Search results for: object-oriented databases.

Authors: A. Kebbeb, M. Mostefai, F. Benmerzoug, Y. Chahir

Abstract:

The multimodal biometric identification is the combination of several biometric systems; the challenge of this combination is to reduce some limitations of systems based on a single modality while significantly improving performance. In this paper, we propose a new approach to the construction and the protection of a multimodal biometric database dedicated to an identification system. We use a topological watermarking to hide the relation between face image and the registered descriptors extracted from other modalities of the same person for more secure user identification.

Keywords: Biometric databases, Multimodal biometrics, security authentication, Digital watermarking.

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Authors: A. S. Tolba, A.H. El-Baz, A.A. El-Harby

Abstract:

The task of face recognition has been actively researched in recent years. This paper provides an up-to-date review of major human face recognition research. We first present an overview of face recognition and its applications. Then, a literature review of the most recent face recognition techniques is presented. Description and limitations of face databases which are used to test the performance of these face recognition algorithms are given. A brief summary of the face recognition vendor test (FRVT) 2002, a large scale evaluation of automatic face recognition technology, and its conclusions are also given. Finally, we give a summary of the research results.

Keywords: Combined classifiers, face recognition, graph matching, neural networks.

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Authors: S. H. Oudjana, A. Hellal, I. Hadj Mahammed

Abstract:

Photovoltaic power generation forecasting is an important task in renewable energy power system planning and operating. This paper explores the application of neural networks (NN) to study the design of photovoltaic power generation forecasting systems for one week ahead using weather databases include the global irradiance, and temperature of Ghardaia city (south of Algeria) using a data acquisition system. Simulations were run and the results are discussed showing that neural networks Technique is capable to decrease the photovoltaic power generation forecasting error.

Keywords: Photovoltaic Power Forecasting, Regression, Neural Networks.

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Authors: Fahad Shahbaz Khan, Rao Muhammad Anwer, Olof Torgersson, Göran Falkman

Abstract:

Data mining has been used very frequently to extract hidden information from large databases. This paper suggests the use of decision trees for continuously extracting the clinical reasoning in the form of medical expert-s actions that is inherent in large number of EMRs (Electronic Medical records). In this way the extracted data could be used to teach students of oral medicine a number of orderly processes for dealing with patients who represent with different problems within the practice context over time.

Keywords: Data mining, Oral Medicine, Decision Trees, WEKA.

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Authors: Sherif Abdelazeem

Abstract:

A comparison between the performance of Latin and Arabic handwritten digits recognition problems is presented. The performance of ten different classifiers is tested on two similar Arabic and Latin handwritten digits databases. The analysis shows that Arabic handwritten digits recognition problem is easier than that of Latin digits. This is because the interclass difference in case of Latin digits is smaller than in Arabic digits and variances in writing Latin digits are larger. Consequently, weaker yet fast classifiers are expected to play more prominent role in Arabic handwritten digits recognition.

Keywords: Handwritten recognition, Arabic recognition, Digits recognition, Document recognition

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Authors: Fadl M. Ba-Alwi, Kamal K. Bharadwaj

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In this paper a novel algorithm is proposed that integrates the process of fuzzy hierarchy generation and rule discovery for automated discovery of Production Rules with Fuzzy Hierarchy (PRFH) in large databases.A concept of frequency matrix (Freq) introduced to summarize large database that helps in minimizing the number of database accesses, identification and removal of irrelevant attribute values and weak classes during the fuzzy hierarchy generation.Experimental results have established the effectiveness of the proposed algorithm.

Keywords: Data Mining, Degree of subsumption, Freq matrix, Fuzzy hierarchy.

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Authors: Mehran Yazdi, Kazem Gheysari

Abstract:

In this paper, we propose an approach for the classification of fingerprint databases. It is based on the fact that a fingerprint image is composed of regular texture regions that can be successfully represented by co-occurrence matrices. So, we first extract the features based on certain characteristics of the cooccurrence matrix and then we use these features to train a neural network for classifying fingerprints into four common classes. The obtained results compared with the existing approaches demonstrate the superior performance of our proposed approach.

Keywords: Biometrics, fingerprint classification, gray level cooccurrence matrix, regular texture representation.

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Authors: Abdelhadi Lotfi, Abdelkader Benyettou

Abstract:

In this work, a training algorithm for probabilistic neural networks (PNN) is presented. The algorithm addresses one of the major drawbacks of PNN, which is the size of the hidden layer in the network. By using a cross-validation training algorithm, the number of hidden neurons is shrunk to a smaller number consisting of the most representative samples of the training set. This is done without affecting the overall architecture of the network. Performance of the network is compared against performance of standard PNN for different databases from the UCI database repository. Results show an important gain in network size and performance.

Keywords: Classification, probabilistic neural networks, network optimization, pattern recognition.

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Authors: A. Buzzanca, G. Castellano, A.M. Fanelli

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In this paper we address the problem of musical style classification, which has a number of applications like indexing in musical databases or automatic composition systems. Starting from MIDI files of real-world improvisations, we extract the melody track and cut it into overlapping segments of equal length. From these fragments, some numerical features are extracted as descriptors of style samples. We show that a standard Bayesian classifier can be conveniently employed to build an effective musical style classifier, once this set of features has been extracted from musical data. Preliminary experimental results show the effectiveness of the developed classifier that represents the first component of a musical audio retrieval system

Keywords: Musical style, Bayesian classifier.

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Authors: Thiago M. R Dias, Gray F. Moita

Abstract:

The analysis of scientific collaboration networks has contributed significantly to improving the understanding of how does the process of collaboration between researchers and also to understand how the evolution of scientific production of researchers or research groups occurs. However, the identification of collaborations in large scientific databases is not a trivial task given the high computational cost of the methods commonly used. This paper proposes a method for identifying collaboration in large data base of curriculum researchers. The proposed method has low computational cost with satisfactory results, proving to be an interesting alternative for the modeling and characterization of large scientific collaboration networks.

Keywords: Extraction and data integration, Information Retrieval, Scientific Collaboration.

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Authors: A.Ö. İlkay, C.O. Zeynep

Abstract:

Information and Communication Technologies (ICT) has changed our life and learn. ICT bares doors to new innovative methods to deliver education. E-learning is a part of ICT and has been endorsed as a tool for developing “21st century skills” in higher education. The aim of this review is to establish the impacts of e-learning in undergraduate nursing education. A systematic literature review was conducted to assess the impacts of e-learning in nursing education by using Akdeniz University electronic databases. According to results, we can decelerate that the nursing faculties cannot treat e-learning methods as a single tool. E-learning should be used with a good understanding of learners’ needs.

Keywords: E-learning, nursing education, systematic literature review.

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Authors: Liton Jude Rozario, Mohammad Reduanul Haque, Md. Ziarul Islam, Mohammad Shorif Uddin

Abstract:

Face recognition is a technique to automatically identify or verify individuals. It receives great attention in identification, authentication, security and many more applications. Diverse methods had been proposed for this purpose and also a lot of comparative studies were performed. However, researchers could not reach unified conclusion. In this paper, we are reporting an extensive quantitative accuracy analysis of four most widely used face recognition algorithms: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) using AT&T, Sheffield and Bangladeshi people face databases under diverse situations such as illumination, alignment and pose variations.

Keywords: PCA, ICA, LDA, SVM, face recognition, noise.

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Authors: Leo Galambos

Abstract:

The majority of today's IR systems base the IR task on two main processes: indexing and searching. There exists a special group of dynamic IR systems where both processes (indexing and searching) happen simultaneously; such a system discards obsolete information, simultaneously dealing with the insertion of new in¬formation, while still answering user queries. In these dynamic, time critical text document databases, it is often important to modify index structures quickly, as documents arrive. This paper presents a method for dynamization which may be used for this task. Experimental results show that the dynamization process is possible and that it guarantees the response time for the query operation and index actualization.

Keywords: Search engine, inverted file, index management.

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Authors: Dhruv Sharma, Lynne Blair, Stephen Clune

Abstract:

Later life loneliness is a social issue that is increasing alongside an upward global population trend. As a society, one way that we have responded to this social challenge is through developing non-pharmacological interventions such as befriending services, activity clubs, meet-ups, etc. Through a systematic literature review, this paper suggests that currently there is an underrepresentation of radical innovation, and underutilization of digital technologies in developing loneliness interventions for older adults. This paper examines intervention studies that were published in English language, within peer reviewed journals between January 2005 and December 2014 across 4 electronic databases. In addition to academic databases, interventions found in grey literature in the form of websites, blogs, and Twitter were also included in the overall review. This approach yielded 129 interventions that were included in the study. A systematic approach allowed the minimization of any bias dictating the selection of interventions to study. A coding strategy based on a pattern analysis approach was devised to be able to compare and contrast the loneliness interventions. Firstly, interventions were categorized on the basis of their objective to identify whether they were preventative, supportive, or remedial in nature. Secondly, depending on their scope, they were categorized as one-to-one, community-based, or group based. It was also ascertained whether interventions represented an improvement, an incremental innovation, a major advance or a radical departure, in comparison to the most basic form of a loneliness intervention. Finally, interventions were also assessed on the basis of the extent to which they utilized digital technologies. Individual visualizations representing the four levels of coding were created for each intervention, followed by an aggregated visual to facilitate analysis. To keep the inquiry within scope and to present a coherent view of the findings, the analysis was primarily concerned the level of innovation, and the use of digital technologies. This analysis highlights a weak but positive correlation between the level of innovation and the use of digital technologies in designing and deploying loneliness interventions, and also emphasizes how certain existing interventions could be tweaked to enable their migration from representing incremental innovation to radical innovation for example. This analysis also points out the value of including grey literature, especially from Twitter, in systematic literature reviews to get a contemporary view of latest work in the area under investigation.

Keywords: Loneliness, ageing, innovation, digital.

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Authors: Juan Sepúlveda

Abstract:

Energy is a fundamental component in sustainability, the access and use of this resource is related with economic growth, social improvements, and environmental impacts. In this sense, energy efficiency has been studied as a factor that enhances the positive impacts of energy in communities; however, the implementation of efficiency requires strong policy and strategies that usually rely on individual measures focused in independent dimensions. In this paper, the problem of energy efficiency as a multi-objective problem is studied, using scientometric analysis to discover trends and patterns that allow to identify the main variables and study approximations related with a further development of models to integrate energy efficiency and MCA into policy making for small communities.

Keywords: Energy efficiency, MCA, Scientometrics, trends.

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Authors: R. AliMohammadzadeh, M. Rahgozar, A. Zarnani

Abstract:

The inherent flexibilities of XML in both structure and semantics makes mining from XML data a complex task with more challenges compared to traditional association rule mining in relational databases. In this paper, we propose a new model for the effective extraction of generalized association rules form a XML document collection. We directly use frequent subtree mining techniques in the discovery process and do not ignore the tree structure of data in the final rules. The frequent subtrees based on the user provided support are split to complement subtrees to form the rules. We explain our model within multi-steps from data preparation to rule generation.

Keywords: XML, Data Mining, Association Rule Mining.

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Authors: N. Soussi, M. Bahaj

Abstract:

The enormous amount of information stored on the web increases from one day to the next, exposing the web currently faced with the inevitable difficulties of research pertinent information that users really want. The problem today is not limited to expanding the size of the information highways, but to design a system for intelligent search. The vast majority of this information is stored in relational databases, which in turn represent a backend for managing RDF data of the semantic web. This problem has motivated us to write this paper in order to establish an effective approach to support semantic transformation algorithm for SPARQL queries to SQL queries, more precisely SPARQL SELECT queries; by adopting this method, the relational database can be questioned easily with SPARQL queries maintaining the same performance.

Keywords: RDF, Semantic Web, SPARQL, SPARQL Query Transformation, SQL.

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Authors: M. Giannopoulou, I. Roukounis, A. Roukouni.

Abstract:

Geographical Information Systems are an integral part of planning in modern technical systems. Nowadays referred to as Spatial Decision Support Systems, as they allow synergy database management systems and models within a single user interface machine and they are important tools in spatial design for evaluating policies and programs at all levels of administration. This work refers to the creation of a Geographical Information System in the context of a broader research in the area of influence of an under construction station of the new metro in the Greek city of Thessaloniki, which included statistical and multivariate data analysis and diagrammatic representation, mapping and interpretation of the results.

Keywords: Databases, Geographical information systems (GIS), Land-use planning, Metro stations

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Authors: Anouar Ben Khalifa, Sami Gazzah, Najoua Essoukri BenAmara

Abstract:

The Choquet integral is a tool for the information fusion that is very effective in the case where fuzzy measures associated with it are well chosen. In this paper, we propose a new approach for calculating fuzzy measures associated with the Choquet integral in a context of data fusion in multimodal biometrics. The proposed approach is based on genetic algorithms. It has been validated in two databases: the first base is relative to synthetic scores and the second one is biometrically relating to the face, fingerprint and palmprint. The results achieved attest the robustness of the proposed approach.

Keywords: Multimodal biometrics, data fusion, Choquet integral, fuzzy measures, genetic algorithm.

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Authors: S. Vural, H. Yamauchi

Abstract:

Current systems for face recognition techniques often use either SVM or Adaboost techniques for face detection part and use PCA for face recognition part. In this paper, we offer a novel method for not only a powerful face detection system based on Six-segment-filters (SSR) and Adaboost learning algorithms but also for a face recognition system. A new exclusive face detection algorithm has been developed and connected with the recognition algorithm. As a result of it, we obtained an overall high-system performance compared with current systems. The proposed algorithm was tested on CMU, FERET, UNIBE, MIT face databases and significant performance has obtained.

Keywords: Adaboost, Face Detection, Face recognition, SVM, Gabor filters, PCA-ICA.

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Authors: Mehdi Emadi, Masoud Rahgozar, Adel Ardalan, Alireza Kazerani, Mohammad Mahdi Ariyan

Abstract:

The volume of XML data exchange is explosively increasing, and the need for efficient mechanisms of XML data management is vital. Many XML storage models have been proposed for storing XML DTD-independent documents in relational database systems. Benchmarking is the best way to highlight pros and cons of different approaches. In this study, we use a common benchmarking scheme, known as XMark to compare the most cited and newly proposed DTD-independent methods in terms of logical reads, physical I/O, CPU time and duration. We show the effect of Label Path, extracting values and storing in another table and type of join needed for each method's query answering.

Keywords: XML Data Management, XPath, DTD-IndependentXML Data

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Authors: Mehdi Emadi, Masoud Rahgozar, Adel Ardalan, Alireza Kazerani, Mohammad Mahdi Ariyan

Abstract:

The volume of XML data exchange is explosively increasing, and the need for efficient mechanisms of XML data management is vital. Many XML storage models have been proposed for storing XML DTD-independent documents in relational database systems. Benchmarking is the best way to highlight pros and cons of different approaches. In this study, we use a common benchmarking scheme, known as XMark to compare the most cited and newly proposed DTD-independent methods in terms of logical reads, physical I/O, CPU time and duration. We show the effect of Label Path, extracting values and storing in another table and type of join needed for each method-s query answering.

Keywords: XML Data Management, XPath, DTD-Independent XML Data.

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Authors: Yali Zheng, Peipei Luo, Shinan Chen, Jiasheng Hao, Hong Cheng

Abstract:

Most of traditional visual indoor navigation algorithms and methods only consider the localization in ordinary daytime, while we focus on the indoor re-localization in low light in the paper. As RGB images are degraded in low light, less discriminative infrared and depth image pairs are taken, as the input, by RGB-D cameras, the most similar candidates, as the output, are searched from databases which is built in the bag-of-word framework. Epipolar constraints can be used to relocalize the query infrared and depth image sequence. We evaluate our method in two datasets captured by Kinect2. The results demonstrate very promising re-localization results for indoor navigation system in low light environments.

Keywords: Indoor navigation, low light, RGB-D camera, vision based.

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Authors: Shimal Das, Dibyendu Ghoshal

Abstract:

The effect of directional search using iterated functional system has been studied on four images taken from databases. The images are portioned successively towards smaller dimension. Presented method provides the faster rate of convergence with respect to processing time in the flat region, but the same has been found to be slower at the border of the images and edges. It has also been revealed that the PSNR is lower at the edges and border portions of the image, and it is found to be higher in the uniform gray region, under the same external illumination and external noise environment.

Keywords: Iterated functional system, fractal compression, structural similarity index measure, fractal block coding, affine transformations.

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Authors: Chaimaa Messaoudi, Rachida Fissoune, Hassan Badir

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Technological advances of computer science and data analysis are helping to provide continuously huge volumes of biological data, which are available on the web. Such advances involve and require powerful techniques for data integration to extract pertinent knowledge and information for a specific question. Biomedical exploration of these big data often requires the use of complex queries across multiple autonomous, heterogeneous and distributed data sources. Semantic integration is an active area of research in several disciplines, such as databases, information-integration, and ontology. We provide a survey of some approaches and techniques for integrating biological data, we focus on those developed in the ontology community.

Keywords: Semantic data integration, biological ontology, linked data, semantic web, OWL, RDF.

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Authors: S. Sarumathi, N. Shanthi, M. Sharmila

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Clustering in data mining is an unsupervised learning technique of aggregating the data objects into meaningful groups such that the intra cluster similarity of objects are maximized and inter cluster similarity of objects are minimized. Over the past decades several clustering tools were emerged in which clustering algorithms are inbuilt and are easier to use and extract the expected results. Data mining mainly deals with the huge databases that inflicts on cluster analysis and additional rigorous computational constraints. These challenges pave the way for the emergence of powerful expansive data mining clustering softwares. In this survey, a variety of clustering tools used in data mining are elucidated along with the pros and cons of each software.

Keywords: Cluster Analysis, Clustering Algorithms, Clustering Techniques, Association, Visualization.

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Authors: Tarandeep Singh, Parvinder S. Sandhu

Abstract:

The Economic factors are leading to the rise of infrastructures provides software and computing facilities as a service, known as cloud services or cloud computing. Cloud services can provide efficiencies for application providers, both by limiting up-front capital expenses, and by reducing the cost of ownership over time. Such services are made available in a data center, using shared commodity hardware for computation and storage. There is a varied set of cloud services available today, including application services (salesforce.com), storage services (Amazon S3), compute services (Google App Engine, Amazon EC2) and data services (Amazon SimpleDB, Microsoft SQL Server Data Services, Google-s Data store). These services represent a variety of reformations of data management architectures, and more are on the horizon.

Keywords: Data Management in Cloud, AWS, EC2, S3, SQS, TQG.

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Authors: Neerja Bhatnagar, Benjoe A. Juliano, Renee S. Renner

Abstract:

This paper presents data annotation models at five levels of granularity (database, relation, column, tuple, and cell) of relational data to address the problem of unsuitability of most relational databases to express annotations. These models do not require any structural and schematic changes to the underlying database. These models are also flexible, extensible, customizable, database-neutral, and platform-independent. This paper also presents an SQL-like query language, named Annotation Query Language (AnQL), to query annotation documents. AnQL is simple to understand and exploits the already-existent wide knowledge and skill set of SQL.

Keywords: annotation query language, data annotations, data annotation models, semantic data annotations

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Authors: Mohamed Hanine, Abdesadik Bendarag, Omar Boutkhoum

Abstract:

Currently, the field of data migration is very topical. As the number of applications developed rapidly, the ever-increasing volume of data collected has driven the architectural migration from Relational Database Management System (RDBMS) to NoSQL (Not Only SQL) database. This very recent technology is important enough in the field of database management. The main aim of this paper is to present a methodology for data migration from RDBMS to NoSQL database. To illustrate this methodology, we implement a software prototype using MySQL as a RDBMS and MongoDB as a NoSQL database. Although this is a hard engineering work, our results show that the proposed methodology can successfully accomplish the goal of this study.

Keywords: Data Migration, MySQL, RDBMS, NoSQL, MongoDB.

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Authors: Sarra Senhadji, Imene Benzeguimi, Zohra Yagoub

Abstract:

Big Data represents the recent technology of manipulating voluminous and unstructured data sets over multiple sources. Therefore, NOSQL appears to handle the problem of unstructured data. Association rules mining is one of the popular techniques of data mining to extract hidden relationship from transactional databases. The algorithm for finding association dependencies is well-solved with Map Reduce. The goal of our work is to reduce the time of generating of frequent itemsets by using Map Reduce and NOSQL database oriented document. A comparative study is given to evaluate the performances of our algorithm with the classical algorithm Apriori.

Keywords: Apriori, Association rules mining, Big Data, data mining, Hadoop, Map Reduce, MongoDB, NoSQL.

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