Early anomaly detection in smart home: A causal association rule-based approach

Sfar, Hela; Bouzeghoub, Amel; Raddaoui, Badran

doi:10.1016/j.artmed.2018.06.001

Cited by 46 publications

(24 citation statements)

References 20 publications

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“…Analyzing the papers selected and summarized in Table S13, it can be observed that 78% of them exclusively analyze smart homes, 16% take into consideration smart buildings in general, 3% analyze both smart homes and buildings, while the remaining 3% of the selected papers refer to smart workplace environments. The authors of these scientific articles make use of different types of sensors in their analyses, including wireless sensor networks [70,115,[119][120][121][122][123][124]; passive infrared motion sensors [82,97,117,118,122,125,126]; motion sensors [25,70,81,118,120,127,128]; environmental sensors [10,25,81,82,[116][117][118]123,[127][128][129][130][131][132]; temperature sensors [116,118,120,123,125,[131][132][133]; humidity sensors…”

Section: Unsupervised Learningmentioning

confidence: 99%

“…The performance metrics that chosen by the authors of the scientific papers using the Hidden Markov Model integrated with sensor devices in smart buildings included: Accuracy [3,10,25,33,70,115,117,120,122,123,125,127,131,133,136,138]; Precision [25,118,128,133,135,137]; Recall [25,118,128,135]; F-Measure [25,81,121,130,133]; Sensitivity and Specificity [25,33,133]; F1 Score [116,133]; Confusion Matrix [116,127,129]; and Correctness [97,118]. In addition to the above-mentioned performance metrics, other methods that were used to assess the performance of the developed methods by the authors of the scientific papers selected and summarized in Table S13 included: a numerical case study highlighting the efficiency of the developed model [134]; thread latency [119]; evaluation of energy savings…”

Section: Unsupervised Learningmentioning

confidence: 99%

“…In paper [118], Sfar et al developed an approach for early detection of abnormal behavior in elderly people living in smart homes, in order to prevent risks related to their health, based on identifying and extracting anomalous causes from datasets, making use of causal association rules mining. These causes were subsequently used in order to detect the risks of anomalies occurring by using the Markov Logic Network Machine Learning method.…”

Section: Unsupervised Learningmentioning

confidence: 99%

See 2 more Smart Citations

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

et al. 2019

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Lately, many scientists have focused their research on subjects like smart buildings, sensor devices, virtual sensing, buildings management, Internet of Things (IoT), artificial intelligence in the smart buildings sector, improving life quality within smart homes, assessing the occupancy status information, detecting human behavior with a view to assisted living, maintaining environmental health, and preserving natural resources. The main purpose of our review consists of surveying the current state of the art regarding the recent developments in integrating supervised and unsupervised machine learning models with sensor devices in the smart building sector with a view to attaining enhanced sensing, energy efficiency and optimal building management. We have devised the research methodology with a view to identifying, filtering, categorizing, and analyzing the most important and relevant scientific articles regarding the targeted topic. To this end, we have used reliable sources of scientific information, namely the Elsevier Scopus and the Clarivate Analytics Web of Science international databases, in order to assess the interest regarding the above-mentioned topic within the scientific literature. After processing the obtained papers, we finally obtained, on the basis of our devised methodology, a reliable, eloquent and representative pool of 146 papers scientific works that would be useful for developing our survey. Our approach provides a useful up-to-date overview for researchers from different fields, which can be helpful when submitting project proposals or when studying complex topics such those reviewed in this paper. Meanwhile, the current study offers scientists the possibility of identifying future research directions that have not yet been addressed in the scientific literature or improving the existing approaches based on the body of knowledge. Moreover, the conducted review creates the premises for identifying in the scientific literature the main purposes for integrating Machine Learning techniques with sensing devices in smart environments, as well as purposes that have not been investigated yet.

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Section: Unsupervised Learningmentioning

confidence: 99%

Section: Unsupervised Learningmentioning

confidence: 99%

Section: Unsupervised Learningmentioning

confidence: 99%

See 1 more Smart Citation

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

et al. 2019

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“…Association rule mining (ARM) has been extensively used to extract hidden and interesting rules from a large collection of data [1]. It has been an active research area owing to the challenges it presents and to its wide applications in various fields such as market basket analysis [2], recommendation systems [3], anomaly detection [4], bioinformatics [5], and text mining [6]. The ARM process involves finding patterns whose support and confidence values are at least the user-defined thresholds.…”

Section: Introductionmentioning

confidence: 99%

Multitask-based association rule mining

Taşer¹,

Birant²,

Birant³

2020

Turk J Elec Eng & Comp Sci

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Recently, there has been a growing interest in association rule mining (ARM) in various fields. However, standard ARM algorithms fail to discover rules for multitask problems as they do not consider task-oriented investigation and, therefore, they ignore the correlation among the tasks. Considering this situation, this paper proposes a novel algorithm, named multitask association rule miner (MTARM), that tends to jointly discover rules by considering multiple tasks. This paper also introduces two novel concepts: single-task rule and multiple-task rule. In the first phase of the proposed approach, highly frequent local rules (single-task rules) are explored for each task separately and then these local rules are combined to produce the global result (multitask rules) using a majority voting mechanism. Experiments were conducted on four different real-world multitask learning datasets. The experimental results indicated that the proposed MTARM approach discovers more information than that of traditional ARM algorithms by jointly considering the relationships among multiple tasks. This work is licensed under a Creative Commons Attribution 4.0 International License. 933 YILDIRIM TAŞER et al./Turk J Elec Eng & Comp Scisubset of them, are familiar to one mutual rule set with a small difference. The proposed approach can discover common association rules by responding to different tasks and by applying an algorithm to consider all the tasks collectively. Accordingly, the rule X → Y can have high support and confidence in a task. A rule may not be frequent in the entire dataset; however, it may be frequent in several specific tasks. Therefore, finding task-based frequent rules in an effective way is important. This paper proposes a novel algorithm, named multitask association rule miner (MTARM), that tends to discover rules by jointly considering multiple tasks. The proposed algorithm mines datasets that include task information. The MTARM approach consists of two phases. In the first phase, the algorithm discovers local frequent rules (single-task rules) from the data of each task separately, and in the second phase, it combines these local rules to produce the global result (multitask rules). The discovered multitask rules can be used to deploy information systems that support the execution of their associated task or to improve decision-making in applications.The main contributions and novelty of this study are threefold: (i) this is the first study that applies ARM to multitask problems; (ii) it proposes the MTARM approach, which attempts to find intertask rules based solely on the task identities and the observed data for each task; and, finally (iii) it also introduces two novel concepts: single-task rule and multitask rule. This paper also provides a brief survey of ARM as it is an active research area of data mining. In addition, it presents the results of experimental studies conducted on four different real-world datasets to demonstrate the capability of the proposed algorithm.The rest of the paper is organized...

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“…The Smart homes are becoming more and more popular for healthcare applications. In Sfar [4] they show a new approach for detecting the risk of anomalies in the environment regarding user activities. Their approach is based on anomaly-cause extraction from a given dataset using causal association rules mining.…”

mentioning

confidence: 99%