Deep Belief Network

Runoff prediction has recently become an essential task with respect to assessing the impact of climate change to people’s livelihoods and production. However, the runoff time series always exhibits nonlinear and non-stationary features, which makes it very difficult to be accurately predicted.

12p visystrom 22-11-2023 6 5   Download

Ebook "Machine learning for adaptive many core machines - A practical approach (studies in big data) 2015" includes content: Motivation and preliminaries, GPU machine learning library (GPUMLib), neural networks, handling missing data, support vector machines, incremental hypersphere classifier, non negative matrix factorization, deep belief networks

251p haojiubujain07 20-09-2023 3 3   Download

In this paper, we investigate the use of a deep learning method, Deep Belief Network (DBN), combined with chaos theory to forecast chaotic time series. DBN should be used to forecast chaotic time series. First, the chaotic time series are analyzed by calculating the largest Lyapunov exponent, reconstructing the time series by phase-space reconstruction and determining the best embedding dimension and the best delay time. When the forecasting model is constructed, the deep belief network is used to feature learning and the neural network is used for prediction.

11p trinhthamhodang9 04-12-2020 22 2   Download

Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem.

9p vioklahoma2711 19-11-2020 6 0   Download

RNAs play key roles in cells through the interactions with proteins known as the RNA-binding proteins (RBP) and their binding motifs enable crucial understanding of the post-transcriptional regulation of RNAs. How the RBPs correctly recognize the target RNAs and why they bind specific positions is still far from clear.

14p vioklahoma2711 19-11-2020 9 1   Download

One approach to improving the personalized treatment of cancer is to understand the cellular signaling transduction pathways that cause cancer at the level of the individual patient. In this study, we used unsupervised deep learning to learn the hierarchical structure within cancer gene expression data.

13p viflorida2711 30-10-2020 15 2   Download

Studies have shown that enhancers are significant regulatory elements to play crucial roles in gene expression regulation. Since enhancers are unrelated to the orientation and distance to their target genes, it is a challenging mission for scholars and researchers to accurately predicting distal enhancers.

7p viconnecticut2711 29-10-2020 10 1   Download

Deciphering the meaning of the human DNA is an outstanding goal which would revolutionize medicine and our way for treating diseases. In recent years, non-coding RNAs have attracted much attention and shown to be functional in part.

15p vikuala271 13-06-2020 11 2   Download

In this paper, a procedure to develop an automatic CNC program for machining of different types of holes by using different machine learning algorithms is developed.

14p kelseynguyen 28-05-2020 16 0   Download

The efficiency of drug development defined as a number of successfully launched new pharmaceuticals normalized by financial investments has significantly declined.

15p vimax2711 30-03-2020 11 1   Download

In this paper, motivated by significant advantages and lots of achieved successes of deep learning in data mining, we apply Deep Belief Network (DBN), which is one of the breakthrough models laid the foundation for deep learning, to detect epileptic spikes in EEG data. It is really useful in practice because the promising quality evaluation of the spike detection system is higher than 90%. In particular, to construct the accurate detection model for non-spikes and spikes, a new set of detailed features of epileptic spikes is proposed that gives a good description of spikes.

13p truongtien_09 10-04-2018 35 5   Download

Quantifying the semantic relevance between questions and their candidate answers is essential to answer detection in social media corpora. In this paper, a deep belief network is proposed to model the semantic relevance for question-answer pairs. Observing the textual similarity between the community-driven questionanswering (cQA) dataset and the forum dataset, we present a novel learning strategy to promote the performance of our method on the social community datasets without hand-annotating work. ...

9p hongdo_1 12-04-2013 43 1   Download