Classification Techniques for Arrhythmia Patterns Using Convolutional Neural Networks and Internet of Things (IoT) Devices

Agyeman, Michael Opoku; Guerrero, Andres Felipe; Vien, Quoc-Tuan

doi:10.1109/access.2022.3192390

Cited by 19 publications

(9 citation statements)

References 70 publications

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“…Local Binary Pattern (LBP) is a simple and efficient texture description operator. Wavelet analysis not only integrates its localization characteristics, but also links the window size and frequency changes, and is an effective tool for image and signal transformation [9][10].…”

Section: Image Segmentation Methodsmentioning

confidence: 99%

Image Content Segmentation Based on Convolutional Neural Network

2022

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In real life, images are widely used in medicine, commercial marketing and other fields. Image recognition technology has developed rapidly and has played a huge role in our daily life and work. So in order to make better use of image technology, this paper intends to delve into image content segmentation in convolutional neural networks. This paper mainly studies the role of convolutional neural networks and image segmentation in specific operations through experimental methods and interactive ratio IoU analysis methods. The experimental data show that in the PASCAL VOC 2015 and IRCAD datasets, the optimal parameters obtained by G-FCN are α=0.6, β=0.6. Therefore, the result of using the improved algorithm is close to the marked result, which can better reflect the details of the image, and the segmentation effect is better.

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Section: Image Segmentation Methodsmentioning

confidence: 99%

Image Content Segmentation Based on Convolutional Neural Network

2022

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“…Studies show that for identifying pneumonia, MAG-superior SDs outperform cutting-edge models. According to research, one's judgment deteriorates under stress [31]. Doctors can make better choices if they are able to identify stress symptoms early on.…”

Section: In-depth Review Of Different Models For Identification Of Hu...mentioning

confidence: 99%

Review Of Models & Techniques For Identification Of Human Health Diseases

2022

pnr

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Human health diseases are caused due to genetic disorders, environmental conditions, presence of Alpha synuclein bodies, etc. Identification of this disease can be done via estimation of body tremors, slowed movements, fever, change in corpuscles, rigid muscles, impaired posture & balance, pain analysis, loss of automatic movements, speech changes, and writing changes. To estimate these effects, researchers have proposed a wide variety of machine learning techniques that can analyze speech signals, electroencephalogram (EEG) signals, electrocardiogram (ECG) signals, handwriting variations, gait identification & classification, etc. These models are highly variant in terms of their internal operating characteristics, which makes it ambiguous for clinical designers to identify optimal models for their deployments. Furthermore, these models also showcase highly variant performance levels as per their input feature sets. Thus, it is difficult to identify optimal models for a given set of input parameters. To overcome these issues, this paper initially reviews a wide variety of Human Health disease identification models in terms of their contextual nuances, operating advantages, functional limitations, quantitative characteristics, and deployment-specific future scopes. This will assist readers to identify functional models that can be applied for their operation-specific use cases. After referring this comparison, readers will be presented with a comparative analysis of these models in terms of their detection accuracy, classification delay, input datasets, computational complexity, and scalability levels. Based on this comparison, readers will be able to identify optimally performing models as per their qualitative characteristics.

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“…Some Frontiers in Ocean Engineering scholars believe that the focus of growth should be on how to plan the National Coastal Zone scientifically and reasonably and how to use limited space to solve the maritime traffic problem. Some scholars proposed to start with the protection of the environment [5][6]. Second, based on domestic and foreign literature, combined with the actual situation of China, some new research methods are constructed.…”

Section: Introductionmentioning

confidence: 99%

Relationship between Marine Resources growth and Marine Economy Based on Neural Network

2022

FOE

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With the rapid growth of China's economy, people pay more and more attention to marine resources. In the process of growth and utilization, how to deal with the relationship with the marine ecological environment is also a great challenge for us. In this paper, the domestic and foreign research and analysis on protecting the marine environment, carrying out large-scale marine engineering activities and promoting sustainable fishery production are carried out, and classified and summarized. Through the establishment of models to predict the factors affecting the coastline of the coastal seabed zone and their change rules in different stages, according to the historical data of marine resources, artificial neural networks(NN) are constructed using NN algorithms to study the regional water and soil conservation planning and growth measures based on the marine ecological environment. Finally, the model is simulated and tested. The test results show that the marine resource growth model based on neural algorithm has short data processing time and low delay time, which indicates that the algorithm can meet the operational requirements of the marine resource model.

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Classification Techniques for Arrhythmia Patterns Using Convolutional Neural Networks and Internet of Things (IoT) Devices

Cited by 19 publications

References 70 publications

Image Content Segmentation Based on Convolutional Neural Network

Image Content Segmentation Based on Convolutional Neural Network

Review Of Models & Techniques For Identification Of Human Health Diseases

Relationship between Marine Resources growth and Marine Economy Based on Neural Network

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