Search results for: Data EnvelopmentAnalysis

Authors: Vasanta Duggirala, Y. Viswanatha Naidu

Abstract:

This paper makes a contribution to the on-going debate on conceptualization and lexicalization of cutting and breaking (C&B) verbs by discussing data from Telugu, a language of India belonging to the Dravidian family. Five Telugu native speakers- verbalizations of agentive actions depicted in 43 short video-clips were analyzed. It was noted that verbalization of C&B events in Telugu requires formal units such as simple lexical verbs, explicator compound verbs, and other complex verb forms. The properties of the objects involved, the kind of instruments used, and the manner of action had differential influence on the lexicalization patterns. Further, it was noted that all the complex verb forms encode 'result' and 'cause' sub-events in that order. Due to the polysemy associated with some of the verb forms, our data does not support the straightforward bipartition of this semantic domain.

Keywords: Cluster analysis, Cutting and breaking events, Polysemy, Semantic extension, Telugu.

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Authors: K. Anitha Sheela, J. Tarun Kumar

Abstract:

HSDPA is a new feature which is introduced in Release-5 specifications of the 3GPP WCDMA/UTRA standard to realize higher speed data rate together with lower round-trip times. Moreover, the HSDPA concept offers outstanding improvement of packet throughput and also significantly reduces the packet call transfer delay as compared to Release -99 DSCH. Till now the HSDPA system uses turbo coding which is the best coding technique to achieve the Shannon limit. However, the main drawbacks of turbo coding are high decoding complexity and high latency which makes it unsuitable for some applications like satellite communications, since the transmission distance itself introduces latency due to limited speed of light. Hence in this paper it is proposed to use LDPC coding in place of Turbo coding for HSDPA system which decreases the latency and decoding complexity. But LDPC coding increases the Encoding complexity. Though the complexity of transmitter increases at NodeB, the End user is at an advantage in terms of receiver complexity and Bit- error rate. In this paper LDPC Encoder is implemented using “sparse parity check matrix" H to generate a codeword at Encoder and “Belief Propagation algorithm "for LDPC decoding .Simulation results shows that in LDPC coding the BER suddenly drops as the number of iterations increase with a small increase in Eb/No. Which is not possible in Turbo coding. Also same BER was achieved using less number of iterations and hence the latency and receiver complexity has decreased for LDPC coding. HSDPA increases the downlink data rate within a cell to a theoretical maximum of 14Mbps, with 2Mbps on the uplink. The changes that HSDPA enables includes better quality, more reliable and more robust data services. In other words, while realistic data rates are only a few Mbps, the actual quality and number of users achieved will improve significantly.

Keywords: AMC, HSDPA, LDPC, WCDMA, 3GPP.

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Authors: Meseret Teshome

Abstract:

This study has the objective of assessing the effect of kaizen (5S, Muda elimination and Quality Control Circle (QCC) on employees’ affective attitude (job satisfaction, commitment and job stress) in Kombolcha Textile Share Company. A conceptual model was developed to describe the relationship between Kaizen and Employees’ Affective Attitude (EAA) factors. The three factors of Employee Affective Attitude were measured using questionnaire derived from other validated questionnaire. In the data collection to conduct this study; questionnaire, unstructured interview, written documents and direct observations are used. To analyze the data, SPSS and Microsoft Excel were used. In addition, the internal consistency of similar items in the questionnaire instrument was measured for their equivalence by using the cronbach’s alpha test. In this study, the effect of 5S, Muda elimination and QCC on job satisfaction, commitment and job stress in Kombolcha Textile Share Company is assessed and factors that reduce employees’ job satisfaction with respect to kaizen implementation are identified. The total averages of means from the questionnaire are 3.1 for job satisfaction, 4.31 for job commitment and 4.2 for job stress. And results from interview and secondary data show that kaizen implementation have effect on EAA. In general, based on the thesis results it was concluded that kaizen (5S, muda elimination and QCC) have positive effect for improving EAA factors at KTSC. Finally, recommendations for improvement are given based on the results.

Keywords: Kaizen, job satisfaction, job commitment, job stress.

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Authors: Khaled M. EL-Naggar

Abstract:

This paper presents a new method for estimating the mean curve of impulse voltage waveforms that are recorded during impulse tests. In practice, these waveforms are distorted by noise, oscillations and overshoot. The problem is formulated as an estimation problem. Estimation of the current signal parameters is achieved using a fast and accurate technique. The method is based on discrete dynamic filtering algorithm (DDF). The main advantage of the proposed technique is its ability in producing the estimates in a very short time and at a very high degree of accuracy. The algorithm uses sets of digital samples of the recorded impulse waveform. The proposed technique has been tested using simulated data of practical waveforms. Effects of number of samples and data window size are studied. Results are reported and discussed.

Keywords: Digital Filtering, Estimation, Impulse wave, Stochastic filtering.

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Authors: Fereydoon Sarmadian, Ali Keshavarzi

Abstract:

Study of soil properties like field capacity (F.C.) and permanent wilting point (P.W.P.) play important roles in study of soil moisture retention curve. Although these parameters can be measured directly, their measurement is difficult and expensive. Pedotransfer functions (PTFs) provide an alternative by estimating soil parameters from more readily available soil data. In this investigation, 70 soil samples were collected from different horizons of 15 soil profiles located in the Ziaran region, Qazvin province, Iran. The data set was divided into two subsets for calibration (80%) and testing (20%) of the models and their normality were tested by Kolmogorov-Smirnov method. Both multivariate regression and artificial neural network (ANN) techniques were employed to develop the appropriate PTFs for predicting soil parameters using easily measurable characteristics of clay, silt, O.C, S.P, B.D and CaCO3. The performance of the multivariate regression and ANN models was evaluated using an independent test data set. In order to evaluate the models, root mean square error (RMSE) and R2 were used. The comparison of RSME for two mentioned models showed that the ANN model gives better estimates of F.C and P.W.P than the multivariate regression model. The value of RMSE and R2 derived by ANN model for F.C and P.W.P were (2.35, 0.77) and (2.83, 0.72), respectively. The corresponding values for multivariate regression model were (4.46, 0.68) and (5.21, 0.64), respectively. Results showed that ANN with five neurons in hidden layer had better performance in predicting soil properties than multivariate regression.

Keywords: Artificial neural network, Field capacity, Permanentwilting point, Pedotransfer functions.

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Authors: Safaa R. Amer

Abstract:

Missing data yields many analysis challenges. In case of complex survey design, in addition to dealing with missing data, researchers need to account for the sampling design to achieve useful inferences. Methods for incorporating sampling weights in neural network imputation were investigated to account for complex survey designs. An estimate of variance to account for the imputation uncertainty as well as the sampling design using neural networks will be provided. A simulation study was conducted to compare estimation results based on complete case analysis, multiple imputation using a Markov Chain Monte Carlo, and neural network imputation. Furthermore, a public-use dataset was used as an example to illustrate neural networks imputation under a complex survey design

Keywords: Complex survey, estimate, imputation, neural networks, variance.

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Authors: Hazem M. El-Bakry, Qiangfu Zhao

Abstract:

Here, a new idea to speed up the operation of complex valued time delay neural networks is presented. The whole data are collected together in a long vector and then tested as a one input pattern. The proposed fast complex valued time delay neural networks uses cross correlation in the frequency domain between the tested data and the input weights of neural networks. It is proved mathematically that the number of computation steps required for the presented fast complex valued time delay neural networks is less than that needed by classical time delay neural networks. Simulation results using MATLAB confirm the theoretical computations.

Keywords: Fast Complex Valued Time Delay Neural Networks, Cross Correlation, Frequency Domain

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Authors: N.Poolsawad, C.Kambhampati, J. G. F. Cleland

Abstract:

In this paper, we investigated the characteristic of a clinical dataseton the feature selection and classification measurements which deal with missing values problem.And also posed the appropriated techniques to achieve the aim of the activity; in this research aims to find features that have high effect to mortality and mortality time frame. We quantify the complexity of a clinical dataset. According to the complexity of the dataset, we proposed the data mining processto cope their complexity; missing values, high dimensionality, and the prediction problem by using the methods of missing value replacement, feature selection, and classification.The experimental results will extend to develop the prediction model for cardiology.

Keywords: feature selection, missing values, classification, clinical dataset, heart failure.

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Authors: Duong Vannak, Mohd Shahir Liew, Guo Zheng Yew

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Data of wave height and wind speed were collected from three existing oil fields in South China Sea – offshore Peninsular Malaysia, Sarawak and Sabah regions. Extreme values and other significant data were employed for analysis. The data were recorded from 1999 until 2008. The results show that offshore structures are susceptible to unacceptable motions initiated by wind and waves with worst structural impacts caused by extreme wave heights. To protect offshore structures from damage, there is a need to quantify descriptive statistics and determine spectra envelope of wind speed and wave height, and to ascertain the frequency content of each spectrum for offshore structures in the South China Sea shallow waters using measured time series. The results indicate that the process is nonstationary; it is converted to stationary process by first differencing the time series. For descriptive statistical analysis, both wind speed and wave height have significant influence on the offshore structure during the northeast monsoon with high mean wind speed of 13.5195 knots (
= 6.3566 knots) and the high mean wave height of 2.3597 m (
= 0.8690 m). Through observation of the spectra, there is no clear dominant peak and the peaks fluctuate randomly. Each wind speed spectrum and wave height spectrum has its individual identifiable pattern. The wind speed spectrum tends to grow gradually at the lower frequency range and increasing till it doubles at the higher frequency range with the mean peak frequency range of 0.4104 Hz to 0.4721 Hz, while the wave height tends to grow drastically at the low frequency range, which then fluctuates and decreases slightly at the high frequency range with the mean peak frequency range of 0.2911 Hz to 0.3425 Hz.

Keywords: Metocean, Offshore Engineering, Time Series, Descriptive Statistics, Autospectral Density Function, Wind, Wave.

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Authors: Nahid Ghasemi, Mohammad Goodarzi, Morteza Khosravi

Abstract:

Simultaneous determination of multicomponents of phenol, resorcinol and catechol with a chemometric technique a PCranking artificial neural network (PCranking-ANN) algorithm is reported in this study. Based on the data correlation coefficient method, 3 representative PCs are selected from the scores of original UV spectral data (35 PCs) as the original input patterns for ANN to build a neural network model. The results obtained by iterating 8000 .The RMSEP for phenol, resorcinol and catechol with PCranking- ANN were 0.6680, 0.0766 and 0.1033, respectively. Calibration matrices were 0.50-21.0, 0.50-15.1 and 0.50-20.0 μg ml-1 for phenol, resorcinol and catechol, respectively. The proposed method was successfully applied for the determination of phenol, resorcinol and catechol in synthetic and water samples.

Keywords: Phenol, Resorcinol, Catechol, Principal componentrankingArtificial Neural Network, Chemometrics.

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Authors: Liming Zhang

Abstract:

This paper introduces a new instantaneous frequency computation approach
-Counting Instantaneous Frequency for a general class of signals called simple waves. The classsimple wave contains a wide range of continuous signals for which the concept instantaneous frequency has a perfect physical sense. The concept of
-Counting Instantaneous Frequency also applies to all the discrete data. For all the simple wave signals and the discrete data,
-Counting instantaneous frequency can be computed directly without signal decomposition process. The intrinsic mode functions obtained through empirical mode decomposition belongs to simple wave. So
-Counting instantaneous frequency can be used together with empirical mode decomposition.

Keywords: Instantaneous frequency, empirical mode decomposition, intrinsic mode function.

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Authors: Attila Vámosi, Tamás Mankovits, Dávid Huri, Imre Kocsis, Tamás Szabó

Abstract:

Non-linear FEM calculations are indispensable when important technical information like operating performance of a rubber component is desired. For example rubber bumpers built into air-spring structures may undergo large deformations under load, which in itself shows non-linear behavior. The changing contact range between the parts and the incompressibility of the rubber increases this non-linear behavior further. The material characterization of an elastomeric component is also a demanding engineering task. The shape optimization problem of rubber parts led to the study of FEM based calculation processes. This type of problems was posed and investigated by several authors. In this paper the time demand of certain calculation methods are studied and the possibilities of time reduction is presented.

Keywords: Rubber bumper, data acquisition, finite element analysis, support vector regression.

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Authors: Dariush Jafari, Seyed Ali Jafari

Abstract:

The recommended limit for cadmium concentration in potable water is less than 0.005 mg/L. A continuous biosorption process using indigenous red seaweed, Gracilaria corticata, was performed to remove cadmium from the potable water. The process was conducted under fixed conditions and the breakthrough curves were achieved for three consecutive sorption-desorption cycles. A modeling based on Artificial Neural Network (ANN) was employed to fit the experimental breakthrough data. In addition, a simplified semi empirical model, Thomas, was employed for this purpose. It was found that ANN well described the experimental data (R2>0.99) while the Thomas prediction were a bit less successful with R2>0.97. The adjusted design parameters using the nonlinear form of Thomas model was in a good agreement with the experimentally obtained ones. The results approve the capability of ANN to predict the cadmium concentration in potable water.

Keywords: ANN, biosorption, cadmium, packed-bed, potable water.

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Authors: Khin Fai Chen, Jee-Hou Ho, Eng Hwa Yap

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An Acoustic Micro-Energy Harvester (AMEH) is developed to convert wasted acoustical energy into useful electrical energy. AMEH is mathematically modeled using Lumped Element Modelling (LEM) and Euler-Bernoulli beam (EBB) modelling. An experiment is designed to validate the mathematical model and assess the feasibility of AMEH. Comparison of theoretical and experimental data on critical parameter value such as Mm, Cms, dm and Ceb showed the variances are within 1% to 6%, which is reasonably acceptable. Then, AMEH undergoes bandwidth tuning for performance optimization. The AMEH successfully produces 0.9V/(m/s^2) and 1.79μW/(m^2/s^4) at 60Hz and 400kΩ resistive load which only show variances about 7% compared to theoretical data. At 1g and 60Hz resonance frequency, the averaged power output is about 2.2mW which fulfilled a range of wireless sensors and communication peripherals power requirements. Finally, the design for AMEH is assessed, validated and deemed as a feasible design.

Keywords: Piezoelectric, acoustic, energy harvester, thermoacoustic.

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Authors: Gulnaziya Issabayeva, Mohamed Kheireddine Aroua

Abstract:

commercially produced in Malaysia granular palm shell activated carbon (PSAC) was biomodified with bacterial biomass (Bacillus subtilis) to produce a hybrid biosorbent of higher efficiency. The obtained biosorbent was evaluated in terms of adsorption capacity to remove copper and zinc metal ions from aqueous solutions. The adsorption capacity was evaluated in batch adsorption experiments where concentrations of metal ions varied from 20 to 350 mg/L. A range of pH from 3 to 6 of aqueous solutions containing metal ions was tested. Langmuir adsorption model was used to interpret the experimental data. Comparison of the adsorption data of the biomodified and original palm shell activated carbon showed higher uptake of metal ions by the hybrid biosorbent. A trend in metal ions uptake increase with the increase in the solution-s pH was observed. The surface characterization data indicated a decrease in the total surface area for the hybrid biosorbent; however the uptake of copper and zinc by it was at least equal to the original PSAC at pH 4 and 5. The highest capacity of the hybrid biosorbent was observed at pH 5 and comprised 22 mg/g and 19 mg/g for copper and zinc, respectively. The adsorption capacity at the lowest pH of 3 was significantly low. The experimental results facilitated identification of potential factors influencing the adsorption of copper and zinc onto biomodified and original palm shell activated carbon.

Keywords: Adsorption, biomodification, copper, zinc, palm shell carbon.

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Authors: A.O. Boudraa, J.C. Cexus, Z. Saidi

Abstract:

This paper introduces a new signal denoising based on the Empirical mode decomposition (EMD) framework. The method is a fully data driven approach. Noisy signal is decomposed adaptively into oscillatory components called Intrinsic mode functions (IMFs) by means of a process called sifting. The EMD denoising involves filtering or thresholding each IMF and reconstructs the estimated signal using the processed IMFs. The EMD can be combined with a filtering approach or with nonlinear transformation. In this work the Savitzky-Golay filter and shoftthresholding are investigated. For thresholding, IMF samples are shrinked or scaled below a threshold value. The standard deviation of the noise is estimated for every IMF. The threshold is derived for the Gaussian white noise. The method is tested on simulated and real data and compared with averaging, median and wavelet approaches.

Keywords: Empirical mode decomposition, Signal denoisingnonstationary process.

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Authors: M. Castellano, G. Mastronardi, A. Aprile, G. Tarricone

Abstract:

Text Mining is an important step of Knowledge Discovery process. It is used to extract hidden information from notstructured o semi-structured data. This aspect is fundamental because much of the Web information is semi-structured due to the nested structure of HTML code, much of the Web information is linked, much of the Web information is redundant. Web Text Mining helps whole knowledge mining process to mining, extraction and integration of useful data, information and knowledge from Web page contents. In this paper, we present a Web Text Mining process able to discover knowledge in a distributed and heterogeneous multiorganization environment. The Web Text Mining process is based on flexible architecture and is implemented by four steps able to examine web content and to extract useful hidden information through mining techniques. Our Web Text Mining prototype starts from the recovery of Web job offers in which, through a Text Mining process, useful information for fast classification of the same are drawn out, these information are, essentially, job offer place and skills.

Keywords: Web text mining, flexible architecture, knowledgediscovery.

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Authors: Somayeh Tourani, Alireza Behvandi

Abstract:

The peng-Robinson (PR), a cubic equation of state (EoS), is extended to polymers by using a single set of energy (A1, A2, A3) and co-volume (b) parameters per polymer fitted to experimental volume data. Excellent results for the volumetric behavior of the 11 polymer up to 2000 bar pressure are obtained. The EoS is applied to the correlation and prediction of Henry constants in polymer solutions comprising three polymer and many nonpolar and polar solvents, including supercritical gases. The correlation achieved with two adjustable parameter is satisfactory compared with the experimental data. As a result, the present work provides a simple and useful model for the prediction of Henry's constant for polymer containing systems including those containing polar, nonpolar and supercritical fluids.

Keywords: Equation of state, Henry's constant, Peng-Robinson, polymer solution.

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Authors: Zs. Csanádi, A. Szabó Nagy, J. Szabó, J. Erdős

Abstract:

Atmospheric aerosols have several important environmental impacts and health effects in point of air quality. Monitoring the PM10-bound polycyclic aromatic hydrocarbons (PAHs) could have important environmental significance and health protection aspects. Benzo(a)pyrene (BaP) is the most relevant indicator of these PAH compounds. In Hungary, the Hungarian Air Quality Network provides air quality monitoring data for several air pollutants including BaP, but these data show only the annual mean concentrations and maximum values. Seasonal variation of BaP concentrations comparing the heating and non-heating periods could have important role and difference as well. For this reason, the main objective of this study was to assess the annual concentration and seasonal variation of BaP associated with PM10 in the ambient air of Northwestern Hungary seven different sampling sites (six urban and one rural) in the sampling period of 2008–2013. A total of 1475 PM10 aerosol samples were collected in the different sampling sites and analyzed for BaP by gas chromatography method. The BaP concentrations ranged from undetected to 8 ng/m3 with the mean value range of 0.50-0.96 ng/m3 referring to all sampling sites. Relatively higher concentrations of BaP were detected in samples collected in each sampling site in the heating seasons compared with non-heating periods. The annual mean BaP concentrations were comparable with the published data of the other Hungarian sites.

Keywords: Air quality, benzo(a)pyrene, PAHs, polycyclic aromatic hydrocarbons.

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Authors: Hrudaya Ku. Tripathy, B. K. Tripathy, Pradip K. Das

Abstract:

Knowledge Discovery in Databases (KDD) has evolved into an important and active area of research because of theoretical challenges and practical applications associated with the problem of discovering (or extracting) interesting and previously unknown knowledge from very large real-world databases. Rough Set Theory (RST) is a mathematical formalism for representing uncertainty that can be considered an extension of the classical set theory. It has been used in many different research areas, including those related to inductive machine learning and reduction of knowledge in knowledge-based systems. One important concept related to RST is that of a rough relation. In this paper we presented the current status of research on applying rough set theory to KDD, which will be helpful for handle the characteristics of real-world databases. The main aim is to show how rough set and rough set analysis can be effectively used to extract knowledge from large databases.

Keywords: Data mining, Data tables, Knowledge discovery in database (KDD), Rough sets.

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Authors: Lee P. Leon, Raymond Charles

Abstract:

This paper presents a method of evaluating the effect of aggregate angularity on hot mix asphalt (HMA) properties and its relationship to the Permanent Deformation resistance. The research concluded that aggregate particle angularity had a significant effect on the Permanent Deformation performance, and also that with an increase in coarse aggregate angularity there was an increase in the resistance of mixes to Permanent Deformation. A comparison between the measured data and predictive data of permanent deformation predictive models showed the limits of existing prediction models. The numerical analysis described the permanent deformation zones and concluded that angularity has an effect of the onset of these zones. Prediction of permanent deformation help road agencies and by extension economists and engineers determine the best approach for maintenance, rehabilitation, and new construction works of the road infrastructure.

Keywords: Aggregate angularity, asphalt concrete, permanent deformation, rutting prediction.

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Authors: Ahmad Aljaafreh

Abstract:

Safer driver behavior promoting is the main goal of this paper. It is a fact that drivers behavior is relatively safer when being monitored. Thus, in this paper, we propose a monitoring system to report specific driving event as well as the potentially aggressive events for estimation of the driving performance. Our driving monitoring system is composed of two parts. The first part is the in-vehicle embedded system which is composed of a GPS receiver, a two-axis accelerometer, radar sensor, OBD interface, and GPRS modem. The design considerations that led to this architecture is described in this paper. The second part is a web server where an adaptive hierarchical fuzzy system is proposed to classify the driving performance based on the data that is sent by the in-vehicle embedded system and the data that is provided by the geographical information system (GIS). Our system is robust, inexpensive and small enough to fit inside a vehicle without distracting the driver.

Keywords: Driving monitoring system, In-vehicle embedded system, Hierarchical fuzzy system.

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Authors: Chunling Cong, Chris.P.Tsokos

Abstract:

The objective of the present research manuscript is to perform parametric, nonparametric, and decision tree analysis to evaluate two treatments that are being used for breast cancer patients. Our study is based on utilizing real data which was initially used in “Tamoxifen with or without breast irradiation in women of 50 years of age or older with early breast cancer" [1], and the data is supplied to us by N.A. Ibrahim “Decision tree for competing risks survival probability in breast cancer study" [2]. We agree upon certain aspects of our findings with the published results. However, in this manuscript, we focus on relapse time of breast cancer patients instead of survival time and parametric analysis instead of semi-parametric decision tree analysis is applied to provide more precise recommendations of effectiveness of the two treatments with respect to reoccurrence of breast cancer.

Keywords: decision tree, breast cancer treatments, parametricanalysis, non-parametric analysis

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Authors: A. Bouzekri, H. Benmassaud

Abstract:

Aurèsregion is one of the arid and semi-arid areas that have suffered climate crises and overexploitation of natural resources they have led to significant land degradation. The use of remote sensing data allowed us to analyze the land and its spatiotemporal changes in the Aurès between 1987 and 2013, for this work, we adopted a method of analysis based on the exploitation of the images satellite Landsat TM 1987 and Landsat OLI 2013, from the supervised classification likelihood coupled with field surveys of the mission of May and September of 2013. Using ENVI EX software by the superposition of the ground cover maps from 1987 and 2013, one can extract a spatial map change of different land cover units. The results show that between 1987 and 2013 vegetation has suffered negative changes are the significant degradation of forests and steppe rangelands, and sandy soils and bare land recorded a considerable increase. The spatial change map land cover units between 1987 and 2013 allows us to understand the extensive or regressive orientation of vegetation and soil, this map shows that dense forests give his place to clear forests and steppe vegetation develops from a degraded forest vegetation and bare, sandy soils earn big steppe surfaces that explain its remarkable extension. The analysis of remote sensing data highlights the profound changes in our environment over time and quantitative monitoring of the risk of desertification.

Keywords: Aurès, Land use, remote sensing, spatiotemporal.

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Authors: J. K. Alhassan, B. Attah, S. Misra

Abstract:

Human beings have the ability to make logical decisions. Although human decision - making is often optimal, it is insufficient when huge amount of data is to be classified. Medical dataset is a vital ingredient used in predicting patient’s health condition. In other to have the best prediction, there calls for most suitable machine learning algorithms. This work compared the performance of Artificial Neural Network (ANN) and Decision Tree Algorithms (DTA) as regards to some performance metrics using diabetes data. WEKA software was used for the implementation of the algorithms. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) were the two algorithms used for ANN, while RegTree and LADTree algorithms were the DTA models used. From the results obtained, DTA performed better than ANN. The Root Mean Squared Error (RMSE) of MLP is 0.3913 that of RBF is 0.3625, that of RepTree is 0.3174 and that of LADTree is 0.3206 respectively.

Keywords: Artificial neural network, classification, decision tree, diabetes mellitus.

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Authors: Celso L. Fernando, Toshio Yoshii, Takahiro Tsubota

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Understanding the causes of a road accident and predicting their occurrence is key to prevent deaths and serious injuries from road accident events. Traditional statistical methods such as the Poisson and the Logistics regressions have been used to find the association of the traffic environmental factors with the accident occurred; recently, an artificial neural network, ANN, a computational technique that learns from historical data to make a more accurate prediction, has emerged. Although the ability to make accurate predictions, the ANN has difficulty dealing with highly unbalanced attribute patterns distribution in the training dataset; in such circumstances, the ANN treats the minority group as noise. However, in the real world data, the minority group is often the group of interest; e.g., in the road traffic accident data, the events of the accident are the group of interest. This study proposes a combination of the k-means with the ANN to improve the predictive ability of the neural network model by alleviating the effect of the unbalanced distribution of the attribute patterns in the training dataset. The results show that the proposed method improves the ability of the neural network to make a prediction on a highly unbalanced distributed attribute patterns dataset; however, on an even distributed attribute patterns dataset, the proposed method performs almost like a standard neural network. 

Keywords: Accident risks estimation, artificial neural network, deep learning, K-mean, road safety.

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Authors: X. Mercilin Raajini, R. Raja Kumar, P. Indumathi, V. Praveen

Abstract:

Many cluster based routing protocols have been proposed in the field of wireless sensor networks, in which a group of nodes are formed as clusters. A cluster head is selected from one among those nodes based on residual energy, coverage area, number of hops and that cluster-head will perform data gathering from various sensor nodes and forwards aggregated data to the base station or to a relay node (another cluster-head), which will forward the packet along with its own data packet to the base station. Here a Game Theory based Diligent Energy Utilization Algorithm (GTDEA) for routing is proposed. In GTDEA, the cluster head selection is done with the help of game theory, a decision making process, that selects a cluster-head based on three parameters such as residual energy (RE), Received Signal Strength Index (RSSI) and Packet Reception Rate (PRR). Finding a feasible path to the destination with minimum utilization of available energy improves the network lifetime and is achieved by the proposed approach. In GTDEA, the packets are forwarded to the base station using inter-cluster routing technique, which will further forward it to the base station. Simulation results reveal that GTDEA improves the network performance in terms of throughput, lifetime, and power consumption.

Keywords: Cluster head, Energy utilization, Game Theory, LEACH, Sensor network.

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Authors: M. Naserian, M. Jooshaki, M. Fotuhi-Firuzabad, M. Hossein Mohammadi Sanjani, A. Oraee

Abstract:

In recent years, the development of communication infrastructure and smart meters have facilitated the utilization of demand-side resources which can enhance stability and economic efficiency of power systems. Direct load control programs can play an important role in the utilization of demand-side resources in the residential sector. However, investments required for installing control equipment can be a limiting factor in the development of such demand response programs. Thus, selection of consumers with higher potentials is crucial to the success of a direct load control program. Heating, ventilation, and air conditioning (HVAC) systems, which due to the heat capacity of buildings feature relatively high flexibility, make up a major part of household consumption. Considering that the consumption of HVAC systems depends highly on the ambient temperature and bearing in mind the high investments required for control systems enabling direct load control demand response programs, in this paper, a solution is presented to uncover consumers with high air conditioner demand among a large number of consumers and to measure the demand response potential of such consumers. This can pave the way for estimating the investments needed for the implementation of direct load control programs for residential HVAC systems and for estimating the demand response potentials in a distribution system. In doing so, we first cluster consumers into several groups based on the correlation coefficients between hourly consumption data and hourly temperature data using K-means algorithm. Then, by applying a recent algorithm to the hourly consumption and temperature data, consumers with high air conditioner consumption are identified. Finally, demand response potential of such consumers is estimated based on the equivalent desired temperature setpoint changes.

Keywords: Data-driven analysis, demand response, direct load control, HVAC system.

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Authors: Asish O. Mathew, Lewlyn L. R. Rodrigues

Abstract:

The organizations in the knowledge economy era have recognized the importance of building knowledge assets for sustainable growth and development. In comparison to other industries, Information Technology (IT) enterprises, holds an edge in developing an effective Knowledge Management (KM) programmethanks to their in-house technological abilities. This paper tries to study the various knowledge based incentive programmes and its effect on Knowledge Sharing and Learning in the context of the Indian IT sector. A conceptual model is developed linking KM Incentives, Knowledge Sharing and Learning. A questionnaire study is conducted to collect primary data from the knowledge workers of the IT organizations located in India. The data was analysed using Structural Equation Modeling using Partial Least Square method. The results show a strong influence of knowledge management incentives on knowledge sharing and an indirect influence on learning.

Keywords: Knowledge Management, Knowledge Management Incentives, Knowledge Sharing, Learning.

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Authors: Alexandros Leontitsis, Archana P. Sangole

Abstract:

This article presents a short discussion on optimum neighborhood size selection in a spherical selforganizing feature map (SOFM). A majority of the literature on the SOFMs have addressed the issue of selecting optimal learning parameters in the case of Cartesian topology SOFMs. However, the use of a Spherical SOFM suggested that the learning aspects of Cartesian topology SOFM are not directly translated. This article presents an approach on how to estimate the neighborhood size of a spherical SOFM based on the data. It adopts the L-curve criterion, previously suggested for choosing the regularization parameter on problems of linear equations where their right-hand-side is contaminated with noise. Simulation results are presented on two artificial 4D data sets of the coupled Hénon-Ikeda map.

Keywords: Parameter estimation, self-organizing feature maps, spherical topology.

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