A Tree Augmented Naïve Bayes-based methodology for classifying cryptocurrency trends

Dağ, Ali; Dag, Asli Z.; Asilkalkan, Abdullah; Simsek, Serhat; Delen, Dursun

doi:10.1016/j.jbusres.2022.113522

Cited by 11 publications

(2 citation statements)

References 31 publications

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“…BBNs have been successfully used in different domains and industries, such as healthcare [ 4 , 50 , 51 ], education [ 52 ], safety analysis [ 53 ], finance [ 54 ], risk management [ 55 ], disaster management [ 56 ], and traffic management [ 57 ]. BBNs account for noise in stochastic events, ensuring that strong interactions are highlighted across the data.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessment of the organizational factors in incident management practices in healthcare: A tree augmented Naive Bayes model

Albreiki,

Simsekler,

Qazi

et al. 2024

PLoS ONE

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Despite the exponential transformation occurring in the healthcare industry, operational failures pose significant challenges in the delivery of safe and efficient care. Incident management plays a crucial role in mitigating these challenges; however, it encounters limitations due to organizational factors within complex and dynamic healthcare systems. Further, there are limited studies examining the interdependencies and relative importance of these factors in the context of incident management practices. To address this gap, this study utilized aggregate-level hospital data to explore the influence of organizational factors on incident management practices. Employing a Bayesian Belief Network (BBN) structural learning algorithm, Tree Augmented Naive (TAN), this study assessed the probabilistic relationships, represented graphically, between organizational factors and incident management. Significantly, the model highlighted the critical roles of morale and staff engagement in influencing incident management practices within organizations. This study enhances our understanding of the importance of organizational factors in incident management, providing valuable insights for healthcare managers to effectively prioritize and allocate resources for continuous quality improvement efforts.

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Section: Literature Reviewmentioning

confidence: 99%

Assessment of the organizational factors in incident management practices in healthcare: A tree augmented Naive Bayes model

Albreiki,

Simsekler,

Qazi

et al. 2024

PLoS ONE

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“…The Naive Bayes technique can handle issues with many features and minimal computing cost, making it appropriate for text classification and sentiment analysis. This algorithm excels at quickly and successfully deriving patterns from training data [26].…”

Section: Classificationmentioning

confidence: 99%

Classification of Sentiment Analysis and Community Opinion Modeling Topics for Application of ICT in Government Operations

Risal

Fathahillah

Sulaiman

2023

Int. J. Environ. Eng. Educ.

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Utilizing information systems is very useful in the current era. Digitizing administration in the Village is beneficial in the service process to the public. This is seen as a change in service that can make it easier or more difficult for the people of Sanrobone Village to take care of administration at the village office. This study aims to analyze public opinion regarding the use of e-government, predict public opinion regarding the use of e-government, and analyze modeling topics related to the use of e-government. This research applies a text mining algorithm with a sentiment analysis method to see positive, negative, and neutral public perceptions and also uses topic modeling to get the most frequently appearing topics in the data. Stages in this study include Data Collection, Text Pre-processing, Sentiment Analysis, Topic Modelling, Classification, and Evaluation. The results obtained are the ten words that appear most often in the responses of the Village community: easy 122, help 96, village 80, accessed 80, letter 80, permit 77, resident 73, manage 60, service 52, and the person with 52 words. The sentiment analysis is positive, with 411 opinions, 37 negative opinions, and 152 neutral opinions. Finally, the performance of the Nave Bayes algorithm in predicting classification results is excellent, with an accuracy rate of 98 percent.

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Analyzing Emotional Trends from X Platform Using SenticNet: A Comparative Analysis with Cryptocurrency Price

Shahiki Tash,

Ahani,

Tash

et al. 2024

Cogn Comput

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A Tree Augmented Naïve Bayes-based methodology for classifying cryptocurrency trends

Cited by 11 publications

References 31 publications

Assessment of the organizational factors in incident management practices in healthcare: A tree augmented Naive Bayes model

Assessment of the organizational factors in incident management practices in healthcare: A tree augmented Naive Bayes model

Classification of Sentiment Analysis and Community Opinion Modeling Topics for Application of ICT in Government Operations

Analyzing Emotional Trends from X Platform Using SenticNet: A Comparative Analysis with Cryptocurrency Price

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