Hybrid Fuzzy Neural Search Retrieval System

Automatic keywords extraction and classification tasks are important research directions in the domains of NLP (natural language processing), information retrieval, and text mining. As the fine granularity abstracted from text data, keywords are also the most important feature of text data, which has great practical and potential value in document classification, topic modeling, information retrieval, and other aspects. The compact representation of documents can be achieved through keywords, which contains massive significant information. Therefore, it may be quite advantageous to realize text classification with high-dimensional feature space. For this reason, this study designed a supervised keyword classification method based on TextRank keyword automatic extraction technology and optimize the model with the genetic algorithm to contribute to modeling the keywords of the topic for text classification.

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Measuring Waste Recyclability Level Using Convolutional Neural Network and Fuzzy Inference System

Ghnemat

Shaout

2022

International Journal of Intelligent Information Technologies

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This paper presents a hybrid model that is used to measure the waste recyclability level using a convolutional neural network (CNN) and fuzzy inference system (FIS; WRL-CNNFIS). The proposed system uses waste images to train a multilayer convolutional neural network to extract the most relevant features that were used in a rule-based fuzzy system to give an accurate percentage of the recyclability level of these images. The proposed model did overcome many challenges in transfer learning models alone, like overfitting and low accuracy. The use of fuzzy rules, improved the performance even with a small data set, Results have shown the effectiveness of the proposed model in terms of all four metrics: accuracy, precision, recall, and F1 score. The performance was measured under two testing scenarios. For all evaluation measurement, in all experiments the validation was conducted using the cross validation in the last step. The proposed approach is a robust and consistent approach for classifying organic and recyclable waste types. WRL-CNNFIS has achieved accuracy rate of more than 98%.

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Hybrid Fuzzy Neural Search Retrieval System

Cited by 3 publications

References 34 publications

Enterprise Information retrieval using Collaborative Fusion and Active Feedback

Enterprise Information retrieval using Collaborative Fusion and Active Feedback

Research on Text Classification Based on Automatically Extracted Keywords

Measuring Waste Recyclability Level Using Convolutional Neural Network and Fuzzy Inference System

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