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Food waste from the perspective of assessment of attitudes and behaviours of young consumers

Radzymińska Monika, Platta Anna

Scientific Journal of Gdynia Maritime University

2022

nr 122

68--81

The subject of wasting food, as it relates to the reduction of losses and waste, holds an important place in European Union and United Nations agendas. Constant monitoring of the causes of this phenomenon among consumers is very useful in its prevention. It enables modifying and properly targeting educational campaigns and shaping social norms. The aim of this study was to examine the attitudes and behaviours of young consumers in relation to wasting food. The study was conducted in 2021–2022 among young consumers studying at universities (n = 507), using the indirect on-line survey measurement method. The results demonstrated that planning rational purchases helps limit food waste in this consumer group. Young consumers carefully purchase products, which may be a consequence of limited budgets. The study has shown that before making purchases, the majority always check their current food supplies, try to limit food waste in their household and try not to throw away any foodstuff. Additionally, the study has shown that a major part of the them know and apply various methods of using leftover food to prepare simple, multi-ingredient dishes. The results presented in this paper may indicate that the wastage-related educational campaigns conducted in Poland are effective.

Fast reduction of large dataset for nearest neighbor classifier

Raniszewski M.

Journal of Medical Informatics & Technologies

2010

Vol. 16

111--116

Accurate and fast classification of large data obtained from medical images is very important. Proper images (data) processing results to construct a classifier, which supports the work of doctors and can solve many medical problems. Unfortunately, Nearest Neighbor classifiers become inefficient and slow for large datasets. A dataset reduction is one of the most popular solution to this problem, but the large size of a dataset causes long time of a reduction phase for reduction algorithms. A simple method to overcome the large dataset reduction problem is a dataset division into smaller subsets. In this paper five different methods of large dataset division are considered. The received subsets are reduced by using an algorithm based on representative measure. The reduced subsets are combined to form the reduced dataset. The experiments were performed on a large (almost 82 000 samples) two–class dataset dating from ultrasound images of certain 3D objects found in a human body.