Search results for: Prediction of financial markets

Authors: J. Uka, B. McKay, T. Minton, O. Adole, R. Lewis, S. J. Glanvill, L. Anguilano

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Effective heat treatment conditions to obtain maximum aluminium swarf recycling are investigated in this work. Aluminium swarf briquettes underwent treatments at different temperatures and cooling times to investigate the improvements obtained in the recovery of aluminium metal. The main issue for the recovery of the metal from swarfs is to overcome the constraints due to the oxide layers present in high concentration in the swarfs since they have a high surface area. Briquettes supplied by Renishaw were heat treated at 650, 700, 750, 800 and 850 ℃ for 1-hour and then cooled at 2.3, 3.5 and 5 ℃/min. The resulting material was analysed using SEM EDX to observe the oxygen diffusion and aluminium coalescence at the boundary between adjacent swarfs. Preliminary results show that, swarf needs to be heat treated at a temperature of 850 ℃ and cooled down slowly at 2.3 ℃/min to have thin and discontinuous alumina layers between the adjacent swarf and consequently allowing aluminium coalescence. This has the potential to save energy and provide maximum financial profit in preparation of swarf briquettes for recycling.

Keywords: Aluminium, swarf, oxide layers, recycle, reuse.

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Authors: A. Abdullah, A. Bakshwain, A. Aslam

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Livestock is one of the fastest-growing sectors in agriculture. If carefully managed, have potential opportunities for economic growth, food sovereignty and food security. In this study we mainly analyse and compare long-term i.e. for year 2030 climate variability impact on predicted productivity of meat i.e. beef, mutton and poultry for the Kingdom of Saudi Arabia w.r.t three factors i.e. i) climatic-change vulnerability ii) CO2 fertilization and iii) water scarcity and compare the results with two countries of the region i.e. Iraq and Yemen. We do the analysis using data from diverse sources, which was extracted, transformed and integrated before usage. The collective impact of the three factors had an overall negative effect on the production of meat for all the three countries, with adverse impact on Iraq. High similarity was found between CO2 fertilization (effecting animal fodder) and water scarcity i.e. higher than that between production of beef and mutton for the three countries considered. Overall, the three factors do not seem to be favorable for the three Middle-East countries considered. This points to possibility of a vegetarian year 2030 based on dependency on indigenous livestock population.

Keywords: Prediction, animal-source foods, pastures, CO2 fertilization, climatic-change vulnerability, water scarcity.

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Authors: Y. C. Mao, G. S. Chen

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Bicycle configuration is not as large as those of motorcycles or automobiles, while it indeed composes a complicated dynamic system. People-s requirements on comfortability, controllability and safety grow higher as the research and development technologies improve. The shock absorber affects the vehicle suspension performances enormously. The absorber takes the vibration energy and releases it at a suitable time, keeping the wheel under a proper contact condition with road surface, maintaining the vehicle chassis stability. Suspension design for mountain bicycles is more difficult than that of city bikes since it encounters dynamic variations on road and loading conditions. Riders need a stiff damper as they exert to tread on the pedals when climbing, while a soft damper when they descend downhill. Various switchable shock absorbers are proposed in markets, however riders have to manually switch them among soft, hard and lock positions. This study proposes a novel design of the bicycle shock absorber, which provides automatic smooth tuning of the damping coefficient, from a predetermined lower bound to theoretically unlimited. An automatic quick releasing valve is involved in this design so that it can release the peak pressure when the suspension fork runs into a square-wave type obstacle and prevent the chassis from damage, avoiding the rider skeleton from injury. This design achieves the automatic tuning process by innovative plunger valve and fluidic passage arrangements without any electronic devices. Theoretical modelling of the damper and spring are established in this study. Design parameters of the valves and fluidic passages are determined. Relations between design parameters and shock absorber performances are discussed in this paper. The analytical results give directions to the shock absorber manufacture.

Keywords: Modelling, Simulation, Bicycle, Shock Absorber, Damping, Releasing Valve

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Authors: P. Wlodarczak, J. Soar, M. Ally

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The dramatic rise in the use of Social Media (SM) platforms such as Facebook and Twitter provide access to an unprecedented amount of user data. Users may post reviews on products and services they bought, write about their interests, share ideas or give their opinions and views on political issues. There is a growing interest in the analysis of SM data from organisations for detecting new trends, obtaining user opinions on their products and services or finding out about their online reputations. A recent research trend in SM analysis is making predictions based on sentiment analysis of SM. Often indicators of historic SM data are represented as time series and correlated with a variety of real world phenomena like the outcome of elections, the development of financial indicators, box office revenue and disease outbreaks. This paper examines the current state of research in the area of SM mining and predictive analysis and gives an overview of the analysis methods using opinion mining and machine learning techniques.

Keywords: Social Media, text mining, knowledge discovery, predictive analysis, machine learning.

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Authors: Shahab Kavehkar, Mohammad Ali Ghorbani, Valeriy Khokhlov, Afshin Ashrafzadeh, Sabereh Darbandi

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Water level forecasting using records of past time series is of importance in water resources engineering and management. For example, water level affects groundwater tables in low-lying coastal areas, as well as hydrological regimes of some coastal rivers. Then, a reliable prediction of sea-level variations is required in coastal engineering and hydrologic studies. During the past two decades, the approaches based on the Genetic Programming (GP) and Artificial Neural Networks (ANN) were developed. In the present study, the GP is used to forecast daily water level variations for a set of time intervals using observed water levels. The measurements from a single tide gauge at Urmia Lake, Northwest Iran, were used to train and validate the GP approach for the period from January 1997 to July 2008. Statistics, the root mean square error and correlation coefficient, are used to verify model by comparing with a corresponding outputs from Artificial Neural Network model. The results show that both these artificial intelligence methodologies are satisfactory and can be considered as alternatives to the conventional harmonic analysis.

Keywords: Water-Level variation, forecasting, artificial neural networks, genetic programming, comparative analysis.

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Authors: Adel Aloraini

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Financial forecasting using machine learning techniques has received great efforts in the last decide . In this ongoing work, we show how machine learning of graphical models will be able to infer a visualized causal interactions between different banks in the Saudi equities market. One important discovery from such learned causal graphs is how companies influence each other and to what extend. In this work, a set of graphical models named Gaussian graphical models with developed ensemble penalized feature selection methods that combine ; filtering method, wrapper method and a regularizer will be shown. A comparison between these different developed ensemble combinations will also be shown. The best ensemble method will be used to infer the causal relationships between banks in Saudi equities market.

Keywords: Causal interactions , banks, feature selection, regularizere,

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Authors: Beata Czarnacka-Chrobot

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Majority of Business Software Systems (BSS) Development and Enhancement Projects (D&EP) fail to meet criteria of their effectiveness, what leads to the considerable financial losses. One of the fundamental reasons for such projects- exceptionally low success rate are improperly derived estimates for their costs and time. In the case of BSS D&EP these attributes are determined by the work effort, meanwhile reliable and objective effort estimation still appears to be a great challenge to the software engineering. Thus this paper is aimed at presenting the most important synthetic conclusions coming from the author-s own studies concerning the main factors of effective BSS D&EP work effort estimation. Thanks to the rational investment decisions made on the basis of reliable and objective criteria it is possible to reduce losses caused not only by abandoned projects but also by large scale of overrunning the time and costs of BSS D&EP execution.

Keywords: Benchmarking data, business software systems development and enhancement projects, effort estimation, software engineering economics, software functional size measurement.

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Authors: M. Nahid, A. Bajit, A. Tamtaoui, E. H. Bouyakhf

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To model the human visual system (HVS) in the region of interest, we propose a new objective metric evaluation adapted to wavelet foveation-based image compression quality measurement, which exploits a foveation setup filter implementation technique in the DWT domain, based especially on the point and region of fixation of the human eye. This model is then used to predict the visible divergences between an original and compressed image with respect to this region field and yields an adapted and local measure error by removing all peripheral errors. The technique, which we call foveation wavelet visible difference prediction (FWVDP), is demonstrated on a number of noisy images all of which have the same local peak signal to noise ratio (PSNR), but visibly different errors. We show that the FWVDP reliably predicts the fixation areas of interest where error is masked, due to high image contrast, and the areas where the error is visible, due to low image contrast. The paper also suggests ways in which the FWVDP can be used to determine a visually optimal quantization strategy for foveation-based wavelet coefficients and to produce a quantitative local measure of image quality.

Keywords: Human Visual System, Image Quality, ImageCompression, foveation wavelet, region of interest ROI.

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Authors: S. Deswal, M. Pal

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The paper aims to compare the performance of vertical and inclined multiple plunging jets and to model and predict their mass transfer capacity by multi-linear regression based approach. The multiple vertical plunging jets have jet impact angle of θ = 90^O; whereas, multiple inclined plunging jets have jet impact angle of θ = 60^O. The results of the study suggests that mass transfer is higher for multiple jets, and inclined multiple plunging jets have up to 1.6 times higher mass transfer than vertical multiple plunging jets under similar conditions. The derived relationship, based on multi-linear regression approach, has successfully predicted the volumetric mass transfer coefficient (K_La) from operational parameters of multiple plunging jets with a correlation coefficient of 0.973, root mean square error of 0.002 and coefficient of determination of 0.946. The results suggests that predicted overall mass transfer coefficient is in good agreement with actual experimental values; thereby, suggesting the utility of derived relationship based on multi-linear regression based approach and can be successfully employed in modeling mass transfer by multiple plunging jets.

Keywords: Mass transfer, multiple plunging jets, multi-linear regression.

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Authors: Ali Keshavarzi, Fereydoon Sarmadian

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Investigation of soil properties like Cation Exchange Capacity (CEC) plays important roles in study of environmental reaserches as the spatial and temporal variability of this property have been led to development of indirect methods in estimation of this soil characteristic. Pedotransfer functions (PTFs) provide an alternative by estimating soil parameters from more readily available soil data. 70 soil samples were collected from different horizons of 15 soil profiles located in the Ziaran region, Qazvin province, Iran. Then, multivariate regression and neural network model (feedforward back propagation network) were employed to develop a pedotransfer function for predicting soil parameter using easily measurable characteristics of clay and organic carbon. The performance of the multivariate regression and neural network model was evaluated using a test data set. In order to evaluate the models, root mean square error (RMSE) was used. The value of RMSE and R2 derived by ANN model for CEC were 0.47 and 0.94 respectively, while these parameters for multivariate regression model were 0.65 and 0.88 respectively. Results showed that artificial neural network with seven neurons in hidden layer had better performance in predicting soil cation exchange capacity than multivariate regression.

Keywords: Easily measurable characteristics, Feed-forwardback propagation, Pedotransfer functions, CEC.

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Authors: Jamal S. Yassin

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This paper is to formulate a mathematical model to predict the amounts of the emissions produced from the combustion process of the gas turbine unit of the jet engine. These emissions have bad impacts on the environment if they are out of standards, which cause real threats to all type of life on the earth. The amounts of the emissions from the gas turbine engine are functions to many operational and design factors. In landing-takeoff (LTO) these amounts are not the same as in taxi or cruise of the plane using jet engines, because of the difference in the activity period during these operating modes. These emissions can be affected by several physical and chemical variables, such as fuel type, fuel to air ratio or equivalence ratio, flame temperature, combustion pressure, in addition to some inlet conditions such as ambient temperature and air humidity. To study the influence of these variables on the amounts of these emissions during the combustion process in the gas turbine unit, a computer program has been developed by using the visual basic 6 software. Here, the analysis of the combustion process is carried out by considering it as a chemical reaction with shifting equilibrium to find the products of the combustion of the octane fuel, at different equivalence ratios, compressor pressure ratios (CPR) and combustion temperatures. The results obtained have shown that there is noticeable influence of the equivalence ratio, CPR, and the combustion temperature on the amounts of the main emissions which are considered pollutants, such as CO, CO₂ and NO.

Keywords: Mathematical model, gas turbine unit, equivalence ratio, emissions, shifting equilibrium.

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Authors: Galal H. Senussi, Muamar Benisa, Sanja Vasin

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In our business field today, one of the most important issues for any enterprises is cost minimization and profit maximization. Second issue is how to develop a strong and capable model that is able to give us desired forecasting of these two issues. Many researches deal with these issues using different methods. In this study, we developed a model for multi-criteria production program optimization, integrated with Artificial Neural Network.

The prediction of the production cost and profit per unit of a product, dealing with two obverse functions at same time can be extremely difficult, especially if there is a great amount of conflict information about production parameters.

Feed-Forward Neural Networks are suitable for generalization, which means that the network will generate a proper output as a result to input it has never seen. Therefore, with small set of examples the network will adjust its weight coefficients so the input will generate a proper output.

This essential characteristic is of the most important abilities enabling this network to be used in variety of problems spreading from engineering to finance etc.

From our results as we will see later, Feed-Forward Neural Networks has a strong ability and capability to map inputs into desired outputs.

Keywords: Project profitability, multi-objective optimization, genetic algorithm, Pareto set, Neural Networks.

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Authors: Urvee B. Trivedi, U. D. Dalal

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As wireless communication services grow quickly; the seriousness of spectrum utilization has been on the rise gradually. An emerging technology, cognitive radio has come out to solve today’s spectrum scarcity problem. To support the spectrum reuse functionality, secondary users are required to sense the radio frequency environment, and once the primary users are found to be active, the secondary users are required to vacate the channel within a certain amount of time. Therefore, spectrum sensing is of significant importance. Once sensing is done, different prediction rules apply to classify the traffic pattern of primary user. Primary user follows two types of traffic patterns: periodic and stochastic ON-OFF patterns. A cognitive radio can learn the patterns in different channels over time. Two types of classification methods are discussed in this paper, by considering edge detection and by using autocorrelation function. Edge detection method has a high accuracy but it cannot tolerate sensing errors. Autocorrelation-based classification is applicable in the real environment as it can tolerate some amount of sensing errors.

Keywords: Cognitive radio (CR), probability of detection (PD), probability of false alarm (PF), primary User (PU), secondary user (SU), Fast Fourier transform (FFT), signal to noise ratio (SNR).

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Authors: Petra Horváthová, Andrej Miklosik

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One part of the total employee’s reward is apart from basic wages or salary, employee’s benefits and intangible remuneration also so called contingent (variable) pay. Contingent pay is connected to performance, contribution, cap competency or skills of individual employees, and to team’s or company-wide performance or to combination of few of the mentioned possibilities. Sometimes among the contingent pay is also incorporated the remuneration based on length of employment, when the financial reward is not connected to performance or skills, but to length of continuous employment either on one working position or in one level of remuneration scale. Main aim of this article is to define, based on available information, contingent pay, describe individual forms, its advantages and disadvantages and possibilities to utilization in practice; but also bring information not only about its extent and level of utilization of contingent pay by companies in one of the Czech Republic’s regions, but also mention their practical experience with this type of remuneration.

Keywords: Contingent pay, individual contingent pay, team contingent pay, company-wide contingent pay

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Authors: Gitanjali Devi, Kandarpa Kumar Sarma, Pranayee Datta, Anjana Kakoti Mahanta

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Cosmic showers, from their places of origin in space, after entering earth generate secondary particles called Extensive Air Shower (EAS). Detection and analysis of EAS and similar High Energy Particle Showers involve a plethora of experimental setups with certain constraints for which soft-computational tools like Artificial Neural Network (ANN)s can be adopted. The optimality of ANN classifiers can be enhanced further by the use of Multiple Classifier System (MCS) and certain data - dimension reduction techniques. This work describes the performance of certain data dimension reduction techniques like Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Self Organizing Map (SOM) approximators for application with an MCS formed using Multi Layer Perceptron (MLP), Recurrent Neural Network (RNN) and Probabilistic Neural Network (PNN). The data inputs are obtained from an array of detectors placed in a circular arrangement resembling a practical detector grid which have a higher dimension and greater correlation among themselves. The PCA, ICA and SOM blocks reduce the correlation and generate a form suitable for real time practical applications for prediction of primary energy and location of EAS from density values captured using detectors in a circular grid.

Keywords: EAS, Shower, Core, ANN, Location.

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Authors: Mustafa Bayram Gücen, Coşkun Yakar

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In this study, we have investigated the strict stability of fuzzy differential systems and we compare the classical notion of strict stability criteria of ordinary differential equations and the notion of strict stability of fuzzy differential systems. In addition that, we present definitions of stability and strict stability of fuzzy differential equations and also we have some theorems and comparison results. Strict Stability is a different stability definition and this stability type can give us an information about the rate of decay of the solutions. Lyapunov’s second method is a standard technique used in the study of the qualitative behavior of fuzzy differential systems along with a comparison result that allows the prediction of behavior of a fuzzy differential system when the behavior of the null solution of a fuzzy comparison system is known. This method is a usefull for investigating strict stability of fuzzy systems. First of all, we present definitions and necessary background material. Secondly, we discuss and compare the differences between the classical notion of stability and the recent notion of strict stability. And then, we have a comparison result in which the stability properties of the null solution of the comparison system imply the corresponding stability properties of the fuzzy differential system. Consequently, we give the strict stability results and a comparison theorem. We have used Lyapunov second method and we have proved a comparison result with scalar differential equations.

Keywords: Fuzzy systems, fuzzy differential equations, fuzzy stability, strict stability.

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Authors: Darwina Arshad, Paul Hughes

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Recent environmental turbulence including financial crisis, intensified competitive forces, rapid technological change and high market turbulence have dramatically changed the current business climate. The managers firms have to plan and decide what the best approaches that best fit their firms in order to pursue superior performance. This research aims to examine the influence of strategic reasoning and top level managers- individual characteristics on the effectiveness of organizational improvisation and firm performance. Given the lack of studies on these relationships in the previous literature, there is significant contribution to the body of knowledge as well as for managerial practices. 128 responses from top management of technology-based companies in Malaysia were used as a sample. Three hypotheses were examined and the findings confirm that (a) there is no relationship between intuitive reasoning and organizational improvisation but there is a link between rational reasoning and organizational improvisation, (b) top level managers- individual characteristics as a whole affect organizational improvisation; and (c) organizational improvisation positively affects firm performance. The theoretical and managerial implications were discussed in the conclusions.

Keywords: Organizational improvisation, strategic reasoning, firm performance.

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Authors: R. Pastusiak, M. Jasiniak, A. Kaźmierska

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The impact of Special Economic Zones (SEZs) has been analyzed for many years by researchers. There are lot of theoretical studies proving the SEZs importance for regional development, however, there is lack of empirical studies (and they are mainly focused on China market) that are based on available data. The theoretical studies indicate the various impacts of enterprises operating within SEZs on the economy. The article proves that, in case of Poland, locating SEZs in municipalities is an important part of increasing municipalities’ income. Therefore SEZs have a positive impact on regional development. Municipality income is understood as taxes paid by taxpayers who depend on SEZ companies’ performance. The analysis includes the Corporate Income Tax (CIT), Personal Income Tax (PIT) and real estate tax. The effects of SEZs on regional development were narrowed to a few variables that are most significant for the financial system. The analysis indicates the significant impact of SEZs on the amount of taxes influencing the municipality budget.

Keywords: Government, local finance, municipal finance, Special Economic Zones.

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Authors: Y. Egercioglu, S. Yalciner

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With the development of technology, the growing trend of fast and safe passenger transport, air pollution, traffic congestion, increase in problems such as the increasing population and the high cost of private vehicle usage made many cities around the world with a population of more or less, start to build rail systems as a means of urban transport in order to ensure the economic and environmental sustainability and more efficient use of land in the city. The implementation phase of rail systems costs much more than other public transport systems. However, social and economic returns in the long term made these systems the most popular investment tool for planned and developing cities. In our country, the purpose, goals and policies of transportation plans are away from integrity, and the problems are not clearly detected. Also, not defined and incomplete assessment of transportation systems and insufficient financial analysis are the most important cause of failure. Rail systems and other transportation systems to be addressed as a whole is seen as the main factor in increasing efficiency in applications that are not integrated yet in our country to come to this point has led to the problem.

Keywords: Urban Transportation Projects, Urban Light Rail Systems, Urbanization, Izmir.

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Authors: Tina Vuko, Marija Maretić, Marko Čular

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The aim of this study is to analyze the role and effectiveness of internal mechanism (audit committee) of corporate governance on credit institutions performance in Croatia. Based on research objective, sample of 78 credit institutions listed on Zagreb Stock Exchange, from 2007 to 2012, has been collected and efficiency index of audit committee (EIAC) has been created. Based on the sample and created EIAC, conclusions are as follows: audit committees of credit institutions have medium efficiency, based on EIAC measurement; there is a significant difference in audit committee effectiveness, in observed period; there is no positive relationship between audit committee effectiveness and credit institution performance; there is a significant difference between level of audit committee effectiveness and audit firm type. Future research should contain increased number of elements in EIAC creation and increased sample, for all obligators who need to establish audit committee.

Keywords: Corporate Governance, Audit Committee, Financial Institutions, Efficiency Index of Audit Committee.

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Authors: Ruchika Malhotra, Megha Khanna

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The development of change prediction models can help the software practitioners in planning testing and inspection resources at early phases of software development. However, a major challenge faced during the training process of any classification model is the imbalanced nature of the software quality data. A data with very few minority outcome categories leads to inefficient learning process and a classification model developed from the imbalanced data generally does not predict these minority categories correctly. Thus, for a given dataset, a minority of classes may be change prone whereas a majority of classes may be non-change prone. This study explores various alternatives for adeptly handling the imbalanced software quality data using different sampling methods and effective MetaCost learners. The study also analyzes and justifies the use of different performance metrics while dealing with the imbalanced data. In order to empirically validate different alternatives, the study uses change data from three application packages of open-source Android data set and evaluates the performance of six different machine learning techniques. The results of the study indicate extensive improvement in the performance of the classification models when using resampling method and robust performance measures.

Keywords: Change proneness, empirical validation, imbalanced learning, machine learning techniques, object-oriented metrics.

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Authors: Huan Chen, Bo-Chao Cheng, Chih-Chuan Cheng, Yi-Geng Chen, Yu Ling Chou

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Wireless Sensor Network (WSN) comprises of sensor nodes which are designed to sense the environment, transmit sensed data back to the base station via multi-hop routing to reconstruct physical phenomena. Since physical phenomena exists significant overlaps between temporal redundancy and spatial redundancy, it is necessary to use Redundancy Suppression Algorithms (RSA) for sensor node to lower energy consumption by reducing the transmission of redundancy. A conventional algorithm of RSAs is threshold-based RSA, which sets threshold to suppress redundant data. Although many temporal and spatial RSAs are proposed, temporal-spatial RSA are seldom to be proposed because it is difficult to determine when to utilize temporal or spatial RSAs. In this paper, we proposed a novel temporal-spatial redundancy suppression algorithm, Codebookbase Redundancy Suppression Mechanism (CRSM). CRSM adopts vector quantization to generate a codebook, which is easily used to implement temporal-spatial RSA. CRSM not only achieves power saving and reliability for WSN, but also provides the predictability of network lifetime. Simulation result shows that the network lifetime of CRSM outperforms at least 23% of that of other RSAs.

Keywords: Redundancy Suppression Algorithm (RSA), Threshold-based RSA, Temporal RSA, Spatial RSA and Codebookbase Redundancy Suppression Mechanism (CRSM)

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Authors: L. A. Muhammad, M. S. Argungu

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Educational data mining (EDM) has recorded substantial considerations. Techniques of data mining in one way or the other have been proposed to dig out out-of-sight knowledge in educational data. The result of the study got assists academic institutions in further enhancing their process of learning and methods of passing knowledge to students. Consequently, the performance of students boasts and the educational products are by no doubt enhanced. This study adopted a student performance prediction model premised on techniques of data mining with Students' Essential Features (SEF). SEF are linked to the learner's interactivity with the e-learning management system. The performance of the student's predictive model is assessed by a set of classifiers, viz. Bayes Network, Logistic Regression, and Reduce Error Pruning Tree (REP). Consequently, ensemble methods of Bagging, Boosting, and Random Forest (RF) are applied to improve the performance of these single classifiers. The study reveals that the result shows a robust affinity between learners' behaviors and their academic attainment. Result from the study shows that the REP Tree and its ensemble record the highest accuracy of 83.33% using SEF. Hence, in terms of the Receiver Operating Curve (ROC), boosting method of REP Tree records 0.903, which is the best. This result further demonstrates the dependability of the proposed model.

Keywords: Ensemble, bagging, Random Forest, boosting, data mining, classifiers, machine learning.

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Authors: Mahtab Davari, Charles Edward Okon, Somayeh Aghanavesi

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Every institute of learning is usually interested in the performance of enrolled students. The level of these performances determines the approach an institute of study may adopt in rendering academic services. The focus of this paper is to evaluate students' academic performance in given courses of study using machine learning methods. This study evaluated various supervised machine learning classification algorithms such as Logistic Regression (LR), Support Vector Machine (SVM), Random Forest, Decision Tree, K-Nearest Neighbors, Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis, using selected features to predict study performance. The accuracy, precision, recall, and F1 score obtained from a 5-Fold Cross-Validation were used to determine the best classification algorithm to predict students’ performances. SVM (using a linear kernel), LDA, and LR were identified as the best-performing machine learning methods. Also, using the LR model, this study identified students' educational habits such as reading and paying attention in class as strong determinants for a student to have an above-average performance. Other important features include the academic history of the student and work. Demographic factors such as age, gender, high school graduation, etc., had no significant effect on a student's performance.

Keywords: Student performance, supervised machine learning, prediction, classification, cross-validation.

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Authors: S. Kamyabi, M. Salari, H. Shahabi

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One of the processes of slope that occurs every year in Iran and some parts of world and cause a lot of criminal and financial harms is called landslide. They are plenty of method to stability landslide in soil and rock slides. The use of the best method with the least cost and in the shortest time is important for researchers. In this research, determining the best method of stability is investigated by using of Decision Support systems. DSS is made for this purpose and was used (for Hasan Salaran area in Kurdistan). Field study data from topography, slope, geology, geometry of landslide and the related features was used. The related data entered decision making managements programs (DSS) (ALES).Analysis of mass stability indicated the instability potential at present. Research results show that surface and sub surface drainage the best method of stabilizing. Analysis of stability shows that acceptable increase in security coefficient is a consequence of drainage.

Keywords: Landslide, Decision Support systems, stability, Hasan Salaran landslide, Kurdistan province, Iran.

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Authors: Mliki Bouchmel, Abbassi Mohamed Ammar, Kamel Geudri, Chrigui Mouldi, Omri Ahmed

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The objective of this work is to use the Fire Dynamics Simulator (FDS) to investigate the behavior of a kerosene small-scale fire. FDS is a Computational Fluid Dynamics (CFD) tool developed specifically for fire applications. Throughout its development, FDS is used for the resolution of practical problems in fire protection engineering. At the same time FDS is used to study fundamental fire dynamics and combustion. Predictions are based on Large Eddy Simulation (LES) with a Smagorinsky turbulence model. LES directly computes the large-scale eddies and the sub-grid scale dissipative processes are modeled. This technique is the default turbulence model which was used in this study. The validation of the numerical prediction is done using a direct comparison of combustion output variables to experimental measurements. Effect of the mesh size on the temperature evolutions is investigated and optimum grid size is suggested. Effect of width openings is investigated. Temperature distribution and species flow are presented for different operating conditions. The effect of the composition of the used fuel on atmospheric pollution is also a focus point within this work. Good predictions are obtained where the size of the computational cells within the fire compartment is less than 1/10th of the characteristic fire diameter.

Keywords: Large eddy simulation, Radiation, Turbulence, combustion, pollution.

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Authors: Walaa Khalaf, Calogero Pace, Manlio Gaudioso

Abstract:

We present an Electronic Nose (ENose), which is aimed at identifying the presence of one out of two gases, possibly detecting the presence of a mixture of the two. Estimation of the concentrations of the components is also performed for a volatile organic compound (VOC) constituted by methanol and acetone, for the ranges 40-400 and 22-220 ppm (parts-per-million), respectively. Our system contains 8 sensors, 5 of them being gas sensors (of the class TGS from FIGARO USA, INC., whose sensing element is a tin dioxide (SnO2) semiconductor), the remaining being a temperature sensor (LM35 from National Semiconductor Corporation), a humidity sensor (HIH–3610 from Honeywell), and a pressure sensor (XFAM from Fujikura Ltd.). Our integrated hardware–software system uses some machine learning principles and least square regression principle to identify at first a new gas sample, or a mixture, and then to estimate the concentrations. In particular we adopt a training model using the Support Vector Machine (SVM) approach with linear kernel to teach the system how discriminate among different gases. Then we apply another training model using the least square regression, to predict the concentrations. The experimental results demonstrate that the proposed multiclassification and regression scheme is effective in the identification of the tested VOCs of methanol and acetone with 96.61% correctness. The concentration prediction is obtained with 0.979 and 0.964 correlation coefficient for the predicted versus real concentrations of methanol and acetone, respectively.

Keywords: Electronic nose, Least square regression, Mixture ofgases, Support Vector Machine.

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Authors: Lorenzo Maiorino, Fabio Fortuna, Giovanni Panebianco, Marco Sanzari, Gabriella Arcese, Valerio Maria Paolozzi

Abstract:

It is well known that sport is a factor of social cohesion and the breaking down of barriers between people. From this point of view, the aim is to demonstrate how, through the (re)generation of sustainable structures, it is possible to give life to a new social, cultural and economic pathway, where possible, in peripheral areas with problems of abandonment and degradation. The aim of this paper is therefore to study realities such as European programs and funds and to highlight the ways in which planning can be used to respond to critical issues such as urban decay, abandonment, and the mitigation of social differences. For this reason, the analysis will be carried out through the Multiannual Financial Framework (MFF) package, the next generation EU, the Recovery and Resilience Facility (RRF), the Cohesion Fund, the European Social Fund, and other managed funds. The procedure will rely on sources and data of unquestionable origin, and the relation to the object of study in question will be highlighted. The project lends itself to be ambitious and explore a further aspect of the sports theme, which as we know, is one of the foundations for a healthy society

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Keywords: Sport, social inclusion, urban regeneration, sport facilities, European funds.

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Authors: A. Al Drees, A. Alahmari, R. Almuwayshir

Abstract:

The advanced information technology is becoming an important factor in the development of financial services industry, especially the banking industry. It has introduced new ways of delivering banking to the customer, such as Internet Banking. Banks began to look at electronic banking (e-banking) as a means to replace some of their traditional branch functions using the Internet as a new distribution channel. Some consumers have at least more than one account, and across banks, and access these accounts using e-banking services. To look at the current net worth position, customers have to login to each of their accounts and get the details and work on consolidation. This not only takes ample time but it is a repetitive activity at a specified frequency. To address this point, an account aggregation concept is added as a solution. E-banking account aggregation, as one of the e-banking types, appeared to build a stronger relationship with customers. Account Aggregation Service generally refers to a service that allows customers to manage their bank accounts maintained in different institutions through a common Internet banking operating a platform, with a high concern to security and privacy. This paper presents an overview of an e-banking account aggregation approach as a new service in the e-banking field.

Keywords: E-banking, security, account aggregation, enterprise application development.

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Authors: F. Demir

Abstract:

Innovation is highly critical for every company, especially for technology-based organizations looking to sustain their competitive advantage. However, this is not an easy task. Regardless of the size of the enterprise, market and location, all organizations face numerous challenges. Even though huge barriers to innovation exist in different countries, firm- and industry-specific challenges can be distinguished. This paper examines innovation strategies and obstacles to innovation in research and technology organizations (RTO) of Turkey. From the most important to the least, nine different challenges are ranked according the results of this survey. The findings reveal that to take the lead in innovation, financial constraint is the biggest challenge, which is consistent with the related literature. It ranked number one in this study. Beyond that, based on a sample of 40 RTOs, regional challenges such as underdeveloped regional innovation ecosystem plays a significant role in hampering innovation. Most of the organizations (55%) embrace an incremental approach to innovation, while only few pursue radical shifts. About 40% of the RTOs focus on product innovation, and 27.5% of them concentrate on technological innovation, while a very limited number aim for operational excellence and customer engagement as the focus of their strategic innovation efforts.

Keywords: Innovation strategies, innovation challenges, emerging economies, research and technology organizations.

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