By Khalid Saeed, Wladyslaw Homenda
This e-book constitutes the complaints of the fifteenth IFIP TC8 foreign convention on machine info platforms and business administration, CISIM 2016, held in Vilnius, Lithuania, in September 2016.
The sixty three commonplace papers provided including 1 inivted paper and five keynotes during this quantity have been conscientiously reviewed and chosen from approximately 89 submissions. the most issues lined are tough set equipment for large information analytics; photos, visualization, class; optimization, tuning; scheduling in production and different purposes; algorithms; judgements; clever dispensed platforms; and biometrics, id, security.
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Additional resources for Computer Information Systems and Industrial Management: 15th IFIP TC8 International Conference, CISIM 2016, Vilnius, Lithuania, September 14-16, 2016, Proceedings
They are: (1) collecting data, (2) cleaning data, (3) interpreting data, (4) analyzing data, (5) visualization of data, (6) narrative science, and (7) the emergence of new paradigms. The last phase has been instantiated by the ﬁve examples at the beginning of this section. Below we will discuss the following topics and their obstacles: Real big data (Sect. 2); Deﬁnitions (Sect. 3); Small data (Sect. 4); Turn around management (Sect. 5); Criminal behavior (Sect. 6); Conclusions (Sect. 7), and Safeguards and recommendations (Sect.
Usually, the nodes in the ﬁrst layer, the 10 B. Macukow Fig. 9. A feedforward network. input layer, merely distribute the inputs to subsequent layers, and do not perform any operations (summation or thresholding). NB.. some authors miss out these nodes. A layer - it is the part of network structure which contains active elements performing some operation. A multilayer network (Fig. 9) receives a number of inputs. These are distributed by a layer of input nodes that do not perform any operation – these inputs are then passed along the ﬁrst layer of adaptive weights to a layer of perceptron-like units, which do sum and threshold their inputs.
For the network with two hidden layers NHN1 ¼ pﬃﬃﬃ m Ã r2 and NHN2 ¼ m Ã r where r ¼ 3 mn . The hidden neuron can influence the error in the nodes to which its output is connected. The stability of neural network is estimated by error. The minimal error denotes better stability, and higher error indicates worst stability. During the training, the network adapts in order to decrease the error emerging from the training patterns. Many researchers have ﬁxed number of hidden neurons based on trial rule.