Controlling Prosthetic Limb Movements Using EEG Signals

2017 ◽  
pp. 211-233
Author(s):  
V. V. Ramalingam ◽  
Mohan S. ◽  
V. Sugumaran ◽  
Vani V. ◽  
B. Rebecca Jeya Vadhanam
Keyword(s):  
Decision Tree ◽  
Naive Bayes ◽  
Decision Tree Algorithm ◽  
Limb Movements ◽  
Eeg Signals ◽  
Tree Algorithm ◽  
Prosthetic Limb ◽  
Bayes Net ◽  
C4.5 Decision Tree ◽  
Bayes Algorithm

This chapter focuses on replacing natural arms with artificial arms with movement controlled by EEG signals. The selected features were classified using C4.5 decision tree algorithm, best first decision tree algorithm, Naïve Bayes algorithm, Bayes net algorithm, K star algorithm and ripple down rule learner algorithm. The results of statistical and histogram features are discussed and conclusions of the study are presented.

scholarly journals Brain tumor prediction using naïve Bayes’ classifier and decision tree algorithms

2018 ◽  
Vol 7 (1.7) ◽  
pp. 137 ◽  
Author(s):  
Danda Shashank Reddy ◽  
Chinta Naga Harshitha ◽  
Carmel Mary Belinda
Keyword(s):  
Brain Tumor ◽  
Decision Tree ◽  
Naive Bayes ◽  
Classification Problem ◽  
Naïve Bayes ◽  
World Health ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Computer Tomography Scan ◽  
Bayes Algorithm

Now a day’s many advanced techniques are proposed in diagnosing the tumor in brain like magnetic resonance imaging, computer tomography scan, angiogram, spinal tap and biospy. Based on diagnosis it is easy to predict treatment. All of the types of brain tumor are officially reclassified by the World Health Organization. Brain tumors are of 120 types, almost each tumor is having same symptoms and it is difficult to predict treatment. For this regard we are proposing more accurate and efficient algorithm in predicting the type of brain tumor is Naïve Bayes’ classification and decision tree algorithm. The main focus is on solving tumor classification problem using these algorithms. Here the main goal is to show that the prediction through the decision tree algorithm is simple and easy than the Naïve Bayes’ algorithm.

scholarly journals ICU Patient Prediction for Moving with Decision Tree C4.5 and Naïve Bayes Algorithm

SinkrOn ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 88 ◽  
Author(s):  
Sumpena Sumpena ◽  
Yuma Akbar ◽  
Nirat Nirat ◽  
Mario Hengky
Keyword(s):  
Intensive Care ◽  
Decision Tree ◽  
Medical Information ◽  
Naive Bayes ◽  
Venous Pressure ◽  
Naïve Bayes ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Sepsis Diagnosis ◽  
Bayes Algorithm

Critical patients need intensive care and supervision by the medical team in the Intensive Care Unit (ICU), including ventilators, monitors, Central Venous Pressure (CVP), Electrocardiogram (ECG), Echocardiogram (ECHO), medical supply, and medical information that is fast, precise, and accurate. In the ICU treatment room requires data that needs to be processed and analyzed for decision making. This study analyzed the ventilator, CVP and also Sepsis Diagnosis related to the data of moving patients and patients dying. This study also uses the decision tree algorithm C.45 and Naive Bayes to determine the level of accuracy of patient care and supervision information in the ICU. The results showed that the decision tree algorithm C.45 has an accuracy of 81.55% and Naive Bayes of 81.54%. The decision tree C.45 algorithm has almost the same advantages as the Naive Bayes algorithm.

scholarly journals To Identify the Improvement Pattern of Self-financing Arts and Science College Student’s Academic Performance using Classification Algorithms

2019 ◽  
Vol 8 (2) ◽  
pp. 2429-2433
Keyword(s):  
Academic Performance ◽  
Decision Tree ◽  
Naive Bayes ◽  
Research Work ◽  
Naïve Bayes ◽  
Classification Algorithms ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Science College ◽  
Bayes Algorithm

The aim of this research work is to identify the improvement pattern of academic performance of final year students of self-financing arts and science colleges. The data was collected from the students of nine Arts and Science Colleges. The data contains demographic, socio-economic, residence and college location, subjects, infrastructural facilities, faculty concern and self-motivation attributes. The classification algorithms like Naïve Bayes, Decision tree and CBPANN are applied on the student’s data. The outcome of the research can be used to improve the academic performance students studying in self-financing arts and science colleges located in educationally backward areas. The experiment results shows that the accuracy value for Naïve Bayes algorithm is 92.63%, accuracy value for Decision Tree algorithm is 96.41% and accuracy value for CBPANN algorithm is 99.49%

scholarly journals Higher Classification of Fake Political News Using Decision Tree Algorithm Over Naive Bayes Algorithm

2021 ◽  
Vol 11 (2) ◽  
pp. 1084-1096
Author(s):  
T. Dinesh
Keyword(s):  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Decision Tree Algorithm ◽  
Fake News ◽  
Tree Algorithm ◽  
Political News ◽  
Machine Learning Classifiers ◽  
Bayes Algorithm

Aim: The main aim of the study proposed is to perform higher classification of fake political news by implementing fake news detectors using machine learning classifiers by comparing their performance. Materials and Methods: By considering two groups such as Decision Tree algorithm and Naive Bayes algorithm. The algorithms have been implemented and tested over a dataset which consists of 44,000 records. Through the programming experiment which is performed using N=10 iterations on each algorithm to identify various scales of fake news and true news classification. Result: After performing the experiment the mean accuracy of 99.6990 by using Decision Tree algorithm and the accuracy of 95.3870 by using Naive Bayes algorithm for fake political news in. There is a statistical significant difference in accuracy for two algorithms is p<0.05 by performing independent samples t-tests. Conclusion: This paper is intended to implement the innovative fake news detection approach on recent Machine Learning Classifiers for prediction of fake political news. By testing the algorithms performance and accuracy on fake political news detection and other issues. The comparison results shows that the Decision Tree algorithm has better performance when compared to Naive Bayes algorithm.

Research on Asset Recognition Algorithm of Information Security Product Based on Decision Tree Algorithm

2013 ◽  
Vol 397-400 ◽  
pp. 2296-2300 ◽  
Author(s):  
Fei Shuai ◽  
Jun Quan Li
Keyword(s):  
Computational Complexity ◽  
Information Security ◽  
Decision Tree ◽  
Recognition Algorithm ◽  
Space Complexity ◽  
Complex Relationship ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree

In current, there are complex relationship between the assets of information security product. According to this characteristic, we propose a new asset recognition algorithm (ART) on the improvement of the C4.5 decision tree algorithm, and analyze the computational complexity and space complexity of the proposed algorithm. Finally, we demonstrate that our algorithm is more precise than C4.5 algorithm in asset recognition by an application example whose result verifies the availability of our algorithm.Keywordsdecision tree, information security product, asset recognition, C4.5

scholarly journals C4.5 DECISION TREE IMPLEMENTATION IN SISTEM INFORMASI ZAKAT (SIZAKAT) TO AUTOMATICALLY DETERMINING THE AMOUNT OF ZAKAT RECEIVED BY MUSTAHIK

2014 ◽  
Vol 10 (1) ◽  
pp. 28 ◽  
Author(s):  
David Bayu Ananda ◽  
Ari Wibisono
Keyword(s):  
Information Systems ◽  
Decision Tree ◽  
Decision Tree Algorithm ◽  
Accuracy Rate ◽  
Tree Algorithm ◽  
C4.5 Decision Tree

Abstract In general, Zakat Information Systems is established to manage the zakat services, so that the data can be well documented. This study proposes the existence of a feature that will determine the amount of zakat received by Mustahik automatically using C4.5 Decision Tree algorithm. This feature is expected to make the process of determining the amount of zakat be done easy and optimal. The data used in this study are the data taken from Masjid An-Nur, Pancoran, South Jakarta. The experiment results show that the proposed feature produces an accuracy rate over 85%.

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