A Comparison Study of Recurrent Neural Networks in Recognition of Handwritten Odia Numerals

Das, Abhishek; Patra, Gyana Ranjan; Mohanty, Mihir Narayan

doi:10.1007/978-981-15-8752-8_26

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…3.3 Image denoising and feature selection RNN model may pass through vanishing gradient problems due to the large size of data, whereas the LSTM and GRU models are free from this problem [34]. Each image of the dataset has 2,500 numbers of pixels.…”

Section: Preprocessingmentioning

confidence: 99%

Design of ensemble recurrent model with stacked fuzzy ARTMAP for breast cancer detection

Das

Mohanty

2022

ACI

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PurposeIn time and accurate detection of cancer can save the life of the person affected. According to the World Health Organization (WHO), breast cancer occupies the most frequent incidence among all the cancers whereas breast cancer takes fifth place in the case of mortality numbers. Out of many image processing techniques, certain works have focused on convolutional neural networks (CNNs) for processing these images. However, deep learning models are to be explored well.Design/methodology/approachIn this work, multivariate statistics-based kernel principal component analysis (KPCA) is used for essential features. KPCA is simultaneously helpful for denoising the data. These features are processed through a heterogeneous ensemble model that consists of three base models. The base models comprise recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU). The outcomes of these base learners are fed to fuzzy adaptive resonance theory mapping (ARTMAP) model for decision making as the nodes are added to the F_2ˆa layer if the winning criteria are fulfilled that makes the ARTMAP model more robust.FindingsThe proposed model is verified using breast histopathology image dataset publicly available at Kaggle. The model provides 99.36% training accuracy and 98.72% validation accuracy. The proposed model utilizes data processing in all aspects, i.e. image denoising to reduce the data redundancy, training by ensemble learning to provide higher results than that of single models. The final classification by a fuzzy ARTMAP model that controls the number of nodes depending upon the performance makes robust accurate classification.Research limitations/implicationsResearch in the field of medical applications is an ongoing method. More advanced algorithms are being developed for better classification. Still, the scope is there to design the models in terms of better performance, practicability and cost efficiency in the future. Also, the ensemble models may be chosen with different combinations and characteristics. Only signal instead of images may be verified for this proposed model. Experimental analysis shows the improved performance of the proposed model. This method needs to be verified using practical models. Also, the practical implementation will be carried out for its real-time performance and cost efficiency.Originality/valueThe proposed model is utilized for denoising and to reduce the data redundancy so that the feature selection is done using KPCA. Training and classification are performed using heterogeneous ensemble model designed using RNN, LSTM and GRU as base classifiers to provide higher results than that of single models. Use of adaptive fuzzy mapping model makes the final classification accurate. The effectiveness of combining these methods to a single model is analyzed in this work.

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Section: Preprocessingmentioning

confidence: 99%

Design of ensemble recurrent model with stacked fuzzy ARTMAP for breast cancer detection

Das

Mohanty

2022

ACI

Self Cite

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“…As a result, the traditional neural network cannot utilize the sequential information included in time series data. Contrary to the conventional neural networks, the recurrent neural network (RNN) approaches are used to generate a sequence of data such that each observation is supposed to be dependent on the previous ones [ 36 ]. As an elegant variation of RNN, LSTM is a recurrent neural network approach that can be applied to model sequential data as well [ 21 ].…”

Section: The Proposed Approachmentioning

confidence: 99%

Deep LSTM-Based Transfer Learning Approach for Coherent Forecasts in Hierarchical Time Series

Sagheer

Hamdoun

Youness

2021

Sensors

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Hierarchical time series is a set of data sequences organized by aggregation constraints to represent many real-world applications in research and the industry. Forecasting of hierarchical time series is a challenging and time-consuming problem owing to ensuring the forecasting consistency among the hierarchy levels based on their dimensional features. The excellent empirical performance of our Deep Long Short-Term Memory (DLSTM) approach on various forecasting tasks motivated us to extend it to solve the forecasting problem through hierarchical architectures. Toward this target, we develop the DLSTM model in auto-encoder (AE) fashion and take full advantage of the hierarchical architecture for better time series forecasting. DLSTM-AE works as an alternative approach to traditional and machine learning approaches that have been used to manipulate hierarchical forecasting. However, training a DLSTM in hierarchical architectures requires updating the weight vectors for each LSTM cell, which is time-consuming and requires a large amount of data through several dimensions. Transfer learning can mitigate this problem by training first the time series at the bottom level of the hierarchy using the proposed DLSTM-AE approach. Then, we transfer the learned features to perform synchronous training for the time series of the upper levels of the hierarchy. To demonstrate the efficiency of the proposed approach, we compare its performance with existing approaches using two case studies related to the energy and tourism domains. An evaluation of all approaches was based on two criteria, namely, the forecasting accuracy and the ability to produce coherent forecasts through through the hierarchy. In both case studies, the proposed approach attained the highest accuracy results among all counterparts and produced more coherent forecasts.

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Breast Cancer Detection Using Concatenated Deep Learning Model

Das

Mohapatra

Mohanty

2022

Smart Innovation, Systems and Technologies

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A Comparison Study of Recurrent Neural Networks in Recognition of Handwritten Odia Numerals

Cited by 4 publications

References 19 publications

Design of ensemble recurrent model with stacked fuzzy ARTMAP for breast cancer detection

Design of ensemble recurrent model with stacked fuzzy ARTMAP for breast cancer detection

Deep LSTM-Based Transfer Learning Approach for Coherent Forecasts in Hierarchical Time Series

Breast Cancer Detection Using Concatenated Deep Learning Model

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