Abstract

Abstract

Abstract

Abstract
Pet owners are increasingly becoming conscious of their pet's necessities and are paying more attention to their overall wellness. The well-being of their pets is intricately linked to their own emotional and physical well-being. Some veterinary system solutions are emerging to provide proactive healthcare options for pets. One such solution offers the continuous monitoring of a pet's activity through accelerometer tracking devices. Based on data collected by this application, in this paper, we study different time aggregation and three unsupervised machine learning techniques to identify anomalies in pet behaviour data. Specifically, three algorithms, Isolation Forest, Local Outlier Factor, and K-Nearest Neighbour, with various thresholds to differentiate between normal and abnormal events. Results conducted on ten pets (five cats and five dogs) show that the most effective approach is to use daily data divided into periods. Moreover, the Local Outlier Factor is the best algorithm for detecting anomalies when prioritizing the identification of true positives. However, it also produces a high false positive ratio.

Abstract
The rise of environmental crimes has become a major concern globally as they cause significant damage to ecosystems, public health and result in economic losses. The availability of vast sensor data provides an opportunity to analyze environmental data proactively. This helps to detect irregularities and uncover potential criminal activities. This paper highlights the critical role played by machine learning (ML) and remote sensing technologies in the continuously evolving scenarios of environmental crime. By examining some case studies on detecting illegal fishing, illegal oil spills, illegal landfills, and illegal logging, we delve into the practical implementation of data-driven approaches for environmental crime detection. Our goal with this study is to provide an overview of the existing research in this area and foster the use of ML and data science techniques to enhance environmental crime detection.

Abstract
Latent Dirichlet Allocation (LDA) is a fundamental method for clustering short text streams. However, when applied to large datasets, it often faces significant challenges, and its performance is typically evaluated in domain-specific datasets such as news and tweets. This study aims to fill this gap by evaluating the effectiveness of short text clustering methods in a large and diverse e-commerce dataset. We specifically investigate how well these clustering algorithms adapt to the complex dynamics and larger scale of e-commerce text streams, which differ from their usual application domains. Our analysis focuses on the impact of high homogeneity scores on the reported Normalized Mutual Information (NMI) values. We particularly examine whether these scores are inflated due to the prevalence of single-element clusters. To address potential biases in clustering evaluation, we propose using the Akaike Information Criterion (AIC) as an alternative metric to reduce the formation of single-element clusters and provide a more balanced measure of clustering performance. We present new insights for applying short text clustering methodologies in real-world situations, especially in sectors like e-commerce, where text data volumes and dynamics present unique challenges.