|Title: Best Practices in E government: A review of Some Innovative Models Proposed in Different Countries|
|Author(s): Sami M. Alhomod and Mohd Mudasir Shafi|
|Pages: 01-06||Paper ID: 122001-7878-IJECS-IJENS||Published: February, 2012|
Abstract: Governments all around the world are heavily investing towards the implementation of e government to improve services to citizens and reduce costs. With the help of ICT, governments can increase efficiency of their operations and can carry out their administrative operations smoothly. Keeping this fact in mind we thought to carry out research identifying the models for international best practices in e government. This paper offers a comparative study of three models and frameworks concerning the development of best practices in e government. The paper reviews each model in detail and provides a view of how each model can help towards the development of best practice in any e government initiative.
|Keywords: Best Practices, Citizens, E governance Models, Services|
|Full Text (.pdf) | 122 KB|
|Title: Improved Algorithm of Newton Raphson Power Flow using GCC limit based on Neural Network|
|Author(s): Mat Syai’in, Adi Soeprijanto|
|Pages: 07-12||Paper ID: 125601-7474-IJECS-IJENS||Published: February, 2012|
Abstract: Power flow is very important tool for analysis power systems. One of the best power flow methods is Newton Rahpson. The current algorithm of Newton Rhapson power flow still used rectangular limit (Pmin-Pmax /Qmin-Qmax) to represent generator capability curve (GCC). Using rectangular limit is not optimum because it is ignore some are inside GCC. Although less optimum the rectangular limit is still used in many power systems applications, because using GCC as limit in power flow needs complicated mathematical equation. Neural Network (NN) is one of the best methods in imitating a curve. In this paper NN is employed to imitate GCC to limit the operating point of generator in power flow using security check algorithm. The Advantages of using GCC based on NN as power flow limit is can minimize the complicated mathematical equations. Also the algorithm is very simple and accurate especially in representing the operating point near steady state limit.
|Keywords: Newton-Raphson, Power-Flow, Generator capability Curve, Neural-Network, Constructive back-propagation|
|Full Text (.pdf) | 141 KB|
|Title: An Experimental Comparative Study on Thyroid Disease Diagnosis Based on Feature Subset Selection and classification|
|Author(s): M. R. Nazari Kousarrizi, F.Seiti, and M. Teshnehlab|
|Pages: 13-19||Paper ID: 126001-8989-IJECS-IJENS||Published: February, 2012|
Abstract: In this study several methods of feature selection and classification for thyroid disease diagnosis, which is one of the most important classification problems, are proposed. Two common diseases of the thyroid gland, which releases thyroid hormones for regulating the rate of body’s metabolism, are hyperthyroidism and hypothyroidism. Classification of these thyroid diseases is a considerable task. An important problem of pattern recognition is to extract or select feature set, which is included in the pre-processing stage. As a case study, Sequential forward selection and sequential backward selection, which are two well-known heuristic schemes, are employed for feature selection. Another feature selection method considered is genetic algorithm, the popular method for nonlinear optimization problems. Support vector machine is used as classifier to separate the thyroid diseases. This study is based on two thyroid disease datasets. The first dataset is taken from UCI machine learning repository and the second one is the real data which has been gathered by the Intelligent System Laboratory of K.N.Toosi University of Technology from Imam Khomeini hospital.
|Keywords: Thyroid diseases diagnosis; Feature selection; Genetic algorithm; Support vector machine|
|Full Text (.pdf) | 149 KB|
|Title: I-SolFramework: An Integrated Solution Framework Six Layers Assessment on Multimedia Information Security Architecture Policy Compliance|
|Author(s): Heru Susanto, Mohammad Nabil Almunawar, Yong Chee Tuan, Mehmet Sabih Aksoy|
|Pages: 20-28||Paper ID: 126501-9494-IJECS-IJENS||Published: February, 2012|
Abstract: Multimedia Information security becomes a important part for the organization’s intangible assets. Level of confidence and stakeholder trusted are performance indicator as successes organization, it is imperative for organizations to use Information Security Management System (ISMS) to effectively manage their multimedia information assets. The main objective of this paper is to Provide a novel practical framework approach to the development of ISMS, Called by the I-SolFramework, implemented in multimedia information security architecture (MISA), it divides a problem into six object domains or six layers, namely organization, stakeholders, tool & technology, policy, knowledge, and culture. In addition, this framework also introduced novelty algorithm and mathematic models as measurement and assessment tools of MISA parameters.
|Keywords: I-SolFramework, Integrated Solution, Multimedia Information Security Architecture, Six Layers Framework, Information Security Management System|
|Full Text (.pdf) | 411 KB|
|Title: Accuracy Evaluation of Arabic Optical Character Recognition Voting Technique: Experimental Study|
|Author(s): Yusof A. Batawi and Osama A. Abulnaja|
|Pages: 29-33||Paper ID: 127301-4848-IJECS-IJENS||Published: February, 2012|
Abstract: In earlier work we have introduced a high accurate Arabic Optical Character Recognition (AOCR) technique. The proposed algorithm is based on the N-Version Programming (NVP) technique and AOCR software. The proposed technique is called Arabic Optical Character Recognition Voting (AOCRV) scheme. This work studies the effect of the AOCRV technique on AOCR accuracy. The research evaluates four accuracy types: AccuracyALL, Accuracy~, Accuracy!, and Accuracy~!.
Keywords: Arabic Optical Character Recognition, N-Version Programming Technique, Arabic Optical Character Recognition Software Accuracy, Accuracy Evaluation
|Full Text (.pdf) | 199 KB|