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LiDAR-Based Maintenance of a Safe Distance between a Human and a Robot Arm
David Podgorelec, Suzana Uran, Andrej Nerat, Božidar Bratina, Sašo Pečnik, Marjan Dimec, Franc Žaberl, Borut Žalik, Riko Šafarič, 2023, izvirni znanstveni članek

Opis: This paper focuses on a comprehensive study of penal policy in Slovenia in the last 70 years, providing an analysis of statistical data on crime, conviction, and prison populations. After a sharp political and penal repression in the first years after World War II, penal and prison policy began paving the way to a unique "welfare sanction system", grounded in ideas of prisoners' treatment. After democratic reforms in the early 1990s, the criminal legislation became harsher, but Slovenia managed to avoid the general punitive trends characterized by the era of penal state and culture of control. The authoritarian socialist regime at its final stage had supported the humanization of the penal system, and this trend continued in the first years of the democratic reforms in the 1990s, but it lost its momentum after 2000. In the following two decades, Slovenia experienced a continuous harshening of criminal law and sanctions on the one hand and an increasing prison population rate on the other. From 2014 onwards, however, there was a decrease in all segments of penal statistics. The findings of the study emphasize the exceptionalism of Slovenian penal policy, characterized by penal moderation, which is the product of the specific local historical, political, economic, and normative developments.
Ključne besede: LIDAR, robot, human-robot collaboration, speed and separation monitoring, intelligent control system, geometric data registration, motion prediction
Objavljeno v DKUM: 16.02.2024; Ogledov: 68; Prenosov: 5
.pdf Celotno besedilo (5,27 MB)
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Naive prediction of protein backbone phi and psi dihedral angles using deep learning
Matic Broz, Marko Jukič, Urban Bren, 2023, izvirni znanstveni članek

Opis: Protein structure prediction represents a significant challenge in the field of bioinformatics, with the prediction of protein structures using backbone dihedral angles recently achieving significant progress due to the rise of deep neural network research. However, there is a trend in protein structure prediction research to employ increasingly complex neural networks and contributions from multiple models. This study, on the other hand, explores how a single model transparently behaves using sequence data only and what can be expected from the predicted angles. To this end, the current paper presents data acquisition, deep learning model definition, and training toward the final protein backbone angle prediction. The method applies a simple fully connected neural network (FCNN) model that takes only the primary structure of the protein with a sliding window of size 21 as input to predict protein backbone φ and ψ dihedral angles. Despite its simplicity, the model shows surprising accuracy for the φ angle prediction and somewhat lower accuracy for the ψ angle prediction. Moreover, this study demonstrates that protein secondary structure prediction is also possible with simple neural networks that take in only the protein amino-acid residue sequence, but more complex models are required for higher accuracies.
Ključne besede: protein structure prediction, backbone dihedral angles, deep neural network, fully connected neural network, FCNN, protein secondary structure prediction
Objavljeno v DKUM: 01.12.2023; Ogledov: 140; Prenosov: 17
.pdf Celotno besedilo (3,60 MB)
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Artificial intelligence based prediction of diabetic foot risk in patients with diabetes : a literature review
Lucija Gosak, Adrijana Svenšek, Mateja Lorber, Gregor Štiglic, 2023, pregledni znanstveni članek

Opis: Diabetic foot is a prevalent chronic complication of diabetes and increases the risk of lower limb amputation, leading to both an economic and a major societal problem. By detecting the risk of developing diabetic foot sufficiently early, it can be prevented or at least postponed. Using artificial intelligence, delayed diagnosis can be prevented, leading to more intensive preventive treatment of patients. Based on a systematic literature review, we analyzed 14 articles that included the use of artificial intelligence to predict the risk of developing diabetic foot. The articles were highly heterogeneous in terms of data use and showed varying degrees of sensitivity, specificity, and accuracy. The most used machine learning techniques were support vector machine (SVM) (n = 6) and K-Nearest Neighbor (KNN) (n = 5). Future research is recommended on larger samples of participants using different techniques to determine the most effective one.
Ključne besede: artificial intelligence, machine learning, thermography, diabetic foot prediction, diabetes, diabetes care, diabetic foot, literature review
Objavljeno v DKUM: 27.11.2023; Ogledov: 225; Prenosov: 10
.pdf Celotno besedilo (654,91 KB)
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Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications : a systematic review of the literature
Lucija Gosak, Kristina Martinović, Mateja Lorber, Gregor Štiglic, 2022, pregledni znanstveni članek

Opis: Aim The aim of this review is to examine the effectiveness of artificial intelligence in predicting multimorbid diabetes-related complications. Background In diabetic patients, several complications are often present, which have a significant impact on the quality of life; therefore, it is crucial to predict the level of risk for diabetes and its complications. Evaluation International databases PubMed, CINAHL, MEDLINE and Scopus were searched using the terms artificial intelligence, diabetes mellitus and prediction of complications to identify studies on the effectiveness of artificial intelligence for predicting multimorbid diabetes-related complications. The results were organized by outcomes to allow more efficient comparison. Key issues Based on the inclusion/exclusion criteria, 11 articles were included in the final analysis. The most frequently predicted complications were diabetic neuropathy (n = 7). Authors included from two to a maximum of 14 complications. The most commonly used prediction models were penalized regression, random forest and Naïve Bayes model neural network. Conclusion The use of artificial intelligence can predict the risks of diabetes complications with greater precision based on available multidimensional datasets and provides an important tool for nurses working in preventive health care. Implications for Nursing Management Using artificial intelligence contributes to a better quality of care, better autonomy of patients in diabetes management and reduction of complications, costs of medical care and mortality.
Ključne besede: artificial intelligence, prediction models, diabetes, prediction of diabetes complications
Objavljeno v DKUM: 03.10.2023; Ogledov: 192; Prenosov: 26
.pdf Celotno besedilo (509,07 KB)
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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, izvirni znanstveni članek

Opis: 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.
Ključne besede: COVID-19, coronavirus, scaling, city size, epidemic, prediction
Objavljeno v DKUM: 12.11.2020; Ogledov: 794; Prenosov: 252
.pdf Celotno besedilo (1,23 MB)
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Prediction of California Bearing Ratio (CBR) and Compaction Characteristics of granular soil
Attique ul Rehman, Khalid Farooq, Hassan Mujtaba, 2017, izvirni znanstveni članek

Opis: 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.
Ključne besede: CBR, regression, model, prediction, compaction characteristics
Objavljeno v DKUM: 18.06.2018; Ogledov: 1242; Prenosov: 211
.pdf Celotno besedilo (830,76 KB)
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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, izvirni znanstveni članek

Opis: 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.
Ključne besede: financial distress prediction, Support vector data description, Fuzzy c-mean
Objavljeno v DKUM: 30.11.2017; Ogledov: 810; Prenosov: 168
.pdf Celotno besedilo (1,10 MB)
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Link prediction on Twitter
Sanda Martinčić-Ipšić, Edvin Močibob, Matjaž Perc, 2017, izvirni znanstveni članek

Opis: 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.
Ključne besede: link prediction, data mining, Twitter, network analysis
Objavljeno v DKUM: 15.09.2017; Ogledov: 1646; Prenosov: 179
.pdf Celotno besedilo (6,98 MB)
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Link prediction in multiplex online social networks
Mahdi Jalili, Yasin Orouskhani, Milad Asgari, Nazanin Alipourfard, Matjaž Perc, 2017, izvirni znanstveni članek

Opis: 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%.
Ključne besede: social networks, complex networks, signed networks, link prediction, machine learning
Objavljeno v DKUM: 08.08.2017; Ogledov: 1371; Prenosov: 442
.pdf Celotno besedilo (940,17 KB)
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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, izvirni znanstveni članek

Opis: 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.
Ključne besede: readmission prediction, predictive modelling, temporal data
Objavljeno v DKUM: 02.08.2017; Ogledov: 1786; Prenosov: 358
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