Comparative Study of Truncating and Statistical Stemming Algorithms

Memon, Sanaullah; Mallah, Ghulam Ali; K.N.Memon,; Shaikh, AG; K.Aasoori, Sunny; Dehraj, Faheem Ul Hussain

doi:10.14569/ijacsa.2020.0110272

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…Moreover, the relationship between these two is dependent. Compared to SVM, CNN performs better when classifying images, thus, we used it [22,24,27,28].…”

Section: Discussionmentioning

confidence: 99%

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Detection of Crackle and Wheeze in Lung Sound using Machine Learning Technique for Clinical Decision Support System

Ali,

Asif,

Rashid

et al. 2023

VAWKUM trans. comput. sci.

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This study aims to develop a computer-based clinical decision support system that will help clinicians and healthcare personnel to make an early and correct decision to prevent the patient from nontransmissible respiratory diseases. The main contribution of this study is to analyze, investigate, and extraction of the useful feature of pathological respiration and Classification of Crackle and Wheeze from recorded lungs sound by using machine learning techniques. In the particular spectrogram, Time-frequency and Mel-Frequency cepstral coefficient (MFCC)technique is applied for feature analysis and data conversion into a format that can be useful for feature extraction and training models. PCA dimensional reduction technique is used to reduce the dimensionality of the extracted feature. In order to apply various machine learning techniques a widely used dataset freely available dataset ICBHI-2017 is used. The respiratory lungs sound is comprised of 126 patients with 920 Chest sound annotations that include adventitious sounds such as “Crackle” and “Wheeze”. Machine learning algorithms such as MusicANN, VGGish, and OpenL3 were applied for testing the better accuracy of the classification model. The accuracy of the utilized classifier with the extracted feature set is determined as 72%, 81%, and 69% respectively.

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“…Moreover, the relationship between these two is dependent. Compared to SVM, CNN performs better when classifying images, thus, we used it [22,24,27,28].…”

Section: Discussionmentioning

confidence: 99%

“…The study found that the OpenL3 embeddings improved the performance of the machine learning model compared to traditional signal processing techniques. [21,22]…”

Section: Classification Of Respiratory Lungs Sound Using Openl3mentioning

confidence: 99%

Detection of Crackle and Wheeze in Lung Sound using Machine Learning Technique for Clinical Decision Support System

Ali,

Asif,

Rashid

et al. 2023

VAWKUM trans. comput. sci.

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show abstract

“…This dataset is an integral part of training the chatbot model to understand everyday language, even when using informal expressions. After gathering the necessary data, the preprocessing stage is implemented using natural language processing approaches, including case folding, tokenizing, word normalization, filtering, and vectorization, to prepare the dataset for model training [11]- [13].…”

Section: A Data Collection and Preprocessingmentioning

confidence: 99%

Enhancing Durrotalk Chatbot Accuracy Utilizing a Hybrid Model Based on Recurrent Neural Network (RNN) Algorithm and Decision Tree

Darmawan,

Arifudin

2024

JUITA

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DurroTalk, a chatbot for new student admissions at Pondok Pesantren Durrotu Ahlissunnah Waljamaah, Semarang, integrates a hybrid model with Recurrent Neural Network (RNN) and Decision Tree. RNN, the base model, employs Natural Language Processing (NLP) to understand sentence structure and context, overcoming vanishing gradient through LSTM layers. The Decision Tree normalizes words, addressing slang and synonyms. The hybrid model boosts chatbot accuracy by 9%, reaching 77% from the initial 68%. This research signifies progress in integrating artificial intelligence into traditional education, showcasing a chatbot adept at handling non-standard language. Decision Tree integration enhances overall performance, making the chatbot proficient in understanding user inputs and generating contextually relevant responses. This study exemplifies the potential of AI, particularly chatbot technology, in modernizing educational processes at traditional institutions.

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“…Therefore, the word after processing by the stemming algorithm may differ from the morphological root of the word. Stemming is used in linguistic morphology and information retrieval [16]. Many search systems use stemming to establish synonymous relationships if they have the same forms after stemming.…”

Section: Using Stems To Form An Author Profilementioning

confidence: 99%

Methods and software for significant indicators determination of the natural language texts author profile

Shynkarenko,

Demydovych

2023

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Methods for the formation and optimization of author profiles are presented. The author profile is an image – a vector in a multidimensional space, which components are author’s texts measurements by a number of methods based on 4-grams, stemming, recurrence analysis and formal stochastic grammar. The author’s profile is a model of his language, including vocabulary, sentence syntax features. A comparative analysis of each of the methods effectiveness is carried out. By means of the genetic algorithm, a reduced profile of the author is formed. Insignificant indicators are excluded, which allows to reduce their number by 20%. The reduced author’s profile contains attributes that are significant for this author and is an effective attribution of a particular author.

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Comparative Study of Truncating and Statistical Stemming Algorithms

Cited by 6 publications

References 10 publications

Detection of Crackle and Wheeze in Lung Sound using Machine Learning Technique for Clinical Decision Support System

Detection of Crackle and Wheeze in Lung Sound using Machine Learning Technique for Clinical Decision Support System

Enhancing Durrotalk Chatbot Accuracy Utilizing a Hybrid Model Based on Recurrent Neural Network (RNN) Algorithm and Decision Tree

Methods and software for significant indicators determination of the natural language texts author profile

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