Mining Sequential Update Summarization with Hierarchical Text Analysis

Zhang, Chunyun; Si, Zhongwei; Ma, Zhanyu; Xi, Xiaoming; Yin, Yilong

doi:10.1155/2016/1340973

Cited by 6 publications

(6 citation statements)

References 35 publications

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“…These technologies typically assisted disaster responders in better understanding general population movement and needs during disasters 30 , 43 , 46 and planning evacuation routes 21 99 , 122 Other uses for models and algorithms included monitoring damage 90 and automating disaster communication to the public 118 …”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Use of Big Data and Information and Communications Technology in Disasters: An Integrative Review

Freeman

Blacker

Hatt

et al. 2018

Disaster med. public health prep.

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Novel approaches to improving disaster response have begun to include the use of big data and information and communication technology (ICT). However, there remains a dearth of literature on the use of these technologies in disasters. We have conducted an integrative literature review on the role of ICT and big data in disasters. Included in the review were 113 studies that met our predetermined inclusion criteria. Most studies used qualitative methods (39.8%, n=45) over mixed methods (31%, n=35) or quantitative methods (29.2%, n=33). Nearly 80% (n=88) covered only the response phase of disasters and only 15% (n=17) of the studies addressed disasters in low- and middle-income countries. The 4 most frequently mentioned tools were geographic information systems, social media, patient information, and disaster modeling. We suggest testing ICT and big data tools more widely, especially outside of high-income countries, as well as in nonresponse phases of disasters (eg, disaster recovery), to increase an understanding of the utility of ICT and big data in disasters. Future studies should also include descriptions of the intended users of the tools, as well as implementation challenges, to assist other disaster response professionals in adapting or creating similar tools. (Disaster Med Public Health Preparedness. 2018;page of 1 to 15).

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Section: Resultsmentioning

confidence: 99%

“…21,99,122 Other uses for models and algorithms included monitoring damage 90 and automating disaster communication to the public. 118 Records identified through database search (n = 1297)…”

Section: Model/algorithmmentioning

confidence: 99%

Use of Big Data and Information and Communications Technology in Disasters: An Integrative Review

Freeman

Blacker

Hatt

et al. 2018

Disaster med. public health prep.

View full text Add to dashboard Cite

show abstract

“…The correlation coefficient is a measure of correlation analysis. The value of the correlation coefficient is between [-1, +1], which represents the degree of association between two random variables [19]. When the correlation coefficient is less than 0, it is called negative correlation; when it is greater than 0, it is called positive correlation; when it is equal to 0, it is called zero correlation.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Prediction of Telecommunication Network Fraud Crime Based on Regression-LSTM Model

Gao

Yin

Zhao

et al. 2022

Wireless Communications and Mobile Computing

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Telecommunication network fraud crimes frequently occur in China. Predicting the number and trend of telecommunication network fraud will be of great significance to combating crimes and protecting the legal property of citizens. This paper proposes a combined model of predicting telecommunication network fraud crimes based on the Regression-LSTM model. First, we find that there is a strong correlation between privacy data illegally sold on the dark web and telecommunication network fraud data. Hence, this paper constructs a Linear Regression model using the privacy data illegally sold on the dark web to predict the number of telecommunication network fraud crimes. Second, an LSTM prediction model is constructed using the data of telecommunication network fraud cases on China Judgments Online based on the time-series feature of telecommunication network fraud crimes. Third, this paper uses the error reciprocal method to combine the two models for prediction. In addition, this paper selects the monthly data set of telecommunication network fraud occurring in 2021 for experimental evaluation. The experimental results show that the accuracy of the Regression-LSTM model constructed in this paper is 86.80%, and the RMSE is 0.149. Compared with the ARIMA, Linear Regression, LSTM, Additive-ARIMA-LSTM, and Multiplicative-ARIMA-LSTM models, the Regression-LSTM model proposed has the highest prediction accuracy.

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“…The improvements in channel modeling were initiated by the big-data DL method, whereas data mining approaches used the mining connection rules to accelerate the development of information detection, to avoid the low-density value of high mobile costs, and to provide efficient transmission in the complex channel [55], [248], [249], [250]. The proposed DL algorithms improved big data by using different methods for huge data reduction approaches [16]- [18], [33], [56].…”

Section: ) Mobile Data Mining and Analytics Layermentioning

confidence: 99%

A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

et al. 2021

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The sixth generation (6G) wireless communication network presents itself as a promising technique that can be utilized to provide a fully data-driven network evaluating and optimizing the end-toend behavior and big volumes of a real-time network within a data rate of Tb/s. In addition, 6G adopts an average of 1000+ massive number of connections per person in one decade (2030 virtually instantaneously). The data-driven network is a novel service paradigm that offers a new application for the future of 6G wireless communication and network architecture. It enables ultra-reliable and low latency communication (URLLC) enhancing information transmission up to around 1 Tb/s data rate while achieving a 0.1 millisecond transmission latency. The main limitation of this technique is the computational power available for distributing with big data and greatly designed artificial neural networks. The work carried out in this paper aims to highlight improvements to the multi-level architecture by enabling artificial intelligence (AI) in URLLC providing a new technique in designing wireless networks. This is done through the application of learning, predicting, and decision-making to manage the stream of individuals trained by big data. The secondary aim of this research paper is to improve a multi-level architecture. This enables user level for device intelligence, cell level for edge intelligence, and cloud intelligence for URLLC. The improvement mainly depends on using the training process in unsupervised learning by developing data-driven resource management. In addition, improving a multi-level architecture for URLLC through deep learning (DL) would facilitate the creation of a data-driven AI system, 6G networks for intelligent devices, and technologies based on an effective learning capability. These investigational problems are essential in addressing the requirements in the creation of future smart networks. Moreover, this work provides further ideas on several research gaps between DL and 6G that are up-to-date unknown.INDEX TERMS Artificial neural networks, artificial intelligence, Internet of Things, sixth-generation wireless communication and network architecture, URLLC.

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Mining Sequential Update Summarization with Hierarchical Text Analysis

Cited by 6 publications

References 35 publications

Use of Big Data and Information and Communications Technology in Disasters: An Integrative Review

Use of Big Data and Information and Communications Technology in Disasters: An Integrative Review

Prediction of Telecommunication Network Fraud Crime Based on Regression-LSTM Model

A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

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