Autonomous/self-driving vehicles have gained significant attention these days, as one of the intelligent transportation systems. However, those vehicles have risks related to their physical implementation and security against cyber threats. Therefore, this study proposes a new security-by-design model for estimating the uncertainty of autonomous vehicles and measuring cyber risks; thus it assists decision-makers in addressing the risks of the physical design and their attack surfaces. The proposed model is developed using neutrosophic sets that efficiently tackle multi-criteria decision-making (MCDM) problems with extensive conflicting criteria and alternatives. The proposed model integrates MCDM, Analytic Hierarchy Process (AHP), Multi-Attributive Border Approximation Area Comparison (MABAC), and Preference Ranking Organization Method for Enrichment Evaluations II (PROMETHEE II), along with single-valued neutrosophic sets (SVNSs). An illustrative case considering ten risks in self-driving vehicles is used to validate the feasibility of the proposed model. Compared to the state-of-the-art methods, the proposed model is considered consistent and reliable to deal with and represent uncertainty and incomplete risk information using neutrosophic sets.

The development of sustainable green buildings (GBs) is a major contribution to the preservation of the environment. Sustainable thinking in GB construction is not a supplementary element, but rather necessary to achieve the building’s functional, economic, and environmental efficiency in order to preserve resources and meet current and future needs. In particular, developing countries can apply the idea of sustainability in GBs by following international policies and standards, combined with their local characteristics, to construct GBs that are aligned with the environment and are in line with the available local capabilities and resources. The paper focuses on the dimensions and indicators of sustainable design for GBs in developing countries to achieve the positive dimensions of building sustainability, such as preserving energy and natural resources, water management, adaptation to the surrounding environment, and respecting the needs of its users. We assess and prioritize the dimensions and indicators of GBs through the use of a multi-criteria decision-making (MCDM) method under a neutrosophic environment. Initially, the Delphi method is employed to capture preference and to determine the dimensions and their indicators in addition to provide preference among sub-indicators. The relative importance of the selected dimensions and indicators is assessed through the analytical hierarchy method (AHP) method. The results indicate that the water efficiency dimension is the most significant, with a weight of 0.330, while the energy efficiency dimension is the least significant for GBs in developing countries, with a weight of 0.100. The paper concludes with a set of administrative implications for applying sustainable development strategies in GBs.

Churn prediction has become one of the core concepts in customer relationship management within the insurances, telecom, and internet service provider industries, which is essential in customer retention. Therefore, this study attempts to analyze the effectiveness of the advanced machine learning and deep learning models for churn prediction in the evaluation of the models’ performance across different sectors. This would help conclude whether the varied patterns of the churn throughout different sectors to the level that affects the model performance and to what extent. The work includes three datasets: namely, insurance churn, internet service provider customer churn, and Telecom churn datasets. The implementation and comparison conducted in this study of models include XGBoost, Convolutional Neural Networks (CNNs), and Ensemble Deep Learning with the pre-trained hybrid approach. The results show that the ensemble deep learning model outperforms other models in terms of accuracy and F1-score, achieving accuracies of up to 95.96% in the insurance churn dataset and of 98.42% in the telecom churn dataset. Moreover, traditional machine learning models like XGBoost also produced competitive results for selected datasets. The proposed deep learning ensembles reveal the strength and possibility for churn prediction and provide a benchmark for future research relevant to customer retention strategies. Also, the proposed ensemble deep learning model shows stable performance across different sectors, which reflects its ability to capture the varied churn patterns of different sectors.

Most studies in recent decades focused on transforming linear economics into circular through recovering and remanufacturing the products. Circular Economies (CE) aim to minimize the usage of resources by utilizing the waste in production as new or raw materials. Interconnectivity between parties in the industrial system provides decision-makers with rich information and anticipation of failure. Industry 4.0 technologies (I4.0) allow for handling such issues, protecting the environment by utilizing resources efficiently, and restructuring the industry to be smarter as well. This paper contributes to achieving cleaner production (CP), CE, and social for manufacturers through the linkage between 6R methodology with new technologies of I4.0 such as Blockchain technology (BCT) and big data analytical technology (BDA). In this paper, the authors proposed a Multi-criteria decision-making (MCDM) decision framework based on the best-worst method (BWM), Decision-Making trial and evaluation laboratory (DEMATEL), Technique for order of preference by similarity to ideal solution (TOPSIS), and Complex Proportional Assessment (COPRAS). The authors contributed to addressing the weaknesses and problems of these subjective MCDM methods through the cooperation of the neutrosophic theory with the usage of MCDM methods in this work. In the first stage, all criteria that influence sustainable manufacturer selection are specified using literature research on this topic. BWM-based neutrosophic theory was combined to get the criteria’s weights with the aid of DEMATEL-based neutrosophic to obtain the least and best criteria used in BWM in the second stage. The optimal sustainable manufacturer was selected based on TOPSIS and COPRAS under neutrosophic theory in the third and fourth stages, respectively. Furthermore, a case study performed indicated manufacturer 2 (A₂) is an optimal sustainable manufacturer in two ranking methods otherwise, manufacturer 4 (A₄) is the worst sustainable manufacturer. The contribution of this work is to propose a hybrid MCDM with an uncertainty theory of neutrosophic for sustainable manufacturer selection based BDA-BCT with 6R. Sensitivity analyses were carried out to show the decision’s flexibility in various scenarios. Finally, the consequences for management viewpoints were considered.

There has been an increasing tendency for the generation of energy from diverse renewable resources because of the application of contemporary pollution mitigation and justification regulations. Precisely a consequence, choosing the best renewable energy source might be considered a challenging issue given the complexity of the future conditions in any society. Environmental, economic, social, and technical aspects are merely some of the factors that are taken into consideration while evaluating renewable energy sources (RnESs). The suitable RES selection problem, which relies on ambiguous and imprecise data, is also influenced by a variety of circumstances. Hence, this study constructs multi-stages intelligent decision-making model (MsIDMM) based on multi-criteria decision making (MCDM) with support with neutrosophic theory especially, interval valued neutrosophic sets to rank the sources of renewable energy. Ultimately, combinative distance-based assessment (CODAS) method under interval-valued neutrosophic sets is used to rank the sources of renewable energy.

Inventory management (InvM) is a critical aspect of supply chain management (SCM) and optimizing inventory levels can lead to significant cost savings and improved customer satisfaction. Hence, business organizations have recently worked to increase the value of their operations by utilizing modern and digital technologies as industry 4.0 (Ind 4.0). In the era of Ind 4.0, the technologies as Internet of Every Things (IoET), AI, BDA…etc. Recognizing the significance of InvM in a supply chain (SC), motivated us to volunteer technologies of Ind 4.0 as machine learning (ML) techniques to boosting Decision Making (DM) process to optimize InvM. Subsequently, this study is constructed for providing an intelligent Decision Support ML framework for automating the process of optimizing inventory management. Our constructed framework ensembles powerful ML prediction algorithms for inventory management, such as Artificial Neural Networks (ANNs), Random Forest (RF), and Support Vector Machine (SVM) for building robust sales regressors. The extensive experimentations on a case study of Walmart suggested that the proposed system has the potential to transform inventory management and improve supply chain performance, but further research is needed to address the challenges of data availability and quality.