RETRACTED ARTICLE: Conventional neural network for blind image blur correction using latent semantics

Gowthami, S.; Harikumar, R.

doi:10.1007/s00500-020-04859-y

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…The specific process includes several steps: the collection of English text data, the preprocessing of English text data, the vectorization of the obtained data, and the establishment of the optimal DNN model and test. The collected English text semantic data are divided into two parts: a test set and a training set [ 16 , 17 ].…”

Section: Model Establishment and Scheme Designmentioning

confidence: 99%

Application of Dual‐Channel Convolutional Neural Network Algorithm in Semantic Feature Analysis of English Text Big Data

2021

Computational Intelligence and Neuroscience

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The current Internet data explosion is expecting an ever-higher demand for text emotion analysis that greatly facilitates public opinion analysis and trend prediction, among others. Therefore, this paper proposes to use a dual-channel convolutional neural network (DCNN) algorithm to analyze the semantic features of English text big data. Following the analysis of the effect of CNN, artificial neural network (ANN), and recurrent neural network (RNN) on English text data analysis, the more effective long short-term memory (LSTM) and the gated recurrent unit (GRU) neural network (NN) are introduced, and each network is combined with the dual-channel CNN, respectively, and comprehensively analyzed under comparative experiments. Second, the semantic features of English text big data are analyzed through the improved SO-pointwise mutual information (SO-PMI) algorithm. Finally, the ensemble dual-channel CNN model is established. Under the comparative experiment, GRU NN has a better feature detection effect than LSTM NN, but the performance increase from dual-channel CNN to GRU NN + dual-channel CNN is not obvious. Under the comparative analysis of GRU NN + dual-channel CNN model and LSTM NN + dual-channel CNN model, GRU NN + dual-channel CNN model ensures the high accuracy of semantic feature analysis and improves the analysis speed of the model. Further, after the attention mechanism is added to the GRU NN + dual-channel CNN model, the accuracy of semantic feature analysis of the model is improved by nearly 1.3%. Therefore, the ensemble model of GRU NN + dual-channel CNN + attention mechanism is more suitable for semantic feature analysis of English text big data. The results will help the e-commerce platform to analyze the evaluation language and semantic features for the current network English short texts.

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Section: Model Establishment and Scheme Designmentioning

confidence: 99%

Application of Dual‐Channel Convolutional Neural Network Algorithm in Semantic Feature Analysis of English Text Big Data

2021

Computational Intelligence and Neuroscience

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“…LSA was applied to test that sleep reduces the semantic coherence of memory recall. In [34], the authors attempted to deploy kernel matrix estimation using LSA to increase the sharpness of the blurred image. e authors in [35] defined the applicability of LSA in determining problems in aerospace science.…”

Section: Introductionmentioning

confidence: 99%

Identifying Major Research Areas and Minor Research Themes of Android Malware Analysis and Detection Field Using LSA

et al. 2021

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Contemporary technologies have ensured the availability of high-quality research data shared over the Internet. This has resulted in a tremendous availability of research literature, which keeps evolving itself. Thus, identification of core research areas and trends in such ever-evolving literature is not only challenging but interesting too. An empirical overview of contemporary machine learning methods, which have the potential to expedite evidence synthesis within research literature, has been explained. This manuscript proposes Simulating Expert comprehension for Analyzing Research trends (SEAR) framework, which can perform subjective and quantitative investigation over enormous literature. TRENDMINER is the use case designed exclusively for the SEAR framework. TRENDMINER uncovered the intellectual structure of a corpus of 444 abstracts of research articles (published during 2010–2019) on Android malware analysis and detection. The study concludes with the identification of three core research areas, twenty-seven research trends. The study also suggests the potential future research directions.

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Estimating Blur Parameters to Reconstruct the Motion Blurred Images

Abbadi¹,

Abdulkhaleq

Hassani

2023

Communications in Computer and Information Science

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RETRACTED ARTICLE: Conventional neural network for blind image blur correction using latent semantics

Cited by 6 publications

References 13 publications

Application of Dual‐Channel Convolutional Neural Network Algorithm in Semantic Feature Analysis of English Text Big Data

Application of Dual‐Channel Convolutional Neural Network Algorithm in Semantic Feature Analysis of English Text Big Data

Identifying Major Research Areas and Minor Research Themes of Android Malware Analysis and Detection Field Using LSA

Estimating Blur Parameters to Reconstruct the Motion Blurred Images

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