Data mining is the process of knowledge discovery in a certain amount of data. Knowledge discovery in data reflects in the application of sophisticated machine learning methods such as regression, classification, clustering, etc. The focus of this study is the analysis of data from the real production system called Edu720, which is intended for internal education of employees in companies and which is used by numerous companies in Bosnia and Herzegovina and its region. A complex process of data preprocessing, including data cleaning and data transformation, was applied to the considered data set so it can be used in numerous classification tasks. The main goal of this study is to predict the success of the education that the company wants to set up for its employees. Information such as the number of questions in education, the average number of words per question in certain education, the number of employees and the duration of the educational video resource represented in seconds were used as attributes for applied classification methods. Class output represents the level of success for certain educations. K-nearest neighbors and decision tree algorithms were used for classification tasks and the accuracy of the classification was determined by the holdout method. The influence of applying the more sophisticated method for data set partitioning, which uses the K-means clustering method, is also presented.

Distribution companies use complex software systems called WMS (Warehouse Management System). The WMS is an important part of the company’s business and it can make processes simple to keep track of. Smart WMS optimizes processes to save resources and to create a more efficient working place. This paper describes the concept of a smart WMS that is implemented in one of the largest distribution companies in Bosnia and Herzegovina. The system uses artificial intelligence and optimization algorithms to improve working process. The paper describes the complete warehouse workflow that includes stock planning, initial product placement, transfer from stock to pick zone, order picking process, transport and tracking. The anomaly detection is used in some processes to improve the whole system. The main contribution of this paper is the presentation of an efficient and in the real world used smart WMS concept.

Two important problems distribution companies face on a daily basis are the routing and tracking of a vehicle fleet. The former is being overcome by solving the famous vehicle routing problem (VRP), a generalization of the traveling salesman problem (TSP), and the later analyses GPS data to get information of the moving vehicles. In this paper a system which uses GPS data to track the vehicles, analyze their routes and improve input data needed for the algorithm for the vehicle routing problem is described. In a real-world scenario, implementing an VRP algorithm is not enough. Algorithms which analyze GPS data ensure that the VRP algorithm takes correct input data and that the driven routes are those that the algorithm proposed.

The production of the Higgs boson in association with a single top quark. tH is a very important process for testing the Standard Model theory. There are three production channels: associated production with W , t-channel and s-channel. In this study only tHq production with H → bb̄ decay and top leptonic decay has been generated and analyzed. The tH production is important for probing the sign of the Higgs-top Yukawa coupling, yt. Namely, there is an interference between diagrams which depends on the relative sign of yt. LO Madgraph is used as a tool for generating events, calculating the cross-sections and Feynmann diagrams. The cross-section is parameterized as a continuous function of yt. The kinematics of the process appears to be dependent on the yt used. In addition, the dominant background from tt̄ production with heavy flavor jets has been generated and potential discriminating variables to separate tH from this background have been analyzed and discussed.