Domain-Oriented Topic Discovery Based on Features Extraction and Topic Clustering

Lu, Xiaofeng; Zhou, Xiao; Wang, Wenting; Lió, Píetro; Hui, Pan

doi:10.1109/access.2020.2994516

Cited by 7 publications

(2 citation statements)

References 21 publications

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“…A novel domain-oriented topic discovery was done by Xiaofeng lu et al [2], to identify emerging cyber threat topics in the domain in real life to analyze open-source platforms and blogs. This Feature Extraction and Topic clustering (FETC) applied to cyber security data threat considers location of word and parts of speech.…”

Section: Related Workmentioning

confidence: 99%

Secure EMR Classification and Deduplication Using MapReduce

Usharani

Attigeri

2022

IEEE Access

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Healthcare providers generate huge amount of data every day through registration, lab results, prescriptions, and others. This is stored in the form of Electronic Medical Records (EMR) in a central repository. A medical record data is very huge, difficult to read and understand. To give an insight to the professionals in analyzing the different domains a patient belongs to, it is necessary to get pointers to a file before classifying it to a particular department for further analysis. This study provides a EMR processing system to automatically classify EMRs based on the important medical terms using TF-IDF and topic modeling. Automatic Classification of EMRs help the healthcare professionals in taking accurate decisions, providing efficient service, and improves the time taken for processing huge amount of data and in better organizing of patient. The data stored on the cloud may contain duplicate copies of EMR on several storage systems at file level thus increasing the network bandwidth, cost, and consuming storage space. Hence, a deduplication mechanism is required to avoid or reduce the data redundancy. Adapting cloud computing for healthcare systems necessitates sharing patient data with cloud service providers, which creates security concerns as the data may contain diagnosis, medication, laboratory results and medical claims. The main aim of this work is to classify the EMRs as per the specialization using KNN algorithm, optimize storage using deduplication and protect the data using DNA encryption algorithm before uploading to Hadoop. Data redundancy is taken care by implementing deduplication techniques using MD5 hashing. Proposed methodology shows an accuracy of 90% for EMR record classification and handles duplication and security aspects. This in-turn proves the state of the art approach for health care data management.

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Section: Related Workmentioning

confidence: 99%

Secure EMR Classification and Deduplication Using MapReduce

Usharani

Attigeri

2022

IEEE Access

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“…As a basic problem of information retrieval, news topic detection can help decision makers detect meaningful topics effectively. Therefore, it has attracted widespread attention in public opinion monitoring, decision support, and emergency management [2][3][4][5] .…”

Section: Introductionmentioning

confidence: 99%

News topic detection based on capsule semantic graph

Yang

Tang

2022

Big Data Min. Anal.

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Most news topic detection methods use word-based methods, which easily ignore the relationship among words and have semantic sparsity, resulting in low topic detection accuracy. In addition, the current mainstream probability methods and graph analysis methods for topic detection have high time complexity. For these reasons, we present a news topic detection model on the basis of capsule semantic graph (CSG). The keywords that appear in each text at the same time are modeled as a keyword graph, which is divided into multiple subgraphs through community detection. Each subgraph contains a group of closely related keywords. The graph is used as the vertex of CSG. The semantic relationship among the vertices is obtained by calculating the similarity of the average word vector of each vertex. At the same time, the news text is clustered using the incremental clustering method, where each text uses CSG; that is, the similarity among texts is calculated by the graph kernel. The relationship between vertices and edges is also considered when calculating the similarity. Experimental results on three standard datasets show that CSG can obtain higher precision, recall, and F1 values than several latest methods. Experimental results on large-scale news datasets reveal that the time complexity of CSG is lower than that of probabilistic methods and other graph analysis methods.

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Towards product evolution: from the perspective of user knowledge

Zhang

Jiang

2022

International Journal of Production Research

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Domain-Oriented Topic Discovery Based on Features Extraction and Topic Clustering

Cited by 7 publications

References 21 publications

Secure EMR Classification and Deduplication Using MapReduce

Secure EMR Classification and Deduplication Using MapReduce

News topic detection based on capsule semantic graph

Towards product evolution: from the perspective of user knowledge

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