Deep Learning (CNN, RNN) Applications for Smart Homes: A Systematic Review

Yu, Jiyeon; Jiménez, Antonio; Villalba-Mora, Elena

doi:10.3390/computers11020026

Cited by 31 publications

(12 citation statements)

References 77 publications

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“…A CNN is a supervised learning algorithm that requires a training dataset containing classified data. The algorithm learns from existing classes and then assigns them to the unseen data [128]. Applications of CNNs include object detection, scene labelling, and classification [127].…”

Section: A Process Of Scenario Generationmentioning

confidence: 99%

“…Features are learned from the memory of the previous input, which results in the problem of storing past information over an extended period [129]. Because RNNs predict the most likely result for the next step, they are often used with sequential data, such as text or video, for natural language processing, speech analysis, or entity extraction [128].…”

Section: B Categorizationmentioning

confidence: 99%

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Categorizing Data-Driven Methods for Test Scenario Generation to Assess Automated Driving Systems

Bäumler,

Linke,

Prokop

2024

IEEE Access

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This survey aims to provide an overview of various methods for generating data-driven test scenarios for assessing automated driving systems (ADS). The survey updates the overall process of scenario generation and categorizes the current methods using a systematic literature review of 64 studies identified between 2017 and 01/2023. Overall, we demonstrate that the data-driven scenario generation process should be updated by another process step, scenario fusion, leading to seven process steps: (1) scope definition, (2) primary data source selection, (3) primary data collection, (4) scenario identification, ( 5) scenario fusion, ( 6) scenario generation, and (7) scenario evaluation. "Scenario fusion" aims to fuse scenarios identified in different data sources for a better coverage of the ADSs' operational design domains (ODDs) and a more comprehensive scenario description. Moreover, we introduce an improved definition for the representativity of test scenario catalogs, which helps improve the collection of traffic data using sampling plans. Also, we show that real driving and police accident data are the most commonly used data input sources. Besides, we illustrate that the ODD is often not defined. Finally, we discuss that the standardization of test scenario generation is difficult because most methods do not address specific ADSs and test environments, and do not provide standardized interfaces. Overall, we recommend comparing existing approaches using the same input data and researching the mutual supplementation of the existing methods. Finally, pre-defined case studies, further standardized terminology, and standards for test execution and evaluation can help speed up the standardization process.INDEX TERMS Advanced driver assistance systems, autonomous driving, system validation, vehicle safety I. INTRODUCTION

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Section: A Process Of Scenario Generationmentioning

confidence: 99%

Section: B Categorizationmentioning

confidence: 99%

Categorizing Data-Driven Methods for Test Scenario Generation to Assess Automated Driving Systems

Bäumler,

Linke,

Prokop

2024

IEEE Access

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“…A simple neuron usually consists of several dendrites, an axon, and several axon-ends (Soufian et al, 2022). Among them, dendrites are mainly used to receive input information, axon ends transmit information to the next neuron, and axons are used to connect dendrites and axon ends (Yu et al, 2022). Thanks to the special memory and information processing mechanisms of neurons, in 1943 psychologist McCulloch and mathematician Pits abstracted the basic structure of deep neural network units: the neuron model, by referring to the structure of biological neurons (Byeon et al, 2021).…”

Section: Model Structure Of Deep Learningmentioning

confidence: 99%

Application of Behavior Recognition Technology Based on Deep Learning in Elderly Care

Zhang,

Mi,

Liu

2024

International Journal of Healthcare Information Systems and Informatics

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China is currently one of the countries with the largest elderly population in the world, and the issue of population aging has become a widespread concern. The behavior recognition algorithm based on deep learning is currently the main behavior recognition algorithm and one of the basic technologies in the field of computer vision. In existing research, the method of constructing complex classification models based on manual feature representation can no longer meet the requirements of high recognition accuracy and applicability, and the introduction of deep learning has brought new development directions for behavior recognition. Therefore, this article aims to study how to apply deep learning-based behavior recognition technology more accurately and effectively in the care of elderly people in the context of “artificial intelligence.”

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“…billion in 2016 [17] . As convolutional neural networks (CNNs) and recurrent neural networks (RNNs), one of the artificial intelligence technologies, have been developed, research on video, text, and voice recognition has been actively conducted [18] .…”

Section: Theoretical Background 1 History Of Artificial Intelligencementioning

confidence: 99%

Grading Classification of Tongue Cancer in Oral Images Using CNN Transfer Learning

Kang,

Lee

2022

j. méd. Imaging (ScholarGen)

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- Oral cancer is one of the top 10 diseases in cancer incidence worldwide and its prognosis is not good as early diagnosis is not made and so the survival rate from it has not been improved greatly. Oral cancer occurs most commonly in tongue, floor of mouth, and lower lip and 5-year survival rate is very low at 50% and if diagnosed early, the average time of survival becomes longer. Therefore, to improve the survival rate of oral cancer patients, early diagnosis and discovery and patient habit improvement including smoking are very important. Oral cancer can be diagnosed with imaging equipment like CT and MRI or through invasive biopsy, but it is not easy to make an approach and generally difficult to discover early. Therefore, to identify if early diagnosis would be possible using the intraoral images obtained through ordinary camera, the reliability of tongue cancer grade classification was evaluated by applying deep learning analysis. The collected images of oral cavity were classified into four groups: normal, inflammatory lesion, pre-cancerous lesion, and malignant tumor depending on clinical importance and three (3) types of image pretreatment were applied. In case of deep neural network, Inception-ResNet-v2 was used and 70%(1,307 images) of the pretreated images were used as training data and after image enhancement processing, transfer-learned with SGDM technique. As test data, the remaining 30% data (561 images) were used. In confusion matrix, it appeared that sensitivity was 90.37%, specificity 95.03%, accuracy 93.21%, positive predictability 92.06%, and negative predictability 93.93%, and in ROC curve, AUC was normal because normal was 0.9781, inflammatory lesion 0.9102, precancerous lesion 0.8944, and malignant tumor 0.9688, and the classification performance of inflammatory lesion or pre-cancerous lesion was inferior to that of malignant tumor. The accuracy depending on pretreatment did not show a statistically significant difference in HE-1 and HE-2 compared to standard image. The performance of the diagnostic classifier applying deep learning into intraoral images showed accuracy at the average rate of 82.9% and so showed more excellent performance than the precedent studies using different RGB images. Deep learning study using such RGB images tends to be rapidly commercialized as it has disclosed big data whose images were easy to obtain and that were preclassified and so noninvasive data can be easily obtained unlike biopsy or blood test. Intraoral image does not have preclassified and disclosed big data yet, but have a distortion in an artifact or lesion in obtaining an image and so if we can standardize the image acquisition and organize big data through correct preclassification in cooperation with clinicians although this is limited, it will be helpful for early diagnosis of tongue cancer.

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Deep Learning (CNN, RNN) Applications for Smart Homes: A Systematic Review

Cited by 31 publications

References 77 publications

Categorizing Data-Driven Methods for Test Scenario Generation to Assess Automated Driving Systems

Categorizing Data-Driven Methods for Test Scenario Generation to Assess Automated Driving Systems

Application of Behavior Recognition Technology Based on Deep Learning in Elderly Care

Grading Classification of Tongue Cancer in Oral Images Using CNN Transfer Learning

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