Effective text classification using multi-level fuzzy neural network

Zobeidi, Shima; Naderan, Marjan; Alavi, Seyed Mohammad

doi:10.1109/cfis.2017.8003664

Cited by 10 publications

(4 citation statements)

References 16 publications

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“…of prediction values yi denotes the m no. of the actual values Table 3 shows the comparison of proposed and existing approach [11]. Figure 3 clearly defines the comparison between the accuracy of the existing approach and proposed approach.…”

Section: Results Analysismentioning

confidence: 99%

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An efficient technique for hybrid classification and feature extraction using normalization

Kaur¹,

Bathla²

2018

IJET

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Text classification is technique for assigning the class or label to a particular document within predefined class labels. Predefined classes examples are sports, business, technical, education and science etc. Classification is supervised learning technique i.e. these classes are trained with certain features and then document is classified based on similarity measure with these trained document set. Text classification is used in many applications like assigning the label to the documents, separating the spam messages from the genuine one, filtering of text, natural language processing etc. Feature selection, extraction and classification are various phases for assigning label to any document. In this paper, PCA is used for feature extraction, ABC is used for feature selection and SVM is used for classification. PCA is improved by applying normalization-using size of features in our proposed approach. It reduces the redundant features to larger extent. There are very few research works, which have implemented PCA, ABC and SVM for complete classification. Evaluation parameters like accuracy, F-measure and G-mean are calculated to check classifier efficiency. The proposed system is deployed on 20-Newsgroup dataset. Experiment analysis proves that accuracy is improved using our proposed approach as compared to existing approaches.

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Section: Results Analysismentioning

confidence: 99%

“…Zobeidi et al [11] stated a novel approach of classification in which PCA is used for the feature extraction and finally for classification ,MLF is used . The data sets used were 20 newsgroup and Reuters-21578.…”

Section: Literature Surveymentioning

confidence: 99%

An efficient technique for hybrid classification and feature extraction using normalization

Kaur¹,

Bathla²

2018

IJET

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“…The individual visual of artificial fish and genetic operator in PSO is incorporated to avoid the maximum local trap. This study (Zobeidi et al, 2017) introduces a filtering based feature ranking and selection approach to reduce dimensionality. This approach combines and uses three methods such as information gain, chi-square statistic and inter-correlation.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Approach for Emotion Recognition Analysis in Text Streams

Liu

Kirubakaran

J.³

2022

International Journal of Technology and Human Interaction

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Social media sites employ various approaches to track feelings, including diagnosing neurological problems, including fear, in people or assessing a population public sentiment. One essential obstacle for automatic emotion recognition principles is variable with fluctuating limitations, language, and interpretation shifts. Therefore, in this paper, a deep learning-based emotion recognition (DL-EM) system has been proposed to describe the various relational effects in emotional groups. A soft classification method is suggested to quantify the tendency and allocate a message to each emotional class. A supervised framework for emotions in text streaming messages is developed and tested. Two of the major activities are offline teaching assignments and interactive emotion classification techniques. The first challenge offers templates in text responses to describe sentiment. The second activity includes implementing a two-stage framework to identify live broadcasts of text messages for dedicated emotion monitoring.

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“…Hossain and Rahaman [11] classified malignant and benign bone cancer using a adaptive neuro fuzzy inference system (ANFIS). In order to train and test ANFIS network, from MR images, extracted.…”

Section: Related Workmentioning

confidence: 99%

A Critical Review on Medical Image Processing Techniques

2020

jcr

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Medical image processing is highly a vital task in the field of health informatics. Various techniques are used to process the images captured using methods like Medical Resonance Imaging (MRI), Computed Tomography(CT), Positron Emission Tomography (PET) and the process includes, denoising, feature extraction, segmentation and classification. Different methods are implemented for every process. Process of eliminating noise from an acquired image is termed as denoising and various filters are designed for the same. Different features like local binary pattern, gray level similarity are extracted for segmentation process. Clustering algorithms like fuzzy-c-means, fuzzy-k-means algorithms are used for image segmentation. Particle Swarm Optimization (PSO), convolutional neural networks are also used for the purpose of image segmentation. Performance of these methods are compared using various performance evaluation metrics like, precision, recall, accuracy, sensitivity.

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Effective text classification using multi-level fuzzy neural network

Cited by 10 publications

References 16 publications

An efficient technique for hybrid classification and feature extraction using normalization

An efficient technique for hybrid classification and feature extraction using normalization

Deep Learning Approach for Emotion Recognition Analysis in Text Streams

A Critical Review on Medical Image Processing Techniques

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