mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification

Razzaq, Muhammad Asif; Villalonga, Claudia; Lee, Sungyoung; Akhtar, Usman; Ali, Mohsen; Kim, Eun-Soo; Khattak, Asad Masood; Seung, Hyonwoo; Hur, Tae Ho; Bang, Jae Hun; Kim, Dohyeong; Khan, Wajahat Ali

doi:10.3390/s17102433

Cited by 11 publications

(4 citation statements)

References 38 publications

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“…Scholars have long pondered the nature of self-awareness [25][26][27] and its neural basis in stochastic, non-deterministic approaches [28]. The emerging research in self-awareness of contexts includes using cross-domain computing environments to automatically identify the context of the user [29]. The environmental impacts on driving styles are predicted using new methods and models [30].…”

Section: The Literature Reviewmentioning

confidence: 99%

Optimal Learning and Self-Awareness Versus PDI

2020

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This manuscript will explore and analyze the effects of different paradigms for the control of rigid body motion mechanics. The experimental setup will include deterministic artificial intelligence composed of optimal self-awareness statements together with a novel, optimal learning algorithm, and these will be re-parameterized as ideal nonlinear feedforward and feedback evaluated within a Simulink simulation. Comparison is made to a custom proportional, derivative, integral controller (modified versions of classical proportional-integral-derivative control) implemented as a feedback control with a specific term to account for the nonlinear coupled motion. Consistent proportional, derivative, and integral gains were used throughout the duration of the experiments. The simulation results will show that akin feedforward control, deterministic self-awareness statements lack an error correction mechanism, relying on learning (which stands in place of feedback control), and the proposed combination of optimal self-awareness statements and a newly demonstrated analytically optimal learning yielded the highest accuracy with the lowest execution time. This highlights the potential effectiveness of a learning control system.

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Section: The Literature Reviewmentioning

confidence: 99%

Optimal Learning and Self-Awareness Versus PDI

2020

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“…The Data Curation Layer (DCL) manages the data in raw as well as processed format through data acquisition and synchronization, life-log representation and mapping, lifelog monitoring and big data storage processes [67]. Information Curation Layer (ICL) identifies end-user's activities and context from multimodal sensory data managed in hierarchical models [83]. It employs emotion, location, and multiple activity recognizers, respectively.…”

Section: A Mining Minds In a Nut Shellmentioning

confidence: 99%

Towards User-Centric Intervention Adaptiveness: Influencing Behavior-Context Based Healthy Lifestyle Interventions

et al. 2020

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In the era of digital well-being, smart gadgets are the unobtrusive sources of acquiring information. A variety of personalized wellness applications support self-quantification based recommendations to provide wellness status for achieving personalized targets. However, these applications are unable to promote the induction of new healthy habits and thus are not too much effective for long term as users tend to loose their interest. Thus, we have proposed a methodology for User-Centric Adaptive Intervention based on behavior change theory for maintaining end-users' interest. The methodology consists of four steps: (1) quantification of behavior based on contributing factors governed by expert-driven rules; (2) behavior-context based mapping for the identification of behavior status of the user; (3) selection of appropriate way of intervention to get fruitful outcomes; and finally (4) feedback based evaluation on the basis of recorded activities and questionnaires for satisfaction. A comprehensive healthy behavior index-based quantification supports the machine learning-based prediction model for behavior-context mapping. Furthermore, the evaluation is performed through implicit and explicit feedback analysis along with the accuracy of the behavior-context prediction model through multiple scenarios to cover comprehensive situations. The ensemble classifier suggests the accuracy of 98.02% for the behavior-context prediction model, which is higher than the other classifiers. The gain in behavior change is drawn from implicit feedback, which depicts that behavior context-based methods have improved the adaptation in behavior at a steady pace for the long term. The explicit feedback from 99 end-users of wellness application based on the proposed methodology obtained Good and Desired status for widely used System Usability Score and AttrakDiff tools respectively.

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“…For this, we used a set of 9 SPARQL-based query templates for retrieving and interpreting rules to deal with underlying temporal sensor state relations, as well as their structural properties. Moreover, the SPARQL queries require additional parameters in order to correlate, interpret, and aggregate sensor states within the endpoints of the sliding window [25]. Some of the initializing parameters include start-time, end-time, and a list of sensors within the sliding window identified based on the start-time and datatype properties.…”

Section: Semantic Segmentationmentioning

confidence: 99%

SemImput: Bridging Semantic Imputation with Deep Learning for Complex Human Activity Recognition

Razzaq

Cleland

Nugent

et al. 2020

Sensors

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The recognition of activities of daily living (ADL) in smart environments is a well-known and an important research area, which presents the real-time state of humans in pervasive computing. The process of recognizing human activities generally involves deploying a set of obtrusive and unobtrusive sensors, pre-processing the raw data, and building classification models using machine learning (ML) algorithms. Integrating data from multiple sensors is a challenging task due to dynamic nature of data sources. This is further complicated due to semantic and syntactic differences in these data sources. These differences become even more complex if the data generated is imperfect, which ultimately has a direct impact on its usefulness in yielding an accurate classifier. In this study, we propose a semantic imputation framework to improve the quality of sensor data using ontology-based semantic similarity learning. This is achieved by identifying semantic correlations among sensor events through SPARQL queries, and by performing a time-series longitudinal imputation. Furthermore, we applied deep learning (DL) based artificial neural network (ANN) on public datasets to demonstrate the applicability and validity of the proposed approach. The results showed a higher accuracy with semantically imputed datasets using ANN. We also presented a detailed comparative analysis, comparing the results with the state-of-the-art from the literature. We found that our semantic imputed datasets improved the classification accuracy with 95.78% as a higher one thus proving the effectiveness and robustness of learned models.

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mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification

Cited by 11 publications

References 38 publications

Optimal Learning and Self-Awareness Versus PDI

Optimal Learning and Self-Awareness Versus PDI

Towards User-Centric Intervention Adaptiveness: Influencing Behavior-Context Based Healthy Lifestyle Interventions

SemImput: Bridging Semantic Imputation with Deep Learning for Complex Human Activity Recognition

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