Search results for: sequence mining

Authors: Essam Al-Daoud

Abstract:

Several combinations of the preprocessing algorithms, feature selection techniques and classifiers can be applied to the data classification tasks. This study introduces a new accurate classifier, the proposed classifier consist from four components: Signal-to- Noise as a feature selection technique, support vector machine, Bayesian neural network and AdaBoost as an ensemble algorithm. To verify the effectiveness of the proposed classifier, seven well known classifiers are applied to four datasets. The experiments show that using the suggested classifier enhances the classification rates for all datasets.

Keywords: AdaBoost, Bayesian neural network, Signal-to-Noise, support vector machine, MCMC.

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Authors: V. Arabadzhi

Abstract:

We attempted investigate a boat model, based on the conversion of energy of surface wave into a sequence of unidirectional pulses of jet spurts, in other words - model of the boat, which is thrusting by the waves field on water surface. These pulses are forming some average reactive stream from the output nozzle on the stern of boat. The suggested model provides the conversion of its oscillatory motions (both pitching and rolling) into a jet flow. This becomes possible due to special construction of the boat and due to several details, sensitive to the local wave field. The boat model presents the uniflow jet engine without slow conversions of mechanical energy into intermediate forms and without any external sources of energy (besides surface waves). Motion of boat is characterized by fast jerks and average onward velocity, which exceeds the velocities of liquid particles in the wave.

Keywords: Flat-bottomed boat, Underwater wing, Input and output nozzles, Wave thrust, Conversion of wave into a jet stream, Oscillatory motion and onward motion, Squid-like pump, Hatch-like pump, The thrust due to lifting float, The thrust due to radiation reaction.

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Authors: M. Omer Faruk, A. M. A. M. Zonaed Siddiki, M. Fazal Karim, Md. Masuduzzaman, S. Chowdhury, Md. Shafiqul Islam, M. Alamgir Hossain

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The dog tapeworms Echinococcus granulosus develop hydatid cysts in various organs in human and domestic animals worldwide including Bangladesh. The aim of this study was to identify and characterize the genotype of E. granulosus isolated from cattle using 12S rRNA and Cytochrome oxidase 1 (COX 1) genes. A total of 43 hydatid cyst samples were collected from 390 examined cattle samples derived from slaughterhouses. Among them, three cysts were fertile. Genomic DNA was extracted from germinal membrane and/or protoscoleces followed by PCR amplification of mitochondrial 12S rRNA and Cytochrome oxidase 1 gene fragments. The sequence data revealed existence of G1 (64.28%) and possible G3 (21.43%) genotypes for the first time in Bangladesh. The study indicates that common sheep strain G1 is the dominant subtype of E. granulosus in Chittagong region of Bangladesh. This will increase our understanding of the epidemiology of hydatidosis in the southern part of the country and will be useful to plan suitable control measures in the long run.

Keywords: Echinococcus granulosus, molecular characterization, cattle, Bangladesh.

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Authors: P.Varzakas

Abstract:

In this paper, an accurate theoretical analysis for the achievable average channel capacity (in the Shannon sense) per user of a hybrid cellular direct-sequence/fast frequency hopping code-division multiple-access (DS/FFH-CDMA) system operating in a Rayleigh fading environment is presented. The analysis covers the downlink operation and leads to the derivation of an exact mathematical expression between the normalized average channel capacity available to each system-s user, under simultaneous optimal power and rate adaptation and the system-s parameters, as the number of hops per bit, the processing gain applied, the number of users per cell and the received signal-tonoise power ratio over the signal bandwidth. Finally, numerical results are presented to illustrate the proposed mathematical analysis.

Keywords: Shannon capacity, adaptive systems, code-division multiple access, fading channels.

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Authors: Shigeaki Sakurai, Ryohei Orihara

Abstract:

This paper proposes a new method for analyzing textual data. The method deals with items of textual data, where each item is described based on various viewpoints. The method acquires 2- class classification models of the viewpoints by applying an inductive learning method to items with multiple viewpoints. The method infers whether the viewpoints are assigned to the new items or not by using the models. The method extracts expressions from the new items classified into the viewpoints and extracts characteristic expressions corresponding to the viewpoints by comparing the frequency of expressions among the viewpoints. This paper also applies the method to questionnaire data given by guests at a hotel and verifies its effect through numerical experiments.

Keywords: Text mining, Multiple viewpoints, Differential analysis, Questionnaire data

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Authors: Alcínia Z. Sampaio, Pedro G. Henriques

Abstract:

Three-dimensional geometric models have been used to present architectural and engineering works, showing their final configuration. When the clarification of a detail or the constitution of a construction step in needed, these models are not appropriate. They do not allow the observation of the construction progress of a building. Models that could present dynamically changes of the building geometry are a good support to the elaboration of projects. Techniques of geometric modeling and virtual reality were used to obtain models that could visually simulate the construction activity. The applications explain the construction work of a cavity wall and a bridge. These models allow the visualization of the physical progression of the work following a planned construction sequence, the observation of details of the form of every component of the works and support the study of the type and method of operation of the equipment applied in the construction. These models presented distinct advantage as educational aids in first-degree courses in Civil Engineering. The use of Virtual Reality techniques in the development of educational applications brings new perspectives to the teaching of subjects related to the field of civil construction.

Keywords: Education, Engineering, virtual reality, visualsimulation.

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Authors: Assyl Bari, Olga Berillo, Saltanat Orazova, Anatoliy Ivashchenko

Abstract:

Among all microRNAs (miRNAs) in 12 plant species investigated in this study, only miR398 targeted the copper chaperone for superoxide dismutase (CCS). The nucleotide sequences of miRNA binding sites were located in the mRNA protein-coding sequence (CDS) and were highly homologous. These binding sites in CCS mRNA encoded a conservative GDLGTL hexapeptide. The binding sites for miR398 in the CDS of superoxide dismutase 1 mRNA encoded GDLGN pentapeptide. The conservative miR398 binding site located in the CDS of superoxide dismutase 2 mRNA encoded the GDLGNI hexapeptide. The miR398 binding site in the CDS of superoxide dismutase 3 mRNA encoded the GDLGNI or GDLGNV hexapeptide. Gene expression of the entire superoxide dismutase family in the studied plant species was regulated only by miR398. All members of the miR398 family, i.e. miR398a,b,c were connected to one site for each CuZnSOD and chaperone mRNA.

Keywords: MicroRNA, mRNA, plant, superoxide dismutase.

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Authors: C. V. Pietreanu, S. E. Zaharia, C. Dinu

Abstract:

The present research is built on three major pillars, commencing by making some considerations on accident investigation methods and pointing out both defining aspects and differences between linear and non-linear analysis. The traditional linear focus on accident analysis describes accidents as a sequence of events, while the latest systemic models outline interdependencies between different factors and define the processes evolution related to a specific (normal) situation. Linear and non-linear accident analysis methods have specific limitations, so the second point of interest is mirrored by the aim to discover the drawbacks of systemic models which becomes a starting point for developing new directions to identify risks or data closer to the cause of incidents/accidents. Since communication represents a critical issue in the interaction of human factor and has been proved to be the answer of the problems made by possible breakdowns in different communication procedures, from this focus point, on the third pylon a new error-modeling instrument suitable for risk assessment/accident analysis will be elaborated.

Keywords: Accident analysis, multi-factorial error modeling, risk, systemic methods.

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Authors: Surat Srinoy, Werasak Kurutach, Witcha Chimphlee, Siriporn Chimphlee

Abstract:

One main drawback of intrusion detection system is the inability of detecting new attacks which do not have known signatures. In this paper we discuss an intrusion detection method that proposes independent component analysis (ICA) based feature selection heuristics and using rough fuzzy for clustering data. ICA is to separate these independent components (ICs) from the monitored variables. Rough set has to decrease the amount of data and get rid of redundancy and Fuzzy methods allow objects to belong to several clusters simultaneously, with different degrees of membership. Our approach allows us to recognize not only known attacks but also to detect activity that may be the result of a new, unknown attack. The experimental results on Knowledge Discovery and Data Mining- (KDDCup 1999) dataset.

Keywords: Network security, intrusion detection, rough set, ICA, anomaly detection, independent component analysis, rough fuzzy .

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Authors: Khalid A. Al-Afandy, El-Sayyed El-Rabaie, Osama Salah, Ahmed El-Mhalaway

Abstract:

This paper presents a high secure data hiding technique using image cropping and Least Significant Bit (LSB) steganography. The predefined certain secret coordinate crops will be extracted from the cover image. The secret text message will be divided into sections. These sections quantity is equal the image crops quantity. Each section from the secret text message will embed into an image crop with a secret sequence using LSB technique. The embedding is done using the cover image color channels. Stego image is given by reassembling the image and the stego crops. The results of the technique will be compared to the other state of art techniques. Evaluation is based on visualization to detect any degradation of stego image, the difficulty of extracting the embedded data by any unauthorized viewer, Peak Signal-to-Noise Ratio of stego image (PSNR), and the embedding algorithm CPU time. Experimental results ensure that the proposed technique is more secure compared with the other traditional techniques.

Keywords: Steganography, stego, LSB, crop.

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Authors: Yasser M. Saad, Amr A. El Hanafy, Saleh A. Alkarim, Hussein A. Almehdar, Elrashdy M. Redwan

Abstract:

Camels are substantial providers of transport, milk, sport, meat, shelter, security and capital in many countries, particularly in Saudi Arabia. Inter simple sequence repeat technique was used to detect the genetic variations among some camel breeds (Majaheim, Safra, Wadah, and Hamara). Actual number of alleles, effective number of alleles, gene diversity, Shannon’s information index and polymorphic bands were calculated for each evaluated camel breed. Neighbor-joining tree that re-constructed for evaluated these camel breeds showed that, Hamara breed is distantly related from the other evaluated camels. In addition, the polymorphic sites, haplotypes and nucleotide diversity were identified for some camelidae cox1 gene sequences (obtained from NCBI). The distance value between C. bactrianus and C. dromedarius (0.072) was relatively low. Analysis of genetic diversity is an important way for conserving Camelus dromedarius genetic resources.

Keywords: Camel, genetics, ISSR, cox1, neighbor-joining.

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Authors: C. Adam, G. Henri, T. Levent, J.-B. Mauro, A. -L. Mayet

Abstract:

This paper is concerning the application of a deterministic decisional pattern to a multi-agent system which would provide intelligence to a distributed energy smart grid at local consumer level. Development of multi-agent application involves agent specifications, analysis, design and realization. It can be implemented by following several decisional patterns. The purpose of present article is to suggest a new approach to control the smart grid system in a decentralized competitive approach. The proposed algorithmic solution results from a deterministic dichotomous approach based on environment observation. It uses an iterative process to solve automatic learning problems. Through memory of collected past tries, the algorithm monotonically converges to very steep system operation point in attraction basin resulting from weak system nonlinearity. In this sense, system is given by (local) constitutive elementary rules the intelligence of its global existence so that it can self-organize toward optimal operating sequence.

Keywords: Decentralized Competitive System, Distributed Smart Grid, Multi-Agent System

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Authors: S. Makal, L. Ozyilmaz, S. Palavaroglu

Abstract:

Gene, principal unit of inheritance, is an ordered sequence of nucleotides. The genes of eukaryotic organisms include alternating segments of exons and introns. The region of Deoxyribonucleic acid (DNA) within a gene containing instructions for coding a protein is called exon. On the other hand, non-coding regions called introns are another part of DNA that regulates gene expression by removing from the messenger Ribonucleic acid (RNA) in a splicing process. This paper proposes to determine splice junctions that are exon-intron boundaries by analyzing DNA sequences. A splice junction can be either exon-intron (EI) or intron exon (IE). Because of the popularity and compatibility of the artificial neural network (ANN) in genetic fields; various ANN models are applied in this research. Multi-layer Perceptron (MLP), Radial Basis Function (RBF) and Generalized Regression Neural Networks (GRNN) are used to analyze and detect the splice junctions of gene sequences. 10-fold cross validation is used to demonstrate the accuracy of networks. The real performances of these networks are found by applying Receiver Operating Characteristic (ROC) analysis.

Keywords: Gene, neural networks, ROC analysis, splice junctions.

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Authors: Hamed Qahri Saremi, Gholam Ali Montazer

Abstract:

With the proliferation of World Wide Web, development of web-based technologies and the growth in web content, the structure of a website becomes more complex and web navigation becomes a critical issue to both web designers and users. In this paper we define the content and web pages as two important and influential factors in website navigation and paraphrase the enhancement in the website navigation as making some useful changes in the link structure of the website based on the aforementioned factors. Then we suggest a new method for proposing the changes using fuzzy approach to optimize the website architecture. Applying the proposed method to a real case of Iranian Civil Aviation Organization (CAO) website, we discuss the results of the novel approach at the final section.

Keywords: Web content, Web navigation, Website system, Webusage mining.

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Authors: Kyoung-jae Kim, Hyunchul Ahn, Sangwon Jeong

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This study proposes a novel recommender system to provide the advertisements of context-aware services. Our proposed model is designed to apply a modified collaborative filtering (CF) algorithm with regard to the several dimensions for the personalization of mobile devices – location, time and the user-s needs type. In particular, we employ a classification rule to understand user-s needs type using a decision tree algorithm. In addition, we collect primary data from the mobile phone users and apply them to the proposed model to validate its effectiveness. Experimental results show that the proposed system makes more accurate and satisfactory advertisements than comparative systems.

Keywords: Location-based advertisement, Recommender system, Collaborative filtering, User needs type, Mobile user.

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Authors: Basheer M. Al-Maqaleh, Kamal K. Bharadwaj

Abstract:

Automated discovery of hierarchical structures in large data sets has been an active research area in the recent past. This paper focuses on the issue of mining generalized rules with crisp hierarchical structure using Genetic Programming (GP) approach to knowledge discovery. The post-processing scheme presented in this work uses flat rules as initial individuals of GP and discovers hierarchical structure. Suitable genetic operators are proposed for the suggested encoding. Based on the Subsumption Matrix(SM), an appropriate fitness function is suggested. Finally, Hierarchical Production Rules (HPRs) are generated from the discovered hierarchy. Experimental results are presented to demonstrate the performance of the proposed algorithm.

Keywords: Genetic Programming, Hierarchy, Knowledge Discovery in Database, Subsumption Matrix.

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Authors: Ines Hamdi, Mohamed Ben Ahmed

Abstract:

The understanding of the system level of biological behavior and phenomenon variously needs some elements such as gene sequence, protein structure, gene functions and metabolic pathways. Challenging problems are representing, learning and reasoning about these biochemical reactions, gene and protein structure, genotype and relation between the phenotype, and expression system on those interactions. The goal of our work is to understand the behaviors of the interactions networks and to model their evolution in time and in space. We propose in this study an ontological meta-model for the knowledge representation of the genetic regulatory networks. Ontology in artificial intelligence means the fundamental categories and relations that provide a framework for knowledge models. Domain ontology's are now commonly used to enable heterogeneous information resources, such as knowledge-based systems, to communicate with each other. The interest of our model is to represent the spatial, temporal and spatio-temporal knowledge. We validated our propositions in the genetic regulatory network of the Aarbidosis thaliana flower

Keywords: Ontological model, spatio-temporal modeling, Genetic Regulatory Networks (GRNs), knowledge representation.

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Authors: Aigerim Uyzbayeva, Valeriya Tyo, Nurlan Ibrayev

Abstract:

Kazakhstan is currently one of the dynamically developing states in its region. The stable growth in all sectors of the economy leads to a corresponding increase in energy consumption. Thus country consumes significant amount of energy due to the high level of industrialisation and the presence of energy-intensive manufacturing such as mining and metallurgy which in turn leads to low energy efficiency. With allowance for this the Government has set several priorities to adopt a transition of Republic of Kazakhstan to a “green economy”. This article provides an overview of Kazakhstan’s energy efficiency situation in for the period of 1991- 2014. First, the dynamics of production and consumption of conventional energy resources are given. Second, the potential of renewable energy sources is summarised followed by the description of GHG emissions trends in the country. Third, Kazakhstan’ national initiatives, policies and locally implemented projects in the field of energy efficiency are described.

Keywords: Energy efficiency in Kazakhstan, greenhouse gases, renewable energy, sustainable development.

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Authors: Girija Chetty, Renuka Biswas

Abstract:

In this paper, we propose novel algorithmic models based on information fusion and feature transformation in crossmodal subspace for different types of residue features extracted from several intra-frame and inter-frame pixel sub-blocks in video sequences for detecting digital video tampering or forgery. An evaluation of proposed residue features – the noise residue features and the quantization features, their transformation in cross-modal subspace, and their multimodal fusion, for emulated copy-move tamper scenario shows a significant improvement in tamper detection accuracy as compared to single mode features without transformation in cross-modal subspace.

Keywords: image tamper detection, digital forensics, correlation features image fusion

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Authors: Jyh-Cherng Chen, Yu-Zen Lin, Wei-Zhi Chen

Abstract:

Torrefaction is one of waste to energy (WTE) technologies developing in Taiwan recently, which can reduce the moisture and impuritiesand increase the energy density of biowaste effectively.To understand the torrefaction characteristics of different biowaste and the influences of different torrefaction conditions, four typical biowaste were selected to carry out the torrefaction experiments. The physical and chemical properties of different biowaste prior to and after torrefaction were analyzed and compared. Experimental results show that the contents of elemental carbon and caloric value of the four biowaste were significantly increased after torrefaction. The increase of combustible and caloric value in bamboo was the greatest among the four biowaste. The caloric value of bamboo can be increased from 1526 kcal/kg to 6104 kcal/kg after 300^oC and 1 hour torrefaction. The caloric valueof torrefied bamboo was almost four times as the original. The increase of elemental carbon content in wood was the greatest (from 41.03% to 75.24%), and the next was bamboo (from 47.07% to 74.63%). The major parameters which affected the caloric value of torrefied biowaste followed the sequence of biowaste kinds, torrefaction time, and torrefaction temperature. The optimal torrefaction conditions of the experiments were bamboo torrefied at 300^oC for 3 hours, and the corresponding caloric value of torrefied bamboo was 5953 kcal/kg. This caloric value is similar to that of brown coal or bituminous coal.

Keywords: Torrefaction, waste to energy, calorie, biofuel.

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Authors: Mouhammad Alkasassbeh, Mohammad Almseidin

Abstract:

Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanisms that is used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity, and availability of the services. The speed of the IDS is a very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The J48, MLP, and Bayes Network classifiers have been chosen for this study. It has been proven that the J48 classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type DOS, R2L, U2R, and PROBE.

Keywords: IDS, DDoS, MLP, KDD.

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Authors: R. Haddad, K. Morris, V. Buchanan-Wollaston

Abstract:

Biochemical and molecular analysis of some antioxidant enzyme genes revealed different level of gene expression on oilseed (Brassica napus). For molecular and biochemical analysis, leaf tissues were harvested from plants at eight different developmental stages, from young to senescence. The levels of total protein and chlorophyll were increased during maturity stages of plant, while these were decreased during the last stages of plant growth. Structural analysis (nucleotide and deduced amino acid sequence, and phylogenic tree) of a complementary DNA revealed a high level of similarity for a family of Catalase genes. The expression of the gene encoded by different Catalase isoforms was assessed during different plant growth phase. No significant difference between samples was observed, when Catalase activity was statistically analyzed at different developmental stages. EST analysis exhibited different transcripts levels for a number of other relevant antioxidant genes (different isoforms of SOD and glutathione). The high level of transcription of these genes at senescence stages was indicated that these genes are senescenceinduced genes.

Keywords: Biochemical analysis, Oilseed, Expression pattern, Growth phases

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Authors: Adi A. Maaita, Hamza A. A. Al_Sewadi

Abstract:

One of the crucial parameters of digital cryptographic systems is the selection of the keys used and their distribution. The randomness of the keys has a strong impact on the system’s security strength being difficult to be predicted, guessed, reproduced, or discovered by a cryptanalyst. Therefore, adequate key randomness generation is still sought for the benefit of stronger cryptosystems. This paper suggests an algorithm designed to generate and test pseudo random number sequences intended for cryptographic applications. This algorithm is based on mathematically manipulating a publically agreed upon information between sender and receiver over a public channel. This information is used as a seed for performing some mathematical functions in order to generate a sequence of pseudorandom numbers that will be used for encryption/decryption purposes. This manipulation involves permutations and substitutions that fulfill Shannon’s principle of “confusion and diffusion”. ASCII code characters were utilized in the generation process instead of using bit strings initially, which adds more flexibility in testing different seed values. Finally, the obtained results would indicate sound difficulty of guessing keys by attackers.

Keywords: Cryptosystems, Information Security agreement, Key distribution, Random numbers.

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Authors: Janet L. Kourik, Peter E. Maher

Abstract:

To investigate the correspondence of theory and practice, a successfully implemented Knowledge Management System (KMS) is explored through the lens of Alavi and Leidner-s proposed KMS framework for the analysis of an information system in knowledge management (Framework-AISKM). The applied KMS system was designed to manage curricular knowledge in a distributed university environment. The motivation for the KMS is discussed along with the types of knowledge necessary in an academic setting. Elements of the KMS involved in all phases of capturing and disseminating knowledge are described. As the KMS matures the resulting data stores form the precursor to and the potential for knowledge mining. The findings from this exploratory study indicate substantial correspondence between the successful KMS and the theory-based framework providing provisional confirmation for the framework while suggesting factors that contributed to the system-s success. Avenues for future work are described.

Keywords: Applied KMS, education, knowledge management (KM), KM framework, knowledge management system (KMS).

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Authors: Dharmveer Singh Rajput , P. K. Singh, Mahua Bhattacharya

Abstract:

Clustering in high dimensional space is a difficult problem which is recurrent in many fields of science and engineering, e.g., bioinformatics, image processing, pattern reorganization and data mining. In high dimensional space some of the dimensions are likely to be irrelevant, thus hiding the possible clustering. In very high dimensions it is common for all the objects in a dataset to be nearly equidistant from each other, completely masking the clusters. Hence, performance of the clustering algorithm decreases. In this paper, we propose an algorithmic framework which combines the (reduct) concept of rough set theory with the k-means algorithm to remove the irrelevant dimensions in a high dimensional space and obtain appropriate clusters. Our experiment on test data shows that this framework increases efficiency of the clustering process and accuracy of the results.

Keywords: High dimensional clustering, sub-space, k-means, rough set, discernibility matrix.

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Authors: Hossam el-ddin Mostafa, Pavel Čičak

Abstract:

The mobile users with Laptops need to have an efficient access to i.e. their home personal data or to the Internet from any place in the world, regardless of their location or point of attachment, especially while roaming outside the home subnet. An efficient interpretation of packet losses problem that is encountered from this roaming is to the centric of all aspects in this work, to be over-highlighted. The main previous works, such as BER-systems, Amigos, and ns-2 implementation that are considered to be in conjunction with that problem under study are reviewed and discussed. Their drawbacks and limitations, of stopping only at monitoring, and not to provide an actual solution for eliminating or even restricting these losses, are mentioned. Besides that, the framework around which we built a Triple-R sequence as a costeffective solution to eliminate the packet losses and bridge the gap between subnets, an area that until now has been largely neglected, is presented. The results show that, in addition to the high bit error rate of wireless mobile networks, mainly the low efficiency of mobile-IP registration procedure is a direct cause of these packet losses. Furthermore, the output of packet losses interpretation resulted an illustrated triangle of the registration process. This triangle should be further researched and analyzed in our future work.

Keywords: Amigos, BER-systems, ns-2 implementation, packetlosses, registration process, roaming.

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Authors: Nora Taleb, Sellami Mokhtar, Michel Simonet

Abstract:

This work concerns the evolution and the maintenance of an ontological resource in relation with the evolution of the corpus of texts from which it had been built. The knowledge forming a text corpus, especially in dynamic domains, is in continuous evolution. When a change in the corpus occurs, the domain ontology must evolve accordingly. Most methods manage ontology evolution independently from the corpus from which it is built; in addition, they treat evolution just as a process of knowledge addition, not considering other knowledge changes. We propose a methodology for managing an evolving ontology from a text corpus that evolves over time, while preserving the consistency and the persistence of this ontology. Our methodology is based on the changes made on the corpus to reflect the evolution of the considered domain - augmented surgery in our case. In this context, the results of text mining techniques, as well as the ARCHONTE method slightly modified, are used to support the evolution process.

Keywords: Corpus, Evolution, Ontology

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Authors: Parvaneh Shabanzadeh, Malik Hj Abu Hassan, Leong Wah June, Maryam Mohagheghtabar

Abstract:

Clustering is one of an interesting data mining topics that can be applied in many fields. Recently, the problem of cluster analysis is formulated as a problem of nonsmooth, nonconvex optimization, and an algorithm for solving the cluster analysis problem based on nonsmooth optimization techniques is developed. This optimization problem has a number of characteristics that make it challenging: it has many local minimum, the optimization variables can be either continuous or categorical, and there are no exact analytical derivatives. In this study we show how to apply a particular class of optimization methods known as pattern search methods to address these challenges. These methods do not explicitly use derivatives, an important feature that has not been addressed in previous studies. Results of numerical experiments are presented which demonstrate the effectiveness of the proposed method.

Keywords: Clustering functions, Non-smooth Optimization, Nonconvex Optimization, Pattern Search Method.

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Authors: L. Mirtskhulava, M. Khunjgurua, N. Lomineishvili, K. Bakuria

Abstract:

Software reliability prediction gives a great opportunity to measure the software failure rate at any point throughout system test. A software reliability prediction model provides with the technique for improving reliability. Software reliability is very important factor for estimating overall system reliability, which depends on the individual component reliabilities. It differs from hardware reliability in that it reflects the design perfection. Main reason of software reliability problems is high complexity of software. Various approaches can be used to improve the reliability of software. We focus on software reliability model in this article, assuming that there is a time redundancy, the value of which (the number of repeated transmission of basic blocks) can be an optimization parameter. We consider given mathematical model in the assumption that in the system may occur not only irreversible failures, but also a failure that can be taken as self-repairing failures that significantly affect the reliability and accuracy of information transfer. Main task of the given paper is to find a time distribution function (DF) of instructions sequence transmission, which consists of random number of basic blocks. We consider the system software unreliable; the time between adjacent failures has exponential distribution.

Keywords: Exponential distribution, conditional mean time to failure, distribution function, mathematical model, software reliability.

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Authors: Shigeaki Sakurai, Ryohei Orihara

Abstract:

This paper proposes an improvement method of classification efficiency in a classification model. The model is used in a risk search system and extracts specific labels from articles posted at bulletin board sites. The system can analyze the important discussions composed of the articles. The improvement method introduces ensemble learning methods that use multiple classification models. Also, it introduces expressions related to the specific labels into generation of word vectors. The paper applies the improvement method to articles collected from three bulletin board sites selected by users and verifies the effectiveness of the improvement method.

Keywords: Text mining, Risk search system, Corporate reputation, Bulletin board site, Ensemble learning

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