RETRACTED ARTICLE: Situational English Language Information Intelligent Retrieval Algorithm Based on Wireless Sensor Network

Ye, Qing

doi:10.1007/s10776-021-00516-9

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…The proposed GM-ShorT improves the system's efficiency by raising the accuracy rate of the categorization process. Ye (2021), an English language intelligent retrieval method, was introduced for wireless sensor networks (WSN). In this case, WSN is employed to amass data necessary for retrieval and to generate a superior data set for subsequent analysis and detection.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED: A predictive typological content retrieval method for real‐time applications using multilingual natural language processing

Baskar

Dhote²,

Dhote

et al. 2022

Expert Systems

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Natural language processing (NLP) is widely used in multi‐media real‐time applications for understanding human interactions through computer aided‐analysis. NLP is common in auto‐filling, voice recognition, typo‐checking applications, and so forth. Multilingual NLP requires vast data processing and interaction recognition features for leveraging content retrieval precision. To strengthen this concept, a predictive typological content retrieval method is introduced in this article. The proposed method maximizes and relies on distributed transfer learning for training multilingual interactions with pitch and tone features. The phonetic pronunciation and the previous content‐based predictions are forwarded using knowledge transfer. This knowledge is modelled using the training data and precise contents identified in the previous processing instances. For this purpose, the auto‐fill and error correction data are augmented with the training and multilingual processing databases. Depending on the current prediction and previous content, the knowledge base is updated, and further training relies on this feature. Therefore, the proposed method accurately identifies the content across multilingual NLP models.

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Section: Related Workmentioning

confidence: 99%

RETRACTED: A predictive typological content retrieval method for real‐time applications using multilingual natural language processing

Baskar

Dhote²,

Dhote

et al. 2022

Expert Systems

View full text Add to dashboard Cite

show abstract

“…PSO identifies the optimal response for a user query. For instance, the metadata of a document can be extracted using this approach [22][23][24][25][26][27]. A text extractor can be built using the AI technique for the automated extraction of key terms from a document [28][29][30][31][32][33][34].…”

Section: Literature Reviewmentioning

confidence: 99%

“…On the one hand, there is an availability of a wide range of IR systems. On the other hand, there is a lack of domainspeci c ontologies or IR systems to serve an organization [26][27][28][29][30]. In the Kingdom of Saudi Arabia (KSA), most organizations o er a sophisticated application for employees and stakeholders to share the information and valuable documents.…”

Section: Introductionmentioning

confidence: 99%

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Alothman

Sait

2022

Computational Intelligence and Neuroscience

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In recent times, artificial intelligence (AI) methods have been applied in document and content management to make decisions and improve the organization’s functionalities. However, the lack of semantics and restricted metadata hinders the current document management technique from achieving a better outcome. E-Government activities demand a sophisticated approach to handle a large corpus of data and produce valuable insights. There is a lack of methods to manage and retrieve bilingual (Arabic and English) documents. Therefore, the study aims to develop an ontology-based AI framework for managing documents. A testbed is employed to simulate the existing and proposed framework for the performance evaluation. Initially, a data extraction methodology is utilized to extract Arabic and English content from 77 documents. Researchers developed a bilingual dictionary to teach the proposed information retrieval technique. A classifier based on the Naïve Bayes approach is designed to identify the documents’ relations. Finally, a ranking approach based on link analysis is used for ranking the documents according to the users’ queries. The benchmark evaluation metrics are applied to measure the performance of the proposed ontological framework. The findings suggest that the proposed framework offers supreme results and outperforms the existing framework.

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“…In addition, the focus of information utilization varies from system to system. From the type of information utilized, most systems utilize rich tourism heterogeneous information, including text, image, and numerical information, while some personalized intelligent tourism systems incorporate user contextual information and some systems introduce user interaction to analyze user preferences [19].…”

Section: Related Workmentioning

confidence: 99%

Multimodal Wireless Situational Awareness‐Based Tourism Service Scene

Liu

Zhu

2021

Journal of Sensors

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Community platforms featuring user sharing and self-expression in social media generate big data on tourism resources, which, if fully utilized in a smart tourism system driven by high-tech and new technologies, will bring new life to the field of smart tourism research and will play an important role in the development of Internet+ tourism. However, tourism data in social media has the following characteristics: diversity, redundancy, heterogeneity, and intelligence. To address the characteristics of tourism data in social media, this thesis focuses on the following challenges: it is difficult to efficiently obtain tourism visualization information (text and images) in social media; it is difficult to effectively utilize tourism multimodal heterogeneous information; it is difficult to properly retrieve multimedia entity information of tourism attractions; and it is difficult to reasonably construct tourism personalized recommendation models. In this paper, an image search reordering method based on a hybrid feature graph model is proposed to realize the rapid acquisition of high-quality Internet images from the web using hybrid visual features and graph models, thus providing data security for the analysis of social media-based tourism images. To address the shortcomings of current search engines for image retrieval, visual information is used to bridge the problem of semantic gap between text-based search and images. To address the limitation of single visual features, we use latent semantic analysis to fuse multiple visual features to obtain hybrid features, which not only combine multiple single features but also preserve the potential relationship between these features. To address the shortcomings of the reordering methods based on classification and clustering, a reordering framework based on the graph model is used to reorder the images and finally complete the image search reordering based on the hybrid feature graph model. This method can obtain image information in social media with high efficiency and quality and then prepare for the subsequent work of tourism image analysis mining and personalized recommendation.

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RETRACTED ARTICLE: Situational English Language Information Intelligent Retrieval Algorithm Based on Wireless Sensor Network

Cited by 5 publications

References 19 publications

RETRACTED: A predictive typological content retrieval method for real‐time applications using multilingual natural language processing

RETRACTED: A predictive typological content retrieval method for real‐time applications using multilingual natural language processing

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Multimodal Wireless Situational Awareness‐Based Tourism Service Scene

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