This paper conducts research on the short-term electric load forecast method under the background of big data. It builds a new electric load forecast model based on Deep Auto-Encoder Networks (DAENs), which takes into account multidimensional load-related data sets including historical load value, temperature, day type, etc. A new distributed short-term load forecast method based on TensorFlow and DAENs is therefore proposed, with an algorithm flowchart designed. This method overcomes the shortcomings of traditional neural network methods, such as over-fitting, slow convergence and local optimum, etc. Case study results show that the proposed method has obvious advantages in prediction accuracy, stability, and expansibility compared with those based on traditional neural networks. Thus, this model can better meet the demands of short-term electric load forecasting under big data scenario.
HMM-TTS synthesis is a popular approach toward flexible, low-footprint, data driven systems that produce highly intelligible speech. In spite of these strengths, speech generated by these systems exhibit some degradation in quality, attributable to an inadequacy in modeling the excitation signal that drives the parametric models of the vocal tract. This paper proposes a novel method for modeling the excitation as a low-dimensional set of coefficients, based on a non-linear map learned through an autoencoder. Through analysis-and-resynthesis experiments, and a formal listening test, we show that this model produces speech of higher perceptual quality compared to conventional pulse-excited speech signals at the p ≪ 0.01 significance level. 1in 1982 to describe the first cases of unusual immune system failure that were identified in the previous year. The human immunodeficiency virus (HIV) was later identified as the cause of AIDS. Risk factor epidemiology examines the individual demographic and social characteristics and attempts to determine factors that place an individual at risk of acquiring a life-threatening disease 2 . In this study, the demographic and social characteristics of the individuals and their behaviour are used to determine the risk of HIV infection; referred to as biomedical individualism 2,3 . By identifying the individual risk factors that lead to the HIV infection, it is possible to modify social conditions, which give rise to the disease, and thus design effective HIV prevention policies 2 . A model will be created and used to classify the HIV status of individuals based on demographic properties. In this study, the model is created using autoencoder neural networks and genetic algorithms, which have been applied to classification. An artificial neural network ( ANN) is an inter-connected structure of processing elements. The ANN structure 4 Previous work has shown that the difficulties in learning deep generative or discriminative models can be overcome by an initial unsupervised learning step that maps inputs to useful intermediate representations. We introduce and motivate a new training principle for unsupervised learning of a representation based on the idea of making the learned representations robust to partial corruption of the input pattern. This approach can be used to train autoencoders, and these denoising autoencoders can be stacked to initialize deep architectures. The algorithm can be motivated from a manifold learning and information theoretic perspective or from a generative model perspective. Comparative experiments clearly show the surprising advantage of corrupting the input of autoencoders on a pattern classification benchmark suite. The annotation of genomic information is a major challenge in biology and bioinformatics. Existing databases of known gene functions are incomplete and prone to errors, and the bimolecular experiments needed to improve these databases are slow and costly. While computational methods are not a substitute for experimental verification, they can help in two ways: algorithms can aid in the curation of gene annotations by automatically suggesting inaccuracies, and they can predict previously-unidentified gene functions, accelerating the rate of gene function discovery. In this work, we develop an algorithm that achieves both goals using deep autoencoder neural networks. With experiments on gene annotation data from the Gene Ontology project, we show that deep autoencoder networks achieve better performance than other standard machine learning methods, including the popular truncated singular value decomposition. Following a number of studies that have interrogated the usability of an autoencoder neural network in various classification and regression approximation problems, this manuscript focuses on its usability in water demand predictive modelling, with the Gauteng Province of the Republic of South Africa being chosen as a case study. Water demand predictive modelling is a regression approximation problem. This autoencoder network is constructed from a simple multi-layer network, with a total of 6 parameters in both the input and output units, and 5 nodes in the hidden unit. These 6 parameters include a figure that represents population size and water demand values of 5 consecutive days. The water demand value of the fifth day is the variable of interest, that is, the variable that is being predicted. The optimum number of nodes in the hidden unit is determined through the use of a simple, less computationally expensive technique. The performance of this network is measured against prediction accuracy, average prediction error, and the time it takes the network to generate a single output. The dimensionality of the network is also taken into consideration. In order to benchmark the performance of this autoencoder network, a conventional neural network is also implemented and evaluated using the same measures of performance. The conventional network is slightly outperformed by the autoencoder network.