Vaclav Snasel

Vaclav Snasel
VŠB-Technical University of Ostrava · Department of Computer Science

PhD

About

907
Publications
183,358
Reads
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7,213
Citations
Citations since 2017
205 Research Items
4208 Citations
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Introduction
Vaclav Snasel's research and development experience includes over 30 years in the Industry and Academia. He works in a multi-disciplinary environment involving social network, formal concept analysis, information retrieval, semantic web, knowledge management, data compression, machine intelligence, neural network, web intelligence, bio-inspired computing, data mining, and applied to various real world problems.
Additional affiliations
September 2001 - present
VŠB-Technical University of Ostrava
Position
  • Professor (Full)

Publications

Publications (907)
Article
Full-text available
On the outbreak of the global COVID-19 pandemic, high-risk and vulnerable groups in the population were at particular risk of severe disease progression. Pregnant women were one of these groups. The infectious disease endangered not only the physical health of pregnant women, but also their mental well-being. Improving the mental health of pregnant...
Article
Full-text available
Parallel implementations of algorithms are usually compared with single-core CPU performance. The advantage of multicore vector processors decreases the performance gap between GPU and CPU computation, as shown in many recent pieces of research. With the AVX-512 instruction set, there will be another performance boost for CPU computations. The avai...
Chapter
Graph Neural Networks have been extensively applied in the field of machine learning to find features of graphs, and recommendation systems are no exception. The ratings of users on considered items can be represented by graphs which are input for many efficient models to find out the characteristics of the users and the items. From these insights,...
Article
Full-text available
This paper introduces a novel algorithm for effective and accurate extraction of non-invasive fetal electrocardiogram (NI-fECG). In NI-fECG based monitoring, the useful signal is measured along with other signals generated by the pregnant women’s body, especially maternal electrocardiogram (mECG). These signals are more distinct in magnitude and ov...
Article
Full-text available
This article presents a comprehensively state-of-the-art investigation of the engineering applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based on application scenarios and solution encoding, and describes these algorithms in detail to help researchers choose appropriate methods to solve related applications....
Chapter
Traditional computing hardware is working to meet the extensive computational load presented by the rapidly growing Machine Learning (ML) and Artificial Intelligence algorithms such as Deep Neural Networks and Big Data. In order to get hardware solutions to meet the low-latency and high-throughput computational needs of these algorithms, Non-Von Ne...
Chapter
Due to the complex topology of the search space, expensive multi-objective evolutionary algorithms (EMOEAs) emphasize enhancing the exploration capability. Many algorithms use ensembles of surrogate models to boost the performance. Generally, the surrogate-based model either works out the solution’s fitness by approximating the evaluation function...
Article
Full-text available
In social influence analysis, viral marketing, and other fields, the influence maximization problem is a fundamental one with critical applications and has attracted many researchers in the last decades. This problem asks to find a k-size seed set with the largest expected influence spread size. Our paper studies the problem of fairness budget dist...
Article
Multiple-criteria group decision-making (MCGDM) problems mainly consist of multiple factors and multiple Decision Makers (DMs) or Users, for which dimension extension is necessary when considering all the entries of DMs together. Tensor, a generalized form of a matrix, displays a multi-way array item, which is the most suitable and practical way to...
Article
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Network alignment, which is also known as user identity linkage, is a kind of network analysis task that predicts overlapping users between two different social networks. This research direction has attracted much attention from the research community, and it is considered to be one of the most important research directions in the field of social n...
Article
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In recent years, the issue of maximizing submodular functions has attracted much interest from research communities. However, most submodular functions are specified in a set function. Meanwhile, recent advancements have been studied for maximizing a diminishing return submodular (DR-submodular) function on the integer lattice. Because plenty of pu...
Chapter
In this chapter, three recent swarm intelligence algorithms are used to solve a challenging optimization problem in the field of photonics, including Grey Wolf Optimizer, Whale Optimization Algorithm, and Moth Flame Optimization Algorithm. The problem is to optimize the radii of several rods in a photonics crystal to minimize light wave loss when t...
Article
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Nowadays social networks such as Twitter, LinkedIn, and Facebook are a popular and necessary platform. It is considered a miniature of an actual social network because of its advantages in connecting and sharing information between users. The analysis of data on online social networks has become a field that has attracted a lot of attention from th...
Article
Most metaheuristic optimizers rely heavily on precisely setting their control parameters and search operators to perform well. Considering the complexity of real-world problems, it is always preferable to adjust control parameter values automatically rather than clamping them to a fixed value. In recent years, Spherical Search (SS) has emerged as a...
Article
Full-text available
Blockchain has found many applications, apart from Bitcoin, in different fields and it has the potential to be very useful in the satellite communications and space industries. Decentralized and secure protocols for processing and manipulating space transactions of satellite swarms in the form of Space Digital Tokens (SDT) can be built using blockc...
Article
Full-text available
Selecting team players is a crucial and challenging task demanding a considerable amount of thinking and hard work by the selectors. The present study formulated the selection of an IPL squad as a multi-objective optimization problem with the objectives of maximizing the batting and bowling performance of the squad, in which a player’s performance...
Article
Full-text available
Unmanned aerial vehicles (UAVs) have emerged as a powerful technology for introducing untraditional solutions to many challenges in non-military fields and industrial applications in the next few years. However, the limitations of a drone’s battery and the available optimal charging techniques represent a significant challenge in using UAVs on a la...
Article
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Coronavirus disease (COVID-19) is rapidly spreading worldwide. Recent studies show that radiological images contain accurate data for detecting the coronavirus. This paper proposes a pre-trained convolutional neural network (VGG16) with Capsule Neural Networks (CapsNet) to detect COVID-19 with unbalanced data sets. The CapsNet is proposed due to it...
Article
Full-text available
This work explores the effectiveness and robustness of quantum computing by conjoining the principles of quantum computing with the conventional computational paradigm for the automatic clustering of colour images. In order to develop such a computationally efficient algorithm, two population-based meta-heuristic algorithms, viz., Particle Swarm Op...
Article
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In this paper, we propose a hybrid meta-heuristic algorithm called MRFO-PSO that hybridizes the Manta ray foraging optimization (MRFO) and particle swarm optimization (PSO) with the aim to balance the exploration and exploitation abilities. In the MRFO-PSO, the concept of velocity of the PSO is incorporated to guide the searching process of the MRF...
Article
Performance measurement is a complex but important task required in all sectors. The problem however arises when usage of different methods for performance assessment provides different results. Under such circumstances when there is a difference of opinions, rank aggregation methods can be used to provide the best solution to decision-makers (DMs)...
Article
Full-text available
Off-grid power systems are often used to supply electricity to remote households, cottages, or small industries, comprising small renewable energy systems, typically a photovoltaic plant whose energy supply is stochastic in nature, without electricity distributions. This approach is economically viable and conforms to the requirements of the Europe...
Article
Full-text available
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...
Chapter
Pan, Jeng-ShyangHu, PeiChu, Shu-ChuanSnášel, Václav Phasmatodea population evolution (PPE) algorithm is a new meta-heuristic, which has presented great performance in engineering optimization problems. A novel version, called K-PPE, is proposed to advance PPE. K-PPE improves the method of generating K optimal solutions and adopts dynamic value of K...
Article
Full-text available
DEA, incepted in 80s, has emerged as a popular decision-making technique, for determining the efficiency of similar units. Due to its simplicity and applicability, DEA has gained the attention of scientists and researchers working in diverse areas, which has contributed towards a rich literature both in terms of theoretical development as well as d...
Article
Engineering design problems are usually large-scale constrained optimization problems, and metaheuristic algorithms are vital for solving such complex problems. Therefore, this paper introduces a new nature-inspired metaheuristic algorithm: the gannet optimization algorithm (GOA). The GOA mathematizes the various unique behaviors of gannets during...
Article
Random Forest is an ensemble of decision trees based on the bagging and random subspace concepts. As suggested by Breiman, the strength of unstable learners and the diversity among them are the ensemble models’ core strength. In this paper, we propose two approaches known as oblique and rotation double random forests. In the first approach, we prop...
Article
Full-text available
The unmanned aerial vehicle (UAV) path planning problem is primarily concerned with avoiding collision with obstacles while determining the best flight path to the target position. This paper first establishes a cost function to transform the UAV route planning issue into an optimization issue that meets the UAV’s feasible path requirements and pat...
Article
Full-text available
The unmanned aerial vehicle (UAV) path planning problem is primarily concerned with avoiding collision with obstacles while determining the best flight path to the target position. This paper first establishes a cost function to transform the UAV route planning issue into an optimization issue that meets the UAV’s feasible path requirements and pat...
Article
Full-text available
With the continuous development of evolutionary computing, many excellent algorithms have emerged, which are applied in all walks of life to solve various practical problems. In this paper, two hybrid fish, bird and insect algorithms based on different architectures are proposed to solve the optimal coverage problem in wireless sensor networks. The...
Article
Combined heat and power economic dispatch (CHPED) is a challenging important optimization task in the economic operation of power systems that aims to minimize the production cost by scheduling the generation and heat outputs to committed units. The interdependency of heat and power production of the CHPED task exhibits non-convexity and non-linear...
Preprint
Full-text available
This paper introduces a novel algorithm for effective and accurate extraction of non-invasive fetal Electrocardiogram (NI-fECG). In NI-fECG based monitoring, the useful signal is measured along with other signals generated by the pregnant women’s body, especially maternal electro-cardiogram (mECG). These signals are more distinct in magnitude and o...
Article
The simulation, control and optimization of photovoltaic (PV) modules require the extraction of parameters from actual data and the construction of highly accurate PV cells. Multiple PV modules supplying power to a common load is the most common form of power distribution in PV systems. In these PV systems, providing separate maximum power point tr...
Article
The object recognition problems realized in uncertain environments have played a paramount role in decision-making. In recent years, neutrosophic soft sets (NS-sets), a combination of soft and neutrosophic sets, have emerged as outstanding candidates in this field. If neutrosophic sets are used to handle problems involving imprecise, indeterminate,...
Article
Full-text available
An important problem in the context of viral marketing in social networks is the Influence Threshold (IT) problem, which aims at finding some users (referred to as a seed set) to begin the process of disseminating their product’s information so that the benefit gained exceeds a predetermined threshold. Even though, marketing strategies exhibit diff...
Article
This paper will present how predictive evaluation can be applied in research on specifying perception thresholds in cognitive approach to understanding images. This approach will be based on predictive methods with cognitive inference for threshold re-production for image patterns. In particular, two basic parameters having an impact on perception...
Article
Increase in global population growth and the consequent rise in the demand for food while ensuring quality, preventing wastage, avoiding deforestation, and carbon footprinting has put tremendous pressure on the global food supply chain (FSC). Due to fast computing facilities and availability of data, blockchain technology has emerged as a potential...
Article
Full-text available
Neutrosophic sets have recently emerged as a tool for dealing with imprecise, indeterminate, inconsistent data, while soft sets may have the potential to deal with uncertainties that classical methods cannot control. Combining these two types of sets results in a unique hybrid structure, a neutrosophic soft set (NS-set), for working effectively in...
Chapter
In the field of population-based multi-objective optimization, a non-dominated sorting approach amounts to sort a set of candidate solutions with multiple objective function values, based on their dominance relations, and to find out solutions distributed into the first front set, second front set, and so on. A fast non-dominated sorting approach u...
Chapter
In many (large)-objective optimization problems, high-dimensional data are involved. Hence, the results of population convergence and population distance in decision space are high-dimensional geometrical objects which are difficult to analyze and interpret. A popular method Parallel Coordinates Plot scales well to high-dimensional data. However, t...
Article
Full-text available
Information hiding can be seen everywhere in our daily life, and this technology improves the security of information. The requirements for information security are becoming higher and higher. The coverless information hiding with the help of mapping relationship has high capacity, but there is still a problem in which the secret message cannot fin...
Article
Equilibrium optimizer (EO) is a new proposed meta-heuristic algorithm by utilizing the mass balance model of the control volume. In order to solve the binary appli-cations, this paper proposes a binary version of equilibrium optimizer (BEO). BEO takes advantage of the structure of EO, only modifying the equations of equilibrium concentra-tion and p...
Chapter
Computational methods based on Artificial Intelligence (AI) can convert or post-process data produced by Numerical Weather Prediction (NWP) systems to predict Photo-Voltaic (PV) power in consideration of a plant specific situation. Their statistical models, developed with historical data series, are more precise if they rely on the latest weather o...
Chapter
Most common of renewable energies sources which are available everywhere around the world are solar and wind energies. These sources are used to generate the electricity as an alternative clean power source. The electric power which is generated by these sources is sometimes available and in other times it is not available depending on the weather...
Book
This book presents key advances in intelligent information technologies for industry. This book of Lecture Notes in Networks and Systems contains the papers presented in the main track of IITI 2021, the Fifth International Scientific Conference on Intelligent Information Technologies for Industry held on September 30 – October 4, 2021 in Sirius, Ru...
Book
This proceeding book constitutes the refereed proceedings of the 7th International Conference on Advanced Intelligent Systems and Informatics (AISI 2021), which took place in Cairo, Egypt, during December 11-13, 2021, and is an international interdisciplinary conference that presents a spectrum of scientific research on all aspects of informatics a...
Book
This book emphasizes the latest developments and achievements in artificial intelligence and related technologies, focusing on the applications of artificial intelligence and medical diagnosis. The book describes the theory, applications, concept visualization, and critical surveys covering most aspects of AI for medical informatics.
Article
Full-text available
Metaheuristic algorithms have successfully been used to solve any type of optimization problem in the field of structural engineering. The newly proposed Arithmetic Optimization Algorithm (AOA) has recently been presented for mathematical problems. The AOA is a metaheuristic that uses the main arithmetic operators' distribution behavior, such as mu...
Chapter
Influence maximization (\({\mathsf {IM}}\)) is an important problem in social influence, viral marketing, and economics. This paper studies a fairness constraint in the Influence Maximization problem, a general \( {\mathsf {IM}}\) version that aims to find a k-size seed set distributed in target communities. Each has certain upper and lower bounds...
Article
Full-text available
This research proposes an Archive-based Multi-Objective Arithmetic Optimization Algorithm (MAOA) as an alternative to the recently established Arithmetic Optimization Algorithm (AOA) for multi-objective problems (MAOA). The original AOA approach was based on the distribution behavior of vital mathematical arithmetic operators, such as multiplicatio...
Article
Since the last three decades, numerous search strategies have been introduced within the framework of different evolutionary algorithms (EAs). Most of the popular search strategies operate on the hypercube (HC) search model, and search models based on other hypershapes, such as hyper-spherical (HS), are not investigated well yet. The recently devel...
Article
Full-text available
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...
Article
Full-text available
Mining of colossal patterns is used to mine patterns in databases with many attributes and values, but the number of instances in each database is small. Although many efficient approaches for extracting colossal patterns have been proposed, they cannot be applied to colossal pattern mining with constraints. In this paper, we solve the challenge of...
Chapter
The influence maximization (IM) is an optimization problem in the information propagation and social network analysis, which has the goal of finding a seed set that can influence largest number of users. There have been many studies on the IM problem, but most of them focus on maximizing influence effects based on individuals rather than on groups...
Preprint
Full-text available
An ensemble of decision trees is known as Random Forest. As suggested by Breiman, the strength of unstable learners and the diversity among them are the ensemble models' core strength. In this paper, we propose two approaches for generating ensembles of double random forest. In the first approach, we propose a rotation based ensemble of double rand...
Article
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...
Article
Student evaluation is an essential part of education and is usually done through examinations. These examinations generally use tests consisting of several questions as crucial factors to determine the quality of the students. Test-making can be thought of as a multi-constraint optimization problem. However, the test-making process that is done by...
Article
Community detection in complex networks has recently become considerable because it is possible to explore community structure, analyze behaviour and action. Detecting these communities brings enormous finance and provides informational value in the complex network. Many algorithms are proposed in previous studies for community structure detection...
Article
Metaheuristic algorithm is a prestigious technique for solving optimization problems. QUATRE is a simple but powerful algorithm. However, QUATRE also shows premature convergence and is easily trapped in local optima for complex optimization problems. This work presents a novel algorithm named two-phase QUasi-Affine Transformation Evolution with fee...
Article
Full-text available
This study aimed to find the most suitable combination of adaptive and non-adaptive methods for extraction of non-invasive fetal electrocardiogram (NI-fECG) using signals recorded from the mother’s abdomen. Among the nine methods considered, the combination of independent component analysis (ICA), fast transversal filter (FTF), and complementary en...
Article
Full-text available
In the pre-processing of the digital thermograms, multi-level thresholding plays a crucial role in the segmentation of thermographic images for better clinical decision support. This paper attempts to optimize the multi-level thresholding method for thermographic image segmentation using Differential Evolution (DE) with the Otsu’s between-class var...