Ivan Zelinka

Institute of Electrical and Electronics Engineers | IEEE · IEEE Computer Society

In the present paper, we demonstrate the possibilities of designing quantum computing circuits using a specific swarm intelligence algorithm — iSOMA in the form of three experiments. All simulations are based on a simple sample of a quantum computing circuit from the Qiskit environment, which was used as a comparison circuit with the results of the...

One of the key tasks in the economy is forecasting the economic agents' expectations of the future values of economic variables using mathematical models. The behavior of mathematical models can be irregular, including chaotic, which reduces their predictive power. In this paper, we study the regimes of behavior of two economic models and identify...

The goal of this paper is the insight into the forecasting of Bitcoin price using machine learning models like AutoRegressive Integrated Moving Average (ARIMA), Support vector machines (SVM), hybrid ARIMA-SVM, and Long short-term memory (LSTM). Depending on the different types of data and the period, various models are used for prediction. A single...

To date, a large number of research papers have been written on the classification of malware, its identification, classification into different families and the distinction between malware and goodware. These works have been based on captured malware samples and have attempted to analyse malware and goodware using various techniques, including tec...

The problem of class imbalance has always been considered as a significant challenge to traditional machine learning and the emerging deep learning research communities. A classification problem can be considered as class imbalanced if the training set does not contain an equal number of labeled examples from all the classes. A classifier trained o...

A social network is one of the efficient tools for information propagation. The content is the bridge between the product and its customers. Evaluating the user’s content creation is a valuable feature to improve information spreading on the social network. This paper proposes a method for extracting brand value with influencers by combining the us...

Large graphs are often used to simulate and model complex systems in various research and application fields. Because of its importance, frequent subgraph mining (FSM) in single large graphs is a vital issue, and recently, it has attracted numerous researchers, and played an important role in various tasks for both research and application purposes...

Irregular, especially chaotic, behavior is often undesirable for economic processes because it presents challenges for predicting their dynamics. In this situation, control of such a process by its mathematical model can be used to suppress chaotic behavior and to transit the system from irregular to regular dynamics. In this paper, we have constru...

This paper presents an approach to solve the variant of the well-known Travelling Salesman Problem (TSP) by using a gamesourcing approach. In contemporary literature is TSP solved by wide spectra of modern as well as classical computational methods. We would like to point out the possibility to solve such problems by computer game plying that is ca...

In many recent applications, a graph is used to simulate many complex systems, such as social networks, traffic models or bioinformatics, and the underlying graphs for these systems are very large. Algorithms for mining all frequent subgraphs from a single large graph have attracted much attention and been studied in more detail lately. Mining freq...

Differential Evolution (DE) has been widely appraised as a simple yet robust population-based, non-convex optimization algorithm primarily designed for continuous optimization. Two important control parameters of DE are the scale factor F, which controls the amplitude of a perturbation step on the current solutions and the crossover rate Cr, which...

Propagating patterns are used to transfer and process information in chemical and physical prototypes of unconventional computing devices. Logical values are represented by fronts of traveling diffusive, trigger or phase waves. We apply this concept of pattern based computation to develop experimental prototypes of computing circuits implemented in...

Recent years have witnessed a dramatic growth in utilizing computational intelligence techniques for various domains. Coherently, malicious actors are expected to utilize these techniques against current security solutions. Despite the importance of these new potential threats, there remains a paucity of evidence on leveraging these research litera...

The 4th edition of the popular as well as prestigious International Conference on Intelligent Computing and Optimization (ICO) 2021, in short ICO’2021, will be held along an “online” platform, herewith respecting the care for everyone as necessitated by the pandemic COVID-19. The physical conference is foreseen to be celebrated at G Hua Hin Resort...

In the contemporary edition, this book of conference proceedings encloses the original and innovative, creative and recreative scientific fields on optimization and optimal control, renewable energy and sustainability, artificial intelligence and operational research, economics and management, smart cities and rural planning, meta-heuristics and bi...

In modern applications, large graphs are usually applied in the simulation and analysis of large complex systems such as social networks, computer networks, maps, traffic networks. Therefore, graph mining is also an interesting subject attracting many researchers. Among them, frequent subgraph mining in a single large graph is one of the most impor...

Mining frequent subgraphs is an interesting and important problem in the graph mining field, in that mining frequent subgraphs from a single large graph has been strongly developed, and has recently attracted many researchers. Among them, MNI-based approaches are considered as state-of-the-art, such as the GraMi algorithm. Besides frequent subgraph...

This article introduces a version of the Self-Organizing Migrating Algorithm with a narrowing search space strategy named iSOMA. Compared to the previous two versions, SOMA T3A and Pareto that ranked 3rd and 5th respectively in the IEEE CEC (Congress on Evolutionary Computation) 2019 competition, the iSOMA is equipped with more advanced features wi...

Random mechanisms including mutations are an internal part of evolutionary algorithms, which are based on the fundamental ideas of Darwin’s theory of evolution as well as Mendel’s theory of genetic heritage. In this paper, we debate whether pseudo-random processes are needed for evolutionary algorithms or whether deterministic chaos, which is not a...

OCR post-processing is an important step for improving the quality of OCR output texts. Long short-term memory (LSTM) is a deep learning model, which has wide-range applications in many domains like time series prediction, natural language processing and speech recognition. In this paper, we propose an OCR error correction model using neural machin...

This work outlines a practically realizable (i.e., deployable and scalable) yet novel autonomous exploration strategy for unmanned aerial vehicles (UAV), which in our case, corresponds to multi-rotor configurations. Concretely, based on a probabilistic map, UAVs are able to modify their trajectory to localize the required target in unknown areas. T...

The widespread development of the malware industry is considered the main threat to our e-society. Therefore, malware analysis should also be enriched with smart heuristic tools that recognize malicious behaviors effectively. Although the generated API calling graph representation for malicious processes encodes worthwhile information about their m...

In the fourth technology revolution, influencer marketing is an essential kind of digital marketing. This marketing uses identified influencers to viral the information of products to target customers. It is useful to support brands exposed to more valuable online consumers. The influencer marketing campaign needs a management system to manage on a...

In recent months and years, with more and more computers and computer systems becoming the target of cyberattacks. These attacks are gaining strength and the sophistication of the approach in terms of how to attack. Attackers and Defenders are increasingly using artificial intelligence methods to maximize the success of their actions. For a success...

In this chapter, we introduce the basic characteristics of swarm intelligence, the path planning problem for robots, and how to apply the self-organizing migrating algorithm, a representative of swarm intelligence to solve that real-world problem. We set up simulations in the Matlab environment with four common possible scenarios to demonstrate the...

The dynamic constrained optimization problems can be a challenge for the optimization algorithms. They must tackle global optimum detection, as well as the change of the environment. Recently, a novel test suite for dynamic constrained optimization was introduced. Furthermore, three well-performed evolutionary algorithms were compared based on it....

Optical character recognition (OCR) systems help to digitize paper-based historical achieves. However, poor quality of scanned documents and limitations of text recognition techniques result in different kinds of errors in OCR outputs. Post-processing is an essential step in improving the output quality of OCR systems by detecting and cleaning the...

Artificial intelligence and its subparts (like evolutionary algorithms, machine learning\(,\ldots \)) are search methods that can be used for solving optimization problems. A particular class of algorithms like bioinspired one mimic working principles from natural evolution (or swarm intelligence) by employing a population-based (swarm) approach, l...

For this edition, the conference proceedings cover the innovative, original, and creative research areas of sustainability, smart cities, meta-heuristics optimization, cybersecurity, block chain, big data analytics, IoTs, renewable energy, artificial intelligence, Industry 4.0, modeling, and simulation. The organizing committee would like to sincer...

The third edition of the International Conference on Intelligent Computing and Optimization (ICO) ICO’2020 will be held via online platform due to COVID-19 pandemic. The physical conference was held at G Hua Hin Resort & Mall, Hua Hin, Thailand, once the COVID-19 pandemic is recovered. The objective of the international conference is to bring toget...

This book gathers papers presented during the 4th International Conference on Electrical Engineering and Control Applications. It covers new control system models, troubleshooting tips and complex system requirements, such as increased speed, precision and remote capabilities. Additionally, the papers discuss not only the engineering aspects of sig...

This present book discusses the application of the methods to astrophysical data from different perspectives. In this book, the reader will encounter interesting chapters that discuss data processing and pulsars, the complexity and information content of our universe, the use of tessellation in astronomy, characterization and classification of astr...

Evolutionary algorithms (EAs) and deterministic chaos, which is a complex behavior produced by complex as well as simple dynamical systems, are tightly joined to create an interdisciplinary fusion of two interesting areas. This chapter discusses the use of EAs for numerical identification of the existence of the so-called hidden attractors (a full...

A numerical method based on the Pontryagin maximum principle for solving an optimal control problem with static and dynamic phase constraints for a group of objects is considered. Dynamic phase constraints are introduced to avoid collisions between objects. Phase constraints are included in the functional in the form of smooth penalty functions. Ad...

OCR post-processing is an essential step to improve the accuracy of OCR-generated texts by detecting and correcting OCR errors. In this paper, the OCR texts are resulted from an OCR engine which is based on the attention-based encoder-decoder model for unconstrained Vietnamese handwriting. We identify various kinds of Vietnamese OCR errors and thei...

n recent years, computational intelligence methodologies in general and swarm intelligence in particular have become increasingly popular and attracted interest with a dramatic increase in the number of relevant publications. Swarm intelligence (SI) and bio-inspired computing, in general, have successfully adopted in many areas of science and engin...

Along with the rapid increase in the popularity of online media , the proliferation of fake news and its propagation is also rising. Fake news can propagate with an uncontrollable speed without verification and can cause severe damages. Various machine learning and deep learning approaches have been attempted to classify the real and the false news...

Artificial neural networks, evolutionary algorithms (and swarm intelligence) are common algorithms belonging to well known soft-computing algorithms. Both classes of algorithms have their history, principles and represent two different biological areas, converted to computer technology. Despite fact that scientists already exhibited that both syste...

The focus of this work is the deeper insight into arising serious research questions connected with the growing popularity of combining metaheuristic algorithms and chaotic sequences showing quasi-periodic patterns. This paper reports analysis on the performance of popular and CEC 2019 competition winning strategy of Differential Evolution (DE), wh...

The paper proposes a method for the drone to catch the given target and avoid detected obstacles in its path based on the self-organizing migrating algorithm. In particular, a two-component fitness function is proposed based on the principle that the closer the target, the lower the fitness value, and the closer the obstacle, the higher the fitness...

In this paper, we propose an original deep learning framework for malware classifying based on the malware behavior data. Currently, machine learning techniques are becoming popular for classifying malware. However, most of the existing machine learning methods for malware classifying use shallow learning algorithms such as Support Vector Machine,...

With the rapid growth of technology in the digital landscape, cybercriminals attempt to utilize new and sophisticated techniques to autonomous and increase the speed and scale of their attacks. Meanwhile, the Dark Web infrastructures such as Tor, plays a crucial role in the criminal underground, especially for malware devel-opers' communities. It i...

In recent years, swarm intelligence in particular, and bio-inspired computing, in general, have successfully utilized in a number of majors from science, engineering to industry. Therefore, it is logical to investigate how such techniques might contribute in the field of cybersecurity. This paper covers swarm-based intelligence techniques for enhan...

This paper is discussing our new research direction in the Voynich manuscript research. While our previous papers have been dealing with the research that has been based on fractal property analyses or graph properties analyses, where the graph has been constructed from the Voynich manuscript word sequences (Fig. 1), this paper discusses another ki...

Rule 22 elementary cellular automaton (ECA) has a 3--cell neighborhood, binary cell states, where a cell takes state `1' if there is exactly one neighbor, including the cell itself, in state `1'. In Boolean terms the cell-state transition is a XOR function of three cell states. In physico--chemical terms the rule might be seen as describing propaga...

Influencer marketing is a modern method that uses influential users to approach goal customers easily and quickly. An online social network is a useful platform to detect the most effective influencer for a brand. Thus, we have an issue: how can we extract user data to determine an influencer? In this paper, a model for representing a social networ...

Mining frequent subgraphs is an important issue in graph mining. It is defined as finding all subgraphs whose occurrences in the dataset are greater than or equal to a given frequency threshold. In recent applications, such as social networks, the underlying graphs are very large. Algorithms for mining frequent subgraphs from a single large graph h...

This extended study presents a hybridization of particle swarm optimization (PSO) with complex network construction and analysis. A partial population restart is performed in certain moments of the run of the algorithm based on the information obtained from a complex network analysis. The complex network structure represents the communication in th...

Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are...

Malware API call graph derived from API call sequences is considered as a representative technique to understand the malware behavioral characteristics. However, it is troublesome in practice to build a behavioral graph for each malware. To resolve this issue, we examine how to generate a simple behavioral graph that characterizes malware. In this...

The dramatic improvements in computational intelligence techniques over recent years have influenced many domains. Hence, it is reasonable to expect that virus writers will taking advantage of these techniques to defeat existing security solution. In this article, we outline a possible dynamic swarm smart malware, its structure, and functionality a...

The focus of this work is the deeper insight into arising serious research questions connected with the growing popularity of combining metaheuristic algorithms and chaotic sequences showing quasi-periodic patterns. This paper reports an analysis of population dynamics by linking three elements like distribution of the results, population diversity...

In this article, we describe the design and implementation of a variant version of SOMA named SOMA Pareto to solve ten hard problems of the 100-Digit Challenge. The algorithm consists of the following operations: Organization, Migration, and Update. In which, we focus on improving the Organization operation with the adaptive parameters of PRT and S...

In this paper, we address 10 basic test functions of the 100-Digit Challenge of the SEMCCO 2019 & FANCCO 2019 Competition by using team-to-team adaptive seft-organizing migrating algorithm - SOMA T3A with many improvements in the Organization, Migration, and Update process, as well as the linear adaptive PRT and the cosine-based adaptive Step. The...

In this paper, we present a robust fuzzy model predictive control (RFMPC) for a class of discrete-time system with input delays. The system is represented into a Takagi-Sugeno (T-S) discrete fuzzy model. Based on the Lyapunov functions theory, some required sufficient conditions are established in terms of linear-matrix inequalities (LMIs). The pro...

This paper presents a method for swarm robots catching multiple moving targets without colliding any dynamic obstacles and other robots in an unknown complex environment. An imaginary map, including multi-layers corresponding to the number of robots, is built in which the starting position, the target, the obstacles, and the robot denoted by the hi...

In this article we continue in our research which combines three different areas - swarm and evolutionary algorithms, networks and coupled map lattices control. Main aim of this article is to compare networks obtained from best and worst self-organizing migrating algorithm runs. All experiments were done on well known CEC 2014 benchmark functions....

In the businesses, the sentiment analysis makes the brands understanding the sentiment of their customers. They can know what people are saying, how they’re saying it, and what they mean. There are many methods for sentiment analysis; however, they are not effective when were applied in Vietnamese language. In this paper, a method for Vietnamese se...

Deep learning methods such as recurrent neural network and long short-term memory have attracted a great amount of attentions recently in many fields including computer vision, natural language processing and finance. Long short-term memory is a special type of recurrent neural network capable of predicting future values of sequential data by takin...

Recent developments in Artificial intelligence (AI) have a vast transformative potential for both cybersecurity defenders and cybercriminals. Anti-malware solutions adopt intelligent techniques to detect and prevent threats to the digital space. In contrast, cybercriminals are aware of the new prospects too and will probably try to use it in their...

Current commercial antivirus detection engines still rely on signature-based methods. However, with the huge increase in the number of new malware, current detection methods become not suitable. In this paper, we introduce a malware detection model based on ensemble learning. The model is trained using the minimum number of signification features t...

Post-processing is an essential step in detecting and correcting errors in OCR-generated texts. In this paper, we present an automatic OCR post-processing model which comprises both error detection and error correction phases for OCR output texts of unconstrained Vietnamese handwriting. We propose a hybrid approach of generating and scoring correct...

The second edition of the International Conference on Intelligent Computing and Optimization (ICO)—ICO 2019 is held during October 3–4, 2019, at Baywater Resort in Koh Samui, Thailand. The objective of the international conference is to bring together the global research scholars, experts and scientists in the research areas of Intelligent Computin...

The emerging challenges of science and engineering, of economies and societies, of sustainability and social complexity, of Operational Research and decision-making, are becoming recognized and acknowledged worldwide more and more. Thorough multidisciplinary research is urgently needed for finding accurate and, at the same time, stable solutions, f...

The self-organizing migrating algorithm is a population-based algorithm belonging to swarm intelligence, which has been successfully applied in several areas for solving non-trivial optimization problems. However, based on our experiments, the original formulation of this algorithm suffers with some shortcomings as loss of population diversity, pre...

In this article, we review how to capture the swarm and evolutionary algorithm dynamic into a network, and we show how we can cover the dynamical network into a coupled map lattices. The main part of this article focuses on the control of the algorithm via the coupled map lattices. Mainly, we concentrate on the statical control and the differential...

Natural systems often exhibit chaotic behavior in their space-time evolution. Systems transiting between chaos and order manifest a potential to compute, as shown with cellular automata and artificial neural networks. We demonstrate that swarm optimization algorithms also exhibit transitions from chaos, analogous to a motion of gas molecules, when...