Smart City Data Sensing during COVID-19: Public Reaction to Accelerating Digital Transformation

Kharlamov, Alexander A.; Расходчиков, Алексей Николаевич; Pilgún, María

doi:10.3390/s21123965

Cited by 15 publications

(7 citation statements)

References 43 publications

(22 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Te pooling layer performs feature dimension reduction [21]. Te proposed method uses the max-pooling method to extract the largest feature from the local feature set obtained by the convolution layer to replace the entire local feature 􏽢 f i , and the calculation is as follows:…”

Section: Pooling Layermentioning

confidence: 99%

Social Network Text Sentiment Analysis Method Based on CNN-BiGRU in Big Data Environment

Shan

2023

Mobile Information Systems

View full text Add to dashboard Cite

Aiming at the serious colloquialism of social network texts and the sparse semantic features, this article proposes a CNN-BiGRU-based sentiment analysis method for social network texts in the big data environment. First, the dependency syntax tree is introduced to represent the dependency relationship between words to construct the word vector to represent the text. Then, sentiment features with different granularity are extracted by multiple convolution kernels of different sizes in a convolution neural network (CNN). These sentiment features are input into bidirectional gated recurrent unit (BiGRU) network for analysis to obtain deeper sentiment features. Finally, a certain number of neurons are discarded by the Dropout method, and sentiment types are classified by the Sigmoid activation function. The Weibo_senti_100k Weibo data set is used to demonstrate the proposed method. The results show that if the Dropout value is set to 0.25 and the Adam optimizer is selected, the analysis performance is the best. The accuracy, precision, recall, and AUC are about 94.09%, 95.13%, 92.87%, and 0.953, respectively, which has certain application value.

show abstract

Section: Pooling Layermentioning

confidence: 99%

Social Network Text Sentiment Analysis Method Based on CNN-BiGRU in Big Data Environment

Shan

2023

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…Data mining technology can also analyze citizens' attitudes towards daily life status or policies. Kharlamov et al (2021) The researchers found that due to the COVID-19 pandemic, there are differences in how well people adapt to IT technology. Digital services in smart cities have received mixed reviews.…”

Section: Smart Iot Data and Smart Service And Applicationmentioning

confidence: 99%

Integrating Data Mining Methods Across all Domains of a Smart City

Dave¹,

Xu²,

Arneberg³

et al. 2022

Journal of Computer Science

View full text Add to dashboard Cite

As modern cities continue to develop, smart devices are being used to improve citizens' lives. As these devices become more sophisticated, the amount of information that they collect increases. A Smart City is a city that uses a large amount of generated data to constantly improve the services offered to the people that live there. Data mining techniques can be used to sift through the data and mine out meaningful patterns. Our research project focused on seven different disciplines within a Smart City, surveying the current state of research in each category. Smart Transportation is focused on decreasing congestion, increasing efficiency in public transportation, and improving the safety of pedestrians. Smart Healthcare is focused on modern healthcare monitoring systems and ambulance dispatch services. Smart Energy is focused on decreasing energy consumption and promoting green energy through WiFi thermostat optimization and a smart electric grid. Smart City Utilities is focused on creating algorithms to improve waste collection techniques and air quality. Smart City Planning is focused on land use and the placement of green spaces. Smart Networks and Privacy is focused on secure networks. Lastly, the Smart IoT (Internet of Things) Application is focused on next-generation networks like 5G.

show abstract

“…Information entropy represents the information gain of uncertainty. It can measure the impact of a feature on the classification results of enterprise transformation effectiveness [15,16]. Information entropy is expressed as follows:…”

Section: Information Gainmentioning

confidence: 99%

Decision Modeling and Evaluation of Enterprise Digital Transformation Using Data Mining

Cheng¹

2022

Mobile Information Systems

View full text Add to dashboard Cite

The paper discusses the commercial opportunities for digitalization, as well as the changes that occur when digital technology is adopted throughout all business areas. A survey of digital firms found that maturing digital organisations are concentrating on integrating digital technologies to revolutionise how businesses operate. A digital strategy supported by management who nurture a culture of change and innovation is a primary predictor of a company’s ability to digitally remake itself. Companies that integrate big data, cloud, mobile, and social technologies into their infrastructure are more lucrative, have larger sales, and have a better market valuation than competitors with a weak vision. This paper aims to conduct an in-depth study on the evaluation and decision-making model of the effectiveness of enterprise transforming to digitalization based on data mining in order to address the shortcomings of self-evaluation, promote and improve the process and promote enterprise transforming to digitalization. First, it determines the evaluation index and uses triangular fuzzy numbers to clarify the index weight. It also gathers the opinions of various experts in the decision-making process. Afterward, the decision tree evaluation model is generated through the information gain and ROC curve. Based on enterprises’ relevant transforming to digitalization data, the decision tree model is used as the evaluation decision model, and the research on the evaluation model of the point of enterprise transforming to digitalization is completed. Experiments show that the method proposed in this paper has high accuracy, fast algorithm operation efficiency, and robust data mining ability. It can effectively improve and promote the progress of enterprise transforming to digitalization.

show abstract

Smart City Data Sensing during COVID-19: Public Reaction to Accelerating Digital Transformation

Cited by 15 publications

References 43 publications

Social Network Text Sentiment Analysis Method Based on CNN-BiGRU in Big Data Environment

Social Network Text Sentiment Analysis Method Based on CNN-BiGRU in Big Data Environment

Integrating Data Mining Methods Across all Domains of a Smart City

Decision Modeling and Evaluation of Enterprise Digital Transformation Using Data Mining

Contact Info

Product

Resources

About