learning models

Open Access Article

1 - Management of Managers and Workers Reward of Organization with Learning
N. Feghhi Farahmand

This paper studies the dispersion around the workers expected reward of before and after controlling theorganizational hierarchical positions in cross section data samples. Data collected form 694 managers andworkers of 69 industrial and business organizations, showd th More

This paper studies the dispersion around the workers expected reward of before and after controlling theorganizational hierarchical positions in cross section data samples. Data collected form 694 managers andworkers of 69 industrial and business organizations, showd that this dispersion decreases with education andwork experience before entering the current job and increases with job tenure. These finding contrasts withprevious researches show a positive association between reward dispersion and education and workexperience. This study explains the new finding through a model of learning that separates from rewarddispersion between jobs and within organizational hierarchical positions jobs. The model takes advantage ofthe information revealed when workers are promoted to their current hierarchical positions and allows formore robust tests of learning theories. This study also contributes to the existing literature through a new twoequation empirical model, one for the level of reward and another for conditional dispersion, in order to testthe theoretical predictions. The paper is organized as follows:Summarize the empirical implications of learning models in studying the determinants of workers' rewardwhen information is available about managers and workers' job positions.Description of the database and methodology and so present the results of the estimation of the empiricalmodel. The discussion of the evidence supporting the basic theory and conclusion with a brief summary ofthe main findings Manuscript profile

Open Access Article

2 - Examination of Inflation Expectation formation: Experimental Approach(Case Study: Kermanshah Capital Market Activists)
Shahram Fattahi Kiumars Soheili Mohammad Bahmani

Inflation expectations as one of the factors influencing inflation formation plays a decisive role in economic policy. Decision making and decision making in relation to issues such as consumption, savings, production, investment, the choice of asset portfolios, wages, More

Inflation expectations as one of the factors influencing inflation formation plays a decisive role in economic policy. Decision making and decision making in relation to issues such as consumption, savings, production, investment, the choice of asset portfolios, wages, exchange rates and interest rates are all shaped by inflationary expectations.In this study, the problem of formation of inflationary expectations among capital market participants in laboratory space using Z-tree software has been studied. To this end, Taylor's expanded rule first examined the central bank's behavior toward inflationary depreciation and estimated the Bank's central counterpart effect. The average inflation expectations of the participants in this experiment was 14.12%. Based on the results of this study, at a significant level, 95% of 34.7% of participants in this experiment formed their expectations based on the rational expectation pattern, 22.1% of individuals based on the matching pattern of expectations and 43.2% based on learning models. Therefore, monetary authorities, with the knowledge of how the formation of inflationary expectations among capital market participants can be appropriate policies, in line with the direction of existing liquidity towards the capital market, which controls inflation and prevents the movement of liquidity in the community towards the foreign exchange market and gold To adopt. Manuscript profile

Open Access Article

3 - Spatial flood susceptibility assessment using boosting and bagging in machine learning techniques
hossein aghamohammadi Mohammad Hassan vahidnia Zahra Azizi

Every year flooding causes countries billions of dollars’ worth of damage that threatens the livelihood of individuals. As a result, it poses significant socio-economic threats to populations worldwide. Therefore, it should be controlled and restrained. In this re More

Every year flooding causes countries billions of dollars’ worth of damage that threatens the livelihood of individuals. As a result, it poses significant socio-economic threats to populations worldwide. Therefore, it should be controlled and restrained. In this regard, machine learning algorithms, along with geographic information systems, are primary tools that are effective in flood control modeling and analysis. The purpose of this research is to identify a part of flood-sensitive regions across the Heraz catchment area in Mazandaran province using ensemble methods in machine learning algorithms. The research process is as follows: first, the data of flood points were prepared. Next, 70% of approximately 240 sample positions were used for modeling and map preparation. The remaining 30%, which were randomly selected, were used to validate the produced maps. Then, the effective factors, including slope angle, slope direction, topography, soil type, land cover, distance from the river, annual rainfall, normalized difference vegetation index, index of sediment transmittance, index of topographic wetness, and index of stream density have been used to weight the impact of each factor using machine learning algorithms. Based on the results of this study, the system performance characteristic curve (ROC) was drawn, and the area under the curve (AUC) was calculated to validate the flood-prone area map. Findings demonstrated that the Adaptive Boosting model is more accurate than the Bagging model in preparing a flood sensitivity map. Predictive susceptibility mapping plays a pivotal role in enabling urban planners and managers to mitigate and safeguard proactively against the adverse consequences of flooding. Flood management authorities in the Ministry of Energy can employ the proposed ensemble model to assist disaster management and mitigate hazards in future studies. . Manuscript profile

Open Access Article

4 - Body Weight Prediction of Dromedary Camels Using the Machine Learning Models
N. Asadzadeh M. Bitaraf Sani E. Shams Davodly J. Zare Harofte M. Khojestehkey S. Abbaasi A. Shafie Naderi

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Open Access Article

5 - 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 Rostami

10.30495/fed.2023.700133

Abstract 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 More

Abstract 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

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