A Complete Home Automation Strategy Using Internet of Things

Bheesetti, Deva Sai Kumar; Bhogadi, Venkata Nikhil; Kintali, Saran Kumar; Rahman, Zia Ur

doi:10.1007/978-981-15-7961-5_36

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…This option is not available in any CNN deep learning algorithms. ------------------------------------------------------------------------ (24) Equation 24 represents that k fold validation function; this can give the principal component values in the LeNet 10. Above all steps are used to classify the ultra sound fetal heart abnormalities diagnosis and affected area classification.…”

Section: Fig 5 -Lenet 10 Architecturementioning

confidence: 99%

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A Real and Accurate Ultrasound Fetal Imaging Based Heart Disease Detection Using Deep Learning Technology

Edupuganti¹,

Rathikarani²,

Chaduvula³

2022

IJIE

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The heart anomalies detection is a significant task in cardiac medical research. The CT, ULTRASOUND, CTA and MRI scans have been used to detect heart diseases but giving false experimental outcomes in longer time of conversion (ToC). Therefore, patients haven’t getting better treatment from doctors. So that in this research work an ultrasound image scan-based heart disease prediction and classification is performed with deep learning technology. The LeNet 10 deep learning classifier has been trained Kaggle dataset using appropriate CNN layers. Proposed CNN LeNet -10 is a 165 layers technology consists of flattened layer, dense layer, convolution layer, max pooling layer and etc. Classification and feature extraction has been performed to loading with LeNet-10 architecture. The real time heart ultrasound test images are collecting from Manipal super specialty hospital Vijayawada, these test features are managed to test.CSV file. In pre-processing step, Ostu segmentation and histogram equalization is applied to make heart ultrasound images to be clear. In Segmentation, edge and region-based convolutional steps are applied such that deep features have been identified. LeNet-10 classification is used to find affected area as well as abnormality location. Finally proposed deep learning with confusion matrix can generating application measures. Implementation has been performed on python 3.9 and DL (Deep learning) packages like TensorFlow, keras, sklearn and etc. The measures like Accuracy 98.37%, sensitivity 97.81%, Recall 98.34% and F1 score 98.98% had been attained, proposed heart disease estimation application is more robust and compete with present technology.

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Section: Fig 5 -Lenet 10 Architecturementioning

confidence: 99%

“…The localization and impacted region estimation can be performed through training and testing procedure. Finally, performance parameters such as accuracy, sensitivity, recall, and throughput are measured and differentiated [24]. The most potential pathways for future research as well as enhancements in current methods are discussed briefly.…”

mentioning

confidence: 99%

A Real and Accurate Ultrasound Fetal Imaging Based Heart Disease Detection Using Deep Learning Technology

Edupuganti¹,

Rathikarani²,

Chaduvula³

2022

IJIE

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IoT BASED SOIL MOISTURE MANAGEMENT USING CAPACITIVE SENSOR AND USER-FRIENDLY SMARTPHONE APPLICATION

Sarmphim

SUTTHIPHON

JAROENSONG

et al. 2022

INMATEH

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The Internet of Things (IoT) based system was assembled to monitor the moisture of soils for both indoor and outdoor uses. The SKU:SEN0193 capacitive soil moisture sensor exhibited a linear response to a variation in water volume added to the soil. Microcontroller Arduino NodeMCU was used with ESP8266 Wi-Fi module to transfer the sensing data in real-time, and the soil moisture data was displayed by the Blynk application on a smartphone. When the moisture dropped under the pre-defined threshold, the user was informed via the Line application and able to remotely trigger the irrigation pump

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A Complete Home Automation Strategy Using Internet of Things

Cited by 2 publications

References 12 publications

A Real and Accurate Ultrasound Fetal Imaging Based Heart Disease Detection Using Deep Learning Technology

A Real and Accurate Ultrasound Fetal Imaging Based Heart Disease Detection Using Deep Learning Technology

IoT BASED SOIL MOISTURE MANAGEMENT USING CAPACITIVE SENSOR AND USER-FRIENDLY SMARTPHONE APPLICATION

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