Cluster-Based Knowledge Graph and Entity-Relation Representation on Tourism Economical Sentiments

Mishra, Ram Krishn; Raj, Harshit; Urolagin, Siddhaling; Jothi, J. Angel Arul; Nawaz, Nishad

doi:10.3390/app12168105

Cited by 13 publications

(6 citation statements)

References 46 publications

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“…The pre-processing procedure seeks to enhance the data's quality and produce customised datasets from the original dataset in accordance with requirements in order to get better outcomes [39,40,41]. Our aim in this work is to use single-label classification method to improve the robustness of the deep learning model and attain higher accuracy.…”

Section: Pre-processing For Customized Daisee Dataset Creationmentioning

confidence: 99%

Detecting Academic Affective States of Learners in Online Learning Environments Using Deep Transfer Learning

Komaravalli,

2023

SCPE

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Online Learning Environments (OLEs) have become essential in global education, especially during and after the COVID-19 pandemic. However, OLEs face a challenge in recognizing student emotions, hindering educators' ability to provide effective support. To address this issue, researchers emphasize the importance of a balanced dataset and a precise model for academic emotion detection in OLEs. However, the widely-used DAiSEE dataset is imbalanced and contains videos captured in well-lit environments. However, real-time observations reveal students' diverse lighting conditions and proximity to cameras. Consequently, models trained on DAiSEE dataset exhibit poor accuracy. In response, this work suggests a customized DAiSEE dataset and proposes the Xception-based transfer learned model and AffectXception model. Our customization process involves selectively extracting single-label frames with intensity levels 2 or 3 from the original DAiSEE dataset. To enhance dataset diversity and tackle the issue of dataset imbalance, we meticulously apply data augmentation techniques on these extracted frames. This results in frames that showcase variations in lighting, both low and high, as well as diverse camera perspectives. As a result, the customized DAiSEE dataset is now well-balanced and exceptionally suitable for training deep learning models to detect academic emotions in online learners. Then we trained and tested both proposed models on this dataset. The AffectXception model outperforms existing models, achieving significant improvements. For Boredom, Engagement, Confusion, and Frustration, it attains accuracy rates of 77%, 79.28%, 83.76%, and 91.87%, respectively. Additionally, we evaluate the AffectXception model on the Online Learning Spontaneous Facial Expression Database (OL-SFED), obtaining competitive results across various emotion classes. This work empowers educators to adjust their content and delivery methods based on learners' emotional states, resulting in more effective and informative online sessions. As OLEs continue to play a crucial role in education, our approach enhances their capacity to address students' emotional needs.

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Section: Pre-processing For Customized Daisee Dataset Creationmentioning

confidence: 99%

Detecting Academic Affective States of Learners in Online Learning Environments Using Deep Transfer Learning

Komaravalli,

2023

SCPE

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“…K-means clustering was employed to group districts based on vaccination rates, using random centroid initialization [29]. The Silhouette score method was then used to evaluate cluster quality, with scores ranging from −1 to +1, where higher values indicate better intra-cluster matching and sub-optimal inter-cluster matching [30].…”

Section: Cluster and Regression Analysismentioning

confidence: 99%

Social Response and Measles Dynamics

Adebanji,

Aschl,

Chumo

et al. 2023

Stats

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Measles remains one of the leading causes of death among young children globally, even though a safe and cost-effective vaccine is available. Vaccine hesitancy and social response to vaccination continue to undermine efforts to eradicate measles. In this study, we consider data about measles vaccination and measles prevalence in Germany for the years 2008–2012 in 345 districts. In the first part of the paper, we show that the probability of a local outbreak does not significantly depend on the vaccination coverage, but—if an outbreak does take place—the scale of the outbreak depends significantly on the vaccination coverage. Additionally, we show that the willingness to be vaccinated is significantly increased by local outbreaks, with a delay of about one year. In the second part of the paper, we consider a deterministic delay model to investigate the consequences of the statistical findings on the dynamics of the infection. Here, we find that the delay might induce oscillations if the vaccination coverage is rather low and the social response to an outbreak is sufficiently strong. The relevance of our findings is discussed at the end of the paper.

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“…It is able to identify the natural groupings and patterns in the data without establishing any rules or criteria beforehand and discovering the patterns that might not be apparent from prior knowledge. Therefore, with the help of the clustering algorithms, the underlying patterns and trends for marketing and advertising motives with the huge amount of data can be used to classify the audience into meaningful categories [26].…”

Section: Audience Segmentationmentioning

confidence: 99%

New RFI Model for Behavioral Audience Segmentation in Wi-Fi Advertising System

Lim,

Ong,

Leow

2023

Future Internet

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In this technological era, businesses tend to place advertisements via the medium of Wi-Fi advertising to expose their brands and products to the public. Wi-Fi advertising offers a platform for businesses to leverage their marketing strategies to achieve desired goals, provided they have a thorough understanding of their audience’s behaviors. This paper aims to formulate a new RFI (recency, frequency, and interest) model that is able to analyze the behavior of the audience towards the advertisement. The audience’s interest is measured based on the relationship between their total view duration on an advertisement and its corresponding overall click received. With the help of a clustering algorithm to perform the dynamic segmentation, the patterns of the audience behaviors are then being interpreted by segmenting the audience based on their engagement behaviors. In the experiments, two different Wi-Fi advertising attributes are tested to prove the new RFI model is applicable to effectively interpret the audience engagement behaviors with the proposed dynamic characteristics range table. The weak and strongly engaged behavioral characteristics of the segmented behavioral patterns of the audience, such as in a one-time audience, are interpreted successfully with the dynamic-characteristics range table.

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Cluster-Based Knowledge Graph and Entity-Relation Representation on Tourism Economical Sentiments

Cited by 13 publications

References 46 publications

Detecting Academic Affective States of Learners in Online Learning Environments Using Deep Transfer Learning

Detecting Academic Affective States of Learners in Online Learning Environments Using Deep Transfer Learning

Social Response and Measles Dynamics

New RFI Model for Behavioral Audience Segmentation in Wi-Fi Advertising System

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