Hierarchy based firefly optimized K-means clustering for complex question answering

Reddy, A. Chandra Obula; Madhavi, K. Reddy

doi:10.11591/ijeecs.v17.i1.pp264-272

Cited by 6 publications

(6 citation statements)

References 21 publications

(21 reference statements)

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“…The program contains data that can produce answers to questions that are submitted by users. Thus, a resulting semantic question-and-answer system was developed, and the resulting words are not certain to be in the form of questions [13]. The application of chatbots in a question-and-answer system is expected to answer these challenges.…”

Section: Methodsmentioning

confidence: 99%

Comparative Analysis of Performance between Multimodal Implementation of Chatbot Based on News Classification Data Using Categories

2021

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In the modern era, the implementation of chatbot can be used in various fields of science. This research will focus on the application of sentence classification using the News Aggregator Dataset that is used to test the model against the categories determined to create the chatbot program. The results of the chatbot program trial by multimodal implementation applied four models (GRU, Bi-GRU, 1D CNN, 1D CNN Transpose) with six variations of parameters to produce the best results from the entire trial. The best test results from this research for the chatbot program using the 1D CNN Transpose model are the best models with detailed characteristics in this research, which produces an accuracy value of 0.9919. The test results on both types of chatbot are expected to produce sentence prediction results and precise and accurate detection results. The stages in making the program are explained in detail; therefore, it is hoped that program users can understand not only how to use the program by entering an input and receiving program output results that are explained in more detail in each sub-topic of this study.

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Section: Methodsmentioning

confidence: 99%

Comparative Analysis of Performance between Multimodal Implementation of Chatbot Based on News Classification Data Using Categories

2021

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“…Question-answering (QA) systems in information retrieval are tasks that automatically answer the questions asked by humans in natural language using either a pre-structured database or a collection of natural language documents [7], [8]. In order to better understand what QA systems are, the associated terminology is firstly provided, namely Question Phrase, Question Type, Answer Type and Question Focus.…”

Section: Basic Concepts 21 Questionn Answering Systemmentioning

confidence: 99%

An ontology driven question answering system for fatawa retrieval

Benlaharche

Laboudi

Nouaouria

et al. 2021

IJEECS

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This work aims to propose a system for the Algerian Fatawa House in orderto facilitate the task of the Expert Mufti who is responsible of giving fatawa for Algerian people inquiries. In fact, as this house is recent and does not have sufficient human resources, it is difficult to satisfy all inquiries coming daily, this leads the askers to wait for a long time before getting answers. The proposed system allows the askers to express concerns they may have. By using a case-based reasoning mechanism combined with ontology domain, the system tries to retrieve similar cases from the knowledge base. In the casewhere the response already exists, the system immediately provides the answer to the askers. Otherwise, an inquery is automatically formulated and sent to the expert Mufti-which is a certified scholar-in order either to validate the generated response by the system or give a new answer. Such a question-answering system may be very helpful for askers to get their answers faster since it allows both the storage of previous <em>fatawas</em> and their retrieval for processing coming inquiries. To validate our proposal, we rely on <em>fatawas</em> concerning the Islamic finance and banking transactions domain. Overall, the results are encouraging and satisfactory.

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“…The feature analysis on each channel uses k-mean clustering, the data for each feature in the word sample is calculated for the cluster center [23], [28]. Identification determines whether a sample of words is identical or not by comparing the distance to the center of the cluster.…”

Section: K-mean Clusteringmentioning

confidence: 99%

Feature extraction with mel scale separation method on noise audio recordings

Huizen

Kurniati

2021

IJEECS

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This paper focuses on improving the accuracy of noise audio recordings. High-quality audio recording, extraction using the mel frequency cepstral coefficients (MFCC) method produces high accuracy. While the low-quality is because of noise, the accuracy is low. Improved accuracy by investigating the effect of bandwidth on the mel scale. The proposed improvement uses the mel scale separation methods into two frequency channels (MFCC dual-channel). For the comparison method using the mel scale bandwidth without separation (MFCC single-channel). Feature analysis using k-mean clustering. The data uses a noise variance of up to -16 dB. Testing on the MFCC single-channel method for -16 dB noise has an accuracy of 47.5%, while the MFCC dual-channel method has an accuracy better of 76.25%. The next test used adaptive noise-canceling (ANC) to reduce noise before extraction. The result is that the MFCC single-channel method has an accuracy of 82.5% and the MFCC dual-channel method has an accuracy better of 83.75%. High-quality audio recording testing for the MFCC single-channel method has an accuracy of 92.5% and the MFCC dual-channel method has an accuracy better of 97.5%. The test results show the effect of mel scale bandwidth to increase accuracy. The MFCC dual-channel method has higher accuracy.

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Hierarchy based firefly optimized K-means clustering for complex question answering

Cited by 6 publications

References 21 publications

Comparative Analysis of Performance between Multimodal Implementation of Chatbot Based on News Classification Data Using Categories

Comparative Analysis of Performance between Multimodal Implementation of Chatbot Based on News Classification Data Using Categories

An ontology driven question answering system for fatawa retrieval

Feature extraction with mel scale separation method on noise audio recordings

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