Search results for: Knowledge Representation

Authors: Petra Horváthová, Irena Durdová

Abstract:

The article is aimed at bringing information on the scope and the level of use of talent management by organizations in one of the Czech Republic regions, in the Moravian-Silesian Region. On the basis of data acquired by a questionnaire survey it has been found out that organizations in the above-mentioned region are implementing the system of talent management on a small scale: this approach is used by 3.8 % of organizations only that is 9 from 237 (100 %) of the approached respondents. The main reasons why this approach is not used is either that organizations have no knowledge of it or there is lack of financial and personnel resources. In the article recommendations suggested by the author can be found for a wider application of talent management in the Czech practice.

Keywords: Talent, talent management, use, mind map of talent management.

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Authors: Qurban A Memon, Mohammad S. Laghari

Abstract:

Integration of system process information obtained through an image processing system with an evolving knowledge database to improve the accuracy and predictability of wear debris analysis is the main focus of the paper. The objective is to automate intelligently the analysis process of wear particle using classification via self-organizing maps. This is achieved using relationship measurements among corresponding attributes of various measurements for wear debris. Finally, visualization technique is proposed that helps the viewer in understanding and utilizing these relationships that enable accurate diagnostics.

Keywords: Relationship Network, Relationship Measurement, Self-organizing Clusters, Wear Debris Analysis, Kohonen Network

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Authors: Abd Mutalib Embong, Azelin M Noor, Razol Mahari M Ali, Zulqarnain Abu Bakar, Abdur- Rahman Mohamed Amin

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At the time where electronic books, or e-Books, offer students a fun way of learning , teachers who are used to the paper text books may find it as a new challenge to use it as a part of learning process. Precisely, there are various types of e-Books available to suit students- knowledge, characteristics, abilities, and interests. The paper discusses teachers- perceptions on the use of ebooks as a paper text book in the classroom. A survey was conducted on 72 teachers who use e-books as textbooks. It was discovered that a majority of these teachers had good perceptions on the use of ebooks. However, they had little problems using the devices. It can be overcome with some strategies and a suggested framework.

Keywords: Classroom, E-books, perception, teacher.

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Authors: Feyzan Tuzkaya, Nihan S. Soylu, Çağlar Solak, Hilal Peker, Mehmet Peker, Kemal Özeralp, Ceren Mete, Ezgi Mehmetoğlu, Mehmet Karasu, Cihan Elçi, Ece Akca, Melek Göregenli

Abstract:

Perception, evaluation and representation of the environment have been the subject of many disciplines including psychology, geography and architecture. In environmental and social psychology literature there are several evidences which suggest that cognitive representations about a place consisted of not only geographic items but also social and cultural. Mental representations of residence area or a country are influenced and determined by social-demographics, the physical and social context. Thus, all mental representations of a given place are also social representations. Cognitive maps are the main and common instruments that are used to identify spatial images and the difference between physical and subjective environments. The aim of the current study is investigating the mental and social representations of Turkey in university students’ minds. Data was collected from 249 university students from different departments (i.e. psychology, geography, history, tourism departments) of Ege University. Participants were requested to reflect Turkey in their mind onto the paper drawing sketch maps. According to the results, cognitive maps showed geographic aspects of Turkey as well as the context of symbolic, cultural and political reality of Turkey. That is to say, these maps had many symbolic and verbal items related to critics on social and cultural problems, ongoing ethnic and political conflicts, and actual political agenda of Turkey. Additionally, one of main differentiations in these representations appeared in terms of the East and West side of the Turkey, and the representations of the East and West was varied correspondingly participants’ cultural background, their ethnic values, and where they have born. The results of the study were discussed in environmental and social psychological perspective considering cultural and social values of Turkey and current political circumstances of the country.

Keywords: Cognitive maps, East and West, politics, social representations, Turkey.

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Authors: M. S. Suhaida, N. M. Tawil, N. Hamzah, A. I. Che-Ani, M.M. Tahir

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Socioeconomic stability and development of a country, can be describe by housing affordability. It is aimed to ensure the housing provided as one of the key factors that is affordable by every income earner group whether low-income, middle income and high income group. This research carried out is to find out affordability of home ownership level for first medium cost landed-house by the middle-income group in Selangor, Malaysia. It is also hope that it could be seen as able to contribute to the knowledge and understanding on housing affordability level for the middleincome group and variables that influenced the medium income group-s ability to own first medium-cost houses.

Keywords: Residential, Housing Affordability, Middle income.

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Authors: Morten Brøgger, Kim Wittchen

Abstract:

Focus on reducing energy consumption in existing buildings at large scale, e.g. in cities or countries, has been increasing in recent years. In order to reduce energy consumption in existing buildings, political incentive schemes are put in place and large scale investments are made by utility companies. Prioritising these investments requires a comprehensive overview of the energy consumption in the existing building stock, as well as potential energy-savings. However, a building stock comprises thousands of buildings with different characteristics making it difficult to model energy consumption accurately. Moreover, the complexity of the building stock makes it difficult to convey model results to policymakers and other stakeholders. In order to manage the complexity of the building stock, building archetypes are often employed in building stock energy models (BSEMs). Building archetypes are formed by segmenting the building stock according to specific characteristics. Segmenting the building stock according to building type and building age is common, among other things because this information is often easily available. This segmentation makes it easy to convey results to non-experts. However, using a single archetypical building to represent all buildings in a segment of the building stock is associated with loss of detail. Thermal characteristics are aggregated while other characteristics, which could affect the energy efficiency of a building, are disregarded. Thus, using a simplified representation of the building stock could come at the expense of the accuracy of the model. The present study evaluates the accuracy of a conventional archetype-based BSEM that segments the building stock according to building type- and age. The accuracy is evaluated in terms of the archetypes’ ability to accurately emulate the average energy demands of the corresponding buildings they were meant to represent. This is done for the buildings’ energy demands as a whole as well as for relevant sub-demands. Both are evaluated in relation to the type- and the age of the building. This should provide researchers, who use archetypes in BSEMs, with an indication of the expected accuracy of the conventional archetype model, as well as the accuracy lost in specific parts of the calculation, due to use of the archetype method.

Keywords: Building stock energy modelling, energy-savings, archetype.

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Authors: Henri El Zakhem, Christina Romanos, Charlie Bakhos, Hassan Chahal, Jessica Koura

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Squalene is a valuable component of the oil composed of 30 carbon atoms and is mainly used for cosmetic materials. The main concern of this article is to study the Squalene composition in the Lebanese olive oil and to compare it with foreign oil results. To our knowledge, extraction of Squalene from the Lebanese olive oil has not been conducted before. Three different techniques were studied and experiments were performed on three brands of olive oil, Al Wadi Al Akhdar, Virgo Bio and Boulos. The techniques performed are the Fractional Crystallization, the Soxhlet and the Esterification. By comparing the results, it is found that the Lebanese oil contains squalene and Soxhlet method is the most effective between the three methods extracting about 6.5E-04 grams of Squalene per grams of olive oil.

Keywords: Squalene, extraction, crystallization, Soxhlet.‎

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Authors: Laura de Zwaan, Tracey West

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There is a sustained observable gender gap in financial literacy, with females consistently having lower levels than males. This research explores the knowledge and experiences of high school students in Australia aged 14 to 18 in order to understand how this gap can be improved. Using a predominantly qualitative approach, we find evidence to support impacts on financial literacy from financial socialization and socio-economic environment. We also find evidence that current teaching and assessment approaches to financial literacy may disadvantage female students. We conclude by offering recommendations to improve the way financial literacy education is delivered within the curriculum.

Keywords: Financial literacy, financial socialization, gender, maths.

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Authors: Jyoti Dua

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One image is worth more than thousand words. Images if analyzed can reveal useful information. Low level image processing deals with the extraction of specific feature from a single image. Now the question arises: What technique should be used to extract patterns of very large and detailed image database? The answer of the question is: “Image Mining”. Image Mining deals with the extraction of image data relationship, implicit knowledge, and another pattern from the collection of images or image database. It is nothing but the extension of Data Mining. In the following paper, not only we are going to scrutinize the current techniques of image mining but also present a new technique for mining images using Genetic Algorithm.

Keywords: Image Mining, Data Mining, Genetic Algorithm.

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Authors: N. Nacereddine, L. Hamami, M. Tridi, N. Oucief

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In non destructive testing by radiography, a perfect knowledge of the weld defect shape is an essential step to appreciate the quality of the weld and make decision on its acceptability or rejection. Because of the complex nature of the considered images, and in order that the detected defect region represents the most accurately possible the real defect, the choice of thresholding methods must be done judiciously. In this paper, performance criteria are used to conduct a comparative study of four non parametric histogram thresholding methods for automatic extraction of weld defect in radiographic images.

Keywords: Radiographic images, non parametric methods, histogram thresholding, performance criteria.

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Authors: Toshimi Munehira

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In order to optimize annual IT spending and to reduce the complexity of an entire system architecture, SOA trials have been started. It is common knowledge that to design an SOA system we have to adopt the top-down approach, but in reality silo systems are being made, so these companies cannot reuse newly designed services, and cannot enjoy SOA-s economic benefits. To prevent this situation, we designed a generic SOA development process referred to as the architecture of “mass customization." To define the generic detail development processes, we did a case study on an imaginary company. Through the case study, we could define the practical development processes and found this could vastly reduce updating development costs.

Keywords: SOA, BPM, Generic Model, MassCustomization

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Authors: Abdel-Badeeh M. Salem

Abstract:

Case based reasoning (CBR) methodology presents a foundation for a new technology of building intelligent computeraided diagnoses systems. This Technology directly addresses the problems found in the traditional Artificial Intelligence (AI) techniques, e.g. the problems of knowledge acquisition, remembering, robust and maintenance. This paper discusses the CBR methodology, the research issues and technical aspects of implementing intelligent medical diagnoses systems. Successful applications in cancer and heart diseases developed by Medical Informatics Research Group at Ain Shams University are also discussed.

Keywords: Medical Informatics, Computer-Aided MedicalDiagnoses, AI in Medicine, Case-Based Reasoning.

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Authors: Ya Hong Dong, Conrad T.C. Wong, S. Thomas Ng, James M.W. Wong

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Precast concrete has been widely adopted in public housing construction of Hong Kong since the mid-1980s. While pre-casting is considered an environmental friendly solution, there is lack of study to investigate the life cycle performance of precast concrete units. This study aims to bridge the knowledge gap by providing a comprehensive life cycle assessment (LCA) study for two precast elements namely façade and bathroom. The results show that raw material is the most significant contributor of environmental impact accounting for about 90% to the total impact. Furthermore, human health is more affected by the production of precast concrete than the ecosystems.

Keywords: Environment, green, LCA, LCIA, precast.

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Authors: R. Khaldi, N. Benamrouche, M. Bouheraoua

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The ability to predict an accurate temperature distribution requires the knowledge of the losses, the thermal characteristics of the materials, and the cooling conditions, all of which are very difficult to quantify. In this paper, the impact of the effects of iron and copper losses are investigated separately and their effects on the heating in various points of the stator of an induction motor, is highlighted by using two simple tests. In addition, the effect of a defect, such as an open circuit in a phase of the stator, on the heating is also obtained by a no-load test. The squirrel cage induction motor is rated at 2.2 kW; 380 V; 5.2 A; Δ connected; 50 Hz; 1420 rpm and the class of insulation F, has been thermally tested under several load conditions. Several thermocouples were placed in strategic points of the stator.

Keywords: induction motor, temperature, heating, losses

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Authors: M. Uysal, M. Yilmaz, I. Tiryakioğlu

Abstract:

Digital Terrain Model (DTM) is a digital numerical representation of the Earth's surface. DTMs have been applied to a diverse field of tasks, such as urban planning, military, glacier mapping, disaster management. In the expression of the Earth' surface as a mathematical model, an infinite number of point measurements are needed. Because of the impossibility of this case, the points at regular intervals are measured to characterize the Earth's surface and DTM of the Earth is generated. Hitherto, the classical measurement techniques and photogrammetry method have widespread use in the construction of DTM. At present, RADAR, LiDAR, and stereo satellite images are also used for the construction of DTM. In recent years, especially because of its superiorities, Airborne Light Detection and Ranging (LiDAR) has an increased use in DTM applications. A 3D point cloud is created with LiDAR technology by obtaining numerous point data. However recently, by the development in image mapping methods, the use of unmanned aerial vehicles (UAV) for photogrammetric data acquisition has increased DTM generation from image-based point cloud. The accuracy of the DTM depends on various factors such as data collection method, the distribution of elevation points, the point density, properties of the surface and interpolation methods. In this study, the random data reduction method is compared for DTMs generated from image based point cloud data. The original image based point cloud data set (100%) is reduced to a series of subsets by using random algorithm, representing the 75, 50, 25 and 5% of the original image based point cloud data set. Over the ANS campus of Afyon Kocatepe University as the test area, DTM constructed from the original image based point cloud data set is compared with DTMs interpolated from reduced data sets by Kriging interpolation method. The results show that the random data reduction method can be used to reduce the image based point cloud datasets to 50% density level while still maintaining the quality of DTM.

Keywords: DTM, unmanned aerial vehicle, UAV, random, Kriging.

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Authors: Abdesh Toleubayev, Rinat Zhumatayev, Kulzhazira Toleubayeva

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Mounds are one of the most valuable sources of information on various aspects of life, household skills, rituals and beliefs of the ancient peoples of Kazakhstan. Moreover, the objects associated with the cult of the burial of the dead are the most informative, and often the only source of knowledge about past eras. The present study is devoted to some results of the excavations carried out on the mound "Baygetobe" of Shilikti burial ground. The purpose of the work is associated with certain categories of grave goods and reading "Fine Text" of Shilikti graves, whose structure is the same for burials of nobles and ordinary graves. The safety of a royal burial mounds, the integrity and completeness of the source are of particular value for studying.

Keywords: Animal style, barrow, Baygetobe, dromos, Shilikty.

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Authors: A. Faro, D. Giordano, C. Spampinato

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Traffic management in an urban area is highly facilitated by the knowledge of the traffic conditions in every street or highway involved in the vehicular mobility system. Aim of the paper is to propose a neuro-fuzzy approach able to compute the main parameters of a traffic system, i.e., car density, velocity and flow, by using the images collected by the web-cams located at the crossroads of the traffic network. The performances of this approach encourage its application when the traffic system is far from the saturation. A fuzzy model is also outlined to evaluate when it is suitable to use more accurate, even if more time consuming, algorithms for measuring traffic conditions near to saturation.

Keywords: Neuro-fuzzy networks, computer vision, Fuzzy systems, intelligent transportation system.

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Authors: Arshia Azam, J. Amarnath, Ch. D. V. Paradesi Rao

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The advantage of solving the complex nonlinear problems by utilizing fuzzy logic methodologies is that the experience or expert-s knowledge described as a fuzzy rule base can be directly embedded into the systems for dealing with the problems. The current limitation of appropriate and automated designing of fuzzy controllers are focused in this paper. The structure discovery and parameter adjustment of the Branched T-S fuzzy model is addressed by a hybrid technique of type constrained sparse tree algorithms. The simulation result for different system model is evaluated and the identification error is observed to be minimum.

Keywords: Fuzzy logic, branch T-S fuzzy model, tree modeling, complex nonlinear system.

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Authors: Arslan Qamar Malik, Jehanzeb Ahmad

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The paper presents a novel idea to control computer mouse cursor movement with human eyes. In this paper, a working of the product has been described as to how it helps the special people share their knowledge with the world. Number of traditional techniques such as Head and Eye Movement Tracking Systems etc. exist for cursor control by making use of image processing in which light is the primary source. Electro-oculography (EOG) is a new technology to sense eye signals with which the mouse cursor can be controlled. The signals captured using sensors, are first amplified, then noise is removed and then digitized, before being transferred to PC for software interfacing.

Keywords: Human Computer Interaction, Real-Time System, Electro-oculography, Signal Processing.

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Authors: J. T. Manhire

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A tax authority wants to take actions it knows will foster the greatest degree of voluntary taxpayer compliance to reduce the “tax gap.” This paper suggests that even if a tax authority could attain a state of complete knowledge, there are constraints on whether and to what extent such actions would result in reducing the macro-level tax gap. These limits are not merely a consequence of finite agency resources. They are inherent in the system itself. To show that this is one possible interpretation of the tax gap data, the paper formulates known results in a different way by analyzing tax compliance as a population with a single covariate. This leads to a standard use of the logistic map to analyze the dynamics of non-compliance growth or decay over a sequence of periods. This formulation gives the same results as the tax gap studies performed over the past fifty years in the U.S. given the published margins of error. Limitations and recommendations for future work are discussed, along with some implications for tax policy.

Keywords: Tax law, tax compliance, tax gap, income tax.

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Authors: Widad Jakjoud, Mohamed Bahaj, Jamal Bakkas

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Through this paper we present a method for automatic generation of ontological model from any data source using Model Driven Architecture (MDA), this generation is dedicated to the cooperation of the knowledge engineering and software engineering. Indeed, reverse engineering of a data source generates a software model (schema of data) that will undergo transformations to generate the ontological model. This method uses the meta-models to validate software and ontological models.

Keywords: Meta model, model, ontology, data source.

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Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra, Abdus Sobur

Abstract:

In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of artificial intelligence (AI), specifically deep learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images, representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our approach presents a hybrid model, amalgamating the strengths of two renowned convolutional neural networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.

Keywords: Artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging.

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Authors: Ralf God, Lothar Kerschgens, Leonardo Goratti, Steven Lemaire

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Ubiquitous connectivity is a reality and a basic need for users on ground. Air travel connectivity in the cabin is also becoming increasingly important for passengers during cabin use. Wireless sensor networks that provide information to cabin management systems are being used by airlines to optimize cabin crew workload. In networked cabin systems, communications and digitally transmitted data must be managed by airlines in every direction. Security and privacy, information processing and knowledge management are the current and future requirements for a smart and connected cabin.

Keywords: Smart and connected cabin management, Internet of Things, power management, airline business.

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Authors: Magdalena Pejas

Abstract:

This article concerns the presentation of an integrated method for detection of steganographic content embedded by new unknown programs. The method is based on data mining and aggregated hypothesis testing. The article contains the theoretical basics used to deploy the proposed detection system and the description of improvement proposed for the basic system idea. Further main results of experiments and implementation details are collected and described. Finally example results of the tests are presented.

Keywords: Steganography, steganalysis, data embedding, data mining, feature extraction, knowledge base, system learning, hypothesis testing, error estimation, black box program, file structure.

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Authors: D. Beckwith

Abstract:

This research explored sexual identity for women with physical disability, both congenital and acquired. It also explored whether exposure to violence or negative risk-taking had played a role in their intimate relationships. This phenomenological research used semi-structured interviews and photo elicitation with the researcher’s insider knowledge adding experiential substance and understanding to the discussion. Findings confirm sexuality for women with physical disability is marginalised and de-gendered making it less of a priority for professionals and policy makers and emphasising the need to more effectively support women with disability in relation to their sexuality, sexual expression and violence.

Keywords: Lived-experience, PhotoVoice, sexuality, violence.

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Authors: Larisa N. Pop, Irina M. Ban

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This paper aims to present the main instruments used in the economic literature for measuring the price risk, pointing out on the advantages brought by the conditional variance in this respect. The theoretical approach will be exemplified by elaborating an EGARCH model for the price returns of wheat, both on Romanian and on international market. To our knowledge, no previous empirical research, either on price risk measurement for the Romanian markets or studies that use the ARIMA-EGARCH methodology, have been conducted. After estimating the corresponding models, the paper will compare the estimated conditional variance on the two markets.

Keywords: conditional variance, GARCH models, price risk, volatility

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Authors: Ephraim Gower, Thato Tsalaile, Monageng Kgwadi, Malcolm Hawksford.

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In this paper, the statistical properties of filtered or convolved signals are considered by deriving the resulting density functions as well as the exact mean and variance expressions given a prior knowledge about the statistics of the individual signals in the filtering or convolution process. It is shown that the density function after linear convolution is a mixture density, where the number of density components is equal to the number of observations of the shortest signal. For circular convolution, the observed samples are characterized by a single density function, which is a sum of products.

Keywords: Circular Convolution, linear Convolution, mixture density function.

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Authors: S. Maheswari, P. Arockia Jansi Rani

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Human action is recognized directly from the video sequences. The objective of this work is to recognize various human actions like run, jump, walk etc. Human action recognition requires some prior knowledge about actions namely, the motion estimation, foreground and background estimation. Region of interest (ROI) is extracted to identify the human in the frame. Then, optical flow technique is used to extract the motion vectors. Using the extracted features similarity measure based classification is done to recognize the action. From experimentations upon the Weizmann database, it is found that the proposed method offers a high accuracy.

Keywords: Background subtraction, human silhouette, optical flow, classification.

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Authors: M. R. Aghaebrahimi, S. H. Zahiri, M. Amiri

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In this paper a data miner based on the learning automata is proposed and is called LA-miner. The LA-miner extracts classification rules from data sets automatically. The proposed algorithm is established based on the function optimization using learning automata. The experimental results on three benchmarks indicate that the performance of the proposed LA-miner is comparable with (sometimes better than) the Ant-miner (a data miner algorithm based on the Ant Colony optimization algorithm) and CNZ (a well-known data mining algorithm for classification).

Keywords: Data mining, Learning automata, Classification rules, Knowledge discovery.

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Authors: Hoda Waguih

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A serious problem on the WWW is finding reliable information. Not everything found on the Web is true and the Semantic Web does not change that in any way. The problem will be even more crucial for the Semantic Web, where agents will be integrating and using information from multiple sources. Thus, if an incorrect premise is used due to a single faulty source, then any conclusions drawn may be in error. Thus, statements published on the Semantic Web have to be seen as claims rather than as facts, and there should be a way to decide which among many possibly inconsistent sources is most reliable. In this work, we propose a trust model for the Semantic Web. The proposed model is inspired by the use trust in human society. Trust is a type of social knowledge and encodes evaluations about which agents can be taken as reliable sources of information or services. Our proposed model allows agents to decide which among different sources of information to trust and thus act rationally on the semantic web.

Keywords: Semantic Web, Trust, Web of Trust, WWW.

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