Prediction of the Attention Area in Ambient Intelligence Tasks

Shafi, Jawad; Angelov, Plamen; Umair, Muhammad

doi:10.1007/978-3-319-27267-2_2

Cited by 2 publications

(5 citation statements)

References 23 publications

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“…Among others, in the past literature, the eye-gaze is the most widely used cue to find out the user attention area to provide numerous services in AmI applications, e.g., as cited in [47][48][49]. However, human eye-gaze attention is dynamic, non-linear, and non-stationary [3,9]. Therefore, offline and fixed model structure approaches have severe limitations (see Sections 2 and 3); thus, they are not adoptable for the eye-gaze attention type of task.…”

Section: Problem Statement For the Visual Attention Area Forecasting ...mentioning

confidence: 99%

“…The VAAPeNFS is the eTS+ model, DeTS refers to the dynamically evolving Takagi-Sugeno neuro-fuzzy model, ANFIS refers to an Adaptive Neuro-Fuzzy Inference System [26], and the recursive least squares (SLR) [53]. The reason for shortlisting the ANFIS for comparison with our newly developed technique is its code availability in Matlab, one of the most commonly used neuro-fuzzy models for the offline approach and one that has been used in a similar type of study in the recent past [9]. The ANFIS works in offline mode; therefore, the dataset is divided into training and testing.…”

Section: Approachesmentioning

confidence: 99%

“…Computational intelligence techniques are useful to model the environmental context and the relationships among the events; thus, they are useful for an AmI environment. Hence, AmI-based computational intelligence has received significant research 2 of 24 attention [8,9]. However, research and development in relation to AmI using computational methodologies/techniques are still challenging due to several reasons: measurement errors and non-linear relationships between (in) dependent data variables.…”

Section: Introductionmentioning

confidence: 99%

“…As discussed earlier, the AmI requirement can differ from one environment to another and from person to person, so writing separate models for all those environments is entirely inconvenient. This paper is thus novel in four ways: (i) an evolving version of the Takagi-Sugeno fuzzy model is adapted in order to be suitable for attention area application in AmI, (ii) the fuzzy rules are generated automatically, (iii) the fuzzy forecasting techniques are evaluated and compared in the context of visual attention area prediction with the help of an ordinary inexpensive web camera, and (iv) the results of various evolving models (eTS and DeTS) are compared with offline techniques (Adaptive Neuro-Fuzzy Inference System (ANFIS) [9,26] and SLR). Experimental result show our proposed Visual Attention Area Prediction using Evolving Neuro-Fuzzy Systems (hereafter VAAPeNFS) approach can predict the user's attention area with high accuracy and with improved transparency and flexibility as compared to conventional techniques.…”

Section: Introductionmentioning

confidence: 99%

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A Method for Predicting the Visual Attention Area in Real-Time Using Evolving Neuro-Fuzzy Models

et al. 2023

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This research paper presents the prediction of the visual attention area on a visual display using an evolving rule-based fuzzy model: evolving Takagi–Sugeno (eTS). The evolving fuzzy model is feasible for predicting the visual attention area because of its non-iterative, recursive, online, and real-time nature. Visual attention area prediction through a web camera is a problem that requires online adaptive systems with higher accuracy and greater performance. The proposed approach using an evolving fuzzy model to predict the eye-gaze attention area on a visual display in an ambient environment (to provide further services) mimics the human cognitive process and its flexibility to generate fuzzy rules without any prior knowledge. The proposed Visual Attention Area Prediction using Evolving Neuro-Fuzzy Systems (VAAPeNFS) approach can quickly generate compact fuzzy rules from new data. Numerical experiments conducted in a simulated environment further validate the performance and accuracy of the proposed model. To validate the model, the forecasting results of the eTS model are compared with DeTS and ANFIS. The result shows high accuracy, transparency and flexibility achieved by applying the evolving online versions compared to other offline techniques. The proposed approach significantly reduces the computational overhead, which makes it suitable for any sort of AmI application. Thus, using this approach, we achieve reusability, robustness, and scalability with better performance with high accuracy.

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Section: Problem Statement For the Visual Attention Area Forecasting ...mentioning

confidence: 99%

Section: Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Method for Predicting the Visual Attention Area in Real-Time Using Evolving Neuro-Fuzzy Models

et al. 2023

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“…The resultant NFS combines parallel computation and learning abilities of the ANN with the human reasoning of fuzzy systems and clarity of systems representation. Therefore, the ANN becomes more transparent, and the fuzzy logic system becomes capable of learning [27]. The integration of the ANN with the fuzzy logic system can be done in three ways: cooperative, concurrent, and hybrid.…”

Section: An Overview Of Nfsmentioning

confidence: 99%

Efficient NFS Model for Risk Estimation in a Risk-Based Access Control Model

Atlam

Azad

Fadhel

2022

Sensors

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Providing a dynamic access control model that uses real-time features to make access decisions for IoT applications is one of the research gaps that many researchers are trying to tackle. This is because existing access control models are built using static and predefined policies that always give the same result in different situations and cannot adapt to changing and unpredicted situations. One of the dynamic models that utilize real-time and contextual features to make access decisions is the risk-based access control model. This model performs a risk analysis on each access request to permit or deny access dynamically based on the estimated risk value. However, the major issue associated with building this model is providing a dynamic, reliable, and accurate risk estimation technique, especially when there is no available dataset to describe risk likelihood and impact. Therefore, this paper proposes a Neuro-Fuzzy System (NFS) model to estimate the security risk value associated with each access request. The proposed NFS model was trained using three learning algorithms: Levenberg–Marquardt (LM), Conjugate Gradient with Fletcher–Reeves (CGF), and Scaled Conjugate Gradient (SCG). The results demonstrated that the LM algorithm is the optimal learning algorithm to implement the NFS model for risk estimation. The results also demonstrated that the proposed NFS model provides a short and efficient processing time, which can provide timeliness risk estimation technique for various IoT applications. The proposed NFS model was evaluated against access control scenarios of a children’s hospital, and the results demonstrated that the proposed model can be applied to provide dynamic and contextual-aware access decisions based on real-time features.

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Prediction of the Attention Area in Ambient Intelligence Tasks

Cited by 2 publications

References 23 publications

A Method for Predicting the Visual Attention Area in Real-Time Using Evolving Neuro-Fuzzy Models

A Method for Predicting the Visual Attention Area in Real-Time Using Evolving Neuro-Fuzzy Models

Efficient NFS Model for Risk Estimation in a Risk-Based Access Control Model

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