Various approaches to improve the classification rate of neural networks (NN) exist. Nevertheless, the application of integrity constraints to mitigate this rate is novel. This paper investigates the effectiveness of integrity constraints (ICs or short: constraints) to improve the performance of neural networks (NNs). This applies in particular to data reduction in training data. The study starts with the application of ICs to the initial NN classification, focusing on the development of data set-specific constraints. These constraints are created by machine learning algorithms, such as multiple linear regression. The method consists of applying these constraints to the misclassified data sets from different tests with the aim of reducing the misclassification rate. The effectiveness of this approach is quantified by comparing the original misclassification rates with those after applying the IC, where a significant reduction was observed in three different test cases. For example, one test case can be described as significant: In Test 1, the misclassification rate decreased from 0.78% to 0.19%, which corresponds to a reduction of 75.6%. Similar improvements were observed in the subsequent tests, underlining the potential of ICs to improve classification accuracy. Thus, this study provides convincing evidence that the NNIC approach is a valuable tool for mitigating misclassification problems in neural network applications. The combination of training a NN and subsequently apply ICs from the training data (NNIC approach) is new and the experimental results indicate evidence for improving the classification rate.

Artificial Intelligence (AI), in particular generative AI (GAI) and neural networks (NN), is emerging as a transformative force in the evolving landscape of digital transformation in industries such as medicine and marketing. The paper describes the actual and potential impact of AI on the advancement of sustainable practices in these areas through a theoretical study of existing research. Through case studies, from NN in medicine, e.g., skin cancer diagnosis to GAI in marketing strategy, we highlight current applications and challenges. Despite the undeniable capabilities of AI in these domains, there remains a dearth of research that links AI to tangible sustainability outcomes. Our findings on AI in marketing highlight its importance in enhancing branding, optimizing pricing, refining channels and personalizing promotions. Furthermore, the application possibilities of NN in medical are presented. Due to the high model quality in the field of cancer detection, diagnostic processes can be designed more efficiently by NN and thus staff shortages can be compensated cost-efficiently and patient-centered. However, while AI offers promising opportunities, it also requires an ethical, socially responsible approach, as underscored by recent studies. This research underscores the imperative of AI integration for holistic, sustainable outcomes in the medical and marketing sector.

Assessing the power and trustworthiness of AI remains a major challenge in scientific advancement. The distinction between weak and strong AI can be found in the literature, but this definition often falls short when put into practice due to a lack of clear and generally applicable definition for AI. As a result, limitations start arising even with the description of the forms of weak and strong AI. The author addresses these shortcomings. First, the text provides a historical review of the definition of AI. First, the text provides a historical review of the definition of AI. First, the text provides a historical review of the definition of AI. Next, it outlines various milestones in AI development, which together present a comprehensive picture of AI’s complexity. The traditional categorization of AI into weak and strong systems is also introduced and critically analyzed.
First, the text provides a historical review of the definition of AI. One problematic aspect of this classification is that some AI systems cannot be definitively assigned to one category or the other.