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Open Access Article
1 - Modeling of Qaleh Rudkhan river sediment rate prediction, using LSTM neural network
Mahbobeh Shadabi bejand Ebrahim AmiriBackground and Objective: Proper estimation of the amount of sediment flowing in rivers is important as a data base for many river engineering designs and processes. Qaleh Rudkhan River is one of the most important water basins in the west of Gilan province. The most im MoreBackground and Objective: Proper estimation of the amount of sediment flowing in rivers is important as a data base for many river engineering designs and processes. Qaleh Rudkhan River is one of the most important water basins in the west of Gilan province. The most important branches of the basin are two branches named Gasht Rudkhan and Ghaleh Rudkhan. The river (Qaleh Rudkhan) is made up of two branches (Heydaralat) and (Nazaralat). Therefore, the purpose of this study was to model the prediction of sediment rate in Qaleh Rudkhan River using long short-term memory neural network (LSTM). Material and Methodology: In this research, the recorded Debi-sediment statistics related to the statistical period of 1381 to 1395 has been used. These statistics include daily instantaneous Debi in cubic meter per second and daily instantaneous sediment in ton per day, which are measured simultaneously. The data used to model the artificial neural network are Debi-sediment values the accuracy of the predictions was evaluated with three error criteria. Findings: The three criteria considered are AFE, FFE and n-AFE, respectively. Discussion and Conclusion: Among these criteria, the FFE criterion showed that the correlation between the model output and the measured sediment data is appropriate. As a result, the LSTM model has the appropriate accuracy to predict the amount of sediment in the two rivers of Qala-e-Rudkhan. Manuscript profile -
Open Access Article
2 - Designing an intelligent model to optimize the safety risk of the takeoff flight using BIM-LSTM
mansour yahyavi Abbass toloie eshlaghi Mohammad Ali Afsharkazemi Reza RadfarThis article presents a new model for optimizing the safety risk of take-off, as the most important and dangerous flight process, using a combination of BI algorithm and recurrent neural network LSTM. The goal is to train an effective neural network with past data recor MoreThis article presents a new model for optimizing the safety risk of take-off, as the most important and dangerous flight process, using a combination of BI algorithm and recurrent neural network LSTM. The goal is to train an effective neural network with past data records of air accidents to predict safety risk parameters. For this purpose, 17 safety features, such as weather conditions, aircraft configuration and preparation, flight information and air traffic were obtained. The data related to 2019 to 2020 was selected after performing exploration, summarization, cleaning, normalization operations with 28813 data records. Due to the dependence of flight data on their previous inputs and the need for a kind of memory, training was performed by deep learning algorithm (LSTM) in Python environment. After learning, the learning error was about 6 percent and the mean square error was about 116/0. It shows that the error percentage is negligible and the proposed model has high validity. Also, this model solved the problem of exploration and cleaning of bulk flight data by having advanced tools such as ETL, metadata and real-time monitoring and was able to predict the most important safety risk factor (speed V1) with high accuracy. This pattern helps the flight service in controlling the important parameters of safety risk, such as the speed of aircraft taking off from the runway, controlling the safe take-off speed and most importantly controlling the loss of flight with a reliable strategy. Manuscript profile -
Open Access Article
3 - Improving Students' Performance Prediction using LSTM and Neural Network
Hussam Abduljabar Salim Ahmed Razieh Asgarnezhad -
Open Access Article
4 - A Stock Market Prediction Model Based on Deep Learning Networks
seyyedeh mozhgan Beheshti Masalegou Mohammad-Ali Afshar-Kazemie jalal haghighat monfared Ali Rezaeian -
Open Access Article
5 - A Hybrid Type-2 Fuzzy-LSTM Model for Prediction of Environmental Temporal Patterns
Aref Safari Rahil Hosseini -
Open Access Article
6 - Optimization and Improvement of Spam Email Detection using Deep Learning Approaches
Mohsen Nooraee Hamid Reza Ghaffari -
Open Access Article
7 - Forecasting the discharge of the Zayandeh Rood River at the Ghleeh Shahrokh station using deep learning techniques
Mohammad MehraniAbstract- Water discharge is a term in the water industry that refers to the amount of water that passes through a certain point per unit of time. Discharge rate is the amount of water that passes through a specific point such as a river,, water channel, dam valve, pipe MoreAbstract- Water discharge is a term in the water industry that refers to the amount of water that passes through a certain point per unit of time. Discharge rate is the amount of water that passes through a specific point such as a river,, water channel, dam valve, pipe or any other structure such as a faucet cartridge in a unit of time. In the metric system, water discharge rate is expressed in terms of cubic meters per second, cubic meters per hour, or liters per second. The unit of cubic meters per second is used for large flows such as rivers and large canals, and the unit of liters per second is used for the flow of water in wells and water that enters leaks. Measuring the discharge of the river has many effects on people's lives. Knowing the amount of water entering the areas of a river's catchment area is very important in agriculture, potential risks to human and animal life, industries, etc. Therefore, predicting river discharge can lead to effective management and prevent serious damage in the mentioned areas. According to the mentioned cases, the purpose of the presented paper is to predict the river discharge using deep learning techniques. In order to do this, the discharge of the Zayandeh Rood River at Qala Shahrokh station has been investigated and predicted using two techniques - ANFIS and LSTM. The simulation results show 93% to 94% accuracy in predicting the discharge of the studied river. Manuscript profile -
Open Access Article
8 - Presenting the Forecasting Model of Bitcoin Return Using the hybrid Method of Deep Learning - Signal Decomposition Algorithm (CEEMD-DL)
sakineh sayyadi nezhad Ali Esmaeil Zadeh Mohammad Reza RostamiAbstract With the increasing popularity and widespread use of cryptocurrencies, the creation and development of methods for predicting price movements in this field has attracted a lot of attention. In between, recent developments in deep learning (DL) models with stru MoreAbstract With the increasing popularity and widespread use of cryptocurrencies, the creation and development of methods for predicting price movements in this field has attracted a lot of attention. In between, recent developments in deep learning (DL) models with structures such as long-short-term memory (LSTM) and convolutional neural network (CNN) have made improvements in the analysis of this type of data. Another approach that can be effective in the analysis of cryptocurrencies time series is the decomposition through algorithms such as complete integrated empirical mode decomposition (CEEMD). Considering the importance of forecasting in the cryptocurrencies field, in this research, by combining deep learning models and complete integrated empirical mode decomposition (CEEMD), The hybrid CEEMD-DL(LSTM) model has been used to forecast the bitcoin return (as the most popular currency). In this regard, the daily data of the total index of the Tehran Stock Exchange was used in the period of 2013/01/01 – 2022/05/28 and the results obtained were compared with the results of competing models based on efficiency measurement criteria. Based on the obtained results, the use of the introduced model (CEEMD-DL(LSTM)) has increased the efficiency and accuracy of bitcoin return forecasting. Accordingly, the use of this model in this field is suggested. Manuscript profile -
Open Access Article
9 - The Assessment of the optimal Deep Learning Algorithm on Stock Price Prediction (Long Short-Term Memory Approach)
Amir Sharif far Maryam Khalili Araghi Iman Raeesi Vanani Mirfeiz FallahForecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. Deep Learning (DL) is a type of Artificial Neural Network (ANN) that consists of multiple processing layers and enables hi MoreForecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. Deep Learning (DL) is a type of Artificial Neural Network (ANN) that consists of multiple processing layers and enables high-level abstraction to model data. The key advantage of DL models is extracting the good features of input data automatically using a general-purpose learning procedure which is suitable for dynamic time series such as stock price.In this research the ability of Long Short-Term Memory (LSTM) to predict the stock price is studied; moreover, the factors that have significant effects on the stock price is classified and legal and natural person trading is introduced as an important factor which has influence on the stock price. Price data, technical indexes and legal and natural person trading is used as an input data for running the model. The results obtained from LSTM with Dropout layer are better and more stable than simple form of LSTM and RNN models. Manuscript profile -
Open Access Article
10 - Presenting a market direction prediction model for gold coin trades in Iran’s Commodity Exchange market using Long Short-Term Memory (LSTM) algorithm
Soheil Zoghi Reza Raei Saeed FalahporIn recent years, deep learning neural networks have been recognized as powerful tools for solving complex problems. Deep learning is a subfield of artificial intelligence in which complex problems with numerous parameters and inputs are modeled based on a set of algorit MoreIn recent years, deep learning neural networks have been recognized as powerful tools for solving complex problems. Deep learning is a subfield of artificial intelligence in which complex problems with numerous parameters and inputs are modeled based on a set of algorithms. In this research, a new framework of deep learning is presented. Using wavelet transform, stacked auto-encoders, and the Long Short-Term Memory or LSTM, we predict the market direction in the future contracts of gold coins of Iran's Commodity Exchange market. The input data is first denoised using the wavelet transformer in the proposed method. Then, using the stacked auto-encoder, the indicators influencing the market direction are identified. Ultimately, these indicators are given as input to the LSTM architecture to predict the market direction. Proposing several new technical indicators to increase the accuracy of the proposed model, adjusting the parameters of the utilized algorithms, including LSTM, for this problem, and suggesting a trading strategy to achieve appropriate profitability are among the contributions of the present study. Investigations reveal that the proposed method outperforms other approaches and achieves higher accuracy and efficiency. Manuscript profile -
Open Access Article
11 - Price predicting with LSTM artificial neural network and portfolio selection model of financial assets and digital currencies
Faranak Khonsarian Babak teimourpour Mohammad Ali RastegarFinding solutions for price prediction, forming an optimal portfolio and achieving more profit are the basic goals of financial market activists. The purpose of this research is to predict the price of financial assets such as several stocks, gold, coin and a number of MoreFinding solutions for price prediction, forming an optimal portfolio and achieving more profit are the basic goals of financial market activists. The purpose of this research is to predict the price of financial assets such as several stocks, gold, coin and a number of digital currencies using the LSTM neural network model and then form an optimal portfolio by calculating the rate of return, risk and the Sharpe ratio. The data used is from the archives of the Tehran Stock Exchange website, the website of the gold, coin and currency information network, as well as the website of buying and selling digital currencies. The time series of the prices of the investigated assets is between 2017 and 2020. Also, we used Python programming language and Gephi software to build the model and analyze the data. In the end, it was found that the LSTM neural network model is capable of predicting the price of financial assets with a very low error rate in each asset, and according to the Sharpe ratio obtained for each financial asset and the correlation matrix, Vebank stock, Khbahman 1 stock, and Digital currencies TRON, Tether and Bitcoin allocate more shares in the proposed portfolio. Manuscript profile -
Open Access Article
12 - Developing an LSTM neural network model for predicting blocktrade transaction valuation
Adeleh Bahreini Maryam Akbaryan Fard Mehdi KhoshnoodObjective: The capability of intelligent systems in predicting economic and financial variables, particularly stock prices, has been confirmed in previous research in Iran and other countries. However, the valuation of block transactions is calculated for the first time MoreObjective: The capability of intelligent systems in predicting economic and financial variables, particularly stock prices, has been confirmed in previous research in Iran and other countries. However, the valuation of block transactions is calculated for the first time in this study. The aim is to investigate the outcomes and information from the financial reports of listed companies on the Tehran Stock Exchange using 15 financial indices and determine the impact of these indices on the valuation of block transactions by employing the RMSE test on the Test dataset.Research Methodology: For this purpose, financial information from 64 companies within the accepted companies of the Tehran Stock Exchange for the period from 1390 to 1400 has been utilized. The research hypothesis is tested using the Long Short-Term Memory (LSTM) deep learning neural network model.Findings: The LSTM neural network, due to its high capability in training data, appropriate weights for these data, and creating a path that efficiently and accurately produces acceptable results for predicting the valuation of block transactions.Originality/Value: In the proposed model, by measuring the valuation of block transactions, we will scrutinize the prices of these transactions and the effects of information and liquidity in large-sized transactions. Manuscript profile -
Open Access Article
13 - An Interval Type-2 Fuzzy LSTM Algorithm for Modeling Environmental Time-Series Prediction
Aref Safari Rahil Hosseini
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