Comparing Predictive Ability of Classifiers in Forecasting Online Buying Behaviour

Online Shopping ◽

Predictive Ability ◽

Future Research ◽

Support Vector ◽

Market Demand ◽

Artificial Neural ◽

Buying Behaviour ◽

Online Buying

With Internet invading geographic boundaries and diverse demographic strata, online shopping is growing at exponential rate. Expected to grow by 45 per cent to $7.69 billion by the end of 2015, India's ecommerce market has emerged as one of the most anticipated destinations for both multinational and domestic retailers. Since their success will depend on their ability to attract shoppers to buy online, it becomes relevant for them to decipher Indian consumers' attitude and behaviour towards online shopping and to predict online buying potential in India. The effectiveness of marketing and promotional strategies and action plans also will have to be pivoted around the potential available in the market. This empirical study explores the accuracy, precision and recall of four different classifying techniques used in predicting online buying. The forecasting ability of logistic regression (LR), artificial neural network (ANN), support vector machines (SVM) and random forest (RF) in the context of willingness of shoppers' to buy online has been compared. Analysis of the data supported most of the predictions albeit with varying level of accuracy. The outcome of the study reflects the superiority of artificial neural network over the other three models in terms of the predicting power. This paper adds to the knowledge body for online retailers in reducing their vulnerability with respect to market demand and improves their preparedness to handle the market response. Managerial implications of the findings and scope for future research have been deliberated.

Predictive Ability of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) to Approximate Biogas Yield in a Modular Biodigester

10.1007/978-3-030-85030-2_17 ◽

2021 ◽

pp. 202-215

Author(s):

Modestus O. Okwu ◽

Lagouge K. Tartibu ◽

Olusegun D. Samuel ◽

Henry O. Omoregbee ◽

Anna E. Ivbanikaro

Keyword(s):

Neural Network ◽

Response Surface Methodology ◽

Response Surface ◽

Predictive Ability ◽

Biogas Yield ◽

Artificial Neural ◽

Artificial Neural Network for Power System Static Security Assessment: A Survey

Jurnal Teknologi ◽

10.11113/jt.v66.1802 ◽

2013 ◽

Vol 66 (1) ◽

Cited By ~ 2

Author(s):

I. S. Saeh ◽

M. W. Mustafa

Keyword(s):

Neural Network ◽

Power System ◽

Future Research ◽

Security Assessment ◽

Advantages And Disadvantages ◽

Hybrid Techniques ◽

Static Security ◽

Artificial Neural ◽

According to the growth rate of Machine Learning (ML) application in some power system subjects, this paper introduce a comprehensive survey of Artificial Neural Network (ANN) in Static Security Assessment (SSA). Advantages and disadvantages of using ANN in above mentioned subjects and the main challenges in these fields have been explained, too. We explore the links between the fields of SSA and NN in a unified presentation and identify key areas for future research. Recent developments in the solution methods for SSA are reviewed. Hybrid techniques in SSA are also discussed and reviewed and future directions for research are suggested.

Optimisation of the predictive ability of artificial neural network (ANN) models: A comparison of three ANN programs and four classes of training algorithm

European Journal of Pharmaceutical Sciences ◽

10.1016/j.ejps.2005.04.010 ◽

2005 ◽

Vol 25 (4-5) ◽

pp. 395-405 ◽

Cited By ~ 76

Author(s):

A. Philip Plumb ◽

Raymond C. Rowe ◽

Peter York ◽

Martin Brown

Keyword(s):

Neural Network ◽

Predictive Ability ◽

Training Algorithm ◽

Ann Models ◽

Artificial Neural ◽

Forecasting Buying Intention Through Artificial Neural Network: An Algorithmic Solution on Direct-to-Consumer Brands

FIIB Business Review ◽

10.1177/23197145211046126 ◽

2021 ◽

pp. 231971452110461

Author(s):

Bikram Prasad ◽

Indrajit Ghosal

Keyword(s):

Neural Network ◽

Predictive Model ◽

Business Strategy ◽

Direct To Consumer ◽

Traditional Mode ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Algorithmic Model ◽

Buying Behaviour

The direct-to-consumer (DTC) brands are emerging to reach more number of consumers with more potential to meet their expectations. They are characterized through their metamorphosis as the vertical brands sell their products from the manufacturer to consumers directly without any interruptions from distribution channels as in traditional mode of doing business. They are annihilating themselves in the virtual platforms and later disrupting their existing linear sales models. This empirical investigation is targeted to construct an algorithmic model through a deep learning process which has been instrumental to predict the purchase decision. This investigation has churned a predictive model that is based on the attributes of the buying behaviour of the consumers. The attributes of online buying behaviour like safety of transaction, availability of innovative products and quality of products have been considered to build a predictive model through artificial neural network (ANN). The accuracy of training and testing data are closer, which infers about the consistency and validity of the predictive model. There are several consequences arising from the predictive model obtained that can be seeded from customer-centred marketing and further stemmed from the framing of business strategy, gaining insights into market architecture and choice of customer

How to estimate and predict the expenses incurred by diabetes treatment using Artificial Neural Network (ANN)

Journal of Biology and Today s World ◽

10.15412/j.jbtw.01030101 ◽

2014 ◽

Vol 3 (1) ◽

Author(s):

M. Mahboubi ◽

M. Rahimi ◽

M. Mohebbi ◽

F. Ghahramani

Keyword(s):

Neural Network ◽

Diabetes Treatment ◽

Artificial Neural ◽

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

International Review of Automatic Control (IREACO) ◽

10.15866/ireaco.v12i3.16455 ◽

2019 ◽

Vol 12 (3) ◽

pp. 145 ◽

Cited By ~ 1

Author(s):

Epyk Sunarno ◽

Ramadhan Bilal Assidiq ◽

Syechu Dwitya Nugraha ◽

Indhana Sudiharto ◽

Ony Asrarul Qudsi ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural ◽

LOAD SCHEDULING FOR SMART HOME USING DAY-AHEAD PREDICTION FROM ARTIFICIAL NEURAL NETWORK (ANN)

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2020/9291.42020 ◽

2020 ◽

Vol 9 (1.4) ◽

pp. 658-663

Author(s):

Joharry S. H.

Keyword(s):

Neural Network ◽

Smart Home ◽

Load Scheduling ◽

Artificial Neural ◽

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

Engineering and Technology Journal ◽

10.30684/etj.v38i4a.399 ◽

2020 ◽

Vol 38 (4A) ◽

pp. 510-514

Author(s):

Tay H. Shihab ◽

Amjed N. Al-Hameedawi ◽

Ammar M. Hamza

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Random Forest ◽

Satellite Image ◽

Landsat 8 ◽

Optical Remote Sensing ◽

Data Set ◽

Artificial Neural ◽

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019. They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

Engineering and Technology Journal ◽

10.30684/etj.v38i2a.319 ◽

2020 ◽

Vol 38 (2A) ◽

pp. 255-264

Author(s):

Hanan A. R. Akkar ◽

Sameem A. Salman

Keyword(s):

Neural Network ◽

Mean Square Error ◽

Average Rate ◽

Mean Square ◽

Performance Measurements ◽

Artificial Intelligent ◽

Average Mean Square Error ◽

Artificial Neural ◽

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.