Int. J. Artif. Intell. Mach. Learn.最新文献_第3页

A Method Based on a New Word Embedding Approach for Process Model Matching 一种基于词嵌入的过程模型匹配方法

Int. J. Artif. Intell. Mach. Learn. Pub Date : 2021-01-01 DOI: 10.4018/ijaiml.2021010101

M. Abdelkader, Mekour Mansour

引用次数: 1

Comparative Analysis and Detection of Brain Tumor Using Fusion Technique of T1 and T2 Weighted MR Images 磁共振T1与T2加权图像融合技术对脑肿瘤的对比分析与检测

Int. J. Artif. Intell. Mach. Learn. Pub Date : 2021-01-01 DOI: 10.4018/ijaiml.2021010105

Padmanjali A Hagargi

引用次数: 0

Efficient Closure Operators for FCA-Based Classification 基于fca分类的高效闭包算子

Int. J. Artif. Intell. Mach. Learn. Pub Date : 2020-07-01 DOI: 10.4018/ijaiml.2020070105

Nida Meddouri, Mondher Maddouri

{"title":"Efficient Closure Operators for FCA-Based Classification","authors":"Nida Meddouri, Mondher Maddouri","doi":"10.4018/ijaiml.2020070105","DOIUrl":"https://doi.org/10.4018/ijaiml.2020070105","url":null,"abstract":"Knowledge discovery in databases (KDD) aims to exploit the large amounts of data collected every day in various fields of computing application. The idea is to extract hidden knowledge from a set of data. It gathers several tasks that constitute a process, such as: data selection, pre-processing, transformation, data mining, visualization, etc. Data mining techniques include supervised classification and unsupervised classification. Classification consists of predicting the class of new instances with a classifier built on learning data of labeled instances. Several approaches were proposed such as: the induction of decision trees, Bayes, nearest neighbor search, neural networks, support vector machines, and formal concept analysis. Learning formal concepts always refers to the mathematical structure of concept lattice. This article presents a state of the art on formal concept analysis classifier. The authors present different ways to calculate the closure operators from nominal data and also present new approach to build only a part of the lattice including the best concepts. This approach is based on Dagging (ensemble method) that generates an ensemble of classifiers, each one represents a formal concept, and combines them by a voting rule. Experimental results are given to prove the efficiency of the proposed method.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125511665","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: 0

Internet of Things in E-Government: Applications and Challenges 电子政务中的物联网:应用与挑战

Int. J. Artif. Intell. Mach. Learn. Pub Date : 2020-07-01 DOI: 10.4018/ijaiml.2020070106

Panagiota Papadopoulou, Kostas Kolomvatsos, S. Hadjiefthymiades

引用次数: 9

Robotics and Artificial Intelligence 机器人与人工智能

Int. J. Artif. Intell. Mach. Learn. Pub Date : 2020-07-01 DOI: 10.4018/ijaiml.2020070104

Estifanos Tilahun Mihret

引用次数: 2

A Literature Review on Cross Domain Sentiment Analysis Using Machine learning 基于机器学习的跨领域情感分析的文献综述

Int. J. Artif. Intell. Mach. Learn. Pub Date : 2020-07-01 DOI: 10.4018/ijaiml.2020070103

Nancy Kansal, Lipika Goel, Sonam Gupta

引用次数: 5

Palmprint And Dorsal Hand Vein Multi-Modal Biometric Fusion Using Deep Learning 基于深度学习的掌纹和手背静脉多模态生物特征融合

Int. J. Artif. Intell. Mach. Learn. Pub Date : 2020-07-01 DOI: 10.4018/ijaiml.2020070102

Norah Abdullah Al-johani, Lamiaa A. Elrefaei

{"title":"Palmprint And Dorsal Hand Vein Multi-Modal Biometric Fusion Using Deep Learning","authors":"Norah Abdullah Al-johani, Lamiaa A. Elrefaei","doi":"10.4018/ijaiml.2020070102","DOIUrl":"https://doi.org/10.4018/ijaiml.2020070102","url":null,"abstract":"Advancements in biometrics have attained relatively high recognition rates. However, the need for a biometric system that is reliable, robust, and convenient remains. Systems that use palmprints (PP) for verification have a number of benefits including stable line features, reduced distortion and simple self-positioning. Dorsal hand veins (DHVs) are distinctive for every person, such that even identical twins have different DHVs. DHVs appear to maintain stability over time. In the past, different features algorithms were used to implement palmprint (PP) and dorsal hand vein (DHV) systems. Previous systems relied on handcrafted algorithms. The advancements of deep learning (DL) in the features learned by the convolutional neural network (CNN) has led to its application in PP and DHV recognition systems. In this article, a multimodal biometric system based on PP and DHV using (VGG16, VGG19 and AlexNet) CNN models is proposed. The proposed system is uses two approaches: feature level fusion (FLF) and Score level fusion (SLF). In the first approach, the features from PP and DHV are extracted with CNN models. These extracted features are then fused using serial or parallel fusion and used to train error-correcting output codes (ECOC) with a support vector machine (SVM) for classification. In the second approach, the fusion at score level is done with sum, max, and product methods by applying two strategies: Transfer learning that uses CNN models for features extraction and classification for PP and DHV, then score level fusion. For the second strategy, features are extracted with CNN models for PP and DHV and used to train ECOC with SVM for classification, then score level fusion. The system was tested using two DHV databases and one PP database. The multimodal system is tested two times by repeating PP database for each DHV database. The system achieved very high accuracy rate.","PeriodicalId":217541,"journal":{"name":"Int. J. Artif. Intell. Mach. Learn.","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117161084","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

Assessing Hyper Parameter Optimization and Speedup for Convolutional Neural Networks 评估卷积神经网络的超参数优化和加速

Int. J. Artif. Intell. Mach. Learn. Pub Date : 2020-01-18 DOI: 10.4018/IJAIML.2020070101

S. Nazir, Shushma Patel, D. Patel

引用次数: 5

Development of a Charge Estimator for Piezoelectric Actuators: A Radial Basis Function Approach 压电致动器电荷估计器的研制:径向基函数方法

Int. J. Artif. Intell. Mach. Learn. Pub Date : 2020-01-01 DOI: 10.4018/ijaiml.2020010103

M. Mohammadzaheri, Mohammadreza Emadi, M. Ghodsi, I. Bahadur, M. Zarog, A. Saleem

引用次数: 4

Sensor Fusion of Odometer, Compass and Beacon Distance for Mobile Robots 移动机器人里程表、罗盘和信标距离传感器融合

Int. J. Artif. Intell. Mach. Learn. Pub Date : 2020-01-01 DOI: 10.4018/ijaiml.2020010101

R. Fraanje, René Beltman, Fidelis Theinert, M. V. Osch, Teade Punter, John Bolte

引用次数: 0