Intelligent data analysis

Publishing house | impacting the world of sciencebooks & journals, online & igent data factor 2017 print1088-467xissn online1571-4128 volume21; 6 issuesstatuslast issue (21:5) online on 17 october 2017next issue21:6 scheduled for december 2017back volumes1-20subjectartificial intelligence, computer & communication sciences, linked data, end this title to your utional subscription for ript submission & author cted/indexed igent data analysis provides a forum for the examination of issues related to the research and applications of artificial intelligence techniques in data analysis across a variety of disciplines. These techniques include (but are not limited to): all areas of data visualization, data pre-processing (fusion, editing, transformation, filtering, sampling), data engineering, database mining techniques, tools and applications, use of domain knowledge in data analysis, big data applications, evolutionary algorithms, machine learning, neural nets, fuzzy logic, statistical pattern recognition, knowledge filtering, and post-processing. In particular, papers are preferred that discuss development of new ai related data analysis architectures, methodologies, and techniques and their applications to various ad the journal's flyer ng sible for the big data major (iamd). De carvalho department of computer science, university of sao paulo at sao carlos, sp, ment of mathematics and computer sity of of computer science, the academic college of tel-aviv-yaffo,Institute of sity of aires institute of aires, ment of computer and information jersey institute of technology university ute of information g research ment of computer sion of igent data analysis - an international igent data analysis invites the submission of research and application articles that comply with the aims and scope of the journal. In particular, articles that discuss development of new ai architectures, methodologies, and techniques and their applications to the field of data analysis are preferred.

It allows authors to enrich their articles with lay metadata, add links to related materials and promote their articles through the kudos system to a wider public. For more information, please have a look at our authors ic source completeacm digital librarybusiness source completecambridge scientific abstractscompendexcsa illuminaebsco databasesgoogle scholarinspec ietinternational security & counter-terrorism reference centerio-portpsycinfoscience & technology collectionscopusulrich's periodicals directoryweb of science: current contents/engineering, computing and technologyweb of science: journal citation reports/science editionweb of science: science citation index-expanded (scisearch®). Select this link to jump to igent data analysis - volume 21, issue se individual online access for 1 year to this factor 2017:  igent data analysis provides a forum for the examination of issues related to the research and applications of artificial intelligence techniques in data analysis across a variety of disciplines. These techniques include (but are not limited to): all areas of data visualization, data pre-processing (fusion, editing, transformation, filtering, sampling), data engineering, database mining techniques, tools and applications, use of domain knowledge in data analysis, big data applications, evolutionary algorithms, machine learning, neural nets, fuzzy logic, statistical pattern recognition, knowledge filtering, and particular, papers are preferred that discuss development of new ai related data analysis architectures, methodologies, and techniques and their applications to various marked this result for bulk : 10. This strategy however worsens the issue of data sparsity in that some sessions may have very little or even no interaction for preference inference.

To alleviate the data sparsity issue and avoid errors due to data imputation that is commonly adopted by existing models, a novel session-based dynamic recommendation model that divides a user’s interaction history with dynamic window size is proposed. Activity life cycles using real-world dataset is conducted to demonstrate the nonuniformness and aggregation of the users’ behavior patterns on the time dimension. Extensive empirical experiments over two real datasets demonstrate the effectiveness and superiority of our method by comparing to representative temporal dynamic ds: dynamic clustering, temporal context, latent dirichlet allocation, time : 10. Rezende, solange real-world applications, such as those related to sensors, allow collecting large amounts of inexpensive unlabeled sequential data. However, the use of supervised machine learning methods is frequently hindered by the high costs involved in gathering labels for such data.

These methods assume the availability of a considerable amount of labeled data to build an accurate classification model. Although active learning has been widely used in many problems, most of the methods consider the presence of labeled data. Differently, in this paper, we are interested in the realistic scenario where the active learning is performed from scratch on a fully unlabeled dataset and with the absence of any classifier or prior knowledge about the data. In general, the methods that consider fully unlabeled data use random sampling to select examples to label. The goal of this work is to show a broad experimental evaluation with different unsupervised active learning methods to select examples from fully unlabeled sequential data.

Given our evaluation on a benchmark of sequential data and in a case study of insect species classification, we indicated the sampling based on hierarchical clustering or k -means. Also, increasing input image’s size for face detection and using large training data sets for face recognition demand additional computing resources to achieve real-time processing. Event streams have special features, such as high speeds and large amounts of data, as well as diversity of sources and formats. 1141-1154, this result for bulk ting statistically significant dependent rules for associative s: li, jundong | zaiane, osmar ished associative classification algorithms have shown to be very effective in handling categorical data such as text data. Experimental results on real-world datasets show that sigdirect achieves better performance in terms of classification accuracy when measured with state-of-the-art rule based and associative classifiers.

1155-1172, this result for bulk : pearson gaussian naïve bayes classifier for data stream classification with recurring concept s: babu, d. Ramana, data stream classification, selecting the classifier for the dynamic feature space and considering the concept drift is a challenging task. This paper addresses the major challenges in the data stream classification with recurring concept drift. For the data stream classification, the proposed pgnbc is frequently updated based on the concept drift. It is found that the improvement in terms of sensitivity, specificity and accuracy values are better for the proposed method, with the values of 4%, 1% and 1% respectively, which is higher for the pgnbc method than the rgnbc method for the skin data.

But with the localization data, the improvement in terms of specificity and accuracy values are 6% and 2% respectively which is higher than the ds: data stream, recurring concept drift, naïve bayes, rough set theory, : 10. Established textural and geometric features are initially used to represent medical characteristics, before being used to generate secondary features through classifier scoring using the support vector machine and quadratic discriminant analysis classifiers. Automatically from the data itself and treats distant supervision as a multi-instance learning problem to settle the problem of false positive instances. 1213-1231, igent data analysis research igent data analysis (ida) is an interdisciplinary research group concerned with the effective analysis of data. Our work has led not only to novel research results published in many leading journals in the field, but also to effective implementation of applications that have been successfully used in practical settings, especially in biology and areas of interest include, but are not limited to: data mining, artificial intelligence, machine learning, data pre-processing, text mining, image processing, data analysis methodologies, tools and group is a part of the college of engineering, design and physical mejournal rankingscountry rankingsviz toolshelpabout igent data yunited t area and categorycomputer scienceartificial intelligencecomputer vision and pattern recognitionmathematicstheoretical computer herelsevier ation igent data analysis provides a forum for the examination of issues related to the research and applications of artificial intelligence techniques in data analysis across a variety of disciplines.

These techniques include (but are not limited to): all areas of data visualization, data pre-processing (fusion, editing, transformation, filtering, sampling), data engineering, database mining techniques, tools and applications, use of domain knowledge in data analysis, evolutionary algorithms, machine learning, neural nets, fuzzy logic, statistical pattern recognition, knowledge filtering, and set of journals have been ranked according to their sjr and divided into four equal groups, four quartiles. Data analysis research igent data analysis (ida) is an interdisciplinary research group concerned with the effective analysis of data. Our work has led not only to novel research results published in many leading journals in the field, but also to effective implementation of applications that have been successfully used in practical settings, especially in biology and areas of interest include, but are not limited to: data mining, artificial intelligence, machine learning, data pre-processing, text mining, image processing, data analysis methodologies, tools and group is a part of the college of engineering, design and physical sciences.