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Elazab, Ahmed
- Adaptive Model to Estimate Most Significant Features for Oversampling Medical Data
Abstract Views :197 |
PDF Views:3
Authors
Affiliations
1 Department of Computer Science, Fayoum University, Fayoum, 63514, EG
2 General Organization of Physical Planning, GOPP, Cairo, 11311, EG
1 Department of Computer Science, Fayoum University, Fayoum, 63514, EG
2 General Organization of Physical Planning, GOPP, Cairo, 11311, EG
Source
Artificial Intelligent Systems and Machine Learning, Vol 10, No 8 (2018), Pagination: 172-176Abstract
Computerized classification plays an important role in the classification of cancer stage. Hence, there is a growing need for automatic classification of cancer data. In this paper, a new model for stage classification of cancer data is developed. The proposed model use decision tree data mining technique that is trained on medical data to classify cancer data into several stages based on the weights of the most significant features. Hence, this paper proposes boost strapping approach for generating data oversampling to solve the problems of medical data scarcity. Ultimately, the proposed model improve the gain ratio technique to predict the weights of the factors (attributes) that affected in the staging of each patient case before and after oversampling. The performance of the proposed model is evaluated to develop more cost-effective and easy to use systems that support clinicians. The experimental results show that the proposed model precision is 94% for the original dataset and 90% for the oversampling dataset. The result illustrates the promising capabilities of the model for detecting breast cancer stages by minimum data set and minimum attributes.Keywords
Classification, Cancer Data, Decision Trees, Gain Ratio, Oversampling.
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- Fraud News Detection for Online Social Networks
Abstract Views :180 |
PDF Views:3
Authors
Affiliations
1 Institute of Statistical Studies and Research, Cairo University, EG
1 Institute of Statistical Studies and Research, Cairo University, EG
Source
Artificial Intelligent Systems and Machine Learning, Vol 10, No 8 (2018), Pagination: 177-182Abstract
Social media plays a vital role in all online aspects now, including personal communication, business and economics. It even affects political aspects seriously. A huge amount of available information, especially micro blogs is considered as a massive growth rate of human users, which is represented in the unprecedented diversity of its participants in terms of backgrounds, reasons and languages a revolution in its possibility of sharing public information, besides there is the way it makes its participants use their devices and perform their mission.
Twitter, as a most famous used type of online social networking, contains huge data and news that throw the light on the content investigation in the tweets. This paper has discussed a proposed approach for determining the credibility of spread news on such social networks in two phases: The first phase is to detect the fake users enabling to ignore the news given by fake users. The second phase detects the credibility of the news content for the previously checked
Account users by using the similarity measures and most popular machine learning algorithms such as (Support vector machine, Decision tree, Neural networks, Naive Bayes, Random forest) that enhance the credibility examining. The accuracy of the results of this phase is 99.8 %. In the second phase the news content credibility is detected by using the most popular similarity measures (Jacard, Cosine and Dice), which Jacard ended up with 95.4%percentage of accuracy.
Keywords
Fraud News, Support Vector Machine, Neural Networks, Naive Bayes, Random Forest, Fraud Text.References
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