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A novel similarity measure: Voronoi audio similarity for genre classification 一种新的相似度度量:用于类型分类的Voronoi音频相似度
Int. J. Intell. Syst. Technol. Appl. Pub Date : 2017-11-21 DOI: 10.1504/IJISTA.2017.10008859
Prafulla Kalapatapu, N. Tejas, Siddharth Dalmia, Prakhar Gupta, Bhaswant Inguva, Aruna Malapati
{"title":"A novel similarity measure: Voronoi audio similarity for genre classification","authors":"Prafulla Kalapatapu, N. Tejas, Siddharth Dalmia, Prakhar Gupta, Bhaswant Inguva, Aruna Malapati","doi":"10.1504/IJISTA.2017.10008859","DOIUrl":"https://doi.org/10.1504/IJISTA.2017.10008859","url":null,"abstract":"One of the major challenges in genre classification, recommender systems is to find similarity between the query song and songs in a database. In this paper, we propose a novel similarity measure called Voronoi audio similarity (VAS). We extracted the Content-based features from the audio signal of the song split in frames over a particular time period and we represented each song as a point in 2D space. The proposed system is a two-level classification process, where songs are first clustered by K-means clustering and then a Voronoi diagram is created using centroids from the resulting K-means, which is called the template Voronoi diagram (TVD). This approach learns the decision boundary used for genre classification. The genre of the song could thus be predicted as the genre with the maximum normalised area overlap. Empirical results performed with 10 cross-fold validations on million song subsets of 500 songs showed 78% accuracy.","PeriodicalId":420808,"journal":{"name":"Int. J. Intell. Syst. Technol. Appl.","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115280775","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
A novel Arabic font recognition system based on texture feature and dynamic training 基于纹理特征和动态训练的阿拉伯文字体识别系统
Int. J. Intell. Syst. Technol. Appl. Pub Date : 2017-11-21 DOI: 10.1504/IJISTA.2017.10008858
Faten Kallel Jaiem, M. Kherallah
{"title":"A novel Arabic font recognition system based on texture feature and dynamic training","authors":"Faten Kallel Jaiem, M. Kherallah","doi":"10.1504/IJISTA.2017.10008858","DOIUrl":"https://doi.org/10.1504/IJISTA.2017.10008858","url":null,"abstract":"Recognising an Arabic text with OCR is a complex task caused by the cursive nature of Arabic script for printed and handwritten text. The Arabic letters change forms according to not only their position in the word, but also their font. In fact, developing a font recognition system as a pre-recognition step may help to increase the OCR performances. In this paper, we present an Arabic font recognition system using curvelet transform for feature extraction. Moreover, we expose a new classification strategy based on a back-propagation artificial neural network (BpANN) called a dynamics multi-BpANN-1Class classifier. To validate our proposed system, we first focused our research on a comparative study of five texture analysis techniques. Second, we compared our classifier to a classical BpANN. And finally, we validate the dynamic training for the classification phase.","PeriodicalId":420808,"journal":{"name":"Int. J. Intell. Syst. Technol. Appl.","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126098971","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
Wrapping practical problems into a machine learning framework: using water pipe failure prediction as a case study 将实际问题包装到机器学习框架中:以水管故障预测为案例研究
Int. J. Intell. Syst. Technol. Appl. Pub Date : 2017-07-25 DOI: 10.1504/IJISTA.2017.10005998
Jianlong Zhou, Jinjun Sun, Yang Wang, Fang Chen
{"title":"Wrapping practical problems into a machine learning framework: using water pipe failure prediction as a case study","authors":"Jianlong Zhou, Jinjun Sun, Yang Wang, Fang Chen","doi":"10.1504/IJISTA.2017.10005998","DOIUrl":"https://doi.org/10.1504/IJISTA.2017.10005998","url":null,"abstract":"Despite the recognised value of machine learning (ML) techniques and high expectation of applying ML techniques within various applications, users often find it difficult to effectively apply ML techniques in practice because of complicated interfaces between ML algorithms and users. This paper presents a work flow of wrapping practical problems into an ML framework. The water pipe failure prediction is used as a case study to show that the applying process can be divided into various steps: obtain domain data, interview with domain experts, clean/pre-process and preview original domain data, extract ML features, set up ML models, explain ML results and make decisions, as well as make feedback to the system based on decision making. In this process, domain experts and ML developers need to collaborate closely in order to make this workflow more effective.","PeriodicalId":420808,"journal":{"name":"Int. J. Intell. Syst. Technol. Appl.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130966288","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: 7
Application of (α, β)-soft intersectional sets on BCK/BCI-algebras (α， β)-软交集在BCK/ bci -代数上的应用
Int. J. Intell. Syst. Technol. Appl. Pub Date : 2017-07-25 DOI: 10.1504/IJISTA.2017.10006003
C. Jana, M. Pal
{"title":"Application of (α, β)-soft intersectional sets on BCK/BCI-algebras","authors":"C. Jana, M. Pal","doi":"10.1504/IJISTA.2017.10006003","DOIUrl":"https://doi.org/10.1504/IJISTA.2017.10006003","url":null,"abstract":"In this paper, the notion of (α, β)-soft intersectional set is introduced and explained with some examples. Then, some properties of (α, β)-soft intersectional BCK/BCI-algebras and (α, β)-soft intersectional BCK/BCI-ideals are investigated. Strong (α, β)-soft intersectional BCI-algebra is defined on the basis of BCI-subalgebras. The concept of an (α, β)-intersectional t-support of a soft set is introduced and proposed a condition for an (α, β)-intersectional t-support to be a soft BCK/BCI-subalgebras and soft ideal of a BCK/BCI-algebras. Also, it is shown that the image and inverse image of a BCK/BCI-algebras are also (α, β)-soft intersectional BCK/BCI-algebras. Again, (α, β)-soft intersectional BCK/BCI-ideal is developed on the basis of a homomorphism of BCK/BCI-algebras.","PeriodicalId":420808,"journal":{"name":"Int. J. Intell. Syst. Technol. Appl.","volume":"268 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116489826","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: 6
Writing type, script and language identification in heterogeneous documents 异质文件的书写类型、脚本和语言识别
Int. J. Intell. Syst. Technol. Appl. Pub Date : 2017-07-25 DOI: 10.1504/IJISTA.2017.10006001
Anis Mezghani, Fouad Slimane, M. Kherallah
{"title":"Writing type, script and language identification in heterogeneous documents","authors":"Anis Mezghani, Fouad Slimane, M. Kherallah","doi":"10.1504/IJISTA.2017.10006001","DOIUrl":"https://doi.org/10.1504/IJISTA.2017.10006001","url":null,"abstract":"In this paper, we propose a writing type, script and language text classification method to automatically determine the identity of texts segmented from heterogeneous document images. These documents are written in Arabic, French and English languages with mixed machine-printed and handwritten text. To handle such a problem, we treat each text-line/word image with a fixed-length sliding window. Each window is represented with 23 simple and efficient features to achieve the writing type and the script identification goal using Gaussian mixture models (GMM). The proposed approach for language identification is based on a bi-gram analysis of an optical character recognition (OCR) output. Experiments have been conducted with handwritten and machine-printed text-blocks, text-lines and words extracted from the Maurdor database. The results reveal the feasibility of our proposed method in writing type, script and language identification.","PeriodicalId":420808,"journal":{"name":"Int. J. Intell. Syst. Technol. Appl.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129020484","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
Silkworm cocoon classification using fusion of Zernike moments-based shape descriptors and physical parameters for quality egg production 基于泽尼克矩的形状描述符和物理参数融合的蚕茧分类用于优质产蛋
Int. J. Intell. Syst. Technol. Appl. Pub Date : 2017-07-25 DOI: 10.1504/IJISTA.2017.10006002
Vijayalakshmi G. V. Mahesh, A. Raj, T. Çelik
{"title":"Silkworm cocoon classification using fusion of Zernike moments-based shape descriptors and physical parameters for quality egg production","authors":"Vijayalakshmi G. V. Mahesh, A. Raj, T. Çelik","doi":"10.1504/IJISTA.2017.10006002","DOIUrl":"https://doi.org/10.1504/IJISTA.2017.10006002","url":null,"abstract":"This paper proposes a novel non-destructive vision-based system to perform automated gender classification of silkworm cocoons for the purpose of improving the quality of egg production. Gender classification is achieved by discriminative learning on samples of male and female cocoons acquired from CSR2 and Pure Mysore silkworm cocoon breeds. Features composed of different combinations of weight, volume, geometric and Zernike moments-based shape properties of cocoons are used for training classifiers of types k-nearest neighbour (kNN), linear discriminant analysis (LDA), neural networks (NNs) and support vector machine (SVM). The experimental results show superiority of the proposed method with respect to the state-of-the-art methods. Specifically, the experimental results indicated the excellent performance of NN and SVM classifiers. An overall classification accuracy of 91.3% was achieved with NN for CSR2 cocoon breed and 100% was attained with SVM for pure Mysore cocoon breed.","PeriodicalId":420808,"journal":{"name":"Int. J. Intell. Syst. Technol. Appl.","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125298714","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: 6
Feature selection with ensemble learning using enriched SOM 使用强化SOM的集成学习特征选择
Int. J. Intell. Syst. Technol. Appl. Pub Date : 2017-07-25 DOI: 10.1504/IJISTA.2017.10005999
A. Filali, Chiraz Jlassi, N. Arous
{"title":"Feature selection with ensemble learning using enriched SOM","authors":"A. Filali, Chiraz Jlassi, N. Arous","doi":"10.1504/IJISTA.2017.10005999","DOIUrl":"https://doi.org/10.1504/IJISTA.2017.10005999","url":null,"abstract":"Finding pertinent subspaces in very high-dimensional dataset is a challenging task. The selection of features should be stable, but on the other hand clustering results have to be enhanced. Ensemble methods have successfully increased the stability and clustering accuracy, but their runtime prevents them from scaling up to real-world applications. This paper treats the problem of selecting a subset of the most relevant features for each cluster from a dataset. The proposed model is an extension of the random forests method using enriched self-organising map (SOM) to unlabelled data that assess the out-of-bag (oob) feature importance from an ensemble of partitions. Each partition is produced using a different bootstrap sample and a random subset of the features. We then assessed the accuracy and the scalability of the proposed method on 19 benchmark datasets and we compared its effectiveness against other unsupervised feature selection methods with ensemble learning.","PeriodicalId":420808,"journal":{"name":"Int. J. Intell. Syst. Technol. Appl.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131639829","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
Conceptual search of songs using domain ontology and semantic links 基于领域本体和语义链接的歌曲概念搜索
Int. J. Intell. Syst. Technol. Appl. Pub Date : 2017-05-24 DOI: 10.1504/IJISTA.2017.10005104
Nadia Lachtar, Halima Bahi, Z. Bouras
{"title":"Conceptual search of songs using domain ontology and semantic links","authors":"Nadia Lachtar, Halima Bahi, Z. Bouras","doi":"10.1504/IJISTA.2017.10005104","DOIUrl":"https://doi.org/10.1504/IJISTA.2017.10005104","url":null,"abstract":"Songs retrieval process is complex and implies multiple facets. Nowadays, the most common way of searching songs is through a combination of factual and cultural metadata or by lyrics. Owing to the poor indexing techniques, these systems cannot completely satisfy the users' requests. In this paper, we suggest the use of domain ontology and semantic links to index a collection of songs and perform a conceptual search that offers a benefit way to complement metadata-based methods. A set of experiments were carried over a dedicated dataset and show the superiority of our approach when compared with classical one.","PeriodicalId":420808,"journal":{"name":"Int. J. Intell. Syst. Technol. Appl.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125101881","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
A review on data-driven learning of a talking head model 会说话的头部模型的数据驱动学习研究综述
Int. J. Intell. Syst. Technol. Appl. Pub Date : 2017-05-24 DOI: 10.1504/IJISTA.2017.10005128
K. K. Htike
{"title":"A review on data-driven learning of a talking head model","authors":"K. K. Htike","doi":"10.1504/IJISTA.2017.10005128","DOIUrl":"https://doi.org/10.1504/IJISTA.2017.10005128","url":null,"abstract":"Constructing a talking head model of a person allows generation of a novel talking face animation from an unseen audio sequence of the person. This has important applications such as building virtual avatars of people that can interact with real people in novel situations, model-based video compression, teleconferencing, human-computer interaction, computer graphics and video games. Traditionally, talking head models have been built by manual painstaking work. The advancement of computer vision and machine learning techniques, especially in the past decade, has made possible the automatic learning of a talking head model of a person from data. In this paper, we focus on this area of machine learning based data-driven facial animation and critically review the most common approaches, compare and contrast among them and identify promising research directions and prospects.","PeriodicalId":420808,"journal":{"name":"Int. J. Intell. Syst. Technol. Appl.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121559326","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
Feature extraction of electronic nose for classification of indoor pollution gases based on kernel entropy component analysis 基于核熵分量分析的室内污染气体分类电子鼻特征提取
Int. J. Intell. Syst. Technol. Appl. Pub Date : 2017-05-24 DOI: 10.1504/IJISTA.2017.10005102
Jia Yan, Shukai Duan, Lidan Wang, Pengfei Jia, Tingwen Huang, F. Tian, Kun Lu
{"title":"Feature extraction of electronic nose for classification of indoor pollution gases based on kernel entropy component analysis","authors":"Jia Yan, Shukai Duan, Lidan Wang, Pengfei Jia, Tingwen Huang, F. Tian, Kun Lu","doi":"10.1504/IJISTA.2017.10005102","DOIUrl":"https://doi.org/10.1504/IJISTA.2017.10005102","url":null,"abstract":"Feature extraction is important for electronic nose (E-nose), when it is used to classify different gases or odours. A novel feature extraction technique of E-nose based on kernel entropy component analysis (KECA) is presented in this paper. KECA is integrated with Renyi entropy and extracts the features from the kernel Hilbert space by projecting the input dataset onto the kernel principal component analysis (KPCA) axes that preserve the most Renyi entropy. Besides KECA, independent component analysis and KPCA are also used to deal with the original feature matrix of four different indoor pollution gases acquired by E-nose. Experimental results prove that the classification accuracy of KECA is better than other considered techniques.","PeriodicalId":420808,"journal":{"name":"Int. J. Intell. Syst. Technol. Appl.","volume":"282 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133924842","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
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