Search results for: Social Learning Theory.

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: Il Dong Kim, Jin Rak Lee, Young Jun Ko, Young Taek Jin

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An on-line condition monitoring method for transmission line is proposed using electrical circuit theory and IT technology in this paper. It is reasonable that the circuit parameters such as resistance (R), inductance (L), conductance (g) and capacitance (C) of a transmission line expose the electrical conditions and physical state of the line. Those parameters can be calculated from the linear equation composed of voltages and currents measured by synchro-phasor measurement technique at both end of the line. A set of linear voltage drop equations containing four terminal constants (A, B ,C ,D ) are mathematical models of the transmission line circuits. At least two sets of those linear equations are established from different operation condition of the line, they may mathematically yield those circuit parameters of the line. The conditions of line connectivity including state of connecting parts or contacting parts of the switching device may be monitored by resistance variations during operation. The insulation conditions of the line can be monitored by conductance (g) and capacitance(C) measurements. Together with other condition monitoring devices such as partial discharge, sensors and visual sensing device etc.,they may give useful information to monitor out any incipient symptoms of faults. The prototype of hardware system has been developed and tested through laboratory level simulated transmission lines. The test has shown enough evident to put the proposed method to practical uses.

Keywords: Transmission Line, Condition Monitoring, Circuit Parameters, Synchro- phasor Measurement.

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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: Marut Pichetvit

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The research objective aims to search information about storytelling and fable associated with fireflies in Amphawa community, in order to design and create a story book which is appropriate for the interests of children in early childhood. This book should help building the development of learning about the natural environment, imagination, and creativity among children, which then, brings about the promotion of the development, conservation and dissemination of cultural values and uniqueness of the Amphawa community. The population used in this study were 30 students in early childhood aged between 6-8 years-old, grade 1-3 from the Demonstration School of Suan Sunandha Rajabhat University. The method used for this study was purposive sampling and the research conducted by the query and analysis of data from both the document and the narrative field tales and fable associated with the fireflies of Amphawa community. Then, using the results to synthesize and create a conceptual design in a form of 8 visual images which were later applied to 1 illustrated children’s book and presented to the experts to evaluate and test this media.

Keywords: Children’s illustrated book, Fireflies, Amphawa.

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Authors: Petr Teply, Ondrej Vejdovec

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There are three main ways of categorizing capital in banking operations: accounting, regulatory and economic capital. However, the 2008-2009 global crisis has shown that none of these categories adequately reflects the real risks of bank operations, especially in light of the failures Bear Stearns, Lehman Brothers or Northern Rock. This paper deals with the economic capital allocation of global banks. In theory, economic capital should reflect the real risks of a bank and should be publicly available. Yet, as discovered during the global financial crisis, even when economic capital information was publicly disclosed, the underlying assumptions rendered the information useless. Specifically, some global banks that reported relatively high levels of economic capital before the crisis went bankrupt or had to be bailed-out by their government. And, only 15 out of 50 global banks reported their economic capital during the 2007-2010 period. In this paper, we analyze the changes in reported bank economic capital disclosure during this period. We conclude that relative shares of credit and business risks increased in 2010 compared to 2007, while both operational and market risks decreased their shares on the total economic capital of top-rated global banks. Generally speaking, higher levels of disclosure and transparency of bank operations are required to obtain more confidence from stakeholders. Moreover, additional risks such as liquidity risks should be included in these disclosures.

Keywords: global crisis, economic capital, risk management, risk allocation, bank

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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: 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: A. Tellaeche, X. P. Burgos-Artizzu, G. Pajares, A. Ribeiro

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One important objective in Precision Agriculture is to minimize the volume of herbicides that are applied to the fields through the use of site-specific weed management systems. In order to reach this goal, two major factors need to be considered: 1) the similar spectral signature, shape and texture between weeds and crops; 2) the irregular distribution of the weeds within the crop's field. This paper outlines an automatic computer vision system for the detection and differential spraying of Avena sterilis, a noxious weed growing in cereal crops. The proposed system involves two processes: image segmentation and decision making. Image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based attributes measuring the relations among the crops and the weeds. From these attributes, a hybrid decision making approach determines if a cell must be or not sprayed. The hybrid approach uses the Support Vector Machines and the Fuzzy k-Means methods, combined through the fuzzy aggregation theory. This makes the main finding of this paper. The method performance is compared against other available strategies.

Keywords: Fuzzy k-Means, Precision agriculture, SupportVectors Machines, Weed detection.

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Authors: Rubén Bãnos, José Arcos, Oscar Bautista, Federico Méndez

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The Oscillatory electroosmotic flow (OEOF) in power law fluids through a microchannel is studied numerically. A time-dependent external electric field (AC) is suddenly imposed at the ends of the microchannel which induces the fluid motion. The continuity and momentum equations in the x and y direction for the flow field were simplified in the limit of the lubrication approximation theory (LAT), and then solved using a numerical scheme. The solution of the electric potential is based on the Debye-H¨uckel approximation which suggest that the surface potential is small,say, smaller than 0.025V and for a symmetric (z : z) electrolyte. Our results suggest that the velocity profiles across the channel-width are controlled by the following dimensionless parameters: the angular Reynolds number, Reω, the electrokinetic parameter, ¯κ, defined as the ratio of the characteristic length scale to the Debye length, the parameter λ which represents the ratio of the Helmholtz-Smoluchowski velocity to the characteristic length scale and the flow behavior index, n. Also, the results reveal that the velocity profiles become more and more non-uniform across the channel-width as the Reω and ¯κ are increased, so oscillatory OEOF can be really useful in micro-fluidic devices such as micro-mixers.

Keywords: Oscillatory electroosmotic flow, Non-Newtonian fluids, power-law model, low zeta potentials.

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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: Hui-Min Lai, Chin-Pin Chen, Yung-Fu Chang

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The expectation-confirmation model (ECM) is one of the most widely used models for evaluating information system continuance, and this model has been extended to other study backgrounds, or expanded with other theoretical perspectives. However, combining ECM with other theories or investigating the background problem may produce some disparities, thus generating inaccurate conclusions. Habit is considered to be an important factor that influences the user’s continuance behavior. This paper thus critically examines seven pairs of relationships from the original ECM and the habit variable. A meta-analysis was used to tackle the development of ECM research over the last 10 years from a range of journals and conference papers published in 2005–2014. Forty-six journal articles and 19 conference papers were selected for analysis. The results confirm our prediction that a high effect size for the seven pairs of relationships was obtained (ranging from r=0.386 to r=0.588). Furthermore, a meta-analytic structural equation modeling was performed to simultaneously test all relationships. The results show that habit had a significant positive effect on continuance intention at p<=0.05 and that the six other pairs of relationships were significant at p<0.10. Based on the findings, we refined our original research model and an alternative model was proposed for understanding and predicting information system continuance. Some theoretical implications are also discussed.

Keywords: Expectation-confirmation theory, expectation- confirmation model, meta-analysis, meta-analytic structural equation modeling.

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Authors: A. Rashid, N. Hasan

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The present study investigates numerically the phenomenon of vortex-shedding and its suppression in twodimensional mixed convective flow past a square cylinder under the joint influence of buoyancy and free-stream orientation with respect to gravity. The numerical experiments have been conducted at a fixed Reynolds number (Re) of 100 and Prandtl number (Pr) of 0.71, while Richardson number (Ri) is varied from 0 to 1.6 and freestream orientation, α, is kept in the range 0o≤ α ≤ 90o, with 0o corresponding to an upward flow and 90o representing a cross-flow scenario, respectively. The continuity, momentum and energy equations, subject to Boussinesq approximation, are discretized using a finite difference method and are solved by a semi-explicit pressure correction scheme. The critical Richardson number, leading to the suppression of the vortex-shedding (Ric), is estimated by using Stuart-Landau theory at various free-stream orientations and the neutral curve is obtained in the Ri-α plane. The neutral curve exhibits an interesting non-monotonic behavior with Ric first increasing with increasing values of α upto 45o and then decreasing till 70o. Beyond 70o, the neutral curve again exhibits a sharp increasing asymptotic trend with Ric approaching very large values as α approaches 90o. The suppression of vortex shedding is not observed at α = 90o (cross-flow). In the unsteady flow regime, the Strouhal number (St) increases with the increase in Richardson number.

Keywords: bluff body, buoyancy, free-stream orientation, vortex-shedding.

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Authors: Kaishuan Shen, Pan Changyu, Yuhsiang Lu, Zongshao Liu, Chishxsin Chuang, Minyuan Ma

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The culture of riding heavy motorcycles originates from advanced countries and mainly comes from Europe, North America, and Japan. Heavy duty motorcycle riders are different from people who view motorcycles as a convenient mean of transportation. They regard riding them as a kind of enjoyment and high-level taste. The activities of riding heavy duty motorcycles have formes a distinctive landscape in domestic land in Taiwan. Previous studies which explored motorcycle culture in Taiwan still focused on the objects of motorcycle engine displacement under 50 cc.. The study aims to study the heavy duty motorcycles of engine displacement over 550 cc. and explores where their attractiveness is. For finding the attractiveness of heavy duty motorcycle, the study chooses Miryoku Engineering (Preference-Based Design) approach. Two steps are adopted to proceed the research. First, through arranging the letters obtained from interviewing experts, EGM (The Evaluation Grid Method) was applied to find out the structure of attractiveness. The attractive styles are eye-dazzling, leisure, classic, and racing competitive styles. Secondarily, Quantification Theory Type I analysis was adopted as a tool for analyzing the importance of attractiveness. The relationship between style and attractive parts was also discussed. The results could contribute to the design and research development of heavy duty motorcycle industry in Taiwan.

Keywords: attractiveness, evaluation, heavy dutymotorcycle, miryoku engineering

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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: Tero Huhtala, Minna Pikkarainen, Saila Saraniemi

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Information technology has long been used as an enabler of exchange for goods and services. Services are evolving from generic to personalized, and the reverse use of customer data has been discussed in both academia and industry for the past few years. This article presents the results of an empirical case study in the area of preventive health care services. The primary data were gathered in workshops, in which future personal data-based services were conceptualized by analyzing future scenarios from a business perspective. The aim of this study is to understand business model transformation in emerging personal data ecosystems. The work was done as a case study in the context of occupational healthcare. The results have implications to theory and practice, indicating that adopting personal data management principles requires transformation of the business model, which, if successfully managed, may provide access to more resources, potential to offer better value, and additional customer channels. These advantages correlate with the broadening of the business ecosystem. Expanding the scope of this study to include more actors would improve the validity of the research. The results draw from existing literature and are based on findings from a case study and the economic properties of the healthcare industry in Finland.

Keywords: Ecosystem, business model, personal data, preventive healthcare.

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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: S. Bouharati, I. Bouharati, C. Benzidane, F. Alleg, M. Belmahdi

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Sensors possess several properties of physical measures. Whether devices that convert a sensed signal into an electrical signal, chemical sensors and biosensors, thus all these sensors can be considered as an interface between the physical and electrical equipment. The problem is the analysis of the multitudes of saved settings as input variables. However, they do not all have the same level of influence on the outputs. In order to identify the most sensitive parameters, those that can guide users in gathering information on the ground and in the process of model calibration and sensitivity analysis for the effect of each change made. Mathematical models used for processing become very complex. In this paper a fuzzy rule-based system is proposed as a solution for this problem. The system collects the available signals information from sensors. Moreover, the system allows the study of the influence of the various factors that take part in the decision system. Since its inception fuzzy set theory has been regarded as a formalism suitable to deal with the imprecision intrinsic to many problems. At the same time, fuzzy sets allow to use symbolic models. In this study an example was applied for resolving variety of physiological parameters that define human health state. The application system was done for medical diagnosis help. The inputs are the signals expressed the cardiovascular system parameters, blood pressure, Respiratory system paramsystem was done, it will be able to predict the state of patient according any input values.

Keywords: Sensors, Sensivity, fuzzy logic, analysis, physiological parameters, medical diagnosis.

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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: Karim Shiraazi, Zargham OstadiAsl, Vahid Sheikhloie, Ahadollah Azami, Shahin Hassanimehr

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Bridge is an architectural symbol in Iran as Badgir (wind catcher); fire temples and arch are vaults are such. Therefore, from the very old ages, construction of bridges in Iran has mixed with architecture, social customs, alms and charity and holiness. Since long ago, from Mad, Achaemenid, Parthian and Sassanid times which construction of bridges got an inseparable relation with social dependency and architecture, based on those dependency bridges and dams got holy names; as Dokhtar castle and Dokhtar bridges were constructed. This method continued even after Islam and whenever Iranians got free from political fights and the immunity of roads were established the bridge construction did also prospered. In ancient times bridge construction passes through it growing and completion process and in Sassanid time in some way it reached to the peak of art and glory; as after Islam especially during 4th. century (Arab calendar) it put behind a period of glory and in Safavid time it reached to an exceptional glory and magnificence by constructing glorious bridges on Zayandeh Roud River in Isfahan. Having a combined style and changeability into bridge barrier, some of these bridges develop into magnificent constructions. The sustainable structures, mentioned above, are constructed for various reasons as follows: connecting two sides of a river, storing water, controlling floods, using water energy to operate water windmills, making lanes of streams for farms- use, and building recreational places for people, etc. These studies carried in bridges reveals the fact that in construction and designing mentioned above, lots of technological factors have been taken into consideration such as exceeding floods in the rives, hydraulic and hydrology of the rivers and bridges, geology, foundation, structure, construction material, and adopting appropriate executing methods, all of which are being analyzed in this article.

Keywords: Hydraulic-Architectural Bridge, Sustainability, Construction

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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: Lai-Fen Yang

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In this increasingly visual world, how can we best decipher and understand the many ways that our everyday lives are organized around looking practices and the many images we encounter each day? Indeed, how we interact with and interpret visual images is a basic component of human life. Today, however, we are living in one of the most artificial visual and image-saturated cultures in human history, which makes understanding the complex construction and multiple social functions of visual imagery more important than ever before. Themes regarding our experience of a visually pervasive mediated culture, here, termed visual spectacle.

Keywords: Visual culture, contemporary, visual spectacle.

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

Abstract:

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: Xun Ge

Abstract:

Recently, X. Ge and J. Qian investigated some relations between higher mathematics scores and calculus scores (resp. linear algebra scores, probability statistics scores) for Chinese university students. Based on rough-set theory, they established an information system S = (U,CuD,V, f). In this information system, higher mathematics score was taken as a decision attribute and calculus score, linear algebra score, probability statistics score were taken as condition attributes. They investigated importance of each condition attribute with respective to decision attribute and strength of each condition attribute supporting decision attribute. In this paper, we give further investigations for this issue. Based on the above information system S = (U, CU D, V, f), we analyze the decision rules between condition and decision granules. For each x E U, we obtain support (resp. strength, certainty factor, coverage factor) of the decision rule C —>x D, where C —>x D is the decision rule induced by x in S = (U, CU D, V, f). Results of this paper gives new analysis of on higher mathematics scores for Chinese university students, which can further lead Chinese university students to raise higher mathematics scores in Chinese graduate student entrance examination.

Keywords: Rough set, support, strength, certainty factor, coverage factor.

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