Introduction: The changes in river processes due to river discharge and sedimentation as a primary principle driving force can affect the geometry of rivers. Determining the amount of sediment and floodplain and water quality study are prerequisites for river management operations. Any change in the steady-state of the rivers will result in physical changes in the rivers and a new reaction to the rivers' behavior. Morphological studies to determine the quantity and quality of river response will predict future river behavior. The downstream river reaches of the Hamzekhanloo River basin is one of the most important agricultural areas of Germi city due to its fertile flood plains and sufficient water availability, which has undergone many changes in recent years. In this study, the Hamzekhanloo River was investigated based on the Rosgen stream classification scheme. Methodology: The Hec-Ras hydrodynamic model was used to simulate the Hamzekhanloo River cross-sections and floodwater capacity. The processing of the required data for modeling purposes was performed at the ArcGIS software; the classification of stream reaches was done using the Rosgen stream classification system. Rosgen classification system predicts river behavior based on morphology and hydraulic relationship and flow sediment with specific morphology. Based on Rosgen's method, morphological characteristics of rivers are investigated at four different levels but focuses more on two levels of general geomorphic properties and morphological description. Level 1 (General classification): Describes the morphological characteristics of the river obtained by combining information on catchment, landform, and valley morphology. Level two (descriptive classification) of the river. Results and Discussion: The results of the Rosgen classification scheme showed that the studied river had been classified at the C class in some river reaches, which had high flood sensitivity, high vegetation control, high sediment recovery, and sediment supply potential. Also, these reaches had narrow to wide valleys, constructed from alluvial deposition with a well-developed floodplain. Meanwhile, some sections of the study river fall in the B class according to the Rosgen classification. These reaches exist primarily on moderately steep to gently sloped terrain, resulting in narrow valleys that limit the development of a wide floodplain. These streams display a low channel sinuosity, and streambank erosion rates are normally low. The sensitivity to flooding and sediment supply is high; the influence of moderate vegetation control and recovering potential is excellent. Moreover, the cross-section patterns in the river and the parameters affecting the classification and segmentation of reaches are consistent with the overall pattern on the Rosgen classification model. Conclusions: The river bed of the Hamzekhanloo River is a combination of rubble, gravel, and sand. Farmers and gardeners dig the riverbed and store water to irrigate the orchard fields and gardens during the summer, and crop cultivation is observed in the river bed and floodplain. Sand mining is a common activity in the river bed to carry out the development and construction purposes of the area. Sand removal from the riverbed has led to the formation of ponds within the basin, and such alterations have altered the bed and morphology of the Hamzekhanloo River. Thus, Rosgen's model can predict the geomorphic quantification of the Hamzekhanloo River and rivers with similar conditions. This type of river channel morphological classification can be used to develop engineering designs and management implications and river restoration.

The proposed research incorporates the utilization of a heavy-tailed skewed distribution referred to as the inverse Weibull as a link function in the context of a binary classification model. This selection is motivated by the need to address the existence of rare or extreme events in random processes. The study introduces a model that relies on the Inverse Weibull (TYPE II) distribution, and the estimation of model parameters is accomplished through the application of maximum likelihood methods. When the outcomes are compared to those derived from other link functions such as TYPE I (Complementary log) and TYPE III (Weibull) based on extreme value distributions using standard classification data as well as real-life data, it becomes apparent that the Inverse Weibull (TYPE II) model exhibits exceptional performance. This assessment of performance takes into account several criteria, encompassing the Akaike information criterion, Bayesian information criterion, Area under the curve, and Brier scores. In conclusion, the study establishes that the proposed model demonstrates considerable robustness in its performance, rendering it a viable choice for the modeling of binary classification problems.

The use of mixture models for clustering and classification has burgeoned into an important subfield of multivariate analysis. These approaches have been around for a half-century or so, with significant activity in the area over the past decade. The primary focus of this paper is to review work in model-based clustering, classification, and discriminant analysis, with particular attention being paid to two techniques that can be implemented using respective R packages. Parameter estimation and model selection are also discussed. The paper concludes with a summary, discussion, and some thoughts on future work.

The aim of this paper is the classification the economic entropy index in a macroeconomic model. Therefore, the descriptive-analytical methodology and econophysics and systems theory were used. The results show that the economic entropy was divided in four sections, shock entropy, respiration entropy, sleep entropy, and entropy of waste. Increasing the economic entropy index due to the scarcity of environmental resources, predicts the likelihood of an economic catastrophe. This will not only bring about economic growth faces serious problems, but the environment poses a serious problem as a place of residence. According to the results, reducing the instabilities and external stresses to reduce shock entropy, developing the appropriate rules for firms to reduce respiratory entropy, policies based on the reduction of physical and social waste to reduce the entropy of waste and policies for the use of production spaces that are not currently in use to reduce sleep entropy recommended.

Since a robust and accurate classification of tumors is essential for successful treatment of cancer, classification of DNA microarray data has been widely used in effective diagnosis of cancers and some other biological maladies. This paper presents a new ensemble machine learning model for development of robust microarray data classification. The main idea of the proposed model is to build a homogeneous ensemble of base classifiers using different gene subsets to predict the class of each sample. We used stacked generalization as the combination scheme to learn the base classifiers' behavior for each sample. The performance of the proposed model has been evaluated over four publicly available cancer microarray datasets. Experimental results have demonstrated that not only the suggested classifier greatly outperforms existing conventional machine learning methods, but also it is notably more accurate and robust than some traditional ensemble based classifiers and its performance could be compared to some recently introduced outstanding ensemble-based methods.

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.

In the field of logistics, there is a daily need for decision making, i.e. the need to solve business problems by selecting an appropriate solution. During the implementation of decision-making processes, it is necessary to find an optimal solution that will best meet the needs of companies. The selection of an optimal solution is crucial for the profitability, cost-effectiveness and long-term development of companies. The decision-making process in logistics is facilitated by applying various tools such as multi-criteria decision-making methods. In this paper, an integrated SWARA (Step-wise Weight Assessment Ratio Analysis) – MARCOS (Measurement Alternatives and Ranking according to Compromise Solution) model was developed and applied in order to classify products. Fifty alternatives, i.e. products were evaluated based on three criteria. The first criterion is the quantity of purchased products, the second criterion is the unit price of products and the third criterion is the annual value of purchase. The SWARA method was applied to determine the significance of the criteria, while the classification of products was performed using the MARCOS method. According to the results of the originally created MCDM model, the products were grouped into three categories A, B, and C. Then, a sensitivity analysis was performed using a model involving the integration of SWARA method and ABC analysis. Using this model, the classification of products into three groups was performed on the basis of the aforementioned criteria, and then a comparative analysis was conducted.