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Work Breakdown Structure (WBS) is one of the most important planning processes for projects completion. It is considered as the fundamental of other processes such as scheduling, controlling, assigning, performing, evaluating tasks and responsibilities. In this paper, a framework has been introduced which uses neural network to evaluate work breakd...
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Brains can remember information in different forms, i.e images, symbols, sounds, and senses, and the information is connected by logical gate. This information needs imagination and association to construct new meaningful images. The purpose of this research was to describe a method of teaching which based on Tony Buzan's mind mapping technique. Th...
The aim of this study is to examine the effect of mind mapping activities on the maths and science skills of children 48 to 60 months of age. The study was designed using an experimental model with a pre-test post-test and a control group. Accordingly, the hypotheses of the study was that there would be meaningful differences in the values obtained...
The paper describes an evaluation on the user satisfaction towards the multimedia elements of a mind map courseware used in learning History and Basic Concept of Information. The evaluation is done by using a questionnaire, which is based on four multimedia elements: text, interactivity, animation and graphical image. The finding shows that most of...
Creating a memorable and suitable character design for storytelling can be a challenge for students. While most of the students’ design is influenced by media, cultural background, popular trends and ideas, the students attempt to add originality to clichéd character design to create a unique character for animation. Generally, students go through...
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... Failure to quantify the uncertainty of models was another shortcoming of previous research (Table 5). The selection of the best inputs for models and the adjustment of the parameters of soft computing models is another of the challenges faced by previous research (Emami et al. 2013. Each of the soft computing models, which have been presented in Table 1, has its advantages and disadvantages. ...
Evaporation is one of the most important parameters of meteorological science. Therefore, predicting evaporation is necessary for both water resources and planning management. The present study uses Bayesian Model Averaging (BMA) based on developed and optimized Kernel Extreme Learning Machine (KELM) models for predicting daily evaporation in different provinces of Iran with different climates. The Water Strider Algorithm, Salp Swarm Algorithm, Shark Algorithm, and Particle Swarm Optimization were combined with the KELM to predict daily evaporation in the Hormozgan, Mazandaran, Fars, Yazd, and Isfahan provinces. The models’ inputs were average temperature, rainfall, number of sunny hours, wind speed, and relative humidity. The introducing a new hybrid gamma test for determining the adequate inputs, using hybrid and optimized KELM based on developing ELM for predicting evaporation, integrating individual models for predicting evaporation, and quantifying the uncertainty of outputs are the main innovations of the current study. Multiple error indices were used to evaluate the ability of models for predicting evaporation. The standalone and optimized KELM models were used to predict daily evaporation in the first level. In the next level, the BMA based on outputs of standalone and optimized KELM models predicted daily pan evaporation. The general results indicated that the BMA provided the best accuracy among other models in all stations. This study also introduced the new hybrid gamma test (GT-WSA) for choosing the best input combinations. The hybrid GT-WSA gave the best input combination without computing all input combinations (25 - 1). The uncertainty analysis of models also indicated that the uncertainty of BMA and optimized KELM models was lower than that of the KELM model.
... Data-driven models are highly capable of extracting the complex relationships between inputs and outputs. Moreover, they are simpler and faster than numerical algorithms (Ghorbani et al., 2021;Emami et al., 2013). The ARIMA is one of the linear statistical models used for estimating hydrological variables (Choubin and Malekian, 2017). ...
The groundwater resources are the essential sources for irrigation and agriculture management. Forecasting groundwater levels (GWL) for the current and future periods is an essential topic of watershed management. The prediction of GWL helps prevent overexploitation. The Auto-Regressive Integrated Moving Average model (ARIMA) is a widely known linear statistical model. One of the drawbacks of the ARIMA models is that they may not capture all existing patterns, such as non-linear parts of time series. This article introduces a new hybrid model, namely the ARIMA-Long Short-Term Memory (LSTM) neural network, to capture the linear and non-linear components of a GWL time series in the Yazd-Ardekn Plain in Iran. This study applied the ARIMA-LSTM in forecasting three-, six-, and nine-month-ahead GWL. To determine the hyperparameters of the LSTM algorithm, the Salp Swarm Algorithm (SSA), sine cosine optimisation algorithm (SCOA), particle swarm optimisation algorithm (PSOA), and genetic algorithm (GA) were coupled with the LSTM model. Two different scenarios were devised to introduce new input combinations. In the first scenario, the residual values of the ARIMA model and the lagged GWL data were inserted into hybrid and standalone LSTM models for forecasting the GWL. In the second scenario, the summation of the outputs of the ARIMA and LSTM models gave the final outputs. In terms of the content of three-month-ahead GWL predictions for the second scenario, the ARIMA-LSTM-SSA produced better results than the ARIMA-LSTM-SCOA, ARIMA-LSTM-PSOA, ARIMA-LSTM-GA, ATIMA-LSTM, LSTM, and ARIMA algorithms, which had lower mean absolute error values (MAE) of 5%, 9.4%, 15%, 38%, 42%, and 47%, respectively. However, the general results indicated that an increased forecasting horizon reduced the accuracy of the models. The new hybrid ARIMA-LSTM- SSA model was highly capable of forecasting other hydrological variables for capturing non-linear and linear elements of the time series.
The intensive introduction of modern high-tech tools into the production process of organizations of various types creates the need for high-class en-gineering personnel on the labor market. A trained engineering specialist is one of the important com-ponents of the economic stability of the enterprise, since the smoothness of the technical process and the prevention of risk situations associated with errors in the operation of technical equipment de-pends on him. The relevance of interest in engineer-ing professions and analysis of the features of relia-bility of engineering activities is due to the large social demand for the study of the psychological aspects of the labor of specialists-engineers, as well as the low development of this problem. The results of the performed theoretical analysis provide a basis for clarifying the conceptual apparatus of the pro-cess of studying the reliability of professional activi-ty in general, as well as for developing a new ap-proach to the analysis of the reliability of modern labor of an engineer.
