A Novel Deep Learning Approach of Convolutional Neural Network and Random Forest Classifier for Fine-grained Sentiment Classification

Sentiment analysis is one of the active research areas in the field of datamining. Machine learning algorithms are capable to implement sentiment analysis. Due to the capacity of self-learning and massive data handling, most of the researchers are using deep learning neural networks for solving sentiment classification tasks. So, in this paper, a new model is designed under a hybrid framework of machine learning and deep learning which couples Convolutional Neural Network and Random Forest classifier for fine-grained sentiment analysis. The Continuous Bag-of-Word (CBOW) model is used to vectorize the text input. The most important features are extracted by the Convolutional Neural Network (CNN). The extracted features are used by the Random Forest(RF) classifier for sentiment classification. The performance of the proposed hybrid CNNRF model is comparedwith the base model such as Convolutional Neural Network (CNN) and Random Forest (RF) classifier. The experimental result shows that the proposed model far beat the existing base models in terms of classification accuracy and effectively integrated genetically-modified CNN with Random Forest classifier.

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Deep Learning Approach to Parkinson’s Disease Detection Using Voice Recordings and Convolutional Neural Network Dedicated to Image Classification

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ◽

10.1109/embc.2019.8856972 ◽

2019 ◽

Cited By ~ 2

Author(s):

Marek Wodzinski ◽

Andrzej Skalski ◽

Daria Hemmerling ◽

Juan Rafael Orozco-Arroyave ◽

Elmar Noth

Keyword(s):

Neural Network ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Deep Learning ◽

Convolutional Neural Network ◽

Image Classification ◽

Disease Detection ◽

Learning Approach

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Deep Learning-Based Classification of Weld Surface Defects

Applied Sciences ◽

10.3390/app9163312 ◽

2019 ◽

Vol 9 (16) ◽

pp. 3312 ◽

Cited By ~ 4

Author(s):

Zhu ◽

Ge ◽

Liu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Random Forest ◽

Convolutional Neural Network ◽

Surface Defects ◽

Recognition Method ◽

High Level ◽

Intelligent Recognition ◽

Non Destructive

In order to realize the non-destructive intelligent identification of weld surface defects, an intelligent recognition method based on deep learning is proposed, which is mainly formed by convolutional neural network (CNN) and forest random. First, the high-level features are automatically learned through the CNN. Random forest is trained with extracted high-level features to predict the classification results. Secondly, the weld surface defects images are collected and preprocessed by image enhancement and threshold segmentation. A database of weld surface defects is established using pre-processed images. Finally, comparative experiments are performed on the weld surface defects database. The results show that the accuracy of the method combined with CNN and random forest can reach 0.9875, and it also demonstrates the method is effective and practical.

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Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach

Surgical Endoscopy ◽

10.1007/s00464-019-07281-0 ◽

2019 ◽

Vol 34 (11) ◽

pp. 4924-4931 ◽

Cited By ~ 10

Author(s):

Daichi Kitaguchi ◽

Nobuyoshi Takeshita ◽

Hiroki Matsuzaki ◽

Hiroaki Takano ◽

Yohei Owada ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Real Time ◽

Learning Approach ◽

Laparoscopic Sigmoidectomy ◽

Phase Recognition

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Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach

Procedia Computer Science ◽

10.1016/j.procs.2018.05.069 ◽

2018 ◽

Vol 132 ◽

pp. 679-688 ◽

Cited By ~ 39

Author(s):

Sakshi Indolia ◽

Anil Kumar Goswami ◽

S.P. Mishra ◽

Pooja Asopa

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Conceptual Understanding ◽

Learning Approach

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Utilização de técnicas de Machine Learning e de Deep Learning para a predição de casos de internações causadas por dengue em municípios da Paraíba

10.5753/ercemapi.2021.17914 ◽

2021 ◽

Author(s):

Ewerthon Dyego de Araújo Batista ◽

Wellington Candeia de Araújo ◽

Romeryto Vieira Lira ◽

Laryssa Izabel de Araújo Batista

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Random Forest ◽

Convolutional Neural Network ◽

Support Vector Regression ◽

Multilayer Perceptron ◽

Support Vector

Dengue é um problema de saúde pública no Brasil, os casos da doença voltaram a crescer na Paraíba. O boletim epidemiológico da Paraíba, divulgado em agosto de 2021, informa um aumento de 53% de casos em relação ao ano anterior. Técnicas de Machine Learning (ML) e de Deep Learning estão sendo utilizadas como ferramentas para a predição da doença e suporte ao seu combate. Por meio das técnicas Random Forest (RF), Support Vector Regression (SVR), Multilayer Perceptron (MLP), Long ShortTerm Memory (LSTM) e Convolutional Neural Network (CNN), este artigo apresenta um sistema capaz de realizar previsões de internações causadas por dengue para as cidades Bayeux, Cabedelo, João Pessoa e Santa Rita. O sistema conseguiu realizar previsões para Bayeux com taxa de erro 0,5290, já em Cabedelo o erro foi 0,92742, João Pessoa 9,55288 e Santa Rita 0,74551.

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