Book Review: Introduction to Modern Information Retrieval

Naun, Chew Chiat

doi:10.5860/lrts.55n4.239

Cited by 6 publications

(2 citation statements)

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“… and is the proportion of the occurrence frequency of subject and to the sum, and is the degree of difference between subject and ; its value is calculated by Formula 2. Furthermore, is Salton's cosine similarity between the two disciplines [ 55 ]; its value is calculated by Formula 3. Formula 3 calculates the similarity of subjects based on the number of co-occurrences between one subject and the other disciplines as well as the similarity between two associated disciplines.…”

Section: Methodsmentioning

confidence: 99%

Exploring the Interdisciplinary Nature of Precision Medicine：Network Analysis and Visualization

Xu¹,

Hu²,

Lyu³

et al. 2021

JMIR Med Inform

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Background Interdisciplinary research is an important feature of precision medicine. However, the accurate cross-disciplinary status of precision medicine is still unclear. Objective The aim of this study is to present the nature of interdisciplinary collaboration in precision medicine based on co-occurrences and social network analysis. Methods A total of 7544 studies about precision medicine, published between 2010 and 2019, were collected from the Web of Science database. We analyzed interdisciplinarity with descriptive statistics, co-occurrence analysis, and social network analysis. An evolutionary graph and strategic diagram were created to clarify the development of streams and trends in disciplinary communities. Results The results indicate that 105 disciplines are involved in precision medicine research and cover a wide range. However, the disciplinary distribution is unbalanced. Current cross-disciplinary collaboration in precision medicine mainly focuses on clinical application and technology-associated disciplines. The characteristics of the disciplinary collaboration network are as follows: (1) disciplinary cooperation in precision medicine is not mature or centralized; (2) the leading disciplines are absent; (3) the pattern of disciplinary cooperation is mostly indirect rather than direct. There are 7 interdisciplinary communities in the precision medicine collaboration network; however, their positions in the network differ. Community 4, with disciplines such as genetics and heredity in the core position, is the most central and cooperative discipline in the interdisciplinary network. This indicates that Community 4 represents a relatively mature direction in interdisciplinary cooperation in precision medicine. Finally, according to the evolution graph, we clearly present the development streams of disciplinary collaborations in precision medicine. We describe the scale and the time frame for development trends and distributions in detail. Importantly, we use evolution graphs to accurately estimate the developmental trend of precision medicine, such as biological big data processing, molecular imaging, and widespread clinical applications. Conclusions This study can help researchers, clinicians, and policymakers comprehensively understand the overall network of interdisciplinary cooperation in precision medicine. More importantly, we quantitatively and precisely present the history of interdisciplinary cooperation and accurately predict the developing trends of interdisciplinary cooperation in precision medicine.

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Section: Methodsmentioning

confidence: 99%

Exploring the Interdisciplinary Nature of Precision Medicine：Network Analysis and Visualization

Xu¹,

Hu²,

Lyu³

et al. 2021

JMIR Med Inform

View full text Add to dashboard Cite

show abstract

“…Previous studies have found that these two features outperform when used for similar classification tasks [15,16]. Finally, we used tf-idf (short for term frequency-inverse document frequency) to calculate the weight for each term to obtain terms' vector [17]. In a tf-idf scheme, the basic vocabulary is formed by choosing the number of times a term appears in the document collection, here called term frequency (tf).…”

Section: Features Extractionmentioning

confidence: 99%

Development of Social Media Analytics System for Emergency Event Detection and Crisis Management

Khatoon¹,

Alshamari²,

Asif³

et al. 2021

Computers, Materials &Amp; Continua

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Social media platforms have proven to be effective for information gathering during emergency events caused by natural or human-made disasters. Emergency response authorities, law enforcement agencies, and the public can use this information to gain situational awareness and improve disaster response. In case of emergencies, rapid responses are needed to address victims' requests for help. The research community has developed many social media platforms and used them effectively for emergency response and coordination in the past. However, most of the present deployments of platforms in crisis management are not automated, and their operational success largely depends on experts who analyze the information manually and coordinate with relevant humanitarian agencies or law enforcement authorities to initiate emergency response operations. The seamless integration of automatically identifying types of urgent needs from millions of posts and delivery of relevant information to the appropriate agency for timely response has become essential. This research project aims to develop a generalized Information Technology (IT) solution for emergency response and disaster management by integrating social media data as its core component. In this paper, we focused on text analysis techniques which can help the emergency response authorities to filter through the sheer amount of information gathered automatically for supporting their relief efforts. More specifically, we applied state-of-the-art Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL) techniques ranging from unsupervised to supervised learning for an in-depth analysis of social media data for the purpose of extracting real-time information on a critical event to facilitate emergency response in a crisis. As a proof of concept, a case study on the COVID-19 pandemic on the

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An Efficiency Improvement Approach for Library Novelty Search

Xia

2023

2023 3rd International Conference on Intelligent Communications and Computing (ICC)

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Book Review: Introduction to Modern Information Retrieval

Cited by 6 publications

References 0 publications

Exploring the Interdisciplinary Nature of Precision Medicine：Network Analysis and Visualization

Exploring the Interdisciplinary Nature of Precision Medicine：Network Analysis and Visualization

Development of Social Media Analytics System for Emergency Event Detection and Crisis Management

An Efficiency Improvement Approach for Library Novelty Search

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