Journal of Experimental & Theoretical Artificial Intelligence最新文献_第2页

Intelligent system for solid waste classification using combination of image processing and machine learning models 结合图像处理和机器学习模型的固体废物分类智能系统

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2024-02-28 DOI: 10.1080/0952813x.2024.2323043

Hani Abu-Qdais, Nawras Shatnawi, Esra’a AL-Alamie

引用次数: 0

Sewage water management and healthcare monitoring in IoT using Optimized deep residual network 利用优化深度残差网络进行物联网污水管理和医疗监控

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2024-02-23 DOI: 10.1080/0952813x.2023.2300004

Dipali Shende, Yogesh S. Angal

引用次数: 0

LBO-MPAM: Ladybug Beetle Optimization-based multilayer perceptron attention module for segmenting the skin lesion and automatic localization LBO-MPAM：基于瓢虫甲虫优化的多层感知器注意力模块，用于分割皮损和自动定位

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2024-01-21 DOI: 10.1080/0952813x.2023.2301374

Sellam V, Kannan Natrajan, Senthil Pandi S, Sathish Kumar K

引用次数: 0

Gastric cancer classification in saliva data samples using Levy search updated rainfall hybrid deep dual-stage BILSTM 利用利维搜索更新降雨混合深度双级 BILSTM 对唾液数据样本中的胃癌进行分类

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2024-01-12 DOI: 10.1080/0952813x.2023.2301371

M. Kalimuthu, M. Ramya, S. Sreethar, N. Nandhagopal

引用次数: 0

A hybrid sequential forward channel selection method for enhancing EEG-Based emotion recognition 增强基于脑电图的情绪识别的混合顺序前向信道选择方法

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2024-01-11 DOI: 10.1080/0952813x.2023.2301367

Shyam Marjit, Parag Jyoti Das, Upasana Talukdar, Shyamanta M Hazarika

引用次数: 0

Non-invasive anaemia detection based on palm pallor video using tree-structured 3D CNN and vision transformer models 利用树状结构 3D CNN 和视觉变换器模型，基于手掌苍白视频进行无创贫血检测

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2024-01-08 DOI: 10.1080/0952813x.2023.2301401

Abhishek Kesarwani, Sunanda Das, Dakshina Ranjan Kisku, Mamata Dalui

引用次数: 0

Evolutionary multi-objective customer segmentation approach based on descriptive and predictive behaviour of customers: application to the banking sector 基于客户描述性和预测性行为的进化型多目标客户细分方法：在银行业的应用

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2023-11-17 DOI: 10.1080/0952813X.2022.2078886

Chiheb-Eddine Ben Ncir, Mohamed Ben Mzoughia, Alaa Qaffas, Bouaguel Waad

{"title":"Evolutionary multi-objective customer segmentation approach based on descriptive and predictive behaviour of customers: application to the banking sector","authors":"Chiheb-Eddine Ben Ncir, Mohamed Ben Mzoughia, Alaa Qaffas, Bouaguel Waad","doi":"10.1080/0952813X.2022.2078886","DOIUrl":"https://doi.org/10.1080/0952813X.2022.2078886","url":null,"abstract":"ABSTRACT Customer segmentation is a challenging task in marketing that aims to build homogeneous segments of customers based on their similar characteristics and activities. This problem is considered multi-objective since it requires the evaluation of several variables including descriptive and predictive characteristics of customers. However, given that most exiting segmentation methods are based on the optimisation of a single-objective function, the identification of homogeneous customer segments in terms of both predictive and descriptive variables becomes a major issue. Descriptive and predictive characteristics are usually considered as two different and independent objectives, which cannot be optimised together. To deal with this problem, we propose a multi-objective segmentation approach based on three conceptual axes: descriptive, predictive, and quality-validation. In addition to the specificity of design of the multi-objective model, our proposed approach has the specificity of directly optimising the multi-objective problem using a customised genetic algorithm that directly approximates a set of Pareto-optimal solutions. We have applied and evaluated the proposed approach in an empirical study which aims to segment bank credit card customers using their descriptive characteristics and their predictive behaviour. Obtained results have shown the ability of the proposed approach to look for effective homogeneous segments and help decision-makers propose more tailored marketing strategies.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139263514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrated framework for EMD–Boruta-LDA feature extraction and SVM classification in coal and gas outbursts 煤与瓦斯突出中 EMD-Boruta-LDA 特征提取和 SVM 分类的集成框架

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2023-11-17 DOI: 10.1080/0952813X.2022.2067248

Xuning Liu, Zhixiang Li, Zixian Zhang, Shiwu Li, Guoying Zhang

{"title":"Integrated framework for EMD–Boruta-LDA feature extraction and SVM classification in coal and gas outbursts","authors":"Xuning Liu, Zhixiang Li, Zixian Zhang, Shiwu Li, Guoying Zhang","doi":"10.1080/0952813X.2022.2067248","DOIUrl":"https://doi.org/10.1080/0952813X.2022.2067248","url":null,"abstract":"ABSTRACT Coal and gas outbursts classification has become more important than before due to the serious threat to the safety of coal production, in this paper, we proposed a novel combination model consists of feature decomposition and reconstruction, feature selection and feature extraction for classification of coal and gas outbursts. First, EMD is used to decompose the coal and gas outbursts index features into a number of different IMFS; Second, in order to find out the relevance of IMFS with regard to the features, a wrapper algorithm Boruta with the RF classifier is employed, and the IMFS which has high relevance with the feature are selected to form a new index feature, then the new obtained features construct new influencing factors that affect coal and gas outbursts; Furtherly, in order to eliminate the redundancy between the new generated features and the uncorrelation between the index features and outbursts, the LDA is used to extract the features with class differentiation. Finally, the SVM classifiers based on the optimal parameters by Bayesian optimisation algorithm is employed to evaluate the proposed feature extraction scheme. Experimental results show that the proposed comprehensive model can achieve significant performance in terms of classification accuracy and feature size compared to existing methods for coal and gas outbursts classification.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139264213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Selective AnDE based on attributes ranking by Maximin Conditional Mutual Information (MMCMI) 基于最大条件互信息(MMCMI)的属性排序选择性AnDE

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2023-01-02 DOI: 10.1080/0952813X.2022.2062457

Shenglei Chen, Xin Ma, Linyuan Liu, Limin Wang

{"title":"Selective AnDE based on attributes ranking by Maximin Conditional Mutual Information (MMCMI)","authors":"Shenglei Chen, Xin Ma, Linyuan Liu, Limin Wang","doi":"10.1080/0952813X.2022.2062457","DOIUrl":"https://doi.org/10.1080/0952813X.2022.2062457","url":null,"abstract":"ABSTRACT Attribute selection has been proved to be an effective trick to strengthen the classification capability of Bayesian network classifiers, such as Averaged -Dependence Estimators (AnDE). However, conventional mutual information-based attribute ranking considers only the correlation between the attribute and the class, regardless of the redundancies among the attributes. In this paper, we propose a new ranking approach, called Maximin Conditional Mutual Information (MMCMI), which first minimises the conditional mutual information for any unsorted attribute with regard to the sorted attribute sequence, and then maximise the minimal conditional mutual information within all unsorted attributes. When ranking the very first attribute, the mutual information with the class is maximised within all attributes. Extensive empirical results demonstrate that the MMCMI ranking approach together with attribute selection framework achieves significantly superior classification performance and less classification time with respect to regular AnDE and the mutual information counterparts.","PeriodicalId":15677,"journal":{"name":"Journal of Experimental & Theoretical Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83831848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Emotion recognition in election day tweets using optimised kernel extreme learning machine classifier 利用优化的核极限学习机分类器识别选举日推文中的情绪

IF 2.2 4区计算机科学

Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2023-01-01 DOI: 10.1080/0952813x.2021.1960633

D. N.S.B.Kavitha, P. Reddy, K. V. Rao

引用次数: 0