User activities outlier detection system using principal component analysis and fuzzy rule-based system

Mahmoud, Sawsan M.; Lotfi, Ahmad; Langensiepen, Caroline

doi:10.1145/2413097.2413130

Cited by 17 publications

(4 citation statements)

References 16 publications

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“…The evaluation is based on real-world data consisting of 431 traces of normal daily activities and 112 anomalous traces. We also found feature reduction method principal component analysis (PCA) with fuzzy rule-based system [26], [29], that is successfully applied to recognize the anomalous behavior of the user inside the home environment. Fuzzy rulebased system is based on three steps.…”

Section: ) Machine Learning Methodsmentioning

confidence: 99%

Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review

2019

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Anomaly detection has attracted considerable attention from the research community in the past few years due to the advancement of sensor monitoring technologies, low-cost solutions, and high impact in diverse application domains. Sensors generate a huge amount of data while monitoring the physical spaces and objects. These huge collected data streams can be analyzed to identify unhealthy behaviors. It may reduce functional risks, avoid unseen problems, and prevent downtime of the systems. Many research methodologies have been designed and developed to determine such anomalous behaviors in security and risk analysis domains. In this paper, we present the results of a systematic literature review about anomaly detection techniques except for these dominant research areas. We focus on the studies published from 2000 to 2018 in the application areas of intelligent inhabitant environments, transportation systems, health care systems, smart objects, and industrial systems. We have identified a number of research gaps related to the data collection, the analysis of imbalanced large datasets, limitations of statistical methods to process the huge sensory data, and few research articles in abnormal behavior prediction in real scenarios. Based on our analysis, researchers and practitioners can acquaint themselves with the existing approaches, use them to solve real problems, and/or further contribute to developing novel techniques for anomaly detection, prediction, and analysis.INDEX TERMS Statistical learning, machine learning, intelligent environments, smart objects, intelligent transportation systems, industrial systems.

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Section: ) Machine Learning Methodsmentioning

confidence: 99%

Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review

2019

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“…The IIE relies solely on non-intrusive sensors such as door and infrared motion sensors; research has proven that the use of intrusive technology such as surveillance cameras for patient monitoring is generally not accepted [9]. The sensors produce long series of binary multidimensional data that are difficult to analyse and manipulate manually, so a combination of Principal Component Analysis and Fuzzy Rule-based Systems are applied to process the data and identify and also predict deviations from normal behaviour pattern [10], which are relayed to the health clinicians and the patient carer. The research documented in this paper attempts to correlate the identified behavioural patterns to the symptoms of Dementia with the long term objective of developing a semantically-enabled Dementia care decision support system (DCDSS) that can aid health clinicians in the remote assessment of the patients' condition, and thus reduce the demand for on-site care and support longer independent living for the Dementia sufferers.…”

Section: Motivationmentioning

confidence: 99%

Semantic-based decision support for remote care of Dementia patients

Osman

Lotfi

Langensiepen

et al. 2014

2014 IEEE Symposium on Intelligent Agents (IA)

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Abstract-This paper investigates the challenges in developing a semantic-based Dementia Care Decision Support System based on the non-intrusive monitoring of the patient's behaviour. Semantic-based approaches are well suited for modelling context-aware scenarios similar to Dementia care systems, where the patient's dynamic behaviour observations (occupants movement, equipment use) need to be analysed against the semantic knowledge about the patient's condition (illness history, medical advice, known symptoms) in an integrated knowledgebase. However, our research findings establish that the ability of semantic technologies to reason upon the complex interrelated events emanating from the behaviour monitoring sensors to infer knowledge assisting medical advice represents a major challenge. We attempt to address this problem by introducing a new approach that relies on propositional calculus modelling to segregate complex events that are amenable for semantic reasoning from events that require pre-processing outside the semantic engine before they can be reasoned upon. The event pre-processing activity also controls the timing of triggering the reasoning process in order to further improve the efficiency of the inference process. Using regression analysis, we evaluate the response-time as the number of monitored patients increases and conclude that the incurred overhead on the response time of the prototype decision support systems remains tolerable.

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“…Research in the field of digital image filtering has been adapted for point cloud filtering algorithms but it is not direct due to the irregularity, shrinkage and drifting of point clouds. In recent years, a number of filtering methods for 3D point cloud have been developed, such as data clustering [12,13], density-based function [14,15], principal component analysis (PCA) [16][17][18], locally optimal projection (LOP) [19,20], MLS [21,22], nonlocal methods [23,24] and partial differential equations (PDEs) [25,26]. Zaman et al [14] proposed a point cloud denoising method based on a kernel density function.…”

Section: Introductionmentioning

confidence: 99%

A 3D Point Cloud Filtering Method for Leaves Based on Manifold Distance and Normal Estimation

Zhou

2019

Remote Sensing

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Leaves are used extensively as an indicator in research on tree growth. Leaf area, as one of the most important index in leaf morphology, is also a comprehensive growth index for evaluating the effects of environmental factors. When scanning tree surfaces using a 3D laser scanner, the scanned point cloud data usually contain many outliers and noise. These outliers can be clusters or sparse points, whereas the noise is usually non-isolated but exhibits different attributes from valid points. In this study, a 3D point cloud filtering method for leaves based on manifold distance and normal estimation is proposed. First, leaf was extracted from the tree point cloud and initial clustering was performed as the preprocessing step. Second, outlier clusters filtering and outlier points filtering were successively performed using a manifold distance and truncation method. Third, noise points in each cluster were filtered based on the local surface normal estimation. The 3D reconstruction results of leaves after applying the proposed filtering method prove that this method outperforms other classic filtering methods. Comparisons of leaf areas with real values and area assessments of the mean absolute error (MAE) and mean absolute error percent (MAE%) for leaves in different levels were also conducted. The root mean square error (RMSE) for leaf area was 2.49 cm2. The MAE values for small leaves, medium leaves and large leaves were 0.92 cm2, 1.05 cm2 and 3.39 cm2, respectively, with corresponding MAE% values of 10.63, 4.83 and 3.8. These results demonstrate that the method proposed can be used to filter outliers and noise for 3D point clouds of leaves and improve 3D leaf visualization authenticity and leaf area measurement accuracy.

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User activities outlier detection system using principal component analysis and fuzzy rule-based system

Cited by 17 publications

References 16 publications

Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review

Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review

Semantic-based decision support for remote care of Dementia patients

A 3D Point Cloud Filtering Method for Leaves Based on Manifold Distance and Normal Estimation

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