High-performance fuzzy optimized deep convolutional neural network model for big data classification based on the social internet of things

Shaji, B.; Singh, Rohit; Nisha, K. L.

doi:10.1007/s11227-022-04974-7

Cited by 4 publications

(1 citation statement)

References 45 publications

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“…In addition, it is advisable to improve the adaptability of the neural network model to the linguistic design of the data, in particular, for derivatological research, it is advisable to take into account their features by updating lemmatization (turning a word into a dictionary form or lemma, in other words, transforming the form of words into the original one), stemming (reducing words to the base by dropping the ending or suffix), etc. 12 .…”

Section: Data Collection and Processing For Neural Network Modelingmentioning

confidence: 99%

Features of Data Collection and Processing for Neural Network Modeling of Language Categories

Dovhan¹

2023

Trends in the Development of Philological Education in the Era of Digitalization: European and National Contexts

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Section: Data Collection and Processing For Neural Network Modelingmentioning

confidence: 99%

Features of Data Collection and Processing for Neural Network Modeling of Language Categories

Dovhan¹

2023

Trends in the Development of Philological Education in the Era of Digitalization: European and National Contexts

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A systematic survey of fuzzy deep learning for uncertain medical data

Zheng,

Xu,

et al. 2024

Artif Intell Rev

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Intelligent medical industry is in a rapid stage of development around the world, followed by are the expanding market size and basic theories of intelligent medical diagnosis and decision-making. Deep learning models have achieved good practical results in medical domain. However, traditional deep learning is almost calculated and developed by crisp values, while imprecise, uncertain, and vague medical data is common in the process of diagnosis and treatment. It is important and significant to review the contributions of fuzzy deep learning for uncertain medical data, because fuzzy deep learning that originated from fuzzy sets, can effectively deal with uncertain and inaccurate information, providing new viewpoints for alleviating the presence of noise, artifact or high dimensional unstructured information in uncertain medical data. Therefore, taking focus on the intersection of both different fuzzy deep learning models and several types of uncertain medical data, the paper first constructs four types of frameworks of fuzzy deep learning models used for uncertain medical data, and investigates the status from three aspects: fuzzy deep learning models, uncertain medical data and application scenarios. Then the performance evaluation metrics of fuzzy deep learning models are analyzed in details. This work has some original points: (1) four types of frameworks of applying fuzzy deep learning models for uncertain medical data are first proposed. (2) Seven fuzzy deep learning models, five types of uncertain medical data, and five application scenarios are reviewed in details, respectively. (3) The advantages, challenges, and future research directions of fuzzy deep learning for uncertain medical data are critically analyzed, providing valuable suggestions for further deep research.

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Topic and knowledge-enhanced modeling for edge-enabled IoT user identity linkage across social networks

Huang,

Ma,

Rong

et al. 2024

J Cloud Comp

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The Internet of Things (IoT) devices spawn growing diverse social platforms and online data at the network edge, propelling the development of cross-platform applications. To integrate cross-platform data, user identity linkage is envisioned as a promising technique by detecting whether different accounts from multiple social networks belong to the same identity. The profile and social relationship information of IoT users may be inconsistent, which deteriorates the reliability of the effectiveness of identity linkage. To this end, we propose a topic and knowledge-enhanced model for edge-enabled IoT user identity linkage across social networks, named TKM, which conducts feature representation of user generated contents from both post-level and account-level for identity linkage. Specifically, a topic-enhanced method is designed to extract features at the post-level. Meanwhile, we develop an external knowledge-based Siamese neural network for user-generated content alignment at the account-level. Finally, we show the superiority of TKM over existing methods on two real-world datasets. The results demonstrate the improvement in prediction and retrieval performance achieved by utilizing both post-level and account-level representation for identity linkage across social networks.

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High-performance fuzzy optimized deep convolutional neural network model for big data classification based on the social internet of things

Cited by 4 publications

References 45 publications

Features of Data Collection and Processing for Neural Network Modeling of Language Categories

Features of Data Collection and Processing for Neural Network Modeling of Language Categories

A systematic survey of fuzzy deep learning for uncertain medical data

Topic and knowledge-enhanced modeling for edge-enabled IoT user identity linkage across social networks

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