Abstract
Outlier detection is a widely used technique for identifying anomalous or exceptional events across various contexts. It has proven to be valuable in applications like fault detection, fraud detection, and real-time monitoring systems. Detecting outliers in real time is crucial in several industries, such as financial fraud detection and quality control in manufacturing processes. In the context of big data, the amount of data generated is enormous, and traditional batch mode methods are not practical since the entire dataset is not available. The limited computational resources further compound this issue. Boxplot is a widely used batch mode algorithm for outlier detection that involves several derivations. However, the lack of an incremental closed form for statistical calculations during boxplot construction poses considerable challenges for its application within the realm of big data. We propose an incremental/online version of the boxplot algorithm to address these challenges. Our proposed algorithm is based on an approximation approach that involves numerical integration of the histogram and calculation of the cumulative distribution function. This approach is independent of the dataset's distribution, making it effective for all types of distributions, whether skewed or not. To assess the efficacy of the proposed algorithm, we conducted tests using simulated datasets featuring varying degrees of skewness. Additionally, we applied the algorithm to a real-world dataset concerning software fault detection, which posed a considerable challenge. The experimental results underscored the robust performance of our proposed algorithm, highlighting its efficacy comparable to batch mode methods that access the entire dataset. Our online boxplot method, leveraging dataset distribution to define whiskers, consistently achieved exceptional outlier detection results. Notably, our algorithm demonstrated computational efficiency, maintaining constant memory usage with minimal hyperparameter tuning.

Abstract
Integrating Industry 4.0 and Quality 4.0 optimises manufacturing through IoT and ML, improving processes and product quality. The primary challenge involves identifying patterns in computer numerical control (CNC) machining time-series data to boost manufacturing quality control. The proposed solution involves an experimental study comparing one-class and binary classification algorithms. This study aims to classify time-series data from CNC turning machines, offering insight into monitoring and adjusting tool wear to maintain product quality. The methodology entails extracting spectral features from time-series data to train both one-class and binary classification algorithms, assessing their effectiveness and computational efficiency. Although certain models consistently outperform others, determining the best performing is not possible, as a trade-off between classification and computational performance is observed, with gradient boosting standing out for effectively balancing both aspects. Thus, the choice between one-class and binary classification ultimately relies on dataset's features and task objectives.

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.