scholarly journals Prediction of Increasing Production Activities using Combination of Query Aggregation on Complex Events Processing and Neural Network

2016 ◽  
Vol 2 (2) ◽  
pp. 79
Author(s):  
Achmad Arwan
Keyword(s):  
Neural Network ◽  
Complex Events ◽  
Query Aggregation

scholarly journals Prediction of Increasing Production Activities using Combination of Query Aggregation on Complex Events Processing and Neural Network

2016 ◽  
Vol 2 (2) ◽  
pp. 79
Author(s):  
Achmad Arwan
Keyword(s):  
Neural Network ◽  
Manufacturing Industry ◽  
Complex Event Processing ◽  
Event Processing ◽  
The Neural Network ◽  
Event Log ◽  
Complex Events ◽  
Processing Event ◽  
Production Increase ◽  
Neural Network Input

AbstrakProduksi, order, penjualan, dan pengiriman adalah serangkaian event yang saling terkait dalam industri manufaktur. Selanjutnya hasil dari event tersebut dicatat dalam event log. Complex Event Processing adalah metode yang digunakan untuk menganalisis apakah terdapat pola kombinasi peristiwa tertentu (peluang/ancaman) yang terjadi pada sebuah sistem, sehingga dapat ditangani secara cepat dan tepat. Jaringan saraf tiruan adalah metode yang digunakan untuk mengklasifikasi data peningkatan proses produksi. Hasil pencatatan rangkaian proses yang menyebabkan peningkatan produksi digunakan sebagai data latih untuk mendapatkan fungsi aktivasi dari jaringan saraf tiruan. Penjumlahan hasil catatan event log dimasukkan ke input jaringan saraf tiruan untuk perhitungan nilai aktivasi. Ketika nilai aktivasi lebih dari batas yang ditentukan, maka sistem mengeluarkan sinyal untuk meningkatkan produksi, jika tidak, sistem tetap memantau kejadian. Hasil percobaan menunjukkan bahwa akurasi dari metode ini adalah 77% dari 39 rangkaian aliran event.Kata kunci: complex event processing, event, jaringan saraf tiruan, prediksi peningkatan produksi, proses. AbstractProductions, orders, sales, and shipments are series of interrelated events within manufacturing industry. Further these events were recorded in the event log. Complex event processing is a method that used to analyze whether there are patterns of combinations of certain events (opportunities / threats) that occur in a system, so it can be addressed quickly and appropriately. Artificial neural network is a method that we used to classify production increase activities. The series of events that cause the increase of the production used as a dataset to train the weight of neural network which result activation value. An aggregate stream of events inserted into the neural network input to compute the value of activation. When the value is over a certain threshold (the activation value results from training process), the system will issue a signal to increase production, otherwise system will keep monitor the events. Experiment result shows that the accuracy of this method is 77% for 39 series of event streams.Keywords: complex event processing, event, neural networks, process, production increase prediction.

System Security Monitoring Based on Complex Event Processing and Neural Network

2014 ◽  
Vol 571-572 ◽  
pp. 626-637
Author(s):  
Yi Fei Guo ◽  
Shi Si ◽  
Da Wei Jin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real Time ◽  
System Security ◽  
Dynamic Structure ◽  
Complex Event Processing ◽  
Event Processing ◽  
Security Monitoring ◽  
Complex Events

Recently system security monitoring has meet several challenges. Therefore a system security monitoring approach based on complex event processing and dynamic structure-based neural networks is proposed in this paper. Firstly, complex event processing is used to handle real-time event streams and extract complex events from system security sensors. Secondly, the complex events from CEP would be used for further study by dynamic structure-based neural network. Finally the process of system security monitoring is showed and experiments would be applied to validate the feasibility, efficiency and precision of the approach.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural
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