Neural networks: An overview of early research, current frameworks and new challenges

Prieto, Alberto; Prieto, Beatriz; Ortigosa, Eva M.; Ros, Eduardo; Pelayo, Francisco J.; Ortega, Julio; Rojas, Ignacio

doi:10.1016/j.neucom.2016.06.014

Cited by 281 publications

(133 citation statements)

References 303 publications

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“…The importance of this method is emphasized by Prieto et al [24] showing that ANNs have been used in simulators, implementations, and real-world applications for a number of years, and has proven to be competitive in solving real-world problems. ANNs have also significantly contributed to the development of machine learning and other related areas.…”

Section: Neural Networkmentioning

confidence: 99%

Predicting company growth using logistic regression and neural networks

Zekić–Sušac

Šarlija

Has

et al. 2016

CRORR

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Abstract. The paper aims to establish an efficient model for predicting company growth by leveraging the strengths of logistic regression and neural networks. A real dataset of Croatian companies was used which described the relevant industry sector, financial ratios, income, and assets in the input space, with a dependent binomial variable indicating whether a company had high-growth if it had annualized growth in assets by more than 20% a year over a three-year period. Due to a large number of input variables, factor analysis was performed in the pre-processing stage in order to extract the most important input components. Building an efficient model with a high classification rate and explanatory ability required application of two data mining methods: logistic regression as a parametric and neural networks as a non-parametric method. The methods were tested on the models with and without variable reduction. The classification accuracy of the models was compared using statistical tests and ROC curves. The results showed that neural networks produce a significantly higher classification accuracy in the model when incorporating all available variables. The paper further discusses the advantages and disadvantages of both approaches, i.e. logistic regression and neural networks in modelling company growth. The suggested model is potentially of benefit to investors and economic policy makers as it provides support for recognizing companies with growth potential, especially during times of economic downturn.

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Section: Neural Networkmentioning

confidence: 99%

Predicting company growth using logistic regression and neural networks

Zekić–Sušac

Šarlija

Has

et al. 2016

CRORR

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“…For pattern recognition problems, developing a multilayer perceptron (MLP) neural network with backpropagation algorithm is very popular approach [58,59,60,61,62]. The ANNs used in this work are single or multilayer Back Propagation Neural Networks (BPNN).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

A Novel Similar Character Discrimination Method for Online Handwritten Urdu Character Recognition in Half Forms

2018

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Online handwritten Urdu character recognition is one of the key technologies for intelligent interface on smart phones and touch screens. It is a challenging research topic as Urdu script has many similar character groups. A novel similar character discrimination method for online handwritten Urdu character recognition is proposed in this paper which includes pre-classification, feature extraction and fine classification process. The pre-classifier enables the discrimination of similar characters by putting them in distinct smaller subsets according to stroke number and diacritics. Then structural features and wavelet features are extracted. Finally, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Recurrent Neural Network (RNN) classifiers are compared for fine classification within subsets. Results of RNN classifier without using the proposed pre-classifier and features have also been obtained to check the end-to-end capability of the RNN classifier. Experimental results show that the proposed method is efficient and achieves an overall accuracy of 96% on a large-scale self-collected dataset. It is feasible to extend this method for other Arabic scripts.

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“…This scheme results in evaluation of operators, with high accuracy , precise classification when compared to other methods. In the paper [15] it involves two aims that have emerged in neural networks i.e the increase in the ability to understand the behaviour of nervous system and thus taking inspiration from this knowledge and building up of new systems to perform some related tasks presents a comprehensive overview of modelling, simulation and implementation of neural networks. It also includes the growth and evolution in different aspects of neural networks.…”

Section: Related Workmentioning

confidence: 99%

Feature Extraction and Image classification

Marnur¹

2018

IJRASET

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Neural networks: An overview of early research, current frameworks and new challenges

Cited by 281 publications

References 303 publications

Predicting company growth using logistic regression and neural networks

Predicting company growth using logistic regression and neural networks

A Novel Similar Character Discrimination Method for Online Handwritten Urdu Character Recognition in Half Forms

Feature Extraction and Image classification

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