Anomaly Detection Using Hierarchical Temporal Memory (HTM) in Crowd Management

Bamaqa, Amna; Sedky, Mohamed; Bosakowski, Tomasz; Bastaki, Benhur Bakhtiari

doi:10.1145/3416921.3416940

Cited by 13 publications

(7 citation statements)

References 16 publications

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“…HTMs have also proven effective at detecting anomalies in crowd movements [18], traffic patterns [19], human vital signs [20], electrical grids [21], and computer hardware [22].…”

Section: Related Workmentioning

confidence: 99%

Neuroscience-Inspired Algorithms for the Predictive Maintenance of Manufacturing Systems

Malawade

Costa

Muthirayan

et al. 2021

IEEE Trans. Ind. Inf.

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If machine failures can be detected preemptively, then maintenance and repairs can be performed more efficiently, reducing production costs. Many machine learning techniques for performing early failure detection using vibration data have been proposed; however, these methods are often power and data-hungry, susceptible to noise, and require large amounts of data preprocessing. Also, training is usually only performed once before inference, so they do not learn and adapt as the machine ages. Thus, we propose a method of performing online, real-time anomaly detection for predictive maintenance using Hierarchical Temporal Memory (HTM). Inspired by the human neocortex, HTMs learn and adapt continuously and are robust to noise. Using the Numenta Anomaly Benchmark, we empirically demonstrate that our approach outperforms state-of-the-art algorithms at preemptively detecting realworld cases of bearing failures and simulated 3D printer failures. Our approach achieves an average score of 64.71, surpassing state-of-the-art deep-learning (49.38) and statistical

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“…HTMs have also proven effective at detecting anomalies in crowd movements [18], traffic patterns [19], human vital signs [20], electrical grids [21], and computer hardware [22].…”

Section: Related Workmentioning

confidence: 99%

Neuroscience-Inspired Algorithms for the Predictive Maintenance of Manufacturing Systems

Malawade

Costa

Muthirayan

et al. 2021

IEEE Trans. Ind. Inf.

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“…In recent years, some scholars start to implement TPL on hardware [27]. Many scholars combine SPL with TPL and apply HTM to many fields with time series data, such as anomaly detection of time series data [28], heart attack detection [29], medical data flow prediction [25], hydrological intelligent monitoring [30], abnormal ECG detection [31], and abnormal detection in crowd management [32]. However, when the sequence data is learned as rules, TPL in the above research still has some limitations.…”

Section: Htm Is a New Artificial Neural Network Model Based On Jeffmentioning

confidence: 99%

A Temporal Pool Learning Algorithm Based on Location Awareness

Zhu

Cai

et al. 2021

Scientific Programming

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Hierarchical Temporal Memory is a new type of artificial neural network model, which imitates the structure and information processing flow of the human brain. Hierarchical Temporal Memory has strong adaptability and fast learning ability and becomes a hot spot in current research. Hierarchical Temporal Memory obtains and saves the temporal characteristics of input sequences by the temporal pool learning algorithm. However, the current algorithm has some problems such as low learning efficiency and poor learning effect when learning time series data. In this paper, a temporal pool learning algorithm based on location awareness is proposed. The cell selection rules based on location awareness and the dendritic updating rules based on adjacent inputs are designed to improve the learning efficiency and effect of the algorithm. Through the algorithm prototype, three different datasets are used to test and analyze the algorithm performance. The experimental results verify that the algorithm can quickly obtain the complete characteristics of the input sequence. No matter whether there are similar segments in the sequence, the proposed algorithm has higher prediction recall and precision than the existing algorithms.

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“…In particular, they are involved in providing real-time predictions in contexts where continuous and unpredictable changes affect the input data. For example, HTM has been employed for patient health monitoring and human-machine gesture interactions prediction [15], [16], network intrusion detection [17], [18], phone network intention estimation [19], short-term prediction of traffic flows [20] and detection of anomalies in crowd movements [21]. The results reported in these works motivated the investigation of HTM applied to cloud failures prediction.…”

Section: A Applications Of Htmmentioning

confidence: 99%

“…HTM continually updates models as new data to analyze become available. While successfully applied in many other contexts [15]- [21], it has received little attention in the context of cloud failure prediction, with only a preliminary study by Mobilio et al considering its usage [22].…”

Section: Introductionmentioning

confidence: 99%

Cloud Failure Prediction with Hierarchical Temporal Memory: An Empirical Assessment

Riganelli¹,

Saltarel²,

Tundo³

et al. 2021

Preprint

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Hierarchical Temporary Memory (HTM) is an unsupervised learning algorithm inspired by the features of the neocortex that can be used to continuously process stream data and detect anomalies, without requiring a large amount of data for training nor requiring labeled data. HTM is also able to continuously learn from samples, providing a model that is always up-to-date with respect to observations. These characteristics make HTM particularly suitable for supporting online failure prediction in cloud systems, which are systems with a dynamically changing behavior that must be monitored to anticipate problems. This paper presents the first systematic study that assesses HTM in the context of failure prediction.The results that we obtained considering 72 configurations of HTM applied to 12 different types of faults introduced in the Clearwater cloud system show that HTM can help to predict failures with sufficient effectiveness (F-measure = 0.76), representing an interesting practical alternative to (semi-)supervised algorithms.

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Anomaly Detection Using Hierarchical Temporal Memory (HTM) in Crowd Management

Cited by 13 publications

References 16 publications

Neuroscience-Inspired Algorithms for the Predictive Maintenance of Manufacturing Systems

Neuroscience-Inspired Algorithms for the Predictive Maintenance of Manufacturing Systems

A Temporal Pool Learning Algorithm Based on Location Awareness

Cloud Failure Prediction with Hierarchical Temporal Memory: An Empirical Assessment

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