Search the digital library catalog

Abstract: Microbiota analysis can provide valuable insights in various fields, including diet and nutrition, understanding health and disease, and in environmental contexts, such as understanding the role of microorganisms in different ecosystems. Based on the results, we can provide targeted therapies, personalized medicine, or detect environmental contaminants. In our research, we examined the gut microbiota of 16 animal taxa, including humans, as well as the microbiota of cattle and pig manure, where we focused on 16S rRNA V3-V4 hypervariable regions. Analyzing these regions is common in microbiome studies but can be challenging since the results are high-dimensional. Thus, we utilized machine learning techniques and demonstrated their applicability in processing microbial sequence data. Moreover, we showed that techniques commonly employed in natural language processing can be adapted for analyzing microbial text vectors. We obtained the latter through frequency analyses and utilized the proposed hierarchical clustering method over them. All steps in this study were gathered in a proposed microbial sequence data processing pipeline. The results demonstrate that we not only found similarities between samples but also sorted groups’ samples into semantically related clusters. We also tested our method against other known algorithms like the Kmeans and Spectral Clustering algorithms using clustering evaluation metrics. The results demonstrate the superiority of the proposed method over them. Moreover, the proposed microbial sequence data pipeline can be utilized for different types of microbiota, such as oral, gut, and skin, demonstrating its reusability and robustness.
Keywords: machine learning, NLP, hierarchical clustering, microbial data, microbiome, n-grame
Published in DKUM: 04.09.2024; Views: 33; Downloads: 2
Full text (4,48 MB)

Keywords: voltage sag (dip), source localization, supervised and unsupervised learning, convolutional neural network, time-sample-based features
Published in DKUM: 23.08.2024; Views: 61; Downloads: 1
Full text (15,94 MB)

Keywords: waste management, smart waste bin system, central post-sorting, sensor technology, deep learning, convolutional neural networks
Published in DKUM: 23.08.2024; Views: 51; Downloads: 1
Full text (3,64 MB)

Abstract: This paper examines how values and professional socialization in business schools impact the formulation of students’ contextualized view of social responsibility. We propose the empirical concept of a mental gap between the existing and the wished-for level of a business school’s corporate social responsibility and estimate it empirically by using a sample of business school students from Central and South East Europe. Results show that students wish their business schools to reduce their current orientation toward economic outcomes and focus on environmental and social responsibilities. We interpret those empirical results in terms of the students’ wish to balance achieving economic prosperity and enjoyment of life with the prosocial outcomes of their education. New student generations’ perception of corporate social responsibility is not shaped by the professional socialization patterns but rather by the own perceptions, which can be influenced by experiential approaches to academic teaching and learning. Based on these empirical results, implications for academic practice and future research are explored.
Keywords: students, education, social responsibility, economy, culture, human learning, psychological attitudes
Published in DKUM: 06.08.2024; Views: 85; Downloads: 4
Full text (676,12 KB)
This document has many files! More...

Abstract: The goal of this study was to develop an automated monitoring system for the detection of pigs’ bodies, heads and tails. The aim in the first part of the study was to recognize individual pigs (in lying and standing positions) in groups and their body parts (head/ears, and tail) by using machine learning algorithms (feature pyramid network). In the second part of the study, the goal was to improve the detection of tail posture (tail straight and curled) during activity (standing/moving around) by the use of neural network analysis (YOLOv4). Our dataset (n = 583 images, 7579 pig posture) was annotated in Labelbox from 2D video recordings of groups (n = 12–15) of weaned pigs. The model recognized each individual pig’s body with a precision of 96% related to threshold intersection over union (IoU), whilst the precision for tails was 77% and for heads this was 66%, thereby already achieving human-level precision. The precision of pig detection in groups was the highest, while head and tail detection precision were lower. As the first study was relatively time-consuming, in the second part of the study, we performed a YOLOv4 neural network analysis using 30 annotated images of our dataset for detecting straight and curled tails. With this model, we were able to recognize tail postures with a high level of precision (90%).
Keywords: pig, welfare, image processing, object detection, deep learning, smart farming
Published in DKUM: 11.07.2024; Views: 80; Downloads: 3
Full text (48,11 MB)
This document has many files! More...