RETRACTED ARTICLE: IoT complex communication architecture for smart cities based on soft computing models

Li, Daming; Cai, Zhiming; Deng, Lizhen; Yao, Xiang

doi:10.1007/s00500-019-03827-5

Cited by 10 publications

(4 citation statements)

References 24 publications

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“…In the literature [ 9 ], by performing Saliency Analysis of Cooccurrence Histogram (SACH) based on cooccurrence histogram for high-resolution remote sensing images and using a saliency enhancement method based on moving K-means clustering, clear region boundaries are established for the region of interest, while improving the immunity of the algorithm to noise. To reduce the computational complexity of region of interest detection for remote sensing images, the literature [ 10 ] proposes to achieve fast and efficient region of interest detection by segmenting high-resolution remote sensing images into superpixels and generating superpixel-level saliency maps using structural tensor and background contrast, and finally by superpixel-to-pixel-based saliency analysis. To extract high-quality regions of interest with clear boundaries and no background interference from remote sensing images, a GLSA (Global and Local Saliency Analysis) algorithm based on global and local saliency analysis is proposed in the literature [ 11 ] for extracting residential regions in high-resolution remote sensing images.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Support Tensor Product Adaptive Image Classification for the Internet of Things

Shi

2022

Computational Intelligence and Neuroscience

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Computer vision is one of the hottest research directions in artificial intelligence at present, and its research goal is to give computers the ability to perceive and cognize their surroundings from a single image. Image recognition is an important research direction in the field of computer vision, which has important research significance and application value in industrial applications such as video surveillance, biometric identification, unmanned vehicles, human-computer interaction, and medical image recognition. In this article, we propose an end-to-end, pixel-to-pixel IoT-oriented fuzzy support tensor product adaptive image classification method. Considering the problem that traditional support tensor product classification methods are difficult to directly produce pixel-to-pixel classification results, the research is based on the idea of inverse convolution network design, which directly outputs dense pixel-by-pixel classification results for images to be classified of arbitrary size to achieve true end-to-end and pixel-to-pixel high-score image classification and improve the efficiency of support tensor product models for high-score image classification on a pixel-by-pixel basis. Moreover, considering that network supervised classification training using deep learning requires a large amount of labeled data as true values and obtaining a large number of labeled data sources is a difficult problem in the field of image classification, this article proposes using a large amount of unlabeled high-resolution remote sensing images for learning generic structured features through unsupervised to assist the labeled high-resolution remote sensing images for better-supervised feature extraction and classification training. By finding a balance between generic structural feature learning of images and differentiated feature learning related to the target class, the dependence of supervised classification on the number of labeled samples is reduced, and the network robustness of the support tensor product algorithm is improved under a small number of labeled training samples.

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Section: Related Workmentioning

confidence: 99%

Fuzzy Support Tensor Product Adaptive Image Classification for the Internet of Things

Shi

2022

Computational Intelligence and Neuroscience

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“…With smart cities as the top agenda of several countries for the next decade, new devices are constantly being added to the network. Hence, the increases in heterogeneous network nodes lead to over complexity of the architecture of IoTenabled CIs [65].…”

Section: A Iot-enabled Critical Infrastructurementioning

confidence: 99%

Differential Privacy for IoT-Enabled Critical Infrastructure: A Comprehensive Survey

et al. 2021

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The rapid evolution of the Internet of Things (IoT) paradigm during the last decade has lead to its adoption in critical infrastructure. However, the multitude of benefits that are derived from the IoT paradigm are short-lived due to the exponential rise in the associated security and privacy threats. Adversaries carry out privacy-oriented attacks to gain access to the sensitive and confidential data of critical infrastructure for various self-centered, political and commercial gains. In the past, researchers have employed several privacy preservation approaches including cryptographic encryption and k-anonymity to secure IoT-enabled critical infrastructure. However, for various reasons, those proposed solutions are not well suited for modern IoT-enabled critical infrastructure. Therefore, Dwork's differential privacy has emerged as the most viable privacy preservation strategy for IoT-enabled critical infrastructure. This paper provides a comprehensive and extensive survey of the application and implementation of differential privacy in four major application domains of IoT-enabled critical infrastructure: Smart Grids (SGs), Intelligent Transport Systems (ITSs), healthcare and medical systems, and Industrial Internet of Things (IIoT). Finally, we discuss some promising future research directions in differential privacy for IoT-enabled critical infrastructure.

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“…e package structure in the Provider can be adjusted according to your needs. e logical processing layer interacts with the backend data layer using Redis as a cache, and the data in Redis is read first, and if the data is not stored in Redis, Redis is refreshed first [18]. e logical processing layer contains two important business contents, which are also the focus of this thesis, namely, teacher recommendation and video streaming analysis.…”

Section: English Learning Information Platform Designmentioning

confidence: 99%

A Practical Exploration of Constructive English Learning Platform Informatization Based on RBF Algorithm

Tang

Tsai

2021

Mathematical Problems in Engineering

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The constructive English learning approach provides better technical support and learning concepts for English learners. This paper firstly introduces the theoretical basis of constructive English learning and the status of domestic and international research and analyzes the advantages over the traditional English learning approach. This paper also analyzes the RBF neural network model, especially the locally adjusted RBF algorithm, and considers the training model and construction method of the RBF neural network to simulate the learning pattern of good learners of English in grades 4 and 6 to better realize the effect of the English learning platform and allow the English learning platform to play a supportive role in the learning process of students, and through the evaluation of the design, we can better improve the software design, which can be improved by evaluating the design. In the subsequent research, we should add a more user-friendly interface design, use larger model samples to train the model to make it more universal in accuracy and ensure good stability in operation, and integrate multiple algorithms to improve the training algorithm of RBF neural network to obtain a more general algorithm model. The rich teaching methods can cultivate students’ independent inquiry ability, and the construction of this English learning platform can help improve students’ ability to use information technology. By making students become the main subject of teaching and learning, we can stimulate students’ enthusiasm and a good awareness of English learning. Using the radial basis function (RBF) algorithm and the constructive pod language teaching method, this English learning platform is a good and positive aid for students to learn English, and as a better English education tool, it has certain research value and positive significance for the study of English teaching.

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RETRACTED ARTICLE: IoT complex communication architecture for smart cities based on soft computing models

Cited by 10 publications

References 24 publications

Fuzzy Support Tensor Product Adaptive Image Classification for the Internet of Things

Fuzzy Support Tensor Product Adaptive Image Classification for the Internet of Things

Differential Privacy for IoT-Enabled Critical Infrastructure: A Comprehensive Survey

A Practical Exploration of Constructive English Learning Platform Informatization Based on RBF Algorithm

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