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City size and the spreading of COVID-19 in Brazil
Haroldo V. Ribeiro, Andre S. Sunahara, Jack Sutton, Matjaž Perc, Quentin S. Hanley, 2020, original scientific article

Abstract: The current outbreak of the coronavirus disease 2019 (COVID-19) is an unprecedented example of how fast an infectious disease can spread around the globe (especially in urban areas) and the enormous impact it causes on public health and socio-economic activities. Despite the recent surge of investigations about different aspects of the COVID-19 pandemic, we still know little about the effects of city size on the propagation of this disease in urban areas. Here we investigate how the number of cases and deaths by COVID-19 scale with the population of Brazilian cities. Our results indicate small towns are proportionally more affected by COVID-19 during the initial spread of the disease, such that the cumulative numbers of cases and deaths per capita initially decrease with population size. However, during the long-term course of the pandemic, this urban advantage vanishes and large cities start to exhibit higher incidence of cases and deaths, such that every 1% rise in population is associated with a 0.14% increase in the number of fatalities per capita after about four months since the first two daily deaths. We argue that these patterns may be related to the existence of proportionally more health infrastructure in the largest cities and a lower proportion of older adults in large urban areas. We also find the initial growth rate of cases and deaths to be higher in large cities; however, these growth rates tend to decrease in large cities and to increase in small ones over time.
Keywords: COVID-19, coronavirus, scaling, city size, epidemic, prediction
Published in DKUM: 12.11.2020; Views: 433; Downloads: 147
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Prediction of California Bearing Ratio (CBR) and Compaction Characteristics of granular soil
Attique ul Rehman, Khalid Farooq, Hassan Mujtaba, 2017, original scientific article

Abstract: This research is an effort to correlate the index properties of granular soils with the California Bearing Ratio (CBR) and the compaction characteristics. Soil classification, modified proctor and CBR tests conforming to the relevant ASTM methods were performed on natural as well as composite sand samples. The laboratory test results indicated that samples used in this research lie in SW, SP and SP-SM categories based on Unified Soil Classification System and in groups A-1-b and A-3 based on the AASHTO classification system. Multiple linear regression analysis was performed on experimental data and correlations were developed to predict the CBR, maximum dry density (MDD) and optimum moisture content (OMC) in terms of the index properties of the samples. Among the various parameters, the coefficient of uniformity (Cu), the grain size corresponding to 30% passing (D30) and the mean grain size (D50) were found to be the most effective predictors. The proposed prediction models were duly validated using an independent dataset of CBR tests on sandy soils. The comparative results showed that the variation between the experimental and predicted results for CBR falls within ±4% confidence interval and that of the maximum dry density and the optimum moisture content are within ±2%. Based on the correlations developed for CBR, MDD and OMC, predictive curves are proposed for a quick estimation based on Cu , D30 and D50. The proposed models and the predictive curves for the estimation of the CBR value and the compaction characteristics would be very useful in geotechnical & pavement engineering without performing the laboratory compaction and CBR tests.
Keywords: CBR, regression, model, prediction, compaction characteristics
Published in DKUM: 18.06.2018; Views: 765; Downloads: 180
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Financial distress prediction of Iranian companies using data minig techniques
Mahdi Moradi, Mahdi Salehi, Mohammad Ebrahim Ghorgani, Hadi Sadoghi Yazdi, 2013, original scientific article

Abstract: Decision-making problems in the area of financial status evaluation are considered very important. Making incorrect decisions in firms is very likely to cause financial crises and distress. Predicting financial distress of factories and manufacturing companies is the desire of managers and investors, auditors, financial analysts, governmental officials, employees. Therefore, the current study aims to predict financial distress of Iranian Companies. The current study applies support vector data description (SVDD) to the financial distress prediction problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, we use a grid-search technique using 3-fold cross-validation to find out the optimal parameter values of kernel function of SVDD. To evaluate the prediction accuracy of SVDD, we compare its performance with fuzzy c-means (FCM).The experiment results show that SVDD outperforms the other method in years before financial distress occurrence. The data used in this research were obtained from Iran Stock Market and Accounting Research Database. According to the data between 2000 and 2009, 70 pairs of companies listed in Tehran Stock Exchange are selected as initial data set.
Keywords: financial distress prediction, Support vector data description, Fuzzy c-mean
Published in DKUM: 30.11.2017; Views: 482; Downloads: 116
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Link prediction on Twitter
Sanda Martinčić-Ipšić, Edvin Močibob, Matjaž Perc, 2017, original scientific article

Abstract: With over 300 million active users, Twitter is among the largest online news and social networking services in existence today. Open access to information on Twitter makes it a valuable source of data for research on social interactions, sentiment analysis, content diffusion, link prediction, and the dynamics behind human collective behaviour in general. Here we use Twitter data to construct co-occurrence language networks based on hashtags and based on all the words in tweets, and we use these networks to study link prediction by means of different methods and evaluation metrics. In addition to using five known methods, we propose two effective weighted similarity measures, and we compare the obtained outcomes in dependence on the selected semantic context of topics on Twitter. We find that hashtag networks yield to a large degree equal results as all-word networks, thus supporting the claim that hashtags alone robustly capture the semantic context of tweets, and as such are useful and suitable for studying the content and categorization. We also introduce ranking diagrams as an efficient tool for the comparison of the performance of different link prediction algorithms across multiple datasets. Our research indicates that successful link prediction algorithms work well in correctly foretelling highly probable links even if the information about a network structure is incomplete, and they do so even if the semantic context is rationalized to hashtags.
Keywords: link prediction, data mining, Twitter, network analysis
Published in DKUM: 15.09.2017; Views: 1199; Downloads: 124
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Link prediction in multiplex online social networks
Mahdi Jalili, Yasin Orouskhani, Milad Asgari, Nazanin Alipourfard, Matjaž Perc, 2017, original scientific article

Abstract: Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.
Keywords: social networks, complex networks, signed networks, link prediction, machine learning
Published in DKUM: 08.08.2017; Views: 935; Downloads: 378
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Contribution of temporal data to predictive performance in 30-day readmission of morbidly obese patients
Petra Povalej Bržan, Zoran Obradović, Gregor Štiglic, 2017, original scientific article

Abstract: Background: Reduction of readmissions after discharge represents an important challenge for many hospitals and has attracted the interest of many researchers in the past few years. Most of the studies in this field focus on building cross-sectional predictive models that aim to predict the occurrence of readmission within 30-days based on information from the current hospitalization. The aim of this study is demonstration of predictive performance gain obtained by inclusion of information from historical hospitalization records among morbidly obese patients. Methods: The California Statewide inpatient database was used to build regularized logistic regression models for prediction of readmission in morbidly obese patients (n = 18,881). Temporal features were extracted from historical patient hospitalization records in a one-year timeframe. Five different datasets of patients were prepared based on the number of available hospitalizations per patient. Sample size of the five datasets ranged from 4,787 patients with more than five hospitalizations to 20,521 patients with at least two hospitalization records in one year. A 10-fold cross validation was repeted 100 times to assess the variability of the results. Additionally, random forest and extreme gradient boosting were used to confirm the results. Results: Area under the ROC curve increased significantly when including information from up to three historical records on all datasets. The inclusion of more than three historical records was not efficient. Similar results can be observed for Brier score and PPV value. The number of selected predictors corresponded to the complexity of the dataset ranging from an average of 29.50 selected features on the smallest dataset to 184.96 on the largest dataset based on 100 repetitions of 10-fold cross-validation. Discussion: The results show positive influence of adding information from historical hospitalization records on predictive performance using all predictive modeling techniques used in this study. We can conclude that it is advantageous to build separate readmission prediction models in subgroups of patients with more hospital admissions by aggregating information from up to three previous hospitalizations.
Keywords: readmission prediction, predictive modelling, temporal data
Published in DKUM: 02.08.2017; Views: 1252; Downloads: 319
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Prediction of standard lactation curves for primiparous Holstein cows by using corrected regression models
Janez Jeretina, Drago Babnik, Dejan Škorjanc, 2015, original scientific article

Abstract: Prediction of the expected milk yield is important for the management of the primiparous cows (PPC) with a few or no data on their own milk productivity. We developed a system of regression equations for predicting milk yields in standard lactation. The models include the systematic effects of the calving season, the five-year rolling herd average of milk yield of PPC, the breeding values of the parents for milk production, and daily milk recordings. A total of 21,901 lactations of Holstein PPC were collected during the regular monthly milk recordings of cows in the Republic of Slovenia. By including daily milk recordings in the model, the coefficients of determination of regression models for the prediction of milk yield increase: without known recordings (M0) R 2 =0.80; with one recording (M1) R 2 =0.82; with two first consecutive recordings (M2) R 2 =0.86; and with three recordings (M3) R 2 =0.89. Deviations of milk yield up to 500 kg in a standard lactation (<1.6 kg/day) were as follows: with the model M0, they occurred in 53.4% of PPC; with M1, they occurred in 56.3% of PPC; with M2, they occurred in 64.5% of PPC; and with M3, they occurred in 70.9% of PPC. We concluded that the developed system of regression models is an appropriate method for milk yield prediction of PPC.
Keywords: primiparous cows, milk yield, prediction, lactation curves, regression equations
Published in DKUM: 24.07.2017; Views: 860; Downloads: 436
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Prediction of technological parameters of sheet metal bending in two stages using feed-forward neural network
Jernej Šenveter, Jože Balič, Mirko Ficko, Simon Klančnik, 2016, original scientific article

Abstract: This paper describes sheet metal bending in two stages as well as predicting and testing of the final bend angle by means of a feed-forward neural network. The primary objective was to research the technological parameters of bending sheet metal in two stages and to develop an intelligent method that would enable the predicting of those technological parameters. The process of bending sheet metal in two stages is presented by demonstrating the various technological parameters and the test tool used to carry out tests and measurements. The results of the tests and measurements were of decisive guidance in the evaluation of individual technological parameters. Developed method for prediction of the final bend angle is based on a feed-forward neural network that receives signals at the input level. These signals then travel through the hidden level to the output level, where the responses to input signals are received. The input to the neural network is composed of data that affect the selection of the final bend angle. Only five different inputs are used for the total neural network. By choosing the desired final bend angle by means of the trained neural network, bending sheet metal in two stages is optimised and made more efficient.
Keywords: bending in two stages, intelligent system, neural network, prediction of the final bend angle
Published in DKUM: 12.07.2017; Views: 766; Downloads: 397
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Markov analysis of studentsʼ performance and academic progress in higher education
Alenka Brezavšček, Mirjana Pejić Bach, Alenka Baggia, 2017, original scientific article

Abstract: Background: The students’ progression towards completing their higher education degrees possesses stochastic characteristics, and can therefore be modelled as an absorbing Markov chain. Such application would have a high practical value and offer great opportunities for implementation in practice. Objectives: The aim of the paper is to develop a stochastic model for estimation and continuous monitoring of various quality and effectiveness indicators of a given higher education study programme. Method: The study programme is modelled by a finite Markov chain with five transient and two absorbing states. The probability transition matrix is constructed. The quantitative characteristics of the absorbing Markov chain, like the expected time until absorption and the probabilities of absorption, are used to determine chosen indicators of the programme. Results: The model is applied to investigate the pattern of students’ enrolment and their academic performance in a Slovenian higher education institution. Based on the students’ intake records, the transition matrix was developed considering eight consecutive academic seasons from 2008/09 until 2016/17. The students’ progression towards the next stage of the study programme was estimated. The expected time that a student spends at a particular stage as well as the expected duration of the study is determined. The graduation and withdrawal probabilities were obtained. Besides, a prediction on the students’ enrolment for the next three academic years was made. The results were interpreted and discussed. Conclusion: The analysis presented is applicable for all higher education stakeholders. It is especially useful for a higher education institution’s managers seeing that it provides useful information to plan improvements regarding the quality and effectiveness of their study programmes to achieve better position in the educational market.
Keywords: higher education, study programme, effectiveness indicators, enrolment prediction
Published in DKUM: 06.07.2017; Views: 787; Downloads: 343
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Incorporation of duffing oscillator and Wigner-Ville distribution in traffic flow prediction
Anamarija L. Mrgole, Drago Sever, 2017, original scientific article

Abstract: The main purpose of this study was to investigate the use of various chaotic pattern recognition methods for traffic flow prediction. Traffic flow is a variable, dynamic and complex system, which is non-linear and unpredictable. The emergence of traffic flow congestion in road traffic is estimated when the traffic load on a specific section of the road in a specific time period is close to exceeding the capacity of the road infrastructure. Under certain conditions, it can be seen in concentrating chaotic traffic flow patterns. The literature review of traffic flow theory and its connection with chaotic features implies that this kind of method has great theoretical and practical value. Researched methods of identifying chaos in traffic flow have shown certain restrictions in their techniques but have suggested guidelines for improving the identification of chaotic parameters in traffic flow. The proposed new method of forecasting congestion in traffic flow uses Wigner-Ville frequency distribution. This method enables the display of a chaotic attractor without the use of reconstruction phase space.
Keywords: road traffic, congestion prediction, dynamic system, Wigner-Ville distribution, chaotic identification pattern
Published in DKUM: 02.06.2017; Views: 839; Downloads: 315
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