Applications of Evolutionary Neural Networks for Sales Forecasting of Fashionable Products

2010 ◽  
pp. 387-403 ◽  
Author(s):  
Yong Yu ◽  
Tsan-Ming Choi ◽  
Kin-Fan Au ◽  
Zhan-Li Sun
Keyword(s):  
Neural Network ◽  
Evolutionary Computation ◽  
Topology Design ◽  
Future Research ◽  
Binary Representation ◽  
Evolutionary Neural Network ◽  
Network Connection ◽  
Forecasting System ◽  
Future Research Directions ◽  
Artificial Neural Network Ann

The evolutionary neural network (ENN), which is the hybrid combination of evolutionary computation and neural network, is a suitable candidate for topology design, and is widely adopted. An ENN approach with a direct binary representation to every single neural network connection is proposed in this chapter for sales forecasting of fashionable products. In this chapter, the authors will first explore the details on how an evolutionary computation approach can be applied in searching for a desirable network structure for establishing the appropriate sales forecasting system. The optimized ENN structure for sales forecasting is then developed. With the use of real sales data, the authors compare the performances of the proposed ENN forecasting scheme with several traditional methods which include artificial neural network (ANN) and SARIMA. The authors obtain the conditions in which their proposed ENN outperforms other methods. Insights regarding the applications of ENN for forecasting sales of fashionable products are generated. Finally, future research directions are outlined.

scholarly journals A Survey of Supervised Learning Models for Spiking Neural Network

2021 ◽  
pp. 35-49
Author(s):  
Moses Apambila Agebure ◽  
Paula Aninyie Wumnaya ◽  
Edward Yellakuor Baagyere
Keyword(s):  
Neural Network ◽  
Supervised Learning ◽  
Second Generation ◽  
Future Research ◽  
Learning Models ◽  
Research Directions ◽  
Future Research Directions ◽  
The Difference ◽  
Artificial Neural Network Ann ◽  
Made In

There has been a significant attempt to derive supervised learning models for training Spiking Neural Networks (SNN), which is the third and most recent generation of Artificial Neural Network (ANN). Supervised SNN learning models are considered more biologically plausible and thus exploits better the computational efficiency of biological neurons and also, are less computationally expensive than second generation ANN. SNN models have also produced competitive performance in most tasks when compared to second generation ANNs. These advantages, coupled with the difficulty in adopting the well established learning models for second generation networks to train SNN due to the difference in information coding led to the recent introduction of supervised learning models for training SNN. However, lack of comprehensive source of literature detailing strides made in this area, and the challenges and prospects of SNN serves as a hindrance to further exploration and application of SNN models. A comprehensive review of supervised learning methods in SNN is presented in this paper in which some widely used SNN neural models, learning models and their basic concepts, areas of applications, limitations, prospects and future research directions are discussed. The main contribution of this paper is that it presents and discusses trends in supervised learning in SNNwith the aim of providing a reference point for those desiring further knowledge and application of SNN methods.

Comparing Predictive Ability of Classifiers in Forecasting Online Buying Behaviour

2020 ◽  
pp. 1279-1296
Author(s):  
Sanjeev Prashar ◽  
S.K. Mitra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Online Shopping ◽  
Predictive Ability ◽  
Future Research ◽  
Artificial Neural ◽  
Promotional Strategies ◽  
Artificial Neural Network Ann ◽  
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.

scholarly journals Neural Networks for Infectious Diseases Detection: Prospects and Challenges

2021 ◽  
Author(s):  
Shumaila Javaid ◽  
Nasir Saeed
Keyword(s):  
Neural Network ◽  
Data Privacy ◽  
Critical Role ◽  
Disease Diagnosis ◽  
Future Research ◽  
Detection Accuracy ◽  
Privacy And Security ◽  
Research Directions ◽  
Medical Diagnostic ◽  
Artificial Neural Network Ann

Artificial neural network (ANN) ability to learn, correct errors, and transform a large amount of raw data into useful medical decisions for treatment and care have increased its popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients’ healthcare decisions and efficient disease diagnosis. We thoroughly review different types of ANNs presented in the existing literature that advanced ANNs adaptation for complex applications. Moreover, we also investigate ANN’s advances for various disease diagnoses and treatments such as viral, skin, cancer, and COVID-19. Furthermore, we propose a novel deep Convolutional Neural Network (CNN) model called ConXNet for improving the detection accuracy of COVID-19 disease. ConXNet is trained and tested using different datasets, and it achieves more than 97% detection accuracy and precision, which is significantly better than existing models. Finally, we highlight future research directions and challenges such as complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications. <br>

scholarly journals Research on Neural Style Transfer

2021 ◽  
Vol 2079 (1) ◽  
pp. 012029
Author(s):  
Xueyuan Liu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Future Research ◽  
Research Directions ◽  
Style Transfer ◽  
Qualitative And Quantitative ◽  
Existing Problems ◽  
Future Research Directions ◽  
Application Prospects

Abstract The process of using CNN (Convolutional Neural Network) to blend the contents of a picture with different styles is called neural style transfer (NST). The purpose of this paper is to introduce current progress of NST, and introduce in detail the classification of the main NST algorithms based on deep learning, and make qualitative and quantitative comparisons of different algorithms, and then analyze the application prospects of image style migration in related fields, and finally summarize the existing problems and future research directions of NST.

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

2013 ◽  
Vol 66 (1) ◽  
Author(s):  
I. S. Saeh ◽  
M. W. Mustafa
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power System ◽  
Future Research ◽  
Security Assessment ◽  
Advantages And Disadvantages ◽  
Hybrid Techniques ◽  
Static Security ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

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. 

scholarly journals Neural Networks and Its Application in Engineering

10.28945/3317 ◽  
2009 ◽  
Author(s):  
Oludele Awodele ◽  
Olawale Jegede
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Process ◽  
Future Research ◽  
Control Engineering ◽  
Research Directions ◽  
Agricultural Engineering ◽  
Recent Advances ◽  
Future Research Directions ◽  
Network Application

Neural Network (NN) has emerged over the years and has made remarkable contribution to the advancement of various fields of endeavor. The purpose of this work is to examine neural networks and their emerging applications in the field of engineering, focusing more on Controls. In this work, we have examined the various architectures of NN and the learning process. The needs for neural networks, training of neural networks, and important algorithms used in realizing neural networks have also been briefly discussed. Neural network application in control engineering has been extensively discussed, whereas its applications in electrical, civil and agricultural engineering were also examined. We concluded by identifying limitations, recent advances and promising future research directions.

scholarly journals Variable Chromosome Genetic Algorithm for Structure Learning in Neural Networks to Imitate Human Brain

2019 ◽  
Vol 9 (15) ◽  
pp. 3176 ◽  
Author(s):  
Kang-moon Park ◽  
Donghoon Shin ◽  
Sung-do Chi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Structure Learning ◽  
Structural Parameters ◽  
Future Research ◽  
Xor Problem ◽  
Chromosome Attachment ◽  
Artificial Neural Network Ann ◽  
Exclusive Or

This paper proposes the variable chromosome genetic algorithm (VCGA) for structure learning in neural networks. Currently, the structural parameters of neural networks, i.e., number of neurons, coupling relations, number of layers, etc., have mostly been designed on the basis of heuristic knowledge of an artificial intelligence (AI) expert. To overcome this limitation, in this study evolutionary approach (EA) has been utilized to automatically generate the proper artificial neural network (ANN) structures. VCGA has a new genetic operation called a chromosome attachment. By applying the VCGA, the initial ANN structures can be flexibly evolved toward the proper structure. The case study applied to the typical exclusive or (XOR) problem shows the feasibility of our methodology. Our approach is differentiated with others in that it uses a variable chromosome in the genetic algorithm. It makes a neural network structure vary naturally, both constructively and destructively. It has been shown that the XOR problem is successfully optimized using a VCGA with a chromosome attachment to learn the structure of neural networks. Research on the structure learning of more complex problems is the topic of our future research.

Artificial Neural Networks

2018 ◽  
pp. 120-131 ◽  
Author(s):  
Steven Walczak
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Future Research ◽  
Network Architectures ◽  
Advantages And Disadvantages ◽  
History Of ◽  
Future Research Directions ◽  
Artificial Neural

This chapter examines the history of artificial neural networks research through the present day. The components of artificial neural network architectures and both unsupervised and supervised learning methods are discussed. Although a step-by-step tutorial of how to develop artificial neural networks is not included, additional reading suggestions covering artificial neural network development are provided. The advantages and disadvantages of artificial neural networks for research and real-world applications are presented as well as potential solutions to many of the disadvantages. Future research directions for the field of artificial neural networks are presented.

scholarly journals Image captioning Bot

2020 ◽  
Vol 6 (12) ◽  
pp. 348-354
Author(s):  
Mukund Upadhyay and Prof. Shallu Bashambu
Keyword(s):  
Neural Network ◽  
Future Research ◽  
Generation Process ◽  
Image Captioning ◽  
Research Directions ◽  
Benchmark Datasets ◽  
Future Research Directions ◽  
Computer Learn ◽  
Natural Language Process ◽  
The Relationship

Image captioning means automatically generating a caption for an image with the development of deep learning, the combination of computer vision and natural language process has caught great attention in the last few years. Image captioning is a representative of this filed, which makes the computer learn to use one or more sentences to understand the visual content of an image. The meaningful description generation process of highlevel image semantics requires not only the recognition of the object and the scene, but the ability of analyzing the state, the attributes and the relationship among these objects. Neural network based methods are further divided into subcategories based on the specific framework they use. Each subcategory of neural network based methods are discussed in detail. After that, state of the art methods are compared on benchmark datasets. Following that, discussions on future research directions are presented.

Artificial Neural Networks

2019 ◽  
pp. 40-53 ◽  
Author(s):  
Steven Walczak
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Future Research ◽  
Network Architectures ◽  
Advantages And Disadvantages ◽  
History Of ◽  
Future Research Directions ◽  
Artificial Neural

This chapter examines the history of artificial neural networks research through the present day. The components of artificial neural network architectures and both unsupervised and supervised learning methods are discussed. Although a step-by-step tutorial of how to develop artificial neural networks is not included, additional reading suggestions covering artificial neural network development are provided. The advantages and disadvantages of artificial neural networks for research and real-world applications are presented as well as potential solutions to many of the disadvantages. Future research directions for the field of artificial neural networks are presented.

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