AL-TEA: Alternative Tea Algorithm for Healthcare Image in IoT

Babulal, Choudhary Leeladevi; Sharma, Anand

doi:10.17762/ijritcc.v10i6.5620

Cited by 1 publication

(1 citation statement)

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“…Here, in the input layer, channels are given by k 0 . For enhancing the efficiency of computation, 1x1 conv layer is used prior to each 3x3 convlayer which reduces the input feature maps that are usually high compared to the output feature maps k [19]. 1x1 conv layer called bottleneck layer is used which produced 4k feature maps.…”

Section: Figure 2 Overall Architecture Of Auto Encoder Deep Neural Ne...mentioning

confidence: 99%

Construction of Data Driven Decomposition Based Soft Sensors with Auto Encoder Deep Neural Network for IoT Healthcare Applications

Sowmya

Sharma

Bhagat

et al. 2022

Int. j. commun. netw. inf. secur.

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The architecture of IoT healthcare is motivated towards the data-driven realization and patient-centric health models, whereas the personalized assistance is provided by deploying the advanced sensors. According to the procedures in surgery, in the emergency unit, the patients are monitored till they are stable physically and then shifted to ward for further recovery and evaluation. Normally evaluation done in ward doesn’t suggest continuous parameters monitoring for physiological condition and thus relapse of patients are common. In real-time healthcare applications, the vital parameters will be estimated through dedicated sensors, that are still luxurious at the present situation and highly sensitive to harsh conditions of environment. Furthermore, for real-time monitoring, delay is usually present in the sensors. Because of these issues, data-driven soft sensors are highly attractive alternatives. This research is motivated towards this fact and Auto Encoder Deep Neural Network (AutoEncDeepNN) is proposed depending on Health Framework in the internet assisting the patients with trigger-based sensor activation model to manage master and slave sensors. The advantage of the proposed method is that the hidden information are mined automatically from the sensors and high representative features are generated by multiple layer’s iteration. This goal is consistently achieved and thus the proposed model outperforms few standard approaches which are considered like Hierarchical Extreme Learning Machine (HELM), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). It is found that the proposed AutoEncDeepNN method achieves 94.72% of accuracy, 41.96% of RMSE, 34.16% of RAE and 48.68% of MAE in 74.64 ms.

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Section: Figure 2 Overall Architecture Of Auto Encoder Deep Neural Ne...mentioning

confidence: 99%