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Deep Learning Approach for Customer Records Prediction for Future Products Issues

Journal of Computer and Communications 2019年第3期7卷 44-54页

作者： Sriramakrishnan Chandrasekaran ktech Regional Delivery Center KPMG LLP Montvale NJ USA

Artificial neural networks consist of the mechanisms of identifying the solution for the problem with the layers design and implementation, and the hidden layers will have rules to be done by the model. The model consists of different approaches, and based on the priority and the requirement, we need to create a model, and this article is dealing with the prediction related to bank customers and their loan processing, etc. In this article, we have a global dataset collected related to 10000 customers of a single bank and their account and other details related to the customers of the bank. Here, we are implementing using tensor flow and Keras libraries to create an artificial neural network which will work on the model and hidden layers. The hidden layers are the most import part of the presentation, and the virtual environment in the field can be helpful for the better prediction of the related things. Machine learning implementations with the combination of deep learning artificial neural networks and also with tensor flow and Keras will be the most exciting and attractive portion of the research work in any field of science and technology. This architecture and application will help to predict future bank applications, and this can be helpful for the customers to understand their level of applications and products usage based on their account weight.

关键词： Machine Learning Neural Networks Tensor Flow Keras Virtual Environment Hidden Layers

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A Clustering Approach for Customer Billing Prediction in Mall: A Machine Learning Mechanism

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Journal of Computer and Communications 2019年第3期7卷 55-66页

作者： Sriramakrishnan Chandrasekaran Abhishek Kumar Manager KTech Regional Delivery Center KPMG LLP Montvale NJ USA Computer Science Engineering Department ACERC Ajmer India

Machine learning implementations are being done in a long way in science and technology and especially in medical stream. In this article, we are focusing on machine learning implementation on mall customers and based on their income and how they can invest in the purchase in a mall. This explains the features like Customer ID, gender, age, income, and spending score. There, we mentioned a score in purchasing the goods in the mall. In this scenario, we are implementing clustering mechanisms, and here we apply the dataset of mall customers which is a public dataset and create clusters related to the customer purchase. We implement machine learning models for the prediction of whether the visited customer will purchase any product or not. For this kind of works, we require many of the inputs like the features mentioned in the paper. To maintain the features, we require a model with machine learning capability. We are performing K-Means clustering and Hierarchical clustering mechanisms, and finally, we implement a confusion matrix to achieve and identify the highest accuracy in those two algorithms. Here, we consider machine learning mechanisms to predict the category of the customer about whether they can buy a product or not based on the independent variables. This work presents you a simple machine learning prediction model based on which we can predict the category of the customer based on clustering. Before clustering, we don’t know to what group they belong to. But after clustering, we can identify the category that data node belongs to. In this article, we are mentioning the process of determining the employee based information using machine learning clustering mechanisms.

关键词： Clustering Machine Learning Category Technology Hierarchical K-Means

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1D AND 2D CIRCUIT-CODE MODELING OF THE REFURBISHED Z ACCELERATOR

1D AND 2D CIRCUIT-CODE MODELING OF THE REFURBISHED Z ACCELER...

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The 17th International Conference on High-Power Particle Beams

作者： Kenneth W.Struve Lawrence F.Bennett Jean-Paul Davis Mark E.Savage David L.Smith William A.Stygar Timothy F.Chavez David D.Hinshelwood Tim C.Wagoner Sandia National Laboratories P.O.Box 5800AlbuquerqueNM 87185-1194USA EG&G 2420 Comanche Rd.NEAlbuquerqueNM87107 USA Naval Research Laboratory WashingtonDCUSA ktech Corporation 10800 Gibson Blvd.SEAlbuquerqueNM87123USA

＜正＞Now that the refurbishment of the Z machine at Sandia is complete we are routinely operating with currents over 26 MA into various *** routine operation we are able to measure current and voltage throughout the machine and compare with circuit code predictions. These measurements have motivated improvements to the original model to include two-dimensional （2D） and three-dimensional （3D） *** this paper we describe a full-machine （36 module）,one-dimensional （1D） circuit model of Z using the Bertha code developed at the Naval Research *** also describe a model that uses 2D *** addition,we discuss plans for implementing a 3D circuit model to allow investigating azimuthal effects that can be important for tailoring the rise time,shape,and length of the Z output *** tailoring on Z is achieved by staggering the timing of the various pulsed-forming modules,and by operating individual modules in either long or short-pulse *** the individual lines combine at a large diameter and are separated azimuthally, transit-time effects can be important for determining transient field enhancements at the vacuum insulator and in the vacuum transmission lines,as well as pulse "shape. Finally,we compare the 1D and 2D models with data, and provide predictions for both short and long pulse modes for various load configurations.

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