Titlebook: Neural Information Processing; 22nd International C Sabri Arik,Tingwen Huang,Qingshan Liu Conference proceedings 2015 Springer Internationa

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Esmaeel Eftekharian,Amin Khatami,Abbas Khosravi,Saeid Nahavandiva code. The code conversion itself is now automated but not completely. The human reengineer still has to make some adjustments to the automatically generated code and that can lead to errors. These may also be subtle errors in the automated transformation. Therefore, converted code must be tested

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Md. Mamunur Rashid,Iqbal Gondal,Joarder Kamruzzamannsure quality of software, but there is a lack of common knowledge and best practices on it. . The goal of this paper is to investigate the state-of-practice of quality assurance during the implementation phase in software houses. . For this purpose, we conducted a survey in Germany, Austria, and Sw

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Deep Feature-Action Processing with Mixture of Updates,e first layer. We show that this mixture of updates seems to work well for this model. The features layer have been deeply trained by applying a simple PCA on the whole set of images histograms acquired during the first running episode. The model is also able to adapt to a reduced features dimension

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Deep Convolutional Neural Networks for Human Activity Recognition with Smartphone Sensors,e can also benefit from a wider filter size and lower pooling size setting. Lastly, we show that convnet outperforms all the other state-of-the-art techniques in HAR, especially SVM, which achieved the previous best result for the data set.

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A Proposed Blind DWT-SVD Watermarking Scheme for EEG Data,SVD technique, our proposed method achieved blind detection of watermark in which the receiver does not require the original EEG signal to retrieve the watermark. Experimental results show that the proposed EEG watermarking approach maintains the high quality of the EEG signal.

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A Study to Investigate Different EEG Reference Choices in Diagnosing Major Depressive Disorder,ds powers were computed. These EEG features were used as input data to train and test the logistic regression (LR) classifier and the linear kernel support vector machine (SVM). Finally, the results were presented as classification accuracies, sensitivities, and specificities while discriminating th

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