Search results for: multiclass support vector machines.

Authors: Reza Mohammadi, Mahmod R. Sahebi, Mehrnoosh Omati, Milad Vahidi

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Classification of high resolution polarimetric Synthetic Aperture Radar (PolSAR) images plays an important role in land cover and land use management. Recently, classification algorithms based on Bag of Visual Words (BOVW) model have attracted significant interest among scholars and researchers in and out of the field of remote sensing. In this paper, BOVW model with pixel based low-level features has been implemented to classify a subset of San Francisco bay PolSAR image, acquired by RADARSAR 2 in C-band. We have used segment-based decision-making strategy and compared the result with the result of traditional Support Vector Machine (SVM) classifier. 90.95% overall accuracy of the classification with the proposed algorithm has shown that the proposed algorithm is comparable with the state-of-the-art methods. In addition to increase in the classification accuracy, the proposed method has decreased undesirable speckle effect of SAR images.

Keywords: Bag of Visual Words, classification, feature extraction, land cover management, Polarimetric Synthetic Aperture Radar.

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Authors: Amon Lasomboon

Abstract:

Since the world printing industry has to confront globalization with a constant change, the Thai printing industry, as a small but increasingly significant part of the world printing industry, cannot inevitably escape but has to encounter with the similar change and also the need to revamp its production processes, designs and technology to make them more appealing to both international and domestic market. The essential question is what is the Thai competitive edge in the printing industry in changing environment? This research is aimed to study the Thai level of competitive edge in terms of marketing, technology, environment friendly, and the level of satisfaction of the process of using printing machines. To access the extent to which is the trends in competitiveness of Thai printing industry, both quantitative and qualitative study were conducted. The quantitative analysis was restricted to 100 respondents. The qualitative analysis was restricted to a focus group of 10 individuals from various backgrounds in the Thai printing industry. The findings from the quantitative analysis revealed that the overall mean scores are 4.53, 4.10, and 3.50 for the competitiveness of marketing, the competitiveness of technology, and the competitiveness of being environment friendly respectively. However, the level of satisfaction for the process of using machines has a mean score only 3.20. The findings from the qualitative analysis have revealed that target customers have increasingly reordered due to their contentment in both low prices and the acceptable quality of the products. Moreover, the Thai printing industry has a tendency to convert to ambient green technology which is friendly to the environment. The Thai printing industry is choosing to produce or substitute with products that are less damaging to the environment. It is also found that the Thai printing industry has been transformed into a very competitive industry which bargaining power rests on consumers who have a variety of choices.

Keywords: Competitiveness, Printing Industry

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Authors: Tzyy Rong Jinn, Nguyen Tiep Khac, Tzong Yuan Wu

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Highly pathogenic avian influenza (HPAI) H5N1 viruses have created demand for a cost-effective vaccine to prevent a pandemic of the disease. Here, we report that Trichoplusia ni (T. ni) larvae can act as a cost-effective bioreactor to produce recombinant HA5 (rH5HA) proteins as an potential effective vaccine for chickens. To facilitate the recombinant virus identification, virus titer determination and access the infected larvae, we employed the internal ribosome entry site (IRES) derived from Perina nuda virus (PnV, belongs to insect picorna like Iflavirus genus) to construct a bi-cistronic baculovirus expression vector that can express the rH5HA protein and enhanced green fluorescent protein (EGFP) simultaneously. Western blot analysis revealed that the 70 kDa rH5HA protein and partially cleaved products (40 kDa H5HA1) were generated in T. ni larvae infected with recombinant baculovirus carrying the H5HA gene. These data suggest that the baculovirus-larvae recombinant protein expression system could be a cost-effective platform for H5N1 vaccine production.

Keywords: Avian Influenza, baculovirus, hemagglutinin, Trichoplusia ni larvae

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Authors: Anukriti Kumar, Tanmay Singh, Dinesh Kumar Vishwakarma

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Traffic control has been one of the most common and irritating problems since the time automobiles have hit the roads. Problems like traffic congestion have led to a significant time burden around the world and one significant solution to these problems can be the proper implementation of the Intelligent Transport System (ITS). It involves the integration of various tools like smart sensors, artificial intelligence, position technologies and mobile data services to manage traffic flow, reduce congestion and enhance driver's ability to avoid accidents during adverse weather. Road and traffic signs’ recognition is an emerging field of research in ITS. Classification problem of traffic signs needs to be solved as it is a major step in our journey towards building semi-autonomous/autonomous driving systems. The purpose of this work focuses on implementing an approach to solve the problem of traffic sign classification by developing a Convolutional Neural Network (CNN) classifier using the GTSRB (German Traffic Sign Recognition Benchmark) dataset. Rather than using hand-crafted features, our model addresses the concern of exploding huge parameters and data method augmentations. Our model achieved an accuracy of around 97.6% which is comparable to various state-of-the-art architectures.

Keywords: Multiclass classification, convolution neural network, OpenCV, Data Augmentation.

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Authors: S. Nandagopalan, N. Pradeep

Abstract:

The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: Active Contour, Bayesian, Echocardiographic image, Feature vector.

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Authors: Nalinee Sophatsathit

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This research focuses on the use of a recommender system in decision support by means of a used car dealer case study in Bangkok Metropolitan. The goal is to develop an effective used car purchasing system for dealers based on the above premise. The underlying principle rests on content-based recommendation from a set of usability surveys. A prototype was developed to conduct buyers- survey selected from 5 experts and 95 general public. The responses were analyzed to determine the mean and standard deviation of buyers- preference. The results revealed that both groups were in favor of using the proposed system to assist their buying decision. This indicates that the proposed system is meritorious to used car dealers.

Keywords: Recommender Systems, Decision Support, Content- Based Recommendation, used car dealer.

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Authors: Vassilios Moussas, Dimos N. Pantazis, Panagiotis Stratakis

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The aim of this paper is to present the optimization methodology developed in the frame of a Coastal Transport Information System. The system will be used for the effective design of coastal transportation lines and incorporates subsystems that implement models, tools and techniques that may support the design of improved networks. The role of the optimization and decision subsystem is to provide the user with better and optimal scenarios that will best fulfill any constrains, goals or requirements posed. The complexity of the problem and the large number of parameters and objectives involved led to the adoption of an evolutionary method (Genetic Algorithms). The problem model and the subsystem structure are presented in detail, and, its support for simulation is also discussed.

Keywords: Coastal transport, modeling, optimization.

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Authors: Manpreet Singh, Parvinder Singh, Sumitter Bir Singh

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The emergence of information technology has resulted in an ever-increasing demand to use computers for the efficient management and dissemination of information. Keeping in view the strong need of farmers to collect important and updated information for interactive, flexible and quick decision-making, a model of Decision Support System for Farm Management is developed. The paper discusses the use of Internet technology for the farmers to take decisions. A model is developed for the farmers to access online interactive and flexible information for their farm management. The workflow of the model is presented highlighting the information transfer between different modules.

Keywords: Decision Support System, dissemination.

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Authors: Achut Manandhar, Kenneth D. Morton, Peter A. Torrione, Leslie M. Collins

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The current trends in affect recognition research are to consider continuous observations from spontaneous natural interactions in people using multiple feature modalities, and to represent affect in terms of continuous dimensions, incorporate spatio-temporal correlation among affect dimensions, and provide fast affect predictions. These research efforts have been propelled by a growing effort to develop affect recognition system that can be implemented to enable seamless real-time human-computer interaction in a wide variety of applications. Motivated by these desired attributes of an affect recognition system, in this work a multi-dimensional affect prediction approach is proposed by integrating multivariate Relevance Vector Machine (MVRVM) with a recently developed Output-associative Relevance Vector Machine (OARVM) approach. The resulting approach can provide fast continuous affect predictions by jointly modeling the multiple affect dimensions and their correlations. Experiments on the RECOLA database show that the proposed approach performs competitively with the OARVM while providing faster predictions during testing.

Keywords: Dimensional affect prediction, Output-associative RVM, Multivariate regression.

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Authors: Mais Haj Qasem, Maen M. Al Assaf, Ali Rodan

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Parallel hybrid storage systems consist of a hierarchy of different storage devices that vary in terms of data reading speed performance. As we ascend in the hierarchy, data reading speed becomes faster. Thus, migrating the application’ important data that will be accessed in the near future to the uppermost level will reduce the application I/O waiting time; hence, reducing its execution elapsed time. In this research, we implement trace-driven two-levels parallel hybrid storage system prototype that consists of HDDs and SSDs. The prototype uses data mining techniques to classify application’ data in order to determine its near future data accesses in parallel with the its on-demand request. The important data (i.e. the data that the application will access in the near future) are continuously migrated to the uppermost level of the hierarchy. Our simulation results show that our data migration approach integrated with data mining techniques reduces the application execution elapsed time when using variety of traces in at least to 22%.

Keywords: Data mining, hybrid storage system, recurrent neural network, support vector machine.

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Authors: M. Mohsenzadeh, F. Shams, M. Teshnehlab

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Current research on semantic web aims at making intelligent web pages meaningful for machines. In this way, ontology plays a primary role. We believe that logic can help ontology languages (such as OWL) to be more fluent and efficient. In this paper we try to combine logic with OWL to reduce some disadvantages of this language. Therefore we extend OWL by logic and also show how logic can satisfy our future expectations of an ontology language.

Keywords: Logical Programming, OWL, Language Extension.

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Authors: Hiroaki Sumi, Kiichiro Hayashi

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Now, the use of renewable energy power generation has been advanced. In this paper, we compared the usable amount of resource for photovoltaic power generation which was estimated using the NEDO formula and the expansion support policy of photovoltaic power generation which was researched using Internet in the municipality level in Hokkaido and Aichi Prefecture, Japan. This paper will contribute to grasp the current situation especially about the policy. As a result, there were municipalities which seemed to be no consideration of fitting the amount of resources. We think it would need to consider the suitability between the resources and policies.

Keywords: Photovoltaic power generation, expansion support policy, amount of resources, Japan.

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Authors: Kunyanuth Kularbphettong, Cholticha Tongsiri

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This paper aims to create the model for student in choosing an emphasized track of student majoring in computer science at Suan Sunandha Rajabhat University. The objective of this research is to develop the suggested system using data mining technique to analyze knowledge and conduct decision rules. Such relationships can be used to demonstrate the reasonableness of student choosing a track as well as to support his/her decision and the system is verified by experts in the field. The sampling is from student of computer science based on the system and the questionnaire to see the satisfaction. The system result is found to be satisfactory by both experts and student as well. 

Keywords: Data mining technique, the decision support system, knowledge and decision rules.

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Authors: Mahbub C. Mishu, Venketesh N. Dubey, Tamas Hickish, Jonathan Cole

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Pressure ulcer is a common problem for today’s healthcare industry. It occurs due to external load applied to the skin. Also when the subject is immobile for a longer period of time and there is continuous load applied to a particular area of human body, blood flow gets reduced and as a result pressure ulcer develops. Body support surface has a significant role in preventing ulceration so it is important to know the characteristics of support surface under loading conditions. In this paper we have presented mathematical models of different types of viscoelastic materials and also we have shown the validation of our simulation results with experiments.

Keywords: Pressure ulcer, viscoelastic material, mathematical model, experimental validation.

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Authors: J. P. Dubois, O. M. Abdul-Latif

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Support Vector Machine (SVM) is a statistical learning tool that was initially developed by Vapnik in 1979 and later developed to a more complex concept of structural risk minimization (SRM). SVM is playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM was applied to the detection of medical ultrasound images in the presence of partially developed speckle noise. The simulation was done for single look and multi-look speckle models to give a complete overlook and insight to the new proposed model of the SVM-based detector. The structure of the SVM was derived and applied to clinical ultrasound images and its performance in terms of the mean square error (MSE) metric was calculated. We showed that the SVM-detected ultrasound images have a very low MSE and are of good quality. The quality of the processed speckled images improved for the multi-look model. Furthermore, the contrast of the SVM detected images was higher than that of the original non-noisy images, indicating that the SVM approach increased the distance between the pixel reflectivity levels (detection hypotheses) in the original images.

Keywords: LS-SVM, medical ultrasound imaging, partially developed speckle, multi-look model.

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Authors: Jatinderkumar R. Saini, Apurva A. Desai

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e-mail has become an important means of electronic communication but the viability of its usage is marred by Unsolicited Bulk e-mail (UBE) messages. UBE consists of many types like pornographic, virus infected and 'cry-for-help' messages as well as fake and fraudulent offers for jobs, winnings and medicines. UBE poses technical and socio-economic challenges to usage of e-mails. To meet this challenge and combat this menace, we need to understand UBE. Towards this end, the current paper presents a content-based textual analysis of nearly 3000 winnings-announcing UBE. Technically, this is an application of Text Parsing and Tokenization for an un-structured textual document and we approach it using Bag Of Words (BOW) and Vector Space Document Model techniques. We have attempted to identify the most frequently occurring lexis in the winnings-announcing UBE documents. The analysis of such top 100 lexis is also presented. We exhibit the relationship between occurrence of a word from the identified lexisset in the given UBE and the probability that the given UBE will be the one announcing fake winnings. To the best of our knowledge and survey of related literature, this is the first formal attempt for identification of most frequently occurring lexis in winningsannouncing UBE by its textual analysis. Finally, this is a sincere attempt to bring about alertness against and mitigate the threat of such luring but fake UBE.

Keywords: Lexis, Unsolicited Bulk e-mail (UBE), Vector SpaceDocument Model, Winnings, Lottery

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Authors: Ismail El Gayar, Ossama Ismail, Yousri El Gamal

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This paper presented a technique to solve one of the transportation problems that faces us in real life which is the Bus Scheduling Problem. Most of the countries using buses in schools, companies and traveling offices as an example to transfer multiple passengers from many places to specific place and vice versa. This transferring process can cost time and money, so we build a decision support system that can solve this problem. In this paper, a genetic algorithm with the shortest path technique is used to generate a competitive solution to other well-known techniques. It also presents a comparison between our solution and other solutions for this problem.

Keywords: Bus scheduling problem, decision support system, genetic algorithm, operation planning, shortest path, transportation.

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Authors: Antonio Di Leva, Carla Reyneri, Michele Sonnessa

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PARADIGMA (PARticipative Approach to DIsease Global Management) is a pilot project which aims to develop and demonstrate an Internet based reference framework to share scientific resources and findings in the treatment of major diseases. PARADIGMA defines and disseminates a common methodology and optimised protocols (Clinical Pathways) to support service functions directed to patients and individuals on matters like prevention, posthospitalisation support and awareness. PARADIGMA will provide a platform of information services - user oriented and optimised against social, cultural and technological constraints - supporting the Health Care Global System of the Euro-Mediterranean Community in a continuous improvement process.

Keywords: Decision Support Systems, Ontology, Healt Care, Clinical Pathway

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Authors: Elena Ezhova, Vadim Mottl, Olga Krasotkina

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The problem of estimating time-varying regression is inevitably concerned with the necessity to choose the appropriate level of model volatility - ranging from the full stationarity of instant regression models to their absolute independence of each other. In the stationary case the number of regression coefficients to be estimated equals that of regressors, whereas the absence of any smoothness assumptions augments the dimension of the unknown vector by the factor of the time-series length. The Akaike Information Criterion is a commonly adopted means of adjusting a model to the given data set within a succession of nested parametric model classes, but its crucial restriction is that the classes are rigidly defined by the growing integer-valued dimension of the unknown vector. To make the Kullback information maximization principle underlying the classical AIC applicable to the problem of time-varying regression estimation, we extend it onto a wider class of data models in which the dimension of the parameter is fixed, but the freedom of its values is softly constrained by a family of continuously nested a priori probability distributions.

Keywords: Time varying regression, time-volatility of regression coefficients, Akaike Information Criterion (AIC), Kullback information maximization principle.

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Authors: Ashwag O. Maghraby, Nida N. Khan, Hosnia A. Ahmed, Ghufran N. Brohi, Hind F. Assouli, Jawaher S. Melibari

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The Arabic language is one of the most important languages. Learning it is so important for many people around the world because of its religious and economic importance and the real challenge lies in practicing it without grammatical or syntactical mistakes. This research focused on detecting and correcting the syntactic mistakes of Arabic syntax according to their position in the sentence and focused on two of the main syntactical rules in Arabic: Dual and Plural. It analyzes each sentence in the text, using Stanford CoreNLP morphological analyzer and machine-learning approach in order to detect the syntactical mistakes and then correct it. A prototype of the proposed system was implemented and evaluated. It uses support vector machine (SVM) algorithm to detect Arabic grammatical errors and correct them using the rule-based approach. The prototype system has a far accuracy 81%. In general, it shows a set of useful grammatical suggestions that the user may forget about while writing due to lack of familiarity with grammar or as a result of the speed of writing such as alerting the user when using a plural term to indicate one person.

Keywords: Arabic Language acquisition and learning, natural language processing, morphological analyzer, part-of-speech.

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Authors: Arne Koschel, Irina Astrova

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Heterogeneity has to be taken into account when integrating a set of existing information sources into a distributed information system that are nowadays often based on Service- Oriented Architectures (SOA). This is also particularly applicable to distributed services such as event monitoring, which are useful in the context of Event Driven Architectures (EDA) and Complex Event Processing (CEP). Web services deal with this heterogeneity at a technical level, also providing little support for event processing. Our central thesis is that such a fully generic solution cannot provide complete support for event monitoring; instead, source specific semantics such as certain event types or support for certain event monitoring techniques have to be taken into account. Our core result is the design of a configurable event monitoring (Web) service that allows us to trade genericity for the exploitation of source specific characteristics. It thus delivers results for the areas of SOA, Web services, CEP and EDA.

Keywords: ECA, CEP, SOA, and Web services.

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Authors: Ahmed M. Hemeida, Wael A. Farag, Osama A. Mahgoub

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This paper focuses on developing an integrated reliable and sophisticated model for ultra large wind turbines And to study the performance and analysis of vector control on large wind turbines. With the advance of power electronics technology, direct driven multi-pole radial flux PMSG (Permanent Magnet Synchronous Generator) has proven to be a good choice for wind turbines manufacturers. To study the wind energy conversion systems, it is important to develop a wind turbine simulator that is able to produce realistic and validated conditions that occur in real ultra MW wind turbines. Three different packages are used to simulate this model, namely, Turbsim, FAST and Simulink. Turbsim is a Full field wind simulator developed by National Renewable Energy Laboratory (NREL). The wind turbine mechanical parts are modeled by FAST (Fatigue, Aerodynamics, Structures and Turbulence) code which is also developed by NREL. Simulink is used to model the PMSG, full scale back to back IGBT converters, and the grid.

Keywords: FAST, Permanent Magnet Synchronous Generator(PMSG), TurbSim, Vector Control and Pitch Control

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Authors: Sarah Beaver, Renee Bryce

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Audiograms detect hearing impairment, but missing values pose problems. This work explores imputations in an attempt to improve accuracy. This work implements Linear Regression, Lasso, Linear Support Vector Regression, Bayesian Ridge, K Nearest Neighbors (KNN), and Random Forest machine learning techniques to impute audiogram frequencies ranging from 125 Hz to 8000 Hz. The data contain patients who had or were candidates for cochlear implants. Accuracy is compared across two different Nested Cross-Validation k values. Over 4000 audiograms were used from 800 unique patients. Additionally, training on data combines and compares left and right ear audiograms versus single ear side audiograms. The accuracy achieved using Root Mean Square Error (RMSE) values for the best models for Random Forest ranges from 4.74 to 6.37. The R2 values for the best models for Random Forest ranges from .91 to .96. The accuracy achieved using RMSE values for the best models for KNN ranges from 5.00 to 7.72. The R2 values for the best models for KNN ranges from .89 to .95. The best imputation models received R2 between .89 to .96 and RMSE values less than 8dB. We also show that the accuracy of classification predictive models performed better with our imputation models versus constant imputations by a two percent increase.

Keywords: Machine Learning, audiograms, data imputations, single imputations.

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Authors: Abdellatif Kasbi, Abderrafii Rahali

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In an electric power grid connected wind generation system, dynamic control strategy is essential to use the wind energy efficiently as well as for an energy optimization. The present study has focused on decoupled power regulation of doubly fed induction generator, operating in wind turbine, in accordance with the vector control approach by applying fractional order proportional integral (FOPI) controller. The FOPI controller is designed based on a simple method; up such that the response of closed loop process is similar to the response of a specified fractional model whose transfer function is Bode’s ideal function. In this tuning operation, the parameters of the proposed fractional controller are established analytically using the impulse closed-loop response of the controlled process. To show the superior action of the developed FOPI controller in comparison with standard PI controller in different function conditions, the study is validated through simulation using the software MATLAB/Simulink.

Keywords: Wind generation system, DFIG, vector control approach, fractional order PI controller, Bode’s ideal transfer function, impulse response.

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Authors: SeHun Lim, Sanghoon Kim, Sun-Tae Chung, Seongwon Cho

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Face authentication for access control is a face membership authentication which passes the person of the incoming face if he turns out to be one of an enrolled person based on face recognition or rejects if not. Face membership authentication belongs to the two class classification problem where SVM(Support Vector Machine) has been successfully applied and shows better performance compared to the conventional threshold-based classification. However, most of previous SVMs have been trained using image feature vectors extracted from face images of each class member(enrolled class/unenrolled class) so that they are not robust to variations in illuminations, poses, and facial expressions and much affected by changes in member configuration of the enrolled class In this paper, we propose an effective face membership authentication method based on SVM using class discriminating features which represent an incoming face image-s associability with each class distinctively. These class discriminating features are weakly related with image features so that they are less affected by variations in illuminations, poses and facial expression. Through experiments, it is shown that the proposed face membership authentication method performs better than the threshold rule-based or the conventional SVM-based authentication methods and is relatively less affected by changes in member size and membership.

Keywords: Face Authentication, Access control, member ship authentication, SVM.

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Authors: Lin Yang, Zhian Mi, Jiacheng Xiao, Rong Li

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Swimming with the tide of deep learning, the field of music information retrieval (MIR) experiences parallel development and a sheer variety of feature-learning models has been applied to music classification and tagging tasks. Among those learning techniques, the deep convolutional neural networks (CNNs) have been widespreadly used with better performance than the traditional approach especially in music genre classification and prediction. However, regarding the music recommendation, there is a large semantic gap between the corresponding audio genres and the various aspects of a song that influence user preference. In our study, aiming to bridge the gap, we strive to construct an automatic music aesthetic annotation model with MIDI format for better comparison and measurement of the similarity between music pieces in the way of harmonic analysis. We use the matrix of qualification converted from MIDI files as input to train two different classifiers, support vector machine (SVM) and Decision Tree (DT). Experimental results in performance of a tag prediction task have shown that both learning algorithms are capable of extracting high-level properties in an end-to end manner from music information. The proposed model is helpful to learn the audience taste and then the resulting recommendations are likely to appeal to a niche consumer.

Keywords: Harmonic analysis, machine learning, music classification and tagging, MIDI.

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Authors: Matthias Kirmse, Uwe Petersohn, Elief Paffrath

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As in today's semiconductor industries test costs can make up to 50 percent of the total production costs, an efficient test error detection becomes more and more important. In this paper, we present a new machine learning approach to test error detection that should provide a faster recognition of test system faults as well as an improved test error recall. The key idea is to learn a classifier ensemble, detecting typical test error patterns in wafer test results immediately after finishing these tests. Since test error detection has not yet been discussed in the machine learning community, we define central problem-relevant terms and provide an analysis of important domain properties. Finally, we present comparative studies reflecting the failure detection performance of three individual classifiers and three ensemble methods based upon them. As base classifiers we chose a decision tree learner, a support vector machine and a Bayesian network, while the compared ensemble methods were simple and weighted majority vote as well as stacking. For the evaluation, we used cross validation and a specially designed practical simulation. By implementing our approach in a semiconductor test department for the observation of two products, we proofed its practical applicability.

Keywords: Ensemble methods, fault detection, machine learning, semiconductor test.

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Authors: Zhiwei Yan, Jong-Hyouk Lee, Yong-Jin Park, Xiaodong Lee

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In this paper, we analyze NEtwork MObility (NEMO) supporting problems in Content-Centric Networking (CCN), and propose the CCN-NEMO which can well support the deployment of the content-centric paradigm in large-scale mobile Internet. The CCN-NEMO extends the signaling message of the basic CCN protocol, to support the mobility discovery and fast trigger of Interest re-issuing during the network mobility. Besides, the Mobile Router (MR) is extended to optimize the content searching and relaying in the local subnet. These features can be employed by the nested NEMO to maximize the advantages of content retrieving with CCN. Based on the analysis, we compare the performance on handover latency between the basic CCN and our proposed CCN-NEMO. The results show that our scheme can facilitate the content-retrieving in the NEMO scenario with improved performance.

Keywords: CCN, handover, NEMO, mobility management.

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Authors: Aseela Alhinaai, Suliman Abdullah, Adil Albusaidi

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Knowledge has been considered an important asset in private and public organizations. It is utilized in the libraries sector to run different operations of technical services and administrative works. This study aims to identify the impact of the knowledge-sharing factors (technology, collaboration, management support) to improve decision-making at Sultan Qaboos University Libraries. This study conducted a quantitative method using a questionnaire instrument to measure the impact of technology, collaboration, and management support on knowledge sharing that lead to improved decision-making. The study population is the Sultan Qaboos University (SQU) libraries (Main Library, Medical Library, College of Economic and Political Science Library, and Art Library). The results showed that management support, collaboration, and technology use have a positive impact on the knowledge-sharing process, and knowledge sharing positively affects decision making process.

Keywords: Knowledge sharing, decision making, information technology, management support, corroboration, Sultan Qaboos University.

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Authors: Mohammad Yasrebi, Somayeh Khosravi

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Machine-understandable data when strongly interlinked constitutes the basis for the SemanticWeb. Annotating web documents is one of the major techniques for creating metadata on the Web. Annotating websitexs defines the containing data in a form which is suitable for interpretation by machines. In this paper, we present a better and improved approach than previous [1] to annotate the texts of the websites depends on the knowledge base.

Keywords: Knowledge base, ontology, semantic annotation, XML.

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