Adaptive context aware decision computing paradigm for intensive health care delivery in smart cities—A case analysis

Aborokbah, Majed; Almutairi, Saad; Sangaiah, Arun Kumar; Samuel, Oluwarotimi Williams

doi:10.1016/j.scs.2017.09.004

Cited by 55 publications

(16 citation statements)

References 26 publications

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“…Chui, Lytras, and Visvizi [62] propose an innovative approach to evaluate energy sustainability in smart cities. Ultimately, Aborokbah et al [63] and Muhammad et al [64] utilize SVMs to provide solutions for the health industry of smart cities.…”

Section: Support Vector Machinesmentioning

confidence: 99%

State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

Nosratabadi¹,

Mosavi²,

Keivani³

et al. 2019

Preprint

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Deep learning (DL) and machine learning (ML) methods have recently contributed to the advancement of models in the various aspects of prediction, planning, and uncertainty analysis of smart cities and urban development. This paper presents the state of the art of DL and ML methods used in this realm. Through a novel taxonomy, the advances in model development and new application domains in urban sustainability and smart cities are presented. Findings reveal that five DL and ML methods have been most applied to address the different aspects of smart cities. These are artificial neural networks; support vector machines; decision trees; ensembles, Bayesians, hybrids, and neuro-fuzzy; and deep learning. It is also disclosed that energy, health, and urban transport are the main domains of smart cities that DL and ML methods contributed in to address their problems.

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Section: Support Vector Machinesmentioning

confidence: 99%

State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

Nosratabadi¹,

Mosavi²,

Keivani³

et al. 2019

Preprint

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“…In similar trends with other results, the multi-features outperformed the single features when 2-channels, 4-channels and 6-channels were considered. Finally, by critically analyzing of our results, we discovered that when classification error, computation time, and number of electrodes were considered together, most feature sets achieved good classification performance with optimal windowing parameters of 250 ms/100 ms. Also, discoveries from this study through the systematic approach adopted can facilitate positive development in other areas where optimal features and machine learning driven approaches are required [41][42][43][44][45][46][47][48][49][50]. Last, one limitation of the current work is that the EMG pattern recognition system for movement intent decoding was analyzed in an off-line mode, and we hope to conduct online and real-time analysis in our future work.…”

Section: Discussionmentioning

confidence: 99%

Appropriate Feature Set and Window Parameters Selection for Efficient Motion Intent Characterization towards Intelligently Smart EMG-PR System

et al. 2020

Self Cite

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The constantly rising number of limb stroke survivors and amputees has motivated the development of intelligent prosthetic/rehabilitation devices for their arm function restoration. The device often integrates a pattern recognition (PR) algorithm that decodes amputees’ limb movement intent from electromyogram (EMG) signals, characterized by neural information and symmetric distribution. However, the control performance of the prostheses mostly rely on the interrelations among multiple dynamic factors of feature set, windowing parameters, and signal conditioning that have rarely been jointly investigated to date. This study systematically investigated the interaction effects of these dynamic factors on the performance of EMG-PR system towards constructing optimal parameters for accurately robust movement intent decoding in the context of prosthetic control. In this regard, the interaction effects of various features across window lengths (50 ms~300 ms), increments (50 ms~125 ms), robustness to external interferences and sensor channels (2 ch~6 ch), were examined using EMG signals obtained from twelve subjects through a symmetrical movement elicitation protocol. Compared to single features, multiple features consistently achieved minimum decoding error below 10% across optimal windowing parameters of 250 ms/100 ms. Also, the multiple features showed high robustness to additive noise with obvious trade-offs between accuracy and computation time. Consequently, our findings may provide proper insight for appropriate parameter selection in the context of robust PR-based control strategy for intelligent rehabilitation device.

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“…Chui et al [62] propose an innovative approach to evaluate energy sustainability in smart cities. Ultimately, Aborokbah et al [63] and Muhammad et al [64] utilize SVMs to provide solutions for the health industry of smart cities.…”

Section: Support Vector Machinesmentioning

confidence: 99%

State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

Nosratabadi

Mosavi

Keivani

et al. 2020

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Deep learning (DL) and machine learning (ML) methods have recently contributed to the advancement of models in the various aspects of prediction, planning, and uncertainty analysis of smart cities and urban development. This paper presents the state of the art of DL and ML methods used in this realm. Through a novel taxonomy, the advances in model development and new application domains in urban sustainability and smart cities are presented. Findings reveal that five DL and ML methods have been most applied to address the different aspects of smart cities. These are artificial neural networks; support vector machines; decision trees; ensembles, Bayesians, hybrids, and neurofuzzy; and deep learning. It is also disclosed that energy, health, and urban transport are the main domains of smart cities that DL and ML methods contributed in to address their problems.

show abstract

Adaptive context aware decision computing paradigm for intensive health care delivery in smart cities—A case analysis

Cited by 55 publications

References 26 publications

State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

Appropriate Feature Set and Window Parameters Selection for Efficient Motion Intent Characterization towards Intelligently Smart EMG-PR System

State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

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