Search results for: learning community

Authors: Kawai Nobuaki

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This paper describes the design and implementation of web system for continuable and viable collaboration. This study proposes the improvement of the system based on a result of a certain practice. As contemporary higher education information environments transform, this study highlights the significance of university identity and college identity that are formed continuously through independent activities of the students. Based on these discussions, the present study proposes a practical media environment design which facilitates the processes of organizational identity formation based on a continuous and cyclical model. Even if users change by this system, the communication system continues operation and cooperation. The activity becomes the archive and produces new activity. Based on the result, this study elaborates a plan with a re-design by a system from the viewpoint of second-order cybernetics. Systems theory is a theoretical foundation for our study.

Keywords: Collaborative work, learning management system, second-order cybernetics, systems theory, user generated contents, viable system model.

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

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

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

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Authors: Belkacem Laimouche

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With the field of Artificial Intelligence (AI) experiencing exponential growth, fueled by technological advancements that pave the way for increasingly innovative and promising applications, there is an escalating need to develop rigorous methods for assessing their performance in pursuit of transparency and equity. This article proposes a metrology-inspired statistical framework for evaluating bias and explainability in AI systems. Drawing from the principles of metrology, we propose a pioneering approach, using a concrete example, to evaluate the accuracy and precision of AI models, as well as to quantify the sources of measurement uncertainty that can lead to bias in their predictions. Furthermore, we explore a statistical approach for evaluating the explainability of AI systems based on their ability to provide interpretable and transparent explanations of their predictions.

Keywords: Artificial intelligence, metrology, measurement uncertainty, prediction error, bias, machine learning algorithms, probabilistic models, inter-laboratory comparison, data analysis, data reliability, bias impact assessment, bias measurement.

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Authors: Hany Alashwal, Safaai Deris, Razib M. Othman

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The protein domain structure has been widely used as the most informative sequence feature to computationally predict protein-protein interactions. However, in a recent study, a research group has reported a very high accuracy of 94% using hydrophobicity feature. Therefore, in this study we compare and verify the usefulness of protein domain structure and hydrophobicity properties as the sequence features. Using the Support Vector Machines (SVM) as the learning system, our results indicate that both features achieved accuracy of nearly 80%. Furthermore, domains structure had receiver operating characteristic (ROC) score of 0.8480 with running time of 34 seconds, while hydrophobicity had ROC score of 0.8159 with running time of 20,571 seconds (5.7 hours). These results indicate that protein-protein interaction can be predicted from domain structure with reliable accuracy and acceptable running time.

Keywords: Bioinformatics, protein-protein interactions, support vector machines, protein features.

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Authors: Ilario Gabriele Gerloni, Vincenza Carchiolo, Alessandro Longheu, Ugo Becciani, Eva Sciacca, Fabio Vitello

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ICT tools and platforms endorse more and more educational process. Many models and techniques for people to be educated and trained about specific topics and skills do exist, as classroom lectures with textbooks, computers, handheld devices and others. The choice to what extent ICT is applied within learning contexts is related to personal access to technologies as well as to the infrastructure surrounding environment. Among recent techniques, the adoption of Virtual Reality (VR) and Augmented Reality (AR) provides significant impulse in fully engaging users senses. In this paper, an application of AR/VR within Geographic Information Systems (GIS) context is presented. It aims to provide immersive environment experiences for educational and training purposes (e.g. for civil protection personnel), useful especially for situations where real scenarios are not easily accessible by humans. First acknowledgments are promising for building an effective tool that helps civil protection personnel training with risk reduction.

Keywords: Education, virtual reality, augmented reality, civil protection.

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Authors: Huay Lit Woo, Qiyun Wang

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Weblog is an Internet tool that is believed to possess great potential to facilitate learning in education. This study wants to know if weblog can be used to promote students- critical thinking. It used a group of secondary two students from a Singapore school to write weblogs as a means of substitution for their traditional handwritten assignments. The topics for the weblogging are taken from History syllabus but modified to suit the purpose of this study. Weblogs from the students were collected and analysed using a known coding system for measuring critical thinking. Results show that the topic for blogging is crucial in determining the types of critical thinking employed by the students. Students are seen to display critical thinking traits in the areas of information sourcing, linking information to arguments and viewpoints justification. Students- criticalness is more profound when the information for writing a topic is readily available. Otherwise, they tend to be less critical and subjective. The study also found that students lack the ability to source for external information suggesting that students may need to be taught information literacy in order to widen their use of critical thinking skills.

Keywords: Affordance, blog, critical thinking, perception, weblog.

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Authors: V. C. Shruti, P. F. Rodríguez-Espinosa, D. C. Escobedo-Urías, Estefanía Martinez Tavera, M. P. Jonathan

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Atoyac River, a major south-central river flowing through the states of Puebla and Tlaxcala in Mexico is significantly impacted by the natural volcanic inputs in addition with wastewater discharges from urban, agriculture and industrial zones. In the present study, core samples were collected from R. Atoyac and analyzed for sediment granularity, major (Al, Fe, Ca, Mg, K, P and S) and trace elemental concentrations (Ba, Cr, Cd, Mn, Pb, Sr, V, Zn, Zr). The textural studies reveal that the sediments are mostly sand sized particles exceeding 99% and with very few to no presence of mud fractions. It is observed that most of the metals like (avg: all values in μg g^-1) Ca (35,528), Mg (10,789), K (7453), S (1394), Ba (203), Cr (30), Cd (4), Pb (11), Sr (435), Zn (76) and Zr (88) are enriched throughout the sediments mainly sourced from volcanic inputs, source rock composition of Atoyac River basin and industrial influences from the Puebla city region. Contamination indices, such as anthropogenic factor (AF), enrichment factor (EF) and geoaccumulation index (I_geo), were used to investigate the level of contamination and toxicity as well as quantitatively assess the influences of human activities on metal concentrations. The AF values (>1) for Ba, Ca, Mg, Na, K, P and S suggested volcanic inputs from the study region, where as Cd and Zn are attributed to the impacts of industrial inputs in this zone. The EF and I_geo values revealed an extreme enrichment of S and Cd. The ecological risks were evaluated using potential ecological risk index (RI) and the results indicate that the metals Cd and V pose a major hazard for the biological community.

Keywords: Atoyac River, contamination indices, metal concentrations, Mexico, textural studies.

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Authors: Pedro Barbosa Cabral, Neuza Pedro, Ana Mafalda Gonçalves

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The purpose of this research is the analysis of the impact of ICT-related training in the adoption of a learning management systems (LMS) for teaching practicesby faculties in a higher education institution. Based on comparative analyses the impact will be obtained by the number of LMS courses created and managed by participants in ICT for teaching workshops and those who have not attended to any workshops. Involving near 1320 LMS courses and 265 faculties, the results evidence that(i) faculties who have not attend any workshop present a larger distribution of empty courses and (ii) faculties who have attended three or more workshops managed a higher distribution of courses with a considerable level of use intensity, when compared to the others groups. These findings supportthe idea that faculty training is a crucial factor in the process of LMS integration in higher education institutions and that faculties who have been enrolled in three or more workshops develop a higher level of technical and pedagogical proficiency in LMS.

Keywords: Higher Education, Faculty Training, LMS, Comparative Analyses

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Authors: Daniel Morariu, Lucian N. Vintan, Volker Tresp

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Text categorization is the problem of classifying text documents into a set of predefined classes. After a preprocessing step the documents are typically represented as large sparse vectors. When training classifiers on large collections of documents, both the time and memory restrictions can be quite prohibitive. This justifies the application of features selection methods to reduce the dimensionality of the document-representation vector. Four feature selection methods are evaluated: Random Selection, Information Gain (IG), Support Vector Machine (called SVM_FS) and Genetic Algorithm with SVM (GA_FS). We showed that the best results were obtained with SVM_FS and GA_FS methods for a relatively small dimension of the features vector comparative with the IG method that involves longer vectors, for quite similar classification accuracies. Also we present a novel method to better correlate SVM kernel-s parameters (Polynomial or Gaussian kernel).

Keywords: Features selection, learning with kernels, support vector machine, genetic algorithms and classification.

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Authors: An-Jui. Li, Kelvin Lim, Chien-Kuo Chiu, Benson Hsiung

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This paper details the utilization of artificial intelligence (AI) in the field of slope stability whereby quick and convenient solutions can be obtained using the developed tool. The AI tool used in this study is the artificial neural network (ANN), while the slope stability analysis methods are the finite element limit analysis methods. The developed tool allows for the prompt prediction of the safety factors of fill slopes and their corresponding probability of failure (depending on the degree of variation of the soil parameters), which can give the practicing engineer a reasonable basis in their decision making. In fact, the successful use of the Extreme Learning Machine (ELM) algorithm shows that slope stability analysis is no longer confined to the conventional methods of modeling, which at times may be tedious and repetitive during the preliminary design stage where the focus is more on cost saving options rather than detailed design. Therefore, similar ANN-based tools can be further developed to assist engineers in this aspect.

Keywords: Landslide, limit analysis, ANN, soil properties.

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Authors: Alicia Y. C. Tang, Sharifuddin M. Zain, Noorsaadah A. Rahman, Rukaini Abdullah

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This paper discusses a qualitative simulator QRiOM that uses Qualitative Reasoning (QR) technique, and a process-based ontology to model, simulate and explain the behaviour of selected organic reactions. Learning organic reactions requires the application of domain knowledge at intuitive level, which is difficult to be programmed using traditional approach. The main objective of QRiOM is to help learners gain a better understanding of the fundamental organic reaction concepts, and to improve their conceptual comprehension on the subject by analyzing the multiple forms of explanation generated by the software. This paper focuses on the generation of explanation based on causal theories to explicate various phenomena in the chemistry subject. QRiOM has been tested with three classes problems related to organic chemistry, with encouraging results. This paper also presents the results of preliminary evaluation of QRiOM that reveal its explanation capability and usefulness.

Keywords: Artificial intelligence, explanation, ontology, organicreactions, qualitative reasoning, QPT.

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Authors: Enas M. F. El Houby, Marwa S. Hassan

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Combined therapy using Interferon and Ribavirin is the standard treatment in patients with chronic hepatitis C. However, the number of responders to this treatment is low, whereas its cost and side effects are high. Therefore, there is a clear need to predict patient’s response to the treatment based on clinical information to protect the patients from the bad drawbacks, Intolerable side effects and waste of money. Different machine learning techniques have been developed to fulfill this purpose. From these techniques are Associative Classification (AC) and Decision Tree (DT). The aim of this research is to compare the performance of these two techniques in the prediction of virological response to the standard treatment of HCV from clinical information. 200 patients treated with Interferon and Ribavirin; were analyzed using AC and DT. 150 cases had been used to train the classifiers and 50 cases had been used to test the classifiers. The experiment results showed that the two techniques had given acceptable results however the best accuracy for the AC reached 92% whereas for DT reached 80%.

Keywords: Associative Classification, Data mining, Decision tree, HCV, interferon.

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Authors: Chantana Insra

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The research “Buddha Images in Mudras Representing Days of a Week: Tactile Texture Design for the Blind” aims to provide original tactile format to institutions for the blind, as supplementary textbooks, to accumulate Buddhist knowledge, so that it could be extracurricular learning. The research studied on 33 students with both total and partial blindness, the latter with the ability to read Braille’s signs, of elementary 4 – 6, who are pursuing their studies on the second semester of the academic year 2013 at Bangkok School for the Blind. The researcher opted samples specifically, studied data acquired from both documents and fieldworks. Those methods must be related to the blind, tactile format production, and Buddha images in mudras representing days of a week. Afterwards, the formats will be analyzed and designed so that there would be 8 format pictures of Buddha images in mudras representing days of the week. Experts will next evaluate the media and try out.

Keywords: Blind, tactile texture, Thai Buddha images in Mudras representing days of the week.

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Authors: Tang Mui Joo, Chan Eang Teng

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The use of social media has become more defined. It has been widely used for the purpose of business. More marketers are now using social media as tools to enhance their businesses. Whereas on the other hand, there are more and more people spending their time through mobile apps to be engaged in the social media sites like YouTube, Facebook, Twitter and others. Social media has even become common in Public Relations (PR). It has become number one platform for creating and sharing content. In view to this, social media has changed the rules in PR where it brings new challenges and opportunities to the profession. Although corporate websites, chat-rooms, email customer response facilities and electronic news release distribution are now viewed as standard aspects of PR practice, many PR practitioners are still struggling with the impact of new media though the implementation of social media is potentially reducing the cost of communication. It is to the point that PR practitioners are not fully embracing new media, they are ill-equipped to do so and they have a fear of the technology. Somehow that social media has become a new style of communication that is characterized by conversation and community. It has become a platform that allows individuals to interact with one another and build relationship among each other. Therefore, in the use of business world, consumers are able to interact with those companies that have joined any social media. Based on their experiences with social networking site interactions, they are also exposed to personal interaction while communicating. This paper is to study the impact of social media to PR. This paper discovers the potential changes of PR practices in a developing country like Malaysia. Eventually the study reflects on how PR practitioners are actually using social media in the country. This paper is based on two theories in its development of this research foundation. Media Ecology Theory is to support the impact and changes to PR. Social Penetration Theory is to reflect on how the use of social media is among PRs. This research is using survey with PR practitioners in its data collection. The results have shown that PR professionals value social media more than they actually use it and the way of organizations communicate had been changed due to the transformation of social media.

Keywords: New media, social media, PR.

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Authors: Sam Moshaver

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Reasonably priced and well-constructed housing must be an integral and element supporting a healthy society. The absence of housing everyone in society can afford negatively affects the people's health, education, ability to get jobs, develop their community. Without access to decent housing, economic development, integration of immigrants and inclusiveness, the society is negatively impacted. Canada has a sterling record in creating housing compared to many other nations around the globe. Canadian housing gets support from a mature and responsive mortgage network and a top-quality construction industry as well as safe and excellent quality building materials that are readily available. Yet 1.7 million Canadian households occupy substandard abodes. During the past hundred years, Canada's government has made a wide variety of attempts to provide decent residential facilities every Canadian can afford. Despite these laudable efforts, today Canada is left with housing that is inadequate for many Canadians. People who own their housing are given all kinds of privileges and perks, while people with relatively low incomes who rent their apartments or houses are discriminated against. To help solve these problems, zoning that is based on an "inclusionary" philosophy is tool developed to help provide people the affordable residences that they need. No, thirty years after its introduction, this type of zoning has been shown effective in helping build and provide Canadians with a houses or apartments they can afford to pay for. Using this form of zoning can have different results +depending on where and how it is used. After examining Canadian affordable housing and four American cases where this type of zoning was enforced in the USA, this makes various recommendations for expanding Canadians' access to housing they can afford.

Keywords: Affordable Housing, Inclusionary Zoning Low- Income Housing, Toronto Housing.

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Authors: Kjersti Engan, Karl Skretting, Jostein Herredsvela, Thor Ole Gulsrud

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Texture classification is an important image processing task with a broad application range. Many different techniques for texture classification have been explored. Using sparse approximation as a feature extraction method for texture classification is a relatively new approach, and Skretting et al. recently presented the Frame Texture Classification Method (FTCM), showing very good results on classical texture images. As an extension of that work the FTCM is here tested on a real world application as detection of abnormalities in mammograms. Some extensions to the original FTCM that are useful in some applications are implemented; two different smoothing techniques and a vector augmentation technique. Both detection of microcalcifications (as a primary detection technique and as a last stage of a detection scheme), and soft tissue lesions in mammograms are explored. All the results are interesting, and especially the results using FTCM on regions of interest as the last stage in a detection scheme for microcalcifications are promising.

Keywords: detection, mammogram, texture classification, dictionary learning, FTCM

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Authors: Yogesh Aggarwal

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The paper discusses the results obtained to predict reinforcement in singly reinforced beam using Neural Net (NN), Support Vector Machines (SVM-s) and Tree Based Models. Major advantage of SVM-s over NN is of minimizing a bound on the generalization error of model rather than minimizing a bound on mean square error over the data set as done in NN. Tree Based approach divides the problem into a small number of sub problems to reach at a conclusion. Number of data was created for different parameters of beam to calculate the reinforcement using limit state method for creation of models and validation. The results from this study suggest a remarkably good performance of tree based and SVM-s models. Further, this study found that these two techniques work well and even better than Neural Network methods. A comparison of predicted values with actual values suggests a very good correlation coefficient with all four techniques.

Keywords: Linear Regression, M5 Model Tree, Neural Network, Support Vector Machines.

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Authors: Chantana Insra

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The main purpose of this research aimed to create tactile texture designed media for the blind used for extra learning outside classrooms in order to enhance imagination of the blind about Himmapan creatures, furthermore, the main objective of the research focused on improving the visual disabled perception to be equal to normal people. The target group of the research is blinded students studying in The Bangkok school for the blind between grade 4-6 in the second semester of 2011 who are able to read the braille language. The research methodology consisted of the field study and the documentary study related to the blind, tactile texture designed media and Himmapan creatures. 10 pictures of tactile texture designed media were created in the designing process which began after the analysis had conducted based the primary and secondary data. The works had presented to experts in the visual disabled field who evaluated the works. After approval, the works used as prototype to teach the blind. KeywordsBlind, Himmapan Creatures, Tactile Texture.

Keywords: Blind, Himmapan Creatures, Tactile Texture.

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

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

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

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Authors: R. Sutjiati, Rubianto, I. B. Narmada, I. K. Sudiana, R. P. Rahayu

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The prevalence of post-treatment relapse in orthodontics in the community is high enough; therefore, relapses in orthodontic treatment must be prevented well. The aim of this study is to experimentally test the inhibition of relapse of orthodontics tooth movement in NaF of expression TGF-β1, Runx2, alkaline phosphatase (ALP) and microscopic of woven bone. The research method used was experimental laboratory research involving 30 rats, which were divided into three groups. Group A: rats were not given orthodontic tooth movement and without NaF. Group B: rats were given orthodontic tooth movement and without 11.5 ppm by topical application. Group C: rats were given orthodontic tooth movement and 11.75 ppm by topical application. Orthodontic tooth movement was conducted by applying ligature wires of 0.02 mm in diameter on the molar-1 (M-1) of left permanent maxilla and left insisivus of maxilla. Immunohistochemical examination was conducted to calculate the number of osteoblast to determine TGF β1, Runx2, ALP and haematoxylin to determine woven bone on day 7 and day 14. Results: It was shown that administrations of Natrium Fluoride topical application proved effective to increase the expression of TGF-β1, Runx2, ALP and to increase woven bone in the tension area greater than administration without natrium fluoride topical application (p < 0.05), except the expression of ALP on day 7 and day 14 which was significant. The results of the study show that NaF significantly increases the expressions of TGF-β1, Runx2, ALP and woven bone. The expression of the variables enhanced on day 7 compared on that on day 14, except ALP. Thus, it can be said that the acceleration of woven bone occurs on day 7.

Keywords: TGF-β1, Runx2, ALP, woven bone, natrium fluoride.

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Authors: Sultan Noman Qasem

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This paper presents an evolutionary algorithm for solving multi-objective optimization problems-based artificial neural network (ANN). The multi-objective evolutionary algorithm used in this study is genetic algorithm while ANN used is radial basis function network (RBFN). The proposed algorithm named memetic elitist Pareto non-dominated sorting genetic algorithm-based RBFN (MEPGAN). The proposed algorithm is implemented on medical diseases problems. The experimental results indicate that the proposed algorithm is viable, and provides an effective means to design multi-objective RBFNs with good generalization capability and compact network structure. This study shows that MEPGAN generates RBFNs coming with an appropriate balance between accuracy and simplicity, comparing to the other algorithms found in literature.

Keywords: Radial basis function network, Hybrid learning, Multi-objective optimization, Genetic algorithm.

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Authors: Georges Bou Ghantous

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This research delves into the transformative impact of Artificial Intelligence (AI) on Infrastructure as Code (IaC) practices, specifically focusing on the redefinition of infrastructure orchestration. By harnessing AI technologies such as machine learning algorithms and predictive analytics, organizations can achieve unprecedented levels of efficiency and optimization in managing their infrastructure resources. AI-driven IaC introduces proactive decision-making through predictive insights, enabling organizations to anticipate and address potential issues before they arise. Dynamic resource scaling, facilitated by AI, ensures that infrastructure resources can seamlessly adapt to fluctuating workloads and changing business requirements. Through case studies and best practices, this paper sheds light on the tangible benefits and challenges associated with AI-driven IaC transformation, providing valuable insights for organizations navigating the evolving landscape of digital infrastructure management.

Keywords: Artificial intelligence, AI, infrastructure as code, IaC, efficiency optimization, predictive insights, dynamic resource scaling, proactive decision-making.

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Authors: S.H. Zulkarnain, E.M.A Zawawi, M.Y. A. Rahman, N.K.F. Mustafa

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Building maintenance plays an important role among other activities in building operation. Building defect and damages are part of the building maintenance 'bread and butter' as their input indicated in the building inspection is very much justified, particularly as to determine the building performance. There will be no escape route or short cut from building maintenance work. This study attempts to identify a competitive performance that translates the Critical Success Factor achievements and satisfactorily meet the university-s expectation. The quality and efficiency of maintenance management operation of building depends, to some extent, on the building condition information, the expectation from the university sector and the works carried out for each maintenance activity. This paper reviews the critical success factor in building maintenance management practice for university sectors from four (4) perspectives which include (1) customer (2) internal processes (3) financial and (4) learning and growth perspective. The enhancement of these perspectives is capable to reach the maintenance management goal for a better living environment in university campus.

Keywords: Building maintenance, Critical Success Factor, Management, University

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Authors: Moeiz Miraoui, Chakib Tadj, Chokri ben Amar

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Devices in a pervasive computing system (PCS) are characterized by their context-awareness. It permits them to provide proactively adapted services to the user and applications. To do so, context must be well understood and modeled in an appropriate form which enhance its sharing between devices and provide a high level of abstraction. The most interesting methods for modeling context are those based on ontology however the majority of the proposed methods fail in proposing a generic ontology for context which limit their usability and keep them specific to a particular domain. The adaptation task must be done automatically and without an explicit intervention of the user. Devices of a PCS must acquire some intelligence which permits them to sense the current context and trigger the appropriate service or provide a service in a better suitable form. In this paper we will propose a generic service ontology for context modeling and a context-aware service adaptation based on a service oriented definition of context.

Keywords: Pervasive computing system, context, contextawareness, service, context modeling, ontology, adaptation, machine learning.

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Authors: Mina Fenercioglu

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This study discusses a Turkish music education model similar to its Venezuelan counterpart El Sistema, in which socialization and human development are the main goals. The Music for Peace (Baris Icin Muzik) model, founded in 2005 by an idealist humanitarian in Istanbul, started as a pilot project with accordion and today makes symphonic music education. The program aims to offer social change through free-of-charge. In such a big city like Istanbul, in a deprived inner city center people have poor economic, social and cultural conditions. In that Edirnekapi district people don’t have opportunities to join the cultural and social life, like music or sports. It is believed that this initiative covered a part of this gap by giving children the opportunities to participate in social and cultural life. In this study it is planned to understand what social changes could music education could make in children’s lives. In the complimentary music lessons children works in groups, which helps them to learn the feelings of solidarity, friendship, communion and sharing. By Music for Peace project children connect with the community, they have the belief to succeed in life because they feel that they are loved by their friends, instructors and families. In short they feel that they are important, thus brings the success in life. Additionally, it is believed that, this program has achieved success. Today approximately 400 children participate in this programs orchestras and choirs. Some of the students get into the conservatories. And the center is not just a place where they get music lessons but also a place where they get socialized. And music education helps children to have strong sense of identity, self-confidence and self-esteem.

Keywords: El Sistema, music education, Music for Peace, socialization.

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Authors: Elizabeth B. Varghese, M. Wilscy

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A face recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame. A lot of algorithms have been proposed for face recognition. Vector Quantization (VQ) based face recognition is a novel approach for face recognition. Here a new codebook generation for VQ based face recognition using Integrated Adaptive Fuzzy Clustering (IAFC) is proposed. IAFC is a fuzzy neural network which incorporates a fuzzy learning rule into a competitive neural network. The performance of proposed algorithm is demonstrated by using publicly available AT&T database, Yale database, Indian Face database and a small face database, DCSKU database created in our lab. In all the databases the proposed approach got a higher recognition rate than most of the existing methods. In terms of Equal Error Rate (ERR) also the proposed codebook is better than the existing methods.

Keywords: Face Recognition, Vector Quantization, Integrated Adaptive Fuzzy Clustering, Self Organization Map.

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Authors: Li Ni, Pen ManMan, Li KenLi

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Clustering is an intensive research for some years because of its multifaceted applications, such as biology, information retrieval, medicine, business and so on. The expectation maximization (EM) is a kind of algorithm framework in clustering methods, one of the ten algorithms of machine learning. Traditionally, optimization of objective function has been the standard approach in EM. Hence, research has investigated the utility of evolutionary computing and related techniques in the regard. Chemical Reaction Optimization (CRO) is a recently established method. So the property embedded in CRO is used to solve optimization problems. This paper presents an algorithm framework (EM-CRO) with modified CRO operators based on EM cluster problems. The hybrid algorithm is mainly to solve the problem of initial value sensitivity of the objective function optimization clustering algorithm. Our experiments mainly take the EM classic algorithm:k-means and fuzzy k-means as an example, through the CRO algorithm to optimize its initial value, get K-means-CRO and FKM-CRO algorithm. The experimental results of them show that there is improved efficiency for solving objective function optimization clustering problems.

Keywords: Chemical reaction optimization, expectation maximization, initial, objective function clustering.

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Authors: Berna Cantürk-Günhan, Esra Bukova-Güzel

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The purpose of the study is to determine the primary mathematics student teachers- views related to use instructional technology tools in course of the learning process and to reveal how the sample presentations towards different mathematical concepts affect their views. This is a qualitative study involving twelve mathematics students from a public university. The data gathered from two semi-structural interviews. The first one was realized in the beginning of the study. After that the representations prepared by the researchers were showed to the participants. These representations contain animations, Geometer-s Sketchpad activities, video-clips, spreadsheets, and power-point presentations. The last interview was realized at the end of these representations. The data from the interviews and content analyses were transcribed and read and reread to explore the major themes. Findings revealed that the views of the students changed in this process and they believed that the instructional technology tools should be used in their classroom.

Keywords: Integrating Technology, Mathematics Education, Primary Education, Teacher Education.

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Authors: R. Bremananth

Abstract:

Biometric authentication is an essential task for any kind of real-life applications. In this paper, we contribute two primary paradigms to Iris recognition such as Robust Eyelash Detection (RED) using pathway kernels and hair curve fitting synthesized model. Based on these two paradigms, rotation invariant iris recognition is enhanced. In addition, the presented framework is tested with real-life iris data to provide the authentication for LRC (Learning Resource Center) users. Recognition performance is significantly improved based on the contributed schemes by evaluating real-life irises. Furthermore, the framework has been implemented using Java programming language. Experiments are performed based on 1250 diverse subjects in different angles of variations on the authentication process. The results revealed that the methodology can deploy in the process on LRC management system and other security required applications.

Keywords: Authentication, biometric, eye lashes detection, iris scanning, LRC security, secure access.

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Authors: A. van Onselen, C. M. Walsh, F. J. Veldman, C. Brand

Abstract:

Objectives: To determine the nutritional status and risk factors associated with women practicing geophagia in QwaQwa, South Africa. Materials and Methods: An observational epidemiological study design was adopted which included an exposed (geophagia) and nonexposed (control) group. A food frequency questionnaire, anthropometric measurements and blood sampling were applied to determine nutritional status of participants. Logistic regression analysis was performed in order to identify factors that were likely to be associated with the practice of geophagia. Results: The mean total energy intake for the geophagia group (G) and control group (C) were 10324.31 ± 2755.00 kJ and 10763.94 ± 2556.30 kJ respectively. Both groups fell within the overweight category according to the mean Body Mass Index (BMI) of each group (G= 25.59 kg/m2; C= 25.14 kg/m2). The mean serum iron levels of the geophagia group (6.929 μmol/l) were significantly lower than that of the control group (13.75 μmol/l) (p = 0.000). Serum transferrin (G=3.23g/l; C=2.7054g/l) and serum transferrin saturation (G=8.05%; C=18.74%) levels also differed significantly between groups (p=0.00). Factors that were associated with the practice of geophagia included haemoglobin (Odds ratio (OR):14.50), serumiron (OR: 9.80), serum-ferritin (OR: 3.75), serum-transferrin (OR: 6.92) and transferrin saturation (OR: 14.50). A significant negative association (p=0.014) was found between women who were wageearners and those who were not wage-earners and the practice of geophagia (OR: 0.143; CI: 0.027; 0.755). These findings seem to indicate that a permanent income may decrease the likelihood of practising geophagia. Key Findings: Geophagia was confirmed to be a risk factor for iron deficiency in this community. The significantly strong association between geophagia and iron deficiency emphasizes the importance of identifying the practice of geophagia in women, especially during their child bearing years.

Keywords: Anaemia, anthropometry, dietary intake, geophagia, iron deficiency.

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