Today's, Organizations are exposed with huge and diversity of INFORMATION and INFORMATION assets that are produced in different systems shuch as KMS, financial and accounting systems, official and industrial automation systems and so on and protection of these INFORMATION is necessary.Cloud computing is a model for enabling ubiquitous, convenient, on demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released. several benefits of this model cuses that organization has a great trend to implementing Cloud computing.Maintaining and management of INFORMATION security is the main challenges in developing and accepting of this model. In this paper, at first, according to "design science research methodology" and compatible with "design process at INFORMATION systems research", a complete categorization of organizational assets, including 355 different types of INFORMATION assets in 7 groups and 3 level, is presented to managers be able to plan corresponding security controls according to importance of each groups.Then, for directing of organization to architect it’s INFORMATION security in cloud computing environment, appropriate methodology is presented.Presented cloud computing security architecture, resulted proposed methodology, and presented CLASSIFICATION model according to Delphi method and expers comments discussed and verified.

Among all measures of independence between random variables, mutual INFORMATION is the only one that is based on INFORMATION theory. Mutual INFORMATION takes into account of all kinds of dependencies between variables, i.e., both the linear and non-linear dependencies. In this paper we have classified some well-known bivariate distributions into two classes of distributions based on their mutual INFORMATION. The distributions within each class have the same mutual INFORMATION. These distributions have been used extensively as survival distributions of two component systems in reliability theory.

Introduction: Evaluation of hospital INFORMATION system (HIS) is a complex endeavor, in which all human, technical and organizational aspects should be considered. This study aimed to develop indicators for HIS evaluation.Methods: Present qualitative study was carried out through a cross-sectional method in 2012 in Kerman province, using Delphi technique. Given the objectives of this study, three independent phases were performed including literature review, providing draft indicators for HIS evaluation and reaching consensus. Required data were obtained through interviews and designed forms. Twenty-three experts composed the study population in interview and reaching consensus phases. Validity and reliability were confirmed through content validity and test-retest method, respectively. Data were analyzed using descriptive statistics.Results: Final set of indicators for HIS evaluation consisted of ninety-one indicators under 8 main topics, i.e. technical quality, software quality, architecture and interface quality, vendor quality, after-sale services quality, workflow support quality, support department, outcome quality and HIS cost.Conclusion: Given the complexity of INFORMATION system evaluation, all human, technical and organizational aspects have to be taken into account in any evaluation. Proposed indicators provide the possibility of comprehensive evaluation of HIS.

Background: CLASSIFICATION and use of produced aggregate INFORMATION in health systems and especially in mental and behavioral disorders plays an important role in presenting correct statistics in public health and increasing the health level of the society. Therefore, the aim of this study was to determine the importance of use of mental and behavioral disorders CLASSIFICATION with the approach of international CLASSIFICATION of diseases.Materials and Methods: This study is a review study that was done in 2016. Related INFORMATION was acquired through searching in Pub-Med and Mag Iran data bases, google and yahoo search engine, World Health Organization website and survey in librarian resources and then the collected data were reported based on the study goals. Results: ICD published by WHO, classified the mental and behavioral disorders in chapter 5 of it. Due to the importance of CLASSIFICATION and use of related mental and behavioral INFORMATION, WHO decided to create and publish a special CLASSIFICATION related to it called Clinical Descriptions and Diagnostic Guidelines (CDDG).Conclusion: Concerning the importance of mental and behavioral related INFORMATION, the advanced countries in psychology and psychiatry such as the USA has started to design and produce DSM. With regard to the benefits of CLASSIFICATION and accurate statistics in different aspects such as treatments, statistics, researches, legal issues etc., it is recommended to use the CLASSIFICATION of DSM-5.

CLASSIFICATION of hyperspectral images is one of the most important processes on these images. Hyperspectral images are high dimensional, so CLASSIFICATION of these images is difficult. Therefore, methods that extract low-dimensional subspace structures from the hyperspectral image are considered. The low-rank representation method can extract the low-dimensional subspace structure in the data. This method considers the global structure of the data. In this paper, to preserve the global and local structure in the data, spares and low-rank representation feature extraction method based on spectral and spatial INFORMATION is presented. The data structure is better revealed using this model, and the discrimination of the features is increased. In this model, each pixel is expressed by a linear combination of dictionary atoms. In addition, to solve the optimization problem, the alternating direction method of multipliers has been used. The simulation results show that the proposed model has better results than other methods. For Indian hyperspectral dataset, the proposed method has improved accuracy by more than 1. 4% compared to the state of the art methods.

SLAM Loop Closure Detection INFORMATION Theory Kolmogorov Complexity In this paper the problem of 3D scene and object CLASSIFICATION from depth data is addressed. In contrast to high-dimensional feature-based representation, the depth data is described in a low dimensional space. In order to remedy the curse of dimensionality problem, the depth data is described by a sparse model over a learned dictionary. Exploiting the algorithmic INFORMATION theory, a new definition for the Kolmogorov complexity is presented based on the Earth Mover’s Distance (EMD). Finally the CLASSIFICATION of 3D scenes and objects is accomplished by means of a normalized complexity distance, where its applicability in practice is proved by some experiments on publicly available datasets. Also, the experimental results are compared to some state-of-the-art 3D object CLASSIFICATION methods. Furthermore, it has been shown that the proposed method outperforms FAB-Map 2.0 in detecting loop closures, in the sense of the precision and recall.

Brain-computer interface systems based on CLASSIFICATION of the motor imageries (MI) using multi-channel EEG signal play a major role in the control of artificial limbs and machines by people with disabilities. One of the main problems in classifying these signals to recognize different MI tasks is the large number of channels. The large number of channels causes a lot of cost and hassle during the measurement process, increasing computational load of the preprocessing, feature extraction, and CLASSIFICATION, difficulty of interpretation of results, and over-fitting of the classifier due to the limited number and noisy training samples. Since not all measured channels for classifying a particular MI task have useful INFORMATION, it would be beneficial to select the optimal channels for classifying desired MI tasks. Channel selection methods are categorized into wrapper, filtered, hybrid, and embedded categories. In this paper, a filtering method is used due to less computational cost and the independence of the classifier. The used criterion is very important in filtering methods. Criteria based on first-and second-order data moments are less efficient for non-Gaussian classes. The proposed method uses mutual INFORMATION between candidate channels and class label as a comprehensive criterion and sequential forward selection search strategy. One of the problems in using this criterion is the accurate estimation of mutual INFORMATION in the high dimensional spaces. The kpn entropy estimator is used to accurately estimate the mutual INFORMATION in high dimensional space with limited number of training samples. The power of 2 Hz non overlapping sub-bands in the 8-30 Hz band and in 250 milliseconds non overlapping intervals in half to two and a half seconds after the onset of MI are extracted as features for each channel. The extracted features are reduced to 10 for each channel by combining the unsupervised L1-PCA and supervised NWFE dimensionality reduction methods. The reported results show the ability of the proposed method to select effective channels for classifying left and right hand and feet MI tasks. The overall accuracy of the SVM classifier on test samples for two subjects labeled aa and al from the BCI III competition dataset is 94. 87% and 96. 51%, respectively, while the number of channels is reduced from 118 to 7 channels.

Reducing the number of features is essential to improve the accuracy, efficiency and scalability of a CLASSIFICATION process. There are two main reasons to keep the dimensionality of the input features: computational cost and CLASSIFICATION accuracy. Reducing the number of input features can be done by selecting relevant features (i.e., feature selection) or extracting new features containing maximal INFORMATION about the class label from the original ones (i.e., feature extraction). In this work, we use a mutual INFORMATION based feature extraction (MIFX) algorithm for CLASSIFICATION of electroencephalogram (EEG) in brain-computer interface (BCI). The tasks to be discriminated are the imaginative hand movement and the resting state. The experiments were conducted on four healthy subjects on different days. The results show that the CLASSIFICATION accuracy obtained by MIFX is higher than that achieved by full feature set. Moreover, the results indicate that the performance obtained using MIFX is higher than that obtained using principle component analysis.