Using deep learning to detecting abnormal behavior in internet of things

Al-Shabi, Mohammed; Abuhamdah, Anmar

doi:10.11591/ijece.v12i2.pp2108-2120

Cited by 18 publications

(14 citation statements)

References 25 publications

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“…The extended middle layer uses lightweight depth convolution to filter features with non-linear sources. The MobileNetV2 design contains a full convolutional layer with 32 filters and 19 bottleneck layers [24]- [27]. In Figure 6 shows the proposed architecture.…”

Section: Mobilenetv2mentioning

confidence: 99%

A novel automated deep learning approach for Alzheimer's disease classification

Aparna¹,

Rao²

2023

IJ-AI

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Alzheimer's disease is a degenerative brain illness, incurable and progressive. Globally for every two seconds, someone is affected by Alzheimer's disease. Alzheimer's disease in the elderly is difficult to diagnose due to the complexity of the brain structure. Its pixel intensity is similar and systematic distinction is necessary. Deep learning has inspired a lot of interest in recent years in tackling challenges in a variety of fields, including medical imaging. One of the drawbacks of deep learning approach is the inability to detect changes in functional connectivity in MCI (mild cognitive impairment) patients' functional brain networks. In this paper, we utilize deep features extracted from two pre-trained deep learning models to tackle this issue. The proposed models DenseNet121 and MobileNetV2 is used to perform the task of Alzheimer's disease multi-class classification. In this method, initially we increased 70 % of dataset and generated images by using CycleGAN (generative adversarial networks). We achieved 98.82% of accuracy with proposed models. It gives best results compared to existing models.

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Section: Mobilenetv2mentioning

confidence: 99%

A novel automated deep learning approach for Alzheimer's disease classification

Aparna¹,

Rao²

2023

IJ-AI

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“…Due to the huge new data generated by IoT devices, it is necessary to think about how to control and benefit from this data. ML is considered one of the most promising areas, capable of solving a wide range of problems [51]. In healthcare, there is a new approach to monitor patients, called personalized healthcare (PH) [52].…”

Section: And Iotmentioning

confidence: 99%

Blockchain and machine learning in the internet of things: a review of smart healthcare

Al-Blihed

Al-Mufadi²,

Al-Harbi³

et al. 2023

IJ-AI

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<span lang="EN-US">The healthcare sector has benefited from digital transformation and modern technology. As well is expected to rely even more on the internet of things (IoT) technologies in the near future. Due to the availability of portable medical devices, applications, and mobile health services, all of which have contributed to the development of innovative features for the delivery of healthcare services. With the large number of data issued from the IoT and the importance of using data to benefit from contained in diagnosing diseases, medical records, or monitoring. Furthermore, the expansion of emerging technologies such as robots and machine learning (ML) is supported by the ease with exchanged and shared medical information. Moreover, Blockchain technology enables the creation of secure records for storing medical data in a safe and timely manner. The paper reviews various IoT, Blockchain, and ML applications and systems in the smart healthcare sector to discover many challenges, consequently, it will be easy for researchers who have an interest in these fields to find today and future solutions. This, in turn, will help to enhance the technical services depending on the IoT in ML and Blockchain in the smart healthcare field.</span>

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“…Deep learning is quite effective at dealing with such massive amounts of data and can generate highly accurate results. As a result, Internet of Things (IoT), big data analytics, and deep learning are all important to the growth of a high-tech society [22], [28], [74], [77], [89], [92], [93], [101], [103], [110], [114], [126], [149], [152], [155], [183], [188], [202], [232], [265], [318], [352].…”

Section: Big Data Analytics In Iotmentioning

confidence: 99%

Insights on the internet of things: past, present, and future directions

Sutikno

Thalmann

2022

TELKOMNIKA

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The internet of things (IoT) is rapidly expanding and improving operations in a wide range of real-world applications, from consumer IoT and enterprise IoT to manufacturing and industrial IoT (IIoT). Consumer markets, wearable devices, healthcare, smart buildings, agriculture, and smart cities are just a few examples. This paper discusses the current state of the IoT ecosystem, its primary applications and benefits, important architectural stages, some of the problems and challenges it faces, and its future. This paper explains how an appropriate IoT architecture that saves data, analyzes it, and recommends corrective action improves the process's ground reality. The IoT system architecture is divided into three layers: device, gateway, and platform. This then cascades into the four stages of the IoT architectural layout: sensors and actuators; gateways and data acquisition systems; edge IT data processing; and datacenter and cloud, which use high-end apps to collect data, evaluate it, process it, and provide remedial solutions. This elegant combination provides excellent value in automatic action. In the future, IoT will continue to serve as the foundation for many technologies. Machine learning will become more popular in the coming years as IoT networks take center stage in a variety of industries.

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Using deep learning to detecting abnormal behavior in internet of things

Cited by 18 publications

References 25 publications

A novel automated deep learning approach for Alzheimer's disease classification

A novel automated deep learning approach for Alzheimer's disease classification

Blockchain and machine learning in the internet of things: a review of smart healthcare

Insights on the internet of things: past, present, and future directions

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