An Adaptive Deep Learning Framework for Dynamic Image Classification in the Internet of Things Environment
In the modern era of digitization, the analysis in the Internet of Things (IoT) environment demands a brisk amalgamation of domains such as high-dimension (images) data sensing technologies, robust internet connection (4 G or 5 G) and dynamic (adaptive) deep learning approaches. This is required for...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/20/5811 |
| _version_ | 1827704374851272704 |
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| author | Syed Muslim Jameel Manzoor Ahmed Hashmani Mobashar Rehman Arif Budiman |
| author_facet | Syed Muslim Jameel Manzoor Ahmed Hashmani Mobashar Rehman Arif Budiman |
| author_sort | Syed Muslim Jameel |
| collection | DOAJ |
| description | In the modern era of digitization, the analysis in the Internet of Things (IoT) environment demands a brisk amalgamation of domains such as high-dimension (images) data sensing technologies, robust internet connection (4 G or 5 G) and dynamic (adaptive) deep learning approaches. This is required for a broad range of indispensable intelligent applications, like intelligent healthcare systems. Dynamic image classification is one of the major areas of concern for researchers, which may take place during analysis under the IoT environment. Dynamic image classification is associated with several temporal data perturbations (such as novel class arrival and class evolution issue) which cause a massive classification deterioration in the deployed classification models and make them in-effective. Therefore, this study addresses such temporal inconsistencies (novel class arrival and class evolution issue) and proposes an adapted deep learning framework (ameliorated adaptive convolutional neural network (CNN) ensemble framework), which handles novel class arrival and class evaluation issue during dynamic image classification. The proposed framework is an improved version of previous adaptive CNN ensemble with an additional online training (OT) and online classifier update (OCU) modules. An OT module is a clustering-based approach which uses the Euclidean distance and silhouette method to determine the potential new classes, whereas, the OCU updates the weights of the existing instances of the ensemble with newly arrived samples. The proposed framework showed the desirable classification improvement under non-stationary scenarios for the benchmark (CIFAR10) and real (ISIC 2019: Skin disease) data streams. Also, the proposed framework outperformed against state-of-art shallow learning and deep learning models. The results have shown the effectiveness and proven the diversity of the proposed framework to adapt the new concept changes during dynamic image classification. In future work, the authors of this study aim to develop an IoT-enabled adaptive intelligent dermoscopy device (for dermatologists). Therefore, further improvements in classification accuracy (for real dataset) is the future concern of this study. |
| first_indexed | 2024-03-10T15:37:46Z |
| format | Article |
| id | doaj.art-b0dc5464dfa74e038d2be8476ff2e3fd |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T15:37:46Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b0dc5464dfa74e038d2be8476ff2e3fd2023-11-20T17:05:08ZengMDPI AGSensors1424-82202020-10-012020581110.3390/s20205811An Adaptive Deep Learning Framework for Dynamic Image Classification in the Internet of Things EnvironmentSyed Muslim Jameel0Manzoor Ahmed Hashmani1Mobashar Rehman2Arif Budiman3Department of Computer and Information Sciences, Universiti Teknologi PETRONAS (UTP), Seri Iskandar 32610, MalaysiaDepartment of Computer and Information Sciences, Universiti Teknologi PETRONAS (UTP), Seri Iskandar 32610, MalaysiaFaculty of Information and Communication Technology, Universiti Tunku Abdul Rahman, Kampar 31900, MalaysiaFaculty of Computer Science, University of Indonesia, West Java Depok 16424, IndonesiaIn the modern era of digitization, the analysis in the Internet of Things (IoT) environment demands a brisk amalgamation of domains such as high-dimension (images) data sensing technologies, robust internet connection (4 G or 5 G) and dynamic (adaptive) deep learning approaches. This is required for a broad range of indispensable intelligent applications, like intelligent healthcare systems. Dynamic image classification is one of the major areas of concern for researchers, which may take place during analysis under the IoT environment. Dynamic image classification is associated with several temporal data perturbations (such as novel class arrival and class evolution issue) which cause a massive classification deterioration in the deployed classification models and make them in-effective. Therefore, this study addresses such temporal inconsistencies (novel class arrival and class evolution issue) and proposes an adapted deep learning framework (ameliorated adaptive convolutional neural network (CNN) ensemble framework), which handles novel class arrival and class evaluation issue during dynamic image classification. The proposed framework is an improved version of previous adaptive CNN ensemble with an additional online training (OT) and online classifier update (OCU) modules. An OT module is a clustering-based approach which uses the Euclidean distance and silhouette method to determine the potential new classes, whereas, the OCU updates the weights of the existing instances of the ensemble with newly arrived samples. The proposed framework showed the desirable classification improvement under non-stationary scenarios for the benchmark (CIFAR10) and real (ISIC 2019: Skin disease) data streams. Also, the proposed framework outperformed against state-of-art shallow learning and deep learning models. The results have shown the effectiveness and proven the diversity of the proposed framework to adapt the new concept changes during dynamic image classification. In future work, the authors of this study aim to develop an IoT-enabled adaptive intelligent dermoscopy device (for dermatologists). Therefore, further improvements in classification accuracy (for real dataset) is the future concern of this study.https://www.mdpi.com/1424-8220/20/20/5811adaptive deep learning algorithmdynamic image classificationInternet of Things (IoT)concept drifthigh dimensional stream analysis |
| spellingShingle | Syed Muslim Jameel Manzoor Ahmed Hashmani Mobashar Rehman Arif Budiman An Adaptive Deep Learning Framework for Dynamic Image Classification in the Internet of Things Environment Sensors adaptive deep learning algorithm dynamic image classification Internet of Things (IoT) concept drift high dimensional stream analysis |
| title | An Adaptive Deep Learning Framework for Dynamic Image Classification in the Internet of Things Environment |
| title_full | An Adaptive Deep Learning Framework for Dynamic Image Classification in the Internet of Things Environment |
| title_fullStr | An Adaptive Deep Learning Framework for Dynamic Image Classification in the Internet of Things Environment |
| title_full_unstemmed | An Adaptive Deep Learning Framework for Dynamic Image Classification in the Internet of Things Environment |
| title_short | An Adaptive Deep Learning Framework for Dynamic Image Classification in the Internet of Things Environment |
| title_sort | adaptive deep learning framework for dynamic image classification in the internet of things environment |
| topic | adaptive deep learning algorithm dynamic image classification Internet of Things (IoT) concept drift high dimensional stream analysis |
| url | https://www.mdpi.com/1424-8220/20/20/5811 |
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