Adaptive Neuro-Fuzzy Inference System for Classification of Texts

Kamil, A S; Rustamov, Samir; Clements, Mark A.; Mustafayev, Elshan

doi:10.1007/978-3-319-75408-6_6

Cited by 16 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Today, ANFIS is widely used in various fields of science and engineering and is classified as classification and prediction. The ANFIS model is able to solve classification problems in medicine (Hosseini & Zekri, 2012; Nwoye, Khor, Dlay, & Woo, 2006), text recognition (Kamil, Rustamov, Clements, & Mustafayev, 2018), engineering (Raj & Murali, 2013), color texture detection (Sengur, 2008), and water quality diagnosis (Yan, Zou, & Wang, 2010).…”

Section: Introductionmentioning

confidence: 99%

Image processing and prediction of leaf area in cereals: A comparison of artificial neural networks, an adaptive neuro‐fuzzy inference system, and regression methods

et al. 2021

View full text Add to dashboard Cite

In this study, the leaf area was estimated by different regression models and new methods of image processing by using artificial intelligence (AI) in bread (Triticum aestivum L.) and durum wheat (Triticum durum L.) and triticale (×Tritosecale Wittm. ex A. Camus) at seedling, booting, and milk development stages. Data on leaf traits in 1,000 plants of breed wheat, triticale, and durum wheat were studied. Among regression models using general data, LA = + √ , LA = + (), and LA = + (∕) models had a R 2 > 90% for all three cultivars (L and W represented length and width of leaves). For wheat bread, models + () and √ × () exhibited a good estimate of the leaf area at all stages of growth, whereas in triticale, the linear models that used the product of √ × and √ were more suitable to estimate leaf areas in booting and milk development. The multilayer perceptron (MLP) neural network modeling indicated that the 'trainlm,' 'trainlm,' and 'traincgb' algorithms with an optimal structure of 2-10-1, 2-3-1, and 2-10-1 had the least amount of the mean square error for bread wheat, triticale, and durum wheat, respectively, for each of the growth stages. Furthermore, based on the adaptive neuro-fuzzy inference system (ANFIS) method, a very accurate estimate of the leaf area was obtained for each of the growth stages. The comparison of different models in the leaf area estimation showed that unlike estimating leaf area by regression, AI-based methods did not depend on plant growth stages. In this study, we suggested the utilization of image processing and artificial intelligence in leaf area estimation. It will accelerate leaf area measurement in the field, by extending these methods and transferring it into smartphone applications.

show abstract

Section: Introductionmentioning

confidence: 99%

Image processing and prediction of leaf area in cereals: A comparison of artificial neural networks, an adaptive neuro‐fuzzy inference system, and regression methods

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Using the informative dissolved gas analysis method (DGAM) based on training with ANFIS method, an effective technique for diagnosing and classifying power transformer problems is proposed that improves robustness and the classification accuracy [64]. An experiment was carried out on a limited Twitter sample training set, and it has not been compared to the most recent top of the line for study conducted by [65], which used ANFIS to solve three separate classification problems: (1) sentence-level subjectivity detection, (2) text sentiment analysis, and (3) user intention identification in a natural language call routing system. The major purpose of the study by [65] is to prevent the use of human annotation or lexical expertise.…”

Section: Adaptive Neuro-fuzzy Inferences Systemmentioning

confidence: 99%

“…An experiment was carried out on a limited Twitter sample training set, and it has not been compared to the most recent top of the line for study conducted by [65], which used ANFIS to solve three separate classification problems: (1) sentence-level subjectivity detection, (2) text sentiment analysis, and (3) user intention identification in a natural language call routing system. The major purpose of the study by [65] is to prevent the use of human annotation or lexical expertise. In this study, the membership degree of each term is determined using trimmed ICF (inverseclass frequency).…”

Section: Adaptive Neuro-fuzzy Inferences Systemmentioning

confidence: 99%

Classification of Covid-19 misinformation on social media based on neuro-fuzzy and neural network: A systematic review

Ravichandran

Keikhosrokiani

2022

Neural Comput & Applic

View full text Add to dashboard Cite

The spread of Covid-19 misinformation on social media had significant real-world consequences, and it raised fears among internet users since the pandemic has begun. Researchers from all over the world have shown an interest in developing deception classification methods to reduce the issue. Despite numerous obstacles that can thwart the efforts, the researchers aim to create an automated, stable, accurate, and effective mechanism for misinformation classification. In this paper, a systematic literature review is conducted to analyse the state-of-the-art related to the classification of misinformation on social media. IEEE Xplore, SpringerLink, ScienceDirect, Scopus, Taylor & Francis, Wiley, Google Scholar are used as databases to find relevant papers since 2018–2021. Firstly, the study begins by reviewing the history of the issues surrounding Covid-19 misinformation and its effects on social media users. Secondly, various neuro-fuzzy and neural network classification methods are identified. Thirdly, the strength, limitations, and challenges of neuro-fuzzy and neural network approaches are verified for the classification misinformation specially in case of Covid-19. Finally, the most efficient hybrid method of neuro-fuzzy and neural networks in terms of performance accuracy is discovered. This study is wrapped up by suggesting a hybrid ANFIS-DNN model for improving Covid-19 misinformation classification. The results of this study can be served as a roadmap for future research on misinformation classification.

show abstract

“…Sentiment analysis of text for Azerbaijani language had been investigated by Aida-zade et al (2013). Multi machine learning algorithms had been applied for news classification in Azerbaijani language in Aida-zade et al, 2018). Cambria (2016) distinguishes three main approaches in the field of sentiment analysis: knowledge-based, statistical and hybrid.…”

Section: Literature Reviewmentioning

confidence: 99%

Sentiment Polarity Detection in Azerbaijani Social News Articles

Mammadli¹,

Huseynov²,

Alkaramov³

et al. 2019

Proceedings - Natural Language Processing in a Deep Learning World

Self Cite

View full text Add to dashboard Cite

Text classification field of natural language processing has been experiencing remarkable growth in recent years. Especially, sentiment analysis has received a considerable attention from both industry and research community. However, only a few research examples exist for Azerbaijani language. The main objective of this research is to apply various machine learning algorithms for determining the sentiment of news articles in Azerbaijani language. Approximately, 30.000 social news articles have been collected from online news sites and labeled manually as negative or positive according to their sentiment categories. Initially, text preprocessing was implemented to data in order to eliminate the noise. Secondly, to convert text to a more machine-readable form, BOW (bag of words) model has been applied. More specifically, two methodologies of BOW model, which are tf-idf and frequency-based model have been used as vectorization methods. Additionally, SVM, Random Forest, and Naive Bayes algorithms have been applied as the classification algorithms, and their combinations with two vectorization approaches have been tested and analyzed. Experimental results indicate that SVM outperforms other classification algorithms.

show abstract

Adaptive Neuro-Fuzzy Inference System for Classification of Texts

Cited by 16 publications

References 10 publications

Image processing and prediction of leaf area in cereals: A comparison of artificial neural networks, an adaptive neuro‐fuzzy inference system, and regression methods

Image processing and prediction of leaf area in cereals: A comparison of artificial neural networks, an adaptive neuro‐fuzzy inference system, and regression methods

Classification of Covid-19 misinformation on social media based on neuro-fuzzy and neural network: A systematic review

Sentiment Polarity Detection in Azerbaijani Social News Articles

Contact Info

Product

Resources

About