Search results for: logistic regression factor analysis

Authors: R. Ziaie Moayed, S. Taghvamanesh

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So far several methods by different researchers have been developed in order to calculate the bearing capacity factors of foundations and retaining walls. In this paper, the bearing capacity factor Ny (shape factor) for different types of foundation have been investigated. The formula for bearing capacity on c–φ–γ soil can still be expressed by Terzaghi’s equation except that the bearing capacity factor Ny depends on the surcharge ratio, and friction angle φ. Many empirical definitions have been used for measurement of the bearing capacity factors N

Keywords: bearing capacity, bearing capacity factor Nγ, irregular foundations, shape factor

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Authors: Raya Al Khayari, Rasha Al Jassim, Muna Al Balushi, Fatma Al Moqbali, Said El Hajjar

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This study utilizes linear regression analysis to investigate the correlation between user account passwords and the probability of civil ID exposure, offering statistical insights into civil ID security. The study employs multiple linear regression (MLR) analysis to further investigate the elements that influence consumers’ views of civil ID security. This aims to increase awareness and improve preventive measures. The results obtained from the MLR analysis provide a thorough comprehension and can guide specific educational and awareness campaigns aimed at promoting improved security procedures. In summary, the study’s results offer significant insights for improving existing security measures and developing more efficient tactics to reduce risks related to civil ID security in Oman. By identifying key factors that impact consumers’ perceptions, organizations can tailor their strategies to address vulnerabilities effectively. Additionally, the findings can inform policymakers on potential regulatory changes to enhance civil ID security in the country.

Keywords: civil-id disclosure, awareness, linear regression, multiple regression

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Authors: Suparman

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Piecewise linear regression models are very flexible models for modeling the data. If the piecewise linear regression models are matched against the data, then the parameters are generally not known. This paper studies the problem of parameter estimation of piecewise linear regression models. The method used to estimate the parameters of picewise linear regression models is Bayesian method. But the Bayes estimator can not be found analytically. To overcome these problems, the reversible jump MCMC algorithm is proposed. Reversible jump MCMC algorithm generates the Markov chain converges to the limit distribution of the posterior distribution of the parameters of picewise linear regression models. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of picewise linear regression models.

Keywords: regression, piecewise, Bayesian, reversible Jump MCMC

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Authors: Fang Yao, Minglei Chen

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City shrinkage is one of the thorny problems that many European cities have to face with nowadays. It is mainly expressed as the decrease of population in these cities. Eastern Germany is one of the pioneers of European shrinking cities with long shrinking history. Selecting one representative shrinking city Halle(Saale) in eastern Germany as research objective, collecting and investigating nearly 20 years (1993-2010) municipal data after the reunification of Germany. These data based on five dimensions, which are demographic, economic, social, spatial and environmental and total 16 eligible variables. Using Factor Analysis to dealing with these variables in order to assess the most important factors affecting shrinking Halle. The Factor Analysis shows that there are three main factors determine the shrinkage of Halle, namely demographical and economical factor, social stability factor, and city vitality factor. Three factors acts at different period of Halle’s shrinkage: from 1993 to 1997 the demographical and economical factor played an important role; from 1997 to 2004 the social stability is significant to city shrinkage; since 2005 city vitality factors determines the shrinkage of Halle. In recent years, the shrinkage in Halle mitigates that shows the sign of growing population. Thus the city Halle should focus on attaching more importance on the city vitality factor to prevent the city from shrinkage. Meanwhile, the city should possess a positive perspective that to shift the growth-oriented development to tap the potential of shrinking cities. This method is expected to apply to further research and other shrinking cities.

Keywords: demography, factor analysis, Halle, shrinking cities

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Authors: Eva Nedeliakova, Juraj Camaj, Jaroslav Masek

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This contribution is focused on the methodology for identifying levels of quality and improving quality through new logistics model in railway transport. It is oriented on the application of dynamic quality models, which represent an innovative method of evaluation quality services. Through this conception, time factor, expected, and perceived quality in each moment of the transportation process within logistics chain can be taken into account. Various models describe the improvement of the quality which emphases the time factor throughout the whole transportation logistics chain. Quality of services in railway transport can be determined by the existing level of service quality, by detecting the causes of dissatisfaction employees but also customers, to uncover strengths and weaknesses. This new logistics model is able to recognize critical processes in logistic chain. It includes service quality rating that must respect its specific properties, which are unrepeatability, impalpability, their use right at the time they are provided and particularly changeability, which is significant factor in the conditions of rail transport as well. These peculiarities influence the quality of service regarding the constantly increasing requirements and that result in new ways of finding progressive attitudes towards the service quality rating.

Keywords: logistics model, quality, railway transport

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Authors: Mohammad Karimizadehardakani, Hooman Minoonejad, Reza Rajabi, Ali Sharifnejad

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Background: Ankle sprain is one of the most important risk factor of anterior cruciate ligament (ACL) injury. Also, functional ankle instability (FAI) population has alterations in lower extremity sagittal plane biomechanics during landing task. We want to examine whether biomechanical alterations demonstrated by FAI patients are associated with the mechanism of ACL injury during high risk and sport related tasks. Methods: Sixteen basketball player with FAI and 16 non-injured control performed a single-leg cross drop landing. Knee sagittal and frontal (ATSF) was calculated. Independent t-tests, multiple linear regression, and Pearson correlation were used for analysis data. Result: Subject with FAI showed more peak ATFS, posterior ground reaction force (GRF) and less knee flexion, compared to the controls (P= 0.001, P= 0.004, P= 0.011). Knee flexion (r= −0.824, P = 0.011) and posterior GRF (r= 0.901, P = .001) were correlated with ATSF; Posterior GRF was factor that most explained the variance in ATSF (R2= 0.645; P = .001) in the FAI group. Conclusions: Result of our study showed there is a potential biomechanical relationship between the presence of FAI and risk factors associated with ACL injury mechanism.

Keywords: functional ankle instability, anterior cruciate ligament, biomechanics, risk factor

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Authors: Ishapathik Das

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The purpose of this article is to find a method of comparing designs for ordinal regression models using quantile dispersion graphs in the presence of linear predictor misspecification. The true relationship between response variable and the corresponding control variables are usually unknown. Experimenter assumes certain form of the linear predictor of the ordinal regression models. The assumed form of the linear predictor may not be correct always. Thus, the maximum likelihood estimates (MLE) of the unknown parameters of the model may be biased due to misspecification of the linear predictor. In this article, the uncertainty in the linear predictor is represented by an unknown function. An algorithm is provided to estimate the unknown function at the design points where observations are available. The unknown function is estimated at all points in the design region using multivariate parametric kriging. The comparison of the designs are based on a scalar valued function of the mean squared error of prediction (MSEP) matrix, which incorporates both variance and bias of the prediction caused by the misspecification in the linear predictor. The designs are compared using quantile dispersion graphs approach. The graphs also visually depict the robustness of the designs on the changes in the parameter values. Numerical examples are presented to illustrate the proposed methodology.

Keywords: model misspecification, multivariate kriging, multivariate logistic link, ordinal response models, quantile dispersion graphs

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Authors: Yongrong Liu, Xin Tang

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Background: Persistent atrial fibrillation is a common arrhythmia closely related to heart failure. Heart rate control is an essential strategy for treating persistent atrial fibrillation. Still, the understanding of the relationship between different heart rate control levels and the risk of heart failure rehospitalization is limited. Objective: The objective of the study is to determine the relationship between different levels of heart rate control in patients with persistent atrial fibrillation and the risk of readmission for heart failure. Methods: We conducted a retrospective dual-centre cohort study, collecting data from patients with persistent atrial fibrillation who received outpatient treatment at two tertiary hospitals in central and western China from March 2019 to March 2020. The collected data included age, gender, body mass index (BMI), medical history, and hospitalization frequency due to heart failure. Patients were divided into three groups based on their heart rate control levels: Group I with a resting heart rate of less than 80 beats per minute, Group II with a resting heart rate between 80 and 100 beats per minute, and Group III with a resting heart rate greater than 100 beats per minute. The readmission rates due to heart failure within one year after discharge were statistically analyzed using propensity score matching in a 1:1 ratio. Differences in readmission rates among the different groups were compared using one-way ANOVA. The impact of varying levels of heart rate control on the risk of readmission for heart failure was assessed using the Cox proportional hazards model. Binary logistic regression analysis was employed to control for potential confounding factors. Results: We enrolled a total of 1136 patients with persistent atrial fibrillation. The results of the one-way ANOVA showed that there were differences in readmission rates among groups exposed to different levels of heart rate control. The readmission rates due to heart failure for each group were as follows: Group I (n=432): 31 (7.17%); Group II (n=387): 11.11%; Group III (n=317): 90 (28.50%) (F=54.3, P<0.001). After performing 1:1 propensity score matching for the different groups, 223 pairs were obtained. Analysis using the Cox proportional hazards model showed that compared to Group I, the risk of readmission for Group II was 1.372 (95% CI: 1.125-1.682, P<0.001), and for Group III was 2.053 (95% CI: 1.006-5.437, P<0.001). Furthermore, binary logistic regression analysis, including variables such as digoxin, hypertension, smoking, coronary heart disease, and chronic obstructive pulmonary disease as independent variables, revealed that coronary heart disease and COPD also had a significant impact on readmission due to heart failure (p<0.001). Conclusion: The correlation between the heart rate control level of patients with persistent atrial fibrillation and the risk of heart failure rehospitalization is positive. Reasonable heart rate control may significantly reduce the risk of heart failure rehospitalization.

Keywords: heart rate control levels, heart failure rehospitalization, persistent atrial fibrillation, retrospective cohort study

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Authors: Elvira Mustikawati P.H., Iis Dewi Ratih, Dita Amelia

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Bojonegoro has proclaimed a policy of zero malnutrition. Therefore, as an effort to solve the cases of malnutrition children in Bojonegoro, this study used the approach geographically Mixed Weighted Regression (MGWR) to determine the factors that influence the percentage of malnourished children under five in which factors can be divided into locally influential factor in each district and global factors that influence throughout the district. Based on the test of goodness of fit models, R2 and AIC values in GWR models are better than MGWR models. R2 and AIC values in MGWR models are 84.37% and 14.28, while the GWR models respectively are 91.04% and -62.04. Based on the analysis with GWR models, District Sekar, Bubulan, Gondang, and Dander is a district with three predictor variables (percentage of vitamin A, the percentage of births assisted health personnel, and the percentage of clean water) that significantly influence the percentage of malnourished children under five.

Keywords: GWR, MGWR, R2, AIC

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Authors: Leonard Simoni, Ilir Alimehmeti, Ervina Shirka, Endri Hasimi, Ndricim Kallashi, Verona Beka, Suerta Kabili, Artan Goda

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Background: Diabetes Mellitus type 2, small vessel calibre, stented length of vessel, complex lesion morphology, and prior bypass surgery have resulted risk factors for In-Stent Restenosis (ISR). However, there are some contradictory results about body mass index (BMI) as a risk factor for ISR. Purpose: We want to identify clinical, lesional and procedural factors that can predict clinical ISR in our patients. Methods: Were enrolled 759 patients who underwent first-time elective PCI with Bare Metal Stents (BMS) from September 2011 to December 2013 in our Department of Cardiology and followed them for at least 1.5 years with a median of 862 days (2 years and 4 months). Only the patients re-admitted with ischemic heart disease underwent control coronary angiography but no routine angiographic control was performed. Patients were categorized in ISR and non-ISR groups and compared between them. Multivariate analysis - Binary Logistic Regression: Forward Conditional Method was used to identify independent predictive risk factors. P was considered statistically significant when <0.05. Results: ISR compared to non-ISR individuals had a significantly lower BMI (25.7±3.3 vs. 26.9±3.7, p=0.004), higher risk anatomy (LM + 3-vessel CAD) (23% vs. 14%, p=0.03), higher number of stents/person used (2.1±1.1 vs. 1.75±0.96, p=0.004), greater length of stents/person used (39.3±21.6 vs. 33.3±18.5, p=0.01), and a lower use of clopidogrel and ASA (together) (95% vs. 99%, p=0.012). They also had a higher, although not statistically significant, prevalence of Diabetes Mellitus (42% vs. 32%, p=0.072) and a greater number of treated vessels (1.36±0.5 vs. 1.26±0.5, p=0.08). In the multivariate analysis, Diabetes Mellitus type 2 and multiple stents used were independent predictors risk factors for In-Stent Restenosis, OR 1.66 [1.03-2.68], p=0.039, and OR 1.44 [1.16-1.78,] p=0.001, respectively. On the other side higher BMI and use of clopidogrel and ASA together resulted protective factors OR 0.88 [0.81-0.95], p=0.001 and OR 0.2 [0.06-0.72] p=0.013, respectively. Conclusion: Diabetes Mellitus and multiple stents are strong predictive risk factors, whereas the use of clopidogrel and ASA together are protective factors for clinical In-Stent Restenosis. Paradoxically High BMI is a protective factor for In-stent Restenosis, probably related to a larger diameter of vessels and consequently a larger diameter of stents implanted in these patients. Further studies are needed to clarify this finding.

Keywords: body mass index, diabetes mellitus, in-stent restenosis, percutaneous coronary intervention

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Authors: Hochan Seok, Woosik Jang, Seung-Heon Han

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The stagnant global construction market has intensified competition since 2008 among firms that aim to win overseas contracts. Against this backdrop, subcontractor selection is identified as one of the most critical success factors in overseas construction project. However, it is difficult to select qualified subcontractors due to the lack of evaluation standards and reliability. This study aims to identify the problems associated with existing subcontractor evaluations using a correlations analysis and a multiple regression analysis with pre-qualification and performance evaluation of 121 firms in six countries.

Keywords: subcontractor evaluation system, pre-qualification, performance evaluation, correlation analysis, multiple regression analysis

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Authors: Srinivasa Rao Gadde

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In this article, we considered a group acceptance sampling plans for exponentiated half logistic distribution when the life-test is truncated at a pre-specified time. It is assumed that the index parameter of the exponentiated half logistic distribution is known. The design parameters such as the number of groups and the acceptance number are obtained by satisfying the producer’s and consumer’s risks at the specified quality levels in terms of medians and 10th percentiles under the assumption that the termination time and the number of items in each group are pre-fixed. Finally, an example is given to illustration the methodology.

Keywords: group acceptance sampling plan, operating characteristic, consumer and producer’s risks, truncated life-test

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Authors: Ebrahim Gharleghi

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This paper aims to examine how customer knowledge management (CKM) can be implemented in Iranian Banks in practice, with the focus on the human resource (people, technology and processes) as important factors of CKM. A conceptual model of an analytical CKM strategy for CKM in this Iranian Banks is developed from the findings and literature review. This article has been based on interviews and distributing the questionnaire. Data were collected from 260 managers from bank managers. The paper finds that hypotheses were tested using student’s t-test (one-sample t-test), Pearson correlation analysis and regression analysis. Test of hypotheses revealed that human, technology and processes factors positively and significantly influenced the implementation of CKM practices. These findings tend to corroborate our conceptual model. Human factor of CKM was found to be more significantly affecting appropriate CKM implementation than others CKM factors, indicating that this factor is more important than the others aspects of CKM. On the other hand, this factor is appropriate in Iranian Banks. Process is in second part and technology is in final part. This indicates that technology infrastructures are so weak in Iranian Banks for CKM implementation. In this paper there is little or no empirical evidence investigating the amount of the execution of the CKM in Iranian Banks. This paper rectifies this imbalance by clarifying the significance human, technology and processes factors in CKM implementation.

Keywords: knowledge management, customer relationship management, customer knowledge management, integration, people, technology, process

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Authors: J. T. Manhire

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A tax authority wants to take actions it knows will foster the greatest degree of voluntary taxpayer compliance to reduce the “tax gap.” This paper suggests that even if a tax authority could attain a state of complete knowledge, there are constraints on whether and to what extent such actions would result in reducing the macro-level tax gap. These limits are not merely a consequence of finite agency resources. They are inherent in the system itself. To show that this is one possible interpretation of the tax gap data, the paper formulates known results in a different way by analyzing tax compliance as a population with a single covariate. This leads to a standard use of the logistic map to analyze the dynamics of non-compliance growth or decay over a sequence of periods. This formulation gives the same results as the tax gap studies performed over the past fifty years in the U.S. given the published margins of error. Limitations and recommendations for future work are discussed, along with some implications for tax policy.

Keywords: income tax, logistic map, tax compliance, tax law

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Authors: Chander Shekhar, Jeetendra Yadav, Shaziya Allarakha

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Hypertension has been described as an 'iceberg disease' as those who suffered are ignored and hence usually seek healthcare services at a very late stage. It is estimated that more than 2 million Indians are suffering from hypertensive heart disease that contributed to above 0.13 million deaths in 2016. The paper study aims to know the prevalence of Hypertension in India and its variation by socioeconomic backgrounds and to find out risk factors discriminating hypertension with special emphasis on consumption of tobacco and alcohol among men aged 15-54 years in India. The paper uses NFHS (2015-16) data. The paper used binary logistic regression and discriminant analysis to find significant predictors and discriminants of interest. The prevalence of hypertension was 16.5% in the study population. The results suggest that consumption of alcohol and tobacco are significant discriminant characteristics in carrying hypertension irrespective of what socioeconomic background characteristic he possesses.

Keywords: hypertention, alcohol, tobacco, discriminant

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Authors: Masahiro Ohmura, Koh Kakusho, Takeshi Okadome

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This paper presents a regression model with autocorrelated errors in which the inputs are social moods obtained by analyzing the adjectives in Twitter posts using a document topic model. The regression model predicts Dow Jones Industrial Average (DJIA) more precisely than autoregressive moving-average models.

Keywords: stock market prediction, social moods, regression model, DJIA

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Authors: Animish Jain

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This study deals with using Multiplicative Weight Update within artificial intelligence and machine learning to create models that can diagnose skin cancer using microscopic images of cancer samples. In this study, the multiplicative weight update method is used to take the predictions of multiple models to try and acquire more accurate results. Logistic Regression, Convolutional Neural Network (CNN), and Support Vector Machine Classifier (SVMC) models are employed within the Multiplicative Weight Update system. These models are trained on pictures of skin cancer from the ISIC-Archive, to look for patterns to label unseen scans as either benign or malignant. These models are utilized in a multiplicative weight update algorithm which takes into account the precision and accuracy of each model through each successive guess to apply weights to their guess. These guesses and weights are then analyzed together to try and obtain the correct predictions. The research hypothesis for this study stated that there would be a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The SVMC model had an accuracy of 77.88%. The CNN model had an accuracy of 85.30%. The Logistic Regression model had an accuracy of 79.09%. Using Multiplicative Weight Update, the algorithm received an accuracy of 72.27%. The final conclusion that was drawn was that there was a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The conclusion was made that using a CNN model would be the best option for this problem rather than a Multiplicative Weight Update system. This is due to the possibility that Multiplicative Weight Update is not effective in a binary setting where there are only two possible classifications. In a categorical setting with multiple classes and groupings, a Multiplicative Weight Update system might become more proficient as it takes into account the strengths of multiple different models to classify images into multiple categories rather than only two categories, as shown in this study. This experimentation and computer science project can help to create better algorithms and models for the future of artificial intelligence in the medical imaging field.

Keywords: artificial intelligence, machine learning, multiplicative weight update, skin cancer

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Authors: Sook Ching Yee, Angela Siew Hoong Lee

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Big data technologies have become a trend to exploit business opportunities and provide valuable business insights through the analysis of big data. However, there are still many organizations that have yet to adopt big data technologies especially small and medium organizations (SME). This study uses the technology acceptance model (TAM) to look into several constructs in the TAM and other additional constructs which are positive affect, negative affect, organizational factor and motivational factor. The conceptual model proposed in the study will be tested on the relationship and influence of positive affect, negative affect, organizational factor and motivational factor towards the intention to use big data technologies to produce an outcome. Empirical research is used in this study by conducting a survey to collect data.

Keywords: big data technologies, motivational factor, negative affect, organizational factor, positive affect, technology acceptance model (TAM)

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Authors: Al Romano, Jeryl C. Hije, Mechaela Marie O. Tabiolo

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The decline in water quality is a major factor in aquatic disease outbreaks and can lead to significant mortality among aquatic organisms. Understanding the relationship between dissolved oxygen (DO) and water temperature is crucial, as these variables directly impact the health, behavior, and survival of fish populations. This study investigated how DO levels, water temperature, and atmospheric temperature interact in Sampaloc Lake to assess the risk of fish mortality. By employing a combination of linear regression models and machine learning techniques, researchers developed predictive models to forecast DO concentrations at various depths. The results indicate that while DO levels generally decrease with depth, the predicted concentrations are sufficient to support the survival of common fish species in Sampaloc Lake during March, April, and May 2025.

Keywords: aquaculture, dissolved oxygen, water temperature, regression analysis, machine learning, fish mass mortality, early warning system

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Authors: D. P. Rajesh, H. N. Anjanayappa, P. Nayana, S. Benakappa

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The present study deals with length-weight relationship of Atropus atropos for which no information is available on this aspect from Mangalore coast. Therefore the present investigation was undertaken. Fish samples were collected from fish landing center (Mangalore) and fish market. The regression co-efficient of male was found to be lower than female. From this observation it may be opined that female gained more weight with increase in length compared to male. Data on seasonal variation in condition factor (Kn) showed that Kn values were more or less similar in both the sexes, indicating almost identical metabolic activity. Gonadal development and high feeding intensity are the factors which influenced the condition factor. The seasonal fluctuations in the relative condition factor of both the sexes could be attributed to the sexual cycle, food intake and environmental factors. From the present study, it can be inferred that the variation in the condition of Atropus atropos was due to feeding activity and gonadal maturity.

Keywords: Atropus atropos, length-weight relationship, Mangalore coast, relative condition factor, Kn

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Authors: K. E. Daryani, H. Mohamad

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Uncertainties in the geo-structure analysis and design have a significant impact on the safety of slopes. Traditionally, uncertainties in the geotechnical design are addressed by incorporating a conservative factor of safety in the analytical model. In this paper, a risk-based approach is adopted to assess the influence of the geotechnical variable uncertainties on the stability of infinite slopes in cohesionless soils using the “partial factor of safety on shear strength” approach as stated in Eurocode 7. Analyses conducted using Monte Carlo simulation show that the same partial factor can have very different levels of risk depending on the degree of uncertainty of the mean values of the soil friction angle and void ratio.

Keywords: Safety, Probability of Failure, Reliability, Infinite Slopes, Sand.

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Authors: Chaudhuri Manoj Kumar Swain, Susmita Das

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This paper explores a detailed procedure of predicting a path loss (PL) model and its application in estimating the coverage probability in a WiMAX network. For this a hybrid approach is followed in predicting an empirical PL model of a 2.65 GHz WiMAX network deployed in a suburban environment. Data collection, statistical analysis, and regression analysis are the phases of operations incorporated in this approach and the importance of each of these phases has been discussed properly. The procedure of collecting data such as received signal strength indicator (RSSI) through experimental set up is demonstrated. From the collected data set, empirical PL and RSSI models are predicted with regression technique. Furthermore, with the aid of the predicted PL model, essential parameters such as PL exponent as well as the coverage probability of the network are evaluated. This research work may assist in the process of deployment and optimisation of any cellular network significantly.

Keywords: WiMAX, RSSI, path loss, coverage probability, regression analysis

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Authors: Sanja Podunavac-Kuzmanović, Strahinja Kovačević, Lidija Jevrić, Evgenija Djurendić, Jovana Ajduković

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There are several methods for determination of the lipophilicity of biologically active compounds, however chromatography has been shown as a very suitable method for this purpose. Chromatographic (C18-RP-HPLC) analysis of a series of 24 17-picolyl and 17-picolinylidene androstane derivatives was carried out. The obtained retention indices (logk, methanol (90%) / water (10%)) were correlated with calculated physicochemical and lipophilicity descriptors. The QSRR analysis was carried out applying principal component regression (PCR) and partial least squares regression (PLS). The PCR and PLS model were selected on the basis of the highest variance and the lowest root mean square error of cross-validation. The obtained PCR and PLS model successfully correlate the calculated molecular descriptors with logk parameter indicating the significance of the lipophilicity of compounds in chromatographic process. On the basis of the obtained results it can be concluded that the obtained logk parameters of the analyzed androstane derivatives can be considered as their chromatographic lipophilicity. These results are the part of the project No. 114-451-347/2015-02, financially supported by the Provincial Secretariat for Science and Technological Development of Vojvodina and CMST COST Action CM1105.

Keywords: androstane derivatives, chromatography, molecular structure, principal component regression, partial least squares regression

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Authors: K. Hema Shankari, R. Thirumalaiselvi, N. V. Balasubramanian

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The present paper addresses to the research in the area of regression testing with emphasis on automated tools as well as prioritization of test cases. The uniqueness of regression testing and its cyclic nature is pointed out. The difference in approach between industry, with business model as basis, and academia, with focus on data mining, is highlighted. Test Metrics are discussed as a prelude to our formula for prioritization; a case study is further discussed to illustrate this methodology. An industrial case study is also described in the paper, where the number of test cases is so large that they have to be grouped as Test Suites. In such situations, a genetic algorithm proposed by us can be used to reconfigure these Test Suites in each cycle of regression testing. The comparison is made between a proprietary tool and an open source tool using the above-mentioned metrics. Our approach is clarified through several tables.

Keywords: APFD metric, genetic algorithm, regression testing, RFT tool, test case prioritization, selenium tool

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Authors: Fidelia A. Orji, Julita Vassileva

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This research aims to develop machine learning models for students' academic performance and study strategy prediction, which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) used in building the models are chosen based on prior studies, which revealed that the attributes are essential in students’ learning process. Previous studies revealed the individual effects of each of these attributes on students’ learning progress. However, few studies have investigated the combined effect of the attributes in predicting student study strategy and academic performance to reduce the dropout rate. To bridge this gap, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process.

Keywords: classification models, learning strategy, predictive modeling, regression models, student academic performance, student motivation, supervised machine learning

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Authors: Sumit Kumar Das, Laishram Ladusingh

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Aim: The aim of the study is to examine the differential of epidemiological transition across fifteen agro-climatic zones of India and its effect on health-seeking behavior. Data and Methods: Unit level data on consumption expenditure on health of India from three decadal rounds conducted by National Sample Survey Organization are used for the analysis. These three rounds are 52nd (1995-96), 60th (2004-05) and 71st (2014-15). The age-adjusted prevalence rate for communicable diseases and non-communicable diseases are estimated for fifteen agro-climatic zones of India for three time periods. Bivariate analysis is used to find out determinants of health-seeking behavior. Multilevel logistic regression is used to examine factors effecting on household health-seeking behavior. Result: The prevalence of communicable diseases is increasing in most of the zones of India. Every South Indian zones, Gujarat plains, and lower Gangetic plain are facing the severe attack of dual burden of diseases. Demand for medical advice has increased in southern zones, and east zones, reliance on private healthcare facilities are increasing in most of the zone. Demographic characteristics of the household head have a significant impact on health-seeking behavior. Conclusion: Proper program implementation is required considering the disease prevalence and differential in the pattern of health seeking behavior. Along with initiation and strengthening of programs for non-communicable, existing programs for communicable diseases need to monitor and supervised strictly.

Keywords: agro-climatic zone, epidemiological transition, health-seeking behavior, multilevel regression

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Authors: Babak Darvish Rouhani, Mohd Nazri Mahrin, Fatemeh Nikpay, Pourya Nikfard, Maryam Khanian Najafabadi

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Enterprise Architecture (EA) is a strategy that is employed by enterprises in order to align their business and Information Technology (IT). EA is managed, developed, and maintained through Enterprise Architecture Implementation Methodology (EAIM). The effectiveness of EA implementation is the degree in which EA helps to achieve the collective goals of the organization. This paper analyzes the results of a survey that aims to explore the factors that affect the effectiveness of EAIM and specifically the relationship between factors and effectiveness of the output and functionality of EA project. The exploratory factor analysis highlights a specific set of five factors: alignment, adaptiveness, support, binding, and innovation. The regression analysis shows that there is a statistically significant and positive relationship between each of the five factors and the effectiveness of EAIM. Consistent with theory and practice, the most prominent factor for developing an effective EAIM is innovation. The findings contribute to the measuring the effectiveness of EA implementation project by providing an indication of the measurement implementation approaches which is used by the Enterprise Architects, and developing an effective EAIM.

Keywords: enterprise architecture, enterprise architecture implementation methodology, implementation methodology, factors, EA, effectiveness

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Authors: Mehdi Moeinaddini, Zohreh Asadi-Shekari, Muhammad Zaly Shah, Amran Hamzah

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Currently, planners try to have more green travel options to decrease economic, social and environmental problems. Therefore, this study tries to find significant urban travel factors to be used to increase the usage of alternative urban travel modes. This paper attempts to identify the relationship between prominent urban mobility indicators and daily trips by public transport in 30 cities from various parts of the world. Different travel modes, infrastructures and cost indicators were evaluated in this research as mobility indicators. The results of multi-linear regression analysis indicate that there is a significant relationship between mobility indicators and the daily usage of public transport.

Keywords: green travel modes, urban travel indicators, daily trips by public transport, multi-linear regression analysis

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Authors: Suparman

Abstract:

Piecewise polynomial regression model is very flexible model for modeling the data. If the piecewise polynomial regression model is matched against the data, its parameters are not generally known. This paper studies the parameter estimation problem of piecewise polynomial regression model. The method which is used to estimate the parameters of the piecewise polynomial regression model is Bayesian method. Unfortunately, the Bayes estimator cannot be found analytically. Reversible jump MCMC algorithm is proposed to solve this problem. Reversible jump MCMC algorithm generates the Markov chain that converges to the limit distribution of the posterior distribution of piecewise polynomial regression model parameter. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of piecewise polynomial regression model.

Keywords: piecewise regression, bayesian, reversible jump MCMC, segmentation

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Authors: Naser Zourikalatehsamad, Seyed Abdorreza Payambarpour, Ibrahim Alwashali, Zahra Abdolkarimi

Abstract:

The paper aims to evaluate the effect of online advertising on consumer purchase behavior in Malaysian organizations. The paper has potential to extend and refine theory. A survey was distributed among Students of UTM university during the winter 2014 and 160 responses were collected. Regression analysis was used to test the hypothesized relationships of the model. Result shows that the predictors (cost saving factor, convenience factor and customized product or services) have positive impact on intention to continue seeking online advertising.

Keywords: consumer purchase, convenience, customized product, cost saving, customization, flow theory, mass communication, online advertising ads, online advertising measurement, online advertising mechanism, online intelligence system, self-confidence, willingness to purchase

Procedia PDF Downloads 473