Öz
Data has gained vital role in science and engineering applications; the proper data analysis has made it possible to boost the economical worthiness of those applications. Machine learning tools are used to classify the big data in order to discover the hidden patterns in them. That may lead to noteworthy advantages that related to future prediction of the data. The resultant information can be used to enhance the practical systems in such way only the profitable thing can be come on then. In other way, it helps to prevent any unpleasant occurrence that may harm the company or the organization. A brain epilepsy disease prediction system is implemented using four different algorithms namely: Naïve Bays algorithm, K-Nearest Neighbours algorithm, Random Forest algorithm and Long Short Term Memory Neural Network. The performance metrics are also initiate in order to evaluate the difference in prediction performance of the four tools. The accuracy of prediction the disease was recorded more likely 33.035, 95, 61.195 and 96.79 for the Naïve Bays, Random Forest, K-Nearest Neighbour and Long Short Term Neural Network.
Anahtar Kelimeler
Destekleyen Kurum
altinbas university
Proje Numarası
1
Teşekkür
thank's for my supervisor
Kaynakça
- Chandra, B., and R.K. Sharma. 2017. On improving recurrent neural network for image classification. In 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, 1904-1907.
- Chen, Z., Y. Liu, and S. Liu. 2017. Mechanical state prediction based on LSTM neural netwok. In 2017 36th Chinese Control Conference (CCC), Dalian, 3876-3881.
- Chen, S., C. Peng, L. Cai, and L. Guo. 2018. A deep neural network model for target-based sentiment analysis. In 2018 international joint conference on neural networks (IJCNN), Rio de Janeiro, 1-7.
- Fente, D.N., and D.K. Singh. 2018. April. Weather forecasting using artificial neural network. In 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, pp. 1757-1761.
- Jędrzejewska, M.K., A. Zjawiński, and B. Stasiak. 2018. Generating Musical Expression of MIDI Music with LSTM Neural Network. In 2018 11th International Conference on Human System Interaction (HSI), Gdansk, 132-138.
- Jithesh, V., M.J. Sagayaraj, and K.G. Srinivasa. 2017. LSTM recurrent neural networks for high resolution range profile based radar target classification. In 2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT), Ghaziabad, 1-6.
- Liu, Y., Y. Zhou, and X. Li. 2018. Attitude estimation of unmanned aerial vehicle based on lstm neural network. In 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, 1-6.
- Lu, Y., and F.M. Salem. 2017. Simplified gating in long short-term memory (lstm) recurrent neural networks. In 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), Boston, 1601-1604.
- Mirza, A.H., and S. Cosan. 2018. Computer network intrusion detection using sequential LSTM neural networks autoencoders. In 2018 26th signal processing and communications applications conference (SIU), Izmir, 1-4.
- Sundermeyer, M., H. Ney, and R. Schlüter. 2015. From feedforward to recurrent LSTM neural networks for language modeling. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 23(3), 517-529.
- Xu, X., H. Ge, and S. Li. 2016. An improvement on recurrent neural network by combining convolution neural network and a simple initialization of the weights. In 2016 IEEE International Conference of Online Analysis and Computing Science (ICOACS), Chongqing, 150-154.
- Wang, Y., J. Zhou, K. Chen, Y. Wang, and L. Liu. 2017. Water quality prediction method based on LSTM neural network. In 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), Nanjing, 1-5.
- Yuhai, G., L. Shuo, and H. Linfeng. 2018. Research on failure prediction using dbn and lstm neural network. In 2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Nara, 1705-1709.
Öz
Bilim ve mühendislik uygulamalarında veriler hayati bir rol oynamıştır; doğru veri analizi, bu uygulamaların ekonomik değerini artırır. Makine öğrenimi araçları büyük verileri sınıflandırmak için kullanılır ve veriler içindeki gizli kalıpların bulunmasını sağlar. Bu gelecek tahmini ile ilgili önemli avantajları sağlayabilir. Sonuçta elde edilen bilgiler pratik sistemleri sadece karlı olan şeyleri geliştirmek için de kullanılabilir. Başka bir şekilde bakıldığında, şirkete veya kuruluşa zarar verebilecek hoş olmayan olayların önlenmesine de yardımcı olur. Beyin epilepsi hastalığı tahmin sistemi dört farklı algoritma kullanılarak uygulanır: Naive Bayes algoritması, K-en yakın komşular algoritması, rastgele orman algoritması ve uzun kısa süreli bellek sinir ağı. Performans ölçümleri de dört aracın tahmin performansındaki farkı değerlendirmek için başlatılır. Tahmin doğruluğu, bu dört yöntem için sırasıyla 33,035, 95, 61,195 ve 96,79 olarak kaydedildi.
Anahtar Kelimeler
Proje Numarası
1
Kaynakça
- Chandra, B., and R.K. Sharma. 2017. On improving recurrent neural network for image classification. In 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, 1904-1907.
- Chen, Z., Y. Liu, and S. Liu. 2017. Mechanical state prediction based on LSTM neural netwok. In 2017 36th Chinese Control Conference (CCC), Dalian, 3876-3881.
- Chen, S., C. Peng, L. Cai, and L. Guo. 2018. A deep neural network model for target-based sentiment analysis. In 2018 international joint conference on neural networks (IJCNN), Rio de Janeiro, 1-7.
- Fente, D.N., and D.K. Singh. 2018. April. Weather forecasting using artificial neural network. In 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, pp. 1757-1761.
- Jędrzejewska, M.K., A. Zjawiński, and B. Stasiak. 2018. Generating Musical Expression of MIDI Music with LSTM Neural Network. In 2018 11th International Conference on Human System Interaction (HSI), Gdansk, 132-138.
- Jithesh, V., M.J. Sagayaraj, and K.G. Srinivasa. 2017. LSTM recurrent neural networks for high resolution range profile based radar target classification. In 2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT), Ghaziabad, 1-6.
- Liu, Y., Y. Zhou, and X. Li. 2018. Attitude estimation of unmanned aerial vehicle based on lstm neural network. In 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, 1-6.
- Lu, Y., and F.M. Salem. 2017. Simplified gating in long short-term memory (lstm) recurrent neural networks. In 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), Boston, 1601-1604.
- Mirza, A.H., and S. Cosan. 2018. Computer network intrusion detection using sequential LSTM neural networks autoencoders. In 2018 26th signal processing and communications applications conference (SIU), Izmir, 1-4.
- Sundermeyer, M., H. Ney, and R. Schlüter. 2015. From feedforward to recurrent LSTM neural networks for language modeling. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 23(3), 517-529.
- Xu, X., H. Ge, and S. Li. 2016. An improvement on recurrent neural network by combining convolution neural network and a simple initialization of the weights. In 2016 IEEE International Conference of Online Analysis and Computing Science (ICOACS), Chongqing, 150-154.
- Wang, Y., J. Zhou, K. Chen, Y. Wang, and L. Liu. 2017. Water quality prediction method based on LSTM neural network. In 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), Nanjing, 1-5.
- Yuhai, G., L. Shuo, and H. Linfeng. 2018. Research on failure prediction using dbn and lstm neural network. In 2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Nara, 1705-1709.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Elektrik Mühendisliği |
| Bölüm | Araştırma Makalesi |
| Yazarlar | |
| Proje Numarası | 1 |
| Yayımlanma Tarihi | 30 Aralık 2020 |
| Gönderilme Tarihi | 10 Mart 2020 |
| Kabul Tarihi | 27 Aralık 2020 |
| Yayımlandığı Sayı | Yıl 2020 Cilt: 4 Sayı: 2 |
