Data Analytics in the Internet of Things: A Survey

Saleem, Tausifa Jan; Chishti, Mohammad Ahsan

doi:10.12694/scpe.v20i4.1562

Cited by 23 publications

(16 citation statements)

References 111 publications

(114 reference statements)

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“…A feed-forward DNN is a collection of neurons arranged in a series of layers, with each layer’s neurons receiving input from the preceding layer and performing a weighted sum of the input followed by a nonlinear activation [ 28 ]. A sophisticated non-linear mapping from the input to the output is implemented by the network’s neurons in concert.…”

Section: Preliminariesmentioning

confidence: 99%

“…AE [ 28 , 29 , 30 ] is a typical unsupervised DL algorithm. It is the most common sort of generative model that uses simple neural networks to compress high-dimensional input into a compact representation.…”

Section: Preliminariesmentioning

confidence: 99%

“…Since the ordinary feed-forward DNN was not capable of modeling the time-series tasks, a modified version of the feed-forward DNN called RNN [ 28 , 29 , 30 ] was developed to analyze such tasks. The RNN’s input at a specific time ‘p’ is input at time ‘p’ and output at time ‘p−1’.…”

Section: Preliminariesmentioning

confidence: 99%

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Deep Learning-Based Diagnosis of Alzheimer’s Disease

Saleem

Zahra

et al. 2022

JPM

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Alzheimer’s disease (AD), the most familiar type of dementia, is a severe concern in modern healthcare. Around 5.5 million people aged 65 and above have AD, and it is the sixth leading cause of mortality in the US. AD is an irreversible, degenerative brain disorder characterized by a loss of cognitive function and has no proven cure. Deep learning techniques have gained popularity in recent years, particularly in the domains of natural language processing and computer vision. Since 2014, these techniques have begun to achieve substantial consideration in AD diagnosis research, and the number of papers published in this arena is rising drastically. Deep learning techniques have been reported to be more accurate for AD diagnosis in comparison to conventional machine learning models. Motivated to explore the potential of deep learning in AD diagnosis, this study reviews the current state-of-the-art in AD diagnosis using deep learning. We summarize the most recent trends and findings using a thorough literature review. The study also explores the different biomarkers and datasets for AD diagnosis. Even though deep learning has shown promise in AD diagnosis, there are still several challenges that need to be addressed.

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Section: Preliminariesmentioning

confidence: 99%

Section: Preliminariesmentioning

confidence: 99%

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Deep Learning-Based Diagnosis of Alzheimer’s Disease

Saleem

Zahra

et al. 2022

JPM

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“…All aspects of various fields, from environmental monitoring to smart manufacturing, cannot be separated from the IoT. However, the key to building a complete IoT system is to solve the problem of the huge amount of data and spatiotemporal data classification of IoT spatiotemporal data [17]. For example, modern wind farms include hundreds of wind turbines, each equipped with hundreds of sensors, each with a frequency between 1 and 100 Hz.…”

Section: Iot Labor Resource Management Information Platformmentioning

confidence: 99%

Optimization and Simulation of Labor Resource Management Information Platform Based on Internet of Things

Liang

Cao

Liu

2021

Wireless Communications and Mobile Computing

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This paper conducts an in-depth analysis and research on the optimization of the labor resource management information platform through the Internet of Things (IoT) technology; through the collection, classification, and data search functions of this application system, it meets the supply and demand of professional talents within a certain enterprise. At the same time, it also realizes the curriculum training application on improving the skills and literacy of the employees of a certain enterprise, and it can learn the enterprise curriculum training from the comments of the employees on the enterprise curriculum. The effect of the enterprise course training can be learned from the comments of the employees on the enterprise course, providing an important reference basis for the future revision of the enterprise course training content. The performance of the participants in the training also has objective data for reference, so that the situation will not be disconnected from reality, and the interaction between enterprise management and employees can achieve a balanced effect. The goal of this workforce resource management system is to create a systematic workforce resource management platform for professional talents and help enterprises achieve the goal of speeding up and increasing efficiency. The system interface provided by the third party is used for horizontal data expansion to realize the sharing of basic information or video data as well as system expansion to realize real-time monitoring and management of project works. The cloud platform realizes efficient management and scientific application of construction site projects by construction management departments, which better solves the current problem of lack of supervision at construction sites.

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“…The Internet of things (IoT) provides an intelligent environment that facilitates data exchange and communication between users and devices as well as between devices via communication modules attached on devices [ 1 , 2 , 3 ]. It connects things in the real world with the virtual world through networks to establish an environment in which communication between a human and an IoT device.…”

Section: Introductionmentioning

confidence: 99%

User Recommendation for Data Sharing in Social Internet of Things

Bok

Kim

Choi

et al. 2021

Sensors

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As various types of data are generated on the social Internet of things (SIoT), which combine the Internet of things (IoT) and social networks, the relations of IoT devices should be established for necessary data exchange. In this paper, we propose a user recommendation scheme that facilitates data sharing through an analysis of an interaction between an IoT device and a user in the SIoT. An interrelation between a user and an IoT device as well as an interrelation between users exist simultaneously in the SIoT. Hence, the interaction between users must be analyzed to identify the interest keywords, and the interaction between IoT devices and users to determine the user’s preference of IoT device. Moreover, the proposed scheme calculates the similarity between users based on the IoT device preference based on IoT device usage frequency and interest keywords, which are identified through an analysis between the user and IoT device and that between users. Subsequently, it recommends top-N users who have a high similarity as the users for data sharing. Furthermore, the performance of the proposed scheme is verified through performance evaluation based on the precision, recall, and F-measure.

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Data Analytics in the Internet of Things: A Survey

Cited by 23 publications

References 111 publications

Deep Learning-Based Diagnosis of Alzheimer’s Disease

Deep Learning-Based Diagnosis of Alzheimer’s Disease

Optimization and Simulation of Labor Resource Management Information Platform Based on Internet of Things

User Recommendation for Data Sharing in Social Internet of Things

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