scholarly journals PSEUDO-MULTIVARIATE LSTM NEURAL NETWORK APPROACH FOR PURCHASE DAY PREDICTION IN B2B

2021 ◽  
Vol 19 (3) ◽  
pp. 151
Author(s):  
Milica Ćirić ◽  
Bratislav Predić
Keyword(s):  
Neural Network ◽  
Input Data ◽  
Neural Network Approach ◽  
Error Metrics ◽  
Input Feature ◽  
Starting Point ◽  
Additional Input ◽  
The Moment ◽  
Product Pair ◽  
Slight Downward Trend

This research focuses on trying to predict the moment of the next purchase for a customer in vendor-customer B2B scenario using an LSTM neural network and comparing prediction results from different input features. In a previous research we performed predictions for a specific customer product pair and used previous purchases for that pair as input data, but  the number of such previous purchases was often very limited which resulted in low accuracy of predictions. By aggregating purchase data for all products a customer purchased, we were able to get more precise predictions of the next purchase. Additionally, expanding our input feature set yielded even better results. We performed an evaluation of LSTM networks trained with the most successful combination of input features for a six month period. Each of the networks was trained with purchase data up to the starting point of the selected period and the predictions were performed, after which additional input for the following seven days was added to the network. This process was then repeated for the entire six month period and a slight downward trend can be noticed for error metrics, leading to the conclusion that the network would perform even better over time with the addition of future purchases.

scholarly journals Applying Neural Networks to Find the Minimum Cost Coverage of a Boolean Function

VLSI Design ◽  
1995 ◽  
Vol 3 (1) ◽  
pp. 13-19 ◽  
Author(s):  
Pong P. Chu
Keyword(s):  
Neural Network ◽  
Boolean Function ◽  
Input Data ◽  
Selection Process ◽  
Optimal Solution ◽  
Minimum Cost ◽  
Hopfield Network ◽  
Neural Network Approach ◽  
Complete Problem ◽  
Np Complete

To find a minimal expression of a boolean function includes a step to select the minimum cost cover from a set of implicants. Since the selection process is an NP-complete problem, to find an optimal solution is impractical for large input data size. Neural network approach is used to solve this problem. We first formalize the problem, and then define an “energy function” and map it to a modified Hopfield network, which will automatically search for the minima. Simulation of simple examples shows the proposed neural network can obtain good solutions most of the time.

Borehole electrical resistivity modeling using neural networks

Geophysics ◽  
2002 ◽  
Vol 67 (6) ◽  
pp. 1790-1797 ◽  
Author(s):  
Lin Zhang ◽  
Mary M. Poulton ◽  
Tsili Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Input Data ◽  
Window Length ◽  
Neural Network Approach ◽  
Earth Resistivity ◽  
Training Samples ◽  
Resistivity Model ◽  
Dual Laterolog ◽  
Hidden Nodes

A neural network approach has been applied to model downhole resistivity tools, i.e., to generate a synthetic tool response for a given earth resistivity model. The microlaterolog (MLL), shallow dual laterolog (DLLs), and deep dual laterolog (DLLd) tools are modeled using neural networks to demonstrate this approach. Efforts have been made to select various neural network parameters, including the type of neural network, the length of input data for training, the number of hidden nodes, and the number of training samples. A modular neural network (MNN) has been selected because it can facilitate the training and prediction of tool responses in formations with large resistivity variations. The input data for training are taken to be the model formation resistivity values sampled over a depth window. The window length is chosen based on the tool lengths. Three different window lengths are used for experiments: 6.1, 9.1, and 30.5 m. We found the longer window lengths generally have higher modeling accuracy for the three different types of logging tools. The number of hidden nodes needed to yield satisfactory training and prediction data varies from 8 to 25, depending on the type of tool and the window length. Up to 30 000 training samples have been collected to train the MNN. Our modeling examples show that the trained MNN can achieve about 90% accuracy for the MLL log response and about 83% accuracy for the DLLs and DLLd responses. The modeling errors can be described roughly with a Gaussian distribution.

A Reliable Model-Based Approach for the Detection of Equipment Faults in a Chemical Reactor

2007 ◽  
Vol 5 (1) ◽  
Author(s):  
Yahya Chetouani
Keyword(s):  
Neural Network ◽  
Moving Average ◽  
Chemical Reactor ◽  
Operating Conditions ◽  
Neural Network Approach ◽  
Real Time System ◽  
Normal Behavior ◽  
Reliable Model ◽  
The Moment ◽  
Hidden Neurons

In this paper a real-time system for detecting changes in dynamic systems is designed. The cumulative sum (CUSUM) or the Page-Hinkley test is intended to reveal any drift from the normal behavior of the process. The process behavior under its normal operating conditions is established by a reliable model. In order to obtain this reliable model for the process dynamics, the black-box identification by means of a NARMAX (Nonlinear Auto-Regressive Moving Average with eXogenous) model has been chosen in this study. It is based on the neural network approach. This paper will show the choice and the performance of this neural network in the training and the test phases. A study is related to the inputs number, hidden neurons used and their influence on the behavior of the neural predictor. Three statistical criterions are used for the validation of the experimental data. After describing the system architecture and the proposed methodology of the fault detection, we present a realistic application such as a reactor in order to show the technique's potential. The purpose is to detect the change presence and pinpoint the moment it occurred.

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

Assessment of Self-Heating Susceptibility of Indian Coal Seams – A Neural Network Approach

2014 ◽  
Vol 59 (4) ◽  
pp. 1061-1076 ◽  
Author(s):  
D.C. Panigrahi ◽  
S.K. Ray
Keyword(s):  
Neural Network ◽  
Spontaneous Combustion ◽  
Potential Method ◽  
Oxidation Potential ◽  
Wet Oxidation ◽  
Experimental Conditions ◽  
Neural Network Approach ◽  
Indian Coal ◽  
Petrographic Analyses ◽  
Artificial Neural Network Ann

Abstract The paper addresses an electro-chemical method called wet oxidation potential technique for determining the susceptibility of coal to spontaneous combustion. Altogether 78 coal samples collected from thirteen different mining companies spreading over most of the Indian Coalfields have been used for this experimental investigation and 936 experiments have been carried out by varying different experimental conditions to standardize this method for wider application. Thus for a particular sample 12 experiments of wet oxidation potential method were carried out. The results of wet oxidation potential (WOP) method have been correlated with the intrinsic properties of coal by carrying out proximate, ultimate and petrographic analyses of the coal samples. Correlation studies have been carried out with Design Expert 7.0.0 software. Further, artificial neural network (ANN) analysis was performed to ensure best combination of experimental conditions to be used for obtaining optimum results in this method. All the above mentioned analysis clearly spelt out that the experimental conditions should be 0.2 N KMnO4 solution with 1 N KOH at 45°C to achieve optimum results for finding out the susceptibility of coal to spontaneous combustion. The results have been validated with Crossing Point Temperature (CPT) data which is widely used in Indian mining scenario.

A neural network approach to anomaly detection in spectra

1997 ◽  
Author(s):  
Daniel Benzing ◽  
Kevin Whitaker ◽  
Dedra Moore ◽  
Daniel Benzing ◽  
Kevin Whitaker ◽  
...  
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Network Approach ◽  
Neural Network Approach
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