Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification

Quezada-Gaibor, Darwin; Torres-Sospedra, Joaquín; Nurmi, Jari; Koucheryavy, Yevgeni; Huerta, Joaquín

doi:10.1109/icl-gnss54081.2022.9797021

Cited by 3 publications

(6 citation statements)

References 24 publications

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“…Three RNNs are used in this paper, which are ELM, RVFL, and SNN. ELM projects the input features into the hidden space randomly and does not need gradient-based backpropagation to adjust the weights [ 28 ]. The most obvious structural difference between RVFL and ELM is that there is a quick connection between input and output in RVFL [ 29 ].…”

Section: Methods’ Resultsmentioning

confidence: 99%

ROENet: A ResNet-Based Output Ensemble for Malaria Parasite Classification

2022

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(1) Background: People may be infected with an insect-borne disease (malaria) through the blood input of malaria-infected people or the bite of Anopheles mosquitoes. Doctors need a lot of time and energy to diagnose malaria, and sometimes the results are not ideal. Many researchers use CNN to classify malaria images. However, we believe that the classification performance of malaria parasites can be improved. (2) Methods: In this paper, we propose a novel method (ROENet) to automatically classify malaria parasite on the blood smear. The backbone of ROENet is the pre-trained ResNet-18. We use randomized neural networks (RNNs) as the classifier in our proposed model. Three RNNs are used in ROENet, which are random vector functional link (RVFL), Schmidt neural network (SNN), and extreme learning machine (ELM). To improve the performance of ROENet, the results of ROENet are the ensemble outputs from three RNNs. (3) Results: We evaluate the proposed ROENet by five-fold cross-validation. The specificity, F1 score, sensitivity, and accuracy are 96.68 ± 3.81%, 95.69 ± 2.65%, 94.79 ± 3.71%, and 95.73 ± 2.63%, respectively. (4) Conclusions: The proposed ROENet is compared with other state-of-the-art methods and provides the best results of these methods.

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Section: Methods’ Resultsmentioning

confidence: 99%

ROENet: A ResNet-Based Output Ensemble for Malaria Parasite Classification

2022

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“…% less than the CNNLoc on average. This chapter has extended the results of the proposed positioning model based on CNN-LSTM that was published in [180], and the proposed CNN-ELM model for Multi-building and Multi-oor classication that was published in [13]. This chapter covers the following points:…”

Section: Discussionmentioning

confidence: 84%

“…The optimal hyperparameter values for the ELM-based model in each dataset are provided in Table 6.2. [13].…”

Section: Convolutional Neural Network (Cnn) Modelmentioning

confidence: 99%

“…The capacity of cloud-based indoor positioning platforms to evolve and adapt has allowed the introduction of Machine Learning (ML) techniques and models to solve regression and classication problems, acquiring high levels of accuracy regardless of the scenario. For instance, Convolutional Neural Networks (CNNs) have been used to learn complex patterns in ngerprinting datasets, which led to a reduction in positioning error, as well as an improvement in the oor-building hit rate in multi-building and multi-story environments [13].…”

Section: Introductionmentioning

confidence: 99%

“…The rst model combines CNN and LSTM to estimate the user or device position (positioning model of the SURIMI framework introduced in Chapter 4) Quezada-Gaibor, Torres-Sospedra, Nurmi, Koucheryavy, and Huerta [180]. The second model uses CNN and ELM to provide a fast and accurate solution for classifying ngerprints into oor and building Quezada-Gaibor, Torres-Sospedra, Nurmi, Koucheryavy, and Huerta [13]. Unlike other models featured within the literature, the two models suggested here have been tested with multiple datasets, demonstrating their robustness for use in dierent and heterogeneous indoor environments.…”

Section: Introductionmentioning

confidence: 99%

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