Trustworthy media news content retrieval from web using truth content discovery algorithm

Solainayagi, P.; Ponnusamy, R.

doi:10.1016/j.cogsys.2019.01.002

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…The typological error causes various problems in an application that reduce the flexibility rate of the system. Accurate content retrieval is a challenging process for any software (Solainayagi & Ponnusamy, 2019). Human‐constructed graph technique is commonly used in the content retrieval process.…”

Section: Introductionmentioning

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: Introductionmentioning

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

“…It decreases question handling time and gives more applicable records.TCD+J48 calculation lessens the question search time by 7.54ms, 5% mistake rate and 0.98 MNAD. Semantic data recovery utilizing Wikipedia, Jargon Organization and Area Ontologies (SIRWWO) [22] coordinates different data sources and is utilized for SIR.…”

Section: Related Workmentioning

confidence: 99%

“…A true content detection (TCD) algorithm is used to separate solid data from various conflicting sources of media bias [21]. The reordering feature is useful for clustering the most important data from different comparable media message URLs.…”

Section: Related Workmentioning

confidence: 99%

Optimal Intelligent Information Retrieval and Reliable Storage Scheme for Cloud Environment And E-Learning Big Data Analytics

Chandrasekar

Venkatachalam

2023

Preprint

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Currently, online learning systems in the education sector are widely used and have become a new trend, generating large amounts of educational data based on students' activities. In order to improve online learning experiences, sophisticated data analysis techniques are required. Adding value to E-learning platforms through the efficient processing of big learning data is possible with Big Data. With time, the E-learning management system's repository expands and becomes a rich source of learning materials. Subject matter experts may benefit from using E-learning resources to reuse previously created content when creating online content. In addition, it might be beneficial to the students by giving them access to the pertinent documents for achieving their learning objectives effectively. An improved intelligent information retrieval and reliable storage (OIIRS) scheme is proposed for E-learning using hybrid deep learning techniques. Assume that relevant E-learning documents are stored in cloud and dynamically updated according to users' status. First, we present a highly robust and lightweight crypto i.e. optimized CLEFIA, for securely storing data in local repositories that improve the reliability of data loading. We develop an improved butterfly optimization (IBO) algorithm to provide an optimal solution for CLEFIA that selects private keys. In addition, a hybrid deep learning method, i.e., backward diagonal search based deep recurrent neural network (BD-DRNN) is introduced for optimal intelligent information retrieval based on keywords rather than semantics. Here, feature extraction and key feature matching are performed by the modified Hungarian optimization (MHO) algorithm that improves searching accuracy. Finally, we test our proposed OIIRS scheme with different benchmark datasets and use simulation results to test the performance.

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“…These dimensions also appear in the TREC Health Misinformation Track 2019 as relevancy , efficacy , and credibility , respectively. Additionally, models by Solainayagi et al [25] and Li et al [26] incorporated similar dimensions, emphasizing source reliability and the credibility of statements. These metrics represent some of the initial efforts to quantitatively assess the effectiveness of information retrieval engines in sourcing high-quality information, marking a shift from the traditional query-document relevance paradigm [27,28].…”

Section: Introductionmentioning

confidence: 99%

Online health search via multi-dimensional information quality assessment based on deep language models

Zhang

Naderi

Mishra

et al. 2023

Preprint

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Background: The presence of widespread misinformation in Web resources and the limited quality control provided by search engines can lead to serious implications for individuals seeking health advice. Objective: We aimed to investigate a multi-dimensional information quality assessment model based on deep learning to enhance the reliability of online healthcare information search results. Methods: In this retrospective study, we simulated online health information search scenarios with a topic set of 35 different health-related inquiries and a corpus containing one billion Web documents from the April 2019 snapshot of Common Crawl. Using state-of-the-art pre-trained language models, we inferred the usefulness, supportiveness, and credibility quality dimensions of the retrieved documents for a given search query. Results: The usefulness model provided the largest distinction between help and harm compatibility documents with a difference of 0.053. The supportiveness model achieved the best harm compatibility (0.024), while the combination of usefulness, supportiveness, and credibility models achieved the best help and harm compatibility on helpful topics. Conclusions: Our results suggest that integrating automatic ranking models created for specific information quality dimensions can increase the effectiveness of health-related information retrieval for decision-making.

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Trustworthy media news content retrieval from web using truth content discovery algorithm

Cited by 8 publications

References 12 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

Optimal Intelligent Information Retrieval and Reliable Storage Scheme for Cloud Environment And E-Learning Big Data Analytics

Online health search via multi-dimensional information quality assessment based on deep language models

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