Search results for: knowledge clustering

Authors: Barbora Chmelíková

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Financial literacy is a widely discussed topic on the national and international level by governments, organizations and academia. For this reason, this study analyses financial knowledge, financial behavior, and financial attitudes of students of finance. The aim of the paper is to determine whether the financial literacy of university students studying finance differs from the level of financial literacy in selected OECD countries. The research was conducted at Masaryk University in the Czech Republic. The empirical study comprises questions related to several aspects of financial literacy, such as financial knowledge, personal finance behavior, or decisionmaking. The results indicate that improvement in financial literacy of university students is still required, even though their major is finance related.

Keywords: Financial literacy, financial behavior, personal finance management, university students.

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Authors: Muthatakul Metasit, Setthasakko Watchaneeporn

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At present, increased concerns about global environmental problems have magnified the importance of sustainability management. To move towards sustainability, companies need to look at everything from a holistic perspective in order to understand the interconnections between economic growth and environmental and social sustainability. This paper aims to gain an understanding of key determinants that drive sustainability management and barriers that hinder its development. It employs semi-structured interviews with key informants, site observation and documentation. The informants are production, marketing and environmental managers of the leading wine producer, which aims to become an Asia-s leader in wine & wine based products. It is found that corporate image and top management leadership are the primary factors influencing the adoption of sustainability management. Lack of environmental knowledge and inefficient communication are identified as barriers.

Keywords: Environmental, knowledge; Sustainability management; Top management leadership; Wine industry

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

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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: L. Belkhiri, L. Mouni, A. Tiri, T.S. Narany

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In the present study, the self-organizing map (SOM) clustering technique was applied to identify homogeneous clusters of hydrochemical parameters in El Milia plain, Algeria, to assess the quality of groundwater for potable and agricultural purposes. The visualization of SOM-analysis indicated that 35 groundwater samples collected in the study area were classified into three clusters, which showed progressive increase in electrical conductivity from cluster one to cluster three. Samples belonging to cluster one are mostly located in the recharge zone showing hard fresh water type, however, water type gradually changed to hard-brackish type in the discharge zone, including clusters two and three. Ionic ratio studies indicated the role of carbonate rock dissolution in increases on groundwater hardness, especially in cluster one. However, evaporation and evapotranspiration are the main processes increasing salinity in cluster two and three.

Keywords: Drinking water, groundwater quality, irrigation water, self-organizing maps.

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Authors: G. Van Dijck, M. M. Van Hulle, M. Wevers

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A genetic algorithm (GA) based feature subset selection algorithm is proposed in which the correlation structure of the features is exploited. The subset of features is validated according to the classification performance. Features derived from the continuous wavelet transform are potentially strongly correlated. GA-s that do not take the correlation structure of features into account are inefficient. The proposed algorithm forms clusters of correlated features and searches for a good candidate set of clusters. Secondly a search within the clusters is performed. Different simulations of the algorithm on a real-case data set with strong correlations between features show the increased classification performance. Comparison is performed with a standard GA without use of the correlation structure.

Keywords: Classification, genetic algorithm, hierarchicalagglomerative clustering, wavelet transform.

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Authors: Ramin Javadzadeh., Zahra Afsahi, MohammadReza Meybodi

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The Artificial immune systems algorithms are Meta heuristic optimization method, which are used for clustering and pattern recognition applications are abundantly. These algorithms in multimodal optimization problems are more efficient than genetic algorithms. A major drawback in these algorithms is their slow convergence to global optimum and their weak stability can be considered in various running of these algorithms. In this paper, improved Artificial Immune System Algorithm is introduced for the first time to overcome its problems of artificial immune system. That use of the small size of a local search around the memory antibodies is used for improving the algorithm efficiently. The credibility of the proposed approach is evaluated by simulations, and it is shown that the proposed approach achieves better results can be achieved compared to the standard artificial immune system algorithms

Keywords: Artificial immune system, Cellular Automata, Cellular learning automata, Cellular learning automata, , Local search, Optimization.

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Authors: Vinay K. Nangia, Cashmira Pramanik

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Academia-industry relationship is not like that of technology donator-acceptor, but is of interactive and collaborative nature, acknowledging and ensuring mutual respect for each other-s role and contributions with an eye to attaining the true purpose of such relationships, namely, bringing about research-outcome synergy. Indeed, academia-industry interactions are a system that requires active and collaborative participations of all the stakeholders. This paper examines various issues associated with academic institutions and industry collaboration with special attention to the nature of resources and potentialities of stakeholders in the context of knowledge management. This paper also explores the barriers of academia-industry interaction. It identifies potential areas where industry-s participation with academia would be most effective for synergism. Lastly, this paper proposes an integrated model of several new collaborative approaches that are possible, mainly in the Indian scenario to strengthen academia-industry interface.

Keywords: academia-industry, interface, knowledge economy, technology transfer.

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Authors: Mouhammad Alkasassbeh, Mohammad Almseidin

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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: Bakhtiar Ul Haq, R. Ahmed, A. Shaari, Mazmira Binti Mohamed, Nisar Ali

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The capability of exploiting the electronic charge and spin properties simultaneously in a single material has made diluted magnetic semiconductors (DMS) remarkable in the field of spintronics. We report the designing of DMS based on zinc-blend ZnO doped with Cr impurity. The full potential linearized augmented plane wave plus local orbital FP-L(APW+lo) method in density functional theory (DFT) has been adapted to carry out these investigations. For treatment of exchange and correlation energy, generalized gradient approximations have been used. Introducing Cr atoms in the matrix of ZnO has induced strong magnetic moment with ferromagnetic ordering at stable ground state. Cr:ZnO was found to favor the short range magnetic interaction that reflect tendency of Cr clustering. The electronic structure of ZnO is strongly influenced in the presence of Cr impurity atoms where impurity bands appear in the band gap.

Keywords: ZnO, Density functional theory, Diluted magnetic semiconductors, Ferromagnetic materials, FP-L(APW+lo).

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Authors: Wee Hock Quik, Nevan Wright

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Collaborative networked learning (hereafter CNL) was first proposed by Charles Findley in his work “Collaborative networked learning: online facilitation and software support" as part of instructional learning for the future of the knowledge worker. His premise was that through electronic dialogue learners and experts could interactively communicate within a contextual framework to resolve problems, and/or to improve product or process knowledge. Collaborative learning has always been the forefront of educational technology and pedagogical research, but not in the mainstream of operations management. As a result, there is a large disparity in the study of CNL, and little is known about the antecedents of network collaboration and sharing of information among diverse employees in the manufacturing environment. This paper presents a model to bridge the gap between theory and practice. The objective is that manufacturing organizations will be able to accelerate organizational learning and sharing of information through various collaborative

Keywords: Collaborative networked learning, Collaborative technologies, Organizational learning, Synchronous and asynchronous networked learning.

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Authors: Mat Syai'in, Adi Soeprijanto

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An Optimal Power Flow based on Improved Particle Swarm Optimization (OPF-IPSO) with Generator Capability Curve Constraint is used by NN-OPF as a reference to get pattern of generator scheduling. There are three stages in Designing NN-OPF. The first stage is design of OPF-IPSO with generator capability curve constraint. The second stage is clustering load to specific range and calculating its index. The third stage is training NN-OPF using constructive back propagation method. In training process total load and load index used as input, and pattern of generator scheduling used as output. Data used in this paper is power system of Java-Bali. Software used in this simulation is MATLAB.

Keywords: Optimal Power Flow, Generator Capability Curve, Improved Particle Swarm Optimization, Neural Network

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Authors: M. M. Muhammed, O. Khuzaima

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This research examined and outlined some of the challenges facing Islam and Muslims in the 21^st century, considering global Islamic representation and knowledge acquisition as key objectives. It was observed that the Western media misrepresentation of Islam and the Western ethos embodied by the acquisition of western civilisation are major challenges faced by Islam and Muslims today. The problem of sectarianism, decline in the socio-economic power of Muslim communities and the archaic nature of the Islamic creed were recorded as major actors to the evolving global Islamic issues. It was therefore concluded that Islam is not the reason for these challenges, rather the action of some Muslims and non-Muslims were the contributing factors to the pandemics faced by Islam and Muslims. Some relevant recommendations were made to the Islamic world that could serve as effectual solutions to these lingering problems.

Keywords: Islam, challenges, Misrepresentation, twenty-first century.

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Authors: Ying Shen, Weihua Zhu

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Medical images are important to help identifying different diseases, for example, Magnetic resonance imaging (MRI) can be used to investigate the brain, spinal cord, bones, joints, breasts, blood vessels, and heart. Image segmentation, in medical image analysis, is usually the first step to find out some characteristics with similar color, intensity or texture so that the diagnosis could be further carried out based on these features. This paper introduces an improved C-means model to segment the MRI images. The model is based on information entropy to evaluate the segmentation results by achieving global optimization. Several contributions are significant. Firstly, Genetic Algorithm (GA) is used for achieving global optimization in this model where fuzzy C-means clustering algorithm (FCMA) is not capable of doing that. Secondly, the information entropy after segmentation is used for measuring the effectiveness of MRI image processing. Experimental results show the outperformance of the proposed model by comparing with traditional approaches.

Keywords: Magnetic Resonance Image, C-means model, image segmentation, information entropy.

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Authors: S. H. R. Lo

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Virtual engineering technology has undergone rapid progress in recent years and is being adopted increasingly by manufacturing companies of many engineering disciplines. There is an increasing demand from industry for qualified virtual engineers. The qualified virtual engineers should have the ability of applying engineering principles and mechanical design methods within the commercial software package environment. It is a challenge to the engineering education in universities which traditionally tends to lack the integration of knowledge and skills required for solving real world problems. In this paper, a case study shows some recent development of a MSc Mechanical Engineering course at Department of Engineering and Technology in MMU, and in particular, two units Simulation of Mechanical Systems(SMS) and Computer Aided Fatigue Analysis(CAFA) that emphasize virtual engineering education and promote integration of knowledge acquisition, skill training and industrial application.

Keywords: Computational modelling and simulation, mechanical engineering education.

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Authors: Joel S. Mtebe, Aron Kondoro, Mussa M. Kissaka, Elia Kibga

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Sub-Saharan Africa is described as the second fastest growing in mobile phone penetration in the world more than in the United States or the European Union. Mobile phones have been used to provide a lot of opportunities to improve people’s lives in the region such as in banking, marketing, entertainment, and paying for various bills such as water, TV, and electricity. However, the potential of mobile phones to enhance teaching and learning has not been explored. This study presents an experience of developing and delivering SMS based quiz questions used to assess mastery of subject content knowledge of science and mathematics secondary school teachers in Tanzania. The SMS quizzes were used as a follow up support mechanism to 500 teachers who participated in a project to upgrade subject content knowledge of teachers in science and mathematics subjects in Tanzania. Quizzes of 10-15 questions were sent to teachers each week for 8 weeks and the results were analyzed using SPSS. Results show that teachers who participated in chemistry and biology subjects have better performance compared to those who participated in mathematics and physics subjects. Teachers reported some challenges that led to poor performance, This research has several practical implications for those who are implementing or planning to use mobile phones in teaching and learning especially in rural secondary schools in sub-Saharan Africa.

Keywords: Mobile learning, e-learning, educational technologies, SMS, secondary education, assessment.

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Authors: Pradeep Singh, Sukhwinder Singh, Gurjinder Kaur

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In recent years, rapid advances in software and hardware in the field of information technology along with a digital imaging revolution in the medical domain facilitate the generation and storage of large collections of images by hospitals and clinics. To search these large image collections effectively and efficiently poses significant technical challenges, and it raises the necessity of constructing intelligent retrieval systems. Content-based Image Retrieval (CBIR) consists of retrieving the most visually similar images to a given query image from a database of images[5]. Medical CBIR (content-based image retrieval) applications pose unique challenges but at the same time offer many new opportunities. On one hand, while one can easily understand news or sports videos, a medical image is often completely incomprehensible to untrained eyes.

Keywords: Classification, clustering, content-based image retrieval (CBIR), relevance feedback (RF), statistical similarity matching, support vector machine (SVM).

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Authors: Cholpassorn Sitthiwarongchai, Chutikarn Sriviboon

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The two primary objectives of this research were (1)  to examine the current knowledge and actual circumstance of  agricultural workers about mangosteen product processing; and (2) to  analyze and evaluate ways to develop capacity of mangosteen  product processing. The population of this study was 15,125 people  who work in the agricultural sector, in this context, mangosteen  production, in the eastern part of Thailand that included Chantaburi  Province, Rayong Province, Trad Province and Pracheenburi  Province. The sample size based on Yamane’s calculation with 95%  reliability was therefore 392 samples. Mixed method was employed  included questionnaire and focus group discussion with  Connoisseurship Model used in order to collect quantitative and  qualitative data. Key informants were used in the focus group  including agricultural business owners, academic people in agro food  processing, local academics, local community development staff,  OTOP subcommittee, and representatives of agro processing  industry professional organizations. The study found that the  majority of the respondents agreed with a high level (in five- rating  scale) towards most of variables of knowledge management in agro  food processing. The result of the current knowledge and actual  circumstance of agricultural human resource in an arena of  mangosteen product processing revealed that mostly, the respondents  agreed at a high level to establish 7 variables. The guideline to  developing the body of knowledge in order to enhance the capacity  of the agricultural workers in mangosteen product processing was  delivered in the focus group discussion. The discussion finally  contributed to an idea to produce manuals for mangosteen product  processing methods, with 4 products chosen: (1) mangosteen soap;  (2) mangosteen juice; (3) mangosteen toffee; and (4) mangosteen  preserves or jam.

Keywords: Capacity Enhancement, Agricultural Workers, Mangosteen Product Processing.

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Authors: Christina G. Siontorou

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This paper presents an integrated knowledge-based approach to multi-scale modeling of aquatic systems, with a view to enhancing predictive power and aiding environmental management and policy-making. The basic phases of this approach have been exemplified in the case of a bay in Saronicos Gulf (Attiki, Greece). The results showed a significant problem with rising phytoplankton blooms linked to excessive microbial growth, arisen mostly due to increased nitrogen inflows; therefore, the nitrification/denitrification processes of the benthic and water column sub-systems have provided the quality variables to be monitored for assessing environmental status. It is thereby demonstrated that the proposed approach facilitates modeling choices and implementation option decisions, while it provides substantial support for knowledge and experience capitalization in long-term water management.

Keywords: Aquatic ecosystem, integrated modeling, multi-scale modeling, ontological platform.

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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: J. McCullagh, T. Whitfort

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Artificial Neural Networks (ANNs) have been used successfully in many scientific, industrial and business domains as a method for extracting knowledge from vast amounts of data. However the use of ANN techniques in the sporting domain has been limited. In professional sport, data is stored on many aspects of teams, games, training and players. Sporting organisations have begun to realise that there is a wealth of untapped knowledge contained in the data and there is great interest in techniques to utilise this data. This study will use player data from the elite Australian Football League (AFL) competition to train and test ANNs with the aim to predict the onset of injuries. The results demonstrate that an accuracy of 82.9% was achieved by the ANNs’ predictions across all examples with 94.5% of all injuries correctly predicted. These initial findings suggest that ANNs may have the potential to assist sporting clubs in the prediction of injuries.

Keywords: Artificial Neural Networks, data, injuries, sport

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Authors: Yunbin Yang, Liangli He, Fengjun Wang

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The essentiality of maintenance assessment and maintenance optimization in design stage is analyzed, and the existent problems of conventional maintenance design method are illuminated. MDMVM (Maintenance Design Method based Virtual Maintenance) is illuminated, and the process of MDMVM established, and the MDMVM architecture is given out. The key techniques of MDMVM are analyzed, and include maintenance design based KBE (Knowledge Based Engineering) and virtual maintenance based physically attribute. According to physical property, physically based modeling, visual object movement control, the simulation of operation force and maintenance sequence planning method are emphatically illuminated. Maintenance design system based virtual maintenance is established in foundation of maintenance design method.

Keywords: Digital mock-up, virtual maintenance, knowledge engineering, maintenance sequence planning.

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Authors: H. Bouhamed, A. Rebai, T. Lecroq, M. Jaoua

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The objective of our work is to develop a new approach for discovering knowledge from a large mass of data, the result of applying this approach will be an expert system that will serve as diagnostic tools of a phenomenon related to a huge information system. We first recall the general problem of learning Bayesian network structure from data and suggest a solution for optimizing the complexity by using organizational and optimization methods of data. Afterward we proposed a new heuristic of learning a Multi-Entities Bayesian Networks structures. We have applied our approach to biological facts concerning hereditary complex illnesses where the literatures in biology identify the responsible variables for those diseases. Finally we conclude on the limits arched by this work.

Keywords: Data-organization, data-optimization, automatic knowledge discovery, Multi-Entities Bayesian networks, score merging.

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Authors: Zaiton Samdin, Kasimu Abdu Bakori, Hamimah Hassan

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This paper attempts to investigate the factors that influence hotel managers- attitudes towards sustainable tourism practices (STP) in Kuala Lumpur and the state of Selangor in Malaysia. The study distributes 104 questionnaires to hotels ranging from one star to five-star categories including budget hotels. Out of this figure, 60 copies of the questionnaires were returned and analyzed. The finding revealed that of all the seven factors investigated, only the variables measuring incentives and knowledge have significantly influenced sustainable tourism practices in the country. Therefore, government and other green bodies within the country should continue to provide hotels with incentives for sound technologies. Moreover, the government agencies should continue to educate hoteliers on the relevance of environmental protection for the successful implementation of sustainable tourism practices.

Keywords: Attitude, incentive, knowledge, sustainable tourism practices

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Authors: M. K. Pradhan, C. K. Biswas,

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In the current research, neuro-fuzzy model and regression model was developed to predict Material Removal Rate in Electrical Discharge Machining process for AISI D2 tool steel with copper electrode. Extensive experiments were conducted with various levels of discharge current, pulse duration and duty cycle. The experimental data are split into two sets, one for training and the other for validation of the model. The training data were used to develop the above models and the test data, which was not used earlier to develop these models were used for validation the models. Subsequently, the models are compared. It was found that the predicted and experimental results were in good agreement and the coefficients of correlation were found to be 0.999 and 0.974 for neuro fuzzy and regression model respectively

Keywords: Electrical discharge machining, material removal rate, neuro-fuzzy model, regression model, mountain clustering.

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Authors: Shivakumar, G. S. Vijay, P. Srinivas Pai, B. R. Shrinivasa Rao

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In the present study, RBF neural networks were used for predicting the performance and emission parameters of a biodiesel engine. Engine experiments were carried out in a 4 stroke diesel engine using blends of diesel and Honge methyl ester as the fuel. Performance parameters like BTE, BSEC, Tex and emissions from the engine were measured. These experimental results were used for ANN modeling. RBF center initialization was done by random selection and by using Clustered techniques. Network was trained by using fixed and varying widths for the RBF units. It was observed that RBF results were having a good agreement with the experimental results. Networks trained by using clustering technique gave better results than using random selection of centers in terms of reduced MRE and increased prediction accuracy. The average MRE for the performance parameters was 3.25% with the prediction accuracy of 98% and for emissions it was 10.4% with a prediction accuracy of 80%.

Keywords: Radial Basis Function networks, emissions, Performance parameters, Fuzzy c means.

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Authors: Khalid Ghoulam, Belaid Bouikhalene, Zakaria Harmouch, Hicham Mouncif

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One of the essential topics in the information systems is the registration management. The objective of this project is to create a web portal designed to help new students on the first access to the Sultan Moulay Slimane University SMSU (Practical Information, Pre-Registration, Placement Test, Terms of use ... etc.) while creating a secure space protecting both data from the institutions of the University and student information. This portal is accessible from any computer connected to the Internet inside and outside the campus. In this work, we present a platform on the first access to the SMSU which is essential for authentication in the digital work space of the university. This platform allows university to make better decisions for students clustering, to avoid traditional manual method, and to reduce the cost in human and material resources.

Keywords: Registration, SMSU, Security, FAUSMS, digital work space, Placement test.

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Authors: Anna Nedyalkova, KrassimirNedyalkov, TeodoraBakardjieva

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In the present paper, we-ll explore how social media tools provide an opportunity for new developments of the e-Learning in the context of managing personal knowledge. There will be a discussion how social media tools provide a possibility for helping knowledge workersand students to gather, organize and manage their personal information as a part of the e-learning process. At the centre of this social software driven approach to e-learning environments are the challenges of personalization and collaboration. We-ll share concepts of how organizations are using social media for e-Learning and believe that integration of these tools into traditional e-Learning is probably not a choice, but inevitability. Students- Survey of use of web technologies and social networking tools is presented. Newly developed framework for semantic blogging capable of organizing results relevant to user requirements is implemented at Varna Free University (VFU) to provide more effective navigation and search.

Keywords: Semantic blogging, social media tools, e-Learning, web 2.0, web 3.0.

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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: Elham Alaee, Mousa Shamsi, Hossein Ahmadi, Soroosh Nazem, Mohammadhossein Sedaaghi

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Human face has a fundamental role in the appearance of individuals. So the importance of facial surgeries is undeniable. Thus, there is a need for the appropriate and accurate facial skin segmentation in order to extract different features. Since Fuzzy CMeans (FCM) clustering algorithm doesn’t work appropriately for noisy images and outliers, in this paper we exploit Possibilistic CMeans (PCM) algorithm in order to segment the facial skin. For this purpose, first, we convert facial images from RGB to YCbCr color space. To evaluate performance of the proposed algorithm, the database of Sahand University of Technology, Tabriz, Iran was used. In order to have a better understanding from the proposed algorithm; FCM and Expectation-Maximization (EM) algorithms are also used for facial skin segmentation. The proposed method shows better results than the other segmentation methods. Results include misclassification error (0.032) and the region’s area error (0.045) for the proposed algorithm.

Keywords: Facial image, segmentation, PCM, FCM, skin error, facial surgery.

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Authors: R. Saraçoğlu, N. Allahverdi

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Searching similar documents and document management subjects have important place in text mining. One of the most important parts of similar document research studies is the process of classifying or clustering the documents. In this study, a similar document search approach that includes discussion of out the case of belonging to multiple categories (multiple categories problem) has been carried. The proposed method that based on Fuzzy Similarity Classification (FSC) has been compared with Rocchio algorithm and naive Bayes method which are widely used in text mining. Empirical results show that the proposed method is quite successful and can be applied effectively. For the second stage, multiple categories vector method based on information of categories regarding to frequency of being seen together has been used. Empirical results show that achievement is increased almost two times, when proposed method is compared with classical approach.

Keywords: Document similarity, Fuzzy classification, Multiple categories, Text mining.

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