Achieving sustainable development goals agenda 2030 is the aspiration of all the United Nation’s member countries. Countries have an uneven distribution of natural resources, economic strength, and capacity building. Many studies analyze these goals using various models and considering different aspects of the kingdom of Saudi Arabia's (KSA) economy. However, none of the studies modelled the key indicators mathematically to quantify the achievement level towards vision 2030; hence aims to bridge the existing literature gap In this paper, the sustainable development goals (SDGs) of KSA are considered, a mathematical model is formulated in light of fuzzy and weighted goal programming using membership function. The model comprises three goals related to the gross domestic product, sustainable energy consumption and employment capacity of the economy's contributing sectors. The analytic hierarchy process integrated to compute the goals’ weights using row geometric mean method. The study established the goals's satisfaction level with 57% overall achievement of the vision 2030. Individually, economic growth goal is 45% achievable. The clean energy consumption-related goal is 67% realizable. The employment-related goal is 78% attainable within the time frame of the vision 2030. The study suggests that KSA diversify its energy sector by concentrating and investing more resources in alternative energy sources, including renewable energy such as solar, wind, biomass, and nuclear energy. Also, there is a need to involve more vibrant and talented youths in the critical decision-making process. The model is simple and can be replicable in a similar country with slight modifications.

Purpose: Supplier selection in the supply chain network (SCN) has strategic importance and involves multiple factors. The multi-criteria nature of the problem coupled with environmental uncertainty requires several procedures and considerations. The issue of decision-making in selecting the best among various qualified suppliers remains the major challenge in the pharmaceutical industry. This study investigated the multi-criteria multi-supplier decision-making process and proposed a model for supplier selection problems based on mixed-integer linear programming.Design/methodology/approach: The concept of principal component analysis (PCA) was used to reduce data dimensionality, and the four best criteria have been considered and selected. The result is subjected to decision-makers’ (DMs’) reliability test using the concept of a triangular fuzzy number (TFN). The importance of each supplier to each measure is established using fuzzy technique for order preference by similarity to an ideal solution approach, and the suppliers have ranked accordingly.Findings: This study proposes a mixed integer linear programming model for supplier selection in a pharmaceutical company. The effectiveness of the proposed model has been demonstrated using a numerical example. The solution shows the model's applicability in making a sound decision in pharmaceutical companies in the space of reality. The model proposed is simple. Readily commercial packages such as LINDO/LINGO and GAMS can solve the model.Research limitations/implications: This research contributed to the systematic manner of supplier selection considering DMs’ value judgement under a fuzzy environment and is limited to the case study area. However, interested researchers can apply the study in other related manufacturing industries. However, the criteria have to be revisited to suit that system and might require varying ratings based on the experts' opinions in that field.Practical implications: This work suggests more insights practically by considering a realistic and precise investigation based on a real-life case study of pharmaceutical companies with six primary criteria and twenty-four sub-criteria. The study outcome will assist organizations and managers in conducting the best decision objectively by selecting the best suppliers with their various standards and terms among many available contenders in the manufacturing industry.Originality/value: In this paper, the authors attempted to identify the most critical attributes to be preserved by the top managers (DMs) while selecting suppliers in pharmaceutical companies. The study proposed an MILP model for supplier selection in the pharmaceutical company using fuzzy TOPSIS.

Malaria is an acute fever sickness caused by the Plasmodium parasite and spread by infected Anopheles female mosquitoes. It causes catastrophic illness if left untreated for an extended period, and delaying exact treatment might result in the development of further complications. The most prevalent method now available for detecting malaria is the microscope. Under a microscope, blood smears are typically examined for malaria diagnosis. Despite its advantages, this method is time-consuming, subjective, and requires highly skilled personnel. Therefore, an automated malaria diagnosis system is imperative for ensuring accurate and efficient treatment. This research develops an innovative approach utilizing an urgent, inception-based capsule network to distinguish parasitized and uninfected cells from microscopic images. This diagnostic model incorporates neural networks based on Inception and Imperative Capsule networks. The inception block extracts rich characteristics from images of malaria cells using a pre-trained model, such as Inception V3, which facilitates efficient representation learning. Subsequently, the dynamic imperative capsule neural network detects malaria parasites in microscopic images by classifying them into parasitized and healthy cells, enabling the detection of malaria parasites. The experiment results demonstrate a significant improvement in malaria parasite recognition. Compared to traditional manual microscopy, the proposed system is more accurate and faster. Finally, this study demonstrates the need to provide robust and efficient diagnostic solutions by leveraging state-of-the-art technologies to combat malaria.