A Facial-Expression Monitoring System for Improved Healthcare in Smart Cities

Muhammad, Ghulam; Alsulaiman, Mansour; Amin, Syed Umar; Ghoneim, Ahmed; Alhamid, Mohammed F.

doi:10.1109/access.2017.2712788

Cited by 117 publications

(45 citation statements)

References 48 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

View full text Add to dashboard Cite

show abstract

“…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

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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 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.

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“…In this study, they analysed audio characteristics measuring voice confidence and metrics in the voice of patients with sadness for therapeutic treatment evaluation. Ghulam Muhammad et al [7] proposed a system that applies a transform called to a face image to extort bands. Then CS-LBP is applied to every band.…”

Section: Related Workmentioning

confidence: 99%

User Satisfaction Detection System for Smart Healthcare using Multimedia

Rajesh¹,

R²

2019

IJITEE

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Emotion plays a critical job ineffectively conveying one’s convictions and intentions. As an outcome, identification of emotion has turned into focus point of few studies recently. Patient observing models are getting to be significant in patient concern and can endow with helpful feedback related to health issues for caregivers and clinicians. In this work, patient fulfilment recognition framework is proposed that uses image frames extracted from the recorded visual-audio modality dataset. The images are treated with techniques such as Local Binary Pattern (LBP) which is a ocular descriptor. The proposed framework incorporates feature extraction from the images and then the Support Vector Machine (SVM) is applied for classification. The three distinct types of emotions are whether the patient is happy, sad or neutral and the same are detected based on the results. The result of such an analysis can be made use of by a group of analysts which include doctors, healthcare experts and system experts to improve smart healthcare system in steps. The reliability of information provided by such a system makes such upgradations more meaningful.

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A Facial-Expression Monitoring System for Improved Healthcare in Smart Cities

Cited by 117 publications

References 48 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

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

User Satisfaction Detection System for Smart Healthcare using Multimedia

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