Iztok Fister jr.

University of Maribor | UM · Faculty of Electrical Engineering and Computer Science (FERI)

A digital twin is a virtual model to reflect a physical object and helps it by making proper decisions. The decision-making process is based on the same input data that the simulated physical object has access to. Due to exploiting artificial intelligence, the decision-making process of the digital twin is more sophisticated than that of the physic...

Numerical association rule mining offers a very efficient way of mining association rules, where algorithms can operate directly with categorical and numerical attributes. These methods are suitable for mining different transaction databases, where data are entered sequentially. However, little attention has been paid to the time series numerical a...

Data squashing is a well-known preprocessing method in Machine Learning that enables construction of smaller datasets from the original ones and provides approximately the same results of data analysis as the original. The paper proposes a new data squashing method for Association Rule Mining based on the Cosine similarity and Euclidean distance si...

First International Conference, ICAIDS 2021, Hyderabad, India, December 17–18, 2021, Revised Selected Papers This book constitutes selected papers presented at the First International Conference on Artificial Intelligence and Data Science, ICAIDS 2021, held in Hyderabad, India, in December 2021. The 43 papers presented in this volume were thorou...

Nowadays, game environments are used not only for entertainment purposes such as playing, but also as simulation tools for various scientific researches. Machine Learning (ML) algorithms, on the other hand, are efficient Artificial Intelligence tools, used in software applications for purposes such as predicting data patterns or for software optimi...

Podjetja dandanes vneto tekmujejo v zagotavljanju najboljših možnih storitev svojim strankam, pri čemer podjetja na trgu električne energije niso izjema. Glede na negotove razmere na področju zagotavljanja energetskih virov, v katerih se je znašel svet, vse večje potrebe po električni energiji in trend strme rasti cen energije je postala optimizaci...

Dostopnost velikih količin podatkov in relativno poceni in dostopne računske moči je v zadnjih nekaj letih pripomogla k enormnemu vzponu računske inteligence. Čeprav se mnoga podjetja in organizacije že dolga leta poslužujejo uporabe različnih tehnik matematične optimizacije, ki se najpogosteje uporabljajo za namen optimizacije poslovnih procesov,...

Large information datasets often impose an immense number of features where many are found redundant and thus inessential for statistical analysis. In the past, a data preprocessing phase was formalized to cope with the problem and take appropriate remedial measures. Traditionally, this was a fixed and stationary process that suffered from a lack o...

This work addresses the IFS-based image reconstruction problem for binary images. Given a binary image as the input, the goal is to obtain all the parameters of an iterated function system whose attractor approximates the input image accurately; the quality of this approximation is measured according to a similarity function between the original an...

Reinforcement learning is a computational approach that mimics learning from interaction and supplements the existing supervised and unsupervised learning methods within the machine learning field. It bases on the mapping of a given situation to the action, and each action is evaluated by a reward. Of crucial concern, here is that the mapping is pe...

Knjiga služi kot uvod v področje strojnega učenja za vse, ki imajo vsaj osnovne izkušnje s programiranjem. Pregledajo se pomembni pojmi strojnega učenja (model znanja, učna in testna množica, algoritem učenja), natančneje pa se predstavi tehnika klasifikacije in način ovrednotenja kvalitete modelov znanja klasifikacije. Spozna se algoritem klasifik...

Statistical reasoning was one of the earliest methods to draw insights from data. However, over the last three decades, association rule mining and online analytical processing have gained massive ground in practice and theory. Logically, both association rule mining and online analytical processing have some common objectives, but they have been i...

Nowadays, it is no secret that modern machine learning methods are amongst the more computationally-intensive learning methods. The rise in the applications of computationally-intensive deep learning, automated machine learning methods, and even metaheuristics for optimization, have increased the consumption of electrical energy dramatically. Conse...

Fractal image reconstruction through iterated function systems (IFS) is an interesting and challenging topic of research. Several methods have been described in the literature to tackle this issue. However, existing methods have focused exclusively on binary or gray level images. To the best of authors’ knowledge, no method has addressed the proble...

Decisions made nowadays by Artificial Intelligence-powered systems are usually hard for users to understand. One of the most important issues faced by developers is on how to create more explainable Machine Learning models. In line with this, more explainable techniques need to be developed, where visual explanation also plays a more important role...

Planning sport sessions automatically is becoming a very important aspect of improving an athlete’s fitness. So far, many Artificial Intelligence methods have been proposed for planning sport training sessions. These methods depend largely on test data, where Machine Learning models are built, yet evaluated later. However, one of the biggest concer...

We present a novel method for generating cycling training routes from geographical property graphs based on an Evolutionary Algorithm. The algorithm operators of crossover and mutation are adjusted for use in the Property Graph domain. Data fusion of geographical data from the OpenStreetMap, EU-DEM digital surface model, and existing training recor...

The rapid development of computer science and telecommunications has brought new ways and practices to sport training. The artificial sport trainer, founded on computational intelligence algorithms, has gained momentum in the last years. However, artificial sport trainer usually suffers from a lack of automatisation in realization and control phase...

Opponent modeling is a research aspect that needs close attention when facing an opponent in a complex game environment. Real-Time Strategy (RTS) games are a representative of one of the highest complex game environments. In RTS games, the players' tactical and strategical decisions need constant adaptation in order to win the game. In this work, a...

This paper presents a method for generating property graphs from OpenStreetMap data as a precursor to track generating methods for cycling sports. The results indicate that OpenStreetMap geographical data can be represented on a property graph. This is beneficial, and needed for use of computational intelligence path generation algorithms. The pape...

The quality of image recognition with neural network models relies heavily on filters and parameters optimized through the training process. These filters are di˙erent compared to how humans see and recognize objects around them. The di˙erence in machine and human recognition yields a noticeable gap, which is prone to exploitation. The workings of...

Computational Intelligence methods for automatic generation of sport training plans in individual sport disciplines have achieved a mature phase. In order to confirm their added value, they have been deployed into practice. As a result, several methods have been developed for generating well formulated training plans on computers automatically that...

Blockchain and Data Mining are not simply buzzwords, but rather concepts that are playing an important role in the modern Information Technology (IT) revolution. Blockchain has recently been popularized by the rise of cryptocurrencies, while data mining has already been present in IT for many decades. Data stored in a blockchain can also be conside...

Uspešnost prepoznavanja slik z uporabo nevronskih mrež je odvisna od parametrov in filtrov, optimiziranih skozi učni proces. Tukaj najdemo razliko v načinu prepoznavanja motivov med ljudmi in stroji. Pojavi se vrzel, ki jo napadalec s pomočjo adversarnih motenj lahko izkoristi. Slike so na videz neopazno spremenjene, ljudje razlike težko zaznajo, v...

The objective of this paper is the proposal of a new approach for the game feature validation of a game space with the eXtended Classifier System (XCS) algorithm. For initial "proof-of-concept" evaluation we used the game space of the Tic-Tac-Toe game, which was placed in a context of real-time characteristics. Evaluation was done with the XCS algo...

Searching for a set of rules, with which the knowledge hidden in data is extracted, can also be applied for multi-class classification. In line with this, a collection of nature-inspired algorithms are selected for determining the set of rules capable of classifying the samples into three or more classes. This set is encoded into representation of...

Using machine learning methods in the real-world is far from being easy, especially because of the number of methods on the one hand, and setting the optimal values of their parameters on the other. Therefore, a lot of so-called AutoML methods have emerged nowadays that also enable automatic construction of classification pipelines to users, who ar...

The main contribution of this paper is to show the linkage between the domains of Smart Sport Training and Nature-Inspired Metaheuristic Algorithms. Every year, the Smart Sport Training domain is becoming more and more crowded by different intelligent solutions that help, support and encourage people in maintaining their healthy lifestyle, as well...

The usage of wearables in different sports has resulted in the potential of recording vast amounts of data that allow us to dive even deeper into sports training. This paper provides a novel approach to classifying stoppage events in cycling, and shows an analysis of interruptions in training that are caused when a cyclist encounters a road interse...

Association Rule Mining is a machine learning method for discovering the interesting relations between the attributes in a huge transaction database. Typically, algorithms for Association Rule Mining generate a huge number of association rules, from which it is hard to extract structured knowledge and present this automatically in a form that would...

Numerical Association Rule Mining is a popular variant of Association Rule Mining, where numerical attributes are handled without discretization. This means that the algorithms for dealing with this problem can operate directly, not only with categorical, but also with numerical attributes. Until recently, a big portion of these algorithms were bas...

In the present day, it is difficult to imagine the development of computer games without the use of artificial intelligence. We see it utilized for gameplay, players modeling, playtesting, or content generation. In this paper, we focused on the content generation of a custom Tower Defense game named Save the Sheep. The Tower defense game is a strat...

Sport can be viewed from two standpoints: professional and recreational [...]

The authors got the motivation for writing the paper based on an issue, with which developers of the newly developed nature-inspired algorithms are usually confronted today: How to select the test benchmark such that it highlights the quality of the developed algorithm most fairly? In line with this, the CEC Competitions on Real-Parameter Single-Ob...

Nowadays, only a few papers exist dealing with Association Rule Mining with numerical attributes. When we are confronted with solving this problem using nature-inspired algorithms, two issues emerge: How to shrink the values of the upper and lower bounds of attributes properly, and How to define the evaluation function properly? This paper proposes...

The use of Rayleigh wave dispersion curve for estimating shear wave velocity (Vs) is a common task in the field of engineering geophysics. However, because of the nonlinear nature of Rayleigh wave dispersion curves, using a proper technique in the inversion procedure in order to find adequate model parameters is a challenging problem. In this study...

This work is an extension of a previous paper (presented at the Cyberworlds 2019 conference) introducing a new method for fractal compression of bitmap binary images. That work is now extended and enhanced through three new valuable features: (1) the bat algorithm is replaced by an improved version based on optimal forage strategy (OFS) and random...

Recently, sports training sessions have been generated automatically according to the TRIMP load quantifier that can be calculated easily using data obtained from mobile devices worn by an athlete during the session. This paper focuses on generating a sport training session in cycling, and bases on data obtained from power-meters that, nowadays, pr...

This paper considers the problem of image segmentation for medical images, in particular, cutaneous lesions. Given a digital image of a skin lesion, our goal is to compute the border curve separating the lesion from the image background. This problem can be formulated as an optimization problem, where the border curve is computed through data fitti...

The results of evolutionary algorithms depends on population diversity that normally decreases by increasing the selection pressure from generation to generation. Usually, this can lead evolution process to get stuck in local optima. The study is focused on mechanisms to avoid this undesired phenomenon by introducing parallel differential evolution...

The number of users of smart mobile devices is growing every day. Because of the popularity of using mobile devices, it is important for business stakeholders to develop mobile applications targeting all mobile platforms in order to ensure that the number of users is as large as possible. One possible solution is the creation of hybrid mobile appli...

In recent years, some sport clubs have adopted web forums for online discussions about planning training sessions, races, club problems, sponsors and supporters, equipment and so on. Mostly, these forums are closed, because some discussions about critical information must be permitted only to registered club members. Indeed, various members are con...

Numerical Association Rule Mining is a popular variant of Association Rule Mining, where numerical attributes are handled without discretization. This means that the algorithms for dealing with this problem can operate directly, not only with categorical, but also with numerical attributes. Until recently, a big portion of these algorithms were bas...

Phishing stands for a fraudulent process, where an attacker tries to obtain sensitive information from the victim. Usually, these kinds of attacks are done via emails, text messages, or websites. Phishing websites, which are nowadays in a considerable rise, have the same look as legitimate sites. However, their backend is designed to collect sensit...

The paper presents a novel software framework for Association Rule Mining named uARMSolver. The framework is written fully in C++ and runs on all platforms. It allows users to preprocess their data in a transaction database, to make discretization of data, to search for association rules and to guide a presentation/visualization of the best rules f...

Decisions made nowadays by Artificial Intelligence powered systems are usually hard for users to understand. One of the more important issues faced by developers is exposed as how to create more explainable Machine Learning models. In line with this, more explainable techniques need to be developed, where visual explanation also plays a more import...

Association Rule Mining belongs to one of the more prominent methods in Data Mining, where relations are looked for among features in a transaction database. Normally, algorithms for Association Rule Mining mine a lot of association rules, from which it is hard to extract knowledge. This paper proposes a new visualization method capable of extracti...

The term Swarm Robotics collectively refers to a population of robotic devices that efficiently undertakes diverse tasks in a collaborative way by virtue of computational intelligence techniques. This paradigm has given rise to a profitable stream of contributions in recent years, all sharing a clear consensus on the performance benefits derived fr...

Advising athletes how to improve their performance after a race is a very important aspect of sport training. It can also be called a post-hoc analysis, which often includes a deep analysis of an athlete's performance, behavior and body characteristics after a race. These analyses help trainers to adapt their training plan according to the athlete'...

Preference time in a triathlon denotes the time that is planned to be achieved by an athlete in a particular competition. Usually, the preference time is calculated some days, weeks, or even months before the competition. Mostly, trainers calculate the proposed preference time according to the current form, body performances of athletes, psychologi...

This paper investigates how does the solution representation in nature-inspired algorithms impact the performance of feature selection in classification problems. Four most suitable nature-inspired algorithms for feature selection were considered in the analysis, namely the Differential Evolution, Artificial Bee Colony, Particle Swarm Optimization,...

With the advent of big data, interest for new data mining methods has increased dramatically. The main drawback of traditional data mining methods is the lack of comprehensibility. In this paper, the firefly algorithm was employed for standalone binary classification, where each solution is represented by two classification rules that are easy unde...

These data contain a comprehensive collection of all Nature-Inspired Algorithms. This collection is a result of two corresponding surveys, where all Nature-Inspired Algorithms that have been published to-date were gathered and preliminary data acquired. The rapidly increasing number of nature-inspired approaches makes it hard for interested researc...

The protection of sensitive data against unauthorized access remains a primary concern of modern life. Over time, many different approaches have been introduced to tackle this problem, from substitution ciphers in classic cryptography to post-quantum cryptography as a representative of modern cryptography. In this paper, we focus on a polyalphabeti...

When it comes to game playing, evolutionary and tree-based approaches are the most popular approximate methods for decision making in the artificial intelligence field of game research. The evolutionary domain therefore draws its inspiration for the design of approximate methods from nature, while the tree-based domain builds an approximate represe...

The rapid transformation of our communities and our way of life due to modern technologies has impacted sports as well. Artificial intelligence, computational intelligence, data mining, the Internet of Things (IoT), and machine learning have had a profound effect on the way we do things. These technologies have brought changes to the way we watch,...

A COVID-19 pandemic has already proven itself to be a global challenge. It proves how vulnerable humanity can be. It has also mobilized researchers from different sciences and different countries in the search for a way to fight this potentially fatal disease. In line with this, our study analyses the abstracts of papers related to COVID-19 and cor...

Association Rule Mining is a data mining method for discovering the interesting relations between attributes in a huge transaction database. Typically, algorithms for association rule mining generate a huge number of association rules, from which it is hard to extract structured knowledge and automatically present this in a form that would be suita...

Nowadays, the majority of data on the Internet is held in an unstructured format, like websites and e-mails. The importance of analyzing these data has been growing day by day. Similar to data mining on structured data, text mining methods for handling unstructured data have also received increasing attention from the research community. The paper...

The success of every stochastic population-based nature-inspired algorithms is characterized through the dichotomy of exploration and exploitation. In general, exploration refers to the evaluation of points in previously untested areas of a search space, while exploitation refers to evaluation of points in close vicinity to previously visited point...

Knowledge discovery in databases is a comprehensive procedure which enables researchers to explore knowledge and information from raw sample data usefully. Some problems may arise during this procedure, for example the Curse of Dimensionality, where the reduction of database is desired to avoid feature redundancy or irrelevancy. In this paper, we p...

This paper outlines a short overview of swarm intelligence algorithms that are used within the software engineering area. Swarm intelligence algorithms have been used in many software engineering tasks, e.g., grammatical inference or mutation testing. However, their presence in the agile software development field is still awakening. As there are s...

The rapid growth of data and the need for its proper analysis still presents a big problem for intelligent data analysis and machine learning algorithms. In order to gain a better insight into the problem being analyzed, researchers today are trying to find solutions for reducing the dimensionality of the data, by adopting algorithms that could rev...