Search results for: Data mining techniques

Authors: Johana Jaramillo, Robin Kalfat, Dmitriy A. Dikin

Abstract:

The cement production process is one of the major sources of carbon dioxide (CO2), a potent greenhouse gas. Indeed, as a result of its cement manufacturing process, concrete contributes approximately 8% of global greenhouse gas emissions. In addition to environmental concerns, concrete also has a low tensile and ductility strength, which can lead to cracks. Graphene Nanoplatelets (GNPs) have proven to be an eco-friendly solution for improving the mechanical and durability properties of concrete. The current research investigates the effects of preparing concrete enhanced with GNPs by using different wet dispersions techniques and mixing methods on its mechanical properties. Concrete specimens were prepared with 0.00 wt%, 0.10 wt%, 0.20 wt%, 0.30 wt% and wt% GNPs. Compressive and flexural strength of concrete at age 7 days were determined. The results showed that the maximum improvement in mechanical properties was observed when GNPs content was 0.20 wt%. The compressive and flexural strength were improved by up to 17.5% and 8.6%, respectively. When GNP dispersions were prepared by the combination of a drill and an ultrasonic probe, mechanical properties experienced maximum improvement.

Keywords: Concrete, dispersion techniques, graphene nanoplatelets, mechanical properties, mixing methods.

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Authors: Nagi Mekhiel

Abstract:

Many applications use vector operations by applying single instruction to multiple data that map to different locations in conventional memory. Transferring data from memory is limited by access latency and bandwidth affecting the performance gain of vector processing. We present a memory system that makes all of its content available to processors in time so that processors need not to access the memory, we force each location to be available to all processors at a specific time. The data move in different orbits to become available to other processors in higher orbits at different time. We use this memory to apply parallel vector operations to data streams at first orbit level. Data processed in the first level move to upper orbit one data element at a time, allowing a processor in that orbit to apply another vector operation to deal with serial code limitations inherited in all parallel applications and interleaved it with lower level vector operations.

Keywords: Memory organization, parallel processors, serial code, vector processing.

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Authors: Gozel Judakova, Markus Bause

Abstract:

We present and analyze reliable numerical techniques for simulating complex flow and transport phenomena related to natural gas transportation in pipelines. Such kind of problems are of high interest in the field of petroleum and environmental engineering. Modeling and understanding natural gas flow and transformation processes during transportation is important for the sake of physical realism and the design and operation of pipeline systems. In our approach a two fluid flow model based on a system of coupled hyperbolic conservation laws is considered for describing natural gas flow undergoing hydratization. The accurate numerical approximation of two-phase gas flow remains subject of strong interest in the scientific community. Such hyperbolic problems are characterized by solutions with steep gradients or discontinuities, and their approximation by standard finite element techniques typically gives rise to spurious oscillations and numerical artefacts. Recently, stabilized and discontinuous Galerkin finite element techniques have attracted researchers’ interest. They are highly adapted to the hyperbolic nature of our two-phase flow model. In the presentation a streamline upwind Petrov-Galerkin approach and a discontinuous Galerkin finite element method for the numerical approximation of our flow model of two coupled systems of Euler equations are presented. Then the efficiency and reliability of stabilized continuous and discontinous finite element methods for the approximation is carefully analyzed and the potential of the either classes of numerical schemes is investigated. In particular, standard benchmark problems of two-phase flow like the shock tube problem are used for the comparative numerical study.

Keywords: Discontinuous Galerkin method, Euler system, inviscid two-fluid model, streamline upwind Petrov-Galerkin method, two-phase flow.

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Authors: M. V. Sudhamani, C. R. Venugopal

Abstract:

This paper deals with the application for contentbased image retrieval to extract color feature from natural images stored in the image database by segmenting the image through clustering. We employ a class of nonparametric techniques in which the data points are regarded as samples from an unknown probability density. Explicit computation of the density is avoided by using the mean shift procedure, a robust clustering technique, which does not require prior knowledge of the number of clusters, and does not constrain the shape of the clusters. A non-parametric technique for the recovery of significant image features is presented and segmentation module is developed using the mean shift algorithm to segment each image. In these algorithms, the only user set parameter is the resolution of the analysis and either gray level or color images are accepted as inputs. Extensive experimental results illustrate excellent performance.

Keywords: Segmentation, Clustering, Image Retrieval, Features.

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Authors: P. Ashok, G. M. Kadhar Nawaz

Abstract:

Rough set theory is used to handle uncertainty and incomplete information by applying two accurate sets, Lower approximation and Upper approximation. In this paper, the rough clustering algorithms are improved by adopting the Similarity, Dissimilarity–Similarity and Entropy based initial centroids selection method on three different clustering algorithms namely Entropy based Rough K-Means (ERKM), Similarity based Rough K-Means (SRKM) and Dissimilarity-Similarity based Rough K-Means (DSRKM) were developed and executed by yeast dataset. The rough clustering algorithms are validated by cluster validity indexes namely Rand and Adjusted Rand indexes. An experimental result shows that the ERKM clustering algorithm perform effectively and delivers better results than other clustering methods. Outlier detection is an important task in data mining and very much different from the rest of the objects in the clusters. Entropy based Rough Outlier Factor (EROF) method is seemly to detect outlier effectively for yeast dataset. In rough K-Means method, by tuning the epsilon (ᶓ) value from 0.8 to 1.08 can detect outliers on boundary region and the RKM algorithm delivers better results, when choosing the value of epsilon (ᶓ) in the specified range. An experimental result shows that the EROF method on clustering algorithm performed very well and suitable for detecting outlier effectively for all datasets. Further, experimental readings show that the ERKM clustering method outperformed the other methods.

Keywords: Clustering, Entropy, Outlier, Rough K-Means, validity index.

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Authors: Herath MPC Jayaweera, Samer Hanoun

Abstract:

Path planning for on-time obstacle avoidance is an essential and challenging task that enables drones to achieve safe operation in any application domain. The level of challenge increases significantly on the obstacle avoidance technique when the drone is following a ground mobile entity (GME). This is mainly due to the change in direction and magnitude of the GMEs velocity in dynamic and unstructured environments. Force field techniques are the most widely used obstacle avoidance methods due to their simplicity, ease of use and potential to be adopted for three-dimensional dynamic environments. However, the existing force field obstacle avoidance techniques suffer many drawbacks including their tendency to generate longer routes when the obstacles are sideways of the drones route, poor ability to find the shortest flyable path, propensity to fall into local minima, producing a non-smooth path, and high failure rate in the presence of symmetrical obstacles. To overcome these shortcomings, this paper proposes an on-time three-dimensional obstacle avoidance method for drones to effectively and efficiently avoid dynamic and static obstacles in unknown environments while pursuing a GME. This on-time obstacle avoidance technique generates velocity waypoints for its obstacle-free and efficient path based on the shape of the encountered obstacles. This method can be utilize on most types of drones that have basic distance measurement sensors and autopilot supported flight controllers. The proposed obstacle avoidance technique is validated and evaluated against existing force field methods for different simulation scenarios in Gazebo and ROS supported PX4-SITL. The simulation results show that the proposed obstacle avoidance technique outperforms the existing force field techniques and is better suited for real-world applications.

Keywords: Drones, force field methods, obstacle avoidance, path planning.

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Authors: Sui Xin, Li Chunsheng, Tian Di

Abstract:

Short message integrated distributed monitoring systems (SM-DMS) are growing rapidly in wireless communication applications in various areas, such as electromagnetic field (EMF) management, wastewater monitoring, and air pollution supervision, etc. However, delay in short messages often makes the data embedded in SM-DMS transmit unreliably. Moreover, there are few regulations dealing with this problem in SMS transmission protocols. In this study, based on the analysis of the command and data requirements in the SM-DMS, we developed a processing model for the control center to solve the delay problem in data transmission. Three components of the model: the data transmission protocol, the receiving buffer pool method, and the timer mechanism were described in detail. Discussions on adjusting the threshold parameter in the timer mechanism were presented for the adaptive performance during the runtime of the SM-DMS. This model optimized the data transmission reliability in SM-DMS, and provided a supplement to the data transmission reliability protocols at the application level.

Keywords: Delay, SMS, reliability, distributed monitoringsystem (DMS), wireless communication.

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Authors: H. Bouhamed, A. Rebai, T. Lecroq, M. Jaoua

Abstract:

The objective of our work is to develop a new approach for discovering knowledge from a large mass of data, the result of applying this approach will be an expert system that will serve as diagnostic tools of a phenomenon related to a huge information system. We first recall the general problem of learning Bayesian network structure from data and suggest a solution for optimizing the complexity by using organizational and optimization methods of data. Afterward we proposed a new heuristic of learning a Multi-Entities Bayesian Networks structures. We have applied our approach to biological facts concerning hereditary complex illnesses where the literatures in biology identify the responsible variables for those diseases. Finally we conclude on the limits arched by this work.

Keywords: Data-organization, data-optimization, automatic knowledge discovery, Multi-Entities Bayesian networks, score merging.

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Authors: Mohammad Khosravi, Ali Kiani, Behroz Dastar, Parvin Showrang

Abstract:

Review was carried out on important anti-nutritional compounds of livestock and poultry foods and the effect of radiation usage. Nowadays, with advancement in technology, different methods have been considered for the optimum usage of nutrients in livestock and poultry foods. Steaming, extruding, pelleting, and the use of chemicals are the most common and popular methods in food processing. Use of radiation in food processing researches in the livestock and poultry industry is currently highly regarded. Ionizing (electrons, gamma) and non-ionizing beams (microwave and infrared) are the most useable rays in animal food processing. In recent researches, these beams have been used to remove and reduce the anti-nutritional factors and microbial contamination and improve the digestibility of nutrients in poultry and livestock food. The evidence presented will help researchers to recognize techniques of relevance to them. Simplification of some of these techniques, especially in developing countries, must be addressed so that they can be used more widely.

Keywords: Antitrypsin, gamma anti-nutritional components, phytic acid, radiation.

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Authors: Dhinu Johnson, Gurdip Singh

Abstract:

Sensor network applications are often data centric and involve collecting data from a set of sensor nodes to be delivered to various consumers. Typically, nodes in a sensor network are resource-constrained, and hence the algorithms operating in these networks must be efficient. There may be several algorithms available implementing the same service, and efficient considerations may require a sensor application to choose the best suited algorithm. In this paper, we present a systematic evaluation of a set of algorithms implementing the data gathering service. We propose a modular infrastructure for implementing such algorithms in TOSSIM with separate configurable modules for various tasks such as interest propagation, data propagation, aggregation, and path maintenance. By appropriately configuring these modules, we propose a number of data gathering algorithms, each of which incorporates a different set of heuristics for optimizing performance. We have performed comprehensive experiments to evaluate the effectiveness of these heuristics, and we present results from our experimentation efforts.

Keywords: Data Centric Protocols, Shortest Paths, Sensor networks, Message passing systems.

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Authors: M. A. Hasan, M. R. Islam, R. A. Tarefder

Abstract:

This study investigates how the site specific traffic data differs from the Mechanistic Empirical Pavement Design Software default values. Two Weigh-in-Motion (WIM) stations were installed in Interstate-40 (I-40) and Interstate-25 (I-25) to developed site specific data. A computer program named WIM Data Analysis Software (WIMDAS) was developed using Microsoft C-Sharp (.Net) for quality checking and processing of raw WIM data. A complete year data from November 2013 to October 2014 was analyzed using the developed WIM Data Analysis Program. After that, the vehicle class distribution, directional distribution, lane distribution, monthly adjustment factor, hourly distribution, axle load spectra, average number of axle per vehicle, axle spacing, lateral wander distribution, and wheelbase distribution were calculated. Then a comparative study was done between measured data and AASHTOWare default values. It was found that the measured general traffic inputs for I-40 and I-25 significantly differ from the default values.

Keywords: AASHTOWare, Traffic, Weigh-in-Motion, Axle load Distribution.

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Authors: Xiaoshu Lu, Tao Lu, Matias Remes, Martti Viljanen

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As the data-driven economy is growing faster than ever and the demand for energy is being spurred, we are facing unprecedented challenges of improving energy efficiency in data centers. Effectively maximizing energy efficiency or minimising the cooling energy demand is becoming pervasive for data centers. This paper investigates overall energy consumption and the energy efficiency of cooling system for a data center in Finland as a case study. The power, cooling and energy consumption characteristics and operation condition of facilities are examined and analysed. Potential energy and cooling saving opportunities are identified and further suggestions for improving the performance of cooling system are put forward. Results are presented as a comprehensive evaluation of both the energy performance and good practices of energy efficient cooling operations for the data center. Utilization of an energy recovery concept for cooling system is proposed. The conclusion we can draw is that even though the analysed data center demonstrated relatively high energy efficiency, based on its power usage effectiveness value, there is still a significant potential for energy saving from its cooling systems.

Keywords: Data center, case study, cooling system, energyefficiency.

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Authors: Barbara Gawda

Abstract:

The aim of this study is to show innovative techniques that describe the effectiveness of individuals diagnosed with antisocial personality disorders (ASPD). The author presents information about hate schemas regarding persons with ASPD and their understanding of the role of hate. The data of 60 prisoners with ASPD, 40 prisoners without ASPD, and 60 men without antisocial tendencies, has been analyzed. The participants were asked to describe their hate inspired by a photograph. The narrative discourse was analyzed, the three groups were compared. The results show the differences between the inmates with ASPD, those without ASPD, and the controls. The antisocial individuals describe hate as an ambivalent feeling with low emotional intensity, i.e., actors (in stories) are presented more as positives than as partners. They use different mechanisms to keep them from understanding the meaning of the emotional situation. The schema's characteristics were expressed in narratives attributed to high Psychopathy.

Keywords: Antisocial personality disorder, Emotional narratives, Hate schemas, Psychopathy

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Authors: M.Machhout, M.Zeghid, W.El hadj youssef, B.Bouallegue, A.Baganne, R.Tourki

Abstract:

Long number multiplications (n ≥ 128-bit) are a primitive in most cryptosystems. They can be performed better by using Karatsuba-Ofman technique. This algorithm is easy to parallelize on workstation network and on distributed memory, and it-s known as the practical method of choice. Multiplying long numbers using Karatsuba-Ofman algorithm is fast but is highly recursive. In this paper, we propose different designs of implementing Karatsuba-Ofman multiplier. A mixture of sequential and combinational system design techniques involving pipelining is applied to our proposed designs. Multiplying large numbers can be adapted flexibly to time, area and power criteria. Computationally and occupation constrained in embedded systems such as: smart cards, mobile phones..., multiplication of finite field elements can be achieved more efficiently. The proposed designs are compared to other existing techniques. Mathematical models (Area (n), Delay (n)) of our proposed designs are also elaborated and evaluated on different FPGAs devices.

Keywords: finite field, Karatsuba-Ofman, long numbers, multiplication, mathematical model, recursivity.

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Authors: David B. Bracewell, Fuji Ren, Shingo Kuroiwa

Abstract:

We present a method to create special domain collections from news sites. The method only requires a single sample article as a seed. No prior corpus statistics are needed and the method is applicable to multiple languages. We examine various similarity measures and the creation of document collections for English and Japanese. The main contributions are as follows. First, the algorithm can build special domain collections from as little as one sample document. Second, unlike other algorithms it does not require a second “general" corpus to compute statistics. Third, in our testing the algorithm outperformed others in creating collections made up of highly relevant articles.

Keywords: Information Retrieval, News, Special DomainCollections,

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Authors: Amir Hossein Daei Sorkhabi, Farid Vakili Tahami

Abstract:

In this paper, creep constitutive equations of base (Parent) and weld materials of the weldment for cold-drawn 304L stainless steel have been obtained experimentally. For this purpose, test samples have been generated from cold drawn bars and weld material according to the ASTM standard. The creep behavior and properties have been examined for these materials by conducting uniaxial creep tests. Constant temperatures and constant load uni-axial creep tests have been carried out at two high temperatures, 680 and 720 oC, subjected to constant loads, which produce initial stresses ranging from 240 to 360 MPa. The experimental data have been used to obtain the creep constitutive parameters using numerical optimization techniques.

Keywords: Creep, Constitutive equation, Cold-drawn 304L stainless steel, Weld, Base material

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Authors: A. M. Omoare, E. O. Fakoya, O. E. Fapojuwo, W. O. Oyediran

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Awareness of value addition of sweet potato has received comparatively little attention in Nigeria despite its potential to reduce perishability and enhanced utilization of the crop in diverse products forms. This study assessed the awareness of value addition of sweet potato in Osun State, Nigeria. Multi-stage random sampling technique was used to select 120 respondents for the study. Data obtained were analyzed using descriptive statistics and multiple regression analysis. Findings showed that most (75.00%) of the respondents were male with mean age of 42.10 years and 96.70% of the respondents had formal education. The mean farm size was 2.30 hectares. Majority (75.00%) of the respondents had more than 10 years farming experience. Awareness of value addition of sweet potato was very low among the respondents. It was recommended that sweet potato farmers should be empowered through effective and efficient extension training on the use of modern processing techniques in order to enhance value addition of sweet potato. 

Keywords: Awareness, value addition, sweet potato, perishability.

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Authors: K. P. K. S. Lahirushan, W. G. V. Gunasekara

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Main purpose of this study is to identify the impact of government expenditure on economic growth in Asian Countries. Consequently, main objective is to analyze whether government expenditure causes economic growth in Asian countries vice versa and then scrutinizing long-run equilibrium relationship exists between them. The study completely based on secondary data. The methodology being quantitative that includes econometrical techniques of cointegration, panel fixed effects model and granger causality in the context of panel data of Asian countries; Singapore, Malaysia, Thailand, South Korea, Japan, China, Sri Lanka, India and Bhutan with 44 observations in each country, totaling to 396 observations from 1970 to 2013. The model used is the random effects panel OLS model. As with the above methodology, the study found the fascinating outcome. At first, empirical findings exhibit a momentous positive impact of government expenditure on Gross Domestic Production in Asian region. Secondly, government expenditure and economic growth indicate a long-run relationship in Asian countries. In conclusion, there is a unidirectional causality from economic growth to government expenditure and government expenditure to economic growth in Asian countries. Hence the study is validated that it is in line with the Keynesian theory and Wagner’s law as well. Consequently, it can be concluded that role of government would play a vital role in economic growth of Asian Countries. However; if government expenditure did not figure out with the economy’s needs it might be considerably inspiration the economy in a negative way so that society bears the costs.

Keywords: Asian Countries, Government Expenditure, Keynesian theory, Wagner’s theory, Random effects panel OLS model.

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Authors: Hwan-Jun Choi, Sung-Bok Choi, Hyoung-Kyu Song

Abstract:

Recently, among the MIMO-OFDM detection techniques, a lot of papers suggested V-BLAST scheme which can achieve high data rate. Therefore, the signal detection of MIMO-OFDM system is important issue. In this paper, efficient iterative V-BLAST detection technique is proposed in wireless communication system. The proposed scheme adjusts the number of candidate symbol and iterative scheme based on channel state. According to the simulation result, the proposed scheme has better BER performance than conventional schemes and similar BER performance of the QRD-M with iterative scheme. Moreover complexity of proposed scheme has 50.6% less than complexity of QRD-M detection with iterative scheme. Therefore the proposed detection scheme can be efficiently used in wireless communication.

Keywords: MIMO-OFDM, V-BLAST, QR-decomposition, QRD-M, DFE, Iterative scheme, Channel condition.

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Authors: Nilson Barbieri, Bruno Matos Martins, Gabriel de Sant'Anna Vitor Barbieri

Abstract:

In more complex systems, such as automotive gearbox, a rigorous treatment of the data is necessary because there are several moving parts (gears, bearings, shafts, etc.), and in this way, there are several possible sources of errors and also noise. The basic objective of this work is the detection of damage in automotive gearbox. The detection methods used are the wavelet method, the bispectrum; advanced filtering techniques (selective filtering) of vibrational signals and mathematical morphology. Gearbox vibration tests were performed (gearboxes in good condition and with defects) of a production line of a large vehicle assembler. The vibration signals are obtained using five accelerometers in different positions of the sample. The results obtained using the kurtosis, bispectrum, wavelet and mathematical morphology showed that it is possible to identify the existence of defects in automotive gearboxes.

Keywords: Automotive gearbox, mathematical morphology, wavelet, bispectrum.

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi

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In this paper, Bayesian online inference in models of data series are constructed by change-points algorithm, which separated the observed time series into independent series and study the change and variation of the regime of the data with related statistical characteristics. variation of statistical characteristics of time series data often represent separated phenomena in the some dynamical system, like a change in state of brain dynamical reflected in EEG signal data measurement or a change in important regime of data in many dynamical system. In this paper, prediction algorithm for studying change point location in some time series data is simulated. It is verified that pattern of proposed distribution of data has important factor on simpler and smother fluctuation of hazard rate parameter and also for better identification of change point locations. Finally, the conditions of how the time series distribution effect on factors in this approach are explained and validated with different time series databases for some dynamical system.

Keywords: Time series, fluctuation in statistical characteristics, optimal learning.

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Authors: D. Vijayakumar, R. K. Nema

Abstract:

Over Current Relays (OCRs) and Directional Over Current Relays (DOCRs) are widely used for the radial protection and ring sub transmission protection systems and for distribution systems. All previous work formulates the DOCR coordination problem either as a Non-Linear Programming (NLP) for TDS and Ip or as a Linear Programming (LP) for TDS using recently a social behavior (Particle Swarm Optimization techniques) introduced to the work. In this paper, a Modified Particle Swarm Optimization (MPSO) technique is discussed for the optimal settings of DOCRs in power systems as a Non-Linear Programming problem for finding Ip values of the relays and for finding the TDS setting as a linear programming problem. The calculation of the Time Dial Setting (TDS) and the pickup current (Ip) setting of the relays is the core of the coordination study. PSO technique is considered as realistic and powerful solution schemes to obtain the global or quasi global optimum in optimization problem.

Keywords: Directional over current relays, Optimization techniques, Particle swarm optimization, Power system protection.

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Authors: K. Ben Khalifa, M.H. Bédoui, M. Dogui, F. Alexandre

Abstract:

Several studies have been carried out, using various techniques, including neural networks, to discriminate vigilance states in humans from electroencephalographic (EEG) signals, but we are still far from results satisfactorily useable results. The work presented in this paper aims at improving this status with regards to 2 aspects. Firstly, we introduce an original procedure made of the association of two neural networks, a self organizing map (SOM) and a learning vector quantization (LVQ), that allows to automatically detect artefacted states and to separate the different levels of vigilance which is a major breakthrough in the field of vigilance. Lastly and more importantly, our study has been oriented toward real-worked situation and the resulting model can be easily implemented as a wearable device. It benefits from restricted computational and memory requirements and data access is very limited in time. Furthermore, some ongoing works demonstrate that this work should shortly results in the design and conception of a non invasive electronic wearable device.

Keywords: Electroencephalogram interpretation, artificialneural networks, vigilance states, hardware implementation

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Authors: Seo Young Kim, Tai Myong Choi

Abstract:

The main goal of microarray experiments is to quantify the expression of every object on a slide as precisely as possible, with a further goal of clustering the objects. Recently, many studies have discussed clustering issues involving similar patterns of gene expression. This paper presents an application of fuzzy-type methods for clustering DNA microarray data that can be applied to typical comparisons. Clustering and analyses were performed on microarray and simulated data. The results show that fuzzy-possibility c-means clustering substantially improves the findings obtained by others.

Keywords: Clustering, microarray data, Fuzzy-type clustering, Validation

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Authors: Stephen V. Crowder, Jonathan W. Lane

Abstract:

With short production development times, there is an increased need to demonstrate product reliability relatively quickly with minimal testing. In such cases there may be few if any observed failures. Thus it may be difficult to assess reliability using the traditional reliability test plans that measure only time (or cycles) to failure. For many components, degradation measures will contain important information about performance and reliability. These measures can be used to design a minimal test plan, in terms of number of units placed on test and duration of the test, necessary to demonstrate a reliability goal. In this work we present a case study involving an electronic component subject to degradation. The data, consisting of 42 degradation paths of cycles to failure, are first used to estimate a reliability function. Bootstrapping techniques are then used to perform power studies and develop a minimal reliability test plan for future production of this component. 

Keywords: Degradation Measure, Time to Failure Distribution, Bootstrap.

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Authors: Alicia Heraz, Claude Frasson

Abstract:

This paper investigates how the use of machine learning techniques can significantly predict the three major dimensions of learner-s emotions (pleasure, arousal and dominance) from brainwaves. This study has adopted an experimentation in which participants were exposed to a set of pictures from the International Affective Picture System (IAPS) while their electrical brain activity was recorded with an electroencephalogram (EEG). The pictures were already rated in a previous study via the affective rating system Self-Assessment Manikin (SAM) to assess the three dimensions of pleasure, arousal, and dominance. For each picture, we took the mean of these values for all subjects used in this previous study and associated them to the recorded brainwaves of the participants in our study. Correlation and regression analyses confirmed the hypothesis that brainwave measures could significantly predict emotional dimensions. This can be very useful in the case of impassive, taciturn or disabled learners. Standard classification techniques were used to assess the reliability of the automatic detection of learners- three major dimensions from the brainwaves. We discuss the results and the pertinence of such a method to assess learner-s emotions and integrate it into a brainwavesensing Intelligent Tutoring System.

Keywords: Algorithms, brainwaves, emotional dimensions, performance.

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Authors: Adtha Lawanna

Abstract:

Software maintenance is one of the essential processes of Software-Development Life Cycle. The main philosophies of retaining software concern the improvement of errors, the revision of codes, the inhibition of future errors, and the development in piece and capacity. While the adjustment has been employing, the software structure has to be retested to an upsurge a level of assurance that it will be prepared due to the requirements. According to this state, the test cases must be considered for challenging the revised modules and the whole software. A concept of resolving this problem is ongoing by regression test selection such as the retest-all selections, random/ad-hoc selection and the safe regression test selection. Particularly, the traditional techniques concern a mapping between the test cases in a test suite and the lines of code it executes. However, there are not only the lines of code as one of the requirements that can affect the size of test suite but including the number of functions and faulty versions. Therefore, a model for test case selection is developed to cover those three requirements by the integral technique which can produce the smaller size of the test cases when compared with the traditional regression selection techniques.

Keywords: Software maintenance, regression test selection, test case.

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Authors: Adtha Lawanna

Abstract:

Software maintenance is one of the essential processes of Software-Development Life Cycle. The main philosophies of retaining software concern the improvement of errors, the revision of codes, the inhibition of future errors, and the development in piece and capacity. While the adjustment has been employing, the software structure has to be retested to an upsurge a level of assurance that it will be prepared due to the requirements. According to this state, the test cases must be considered for challenging the revised modules and the whole software. A concept of resolving this problem is ongoing by regression test selection such as the retest-all selections, random/ad-hoc selection and the safe regression test selection. Particularly, the traditional techniques concern a mapping between the test cases in a test suite and the lines of code it executes. However, there are not only the lines of code as one of the requirements that can affect the size of test suite but including the number of functions and faulty versions. Therefore, a model for test case selection is developed to cover those three requirements by the integral technique which can produce the smaller size of the test cases when compared with the traditional regression selection techniques.

Keywords: Software maintenance, regression test selection, test case.

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Authors: Bireswar Paul, Amitava Datta

Abstract:

Indoor air environment is a big concern in the last few decades in the developing countries, with increased focus on monitoring the air quality. In this work, an experimental study has been conducted to establish the existence of carbon nanoparticles below the size range of 10 nm in the non-sooting zone of a LPG/air partially premixed flame. Mainly, four optical techniques, UV absorption spectroscopy, fluorescence spectroscopy, dynamic light scattering and TEM have been used to characterize and measure the size of carbon nanoparticles in the sampled materials collected from the inner surface of the flame front. The existence of the carbon nanoparticles in the sampled material has been confirmed with the typical nature of the absorption and fluorescence spectra already reported in the literature. The band gap energy shows that the particles are made up of three to six aromatic rings. The size measurement by DLS technique also shows that the particles below the size range of 10 nm. The results of DLS are also corroborated by the TEM image of the same material. 

Keywords: Indoor air, carbon nanoparticles, LPG, partially premixed flame, optical techniques.

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Authors: Mansoor Momeni, Mahmoud Dehghan Nayeri, Ali Faal Ghayoumi, Hoda Ghorbani

Abstract:

This research is aimed to describe the application of robust regression and its advantages over the least square regression method in analyzing financial data. To do this, relationship between earning per share, book value of equity per share and share price as price model and earning per share, annual change of earning per share and return of stock as return model is discussed using both robust and least square regressions, and finally the outcomes are compared. Comparing the results from the robust regression and the least square regression shows that the former can provide the possibility of a better and more realistic analysis owing to eliminating or reducing the contribution of outliers and influential data. Therefore, robust regression is recommended for getting more precise results in financial data analysis.

Keywords: Financial data analysis, Influential data, Outliers, Robust regression.

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