Data mining
Aboosaleh Mohammad Sharifi; Kaveh Khalili Damghani; Farshid Abdi; Soheila Sardar
Abstract
Cryptocurrencies are considered as new financial and economic tools having special and innovative features, among which Bitcoin is the most popular. The contribution of the Bitcoin market continues to grow due to the special nature of Bitcoin. The investors' attention to Bitcoin has increased significantly ...
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Cryptocurrencies are considered as new financial and economic tools having special and innovative features, among which Bitcoin is the most popular. The contribution of the Bitcoin market continues to grow due to the special nature of Bitcoin. The investors' attention to Bitcoin has increased significantly in recent years due to significant growth in its prices. It is important to create a prediction system which works well for investment management and business strategies due to the high chaos and volatility of Bitcoin prices. In this study, in order to improve predictive accuracy, Bitcoin price dataset is first divided into a time interval through time window, then propose a new model based on Long Short-Term Memory (LSTM) neural networks and Metaheuristic algorithms. Chaotic Dolphin Swarm Optimization algorithm is used to optimize the LSTM. Performance evaluation indicated that the proposed model can have more effective predictions and improve prediction accuracy. In addition, the performance of the optimized model is better and more reliable than other models.
Farzad Movahedi Sobhani; Tahereh Madadi
Volume 2, Issue 1 , March 2015, , Pages 15-33
Abstract
The main purpose of this paper is to investigate the suitability of diverse data mining techniques for construction delay analysis. Data of this research obtained from 120 Iranian construction projects. The analysis consists of developing and evaluating various data mining models for factor selection, ...
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The main purpose of this paper is to investigate the suitability of diverse data mining techniques for construction delay analysis. Data of this research obtained from 120 Iranian construction projects. The analysis consists of developing and evaluating various data mining models for factor selection, delay classification, and delay prediction. The results of this research indicate that with respect to accuracy and correlation indexes, genetic algorithm with K-NN learning model is the most suitable model for factor selection. By conducting the genetic algorithm, eight significant variables causing construction delay are identified as: Changes in project manager, Difficulties in financing project by owner, Number of employees, Project duration, Unforeseen events, Project Location, Number of equipment, How to get the project. This research also revealed that in the case of delay classification and prediction, respectively, bagging decision tree and bagging neural network has the least amount of error in comparison with other techniques. In addition, to compare the diversity of data mining methods, the optimized parameter vectors of the selected models were also identified.
S. Mohammad Arabzad; Mohamad Ebrahim Tayebi Araghi; S. Sadi-Nezhad; Nooshin Ghofrani
Volume 1, Issue 3 , September 2014, , Pages 159-179
Abstract
Predicting the results of sports matches is interesting to many, from fans to punters. It is also interesting as a research problem, in part due to its difficulty, because the result of a sports match is dependent on many factors, such as the morale of a team (or a player), skills, current score, etc. ...
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Predicting the results of sports matches is interesting to many, from fans to punters. It is also interesting as a research problem, in part due to its difficulty, because the result of a sports match is dependent on many factors, such as the morale of a team (or a player), skills, current score, etc. So even for sports experts, it is very hard to predict the exact results of sports matches. This research discusses using a machine learning approach, Artificial Neural Networks (ANNs), to predict the outcomes of one week, specifically applied to the Iran Pro League (IPL) 2013-2014 football matches. The data obtained from the past matches in the seven last leagues are used to make better predictions for the future matches. Results showed that neural networks have a remarkable ability to predict the results of football match results.