Novel favipiravir pattern-based learning model for automated detection of specific language impairment disorder using vowels.

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Neural Computing & Applications Pub Date : 2023-01-01 Epub Date: 2022-11-13 DOI:10.1007/s00521-022-07999-4
Prabal Datta Barua, Emrah Aydemir, Sengul Dogan, Mehmet Erten, Feyzi Kaysi, Turker Tuncer, Hamido Fujita, Elizabeth Palmer, U Rajendra Acharya
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引用次数: 0

Abstract

Specific language impairment (SLI) is one of the most common diseases in children, and early diagnosis can help to obtain better timely therapy economically. It is difficult and time-consuming for clinicians to accurately detect SLI through standard clinical assessments. Hence, machine learning algorithms have been developed to assist in the accurate diagnosis of SLI. This work aims to investigate the graph of the favipiravir molecule-based feature extraction function and propose an accurate SLI detection model using vowels. We proposed a novel handcrafted machine learning framework. This architecture comprises the favipiravir molecular structure pattern, statistical feature extractor, wavelet packet decomposition (WPD), iterative neighborhood component analysis (INCA), and support vector machine (SVM) classifier. Two feature extraction models, statistical and textural, are employed in the handcrafted feature generation methodology. A new nature-inspired graph-based feature extractor that uses the chemical depiction of the favipiravir (favipiravir became popular with the COVID-19 pandemic) is employed for feature extraction. Finally, the proposed favipiravir pattern, statistical feature extractor, and wavelet packet decomposition are used to create a feature vector. Moreover, a statistical feature extractor is used in this work. The WPD generates multilevel features, and the most meaningful features are selected using the NCA feature selector. Finally, these chosen features are fed to SVM classifier for automated classification. Two validation methods, (i) leave one subject out (LOSO) and (ii) tenfold cross-validations (CV), are used to obtain robust classification results. Our proposed favipiravir pattern-based model developed using a vowel dataset can detect SLI children with an accuracy of 99.87% and 98.86% using tenfold and LOSO CV strategies, respectively. These results demonstrated the high vowel classification ability of the proposed favipiravir pattern-based model.

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利用元音自动检测特定语言障碍症的新型法非拉韦模式学习模型。
特殊语言障碍(SLI)是儿童最常见的疾病之一,早期诊断有助于及时获得更经济的治疗。临床医生很难通过标准的临床评估准确检测出特殊语言障碍,而且耗时较长。因此,人们开发了机器学习算法来帮助准确诊断 SLI。这项工作旨在研究基于法非拉韦分子的特征提取函数图,并利用元音提出一种准确的 SLI 检测模型。我们提出了一个新颖的手工机器学习框架。该架构由法比拉韦分子结构模式、统计特征提取器、小波包分解(WPD)、迭代邻域成分分析(INCA)和支持向量机(SVM)分类器组成。手工特征生成方法采用了统计和纹理两种特征提取模型。在特征提取时,采用了一种新的基于自然启发的图谱特征提取器,该特征提取器使用了法非拉韦(法非拉韦因 COVID-19 大流行而流行)的化学描述。最后,利用所提出的法非吡拉韦模式、统计特征提取器和小波包分解来创建特征向量。此外,这项工作还使用了统计特征提取器。小波包分解生成多级特征,并使用 NCA 特征选择器选出最有意义的特征。最后,将这些选定的特征输入 SVM 分类器进行自动分类。为了获得稳健的分类结果,我们采用了两种验证方法:(i) 撇除一个对象 (LOSO) 和 (ii) 十倍交叉验证 (CV)。我们提出的基于法非拉韦模式的模型是利用元音数据集开发的,采用十倍交叉验证和LOSO交叉验证策略,检测SLI儿童的准确率分别为99.87%和98.86%。这些结果证明了所提出的基于法非吡韦模式的模型具有很高的元音分类能力。
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来源期刊
Neural Computing & Applications
Neural Computing & Applications 工程技术-计算机:人工智能
CiteScore
11.40
自引率
8.30%
发文量
1280
审稿时长
6.9 months
期刊介绍: Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.
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