Anomaly detection has numerous applications in diverse fields. For example, it has been widely used for discovering network intrusions and malicious events. It has also been used in numerous other applications such as identifying medical malpractice or credit fraud. Detection of anomalies in quantitative data has received a considerable attention in the literature and has a venerable history. By contrast, and despite the widespread availability use of categorical data in practice, anomaly detection in categorical data has received relatively little attention as compared to quantitative data. This is because detection of anomalies in categorical data is a challenging problem. Some anomaly detection techniques depend on identifying a representative pattern then measuring distances between objects and this pattern. Objects that are far from this pattern are declared as anomalies. However, identifying patterns and measuring distances are not easy in categorical data compared with quantitative data. Fortunately, several papers focussing on the detection of anomalies in categorical data have been published in the recent literature. In this article, we provide a comprehensive review of the research on the anomaly detection problem in categorical data. Previous review articles focus on either the statistics literature or the machine learning and computer science literature. This review article combines both literatures. We review 36 methods for the detection of anomalies in categorical data in both literatures and classify them into 12 different categories based on the conceptual definition of anomalies they use. For each approach, we survey anomaly detection methods, and then show the similarities and differences among them. We emphasize two important issues, the number of parameters each method requires and its time complexity. The first issue is critical, because the performance of these methods are sensitive to the choice of these parameters. The time complexity is also very important in real applications especially in big data applications. We report the time complexity if it is reported by the authors of the methods. If it is not, then we derive it ourselves and report it in this article. In addition, we discuss the common problems and the future directions of the anomaly detection in categorical data.

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The sink nodes in large-scale wireless sensor networks (LSWSNs) are responsible for receiving and processing the collected data from sensor nodes. Identifying the locations of sink nodes in LSWSNs play a vital role in term of saving energy. Furthermore, sink nodes have extremely extra resources such as large memory, powerful batteries, long-range antenna, etc. This paper proposes a multi-objective whale optimization algorithm (MOWOA) to determine the lowest number of sink nodes that cover the whole network. The major aim of MOWOA is to reduce the energy consumption and prolongs the lifetime of LSWSNs. To achieve these objectives, a fitness function has been formulated to decrease energy consumption and maximize the network's lifetime. The experimental results revealed that the proposed MOWOA achieved a better efficiency in reducing the total power consumption by 26% compared with four well-known optimization algorithms: multi-objective grasshopper optimization algorithm, multi-objective salp swarm algorithm, multi-objective gray wolf optimization, multi-objective particle swarm optimization over all networks sizes. KeywordsLarge-scale wireless sensor networks (LSWSNs) • Multiple sink node • Multi-objective optimization (MOO) • Pareto front • Whale optimization algorithm (WOA)

Blind and Visually impaired people (BVIP) face a range of practical difficulties when undertaking outdoor journeys as pedestrians. Over the past decade, a variety of assistive devices have been researched and developed to help BVIP navigate more safely and independently. In addition, research in overlapping domains are addressing the problem of automatic environment interpretation using computer vision and machine learning, particularly deep learning, approaches. Our aim in this article is to present a comprehensive review of research directly in, or relevant to, assistive outdoor navigation for BVIP. We breakdown the navigation area into a series of navigation phases and tasks. We then use this structure for our systematic review of research, analysing articles, methods, datasets and current limitations by task. We also provide an overview of commercial and non-commercial navigation applications targeted at BVIP. Our review contributes to the body of knowledge by providing a comprehensive, structured analysis of work in the domain, including the state of the art, and guidance on future directions. It will support both researchers and other stakeholders in the domain to establish an informed view of research progress.