Advanced Deep Learning Approach for Smart Home Appliance Identification Using Recurrent Neural Networks with LSTM
In the Internet of Things (IoT) domain, vast numbers of smart devices are interconnected, generating large volumes of data requiring advanced management mechanisms. One major challenge in smart environments is the ability to accurately distinguish and categorize the various types of objects within t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | IoT |
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| Online Access: | https://www.mdpi.com/2624-831X/5/4/38 |
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| author | Sana Abdelaziz Bkheet Johnson I. Agbinya Gamal Saad Mohamed Khamis |
| author_facet | Sana Abdelaziz Bkheet Johnson I. Agbinya Gamal Saad Mohamed Khamis |
| author_sort | Sana Abdelaziz Bkheet |
| collection | DOAJ |
| description | In the Internet of Things (IoT) domain, vast numbers of smart devices are interconnected, generating large volumes of data requiring advanced management mechanisms. One major challenge in smart environments is the ability to accurately distinguish and categorize the various types of objects within these systems. To address this issue, the study introduces a recurrent neural network (RNN) model designed for classifying data from smart home devices. Using a dataset from Kaggle, the research outlines the processes of data collection, loading, normalization, and model development. The RNN, enhanced with long short-term memory (LSTM) layers, was trained and evaluated, showing notable improvements in training and validation accuracy over ten epochs. The model achieved a test accuracy of 83.25%, a loss of 35.4%, a precision of 85%, and a recall of 81%. The evaluation of the model on the test set includes a detailed analysis using ROC curves, area under the curve (AUC) scores for multi-class classification, and a confusion matrix. With an AUC score of 0.9896, the model demonstrated exceptional performance in accurately classifying IoT device categories. These results suggest that the LSTM-equipped RNN offers strong learning efficiency and generalization, making it a highly suitable approach for IoT device classification. Additionally, the article explores the concept of IoT and reviews recent advancements in using deep learning models across various IoT sectors, including smart homes, industrial systems, and healthcare. Future research could aim to improve the model’s real-time processing abilities and scalability and incorporate a wider variety of IoT data types to enhance its practical applications and expand its utility across more IoT environments. |
| format | Article |
| id | doaj-art-90b07a9bf29344848c3d79afbcb6c2a2 |
| institution | Kabale University |
| issn | 2624-831X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | IoT |
| spelling | doaj-art-90b07a9bf29344848c3d79afbcb6c2a22024-12-27T14:31:39ZengMDPI AGIoT2624-831X2024-11-015483585110.3390/iot5040038Advanced Deep Learning Approach for Smart Home Appliance Identification Using Recurrent Neural Networks with LSTMSana Abdelaziz Bkheet0Johnson I. Agbinya1Gamal Saad Mohamed Khamis2Department of Computer Science, College of Science, Northern Border University (NBU), Arar 73213, Saudi ArabiaSchool of Information Technology and Engineering, Melbourne Institute of Technology, Melbourne, VIC 3000, AustraliaDepartment of Computer Science, College of Science, Northern Border University (NBU), Arar 73213, Saudi ArabiaIn the Internet of Things (IoT) domain, vast numbers of smart devices are interconnected, generating large volumes of data requiring advanced management mechanisms. One major challenge in smart environments is the ability to accurately distinguish and categorize the various types of objects within these systems. To address this issue, the study introduces a recurrent neural network (RNN) model designed for classifying data from smart home devices. Using a dataset from Kaggle, the research outlines the processes of data collection, loading, normalization, and model development. The RNN, enhanced with long short-term memory (LSTM) layers, was trained and evaluated, showing notable improvements in training and validation accuracy over ten epochs. The model achieved a test accuracy of 83.25%, a loss of 35.4%, a precision of 85%, and a recall of 81%. The evaluation of the model on the test set includes a detailed analysis using ROC curves, area under the curve (AUC) scores for multi-class classification, and a confusion matrix. With an AUC score of 0.9896, the model demonstrated exceptional performance in accurately classifying IoT device categories. These results suggest that the LSTM-equipped RNN offers strong learning efficiency and generalization, making it a highly suitable approach for IoT device classification. Additionally, the article explores the concept of IoT and reviews recent advancements in using deep learning models across various IoT sectors, including smart homes, industrial systems, and healthcare. Future research could aim to improve the model’s real-time processing abilities and scalability and incorporate a wider variety of IoT data types to enhance its practical applications and expand its utility across more IoT environments.https://www.mdpi.com/2624-831X/5/4/38the Internet of Things (IoT)smart objectsrecurrent neural network (RNN)long short-term memory (LSTM) |
| spellingShingle | Sana Abdelaziz Bkheet Johnson I. Agbinya Gamal Saad Mohamed Khamis Advanced Deep Learning Approach for Smart Home Appliance Identification Using Recurrent Neural Networks with LSTM IoT the Internet of Things (IoT) smart objects recurrent neural network (RNN) long short-term memory (LSTM) |
| title | Advanced Deep Learning Approach for Smart Home Appliance Identification Using Recurrent Neural Networks with LSTM |
| title_full | Advanced Deep Learning Approach for Smart Home Appliance Identification Using Recurrent Neural Networks with LSTM |
| title_fullStr | Advanced Deep Learning Approach for Smart Home Appliance Identification Using Recurrent Neural Networks with LSTM |
| title_full_unstemmed | Advanced Deep Learning Approach for Smart Home Appliance Identification Using Recurrent Neural Networks with LSTM |
| title_short | Advanced Deep Learning Approach for Smart Home Appliance Identification Using Recurrent Neural Networks with LSTM |
| title_sort | advanced deep learning approach for smart home appliance identification using recurrent neural networks with lstm |
| topic | the Internet of Things (IoT) smart objects recurrent neural network (RNN) long short-term memory (LSTM) |
| url | https://www.mdpi.com/2624-831X/5/4/38 |
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