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MR Brain Image Edge Detection Guided with Distribution of Noise and Artifact
Issue:
Volume 1, Issue 1, March 2018
Pages:
1-7
Received:
26 October 2017
Accepted:
13 November 2017
Published:
20 December 2017
Abstract: Magnetic resonance imaging (MRI) has revolutionized radiology in past four decades. MR image edge detection can identify anatomy boundaries and extract features for image analysis applications like segmentation and recognition of anatomy structures. Traditional MR image edge detection methods directly identify discontinuities in MR image domain without considering distribution of noise and aliasing artifact produced from MR scanner and reconstruction. It is difficult to suppress effects of noise and aliasing artifact during the edge detection process. In this project, a novel MR brain image edge detection method is proposed, which is based on parallel MRI reconstruction method. Distribution of noise and aliasing artifact is characterized by geometry factor map that also guides edge detection process for avoiding detection of noise and aliasing artifact. A collaborative learning strategy is applied on voting edges for producing the final edge detection. Experimental results show that the proposed method not only keep anatomy structure boundaries without missing edge components, but also avoid detection of noise and artifact with wrong edges.
Abstract: Magnetic resonance imaging (MRI) has revolutionized radiology in past four decades. MR image edge detection can identify anatomy boundaries and extract features for image analysis applications like segmentation and recognition of anatomy structures. Traditional MR image edge detection methods directly identify discontinuities in MR image domain wit...
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Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) for Predicting the Kinematic Viscosity and Density of Biodiesel-Petroleum Diesel Blends
Youssef Kassem,
Hüseyin Çamur,
Kamal Elmokhtar Bennur
Issue:
Volume 1, Issue 1, March 2018
Pages:
8-18
Received:
1 November 2017
Accepted:
13 November 2017
Published:
24 December 2017
Abstract: Biodiesel is considered as an alternative source of energy obtained from renewable materials. In the present paper, the investigation of the applicability of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) approaches for modeling the biodiesel blends property including kinematic viscosity and density at various temperatures and the volume fractions of biodiesel. An experimental database of kinematic viscosity and density of biodiesel blends (biodiesel blend with diesel fuel) were used for developing of models, where the input variables in the network were the temperature and volume fractions of biodiesel. The model results were compared with experimental ones for determining the accuracy of the models. The developed models produced idealized results and were found to be useful for predicting the kinematic viscosity and density of biodiesel blends with a limited number of available data. Moreover, the results suggest that the ANFIS approach can be used successfully for predicting the kinematic viscosity and density of biodiesel blends at various volume fractions and temperature compared to another models.
Abstract: Biodiesel is considered as an alternative source of energy obtained from renewable materials. In the present paper, the investigation of the applicability of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) approaches for modeling the biodiesel blends property including kinematic viscosity and density at various tem...
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Complexity Reduction of Explicit Model Predictive Control via Combining Separator Function and Binary Search Trees
Jamal Arezoo,
Karim Salahshoor
Issue:
Volume 1, Issue 1, March 2018
Pages:
19-23
Received:
21 November 2017
Accepted:
29 November 2017
Published:
24 December 2017
Abstract: The explicit Model Predictive Control (MPC) has emerged as a powerful technique to solve the optimization problem offline for embedded applications where computations is performed online. Despite practical obstacles in implementation of explicit model predictive control (MPC), the main drawbacks of MPC, namely the need to solve a mathematical program on line to compute the control action are removed. This paper addresses complexity of explicit model predictive control (MPC) in terms of online evaluation and memory requirement. Complexity reduction approaches for explicit MPC has recently been emerged as techniques to enhance applicability of MPC. Individual deployment of the approaches has not had enough effect on complexity reduction. In this paper, merging the approaches based on complexity reduction is addressed. The binary search tree and complexity reduction via separation are efficient methods which can be confined to small problems, but merging them can result in significant effect and expansion of its applicability. The simulation tests show proposed approach significantly outperforms previous methods.
Abstract: The explicit Model Predictive Control (MPC) has emerged as a powerful technique to solve the optimization problem offline for embedded applications where computations is performed online. Despite practical obstacles in implementation of explicit model predictive control (MPC), the main drawbacks of MPC, namely the need to solve a mathematical progr...
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An Efficient Algorithm for Workflow Scheduling in the Clouds Based on Differential Evolution Method
Toan Phan Thanh,
Loc Nguyen The,
Said Elnaffar
Issue:
Volume 1, Issue 1, March 2018
Pages:
24-30
Received:
27 October 2017
Accepted:
4 December 2017
Published:
2 January 2018
Abstract: The Cloud is a computing platform that provides on-demand access to a shared pool of configurable resources such as networks, servers, storage that can be rapidly provisioned and released with minimal management effort from clients. At its core, Cloud computing focuses on manimizing the effectiveness of the shared resources. Therefore, workflow scheduling is one of the challenges that the Cloud must tackle especially if a large number of tasks are executed on geographically distributed servers. The Cloud is comprised of computational and storage servers that aim to provision efficient access to remote and geographically distributed resources. To that end, many challenges, specifically workflow scheduling, are yet to be solved such. Despite it has been the focus of many researchers, a handful efficient solutions have been proposed for Cloud computing. In this work, we propose a novel algorithm for workflow scheduling that is derived from the Opposition-based Differential Evolution method, MODE. This algorithm not only ensures fast convergence but also averts getting trapped in local extrema. Our simulation experiments Cloud Sim show that MODE is superior to its predecessors. Moreover, the deviation of its solution from the optimal one is negligible.
Abstract: The Cloud is a computing platform that provides on-demand access to a shared pool of configurable resources such as networks, servers, storage that can be rapidly provisioned and released with minimal management effort from clients. At its core, Cloud computing focuses on manimizing the effectiveness of the shared resources. Therefore, workflow sch...
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Developing a Conceptual Model for Applying the Principles of Crisis Management for Risk Reduction on Electronic Banking
Maedeh Babaei Chafjiri,
Abbas Mahmoudabadi
Issue:
Volume 1, Issue 1, March 2018
Pages:
31-38
Received:
30 October 2017
Accepted:
20 November 2017
Published:
2 January 2018
Abstract: Despite many benefits of e-banking for customers, operators and bank managers, e-banking activities are associated with some kinds (types) of risks. Therefore, it is essential to manage E-banking risks utilizing the concepts of risk reduction techniques such as crisis management. The main aim of the present research work is to utilize the principles of crisis management for risk reduction in e-banking. Major risks associated to e-banking including security, provisional, operational, reputational, legal and strategic activities have been identified at the first stage followed by developing a conceptual model for the application of crisis management countermeasures to reduce the risks of electronic banking activities at the second stage. The proposed conceptual model has been validated by analyzing the filled out questionnaires designed for this purpose. In addition to conceptual model approval, results revealed that the principles of crisis management could be applied to reduce the risks which are associated with e-banking activities for both customer relations and internal transactions.
Abstract: Despite many benefits of e-banking for customers, operators and bank managers, e-banking activities are associated with some kinds (types) of risks. Therefore, it is essential to manage E-banking risks utilizing the concepts of risk reduction techniques such as crisis management. The main aim of the present research work is to utilize the principle...
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