scholarly journals A Frame Work for Hospital Readmission Based on Deep Learning Approach and Naive Bayes Classification Model

2019 ◽  
Vol 33 (1) ◽  
pp. 67-74 ◽  
Author(s):  
Thalakola Rao ◽  
Bhanu Battula
Keyword(s):  
Deep Learning ◽  
Hospital Readmission ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Model ◽  
Learning Approach ◽  
Frame Work ◽  
Naive Bayes Classification ◽  
Naïve Bayes Classification

scholarly journals Naïve Bayes Classification Model for the Producer Price Index Prediction

SISTEMASI ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 171
Author(s):  
Melisa Winda Pertiwi ◽  
Mira Kusmira ◽  
Rezkiani Rezkiani ◽  
Bambang Kelana Simpony ◽  
Yanti Apriyani ◽  
...  
Keyword(s):  
Price Index ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Model ◽  
Producer Price ◽  
Producer Price Index ◽  
Naive Bayes Classification ◽  
Naïve Bayes Classification

Novel naïve Bayes classification models for predicting the carcinogenicity of chemicals

2016 ◽  
Vol 97 ◽  
pp. 141-149 ◽  
Author(s):  
Hui Zhang ◽  
Zhi-Xing Cao ◽  
Meng Li ◽  
Yu-Zhi Li ◽  
Cheng Peng
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Models ◽  
Naive Bayes Classification ◽  
Naïve Bayes Classification

scholarly journals Adaptive sensing scheme using naive Bayes classification for environment monitoring with drone

2018 ◽  
Vol 14 (1) ◽  
pp. 155014771875603 ◽  
Author(s):  
Yao-Hua Ho ◽  
Yu-Te Huang ◽  
Hao-Hua Chu ◽  
Ling-Jyh Chen
Keyword(s):  
Environmental Changes ◽  
Naive Bayes ◽  
Cost Effective ◽  
Parameter Tuning ◽  
Naïve Bayes ◽  
Spatial Distance ◽  
Environmental Sensors ◽  
Adaptive Sensing ◽  
Naive Bayes Classification ◽  
Naïve Bayes Classification

Environmental sensors are important for collecting data to understand environmental changes and analyze environmental issues. In order to effectively monitor environmental changes, high-density sensor deployment and evenly distributed spatial distance between sensors become the requirements and desired properties for such applications. In many applications, sensors are deployed in locations that are difficult and dangerous to reach (e.g. mountaintop or skyscraper roof). To collect data from those sensors, unmanned aerial vehicles are used to act as data mules to overcome the problem of collecting data in challenging environments. In this article, we extend the adaptive return-to-home sensing algorithm with a parameter-tuning algorithm that combines naive Bayes classification and binary search to adapt adaptive return-to-home sensing parameters effectively on the fly. The proposed approach is able to (1) optimize number of sensing attempts, (2) reduce oscillation of the distance for consecutive attempts, and (3) reserve enough power for drone to return-to-home. Our results show that the naive Bayes classification–enhanced adaptive return-to-home sensing scheme is able to avoid oscillation in sensing and guarantees return-to-home feature while behaving more cost-effective in parameter tuning than the other machine learning–based approaches.

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