Food demand prediction is a significant issue for both businesses processes improvement and sustainable development issues. The data science methods, including artificial intelligence methods, are often used for this purpose. The aim of this research is to develop the models for food demand prediction based on the Nonlinear Autoregressive Exogenous Neural Network. The research focuses on the processed food, such as bread or butter. Developed models' architectures differing in the number of hidden layers and the number of neurons in the hidden layers, as well as with different sizes of the delay-line, were tested for a given product. Results of the research show that depending on the type of product, prediction performance slightly differed. The results of the R 2 measure ranged from 96,2399 to 99,6477 depending on particular products. The proposed models can be used in a company's intelligent management system for rational control of inventories and food production. It can also lead to reducing food waste.INDEX TERMS food industry, sustainable development, neural networks, machine learning, demand forecasting.
Churn prediction is a Big Data domain, one of the most demanding use cases of recent time. It is also one of the most critical indicators of a healthy and growing business, irrespective of the size or channel of sales. This paper aims to develop a deep learning model for customers’ churn prediction in e-commerce, which is the main contribution of the article. The experiment was performed over real e-commerce data where 75% of buyers are one-off customers. The prediction based on this business specificity (many one-off customers and very few regular ones) is extremely challenging and, in a natural way, must be inaccurate to a certain ex-tent. Looking from another perspective, correct prediction and subsequent actions resulting in a higher customer retention are very attractive for overall business performance. In such a case, predictions with 74% accuracy, 78% precision, and 68% recall are very promising. Also, the paper fills a research gap and contrib-utes to the existing literature in the area of developing a customer churn prediction method for the retail sector by using deep learning tools based on customer churn and the full history of each customer’s transactions.
The aim of the chapter is to develop an approach for improving quality management in flexographic printing on packages using cognitive agent. A hybrid agents' architecture based on the learning intelligent distribution agent architecture (LIDA) and hierarchical temporal memory has been developed. Such approach has not been developed before; therefore, it is the main contribution of this chapter. The first part of the chapter presents the introduction to the research problem and background. Next, research methodology and the LIDA cognitive agents have been described. The main part of chapter presents the cognitive agent's architecture and functionality related to quality management in flexographic printing. The last part presents discussion, future works, and major conclusions.
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