Search results for: training data condensation.

Authors: W. Sutopo, M. Hisjam, Yuniaristanto

Abstract:

In general, small-scale vegetables farmers experience problems in improving the safety and quality of vegetables supplied to high-class consumers in modern retailers. They also lack of information to access market. The farmers group and/or cooperative (FGC) should be able to assist its members by providing training in handling and packing vegetables and enhancing marketing capabilities to sell commodities to the modern retailers. This study proposes an agri-food supply chain (ASC) model that involves the corporate social responsibility (CSR) activities to cultivate the capabilities of farmers to access market. Multi period ASC model is formulated as Weighted Goal Programming (WGP) to analyze the impacts of CSR programs to empower the FGCs in managing the small-scale vegetables farmers. The results show that the proposed model can be used to determine the priority of programs in order to maximize the four goals to be achieved in the CSR programs.

Keywords: agri-food supply chain, corporate social responsibility, small-scale vegetables farmers, weighted goal programming.

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Authors: Sihem Mostefai, Abdelaziz Bouras, Mohamed Batouche

Abstract:

To achieve competitive advantage nowadays, most of the industrial companies are considering that success is sustained to great product development. That is to manage the product throughout its entire lifetime ranging from design, manufacture, operation and destruction. Achieving this goal requires a tight collaboration between partners from a wide variety of domains, resulting in various product data types and formats, as well as different software tools. So far, the lack of a meaningful unified representation for product data semantics has slowed down efficient product development. This paper proposes an ontology based approach to enable such semantic interoperability. Generic and extendible product ontology is described, gathering main concepts pertaining to the mechanical field and the relations that hold among them. The ontology is not exhaustive; nevertheless, it shows that such a unified representation is possible and easily exploitable. This is illustrated thru a case study with an example product and some semantic requests to which the ontology responds quite easily. The study proves the efficiency of ontologies as a support to product data exchange and information sharing, especially in product development environments where collaboration is not just a choice but a mandatory prerequisite.

Keywords: Information exchange, product lifecyclemanagement, product ontology, semantic interoperability.

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Authors: B. Akhmetov, A. Doszhanova, A. Ivanov, T. Kartbayev, A. Malygin

Abstract:

The article examines the methods of protection of citizens' personal data on the Internet using biometric identity authentication technology. It`s celebrated their potential danger due to the threat of loss of base biometric templates. To eliminate the threat of compromised biometric templates is proposed to use neural networks large and extra-large sizes, which will on the one hand securely (Highly reliable) to authenticate a person by his biometrics, and on the other hand make biometrics a person is not available for observation and understanding. This article also describes in detail the transformation of personal biometric data access code. It`s formed the requirements for biometrics converter code for his work with the images of "Insider," "Stranger", all the "Strangers". It`s analyzed the effect of the dimension of neural networks on the quality of converters mystery of biometrics in access code.

Keywords: Biometric security technologies, Conversion of personal biometric data access code, Electronic signature, Large neural networks, quality of converters "Biometrics - the code", the Egovernment.

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Authors: Leila Rashidian

Abstract:

Temperature rise on Earth has had harmful effects on the Earth's surface and has led to change in precipitation patterns all around the world. The present research was aimed to study the process of climate change according to the data simulation in future and compare these parameters with current situation in the studied stations in Semnan province including Garmsar, Shahrood and Semnan. In this regard, LARS-WG software, HADCM₃ model and A₂ scenario were used for the 2010-2030 period. In this model, climatic parameters such as maximum and minimum temperature, precipitation and radiation were used daily. The obtained results indicated that there will be a 4.4% increase in precipitation in Semnan province compared with the observed data, and in general, there will be a 1.9% increase in temperature. This temperature rise has significant impact on precipitation patterns. Most of precipitation will be raining (torrential rains in some cases). According to the results, from west to east, the country will experience more temperature rise and will be warmer.

Keywords: Climate change, Semnan province, LARS-WG model, climate parameters, HADCM3 model.

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Authors: M. Mousavi-Daramoroudi, H. Yousefnia, F. Abbasi-Davani, S. Zolghadri

Abstract:

Dosimetry is an indispensable and precious factor in patient treatment planning to minimize the absorbed dose in vital tissues. In this study, compartmental model was used in order to estimate the human absorbed dose of ¹⁷⁷Lu-DOTATOC from the biodistribution data in wild type rats. For this purpose, ¹⁷⁷Lu-DOTATOC was prepared under optimized conditions and its biodistribution was studied in male Syrian rats up to 168 h. Compartmental model was applied to mathematical description of the drug behaviour in tissue at different times. Dosimetric estimation of the complex was performed using radiation absorbed dose assessment resource (RADAR). The biodistribution data showed high accumulation in the adrenal and pancreas as the major expression sites for somatostatin receptor (SSTR). While kidneys as the major route of excretion receive 0.037 mSv/MBq, pancreas and adrenal also obtain 0.039 and 0.028 mSv/MBq. Due to the usage of this method, the points of accumulated activity data were enhanced, and further information of tissues uptake was collected that it will be followed by high (or improved) precision in dosimetric calculations.

Keywords: Compartmental modeling, human absorbed dose, 177Lu-DOTATOC, Syrian rats.

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Authors: M. Khosravy-el-Hossani, Q. Dorosti

Abstract:

One of the important applications of gas turbines is their utilization for heat recovery steam generator in combine-cycle technology. Exhaust flow and energy are two key parameters for determining heat recovery steam generator performance which are mainly determined by the main gas turbine components performance data. For this reason a method was developed for determining the exhaust energy in the new edition of ASME PTC22. The result of this investigation shows that the method of standard has considerable error. Therefore in this paper a new method is presented for modifying of the performance calculation. The modified method is based on exhaust gas constituent analysis and combustion calculations. The case study presented here by two kind of General Electric gas turbine design data for validation of methodologies. The result shows that the modified method is more precise than the ASME PTC22 method. The exhaust flow calculation deviation from design data is 1.5-2 % by ASME PTC22 method so that the deviation regarding with modified method is 0.3-0.5%. Based on precision of analyzer instruments, the method can be suitable alternative for gas turbine standard performance test. In advance two methods are proposed based on known and unknown fuel in modified method procedure. The result of this paper shows that the difference between the two methods is below than %0.02. In according to reasonable esult of the second procedure (unknown fuel composition), the method can be applied to performance evaluation of gas turbine, so that the measuring cost and data gathering should be reduced.

Keywords: Gas turbine, Performance test code, Combined cycle.

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Authors: Steve J. Drew, Christopher J. Klopper

Abstract:

In this paper, the action research driven design of a context relevant, developmental peer review of teaching model, its implementation strategy and its impact at an Australian university is presented. PRO-Teaching realizes an innovative process that triangulates contemporaneous teaching quality data from a range of stakeholders including students, discipline academics, learning and teaching expert academics, and teacher reflection to create reliable evidence of teaching quality. Data collected over multiple classroom observations allows objective reporting on development differentials in constructive alignment, peer, and student evaluations. Further innovation is realized in the application of this highly structured developmental process to provide summative evidence of sufficient validity to support claims for professional advancement and learning and teaching awards. Design decision points and contextual triggers are described within the operating domain. Academics and developers seeking to introduce structured peer review of teaching into their organization will find this paper a useful reference.

Keywords: Development loop, Multiple data sources, Objective reporting, Peer review of teaching.

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Authors: Jyoti Dua

Abstract:

An algorithm is a well-defined procedure that takes some input in the form of some values, processes them and gives the desired output. It forms the basis of many other algorithms such as searching, pattern matching, digital filters etc., and other applications have been found in database systems, data statistics and processing, data communications and pattern matching. This paper introduces algorithmic “Enhanced Bidirectional Selection” sort which is bidirectional, stable. It is said to be bidirectional as it selects two values smallest from the front and largest from the rear and assigns them to their appropriate locations thus reducing the number of passes by half the total number of elements as compared to selection sort.

Keywords: Bubble sort, cocktail sort, selection sort, heap sort.

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Authors: Muhammad Aqil, Ichiro Kita, Akira Yano, Nishiyama Soichi

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This paper presents an alternate approach that uses artificial neural network to simulate the flood level dynamics in a river basin. The algorithm was developed in a decision support system environment in order to enable users to process the data. The decision support system is found to be useful due to its interactive nature, flexibility in approach and evolving graphical feature and can be adopted for any similar situation to predict the flood level. The main data processing includes the gauging station selection, input generation, lead-time selection/generation, and length of prediction. This program enables users to process the flood level data, to train/test the model using various inputs and to visualize results. The program code consists of a set of files, which can as well be modified to match other purposes. This program may also serve as a tool for real-time flood monitoring and process control. The running results indicate that the decision support system applied to the flood level seems to have reached encouraging results for the river basin under examination. The comparison of the model predictions with the observed data was satisfactory, where the model is able to forecast the flood level up to 5 hours in advance with reasonable prediction accuracy. Finally, this program may also serve as a tool for real-time flood monitoring and process control.

Keywords: Decision Support System, Neural Network, Flood Level

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Authors: Batel Noureddine, Mehenni Mohamed, Dekar Lies

Abstract:

In biomedical implant field, a new formula is given for the study of Radio Frequency power attenuation by simultaneous effects of side and angular misalignment of the supply/data transfer coils. A confrontation with the practical measurements done into a Faraday cage, allowed a checking of the obtained theoretical results. The DC supply systems without material connection and the data transmitters used in the case of biomedical implants, can be well dimensioned by taking into account the possibility of power attenuation by misalignment of transfer coils

Keywords: Biomedical implant field, misalignment coils, powerattenuation, transmitter and receiver coils.

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Authors: Ashwaq Alsulami, Jianhua Shao

Abstract:

Various organisations often need to understand discussions on social media, such as what trending topics are and characteristics of the people engaged in the discussion. A number of approaches have been proposed to extract attributes that would characterise a discussion group. However, these approaches are largely based on supervised learning, and as such they require a large amount of labelled data. We propose an approach in this paper that does not require labelled data, but rely on lexical sources to detect meaningful attributes for online discussion groups. Our findings show an acceptable level of accuracy in detecting attributes for Twitter discussion groups.

Keywords: Attributed community, attribute detection, community, social network.

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Authors: Jaime R. S. Fonseca

Abstract:

Nevertheless the widespread application of finite mixture models in segmentation, finite mixture model selection is still an important issue. In fact, the selection of an adequate number of segments is a key issue in deriving latent segments structures and it is desirable that the selection criteria used for this end are effective. In order to select among several information criteria, which may support the selection of the correct number of segments we conduct a simulation study. In particular, this study is intended to determine which information criteria are more appropriate for mixture model selection when considering data sets with only categorical segmentation base variables. The generation of mixtures of multinomial data supports the proposed analysis. As a result, we establish a relationship between the level of measurement of segmentation variables and some (eleven) information criteria-s performance. The criterion AIC3 shows better performance (it indicates the correct number of the simulated segments- structure more often) when referring to mixtures of multinomial segmentation base variables.

Keywords: Quantitative Methods, Multivariate Data Analysis, Clustering, Finite Mixture Models, Information Theoretical Criteria, Simulation experiments.

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Authors: A. Dal Monte, M. Raciti Castelli, G. B. Bellato, L. Stevanato, E. Benini

Abstract:

The wind resource in the Italian site of Lendinara (RO) is analyzed through a systematic anemometric campaign performed on the top of the bell tower, at an altitude of over 100 m above the ground. Both the average wind speed and the Weibull distribution are computed. The resulting average wind velocity is in accordance with the numerical predictions of the Italian Wind Atlas, confirming the accuracy of the extrapolation of wind data adopted for the evaluation of wind potential at higher altitudes with respect to the commonly placed measurement stations.

Keywords: Anemometric campaign, wind resource, Weibull distribution, wind atlas

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Authors: Henrik L. Funke, Lars Ulke-Winter, Sandra Gelbrich, Lothar Kroll

Abstract:

This work reports about an approach for an automatic adaptation of concrete formulations based on genetic algorithms (GA) to optimize a wide range of different fit-functions. In order to achieve the goal, a method was developed which provides a numerical description of a fibre reinforced concrete (FRC) mixture regarding the production technology and the property spectrum of the concrete. In a first step, the FRC mixture with seven fixed components was characterized by varying amounts of the components. For that purpose, ten concrete mixtures were prepared and tested. The testing procedure comprised flow spread, compressive and bending tensile strength. The analysis and approximation of the determined data was carried out by GAs. The aim was to obtain a closed mathematical expression which best describes the given seven-point cloud of FRC by applying a Gene Expression Programming with Free Coefficients (GEP-FC) strategy. The seven-parametric FRC-mixtures model which is generated according to this method correlated well with the measured data. The developed procedure can be used for concrete mixtures finding closed mathematical expressions, which are based on the measured data.

Keywords: Concrete design, fibre reinforced concrete, genetic algorithms, GEP-FC.

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Authors: Mohammad Ali Baradaran Ghahfarokhi, Parvin Baradaran Ghahfarokhi

Abstract:

In this paper, first we introduce the stable distribution, stable process and theirs characteristics. The a -stable distribution family has received great interest in the last decade due to its success in modeling data, which are too impulsive to be accommodated by the Gaussian distribution. In the second part, we propose major applications of alpha stable distribution in telecommunication, computer science such as network delays and signal processing and financial markets. At the end, we focus on using stable distribution to estimate measure of risk in stock markets and show simulated data with statistical softwares.

Keywords: stable distribution, SaS, infinite variance, heavy tail networks, VaR.

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Authors: Ju-Chun Chien

Abstract:

Rapid aging and depopulation could lead to serious problems, including workforce shortages and health expenditure costs. The current and predicted future LTC workforce shortages could be a real threat to Taiwan’s society. By means of comparison of data from 144 nursing aide trainees and 727 general public, the main purpose of the present study was to determine whether there were any notable differences between the two groups toward engaging in LTC services. Moreover, this study focused on recognizing the attributes of the general public who had the willingness to take LTC jobs but continue to ride the fence. A self-developed questionnaire was designed based on Ajzen’s Theory of Planned Behavior model. After conducting exploratory factor analysis (EFA) and reliability analysis, the questionnaire was a reliable and valid instrument for both nursing aide trainees and the general public. The main results were as follows: Firstly, nearly 70% of nursing aide trainees showed interest in LTC jobs. Most of them were middle-aged female (M = 46.85, SD = 9.31), had a high school diploma or lower, had unrelated work experience in healthcare, and were mostly unemployed. The most common reason for attending the LTC training program was to gain skills in a particular field. The second most common reason was to obtain the license. The third and fourth reasons were to be interested in caring for people and to increase income. The three major reasons that might push them to leave LTC jobs were physical exhaustion, payment is bad, and being looked down on. Secondly, the variables that best-predicted nursing aide trainees’ intention to engage in LTC services were having personal willingness, perceived behavior control, with high school diploma or lower, and supported from family and friends. Finally, only 11.80% of the general public reported having interest in LTC jobs (the disapproval rating was 50% for the general public). In comparison to nursing aide trainees who showed interest in LTC settings, 64.8% of the new workforce for LTC among the general public was male and had an associate degree, 54.8% had relevant healthcare experience, 67.1% was currently employed, and they were younger (M = 32.19, SD = 13.19) and unmarried (66.3%). Furthermore, the most commonly reason for the new workforce to engage in LTC jobs were to gain skills in a particular field. The second priority was to be interested in caring for people. The third and fourth most reasons were to give back to society and to increase income, respectively. The top five most commonly reasons for the new workforce to quitting LTC jobs were listed as follows: physical exhaustion, being looked down on, excessive working hours, payment is bad, and excessive job stress.

Keywords: Long-term care services, nursing aide trainees, Taiwanese people, theory of planned behavior.

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Authors: Javad Khazaei, Rick S. Blum

Abstract:

Distributed control is an efficient and flexible approach for coordination of multi-agent systems. One of the main challenges in designing a distributed controller is identifying the governing dynamics of the dynamical systems. Data-driven system identification is currently undergoing a revolution. With the availability of high-fidelity measurements and historical data, model-free identification of dynamical systems can facilitate the control design without tedious modeling of high-dimensional and/or nonlinear systems. This paper develops a distributed control design using consensus theory for linear and nonlinear dynamical systems using sparse identification of system dynamics. Compared with existing consensus designs that heavily rely on knowing the detailed system dynamics, the proposed model-free design can accurately capture the dynamics of the system with available measurements and input data and provide guaranteed performance in consensus and tracking problems. Heterogeneous damped oscillators are chosen as examples of dynamical system for validation purposes.

Keywords: Consensus tracking, distributed control, model-free control, sparse identification of dynamical systems.

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Authors: Karolína Vozková, Matěj Kuc

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Net fee and commission income is one of the key elements of a bank’s core income. In the current low-interest rate environment, this type of income is gaining importance relative to net interest income. This paper analyses the effects of bank and country specific determinants of net fee and commission income on a set of cooperative banks from European countries in the 2007-2014 period. In order to do that, dynamic panel data methods (system Generalized Methods of Moments) were employed. Subsequently, alternative panel data methods were run as robustness checks of the analysis. Strong positive impact of bank concentration on the share of net fee and commission income was found, which proves that cooperative banks tend to display a higher share of fee income in less competitive markets. This is probably connected with the fact that they stick with their traditional deposit-taking and loan-providing model and fees on these services are driven down by the competitors. Moreover, compared to commercial banks, cooperatives do not expand heavily into non-traditional fee bearing services under competition and their overall fee income share is therefore decreasing with the increased competitiveness of the sector.

Keywords: Cooperative banking, dynamic panel data models, net fee, commission income, system GMM.

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Authors: Khaled Abduesslam. M, Mohammed Ali, Basher H Alsdai, Muhammad Nizam, Inayati

Abstract:

This paper presents the voltage problem location classification using performance of Least Squares Support Vector Machine (LS-SVM) and Learning Vector Quantization (LVQ) in electrical power system for proper voltage problem location implemented by IEEE 39 bus New- England. The data was collected from the time domain simulation by using Power System Analysis Toolbox (PSAT). Outputs from simulation data such as voltage, phase angle, real power and reactive power were taken as input to estimate voltage stability at particular buses based on Power Transfer Stability Index (PTSI).The simulation data was carried out on the IEEE 39 bus test system by considering load bus increased on the system. To verify of the proposed LS-SVM its performance was compared to Learning Vector Quantization (LVQ). The results showed that LS-SVM is faster and better as compared to LVQ. The results also demonstrated that the LS-SVM was estimated by 0% misclassification whereas LVQ had 7.69% misclassification.

Keywords: IEEE 39 bus, Least Squares Support Vector Machine, Learning Vector Quantization, Voltage Collapse.

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Authors: Mangesh R. Phate, V. H. Tatwawadi

Abstract:

This paper is intended to develop an artificial neural network (ANN) based model of material removal rate (MRR) in the turning of ferrous and nonferrous material in a Indian small-scale industry. MRR of the formulated model was proved with the testing data and artificial neural network (ANN) model was developed for the analysis and prediction of the relationship between inputs and output parameters during the turning of ferrous and nonferrous materials. The input parameters of this model are operator, work-piece, cutting process, cutting tool, machine and the environment.

The ANN model consists of a three layered feedforward back propagation neural network. The network is trained with pairs of independent/dependent datasets generated when machining ferrous and nonferrous material. A very good performance of the neural network, in terms of contract with experimental data, was achieved. The model may be used for the testing and forecast of the complex relationship between dependent and the independent parameters in turning operations.

Keywords: Field data based model, Artificial neural network, Simulation, Convectional Turning, Material removal rate.

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Authors: B. Dora Arul Selvi, .N. Kamaraj

Abstract:

Transient Stability is an important issue in power systems planning, operation and extension. The objective of transient stability analysis problem is not satisfied with mere transient instability detection or evaluation and it is most important to complement it by defining fast and efficient control measures in order to ensure system security. This paper presents a new Fuzzy Support Vector Machines (FSVM) to investigate the stability status of power systems and a modified generation rescheduling scheme to bring back the identified unstable cases to a more economical and stable operating point. FSVM improves the traditional SVM (Support Vector Machines) by adding fuzzy membership to each training sample to indicate the degree of membership of this sample to different classes. The preventive control based on economic generator rescheduling avoids the instability of the power systems with minimum change in operating cost under disturbed conditions. Numerical results on the New England 39 bus test system show the effectiveness of the proposed method.

Keywords: Fuzzy Support Vector Machine (FSVM), Incremental Cost, Preventive Control, Transient stability

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Authors: Giovanni Masala, Stefania Marica

Abstract:

Forecasting electricity load plays a crucial role regards decision making and planning for economical purposes. Besides, in the light of the recent privatization and deregulation of the power industry, the forecasting of future electricity load turned out to be a very challenging problem. Empirical data about electricity load highlights a clear seasonal behavior (higher load during the winter season), which is partly due to climatic effects. We also emphasize the presence of load periodicity at a weekly basis (electricity load is usually lower on weekends or holidays) and at daily basis (electricity load is clearly influenced by the hour). Finally, a long-term trend may depend on the general economic situation (for example, industrial production affects electricity load). All these features must be captured by the model. The purpose of this paper is then to build an hourly electricity load model. The deterministic component of the model requires non-linear regression and Fourier series while we will investigate the stochastic component through econometrical tools. The calibration of the parameters’ model will be performed by using data coming from the Italian market in a 6 year period (2007- 2012). Then, we will perform a Monte Carlo simulation in order to compare the simulated data respect to the real data (both in-sample and out-of-sample inspection). The reliability of the model will be deduced thanks to standard tests which highlight a good fitting of the simulated values.

Keywords: ARMA-GARCH process, electricity load, fitting tests, Fourier series, Monte Carlo simulation, non-linear regression.

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Authors: Philip Symonds, Jon Taylor, Zaid Chalabi, Michael Davies

Abstract:

Average temperatures worldwide are expected to continue to rise. At the same time, major cities in developing countries are becoming increasingly populated and polluted. Governments are tasked with the problem of overheating and air quality in residential buildings. This paper presents the development of a model, which is able to estimate the occupant exposure to extreme temperatures and high air pollution within domestic buildings. Building physics simulations were performed using the EnergyPlus building physics software. An accurate metamodel is then formed by randomly sampling building input parameters and training on the outputs of EnergyPlus simulations. Metamodels are used to vastly reduce the amount of computation time required when performing optimisation and sensitivity analyses. Neural Networks (NNs) have been compared to a Radial Basis Function (RBF) algorithm when forming a metamodel. These techniques were implemented using the PyBrain and scikit-learn python libraries, respectively. NNs are shown to perform around 15% better than RBFs when estimating overheating and air pollution metrics modelled by EnergyPlus.

Keywords: Neural Networks, Radial Basis Functions, Metamodelling, Python machine learning libraries.

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Authors: Ondřej Bayer

Abstract:

The paper evaluates the ongoing reform of VAT in the Czech Republic in terms of impacts on individual households. The main objective is to analyse the impact of given changes on individual households. The adopted method is based on the data related to household consumption by individual household quintiles; obtained data are subjected to micro-simulation examining. Results are discussed in terms of vertical tax justice. Results of the analysis reveal that VAT behaves regressively and a sole consolidation of rates at a higher level only increases the regression of this tax in the Czech Republic.

Keywords: Consolidation of rates, household quintiles, tax impact, VAT.

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Authors: Jiashang Jiang

Abstract:

Model updating is an inverse eigenvalue problem which concerns the modification of an existing but inaccurate model with measured modal data. In this paper, an efficient gradient based iterative method for updating the mass, damping and stiffness matrices simultaneously using a few of complex measured modal data is developed. Convergence analysis indicates that the iterative solutions always converge to the unique minimum Frobenius norm symmetric solution of the model updating problem by choosing a special kind of initial matrices.

Keywords: Model updating, iterative algorithm, damped structural system, optimal approximation.

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Authors: Ali M. Jawarneh, Ahmad S. AL-Shyyab

Abstract:

The purpose of this work is to present the potential of solar energy in Zarqa region. The solar radiation along year 2009 was obtained from Pyranometer which measures the global radiation over horizontal surfaces. Solar data in several different forms, over period of 5 minutes, hour-by-hour, daily and monthly data radiation have been presented. Briefly, the yearly global solar radiation in Zarqa is 7297.5 MJ/m2 (2027 kWh/m²) and the average annual solar radiation per day is 20 MJ/m2 (5.5 Kwh/m2). More specifically, the average annual solar radiation per day is 12.9 MJ/m2 (3.57 Kwh/m2) in winter and 25 MJ/m2 (7 Kwh/m2) in summer.

Keywords: Solar Energy, Pyranometer, Zarqa Region

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Authors: Jin Fan, M. Ala Saadeghvaziri

Abstract:

Unmanned aerial vehicles (UAVs), also referred to as drones, equipped with various kinds of advanced detecting or surveying systems, are effective and low-cost in data acquisition, data delivery and sharing, which can benefit the building of infrastructures. This paper will give an overview of applications of drones in planning, designing, construction and maintenance of infrastructures. The drone platform, detecting and surveying systems, and post-data processing systems will be introduced, followed by cases with details of the applications. Challenges from different aspects will be addressed. Opportunities of drones in infrastructure include but not limited to the following. Firstly, UAVs equipped with high definition cameras or other detecting equipment are capable of inspecting the hard to reach infrastructure assets. Secondly, UAVs can be used as effective tools to survey and map the landscape to collect necessary information before infrastructure construction. Furthermore, an UAV or multi-UVAs are useful in construction management. UVAs can also be used in collecting roads and building information by taking high-resolution photos for future infrastructure planning. UAVs can be used to provide reliable and dynamic traffic information, which is potentially helpful in building smart cities. The main challenges are: limited flight time, the robustness of signal, post data analyze, multi-drone collaboration, weather condition, distractions to the traffic caused by drones. This paper aims to help owners, designers, engineers and architects to improve the building process of infrastructures for higher efficiency and better performance.

Keywords: Bridge, construction, drones, infrastructure, information.

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Authors: Fahd Sabry Esmail, M. Badr Senousy, Mohamed Ragaie

Abstract:

In recent years, there has been an explosion in the rate of using technology that help discovering the diseases. For example, DNA microarrays allow us for the first time to obtain a "global" view of the cell. It has great potential to provide accurate medical diagnosis, to help in finding the right treatment and cure for many diseases. Various classification algorithms can be applied on such micro-array datasets to devise methods that can predict the occurrence of Leukemia disease. In this study, we compared the classification accuracy and response time among eleven decision tree methods and six rule classifier methods using five performance criteria. The experiment results show that the performance of Random Tree is producing better result. Also it takes lowest time to build model in tree classifier. The classification rules algorithms such as nearest- neighbor-like algorithm (NNge) is the best algorithm due to the high accuracy and it takes lowest time to build model in classification.

Keywords: Data mining, classification techniques, decision tree, classification rule, leukemia diseases, microarray data.

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Authors: S. Banerjee

Abstract:

The paper describes design of an ontology in the financial domain for mutual funds. The design of this ontology consists of four steps, namely, specification, knowledge acquisition, implementation and semantic query. Specification includes a description of the taxonomy and different types mutual funds and their scope. Knowledge acquisition involves the information extraction from heterogeneous resources. Implementation describes the conceptualization and encoding of this data. Finally, semantic query permits complex queries to integrated data, mapping of these database entities to ontological concepts.

Keywords: Ontology, Semantic Web, Mutual Funds.

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Authors: Samee Ullah Khan, C. Ardil

Abstract:

Fine-grained data replication over the Internet allows duplication of frequently accessed data objects, as opposed to entire sites, to certain locations so as to improve the performance of largescale content distribution systems. In a distributed system, agents representing their sites try to maximize their own benefit since they are driven by different goals such as to minimize their communication costs, latency, etc. In this paper, we will use game theoretical techniques and in particular auctions to identify a bidding mechanism that encapsulates the selfishness of the agents, while having a controlling hand over them. In essence, the proposed game theory based mechanism is the study of what happens when independent agents act selfishly and how to control them to maximize the overall performance. A bidding mechanism asks how one can design systems so that agents- selfish behavior results in the desired system-wide goals. Experimental results reveal that this mechanism provides excellent solution quality, while maintaining fast execution time. The comparisons are recorded against some well known techniques such as greedy, branch and bound, game theoretical auctions and genetic algorithms.

Keywords: Internet, data content replication, static allocation, mechanism design, equilibrium.

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