Search results for: Knowledge representation
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 2361

Search results for: Knowledge representation

1131 Project Management Success for Contractors

Authors: Hamimah Adnan, Norfashiha Hashim, Mohd Arif Marhani, Mohd Asri Yeop Johari

Abstract:

The aim of this paper is to provide a better understanding of the implementation of Project Management practices by UiTM contractors to ensure project success. A questionnaire survey was administered to 120 UiTM contractors in Malaysia. The purpose of this method was to gather information on the contractors- project background and project management skills. It was found that all of the contractors had basic knowledge and understanding of project management skills. It is suggested that a reasonable project plan and an appropriate organizational structure are influential factors for project success. It is recommended that the contractors need to have an effective program of work and up to date information system are emphasized.

Keywords: Project management, success, contractors.

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1130 Quantifying Uncertainties in an Archetype-Based Building Stock Energy Model by Use of Individual Building Models

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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1129 Retrieval Augmented Generation against the Machine: Merging Human Cyber Security Expertise with Generative AI

Authors: Brennan Lodge

Abstract:

Amidst a complex regulatory landscape, Retrieval Augmented Generation (RAG) emerges as a transformative tool for Governance Risk and Compliance (GRC) officers. This paper details the application of RAG in synthesizing Large Language Models (LLMs) with external knowledge bases, offering GRC professionals an advanced means to adapt to rapid changes in compliance requirements. While the development for standalone LLMs is exciting, such models do have their downsides. LLMs cannot easily expand or revise their memory, and they cannot straightforwardly provide insight into their predictions, and may produce “hallucinations.” Leveraging a pre-trained seq2seq transformer and a dense vector index of domain-specific data, this approach integrates real-time data retrieval into the generative process, enabling gap analysis and the dynamic generation of compliance and risk management content. We delve into the mechanics of RAG, focusing on its dual structure that pairs parametric knowledge contained within the transformer model with non-parametric data extracted from an updatable corpus. This hybrid model enhances decision-making through context-rich insights, drawing from the most current and relevant information, thereby enabling GRC officers to maintain a proactive compliance stance. Our methodology aligns with the latest advances in neural network fine-tuning, providing a granular, token-level application of retrieved information to inform and generate compliance narratives. By employing RAG, we exhibit a scalable solution that can adapt to novel regulatory challenges and cybersecurity threats, offering GRC officers a robust, predictive tool that augments their expertise. The granular application of RAG’s dual structure not only improves compliance and risk management protocols but also informs the development of compliance narratives with pinpoint accuracy. It underscores AI’s emerging role in strategic risk mitigation and proactive policy formation, positioning GRC officers to anticipate and navigate the complexities of regulatory evolution confidently.

Keywords: Retrieval Augmented Generation, Governance Risk and Compliance, Cybersecurity, AI-driven Compliance, Risk Management, Generative AI.

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1128 Descriptive Study of Libyan Steles of Grande Kabylia, Algeria

Authors: Samia Ait Ali Yahia

Abstract:

The Libyan steles contain a good number of inscriptions. We find them on blocks of sandstone in the northern part of Grande Kabylia, Algeria. Three Libyan steles recently discovered are added to the currently known and published documents which enrich the Libyan heritage of this region. The aim of this article is to make a descriptive study of the Libyan inscriptions of these steles in order to better understand the characteristics of each stele by comparing them to the different stele already known in the region. It is certain that if other similar specimens were to be added to those we already possess, knowledge of the Libyan would gradually become clearer. The Kabylia region is certainly full of these remains that have not yet been brought to light.

Keywords: Libyan stele, Libyan inscriptions, Paintings, Engraving, Kaylie.

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1127 Achieving Implementable Nature-Based Solutions While Reshaping Architectural Education: A Case Study of URBiNAT and BUILD Solutions

Authors: C. Farinea, A. Conserva, F. Demeur

Abstract:

Nature has often been something humans have fought against. However, with the changing climate and urban challenges such as air pollution and food shortages, to name but a few, it has never been more crucial to work with nature to find solutions that can help us to adapt to the current planetary situation and mitigate the challenges that we will continue to face in the future. Nature-based solutions (NBS) have been gaining ground as one strategy that can help to create more sustainable solutions for our planet and simultaneously, provide several ecosystem services. As designers, there are a lot of insights that can be extracted and gained from nature. However, nature is a complex and sometimes difficult to predict system and its implementation in cities requires a multidisciplinary knowledge. To keep up with the solutions and prepare the future generations of architects and designers with the skills to be able to implement NBS, educational systems also have to adapt with the times. Architecture is no longer solely about drawing buildings with beautiful forms. It is no longer discipline bound. With the input from different disciplines, the implementation of NBS can be significantly more successful. Transdisciplinary strategies can encourage architects and designers to think beyond their discipline, and ensure the success and realization of the NBS. The paper will demonstrate how transdisciplinary teaching methodologies, including also taking part in participatory processes with experts intended as gathering local knowledge, can be implemented with architectural master students to achieve implementable NBS. Through two projects co-funded by the European Union, strategies such as participatory co-design and transdisciplinary start-ups were implemented into seminars that focused on the development of NBS with a transdisciplinary approach. Within the “Design with Living Systems” seminar, students took part in participatory co-design strategies with experts to design solutions that will be implemented in Porto as part of a healthy corridor, and that respond to the needs of the users and site. On the other hand, within the “Design for Living Systems” seminar, the transdisciplinary start-up approach created start-ups with students of architecture, business and biology focusing on identifying a problem and designing a NBS as a product. Both seminars proved to be successful in achieving implementable NBS through strategies of transdisciplinary education and gave the students the skill sets to be able to work with nature in their future careers.

Keywords: Architectural higher education, digital fabrication, nature-based solutions, transdisciplinary approaches.

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1126 Exploring Unexplored Horizons: Advanced Fluid Mechanics Solutions for Sustainable Energy Technologies

Authors: Elvira S. Castillo, Surupa Shaw

Abstract:

This paper explores advanced applications of fluid mechanics in the context of sustainable energy. By examining the integration of fluid dynamics with renewable energy technologies, the research uncovers previously underutilized strategies for improving efficiency. Through theoretical analyses, the study demonstrates how fluid mechanics can be harnessed to optimize renewable energy systems. The findings contribute to expanding knowledge in sustainable energy by offering practical insights and methodologies for future research and technological advancements to address global energy challenges.

Keywords: Fluid mechanics, sustainable energy, energy efficiency, green energy.

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1125 Integrated Reasoning Approach for Car Faulty Diagnosis

Authors: Diana M.L. Wong

Abstract:

This paper presents an integrated case based and rule based reasoning method for car faulty diagnosis. The reasoning method is done through extracting the past cases from the Proton Service Center while comparing with the preset rules to deduce a diagnosis/solution to a car service case. New cases will be stored to the knowledge base. The test cases examples illustrate the effectiveness of the proposed integrated reasoning. It has proven accuracy of similar reasoning if carried out by a service advisor from the service center.

Keywords: component; case based reasoning (CBR), rule basedreasoning (RBR), decision support systems, diagnosis tool.

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1124 Representing Data without Lost Compression Properties in Time Series: A Review

Authors: Nabilah Filzah Mohd Radzuan, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan

Abstract:

Uncertain data is believed to be an important issue in building up a prediction model. The main objective in the time series uncertainty analysis is to formulate uncertain data in order to gain knowledge and fit low dimensional model prior to a prediction task. This paper discusses the performance of a number of techniques in dealing with uncertain data specifically those which solve uncertain data condition by minimizing the loss of compression properties.

Keywords: Compression properties, uncertainty, uncertain time series, mining technique, weather prediction.

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1123 ICCFMS – Set Up Candid Clips Effectiveness

Authors: P. Suparada, D. Eakapotch

Abstract:

The objectives were to analyze the using of new media in the form of set up candid clip that affects the product and presenter, to study the effectiveness of using new media in the form of set up candid clip in order to increase the circulation and audience satisfaction and to use the earned information and knowledge to develop the communication for publicizing and advertising via new media. This research is qualitative research based on questionnaire and in-depth interview from experts. The findings showed the advantages and disadvantages of communication for publicizing and advertising via new media in the form of set up candid clip including with the specific target group for this kind of advertising. It will be useful for fields of publicizing and advertising in the new media forms at the present.

Keywords: Candid Clip, Communication, New Media, Social Network.

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1122 Text Retrieval Relevance Feedback Techniques for Bag of Words Model in CBIR

Authors: Nhu Van NGUYEN, Jean-Marc OGIER, Salvatore TABBONE, Alain BOUCHER

Abstract:

The state-of-the-art Bag of Words model in Content- Based Image Retrieval has been used for years but the relevance feedback strategies for this model are not fully investigated. Inspired from text retrieval, the Bag of Words model has the ability to use the wealth of knowledge and practices available in text retrieval. We study and experiment the relevance feedback model in text retrieval for adapting it to image retrieval. The experiments show that the techniques from text retrieval give good results for image retrieval and that further improvements is possible.

Keywords: Relevance feedback, bag of words model, probabilistic model, vector space model, image retrieval

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1121 A Multilingual Virtual Simulated Patient Framework for Training Primary Health Care Students

Authors: Juan L. Castro, Maria I. NavarroVictor Lopez, Eduardo M. Eisman, Jose M. Zurita

Abstract:

This paper describes the Multilingual Virtual Simulated Patient framework. It has been created to train the social skills and testing the knowledge of primary health care medical students. The framework generates conversational agents which perform in serveral languages as virtual simulated patients that help to improve the communication and diagnosis skills of the students complementing their training process.

Keywords: Medical training, conversational agents, patient modeling.

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1120 Feature Subset Selection Using Ant Colony Optimization

Authors: Ahmed Al-Ani

Abstract:

Feature selection is an important step in many pattern classification problems. It is applied to select a subset of features, from a much larger set, such that the selected subset is sufficient to perform the classification task. Due to its importance, the problem of feature selection has been investigated by many researchers. In this paper, a novel feature subset search procedure that utilizes the Ant Colony Optimization (ACO) is presented. The ACO is a metaheuristic inspired by the behavior of real ants in their search for the shortest paths to food sources. It looks for optimal solutions by considering both local heuristics and previous knowledge. When applied to two different classification problems, the proposed algorithm achieved very promising results.

Keywords: Ant Colony Optimization, ant systems, feature selection, pattern recognition.

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1119 Talent Management and its Use in the Field of Human Resources Management in the Organization of the Czech Republic

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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1118 Building Relationship Network for Machine Analysis from Wear Debris Measurements

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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1117 Teachers- Perceptions on the Use of E-Books as Textbooks in the Classroom

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

Abstract:

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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1116 Comparison of Data Reduction Algorithms for Image-Based Point Cloud Derived Digital Terrain Models

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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1115 Conceptual Overview of Housing Affordability in Selangor, Malaysia

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

Abstract:

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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1114 A Study on the Application of Machine Learning and Deep Learning Techniques for Skin Cancer Detection

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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1113 Extraction of Squalene from Lebanese Olive Oil

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

Abstract:

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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1112 Improving Financial Education for Young Women: A Case Study of Australian School Students

Authors: Laura de Zwaan, Tracey West

Abstract:

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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1111 Survey on Image Mining Using Genetic Algorithm

Authors: Jyoti Dua

Abstract:

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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1110 Non-Parametric Histogram-Based Thresholding Methods for Weld Defect Detection in Radiography

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

Abstract:

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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1109 A study on a Generic Development Process for the BPM+SOA Design and Implementation

Authors: Toshimi Munehira

Abstract:

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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1108 Case Based Reasoning Technology for Medical Diagnosis

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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1107 Life Cycle Assessment of Precast Concrete Units

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

Abstract:

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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1106 An Experimental Investigation of Heating in Induction Motors

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

Abstract:

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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1105 Royal Mound “Baygetobe“ from the Burial Ground Shilikty

Authors: Abdesh Toleubayev, Rinat Zhumatayev, Kulzhazira Toleubayeva

Abstract:

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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1104 Processing Web-Cam Images by a Neuro-Fuzzy Approach for Vehicular Traffic Monitoring

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

Abstract:

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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1103 Auto Regressive Tree Modeling for Parametric Optimization in Fuzzy Logic Control System

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

Abstract:

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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1102 Retina Based Mouse Control (RBMC)

Authors: Arslan Qamar Malik, Jehanzeb Ahmad

Abstract:

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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