Bayesian Network with Association Rules Applied in the Recognition of Handwritten Digits

2011 ◽  
Vol 187 ◽  
pp. 7-12
Author(s):  
Wen Qing Zhao ◽  
Yan Fang Zhang ◽  
Sheng Long Zhang
Keyword(s):  
Bayesian Network ◽  
Association Rules ◽  
Classification Problem ◽  
Hill Climbing ◽  
Classification Algorithms ◽  
Handwritten Numeral Recognition ◽  
Hill Climbing Algorithm ◽  
Bayesian Network Classifier ◽  
Numeral Recognition ◽  
Learn Network

Classification Based on Association (CBA) algorithm built a classifier based on the association rules, but without considering the uncertainty in the classification problem. This paper proposed a Bayesian network classifier based on the association rules. The algorithm extracts the candidate set uses association rules and classification algorithms related to the network, then uses “greedy hill-climbing algorithm” to learn network structure to get a better topology, and verify that this algorithm is valid on handwritten numeral recognition.

scholarly journals Pemodelan Bayesian Network untuk Prediksi Penyakit Saluran Pernapasan

Petir ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 292-302
Author(s):  
Novi Indah Pradasari ◽  
Rizqia Lestika Atimi
Keyword(s):  
Bayesian Network ◽  
Data Cleaning ◽  
Score Function ◽  
Hill Climbing ◽  
Respiratory Tract Disease ◽  
Inference Process ◽  
Bayesian Network Model ◽  
Hill Climbing Algorithm ◽  
Testing Phase ◽  
Tract Disease

This Bayesian network model was developed by analyzing the correlation between the cause of disease symptom variables and disease variables. The Bayesian network is a method that can depict causality between variables in a system. In this research, the Bayesian network was developed with a scoring based method and it was implemented using a hill-climbing algorithm with scoring BIC score function approach. There were 18 variables and 31 arcs representing the interconnection between symptom variable and respiratory tract disease. In the testing phase, the inference process using approximate inference was carried out and the accuracy was nearly 100% for all testing scenarios. The application of this method could result in a representative Bayesian network. Its resulted structure was affected so much by data condition, thus data cleaning was important to do before the training and testing phase. 

scholarly journals A Hybrid Flower Pollination with β-Hill Climbing Algorithm for Global Optimization

2021 ◽  
Author(s):  
Zaid Abdi Alkareem Alyasseri ◽  
Mohammed Azmi Al-Betar ◽  
Mohammed A. Awadallah ◽  
Sharif Naser Makhadmeh ◽  
Ammar Kamal Abasi ◽  
...  
Keyword(s):  
Global Optimization ◽  
Hill Climbing ◽  
Hill Climbing Algorithm ◽  
Flower Pollination

scholarly journals RMHC-MR: Instance selection by random mutation hill climbing algorithm with MapReduce in big data

2017 ◽  
Vol 111 ◽  
pp. 252-259 ◽  
Author(s):  
Lu Si ◽  
Jie Yu ◽  
Wuyang Wu ◽  
Jun Ma ◽  
Qingbo Wu ◽  
...  
Keyword(s):  
Big Data ◽  
Hill Climbing ◽  
Instance Selection ◽  
Random Mutation ◽  
Hill Climbing Algorithm

scholarly journals Development of Intelligent Drone Battery Charging System Based on Wireless Power Transmission Using Hill Climbing Algorithm

2018 ◽  
Vol 1 (4) ◽  
pp. 44 ◽  
Author(s):  
Ali Rohan ◽  
Mohammed Rabah ◽  
Muhammad Talha ◽  
Sung-Ho Kim
Keyword(s):  
Power Transmission ◽  
Inductive Coupling ◽  
Hill Climbing ◽  
Wireless Power ◽  
Wireless Power Transmission ◽  
Battery Charging ◽  
Charging System ◽  
Charging Station ◽  
Hill Climbing Algorithm ◽  
The Poor

In this work, an advanced drone battery charging system is developed. The system is composed of a drone charging station with multiple power transmitters and a receiver to charge the battery of a drone. A resonance inductive coupling-based wireless power transmission technique is used. With limits of wireless power transmission in inductive coupling, it is necessary that the coupling between a transmitter and receiver be strong for efficient power transmission; however, for a drone, it is normally hard to land it properly on a charging station or a charging device to get maximum coupling for efficient wireless power transmission. Normally, some physical sensors such as ultrasonic sensors and infrared sensors are used to align the transmitter and receiver for proper coupling and wireless power transmission; however, in this system, a novel method based on the hill climbing algorithm is proposed to control the coupling between the transmitter and a receiver without using any physical sensor. The feasibility of the proposed algorithm was checked using MATLAB. A practical test bench was developed for the system and several experiments were conducted under different scenarios. The system is fully automatic and gives 98.8% accuracy (achieved under different test scenarios) for mitigating the poor landing effect. Also, the efficiency η of 85% is achieved for wireless power transmission. The test results show that the proposed drone battery charging system is efficient enough to mitigate the coupling effect caused by the poor landing of the drone, with the possibility to land freely on the charging station without the worry of power transmission loss.

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