The last decade was marked by rapid growth and development of technology. One example of that is the automotive industry. This industry has made an enormous progress, and its main goal is to achieve safer and better driving. The vehicle incorporates GPS devices that send information about the current location and speed of the vehicle. Large amounts of collected data can be used in companies for tracking vehicles and various analysis and statistics. Sometimes, however, GPS data is not accurate. In this paper, the potential of real data sets will be used to analyze possible anomalies that may occur when reading GPS position of vehicles. The approach for solving this problem used in this paper consists of calculating distance and time, based on GPS measurements, then calculating average speed based on these two values, and comparing that speed with the speed given by GPS device.

Vehicle Routing Problem (VRP) is the process of selection of the most favorable roads in a road network vehicle should move during the customer service, so as such, it is a generalization of problems of a commercial traveler. Most of the algorithms for successful solution of VRP problems are consisted of several controll parameters and constants, so this paper presents the data-driven prediction model for adjustment of the parameters based on historical data, especially for practical VRP problems with realistic constraints. The approach is consisted of four prediction models and decision making systems for comparing acquired results each of the used models.

Outlier detection represents the problem of finding patterns in data that does not fit in expected behaviour. In this paper, outlier detection is done over real transactional data set of the distribution company. Outlier detection is done over time-series data, and over an ordered number of products that can be found within transactions. Unsupervised techniques and methods, S-H-ESD and LOF, are applied because data set is unlabelled. Implementation is performed in R language, and web application dashboard using R Shiny is made. Based on collected results, a proposal for creating the outlier detection and prevention system is made, and ideas for further improvements and additional analysis are given.

The identification of association rules is the problem of finding associations between different items in the same transactions. In this paper, performance comparison of different variants of Apriori, FP-Growth and ECLAT algorithms was performed over the real transactional data set of the distribution company by using R programming language and its appropriate packages, and the results obtained are later on explained. Then, the identification and visualization of the association rules of the said real data set was performed.

The problem of nonperforming loans is one of the biggest problems in the banking sector. In order to mitigate this problem, it is necessary to improve the methods of credit risk assessment. One way to minimize credit risk is to improve the assessment of the creditworthiness of the applicant. In order to make a more accurate assessment, many models have been developed using classification techniques. This paper demonstrates the use of classification techniques in the form of a single classifier or in a classifier ensemble setting. We proposed bagging as a model ensemble using artificial neural networks. In the experiment conducted with the Bosnian commercial banks dataset, the proposed model showed promising results according to evaluation criteria, especially after the process of feature selection. Both individual and wrapper feature selection methods were used. Bagging with neural network (NNBag) outperforms commonly used techniques with accuracy improvement from 1% to 5%. The superiority of the proposed model (NNBag) is confirmed on two widely available datasets for assessing creditworthiness. Based on experimental results on three datasets, it is proven that NNBag is suitable for use in the assessment of the creditworthiness of applicants.

With this paper we presented architectural redesign of SOA (service oriented architecture) integration platform by following principles of microservices design. Presented SOA platform is currently in use for Real estate sector. Number of messages which need to be processed by platform is growing as well as number of new integrations which requires redesign of platform. Redesign should provide ability for scalability, better resource management, maintenance and deployment. To support this, it is necessary to transform integration platform to be microservices based.

Collaborative filtering methods are widely accepted and used for item recommendation in various applications and domains. Their simplicity and ability to provide recommendations without the need fo...

Implementing a successful warehouse management system from scratch contains many challenges of different nature. The most important aspect is the financial status of the company that is implementing the system. How many resources is the company willing to spend is always a difficult dilemma. The second important issue is the actual usefulness of the developed solution. If the system cannot be implemented in the real world, as advanced as it may be in theory, it has very little or no use at all. In this paper, the current state of the picking zone of a medium-to-large logistics company warehouse is analyzed through a real-world case study. On the top of the collected results, a method for the optimal strategic and quantitative item placement in the picking zone is constructed through the usage of fitting algorithms. One month after the method was released to production, a quality check of the system by supervising the warehouse activities of the picking zone is conducted and the results are analyzed.

Big Data is becoming one of the most important technology trends with a potential for a dramatic change in which organizations use information in order to improve the customer's experience and transform their own business models. Big Data as a concept is new in the world of technology and therefore requires research in technological or business sense. Management and analysis of large amount of network offer huge benefits and challenges for all organizations. The amount of information floating through social networks increases every day, and represents a rich source of data, if it is properly processed. The aim of this paper is to show on a concrete example the profit of the system implemented over Big Data from social networks using Text Mining methods and technologies, as well as semantic processing and clustering, storaging and possibility of later examination and treatment.

Nowadays, we are witnessing the rapid development of medicine and various methods that are used for early detection of diseases. In order to make quality decisions in diagnosis and prevention of disease, various decision support systems based on machine learning methods have been introduced in the medical domain. Such systems play an increasingly important role in medical practice. This paper presents a new web framework concept for disease prediction. The proposed framework is object-oriented and enables online prediction of various diseases. The framework enables online creation of different autonomous prediction models depending on the characteristics of diseases. Prediction process in the framework is based on a hybrid Case Based Reasoning classifier. The framework was evaluated on disease datasets from public repositories. Experimental evaluation shows that the proposed framework achieved high diagnosis accuracy.

The aim of this paper is to promote and demonstrate the usage and successful practical application of e-learning achievements in the field of biomedical engineering education (BME). Biomedical engineers and bioengineers require significant knowledge of both, engineering and biology. Both, biomedical engineering and BME education in Bosnia and Herzegovina are on an extremely low development level. Therefore, it is important to create a system used by both students and professors, which would increase the level of awareness in this field. During the implementation of this system, the best development practices of web-based systems of wide usage were used. The results of required knowledge in the field of BME, based on real data of the usage of this system, have also been demonstrated.

 Abstract — Data mining and classification of objects is the process of data analysis, using various machine learning techniques, which is used today in various fields of research. This paper presents a concept of hybrid classification model improved with the expert knowledge. The hybrid model in its algorithm has integrated several machine learning techniques (Information Gain, K-means, and Case-Based Reasoning) and the expert’s knowledge into one. The knowledge of experts is used to determine the importance of features. The paper presents the model algorithm and the results of the case study in which the emphasis was put on achieving the maximum classification accuracy without reducing the number of features.

Traditional recommender systems utilize user and item profiles in order to predict ratings of unseen items. New users, items and ratings are continuously updated to the system, making data available for detection of changes in user preferences throughout the time. In this work the widely used user-neighborhood recommender system is extended by incorporating temporal information and enhancing measure of neighborhood similarity with information on item features. Unlike other models, we also add time-weight function in the preference prediction step to improve prediction accuracy. Experiments on real data set show an improvement in prediction performance over traditional collaborative filtering model.

Although there are many attempts to engineer a domain specific language for the Internet of Things, most of them forget the fact that with the evolving of the Internet of Things, the end user will probably be a common person without an engineering or software development background. The designers of the UML had the same problem: how to make a language powerful enough for the professionals, but at the same time simple enough to be understood by a non-technical end user that gives the requirements. Inspired by this idea a Visual Domain Specific Modeling Language was developed for the IoT and proved that it is powerful enough for the professional and at the same time simple enough to be used by non-technical users.