Sustainable supplier selection and optimal quantity transportation (S3OQT) play an important role in supply chain management. This research represents a new four-stage solution approach for S3OQT where the multi-crite...
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Sustainable supplier selection and optimal quantity transportation (S3OQT) play an important role in supply chain management. This research represents a new four-stage solution approach for S3OQT where the multi-criteria decision making (MCDM) methods are integrated through an optimization model. In first stage, a new uncertainty interval type-2 spherical fuzzy set (IT2SFS) is introduced to help the decision-makers (DMs) for securing and reliable results in hesitancy situations. We develop a new operator on IT2SFS under Dombi t-norm and t-conorm by integrating Muirhead mean (MM) operator based on Choquet integral (CI). The preferences and priorities to the sustainable criteria based on interaction and interrelationship are represented by CI. Thereafter, the weights of the criteria and sub-criteria are determined by CI-indifference threshold-based attribute ratio analysis (ITARA) method by utilizing the proposed operator. In second stage, to evaluate the weights of the suppliers and to rank these, we construct a new MCDM method CI-TODIM (an acronym in Portuguese of interactive multi-criteria decision-making)-measurement alternatives and ranking according to compromise solution (MARCOS) method by utilizing the proposed operator and then finally design a new ranking function. In third stage, a new model on stochastic multi-objective mixed-integer non-linear solid transportation problem (SM2NSTP) is established to identify suitable supplier under sustainable risk criteria, and then, optimal quantity of products are transported from each supplier. Thereafter, we propose TOPSIS-neutrosophic-game theoretic approach (TNGTA) to obtain Pareto-optimal solution. We apply Ε-constraint method to obtain Pareto-optimal solution from SM2NSTP model. In the fourth stage, a comparative study is drawn among the obtained Pareto-optimal solutions that are extracted from TNGTA and Ε-constraint method. Finally, two MCDM models, CRITIC-TOPSIS and CRITIC-MARCOS, are used to help the DMs for s
In this study, we propose an effective system called RG-Guard that detects potential risks and threats in the use of cryptocurrencies in the metaverse ecosystem. In order for the RG-Guard engine to detect suspicious t...
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In this study, we propose an effective system called RG-Guard that detects potential risks and threats in the use of cryptocurrencies in the metaverse ecosystem. In order for the RG-Guard engine to detect suspicious transactions, Ethereum network transaction information and phishing wallet addresses were collected, and a unique dataset was created after the data preprocessing process. During the data preprocessing process, we manually distinguished the features within the original dataset that contained potential risk indicators. The learning process of the RG-Guard engine in risk classification was achieved by developing a deep learning model based on LSTM + Softmax. In the training process of the model, RG-Guard was optimised for maximum accuracy, and optimum hyperparameters were obtained. The reliability and dataset performance of the preferred LSTM + Softmax model were verified by comparing it with algorithms used in risk classification and detection applications in the literature (Decision tree, XG boost, Random forest and light gradient boosting machine). Accordingly, among the trained models, LSTM + Softmax has the highest accuracy with an F1-score of 0.9950. When a cryptocurrency transaction occurs, RG-Guard extracts the feature vectors of the transaction and assigns a risk level between 1 and 5 to the parameter named βrisk. Since transactions with βrisk > = 3 are labelled as suspicious transactions, RG-Guard blocks this transaction. Thus, thanks to the use of the RG-Guard engine in metaverse applications, it is aimed to easily distinguish potential suspicious transactions from instant transactions. As a result, it is aimed to detect and prevent instant potential suspicious transactions with the RG-Guard engine in money transfers, which have the greatest risk in cryptocurrency transactions and are the target of fraud. The original dataset prepared in the proposed study and the hybrid LSTM + Softmax model developed specifically for the model are expected to c
Today, it can be said that coding has become a key competence for students and people working in many different fields in the business world. It is assumed that those who seek and develop new ways to learn-teach codin...
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Ensuring the safety of human workers collaborating with industrial robots is paramount. This research work presents a novel approach by developing a safety-related intruder detection system for the operational zones o...
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Improvements in deep learning has facilitated traffic management by providing analyzing and prediction capabilities on the road. In this study, vehicle and pedestrian detection with YOLOv8 was carried out with differe...
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Technology advancements have transformed medical science and practice, leading to the vast gathering of a wide range of medical data. Medical researchers use artificial intelligence techniques extensively because they...
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The concept of reliability is crucial in assessing the ability of a transportation network to provide the continuity in operation. Because of this, in the recent transportation business, sources are being pressurized ...
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Pythagorean fuzzy sets have a lot of applications in the field of engineering and scientific problems. Also, besides, the power averaging (PA) operator can reduce the influence of evaluating extreme data from some bia...
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An increasing number of steel factories around the world are integrating two processes: continuous casting and hot rolling. The slab warehouse is the link between these two processes. Slabs of certain sizes are cast o...
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The work is devoted to solving the current scientific and technical problem of constructing a diagnostic decision support system in medicine based on a heterogeneous ensemble classifier model that implements two appro...
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