American Journal of Business and Operations Research

This study addresses the imperative for robust credit risk management strategies by proposing a novel framework tailored for supply chain networks. It aims to bridge existing gaps in credit risk assessment methodologies by amalgamating empirical insights, advanced computational techniques, and comprehensive data analytics. Leveraging a comprehensive dataset encompassing diverse attributes crucial for credit risk assessment, this study employs a meticulous methodology. It integrates machine learning algorithms, notably LightGBM, and exploratory data analysis techniques to preprocess data, examine missing values, assess variable correlations, and construct a predictive model. The empirical journey reveals insightful findings, emphasizing missing value patterns, variable interrelationships, and model performance. Precision-recall and ROC curves depict the model's ability to discern default and non-default cases, showcasing its efficacy in credit risk assessment within supply chain contexts. Our study contributes a foundational framework for strategic credit risk management within supply chain networks, offering actionable insights for stakeholders. While acknowledging limitations and the need for ongoing model refinement, this research sets the stage for future explorations and transformative practices in adaptive risk management strategies for interconnected supply chain networks.

In this paper, we propose some novel similarity measures between picture fuzzy sets. The novel similarity measure is constructed by combining negative functions of each degree membership of picture fuzzy set. We apply them in several pattern recognition problems. Finally, we apply them to find the fault diagnosis of the steam turbine.

The stagnation of the domestic market has brought the majority of the small and medium-sized enterprises (SME) to their knees, leading them to reinvent their way of doing business and find new strategies in order to survive and grow when the environmental conditions are deeply changing. On the one hand, new trends create a strong disruption on a structural level among the productive fabrics, but on the other hand, they represent also an opportunity, which opens new scenarios and new possibilities for the relaunch of SMEs. Among the most important challenges for Italian SMEs is internationalization, which is the possibility for enterprises to trade their goods not only on the domestic market but also on the foreign markets trying to find new opportunities to obtain some advantages. This is a very complicated process, traumatic and challenging in term of resources, but the possibility to have a genetic patrimony and a productive value, as the ones of the “Made in Italy", gives to the products of Italian enterprises a high level of competition and strong differentiation, making this process more accessible.  The growth and competitiveness of enterprises, in particular SMEs, increasingly depend on the ability to apply new knowledge, working methods, and technologies as well as on the opportunity to participate in the commercialization of research developments in order to create new products, services or processes. Therefore, companies should strive to benefit from the opportunities and competitive advantages that innovation brings. SMEs play an important role in economic growth and provide most of the new jobs in Italy. Within the framework of this paper, the insight into the SMEs internationalization process is presented. The article provides an analysis of SMEs in the process of internationalization. Besides it concentrates on the new threats and opportunities represented by the new industrial revolution - Industry 4.0. Analyzing the impact of Industry 4.0 on the internationalization of Italian SMEs, the authors explain the solutions that are being used and the ones that should be taken.

This paper investigates the Role of financial institutions in supporting entrepreneurial success in one of a very important emerging economy in the Middle East, Egypt. Also focuses on different tools used by either governmental or non-governmental agencies   that contributes to help entrepreneurs starting their jobs, and as a result, achieving the sustainable development goals (SDGs). Throughout the paper, reports various documents and academic literature were presented to determine the different types of sustainable development financing instruments available. Moreover, the various financial services provided by governmental and nongovernmental sectors are reviewed. Also, different types of challenges that may face entrepreneurs are also discussed. The interrelationships among those agencies as well as other technical support agencies are formulated in an integrated framework that describes the nature and direction of those relationships for the purpose of providing an adequate support to Egyptian entrepreneurs in various economic sectors.

In this paper, we applied the concept of triangular Neutrosophic number from a special viewpoint. Additionally, we utilized specific varieties of linear triangular Neutrosophic numbers and de-neutrosofication idea which could be very critical for uncertainty concept. Here, an EOQ model has been developed for a linearly dependent demand of non-instantaneous items under shortages. The paper considers holding cost as triangular neutrosophic number (TNN) and optimizes the model. A comparative study is done under crisps and neutrosophic domain and the model gives better result under the later domain. This noble notion will assist us to resolve a plethora of realistic existence problems in neutrosophic area.

The Iris based authentication framework is basically a pattern recognition strategy that utilizes iris designs, which are factually unique. In this study, an efficient iris recognition system is developed with the help of Possibilistic Fuzzy C-Means Clustering (PFCM) and Fuzzy Logic classifier (FLC). The proposed methodology consists of four modules namely, pre-processing, segmentation, normalization and recognition. Initially, in the pre-processing module, the input images are adjusted to do further processing. Then, we segment the iris region from the input image with the help of PFCM. After that, the segmented image is normalized with the help of the Daugman Robber Sheet Model (DRS). Finally, the iris image is recognized with the help of FLC. The performance of the proposed methodology is analyzed in terms of different metrics namely, accuracy, false acceptance ratio (FAR) and false rejection ratio (FRR). Experimental results demonstrate, proposed PFCM+FLC method attains a better accuracy of 97.5%