The rule based categorization approaches such as associative classification have the capability to produce classifiers rival to those learned by traditional categorization approaches such as Naïve Bayes and K-nearest Neighbor. However, the lack of useful discovery and usage of categorization rules are the major challenges of rule based approaches and their performance is declined with large set of rules. Genetic Algorithm (GA) is effective to reduce the high dimensionality and improve categorization performance. However, the usage of GA in most researches is limited in the categorization preprocessing stage and its results is used to simplify the categorization process performed by other categorization algorithms. This paper proposed a hybrid GA rule based categorization method, named genetic algorithm rule based categorization (GARC), to enhance the accuracy of categorization rules and to produce accurate classifier for text mining. The GARC consists of three main stages; namely, search space determination, rule discovery with validation (rule generation), and categorization. The experimental results are carried out on three Arabic text datasets with multiple categories to evaluate the efficiency of GARC. The results show that a promising performance was achieved by using GARC for Arabic text categorization. The GARC achieves the best performance with small feature space in most situations.

The SMEs is currently experiencing many challenges, especially performance improvements. To improve performance, companies are required to increase the effective organizational learning so that it is emphasized on improving human resource development in addition to being able to overcome market uncertainty. This study examined empirically the variables of human resource development and market uncertainty by using the moderating variable of effective organizational learning to increase corporate performance. Using data from 100 SMEs in two regencies in Central Java, and carrying out using AMOS 22, the results prove that effective organizational learning moderates human resource development and market uncertainty in their activities. These results prove that human resource development is needed to improve the effective organizational learning. This study provides the best insight into human resource development, especially in the SMEs. Some of the observed causes have been discussed in this study.

The most used approach to solve tolerance analysis problems for flexible products is the method of influence coefficients that combines the finite element analysis with statistical analysis in order to establish a relationship between the product deviation and part deviation and to foresee the statistical distribution of stresses. The key of this relationship is the sensitivity matrix for the deviations and stresses that can be evaluated by different methods of influence coefficients. Therefore, the aim of this work is to make a review of these methods applying them to evaluate on some single parts the statistical distribution of deviations and stresses due to operating conditions, i.e. due to the displacements applied to the part during its working.

The tourism sector is considered as one of the most important economic sectors and has great importance in increasing the gross domestic product of any country in the world. Jordan is one of the countries attracting tourism from all over the world because of its natural tourist attractions and in-dustrial parks, as well as the availability of security and safety elements compared with neighboring countries. As the tourism sector is influenced by several factors affecting the tourism sector, this study aimed to investigate the role of the cultural dimensions and their relationships with the Jorda-nian society characterized by customs and traditions, values, norms and religious beliefs in promoting the tourism sector in Jordan. The study concluded that there was a statistically significant relationship between the cultural dimensions and the tourism promotion, so that the tourism promotion in Jordan was directly influenced by the change in cultural dimensions. The study concluded that the governments should increase the interest of the tourism sector and the need to pay special attention on educating the Jordanian society about the importance of this sector for the benefit of the country, which would positively affect their level of income, alleviate unemployment, and raise the standards of their lives.

Advanced production systems usually are complex in nature and aim to deal with multiple performance measures simultaneously. Therefore, in most cases, the consideration of a single objective function is not sufficient to properly solve scheduling problems. This paper investigates the multi-objective mixed no-idle flowshop scheduling problem. The addressed optimization case is minimizing makespan subject to an upper bound on total completion time. To solve this problem, we proposed a two-stage iterated greedy and a multi-objective constructive heuristic. Moreover, we developed a new multi-objective improvement procedure focusing on increasing the performance of the developed methods in solving the addressed problem. and a new initialization procedure. We performed several computational tests in order to compare our developed methods with the main algorithms from similar scheduling problems in the literature. It was revealed that the proposed approaches give the best results compared with other state-of-the-art performing methods.

The paper examined the influence of internal control systems on financial performance of Nigeria’s construction firms. It utilized ridge regression to analyze data from a self-administered questionnaire administered to 305 employees at a company. Internal control had a strong positive impact on financial performance, as evidenced by an R2 of 82.92 percent. In the majority of cases, internal control had positive and statistically significant effects and correlations with financial performance. The positive effects of the control environment and risk assessment were statistically insignificant. It suggests upgrading the control system; the control system must be present and functional.

This research explores the impact of social capital on the adoption of digital applications within Micro, Small, and Medium Enterprises (MSMEs). In the current competitive environment, embracing digitalization is essential for improving the competitiveness of MSMEs; however, challenges such as weak social networks can impede technology uptake. Utilizing a quantitative approach, the study distributed questionnaires to 160 MSME participants, with data analysed through path analysis using SmartPLS. The findings indicate that social capital—which includes relationships, networks, and trust among business stakeholders—plays a critical role in facilitating access to and adoption of digital applications. Results suggest that MSMEs with strong social networks are more adept at integrating digital technologies, fostering innovation, and improving overall business performance. The study recommends initiatives to enhance collaboration and strengthen social networks among MSMEs to support their digitalization efforts. Ultimately, this research deepens the understanding of social capital's influence on MSME digitalization and offers practical strategies for stakeholders to increase the utilization of digital applications in this sector.

This study aims to design a new model for selecting most fitting new product development projects in a pool of projects. To catch the best model, we assume new products will be introduced to the competitive markets. Also, we suppose the revenue yielded by completed projects can be reinvested on implementation of other projects. Other sources of financing are borrowing loans from banks and initial capital of the firm. These limited resources determine most evaluated projects to be performed. Several types of interactions among different projects are considered to make the chosen projects more like a portfolio. In addition, some numerical examples from the real world are provided to demonstrate the applicability of the proposed model. These examples show how the particular considerations in the suggested model affect the results.

The purpose of this study was to determine the role of the ideal aspects of ownership structure and capital structure in determining the uncertainty model of operational of Walmart. This type of research is an explanatory survey using qualitative and quantitative approaches. The qualitative method is carried out by descriptive analysis by conducting a field survey using a questionnaire designed in such a way. Respondents of this study were the 78 managers of the community minimart in Medan City, North Sumatera, Indonesia who were selected by purposive sampling method. Meanwhile, the quantitative method was carried out using SEM PLS analysis by analyzing the indicators of aspects of ownership structure, capital structure and dimensions of operational success. The results show that the capital structure variable had a significant effect on Walmart's operational success. Meanwhile, the ownership structure variable had no significant effect on Walmart's operational success. The novelty that is produced from this research is that the success of community self-service is determined by the capital structure. Capital is an obstacle faced by community supermarkets because with limited capital it is difficult for community supermarkets to expand their business.

Clustering is one of the most common unsupervised data mining classification techniques for splitting objects into a set of meaningful groups. However, the traditional k-means algorithm is not applicable to retrieve useful information / clusters, particularly when there is an overwhelming growth of multidimensional data. Therefore, it is necessary to introduce a new strategy to determine the optimal number of clusters. To improve the clustering task on high dimensional data sets, the distance based k-means algorithm is proposed. The proposed algorithm is tested using eighteen sets of normal and non-normal multivariate simulation data under various combinations. Evidence gathered from the simulation reveal that the proposed algorithm is capable of identifying the exact number of clusters.