Product Prediction with Deep Neural Networks.

Explanation-Guided Fairness Testing through Genetic Algorithm [article]

Ming Fan, Wenying Wei, Wuxia Jin, Zijiang Yang, Ting Liu

<span title="2022-05-16">2022</span> <i > arXiv </i>   <span class="release-stage" >pre-print</span>

Moreover, ExpGA only requires prediction probabilities of the tested model, resulting in a better generalization capability to various models. ... Augmented with the discriminatory samples generated by ExpGA, the fairness of tested models has a substantial improvement through retraining. ... The evaluation experiments demonstrate that ExpGA can detect discriminatory samples much faster with a higher success rate than four state-of-the-art methods, both on the text and tabular benchmarks. ...

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This paper proposes a new method of a well control optimization method based on a multi-input deep neural network. ... This method takes the production history data of the reservoir as the main input and the saturation field as the auxiliary input and establishes a multi-input deep neural network for learning, forming ... As can be seen from the table, compared with the singleinput production dynamic prediction model, the MRE of the Geofluids production dynamic prediction model based on the multiinput deep neural network ...

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○ Deep recurrent network* ■ Learns features of a gene sequence and predicts appropriate annotations. ... *in preprint, and can only predict four annotations Our Solution Recurrent Neural Network (RNN) ...

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Agricultural production prediction is a challenging task in the deep neural network field. ... fruits using deep neural networks. ... In our study, we employ a technical analysis using a deep neural network algorithm to predict fruit production. ...

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Deep learning enhanced the state-of-the-art methods in genomics allows it to be used in analysing the biological data with high prediction. ... In this study, deep neural network has been to identify any set of gene deletion models that offers optimal results in xylitol production and its growth yield. ... While in section 2 described the implementation of deep neural network in predicting the bioprocess product. ...

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Artificial intelligence methods such as deep neural network offer an efficient and powerful approach to be used to analyse the xylitol production value and at the same time to predict which genes and pathway ... Results show that, with an absence of genes pgi, tkt and tala, xylitol production can be boosted up to the higher level.</p> ... In this experiment, deep neural network has been used. Deep neural network is one of the methods from deep learning. ...

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This paper aims to fully utilize the information between features and improve the performance of deep neural networks in the CTR prediction task. ... We evaluate our models on CTR prediction tasks compared with classical baselines and show that our deep FINN model outperforms other state-of-the-art deep models such as PNN and DeepFM. ... At last, we feed the cross features into a deep neural network and the network outputs the prediction score. A. ...

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We present an approach that combines self-organizing maps with artificial neural network with multiple hidden layers in order to identify the potential sales that might be addressed for channel distribution ... This paper presents an experiment of using deep learning models for distribution channel management. ... Deep learning relates to artificial neural networks with multiple hidden layers, convolutional neural networks, recurrent neural networks, self-organized maps, Boltzmann machine and auto encoders. ...

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, and we have analysed their quality and performance.Results: Results showed that a deep neural network with 6 layers and a convolutional neural network gave the best results.Conclusions: This paper suggests ... AbstractBackground: This paper addresses the problem of products' terminal call rate (TCR) prediction during the warranty period. ... The results showed that the best two models, deep neural network with 6 layers and a convolutional neural network differed in 1% when predicting TCR at 12 months. ...

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First, the existing EUR evaluation methods of shale gas wells and the deep feedforward neural network algorithm was systematically analyzed. ... The results show that the EUR prediction with high accuracy. In addition, the results are affected by the variety and number of input parameters, the network structure and hyperparameters. ... Second, with the help of the trained deep neural network model, combined with the J o u r n a l P r e -p r o o f prediction data set, prediction of the normalized EUR of shale gas wells was conducted. ...

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Product recommendation is challenging task to e-commerce companies. Traditional Recommender Systems provided the solutions in recommending the products. ... Now a day Deep Learning is using in every domain. Deep Learning techniques in the field of Recommender Systems can be directly applied. Deep Learning has ample number of algorithms. ... CNN based recommender system Deep Cooperative Neural Network [DeepCoNN] [12] is Convolutional Neural Network uses factorization Machines provide users rating predictions. ...

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The purpose of this paper is to select a power consumption forecasting method with high accuracy and low error. ... In this paper, the origin of deep learning technology is introduced, and the LSTMs model of deep learning is built, and the short-term electricity consumption forecasting model is built, which can complete ... SHORT-TERM ELECTRICITY CONSUMPTION PREDICTION MODEL BASED ON DEEP LEARNING In this paper, the long-short-term memory neural network model LSTMs is used to build the electricity consumption prediction model ...

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We develop a general architecture of hybrid deep neural networks (HDNNs) to support mixed inputs. ... Concentrating on reservoir production prediction, a specific HDNN model is configured and applied to an oil development block. ... Qiu for valuable discussions on production characterization of the adopted oil block. ...

<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.08419v1">arXiv:2005.08419v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jqi3jbsukve6laa365jzfexcea">fatcat:jqi3jbsukve6laa365jzfexcea</a> </span>

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The main purpose of this work is to develop a method that can forecast oil production with high accuracy, using Deep neural networks based on the debt data of wells. ... In this paper, a hybrid model based on a combination of the CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory) networks, called CNN-LSTM is proposed for the forecasting of oil production ... The main purpose of this work is to develop a method that can predict the oil production with high accuracy using the Deep neural networks based on the debt data of wells. ...

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The methods used in the prediction are Neural Network, Deep Learning, and k-Nearest Neighbor. ... Therefore, a study is needed to consider the problem-solving in the form of clean water production prediction that can help PDAM determine policy regarding water production. ... The methods used in the prediction were Neural Network, Deep Learning, and k-Nearest Neighbor. ...

<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1088/1742-6596/1217/1/012122">doi:10.1088/1742-6596/1217/1/012122</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/s2yfceu3mbcbhetfhfh7ppp3ku">fatcat:s2yfceu3mbcbhetfhfh7ppp3ku</a> </span>

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