An ensemble model of convolution and recurrent neural network for skin disease classification

2021 ◽  
Author(s):  
Belal Ahmad ◽  
Mohd Usama ◽  
Tanvir Ahmad ◽  
Shabnam Khatoon ◽  
Chaudhary Maqbool Alam
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Skin Disease ◽  
Disease Classification ◽  
Ensemble Model

scholarly journals Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1958 ◽  
Author(s):  
Lilin Cheng ◽  
Haixiang Zang ◽  
Tao Ding ◽  
Rong Sun ◽  
Miaomiao Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Recurrent Neural Network ◽  
Fuzzy Inference ◽  
Ensemble Model ◽  
Probabilistic Forecasting ◽  
Inference System ◽  
Wind Speed Forecasting ◽  
Wind Power Integration ◽  
Neuro Fuzzy

Wind energy is a commonly utilized renewable energy source, due to its merits of extensive distribution and rich reserves. However, as wind speed fluctuates violently and uncertainly at all times, wind power integration may affect the security and stability of power system. In this study, we propose an ensemble model for probabilistic wind speed forecasting. It consists of wavelet threshold denoising (WTD), recurrent neural network (RNN) and adaptive neuro fuzzy inference system (ANFIS). Firstly, WTD smooths the wind speed series in order to better capture its variation trend. Secondly, RNNs with different architectures are trained on the denoising datasets, operating as submodels for point wind speed forecasting. Thirdly, ANFIS is innovatively established as the top layer of the entire ensemble model to compute the final point prediction result, in order to take full advantages of a limited number of deeplearningbased submodels. Lastly, variances are obtained from submodels and then prediction intervals of probabilistic forecasting can be calculated, where the variances inventively consist of modeling and forecasting uncertainties. The proposed ensemble model is established and verified on less than one-hour-ahead ultra-short-term wind speed forecasting. We compare it with other soft computing models. The results indicate the feasibility and superiority of the proposed model in both point and probabilistic wind speed forecasting.

Eff2Net: An efficient channel attention-based convolutional neural network for skin disease classification

2022 ◽  
Vol 73 ◽  
pp. 103406
Author(s):  
R Karthik ◽  
Tejas Sunil Vaichole ◽  
Sanika Kiran Kulkarni ◽  
Ojaswa Yadav ◽  
Faiz Khan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Skin Disease ◽  
Disease Classification

scholarly journals Modelling Particulate Matter Using Multivariate and Multistep Recurrent Neural Networks

2021 ◽  
Vol 9 ◽  
Author(s):  
Tushar Saini ◽  
Pratik Chaturvedi ◽  
Varun Dutt
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Recurrent Neural Network ◽  
Network Models ◽  
Economic Losses ◽  
Ensemble Model ◽  
Neural Network Models ◽  
Ensemble Techniques ◽  
Bidirectional Lstm

Air quality is a major problem in the world, having severe health implications. Long-term exposure to poor air quality causes pulmonary and cardiovascular diseases. Several studies have also found that deteriorating air quality also causes substantial economic losses. Thus, techniques that can forecast air quality with higher accuracy may help reduce health and economic consequences. Prior research has utilized state-of-the-art artificial neural network and recurrent neural network models for forecasting air quality. However, a comprehensive investigation of different architectures of recurrent neural network, especially LSTMs and ensemble techniques, has been less explored. Also, there have been less explorations of long-term air quality forecasts via these methods exists. This research proposes the development and calibration of recurrent neural network models and their ensemble, which can forecast air quality in terms of PM2.5 concentration 6 hours ahead in time. For forecasting air quality, a vanilla-LSTM, a stack-LSTM, a bidirectional-LSTM, a CNN-LSTM, and an ensemble of individual LSTM models were trained on the UCI Machine Learning Beijing dataset. Data were split into two parts, where 80% of data were used for training the models, while the remaining 20% were used for validating the models. For comparative analysis, four regression losses were calculated, namely root mean squared error, mean absolute percentage error, mean absolute error and Pearson’s correlation coefficient. Results revealed that among all models, the ensemble model performed the best in predicting the PM2.5 concentrations. Furthermore, the ensemble model outperformed other models reported in literature by a long margin. Among the individual models, the bidirectional-LSTM performed the best. We highlight the implications of this research on long-term forecasting of air quality via recurrent and ensemble techniques.

scholarly journals Robust Skin Disease Classification by Distilling Deep Neural Network Ensemble for the Mobile Diagnosis of Herpes Zoster

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 20156-20169
Author(s):  
Seunghyeok Back ◽  
Seongju Lee ◽  
Sungho Shin ◽  
Yeonguk Yu ◽  
Taekyeong Yuk ◽  
...  
Keyword(s):  
Neural Network ◽  
Herpes Zoster ◽  
Skin Disease ◽  
Deep Neural Network ◽  
Disease Classification ◽  
Neural Network Ensemble

scholarly journals Ensemble Model for Chunking

2021 ◽  
Author(s):  
Nilamadhaba Mohapatra ◽  
Namrata Sarraf ◽  
Swapna sarit Sahu
Keyword(s):  
Neural Network ◽  
Natural Language ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
Statistical Models ◽  
Neural Nets ◽  
Language Models ◽  
Ensemble Model ◽  
Neural Net ◽  
Transformer Model

Transformer Models have taken over most of the Natural language Inference tasks. In recent times they have proved to beat several benchmarks. Chunking means splitting the sentences into tokens and then grouping them in a meaningful way. Chunking is a task that has gradually moved from POS tag-based statistical models to neural nets using Language models such as LSTM, Bidirectional LSTMs, attention models, etc. Deep neural net Models are deployed indirectly for classifying tokens as different tags defined under Named Recognition Tasks. Later these tags are used in conjunction with pointer frameworks for the final chunking task. In our paper, we propose an Ensemble Model using a fine-tuned Transformer Model and a recurrent neural network model together to predict tags and chunk substructures of a sentence. We analyzed the shortcomings of the transformer models in predicting different tags and then trained the BILSTM+CNN accordingly to compensate for the same.

scholarly journals Evaluation of psoriasis skin disease classification using convolutional neural network

2020 ◽  
Vol 9 (2) ◽  
pp. 349
Author(s):  
Rosniza Binti Roslan ◽  
Iman Najwa Mohd Razly ◽  
Nurbaity Sabri ◽  
Zaidah Ibrahim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Skin Disease ◽  
Skin Diseases ◽  
Human Vision ◽  
Disease Classification ◽  
Immune Mediated ◽  
Image Library ◽  
Guttate Psoriasis

<span lang="EN-GB">Skin disease has lower impact on mortality compared to others but instead it has greater effect on quality of life because it involves symptoms such as pain, stinging and itchiness.  Psoriasis is one of the ordinary skin diseases which are relapsing, chronic and immune-mediated inflammatory disease.  It is estimated about 125 million people worldwide being infected with various types of skin infection.  </span><span lang="EN-GB">Challenges arise when patients only predict the skin type disease they had without being accurately and precisely examined.  This is because as human being, they only observe and look at the diseases on the surface of the skin with their naked eye, where there are some limits, for example, human vision lacks of accuracy, reproducibility and quantification in the collection of image information.  As Plaque and Guttate are the most common Psoriasis skin disease happened among people, this paper presents an evaluation of Psoriasis skin disease classification using Convolutional Neural Network.  A total of 187 images which consist of 82 images for Plaque Psoriasis and 105 images for Guttate Psoriasis has been used which are retrieved from Psoriasis Image Library, International Psoriasis Council (IPC) and DermNet NZ.  Convolutional Neural Network (CNN) is applied in extracting features and analysing the classification of Psoriasis skin disease.  This paper showed the promising used of CNN with the accuracy rate of 82.9% and 72.4% for Plaque and Guttate Psoriasis skin disease, respectively.</span>

scholarly journals Discriminative Feature Learning for Skin Disease Classification Using Deep Convolutional Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 39025-39033 ◽  
Author(s):  
Belal Ahmad ◽  
Mohd Usama ◽  
Chuen-Min Huang ◽  
Kai Hwang ◽  
M. Shamim Hossain ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Skin Disease ◽  
Feature Learning ◽  
Deep Convolutional Neural Network ◽  
Disease Classification ◽  
Discriminative Feature
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