Institute for International Energy Studies

Tehrān, Iran

Institute for International Energy Studies

Tehrān, Iran

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Hassani H.,Institute for International Energy Studies | Huang X.,Bournemouth University | Silva E.S.,University of the Arts London | Ghodsi M.,Institute for International Energy Studies
Statistical Analysis and Data Mining | Year: 2016

Crime continues to remain a severe threat to all communities and nations across the globe alongside the sophistication in technology and processes that are being exploited to enable highly complex criminal activities. Data mining, the process of uncovering hidden information from Big Data, is now an important tool for investigating, curbing and preventing crime and is exploited by both private and government institutions around the world. The primary aim of this paper is to provide a concise review of the data mining applications in crime. To this end, the paper reviews over 100 applications of data mining in crime, covering a substantial quantity of research to date, presented in chronological order with an overview table of many important data mining applications in the crime domain as a reference directory. The data mining techniques themselves are briefly introduced to the reader and these include entity extraction, clustering, association rule mining, decision trees, support vector machines, naive Bayes rule, neural networks and social network analysis amongst others. © 2016 Wiley Periodicals, Inc. Statistical Analysis and Data Mining: The ASA Data Science Journal, 2016 © 2016 Wiley Periodicals, Inc..


Hassani H.,Institute for International Energy Studies | Ghodsi Z.,Bournemouth University | Silva E.S.,University of the Arts London | Heravi S.,University of Cardiff
Digital Signal Processing: A Review Journal | Year: 2016

Many scientific fields consider accurate and reliable forecasting methods as important decision-making tools in the modern age amidst increasing volatility and uncertainty. As such there exists an opportune demand for theoretical developments which can result in more accurate forecasts. Inspired by Colonial Theory, this paper seeks to bring about considerable improvements to the field of time series analysis and forecasting by identifying certain core characteristics of Colonial Theory which are subsequently exploited in introducing a novel approach for the grouping step of subspace based methods. The proposed algorithm shows promising results in terms of improved performances in noise filtering and forecasting of time series. The reliability and validity of the proposed algorithm is evaluated and compared with popular forecasting models with the results being thoroughly evaluated for statistical significance and thereby adding more confidence and value to the findings of this research. © 2016 Elsevier Inc. All rights reserved.


Iranmanesh H.,University of Tehran | Iranmanesh H.,Institute for International Energy Studies | Abdollahzade M.,Institute for International Energy Studies | Abdollahzade M.,Islamic Azad University at Tehran | And 2 more authors.
Energies | Year: 2012

This paper proposes a structure for long-term energy demand forecasting. The proposed hybrid approach, called HPLLNF, uses the local linear neuro-fuzzy (LLNF) model as the forecaster and utilizes the Hodrick-Prescott (HP) filter for extraction of the trend and cyclic components of the energy demand series. Besides, the sophisticated technique of mutual information (MI) is employed to select the most relevant input features with least possible redundancies for the forecast model. Each generated component by the HP filter is then modeled through an LLNF model. Starting from an optimal least square estimation, the local linear model tree (LOLIMOT) learning algorithm increases the complexity of the LLNF model as long as its performance is improved. The proposed HPLLNF model with MI-based input selection is applied to the problem of long-term energy forecasting in three different case studies, including forecasting of the gasoline, crude oil and natural gas demand over the next 12 months. The obtained forecasting results reveal the noteworthy performance of the proposed approach for long-term energy demand forecasting applications. © 2012 by the authors; licensee MDPI, Basel, Switzerland.


Hassani H.,Bournemouth University | Hassani H.,Institute for International Energy Studies | Heravi S.,University of Cardiff | Zhigljavsky A.,University of Cardiff
Journal of Forecasting | Year: 2013

In recent years the singular spectrum analysis (SSA) technique has been further developed and applied to many practical problems. The aim of this research is to extend and apply the SSA method, using the UK Industrial Production series. The performance of the SSA and multivariate SSA (MSSA) techniques was assessed by applying it to eight series measuring the monthly seasonally unadjusted industrial production for the main sectors of the UK economy. The results are compared with those obtained using the autoregressive integrated moving average and vector autoregressive models.We also develop the concept of causal relationship between two time series based on the SSA techniques. We introduce several criteria which characterize this causality. The criteria and tests are based on the forecasting accuracy and predictability of the direction of change. The proposed tests are then applied and examined using the UK industrial production series. © 2012 John Wiley & Sons, Ltd.


Hassani H.,Institute for International Energy Studies | Hassani H.,University of Cardiff | Soofi A.,University of Wisconsin - Platteville | Avazalipour M.S.,Statistical Research and Training Center
Fluctuation and Noise Letters | Year: 2011

We use the Singular Spectrum Analysis (SSA), a forecasting method which is based on the noise reduction procedure, in prediction of the Iranian gross domestic product (GDP). Two different approaches are considered in forecasting the series. In the first approach, we apply SSA to the aggregate GDP series. In the second approach, we predict the GDP by first forecasting the GDP of the sectors of the economy, and then sum the predicted values as the forecast of the aggregate GDP. We measured the prediction accuracy of both approaches using various criteria, and found that predictions based on the disaggregated, sectoral GDP tend to outperform the predictions based on the aggregated data. © 2011 World Scientific Publishing Company.


Rahmatpour A.,Research Institute of Petroleum Industry RIPI | Vakili A.,Institute for International Energy Studies | Azizian S.,Research Institute of Petroleum Industry RIPI
Heteroatom Chemistry | Year: 2013

Polystyrene-supported gallium trichloride (PS/GaCl3) as a highly active and reusable heterogeneous Lewis acid effectively activates hexamethyldisilazane (HMDS) for the efficient silylation of alcohols and phenols at room temperature. In this heterogeneous catalytic system, primary, secondary, and tertiary alcohols as well as phenols were converted to their corresponding trimethylsilyl ethers with short reaction times and high yields under mild reaction conditions. The heterogenized catalyst is of high reusability and stability in the silylation reactions and was recovered several times with negligible loss in its activity or a negligible catalyst leaching, and also there is no need for regeneration. It is noteworthy that this method can be used for chemoselective silylation of different alcohols and phenols with high yields. © 2013 Wiley Periodicals, Inc.


Iranmanesh S.H.,University of Tehran | Miranian A.,Institute for International Energy Studies | Abdollahzade M.,University of Tehran
2012 11th International Conference on Information Science, Signal Processing and their Applications, ISSPA 2012 | Year: 2012

System identification is defined as finding mathematical models of systems, using experimental measurements and observations. This paper proposes an identification approach based on the singular spectrum analysis (SSA) and least squares support vector machines (LS-SVM) model. The SSA is used in the pre-processing stage for de-noising the measurement data and then the LS-SVM model is trained by the de-noised data. The proposed approach was employed for identification of two nonlinear systems. The simulation results demonstrated the promising performance of the proposed approach and favorable capabilities of the SSA for nonlinear system identification. © 2012 IEEE.


Kalantari M.,Payame Noor University | Yarmohammadi M.,Payame Noor University | Hassani H.,Institute for International Energy Studies
Fluctuation and Noise Letters | Year: 2016

In recent years, the singular spectrum analysis (SSA) technique has been further developed and increasingly applied to solve many practical problems. The aim of this research is to introduce a new version of SSA based on L1-norm. The performance of the proposed approach is assessed by applying it to various real and simulated time series, especially with outliers. The results are compared with those obtained using the basic version of SSA which is based on the Frobenius norm or L2-norm. Different criteria are also examined including reconstruction errors and forecasting performances. The theoretical and empirical results confirm that SSA based on L1-norm can provide better reconstruction and forecasts in comparison to basic SSA when faced with time series which are polluted by outliers. © 2016 World Scientific Publishing Company.


Ghodsi M.,Bournemouth University | Ghodsi M.,Institute for International Energy Studies | Yarmohammadi M.,Payame Noor University
Journal of Systems Science and Complexity | Year: 2014

Forecasting exchange rate is undoubtedly an attractive and challenging issue that has been of interest in different domains for many years. The singular spectrum analysis (SSA) technique has been used as a promising technique for time series forecasting including exchange rate series. The SSA technique is based upon two main choices: Window length, L, and the number of singular values, r. These values are very important for the reconstruction stage and forecasting purposes. Here the authors consider an optimum version of the SSA technique for forecasting exchange rates. The forecasting performances of the SSA technique for one-step-ahead forecast of six exchange rate series are used to find the best L and r. © 2014 Institute of Systems Science, Academy of Mathematics and Systems Science, CAS and Springer-Verlag Berlin Heidelberg.


Ghodsi M.,Bournemouth University | Amiri S.,University of Wisconsin - Green Bay | Hassani H.,Institute for International Energy Studies | Ghodsi Z.,Bournemouth University
Meta Gene | Year: 2016

Genome-wide association studies the evaluation of association between candidate gene and disease status is widely carried out using Cochran-Armitage trend test. However, only a small number of research papers have evaluated the distribution of p-values for the Cochran-Armitage trend test. In this paper, an enhanced version of Cochran-Armitage trend test based on bootstrap approach is introduced. The achieved results confirm that the distribution of p-values of the proposed approach fits better to the uniform distribution, and it is thus concluded that the proposed method, which needs less assumptions in comparison with the conventional method, can be successfully used to test the genetic association. © 2016 Elsevier B.V.

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