Int. J. Knowl. Based Intell. Eng. Syst.最新文献_第3页

Rigorous assessment of data mining algorithms in gestational diabetes mellitus prediction 数据挖掘算法在妊娠期糖尿病预测中的严谨评估

Int. J. Knowl. Based Intell. Eng. Syst. Pub Date : 2022-02-18 DOI: 10.3233/kes-210081

S. Reddy, Nilambar Sethi, R. Rajender

{"title":"Rigorous assessment of data mining algorithms in gestational diabetes mellitus prediction","authors":"S. Reddy, Nilambar Sethi, R. Rajender","doi":"10.3233/kes-210081","DOIUrl":"https://doi.org/10.3233/kes-210081","url":null,"abstract":"Gestational diabetes mellitus (GDM) is the type of diabetes that affects pregnant women due to high blood sugar levels. The women with gestational diabetes have a chance of miscarriage during pregnancy and having chance of developing type-2 diabetes in the future. It is a general practice to take proper diabetes test like OGTT to detect GDM. This test is to be done during 24 to 28 weeks of pregnancy. In addition, the use of machine learning could be exploited for predicting gestational diabetes. The main goal of this work is to propose optimal ML algorithms for effective prediction of gestational diabetes mellitus and there by avoid it’s side effects and future complications. In this work different machine algorithms are planned to be compared for their performance in predicting GDM. Before analysing the algorithms they are implemented using 10 fold cross validation technique to obtain better performance. The algorithms implemented are Linear Discriminant Analysis, Mixture Discriminant Analysis, Quadratic Discriminant Analysis, Flexible Discriminant Analysis, Regularized Discriminant Analysis and Feed Forward Neural Networks. These algorithms are compared depending on performance measures accuracy, kappa statistic, sensitivity, specificity, precision and F-measure. Then feed forward neural networks and Flexible Discriminant Analysis are obtained as optimal in this work.","PeriodicalId":210048,"journal":{"name":"Int. J. Knowl. Based Intell. Eng. Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130149220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Algorithms for multiple attribute decision making with dual hesitant Pythagorean fuzzy information and their application to supplier selection 双犹豫毕达哥拉斯模糊信息下的多属性决策算法及其在供应商选择中的应用

Int. J. Knowl. Based Intell. Eng. Syst. Pub Date : 2022-02-18 DOI: 10.3233/kes-210089

Xuyang Li

引用次数: 0

Models for multiple attribute decision making with dual hesitant pythagorean fuzzy information 具有双犹豫毕达哥拉斯模糊信息的多属性决策模型

Int. J. Knowl. Based Intell. Eng. Syst. Pub Date : 2022-02-18 DOI: 10.3233/kes-210085

Linggang Ran

引用次数: 0

Research on the fire safety assessment of high building with intuitionistic fuzzy TOPSIS method 基于直觉模糊TOPSIS法的高层建筑消防安全评价研究

Int. J. Knowl. Based Intell. Eng. Syst. Pub Date : 2022-02-18 DOI: 10.3233/kes-210084

Mingbiao Xu, Dehong Peng

{"title":"Research on the fire safety assessment of high building with intuitionistic fuzzy TOPSIS method","authors":"Mingbiao Xu, Dehong Peng","doi":"10.3233/kes-210084","DOIUrl":"https://doi.org/10.3233/kes-210084","url":null,"abstract":"In our country, fire from high-rise buildings happens from time to time and produced economic loss could be well over 100 billion that will give rise to great harm to safety of the people’s life and property even to the development of the whole national economy. In the fire protection work for High-rise building’s safety assessment, prevention, cause and salvation, all of this controlled by multiple factors. With their different attributes, fire problem and management work became more complex and even harder. When we want to integration and judgment the information which including many main bodies that with multi-aspect and evaluation index characteristics to be evaluated, Fuzzy mathematics evaluation method can help us. It can consider fully the fuzzy and uncertain in the assessment. For the problems in high-rise Building fire protection management. And it is frequently viewed as a multi-attribute group decision-making (MAGDM) issue. Thus, a novel MAGDM method is needed to tackle it. Depending on the conventional TOPSIS (Technique for Order Preferenceby Similarity to Ideal Solution) method and intuitionistic fuzzy sets (IFSs), this essay designs a novel intuitive distance based IF-TOPSIS method for fire safety assessment based on the high building. Afterwards, relying on novel distance measures between IFNs, the conventional TOPSIS method is extended to the intuitionistic fuzzy environment to calculate assessment score of each alternative. Eventually, an application about fire safety assessment based on the high building and some comparative analysis have been given to demonstrate the superiority of the designed method. The results illustrate that the designed framework is useful for fire safety assessment based on the high building.","PeriodicalId":210048,"journal":{"name":"Int. J. Knowl. Based Intell. Eng. Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121264976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Multimodal biometric identification system with deep learning based feature level fusion using maximum orthogonal method 基于最大正交法的深度学习特征融合多模态生物识别系统

Int. J. Knowl. Based Intell. Eng. Syst. Pub Date : 2022-02-18 DOI: 10.3233/kes-210086

P. Shende, Y. Dandawate

引用次数: 1

An efficient and precise dynamic slicing for concurrent component-oriented programs 面向并发组件程序的高效精确动态切片

Int. J. Knowl. Based Intell. Eng. Syst. Pub Date : 2022-02-18 DOI: 10.3233/kes-210088

N. Pujari, Abhishek Ray, Jagannath Singh

引用次数: 0

A systematic analysis of identity based encryption (IBE) 基于身份的加密(IBE)系统分析

Int. J. Knowl. Based Intell. Eng. Syst. Pub Date : 2021-11-15 DOI: 10.3233/kes-210078

Aravind Karrothu, J. Norman

引用次数: 2

Machine hearing system for teleconference authentication with effective speech analysis 具有有效语音分析的电话会议认证机器听力系统

Int. J. Knowl. Based Intell. Eng. Syst. Pub Date : 2021-11-10 DOI: 10.3233/kes-210079

T. Madhusudhana Rao, Suribabu Korada, Yarramalle Srinivas

引用次数: 0

A variant of SWEMDH technique based on variational mode decomposition for speech enhancement 一种基于变分模态分解的语音增强SWEMDH技术

Int. J. Knowl. Based Intell. Eng. Syst. Pub Date : 2021-11-10 DOI: 10.3233/kes-210072

P. Selvaraj, E. Chandra

{"title":"A variant of SWEMDH technique based on variational mode decomposition for speech enhancement","authors":"P. Selvaraj, E. Chandra","doi":"10.3233/kes-210072","DOIUrl":"https://doi.org/10.3233/kes-210072","url":null,"abstract":"In Speech Enhancement (SE) techniques, the major challenging task is to suppress non-stationary noises including white noise in real-time application scenarios. Many techniques have been developed for enhancing the vocal signals; however, those were not effective for suppressing non-stationary noises very well. Also, those have high time and resource consumption. As a result, Sliding Window Empirical Mode Decomposition and Hurst (SWEMDH)-based SE method where the speech signal was decomposed into Intrinsic Mode Functions (IMFs) based on the sliding window and the noise factor in each IMF was chosen based on the Hurst exponent data. Also, the least corrupted IMFs were utilized to restore the vocal signal. However, this technique was not suitable for white noise scenarios. Therefore in this paper, a Variant of Variational Mode Decomposition (VVMD) with SWEMDH technique is proposed to reduce the complexity in real-time applications. The key objective of this proposed SWEMD-VVMDH technique is to decide the IMFs based on Hurst exponent and then apply the VVMD technique to suppress both low- and high-frequency noisy factors from the vocal signals. Originally, the noisy vocal signal is decomposed into many IMFs using SWEMDH technique. Then, Hurst exponent is computed to decide the IMFs with low-frequency noisy factors and Narrow-Band Components (NBC) is computed to decide the IMFs with high-frequency noisy factors. Moreover, VVMD is applied on the addition of all chosen IMF to remove both low- and high-frequency noisy factors. Thus, the speech signal quality is improved under non-stationary noises including additive white Gaussian noise. Finally, the experimental outcomes demonstrate the significant speech signal improvement under both non-stationary and white noise surroundings.","PeriodicalId":210048,"journal":{"name":"Int. J. Knowl. Based Intell. Eng. Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122161943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Speaker identification analysis for SGMM with k-means and fuzzy C-means clustering using SVM statistical technique 基于支持向量机统计技术的k-均值和模糊c -均值聚类的SGMM说话人识别分析

Int. J. Knowl. Based Intell. Eng. Syst. Pub Date : 2021-11-10 DOI: 10.3233/kes-210073

K. Manikandan, E. Chandra

引用次数: 0