Generative AI in Supply Chain Management

A new age of creativity and efficiency is ushered in by the integration of Generative Artificial Intelligence (AI) into supply chain management. This in-depth study examines the diverse effects of generative artificial intelligence on supply chain operations, including risk management, inventory optimization, procurement, logistics, and more. Given the predictive capacity of generative AI, traditional methods have been completely modified, enabling companies to anticipate demand, maximize inventory, and expedite procurement procedures with previously unheard-of accuracy. Real-time adaptation is made possible by its dynamic decision-making skills, which also help to promote resilience against interruptions and enable proactive reactions to changing market conditions. However, there are some challenges in implementing generative AI in supply chains. Obstacles requiring strategic navigation and organizational preparedness include skill gaps, ethical considerations, scalability issues, and data integration complexity. Future directions for generative artificial intelligence in supply networks are extremely promising. Substantial improvements are expected to be driven by advances in explainable AI, predictive analytics, seamless integration, and ethical frameworks. Redefining supply chain models could be facilitated by autonomous supply chains, adaptive resilience to disturbances, and increased transparency in decision-making

Toward enhancement of deep learning techniques using fuzzy logic: a survey

Deep learning has emerged recently as a type of artificial intelligence (AI) and machine learning (ML), it usually imitates the human way in gaining a particular knowledge type. Deep learning is considered an essential data science element, which comprises predictive modeling and statistics. Deep learning makes the processes of collecting, interpreting, and analyzing big data easier and faster. Deep neural networks are kind of ML models, where the non-linear processing units are layered for the purpose of extracting particular features from the inputs. Actually, the training process of similar networks is very expensive and it also depends on the used optimization method, hence optimal results may not be provided. The techniques of deep learning are also vulnerable to data noise. For these reasons, fuzzy systems are used to improve the performance of deep learning algorithms, especially in combination with neural networks. Fuzzy systems are used to improve the representation accuracy of deep learning models. This survey paper reviews some of the deep learning based fuzzy logic models and techniques that were presented and proposed in the previous studies, where fuzzy logic is used to improve deep learning performance. The approaches are divided into two categories based on how both of the samples are combined. Furthermore, the models' practicality in the actual world is revealed

Data for: Generative Adversarial Network-based Semi-supervised Learning for Real-time Risk Warning of Process Industries

Present program consists of MLP, CNN, Ladder network, and DCGAN.Dataset1 is balanced data, Dataset2 is unbalanced data.Present program was codified on tensorflow 1.3.0.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Risk Assessment of Deadly Economic Socio-Political Crisis with Correlational Network and Convolutional Neural Network

From social analysis to biology to machine learning, graphs naturally occur in a wide range of applications. In contrast to studying data one at a time, graphs' unique capacity to capture structural relationships among data enables them to yield additional insights. Nevertheless, the capacity to learn from graphs can be difficult because meaningful connectivity should exist between data and the form of data such as text, numbers or categories should allow for building a graph from their relationships. Investigating hidden patterns in the variation of development indicators and severe socio-political crises that happened in low-income countries is an analytical approach that has been experimented with in this research. Evidence of a correlation between socio-political crises and development indicators suggests that a method to assess the risk of crisis should consider the context of each country, as well as the relative means of crisis. This research reviewed different risk assessment methods and proposed a novel method based on a weighted correlation network, and convolution neural network, to generate images representing the signature of development indicators correlating with a crisis. The convolution neural network trained to identify changes in indicators will be able to find countries with similar signatures and provide insights about important indicators that might reduce the number of deadly crises in a country. This research enhances the knowledge of developing a quantitative risk assessment for crisis prevention with development indicators