Decision Making in IoT Environment through Unsupervised Learning

Piccialli, Francesco; Casolla, Giampaolo; Cuomo, Salvatore; Giampaolo, Fabio; Cola, Vincenzo Schiano di

doi:10.1109/mis.2019.2944783

Cited by 52 publications

(18 citation statements)

References 7 publications

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“…The system model comprises a layered architecture for IoH system. The architecture is inspired from the fact that decision making in IoT environment [19] can be supported by machine learning techniques. Figure 1 depicts the layered architecture which consists of five layers described as follows:…”

Section: System Modelmentioning

confidence: 99%

DeTrAs: deep learning-based healthcare framework for IoT-based assistance of Alzheimer patients

Sharma

Dudeja

Aujla

et al. 2020

Neural Comput & Applic

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Healthcare 4.0 paradigm aims at realization of data-driven and patient-centric health systems wherein advanced sensors can be deployed to provide personalized assistance. Hence, extreme mentally affected patients from diseases like Alzheimer can be assisted using sophisticated algorithms and enabling technologies. Motivated from this fact, in this paper, DeTrAs: Deep Learning-based Internet of Health Framework for the Assistance of Alzheimer Patients is proposed. DeTrAs works in three phases: (1) A recurrent neural network-based Alzheimer prediction scheme is proposed which uses sensory movement data, (2) an ensemble approach for abnormality tracking for Alzheimer patients is designed which comprises two parts: (a) convolutional neural network-based emotion detection scheme and (b) timestamp window-based natural language processing scheme, and (3) an IoT-based assistance mechanism for the Alzheimer patients is also presented. The evaluation of DeTrAs depicts almost 10–20% improvement in terms of accuracy in contrast to the different existing machine learning algorithms.

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Section: System Modelmentioning

confidence: 99%

DeTrAs: deep learning-based healthcare framework for IoT-based assistance of Alzheimer patients

Sharma

Dudeja

Aujla

et al. 2020

Neural Comput & Applic

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“…After pretraining, the DBN is fine tuned by marking samples. The fine-tuning process is described as follows: the output of the RBM in the last layer of the DBN is taken as the input of the back-propagation network and supervised by the backpropagation algorithm from back to front [31], [49]- [53].…”

Section: ) Fine-tuning Process Of the Dbnmentioning

confidence: 99%

Validity Evaluation of Transformer DGA Online Monitoring Data in Grid Edge Systems

Tao

Zhang

et al. 2020

IEEE Access

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Dissolved gas analysis (DGA) is an important method of predicting transformer faults, and the accuracy of DGA measurements is of great significance to the evaluation of the transformer state in grid edge systems. In actual situations, it is difficult to regularly calibrate online DGA monitoring devices in a uniform way. Therefore, this paper proposes a method based on B-EMD and DBN to evaluate the validity of online DGA monitoring data and optimize the corresponding calibration plan. An analysis of actual DGA signals shows that the method proposed in this paper can effectively diagnose faults and improve the reliability of DGA online monitoring devices. INDEX TERMS Dissolved gas analysis, power transformer, validity evaluation, empirical mode decomposition, deep belief network.

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“…Moreover, with significant advances in communication technologies, an era of ''Internet of Things'' (IoT) appears [58,59]. A large amount of IoT data can be collected in various ways [60,61] which may be used to generate networks for healthcare service systems. Identifying influential providers in those generated network-based systems can also help optimize and distribute the healthcare service resource.…”

Section: Outlook and Future Workmentioning

confidence: 99%

A network-based method with privacy-preserving for identifying influential providers in large healthcare service systems

Mei

Cuomo

et al. 2020

Future Generation Computer Systems

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In data science, networks provide a useful abstraction of the structure of many complex systems, ranging from social systems and computer networks to biological networks and physical systems. Healthcare service systems are one of the main social systems that can also be understood using network-based approaches, for example, to identify and evaluate influential providers. In this paper, we propose a network-based method with privacy-preserving for identifying influential providers in large healthcare service systems. First, the provider-interacting network is constructed by employing publicly available information on locations and types of healthcare services of providers. Second, the ranking of nodes in the generated provider-interacting network is conducted in parallel on the basis of four nodal influence metrics. Third, the impact of the top-ranked influential nodes in the provider-interacting network is evaluated using three indicators. Compared with other research work based on patient-sharing networks, in this paper, the provider-interacting network of healthcare service providers can be roughly created according to the locations and the publicly available types of healthcare services, without the need for personally private electronic medical claims, thus protecting the privacy of patients. The proposed method is demonstrated by employing PhysicianandOtherSupplierDataCY2017, and can be applied to other similar datasets to help make decisions for the optimization of healthcare resources in the response to public health emergencies.

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Decision Making in IoT Environment through Unsupervised Learning

Cited by 52 publications

References 7 publications

DeTrAs: deep learning-based healthcare framework for IoT-based assistance of Alzheimer patients

DeTrAs: deep learning-based healthcare framework for IoT-based assistance of Alzheimer patients

Validity Evaluation of Transformer DGA Online Monitoring Data in Grid Edge Systems

A network-based method with privacy-preserving for identifying influential providers in large healthcare service systems

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