landuse/ cover (Case study: Marave Tappeh region, Golestan)
Asghar Farajollahi Hamid Reza Asgari Majid Ownagh Mohammad Reza Mahboubi Abdol-Rasoul Salman MahiniIn this research, land use changes in previous years and the possibility of predicting in the future using Markov chain model were investigated in the Maraveh Tappeh region of Golestan province. Therefore, using images of MSS, ETM+ and OLI sensors of LandSat satellite a MoreIn this research, land use changes in previous years and the possibility of predicting in the future using Markov chain model were investigated in the Maraveh Tappeh region of Golestan province. Therefore, using images of MSS, ETM+ and OLI sensors of LandSat satellite and using ancillary information, land use maps of 1986, 2000 and 2014 was provided and land use map of 2024 was predicted. According to the results, dense forest area decreased during the study period and with passing time but the area of agricultural land increased with the passage of time while the dense rangeland area decreased during the period 1984-2000. The annual growth rate of agricultural land has achieved 113.45 ha during the period 1984-2000 and this change value was obtained 91.27 ha for the period 2000-2014. The results of predicting changes in the time interval 2014-2028, showed it is possible that will be decreased semi-dense forest and dense rangelands and will be increased other land-use areas according to results of model predictions. The highest increase will be belonging to agricultural land use that will be increased to 25.89 ha per year. According to research findings, land-use changes are causing degradation of natural resource areas. However, in recent years, have taken effective actions to protect these areas, but more attention and protection of natural resources and environment in the Marave Tappeh region is essentially still. Manuscript profile -
Open Access Article
10 - Monitoring of land use changes in Shahmirzad city using remote sensing data and spatial information system
amir kamalifardIn order to study urban development and land use changes in subsequent periods, we also obtained land use maps and land survey data from Landsat satellite imagery and land use studies in Shahmirzad city to achieve this. Important software is ENVI 5.3, ARC.GIS10.5 and Te MoreIn order to study urban development and land use changes in subsequent periods, we also obtained land use maps and land survey data from Landsat satellite imagery and land use studies in Shahmirzad city to achieve this. Important software is ENVI 5.3, ARC.GIS10.5 and Terrset. . The results show that over the years studied, the area of horticultural, waste land has declined, and residential and human-made land use has increased. It was 2855094 square meters in 2009 and 2429144 square meters in 2019, following a downward trend. Residential and man-made land in 1999 was 360623 square meters, in 2009 it was 1264976 square meters and in 2019 it was 2495357 square meters, indicating a significant increase. . The change detection revealed that most land use conversions in 1999-2009 were related to conversion of arable land to wastewater by about 20% and from 2009 to 2019 related to conversion of arable land into residential land. With about 16%. Survey results show that in the first 10 years, about 20% of the horticultural land has become waste land and in the second 10 years about 7% of the land has become residential and human-made. Validation of the model with a kappa coefficient of 0.76 indicates that the model may have weaknesses but has acceptable ability to predict changes in the region. Manuscript profile -
Open Access Article
11 - Prediction of Urban Construction Changes Using Satellite Images Based on CA-MARKOV Models (case study: Sari)
Sahab Bidgoli Kashani Mehran Fadavi Valiollah AzizifarAlong with the ever-increasing urban population, the amount of construction in the city space has been developed. The development of construction in the horizontal space and regardless of the existing restrictions has led to environmental, economic and legal problems fo MoreAlong with the ever-increasing urban population, the amount of construction in the city space has been developed. The development of construction in the horizontal space and regardless of the existing restrictions has led to environmental, economic and legal problems for the citizens. Achieving the amount, intensity and direction of construction development from the past to the present and predicting the construction situation in the future is the first step towards the scientific and practical management of the physical development of urban construction, planning and providing suitable solutions in order to create a balance between allocation Spatial-spatial construction and all kinds of legal, economic and environmental considerations. Data and information extracted from satellite images, while showing the historical changes of urban construction, are used as the main, necessary and necessary input data for models to predict its future state. In this research, satellite images of TM, ETM+ and OLI sensors of Landsat satellite were used in the time periods of 1997-2007 and 2007-2017 related to the city of Sari. After performing geometrical corrections, city area maps were prepared. Then, by using the effective parameters in urban construction changes, using the Cellular Automata(CA) Markov Model, the accuracy of the simulations was checked. Finally, for validation, the simulated maps and the ground reality map were matched with each other. The simulation of the construction development process in 2027 using the CA-Markov model showed that if the existing management regulations continue, this area will decrease from 4617.90 hectares in 2017 to 4357.44 hectares in 2027. But the examination of change maps and stability maps showed that new areas will be under construction between 2017 and 2027, which were mainly used for agriculture and barren land. Manuscript profile -
Open Access Article
12 - MHIDCA: Multi Level Hybrid Intrusion Detection and Continuous Authentication for MANET Security
Soheila Mirzagholi Karim Faez -
Open Access Article
13 - An Adaptive Approach to Increase Accuracy of Forward Algorithm for Solving Evaluation Problems on Unstable Statistical Data Set
Omid SojodiShijani Nader Rezazadeh -
Open Access Article
14 - Abnormality Detection in a Landing Operation Using Hidden Markov Model
Hasan Keyghobadi Alireza Seyedin -
Open Access Article
15 - Strategies for monitoring environmental changes: monitoring and predicting land-use land-cover (LULC) change (Case study: South Pars special economic zone, Iran)
Sadegh Mokhtarisabet Afsaneh Shahriari -
Open Access Article
16 - An Analytical algorithm of component-Based Heterogeneous Software Architectural Styles performance prediction
Golnaz Aghaee Ghazvini Sima Emadi -
Open Access Article
17 - Predicting land use changes with emphasis on residential lands using CA-Markov model Case study (Bojnourd plain catchment)
ahmad hoseinzadeh Abdolreza Kashki reza Javidi Sabaghian Mukhtar KaramiUnderstanding temporal and spatial changes in land use is essential for decision makers and community planners. Land use requires knowledge of the current trend and forecasting future developments in land use and land cover. In this study, using Landsat 7, 8 satellite i MoreUnderstanding temporal and spatial changes in land use is essential for decision makers and community planners. Land use requires knowledge of the current trend and forecasting future developments in land use and land cover. In this study, using Landsat 7, 8 satellite images and Ca-Markov model in EDRISI TerrSet software, simulation and prediction of land use changes in Bojnourd catchment area in North Khorasan province has been performed. After making atmospheric and geometric corrections on the images of 2001 and 2019, a map predicting land use changes has been produced for 2040.The validation of the model is done through the kappa coefficient, the value of which is 0.92 for the land use map of 2001 and 0.95 for 2019. The results of the model prediction show that in the study area, residential lands with the increase of more than 5 thousand hectares during the study period have the most changes. Also, most of the changes have been made around the city of Bojnourd. Manuscript profile -
Open Access Article
18 - Forecasting time and place of earthquakes using a Semi-Markov model (with case study in Tehran province)
Ramin Sadeghian -
Open Access Article
19 - Bi-product inventory planning in a three-echelon supply chain with backordering, Poisson demand, and limited warehouse space
Maryam Alimardani Fariborz Jolai Hamed Rafiei -
Open Access Article
20 - Presenting Comprehensive Algorithm for Long Term Scheduling of Preventive Maintenance in the Electric Transmission Networks
Niki Moslemi Mostafa Kazami Seyyed Mostafa Abedi Hadi Khatibzadeh Mohamad Jafarian Saeed Salimi -
Open Access Article
21 - Persian Speech Recognition Through the Combination of ANN/HMM
Ladan Khosravani pour Ali Farrokhi -
Open Access Article
22 - Reliability Assessment of Power Generation Systems in Presence of Wind Farms Using Fuzzy Logic Method
Shohreh Monshizadeh Mahmoud Reza Haghifam Ali Akhavein -
Open Access Article
23 - Iran Stock Market Prediction Based on Bayesian Networks and Hidden Markov Models
Zohreh Alamatian Majid Vafaei JahanStock market behavior is one of the most complex mechanisms, considered by researchers. Financial markets are influenced by the external and internal factors. External factors such as political and social factors are not measurable, so prediction the trend of stock mark MoreStock market behavior is one of the most complex mechanisms, considered by researchers. Financial markets are influenced by the external and internal factors. External factors such as political and social factors are not measurable, so prediction the trend of stock markets is focused on internal factors. This study suggests a hybrid approach based on Bayesian Networks and Hidden Markov Models to predict trend of stock market. The used variables are 6 index of Tehran Stock Exchange, which have the most correlation coefficient with target stock, and 22 technical indicators. Bayesian networks are utilized to find the relationships between variables, and the effect of each variable in prediction considered from conditional probability tables. Hidden Markov Model is designed for sets of extract from Bayesian networks. The proposed model tested on four company’s stock names Mobarakeh Steel, Iran Khodro, Mellat Bank and Iran drug. The average accuracy of the proposed system is 83.26 %. The experimental results show that the suggested procedure has higher performance for prediction of stock markets in comparison with other previous methods. Manuscript profile
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