درخت حوزه‌های تخصصی

Dimensionality reduction is among the data mining process that is used to reduce the noise and complexity of features in various datasets. Feature selection (FS) is one of the most commonly used dimensionalities that reduces the unwanted features from the datasets. FS can be either wrapper or filter. Wrappers select subsets of the feature with better classification performance but are computationally expensive. On the other hand, filters are computationally fast but lack feature interaction among selected subsets of features which in turn affect the classification performance of the chosen subsets of features. This study proposes two concepts of information theory mutual information (MI). As well as entropy (E). Both were used together with binary cuckoo optimization algorithm BCOA (BCOA-MI and BCOA-EI). The target is to improve classification performance (reduce the error rate and computational complexity) on eight datasets with varying degrees of complexity. A support vector machine classifier was used to measure and computes the error rates of each of the datasets for both BCOA-MI and BCOA-E. The analysis of the results showed that BCOA-E selects a fewer number of features and performed better in terms of error rate. In contrast, BCOA-MI is computationally faster but chooses a larger number of features. Comparison with other methods found in the literature shows that the proposed BCOA-MI and BCOA-E performed better in terms of accuracy, the number of selected features, and execution time in most of the datasets.

The core capital of many businesses is their brand. Today companies not only use the brand to differentiate themselves from competitors in the market, but also use the brand to reach the minds and hearts of customers and create a special emotional relationship with them. In this study, due to the great differences between consumer markets and industrial markets, the factors affecting in industrial brand equity have been investigated. The statistical population includes all companies producing composite products and unsaturated polyester resins and vinyl esters in Iran. Due to the limitations of competitive industrial markets, the Convenience sampling method has been used and to test the research hypotheses, structural equation modeling based on the partial least squares method has been used. Research findings have shown that Consumers’ understanding of brand characteristics, Consumers’ brand evaluation, Consumers’ affective towards the brand and customer behavior towards the brand have a positive and significant effect on the brand equity. Also, brand awareness and brand association have a positive and significant effect on Consumers’ understanding of brand characteristics. Reputation, leadership, quality and distinction or uniqueness of the brand has a positive and significant effect on customers' evaluation of the brand. Brand satisfaction, brand commitment, brand trust and brand identity have a positive and significant effect on consumers’ affective towards the brand. Finally, willing to pay a price premium, Word – of – mouth (WOM) recommendation, brand usage and Acceptance of brand extensions has a positive and significant effect on customer behavior towards the brand.

Managing supply chain operations in a reliable manner is a significant concern for decision-makers in competitive industries. In recent years consumers and legislation have been pushing companies to design their activities in such a way as to reduce negative environmental impacts more and more. It is therefore important to examine the optimization of total supply chain costs and environmental impacts together. However, because of the recycling of deteriorated products, the environmental impacts of deteriorating items are more significant than those of non-deteriorating ones. The subject of supply chain of deteriorating products, simultaneously considering costs and environment has gained attention in the academia and from the industry. Particularly for deteriorating and seasonal products, such as fresh produce, the issues of timely supply and disposal of the deteriorated products are of high concerns. The objective of this paper is to develop multi objective mathematical model and to propose a new replenishment policy in a centralized supply chain for deteriorating products. In this model, the manufacturer produces a new product and delivers it to a distant market, and then the distributor buys the product and sells it to the end consumers. This study presents a new mathematical model of the location-routing problem (LRP) of facilities in supply chain network (SCN) for deteriorating products through taking environmental considerations, cost, delivery time and customer satisfaction into account across the entire network and customer satisfaction. In order to solve the model, the combination of the two red deer algorithm (RDA) and annealing simulation (AS) was proposed. We then perform the network optimization in SCN and provide some managerial insights. Finally, more promising directions are suggested for future research.

As a vital practice in human resources management, workforce planning determines that the organization is needed how many workforces, when, and which position. Workforce planning is not formed in a vacuity, and job variables such as skill, ability, and knowledge affect the planning. This study examines workforce planning in the health sector concerning the workforce's flows such as recruitment, promotion, degradation, fired, retention, and termination of the contract according to the skill audit and the role of performance appraisal results in this program. For this purpose, a dynamic model has been applied that can provide optimal answers to the number of workforces required and the workforce's flows at any time during the planning periods. This model has been implemented in a hospital ward with two levels of hierarchy: nurses, supervisors, and nursing directors. The results show that the designed system can converge the existing values to the optimal values by noting delays by the optimal control model.