Search results for: Text Mining

Authors: Carlo Bellettini, Alessandro Marchetto, Andrea Trentini

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Web applications have become very complex and crucial, especially when combined with areas such as CRM (Customer Relationship Management) and BPR (Business Process Reengineering), the scientific community has focused attention to Web applications design, development, analysis, and testing, by studying and proposing methodologies and tools. This paper proposes an approach to automatic multi-dimensional concern mining for Web Applications, based on concepts analysis, impact analysis, and token-based concern identification. This approach lets the user to analyse and traverse Web software relevant to a particular concern (concept, goal, purpose, etc.) via multi-dimensional separation of concerns, to document, understand and test Web applications. This technique was developed in the context of WAAT (Web Applications Analysis and Testing) project. A semi-automatic tool to support this technique is currently under development.

Keywords: Concepts Analysis, Concerns Mining, Multi-Dimensional Separation of Concerns, Impact Analysis.

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Authors: Abd Mutalib Embong, Azelin M Noor, Razol Mahari M Ali, Zulqarnain Abu Bakar, Abdur- Rahman Mohamed Amin

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At the time where electronic books, or e-Books, offer students a fun way of learning , teachers who are used to the paper text books may find it as a new challenge to use it as a part of learning process. Precisely, there are various types of e-Books available to suit students- knowledge, characteristics, abilities, and interests. The paper discusses teachers- perceptions on the use of ebooks as a paper text book in the classroom. A survey was conducted on 72 teachers who use e-books as textbooks. It was discovered that a majority of these teachers had good perceptions on the use of ebooks. However, they had little problems using the devices. It can be overcome with some strategies and a suggested framework.

Keywords: Classroom, E-books, perception, teacher.

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Authors: L. A. Muhammad, M. S. Argungu

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Educational data mining (EDM) has recorded substantial considerations. Techniques of data mining in one way or the other have been proposed to dig out out-of-sight knowledge in educational data. The result of the study got assists academic institutions in further enhancing their process of learning and methods of passing knowledge to students. Consequently, the performance of students boasts and the educational products are by no doubt enhanced. This study adopted a student performance prediction model premised on techniques of data mining with Students' Essential Features (SEF). SEF are linked to the learner's interactivity with the e-learning management system. The performance of the student's predictive model is assessed by a set of classifiers, viz. Bayes Network, Logistic Regression, and Reduce Error Pruning Tree (REP). Consequently, ensemble methods of Bagging, Boosting, and Random Forest (RF) are applied to improve the performance of these single classifiers. The study reveals that the result shows a robust affinity between learners' behaviors and their academic attainment. Result from the study shows that the REP Tree and its ensemble record the highest accuracy of 83.33% using SEF. Hence, in terms of the Receiver Operating Curve (ROC), boosting method of REP Tree records 0.903, which is the best. This result further demonstrates the dependability of the proposed model.

Keywords: Ensemble, bagging, Random Forest, boosting, data mining, classifiers, machine learning.

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Authors: Dewan Md. Farid, Nouria Harbi, Suman Ahmmed, Md. Zahidur Rahman, Chowdhury Mofizur Rahman

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Network security attacks are the violation of information security policy that received much attention to the computational intelligence society in the last decades. Data mining has become a very useful technique for detecting network intrusions by extracting useful knowledge from large number of network data or logs. Naïve Bayesian classifier is one of the most popular data mining algorithm for classification, which provides an optimal way to predict the class of an unknown example. It has been tested that one set of probability derived from data is not good enough to have good classification rate. In this paper, we proposed a new learning algorithm for mining network logs to detect network intrusions through naïve Bayesian classifier, which first clusters the network logs into several groups based on similarity of logs, and then calculates the prior and conditional probabilities for each group of logs. For classifying a new log, the algorithm checks in which cluster the log belongs and then use that cluster-s probability set to classify the new log. We tested the performance of our proposed algorithm by employing KDD99 benchmark network intrusion detection dataset, and the experimental results proved that it improves detection rates as well as reduces false positives for different types of network intrusions.

Keywords: Clustering, detection rate, false positive, naïveBayesian classifier, network intrusion detection.

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Authors: Donna R. Tabangin, Jacqueline C. Flores, Nelson F. Emperador

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Information is power. Geographical information is an emerging science that is advancing the development of knowledge to further help in the understanding of the relationship of “place" with other disciplines such as crime. The researchers used crime data for the years 2004 to 2007 from the Baguio City Police Office to determine the incidence and actual locations of crime hotspots. Combined qualitative and quantitative research methodology was employed through extensive fieldwork and observation, geographic visualization with Geographic Information Systems (GIS) and Global Positioning Systems (GPS), and data mining. The paper discusses emerging geographic visualization and data mining tools and methodologies that can be used to generate baseline data for environmental initiatives such as urban renewal and rejuvenation. The study was able to demonstrate that crime hotspots can be computed and were seen to be occurring to some select places in the Central Business District (CBD) of Baguio City. It was observed that some characteristics of the hotspot places- physical design and milieu may play an important role in creating opportunities for crime. A list of these environmental attributes was generated. This derived information may be used to guide the design or redesign of the urban environment of the City to be able to reduce crime and at the same time improve it physically.

Keywords: Crime mapping, data mining, environmental design, geographic visualization, GIS.

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Authors: Pejman Hosseinioun, Hasan Shakeri, Ghasem Ghorbanirostam

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In recent years, we have seen an increasing importance of research and study on knowledge source, decision support systems, data mining and procedure of knowledge discovery in data bases and it is considered that each of these aspects affects the others. In this article, we have merged information source and knowledge source to suggest a knowledge based system within limits of management based on storing and restoring of knowledge to manage information and improve decision making and resources. In this article, we have used method of data mining and Apriori algorithm in procedure of knowledge discovery one of the problems of Apriori algorithm is that, a user should specify the minimum threshold for supporting the regularity. Imagine that a user wants to apply Apriori algorithm for a database with millions of transactions. Definitely, the user does not have necessary knowledge of all existing transactions in that database, and therefore cannot specify a suitable threshold. Our purpose in this article is to improve Apriori algorithm. To achieve our goal, we tried using fuzzy logic to put data in different clusters before applying the Apriori algorithm for existing data in the database and we also try to suggest the most suitable threshold to the user automatically.

Keywords: Decision support system, data mining, knowledge discovery, data discovery, fuzzy logic.

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Authors: A. Faro, D. Giordano, F. Maiorana

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Data mining uses a variety of techniques each of which is useful for some particular task. It is important to have a deep understanding of each technique and be able to perform sophisticated analysis. In this article we describe a tool built to simulate a variation of the Kohonen network to perform unsupervised clustering and support the entire data mining process up to results visualization. A graphical representation helps the user to find out a strategy to optmize classification by adding, moving or delete a neuron in order to change the number of classes. The tool is also able to automatically suggest a strategy for number of classes optimization.The tool is used to classify macroeconomic data that report the most developed countries? import and export. It is possible to classify the countries based on their economic behaviour and use an ad hoc tool to characterize the commercial behaviour of a country in a selected class from the analysis of positive and negative features that contribute to classes formation.

Keywords: Unsupervised classification, Kohonen networks, macroeconomics, Visual data mining, cluster interpretation.

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Authors: Amna Javed, Hisashi Masuda, Youji Kohda

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This study examines the value analysis in Islamic and conventional banking services in Pakistan. Many scholars have focused on co-creation of values in services but mainly economic values not non-economic. As Islamic banking is based on Islamic principles that are more concerned with non-economic values (well-being, partnership, fairness, trust worthy, and justice) than economic values as money in terms of interest.  This study is important to know the providers point of view about the co-created values, because, it may be more sustainable and appropriate for today’s unpredictable socio-economic environment. Data were collected from 4 banks (2 Islamic and 2 conventional banks). Text mining technique is applied for data analysis, and values with 100% occurrences in Islamic banking are chosen. The results reflect that Islamic banking is more centric towards non-economic values than economic values and it promotes team work and partnership concept by applying Islamic spirit and trust worthiness concept.

Keywords: Economic values, Islamic banking, Non-economic values, Value system.

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Authors: Kanso Hassan, Elhore Ali, Soule-dupuy Chantal, Tazi Said

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Information Retrieval has the objective of studying models and the realization of systems allowing a user to find the relevant documents adapted to his need of information. The information search is a problem which remains difficult because the difficulty in the representing and to treat the natural languages such as polysemia. Intentional Structures promise to be a new paradigm to extend the existing documents structures and to enhance the different phases of documents process such as creation, editing, search and retrieval. The intention recognition of the author-s of texts can reduce the largeness of this problem. In this article, we present intentions recognition system is based on a semi-automatic method of extraction the intentional information starting from a corpus of text. This system is also able to update the ontology of intentions for the enrichment of the knowledge base containing all possible intentions of a domain. This approach uses the construction of a semi-formal ontology which considered as the conceptualization of the intentional information contained in a text. An experiments on scientific publications in the field of computer science was considered to validate this approach.

Keywords: Information research, text analyzes, intentionalstructure, segmentation, ontology, natural language processing.

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Authors: Z. Abdullah, A. R. Hamdan

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Clustering is a process of grouping objects and data into groups of clusters to ensure that data objects from the same cluster are identical to each other. Clustering algorithms in one of the area in data mining and it can be classified into partition, hierarchical, density based and grid based. Therefore, in this paper we do survey and review four major hierarchical clustering algorithms called CURE, ROCK, CHAMELEON and BIRCH. The obtained state of the art of these algorithms will help in eliminating the current problems as well as deriving more robust and scalable algorithms for clustering.

Keywords: Clustering, method, algorithm, hierarchical, survey.

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Authors: Zhibo Xiao, Tharini Nayanika de Silva, Kezhi Mao

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In this paper, we present an evolving knowledge extraction system named AKEOS (Automatic Knowledge Extraction from Online Sources). AKEOS consists of two modules, including a one-time learning module and an evolving learning module. The one-time learning module takes in user input query, and automatically harvests knowledge from online unstructured resources in an unsupervised way. The output of the one-time learning is a structured vector representing the harvested knowledge. The evolving learning module automatically schedules and performs repeated one-time learning to extract the newest information and track the development of an event. In addition, the evolving learning module summarizes the knowledge learned at different time points to produce a final knowledge vector about the event. With the evolving learning, we are able to visualize the key information of the event, discover the trends, and track the development of an event.

Keywords: Evolving learning, knowledge extraction, knowledge graph, text mining.

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Authors: Nhien-An Le-Khac, Sammer Markos, M-Tahar Kechadi

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Today, money laundering (ML) poses a serious threat not only to financial institutions but also to the nation. This criminal activity is becoming more and more sophisticated and seems to have moved from the cliché of drug trafficking to financing terrorism and surely not forgetting personal gain. Most international financial institutions have been implementing anti-money laundering solutions (AML) to fight investment fraud. However, traditional investigative techniques consume numerous man-hours. Recently, data mining approaches have been developed and are considered as well-suited techniques for detecting ML activities. Within the scope of a collaboration project for the purpose of developing a new solution for the AML Units in an international investment bank, we proposed a data mining-based solution for AML. In this paper, we present a heuristics approach to improve the performance for this solution. We also show some preliminary results associated with this method on analysing transaction datasets.

Keywords: data mining, anti money laundering, clustering, heuristics.

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Authors: Mahsa Tavakoli, Seyed Mohammad Hojjati, Yahya Kooch

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In this study, the effect of coal mining activities on lead and cadmium concentrations and distribution in soil was investigated in Hyrcanian forest, North Iran. 16 plots (20×20 m²) were established by systematic-randomly (60×60 m²) in an area of 4 ha (200×200 m²-mine entrance placed at center). An area adjacent to the mine was not affected by the mining activity; considered as the controlled area. In order to investigate soil lead and cadmium concentration, one sample was taken from the 0-10 cm in each plot. To study the spatial pattern of soil properties and lead and cadmium concentrations in the mining area, an area of 80×80m² (the mine as the center) was considered and 80 soil samples were systematic-randomly taken (10 m intervals). Geostatistical analysis was performed via Kriging method and GS⁺ software (version 5.1). In order to estimate the impact of coal mining activities on soil quality, pollution index was measured. Lead and cadmium concentrations were significantly higher in mine area (Pb: 10.97±0.30, Cd: 184.47±6.26 mg.kg^-1) in comparison to control area (Pb: 9.42±0.17, Cd: 131.71±15.77 mg.kg^-1). The mean values of the PI index indicate that Pb (1.16) and Cd (1.77) presented slightly polluted. Results of the NIPI index showed that Pb (1.44) and Cd (2.52) presented slight pollution and moderate pollution respectively. Results of variography and kriging method showed that it is possible to prepare interpolation maps of lead and cadmium around the mining areas in Hyrcanian forest. According to results of pollution and risk assessments, forest soil was contaminated by heavy metals (lead and cadmium); therefore, using reclamation and remediation techniques in these areas is necessary.

Keywords: Traditional coal mining, heavy metals, pollution indicators, geostatistics, caspian forest.

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Authors: Xu Han, Shanxiong Chen, Shiyu Zhu, Xiaoyu Lin, Fujia Zhao, Dingwang Wang

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Character detection is an important issue for character recognition of ancient Yi books. The accuracy of detection directly affects the recognition effect of ancient Yi books. Considering the complex layout, the lack of standard typesetting and the mixed arrangement between images and texts, we propose a character detection method for ancient Yi books based on connected components and regressive character segmentation. First, the scanned images of ancient Yi books are preprocessed with nonlocal mean filtering, and then a modified local adaptive threshold binarization algorithm is used to obtain the binary images to segment the foreground and background for the images. Second, the non-text areas are removed by the method based on connected components. Finally, the single character in the ancient Yi books is segmented by our method. The experimental results show that the method can effectively separate the text areas and non-text areas for ancient Yi books and achieve higher accuracy and recall rate in the experiment of character detection, and effectively solve the problem of character detection and segmentation in character recognition of ancient books.

Keywords: Computing methodologies, interest point, salient region detections, image segmentation.

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Authors: U. Sakthi, R. Hemalatha, R. S. Bhuvaneswaran

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In Virtual organization, Knowledge Discovery (KD) service contains distributed data resources and computing grid nodes. Computational grid is integrated with data grid to form Knowledge Grid, which implements Apriori algorithm for mining association rule on grid network. This paper describes development of parallel and distributed version of Apriori algorithm on Globus Toolkit using Message Passing Interface extended with Grid Services (MPICHG2). The creation of Knowledge Grid on top of data and computational grid is to support decision making in real time applications. In this paper, the case study describes design and implementation of local and global mining of frequent item sets. The experiments were conducted on different configurations of grid network and computation time was recorded for each operation. We analyzed our result with various grid configurations and it shows speedup of computation time is almost superlinear.

Keywords: Association rule, Grid computing, Knowledge grid, Mobility prediction.

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Authors: R.M.Rani

Abstract:

Chess is one of the indoor games, which improves the level of human confidence, concentration, planning skills and knowledge. The main objective of this paper is to help the chess players to improve their chess openings using data mining techniques. Budding Chess Players usually do practices by analyzing various existing openings. When they analyze and correlate thousands of openings it becomes tedious and complex for them. The work done in this paper is to analyze the best lines of Blackmar- Diemer Gambit(BDG) which opens with White D4... using data mining analysis. It is carried out on the collection of winning games by applying association rules. The first step of this analysis is assigning variables to each different sequence moves. In the second step, the sequence association rules were generated to calculate support and confidence factor which help us to find the best subsequence chess moves that may lead to winning position.

Keywords: Blackmar-Diemer Gambit(BDG), Confidence, sequence Association Rules, Support.

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Authors: Sannikumar Patel, Brian Nolan, Markus Hofmann, Philip Owende, Kunjan Patel

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Sentiment analysis and opinion mining have become emerging topics of research in recent years but most of the work is focused on data in the English language. A comprehensive research and analysis are essential which considers multiple languages, machine translation techniques, and different classifiers. This paper presents, a comparative analysis of different approaches for multilingual sentiment analysis. These approaches are divided into two parts: one using classification of text without language translation and second using the translation of testing data to a target language, such as English, before classification. The presented research and results are useful for understanding whether machine translation should be used for multilingual sentiment analysis or building language specific sentiment classification systems is a better approach. The effects of language translation techniques, features, and accuracy of various classifiers for multilingual sentiment analysis is also discussed in this study.

Keywords: Cross-language analysis, machine learning, machine translation, sentiment analysis.

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Authors: Khairuddin Bin Osman, Ngo Boon Kiat, A. Hamid Bin hamidon, Khairul Azha Bin A. Aziz, Hazli Rafis Bin Abdul Rahman, Mazran Bin Esro

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Conventional industrial monitoring systems are tedious, inefficient and the at times integrity of the data is unreliable. The objective of this system is to monitor industrial processes specifically the fluid level which will measure the instantaneous fluid level parameter and respond by text messaging the exact value of the parameter to the user when being enquired by a privileged access user. The development of the embedded program code and the circuit for fluid level measuring are discussed as well. Suggestions for future implementations and efficient remote monitoring works are included.

Keywords: Industrial monitoring system, text messaging, embedded programming.

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Authors: Heba Mohammed Nagy, Walid Mohamed Aly, Osama Fathy Hegazy

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Educational  data mining  is  a  specific  data   mining field applied to data originating from educational environments, it relies on different  approaches to discover hidden knowledge  from  the  available   data. Among these approaches are   machine   learning techniques which are used to build a system that acquires learning from previous data. Machine learning can be applied to solve different regression, classification, clustering and optimization problems.

In  our  research, we propose  a “Student  Advisory  Framework” that  utilizes  classification  and  clustering  to  build  an  intelligent system. This system can be used to provide pieces of consultations to a first year  university  student to  pursue a  certain   education   track   where  he/she  will  likely  succeed  in, aiming  to  decrease   the  high  rate   of  academic  failure   among these  students.  A real case study  in Cairo  Higher  Institute  for Engineering, Computer  Science  and  Management  is  presented using  real  dataset   collected  from  2000−2012.The dataset has two main components: pre-higher education dataset and first year courses results dataset. Results have proved the efficiency of the suggested framework.

Keywords: Classification, Clustering, Educational Data Mining (EDM), Machine Learning.

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Authors: J. K. Alhassan, S. A. Lawal

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This work is on decision tree-based classification for the disbursement of scholarship. Tree-based data mining classification technique is used in other to determine the generic rule to be used to disburse the scholarship. The system based on the defined rules from the tree is able to determine the class (status) to which an applicant shall belong whether Granted or Not Granted. The applicants that fall to the class of granted denote a successful acquirement of scholarship while those in not granted class are unsuccessful in the scheme. An algorithm that can be used to classify the applicants based on the rules from tree-based classification was also developed. The tree-based classification is adopted because of its efficiency, effectiveness, and easy to comprehend features. The system was tested with the data of National Information Technology Development Agency (NITDA) Abuja, a Parastatal of Federal Ministry of Communication Technology that is mandated to develop and regulate information technology in Nigeria. The system was found working according to the specification. It is therefore recommended for all scholarship disbursement organizations.

Keywords: Decision tree, classification, data mining, scholarship.

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Authors: Yuzana, Khin Marlar Tun

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Personal name matching system is the core of essential task in national citizen database, text and web mining, information retrieval, online library system, e-commerce and record linkage system. It has necessitated to the all embracing research in the vicinity of name matching. Traditional name matching methods are suitable for English and other Latin based language. Asian languages which have no word boundary such as Myanmar language still requires sounds alike matching system in Unicode based application. Hence we proposed matching algorithm to get analogous sounds alike (phonetic) pattern that is convenient for Myanmar character spelling. According to the nature of Myanmar character, we consider for word boundary fragmentation, collation of character. Thus we use pattern conversion algorithm which fabricates words in pattern with fragmented and collated. We create the Myanmar sounds alike phonetic group to help in the phonetic matching. The experimental results show that fragmentation accuracy in 99.32% and processing time in 1.72 ms.

Keywords: natural language processing, name matching, phonetic matching

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Authors: Uzair Muhammad, Kashif Bilal, Atif Khan, M. Nasir Khan

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Machine Translation (MT 3) of English text to its Urdu equivalent is a difficult challenge. Lot of attempts has been made, but a few limited solutions are provided till now. We present a direct approach, using an expert system to translate English text into its equivalent Urdu, using The Unicode Standard, Version 4.0 (ISBN 0-321-18578-1) Range: 0600–06FF. The expert system works with a knowledge base that contains grammatical patterns of English and Urdu, as well as a tense and gender-aware dictionary of Urdu words (with their English equivalents).

Keywords: Machine Translation, Multiword Expressions, Urdulanguage processing, POS12 Tagging for Urdu, Expert Systems.

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Authors: Usha Lokala, Srinivas Nowduri, Prabhakar K. Sharma

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Useful information has been extracted from the road accident data in United Kingdom (UK), using data analytics method, for avoiding possible accidents in rural and urban areas. This analysis make use of several methodologies such as data integration, support vector machines (SVM), correlation machines and multinomial goodness. The entire datasets have been imported from the traffic department of UK with due permission. The information extracted from these huge datasets forms a basis for several predictions, which in turn avoid unnecessary memory lapses. Since data is expected to grow continuously over a period of time, this work primarily proposes a new framework model which can be trained and adapt itself to new data and make accurate predictions. This work also throws some light on use of SVM’s methodology for text classifiers from the obtained traffic data. Finally, it emphasizes the uniqueness and adaptability of SVMs methodology appropriate for this kind of research work.

Keywords: Road accident, machine learning, support vector machines.

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Authors: Nevena Stolba, A Min Tjoa

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Evidence-based medicine is a new direction in modern healthcare. Its task is to prevent, diagnose and medicate diseases using medical evidence. Medical data about a large patient population is analyzed to perform healthcare management and medical research. In order to obtain the best evidence for a given disease, external clinical expertise as well as internal clinical experience must be available to the healthcare practitioners at right time and in the right manner. External evidence-based knowledge can not be applied directly to the patient without adjusting it to the patient-s health condition. We propose a data warehouse based approach as a suitable solution for the integration of external evidence-based data sources into the existing clinical information system and data mining techniques for finding appropriate therapy for a given patient and a given disease. Through integration of data warehousing, OLAP and data mining techniques in the healthcare area, an easy to use decision support platform, which supports decision making process of care givers and clinical managers, is built. We present three case studies, which show, that a clinical data warehouse that facilitates evidence-based medicine is a reliable, powerful and user-friendly platform for strategic decision making, which has a great relevance for the practice and acceptance of evidence-based medicine.

Keywords: data mining, data warehousing, decision-support systems, evidence-based medicine.

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Authors: Hemant Kumar, R. N. K. Sharma, A. P. Krishna

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The study has been carried out on the Saranda in Jharkhand and a part of Odisha state. Geospatial data of Hyperion, a remote sensing satellite, have been used. This study has used a wide variety of patterns related to image processing to enhance and extract the mining class of Fe and Mn ores.Landsat-8, OLI sensor data have also been used to correctly explore related minerals. In this way, various processes have been applied to increase the mineralogy class and comparative evaluation with related frequency done. The Hyperion dataset for hyperspectral remote sensing has been specifically verified as an effective tool for mineral or rock information extraction within the band range of shortwave infrared used. The abundant spatial and spectral information contained in hyperspectral images enables the differentiation of different objects of any object into targeted applications for exploration such as exploration detection, mining.

Keywords: Hyperion, hyperspectral, sensor, Landsat-8.

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Authors: Tadayuki Kyoutani, Shigeyuki Haruyama, Ken Kaminishi, Zefry Darmawan

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Japan’s semiconductor industries have developed greatly in recent years. Many were started from a Small and Medium-sized Enterprises (SMEs) that found at a good circumstance and now become the prosperous industries in the world. Sustainable growth factors that support the creation of spirit value inside the Japanese company were strongly embedded through performance. Those factors were not clearly defined among each company. A series of literature research conducted to explore quantitative text mining about the definition of sustainable growth factors. Sustainable criteria were developed from previous research to verify the definition of the factors. A typical frame work was proposed as a systematical approach to develop sustainable growth factor in a specific company. Result of approach was review in certain period shows that factors influenced in sustainable growth was importance for the company to achieve the goal.

Keywords: SME, manufacture, sustainable, growth factor.

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Authors: Nur Atiqah Rochin Demong, Jie Lu, Farookh Khadeer Hussain

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Property investment in the real estate industry has a high risk due to the uncertainty factors that will affect the decisions made and high cost. Analytic hierarchy process has existed for some time in which referred to an expert-s opinion to measure the uncertainty of the risk factors for the risk analysis. Therefore, different level of experts- experiences will create different opinion and lead to the conflict among the experts in the field. The objective of this paper is to propose a new technique to measure the uncertainty of the risk factors based on multidimensional data model and data mining techniques as deterministic approach. The propose technique consist of a basic framework which includes four modules: user, technology, end-user access tools and applications. The property investment risk analysis defines as a micro level analysis as the features of the property will be considered in the analysis in this paper.

Keywords: Uncertainty factors, data mining, multidimensional data model, risk analysis.

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Authors: L. Godinho, N. Teixeira

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Nowadays, massification of Internet access represents one of the major challenges to the traditional powers of the State, among which the power to control its external image. The virtual world has also sparked the interest of social sciences which consider it a new field of study, an immense open text where sense is expressed. In this paper, that immense text has been accessed to so as to understand the perception Internet users from all over the world have of Portugal. Ours is a quantitative and qualitative approach, as we have resorted to buzz, thematic and category analysis. The results confirm the predominance of sea stereotype in others' vision of the Portuguese people, and evidence that national image has adapted to network communication through processes of individuation and paganization.

Keywords: Internet, national image, perception, web analytics.

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Authors: Z. Shaaban

Abstract:

This paper presents a new approach to tackle the problem of recognizing machine-printed Arabic texts. Because of the difficulty of recognizing cursive Arabic words, the text has to be normalized and segmented to be ready for the recognition stage. The new scheme for recognizing Arabic characters depends on multiple parallel neural networks classifier. The classifier has two phases. The first phase categories the input character into one of eight groups. The second phase classifies the character into one of the Arabic character classes in the group. The system achieved high recognition rate.

Keywords: Neural Networks, character recognition, feature extraction, multiple networks, Arabic text.

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Authors: Kyoung-jae Kim

Abstract:

Recommender systems are usually regarded as an important marketing tool in the e-commerce. They use important information about users to facilitate accurate recommendation. The information includes user context such as location, time and interest for personalization of mobile users. We can easily collect information about location and time because mobile devices communicate with the base station of the service provider. However, information about user interest can-t be easily collected because user interest can not be captured automatically without user-s approval process. User interest usually represented as a need. In this study, we classify needs into two types according to prior research. This study investigates the usefulness of data mining techniques for classifying user need type for recommendation systems. We employ several data mining techniques including artificial neural networks, decision trees, case-based reasoning, and multivariate discriminant analysis. Experimental results show that CHAID algorithm outperforms other models for classifying user need type. This study performs McNemar test to examine the statistical significance of the differences of classification results. The results of McNemar test also show that CHAID performs better than the other models with statistical significance.

Keywords: Customer need type, Data mining techniques, Recommender system, Personalization, Mobile user.

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