Feedback Analysis for Digital Marketing in India

2021 ◽  
Vol 11 (1) ◽  
pp. 78-88
Author(s):  
Biswajit Biswas ◽  
Manas Kumar Sanyal ◽  
Tuhin Mukherjee
Keyword(s):  
Learning Algorithm ◽  
Back Propagation ◽  
Digital Marketing ◽  
Purchase Decision ◽  
Feedback Analysis ◽  
Customer Feedback ◽  
Feed Forward Neural Networks ◽  
Points Of View ◽  
New Research ◽  
Research Challenge

In the context of fastest growing Indian online market, the big players like Amazon.in, Flipkart.com, Snapdeal.com, etc. are in a competitive journey to expand their market share. This paper is an attempt in modelling customer feedback for the said e-market players. The paper uses feed forward neural networks with maximum two hidden layers and back propagation kind of supervised learning algorithm. The paper found satisfactory level of success and concludes usefulness of customer feedback for both customers (for purchase decision) and marketers (for product development) points of view. It is a footstep and opens a new research challenge for the post-COVID era of business.

Study on Deep Structure of Extreme Learning Machine (DS-ELM) for Datasets with Noise

2014 ◽  
Vol 989-994 ◽  
pp. 3679-3682 ◽  
Author(s):  
Meng Meng Ma ◽  
Bo He
Keyword(s):  
Extreme Learning Machine ◽  
Simple Structure ◽  
Learning Algorithm ◽  
Deep Structure ◽  
Noisy Data ◽  
Machine Learning Algorithm ◽  
Feed Forward Neural Networks ◽  
Learning Machine ◽  
Hidden Layer ◽  
Auto Associative Neural Network

Extreme learning machine (ELM), a relatively novel machine learning algorithm for single hidden layer feed-forward neural networks (SLFNs), has been shown competitive performance in simple structure and superior training speed. To improve the effectiveness of ELM for dealing with noisy datasets, a deep structure of ELM, short for DS-ELM, is proposed in this paper. DS-ELM contains three level networks (actually contains three nets ): the first level network is trained by auto-associative neural network (AANN) aim to filter out noise as well as reduce dimension when necessary; the second level network is another AANN net aim to fix the input weights and bias of ELM; and the last level network is ELM. Experiments on four noisy datasets are carried out to examine the new proposed DS-ELM algorithm. And the results show that DS-ELM has higher performance than ELM when dealing with noisy data.

Modeling and Implementation of Inter-Campuses Equipment Optimized Management Based on WebGIS

2013 ◽  
Vol 680 ◽  
pp. 534-539
Author(s):  
Wei Feng Ma
Keyword(s):  
Information Management ◽  
Spatial Data ◽  
Large Scale ◽  
New Technology ◽  
Rapid Expansion ◽  
Mathematics Model ◽  
Data Mapping ◽  
Geographically Dispersed ◽  
New Research ◽  
Research Challenge

With the rapid expansion of the campus scale and the increasing of the geographically dispersed campus, how to adopt new theory, new method and new technology to realize the equipment optimized assignment and the information management is a new research challenge. It is the key to safeguard the national fund to use reasonably, and to speed up the development of education healthily. Through analyzing the domestic and foreign related research works, the paper proposed that it can take use of the spatial data expression and analysis with Geographic Information System (GIS) to realize the large-scale and inter-campuses equipment optimized assignment and information management. It discussed the mathematics model and the system architecture. Moreover, the paper described the key implementation technology in great detail such as spatial data mapping with MapInfo professional 9 and the development of WebGIS functions with MapXtreme. The results show that the solution is feasible and effective.

scholarly journals Stock Market Prediction Using Artificial Neural Networks

2012 ◽  
Vol 6-7 ◽  
pp. 1055-1060 ◽  
Author(s):  
Yang Bing ◽  
Jian Kun Hao ◽  
Si Chang Zhang
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Stock Exchange ◽  
Back Propagation ◽  
Composite Index ◽  
Network Models ◽  
Back Propagation Neural Network ◽  
Neural Network Models ◽  
The Neural Network

In this study we apply back propagation Neural Network models to predict the daily Shanghai Stock Exchange Composite Index. The learning algorithm and gradient search technique are constructed in the models. We evaluate the prediction models and conclude that the Shanghai Stock Exchange Composite Index is predictable in the short term. Empirical study shows that the Neural Network models is successfully applied to predict the daily highest, lowest, and closing value of the Shanghai Stock Exchange Composite Index, but it can not predict the return rate of the Shanghai Stock Exchange Composite Index in short terms.

The TP Chromatic Aberration Estimation by Using Neural Networks

2011 ◽  
Vol 121-126 ◽  
pp. 4239-4243 ◽  
Author(s):  
Du Jou Huang ◽  
Yu Ju Chen ◽  
Huang Chu Huang ◽  
Yu An Lin ◽  
Rey Chue Hwang
Keyword(s):  
Neural Networks ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Physical Property ◽  
Chromatic Aberration ◽  
Neural Model ◽  
Artificial Intelligent ◽  
Error Back Propagation ◽  
The Neural Networks ◽  
Simulation Results

The chromatic aberration estimations of touch panel (TP) film by using neural networks are presented in this paper. The neural networks with error back-propagation (BP) learning algorithm were used to catch the complex relationship between the chromatic aberration, i.e., L.A.B. values, and the relative parameters of TP decoration film. An artificial intelligent (AI) estimator based on neural model for the estimation of physical property of TP film is expected to be developed. From the simulation results shown, the estimations of chromatic aberration of TP film are very accurate. In other words, such an AI estimator is quite promising and potential in commercial using.

scholarly journals Forecasting Gold Prices Based on Extreme Learning Machine

2016 ◽  
Vol 11 (3) ◽  
pp. 372 ◽  
Author(s):  
Kumar Chandar Sivalingam ◽  
Sumathi Mahendran ◽  
Sivanandam Natarajan
Keyword(s):  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Learning Ability ◽  
Stock Index ◽  
Feed Forward Back Propagation ◽  
Crude Oil Prices ◽  
The Future ◽  
Good Learning ◽  
Learning Machine

<p>In recent years, the investors pay major attention to invest in gold market ecause of huge profits in the future. Gold is the only commodity which maintains ts value even in the economic and financial crisis. Also, the gold prices are closely elated with other commodities. The future gold price prediction becomes the warning ystem for the investors due to unforeseen risk in the market. Hence, an accurate gold rice forecasting is required to foresee the business trends. This paper concentrates on orecasting the future gold prices from four commodities like historical data’s of gold rices, silver prices, Crude oil prices, Standard and Poor’s 500 stock index (S&amp;P500) ndex and foreign exchange rate. The period used for the study is from 1st January 000 to 31st April 2014. In this paper, a learning algorithm for single hidden layered eed forward neural networks called Extreme Learning Machine (ELM) is used which as good learning ability. Also, this study compares the five models namely Feed orward networks without feedback, Feed forward back propagation networks, Radial asis function, ELMAN networks and ELM learning model. The results prove that he ELM learning performs better than the other methods.</p>

scholarly journals Impact of Digital Marketing on Consumer Purchase Decision

2019 ◽  
Vol 9 (2S2) ◽  
pp. 453-455
Keyword(s):  
Distribution Channels ◽  
Ease Of Use ◽  
Digital Marketing ◽  
Survey Method ◽  
Purchase Decision ◽  
Goods And Services ◽  
Price Range ◽  
Digital Generation ◽  
The Gift ◽  
The Look

promoting is a trifling exchange of goods and services for cost but advertising and marketing is the phenomenon that allows keeping clients other than attracting them. Within the gift era among the numerous modes of marketing, digital marketing occurs to generate the maximum fee. It could be as the mechanism of reaching consumers through the use of diverse virtual distribution channels. The present observes aims to observe the effect of digital marketing and advertising on client purchase conduct and additionally makes an attempt to investigate as to in what ways are the clients absolutely privy to the diverse digital marketing and advertising mediums inside the gift digital generation. The look at is primarily based on the survey method. A questionnaire is prepared and covered 786 respondents for evaluation. The effects of the look at discovered the information that the purchasers are privy to the digital mediums available to them. It also confirmed the results that most of the customers opt to shop online due to its ease of use and in your price range mode of buying. Because of the technological upliftment in the gift generation, digital mediums and digital marketing are gaining significance and is enormously prevalent via each stratum of the society.

scholarly journals APPLICATION OF LEARNING REINFORCEMENT METHOD IN ROBOTIZED AND AUTOMATED FORESTRY SYSTEMS

2020 ◽  
Vol 10 (1) ◽  
pp. 256-265
Author(s):  
Andrey Tolstyh ◽  
D Stupnikov ◽  
Sergey Malyukov ◽  
Aleksandr Luk'yanov ◽  
Yuriy Lunev
Keyword(s):  
Physical Environment ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Industrial Robots ◽  
Environment Model ◽  
Random Position ◽  
Learning Reinforcement ◽  
The Stability ◽  
Two Stages ◽  
The Cost

Abstract Currently, most large enterprises are actively using industrial robots and other automated solutions. This allows a significant increase in productivity and quality of work performed. This article gave a brief overview of modern industrial robots, their operating principle, basic components and systems. A reinforcement learning algorithm was developed and tested. The task of constructing a learning algorithm with reinforcement was divided into two stages: modeling the environment and description and optimization of the cost function. Since industrial robotic systems operate in the real world, the environment model should reflect basic physical laws. Therefore, the pyBullet library of the physical environment was chosen as the physical environment for testing. After modeling the manipulator in the selected physical medium, it was given the trivial task of touching a given object with the capture of the manipulator. An artificial neural network was used as an agent interacting with the environment. The inputs were the coordinates of the object and the existing angles of rotation of the articulated joints of the robot. Outputs - angle of rotation of joints at this step. This network was trained using the back propagation method, Adam modification. The system was trained for about 12 hours. Success is achieved in 95% of cases when testing the stability of the system (random position of the cylinder). In future, it is planned to test the obtained models on bench samples

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