Feature selection is a critical component of machine learning and data mining which addresses challenges like irrelevance, noise, redundancy in large-scale data etc., which often result in the curse of dimensionality. This study employs a K-nearest neighbour wrapper to implement feature selection using six nature-inspired algorithms, derived from human behaviour and mammal-inspired techniques. Evaluated on six real-world datasets, the study aims to compare the performance of these algorithms in terms of accuracy, feature count, fitness, convergence and computational cost. The findings underscore the efficacy of the Human Learning Optimization, Poor and Rich Optimization and Grey Wolf Optimizer algorithms across multiple performance metrics. For instance, for mean fitness, Human Learning Optimization outperforms the others, followed by Poor and Rich Optimization and Harmony Search. The study suggests the potential of human-inspired algorithms, particularly Poor and Rich Optimization, in robust feature selection without compromising classification accuracy.

• Klíčová slova
• Algorithms, Feature reduction, KNN, Metaheuristics, Non-traditional algorithms, Optimization,
• Publikační typ
• časopisecké články MeSH

Balancing diversity and convergence among solutions in many-objective optimization is challenging, particularly in high-dimensional spaces with conflicting objectives. This paper presents the Many-Objective Marine Predator Algorithm (MaOMPA), an adaptation of the Marine Predators Algorithm (MPA) specifically enhanced for many-objective optimization tasks. MaOMPA integrates an elitist, non-dominated sorting and crowding distance mechanism to maintain a well-distributed set of solutions on the Pareto front. MaOMPA improves upon traditional metaheuristic methods by achieving a robust balance between exploration and exploitation using the predator-prey interaction model. The algorithm underwent evaluation on various benchmarks together with complex real-world engineering problems where it showed superior outcomes when compared against state-of-the-art generational distance and hypervolume and coverage metrics. Engineers and researchers can use MaOMPA as an effective reliable tool to address complex optimization scenarios in engineering design. The MaOMPA source code is available at https://github.com/kanak02/MaOMPA .

• Klíčová slova
• Convergence, Diversity, Information feedback mechanism, Many-objective optimization, Marine predator algorithm, Metaheuristic algorithm,
• MeSH
• algoritmy * MeSH
• potravní řetězec * MeSH
• predátorské chování * MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH

Terrestrial laser scanning is a powerful technology for capturing the three-dimensional structure of forests with a high level of detail and accuracy. Over the last decade, many algorithms have been developed to extract various tree parameters from terrestrial laser scanning data. Here we present 3D Forest, an open-source non-platform-specific software application with an easy-to-use graphical user interface with the compilation of algorithms focused on the forest environment and extraction of tree parameters. The current version (0.42) extracts important parameters of forest structure from the terrestrial laser scanning data, such as stem positions (X, Y, Z), tree heights, diameters at breast height (DBH), as well as more advanced parameters such as tree planar projections, stem profiles or detailed crown parameters including convex and concave crown surface and volume. Moreover, 3D Forest provides quantitative measures of between-crown interactions and their real arrangement in 3D space. 3D Forest also includes an original algorithm of automatic tree segmentation and crown segmentation. Comparison with field data measurements showed no significant difference in measuring DBH or tree height using 3D Forest, although for DBH only the Randomized Hough Transform algorithm proved to be sufficiently resistant to noise and provided results comparable to traditional field measurements.

• MeSH
• algoritmy MeSH
• automatizace MeSH
• lesy * MeSH
• zobrazování trojrozměrné * MeSH
• Publikační typ
• časopisecké články MeSH

This review provides a comprehensive update on the diagnostic approaches to chronic pancreatitis (CP), emphasizing recent advancements in imaging techniques, biomarker research, and multivariable scoring systems. Despite substantial progress in these areas, current diagnostic algorithms have limitations, particularly for early and non-calcific CP. Traditional criteria have focused on classic diagnostic signs, but "minimal change" CP is increasingly recognized through advanced imaging and function tests. This article aims to guide clinicians in applying current methods and available strategies for CP diagnosis and outline research efforts in the field.

• Klíčová slova
• Biomarkers, Chronic pancreatitis, Diagnosis, Diagnostic criteria, Imaging, Pancreatic function tests,
• MeSH
• algoritmy MeSH
• biologické markery krev   MeSH
• chronická pankreatitida * diagnóza   diagnostické zobrazování   MeSH
• funkční testy pankreatu MeSH
• lidé MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH
• Názvy látek
• biologické markery MeSH

In this paper, we study the design aspects of an indoor visible light positioning (VLP) system that uses an artificial neural network (ANN) for positioning estimation by considering a multipath channel. Previous results usually rely on the simplistic line of sight model with limited validity. The study considers the influence of noise as a performance indicator for the comparison between different design approaches. Three different ANN algorithms are considered, including Levenberg-Marquardt, Bayesian regularization, and scaled conjugate gradient algorithms, to minimize the positioning error (εp) in the VLP system. The ANN design is optimized based on the number of neurons in the hidden layers, the number of training epochs, and the size of the training set. It is shown that, the ANN with Bayesian regularization outperforms the traditional received signal strength (RSS) technique using the non-linear least square estimation for all values of signal to noise ratio (SNR). Furthermore, in the inner region, which includes the area of the receiving plane within the transmitters, the positioning accuracy is improved by 43, 55, and 50% for the SNR of 10, 20, and 30 dB, respectively. In the outer region, which is the remaining area within the room, the positioning accuracy is improved by 57, 32, and 6% for the SNR of 10, 20, and 30 dB, respectively. Moreover, we also analyze the impact of different training dataset sizes in ANN, and we show that it is possible to achieve a minimum εp of 2 cm for 30 dB of SNR using a random selection scheme. Finally, it is observed that εp is low even for lower values of SNR, i.e., εp values are 2, 11, and 44 cm for the SNR of 30, 20, and 10 dB, respectively.

• Klíčová slova
• Bayesian regularization, artificial neural network (ANN), multipath reflections, non-linear least square, visible light communication (VLC), visible light positioning,
• MeSH
• algoritmy * MeSH
• Bayesova věta MeSH
• metoda nejmenších čtverců MeSH
• neuronové sítě * MeSH
• světlo MeSH
• Publikační typ
• časopisecké články MeSH

Although the field of sleep study has greatly developed over recent years, the most common and efficient way to detect sleep issues remains a sleep examination performed in a sleep laboratory. This examination measures several vital signals by polysomnograph during a full night's sleep using multiple sensors connected to the patient's body. Nevertheless, despite being the gold standard, the sensors and the unfamiliar environment's connection inevitably impact the quality of the patient's sleep and the examination itself. Therefore, with the novel development of accurate and affordable 3D sensing devices, new approaches for non-contact sleep study have emerged. These methods utilize different techniques to extract the same breathing parameters but with contactless methods. However, to enable reliable remote extraction, these methods require accurate identification of the basic region of interest (ROI), i.e., the patient's chest area. The lack of automated ROI segmenting of 3D time series is currently holding back the development process. We propose an automatic chest area segmentation algorithm that given a time series of 3D frames containing a sleeping patient as input outputs a segmentation image with the pixels that correspond to the chest area. Beyond significantly speeding up the development process of the non-contact methods, accurate automatic segmentation can enable a more precise feature extraction. In addition, further tests of the algorithm on existing data demonstrate its ability to improve the sensitivity of a prior solution that uses manual ROI selection. The approach is on average 46.9% more sensitive with a maximal improvement of 220% when compared to manual ROI. All mentioned can pave the way for placing non-contact algorithms as leading candidates to replace existing traditional methods used today.

• Klíčová slova
• 3D data processing, Breathing analysis, Depth sensors, Human-machine interaction, MS Kinect data acquisition, Segmentation,
• MeSH
• algoritmy * MeSH
• dýchání MeSH
• lidé MeSH
• počítačové zpracování obrazu metody   MeSH
• polysomnografie MeSH
• spánek MeSH
• zobrazování trojrozměrné * metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Integration of renewable energy sources (RES) to the grid in today's electrical system is being encouraged to meet the increase in demand of electrical power and also overcome the environmental related problems by reducing the usage of fossil fuels. Power Quality (PQ) is a critical problem that could have an effect on utilities and consumers. PQ issues in the modern electric power system were turned on by a linkage of RES, smart grid technologies and widespread usage of power electronics equipment. Unified Power Quality Conditioner (UPQC) is widely employed for solving issues with the distribution grid caused by anomalous voltage, current, or frequency. To enhance UPQC performance, Fractional Order Proportional Integral Derivative (FOPID) is developed; nevertheless, a number of tuning parameters restricts its performance. The best solution for the FOPID controller problem is found by using a Coati Optimization Algorithm (COA) and Osprey Optimization Algorithm (OOA) are combined to make a hybrid optimization CO-OA algorithm approach to mitigate these problems. This paper proposes an improved FOPID controller to reduce PQ problems while taking load power into account. In the suggested model, a RES is connected to the grid system to supply the necessary load demand during the PQ problems period. Through the use of an enhanced FOPID controller, both current and voltage PQ concerns are separately modified. The pulse signal of UPQC was done using the optimal controller, which analyzes the error value of reference value and actual value to generate pulses. The integrated design mitigates PQ issues in a system at non-linear load and linear load conditions. The proposed model provides THD of 12.15% and 0.82% at the sag period, 10.18% and 0.48% at the swell period, and 10.07% and 1.01% at the interruption period of non-linear load condition. A comparison between the FOPID controller and the traditional PI controller was additionally taken. The results showed that the recommended improved FOPID controller for UPQC has been successful in reducing the PQ challenges in the grid-connected RESs system.

• MeSH
• algoritmy * MeSH
• elektřina MeSH
• obnovitelná energie * MeSH
• teoretické modely MeSH
• zdroje elektrické energie MeSH
• Publikační typ
• časopisecké články MeSH

Several recent studies suggest that quantitative description of signal complexity using algorithms of nonlinear analysis could uncover new information about the autonomic system that is not reflected using common methods applied to measures of autonomic activity. With this aim we have performed complexity analysis of electrodermal activity (EDA) assessed in 106 healthy university students during rest conditions and non-conflicting and conflicting Stroop task. Complexity analysis applied to EDA was performed using Skinner's algorithm for pointwise correlation dimension (PD2). Results have shown that EDA responses during the Stroop Colour Word test are related to significantly increased or decreased complexity. Particularly significant result is that PD2 has a unique ability to predict to an extent the change in EDA response to stress i.e. that subjects with low initial PD2 tended to respond to experimental stress by its increase and subjects with high initial PD2 values tended to respond by its decrease. This response was not found in EDA measures where increase of the EDA presented predominant response to experimental stress in majority of the subjects. These findings suggest that PD2 is more sensitive to subtle aspects of functionally and spatially distributed modulatory influences of various parts of the brain that are involved in the EDA modulation and provides novel information in comparison to traditional methods.

• MeSH
• algoritmy MeSH
• autonomní nervový systém fyziologie   patofyziologie   MeSH
• dospělí MeSH
• galvanická kožní odpověď fyziologie   MeSH
• konflikt (psychologie) MeSH
• lidé MeSH
• mladý dospělý MeSH
• psychický stres patofyziologie   MeSH
• statistika jako téma MeSH
• Stroopův test * MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

The exponential distribution optimizer (EDO) represents a heuristic approach, capitalizing on exponential distribution theory to identify global solutions for complex optimization challenges. This study extends the EDO's applicability by introducing its multi-objective version, the multi-objective EDO (MOEDO), enhanced with elite non-dominated sorting and crowding distance mechanisms. An information feedback mechanism (IFM) is integrated into MOEDO, aiming to balance exploration and exploitation, thus improving convergence and mitigating the stagnation in local optima, a notable limitation in traditional approaches. Our research demonstrates MOEDO's superiority over renowned algorithms such as MOMPA, NSGA-II, MOAOA, MOEA/D and MOGNDO. This is evident in 72.58% of test scenarios, utilizing performance metrics like GD, IGD, HV, SP, SD and RT across benchmark test collections (DTLZ, ZDT and various constraint problems) and five real-world engineering design challenges. The Wilcoxon Rank Sum Test (WRST) further confirms MOEDO as a competitive multi-objective optimization algorithm, particularly in scenarios where existing methods struggle with balancing diversity and convergence efficiency. MOEDO's robust performance, even in complex real-world applications, underscores its potential as an innovative solution in the optimization domain. The MOEDO source code is available at: https://github.com/kanak02/MOEDO .

• Publikační typ
• časopisecké články MeSH

Researchers are increasingly focusing on renewable energy due to its high reliability, energy independence, efficiency, and environmental benefits. This paper introduces a novel multi-objective framework for the short-term scheduling of microgrids (MGs), which addresses the conflicting objectives of minimizing operating expenses and reducing pollution emissions. The core contribution is the development of the Chaotic Self-Adaptive Sine Cosine Algorithm (CSASCA). This algorithm generates Pareto optimal solutions simultaneously, effectively balancing cost reduction and emission mitigation. The problem is formulated as a complex multi-objective optimization task with goals of cost reduction and environmental protection. To enhance decision-making within the algorithm, fuzzy logic is incorporated. The performance of CSASCA is evaluated across three scenarios: (1) PV and wind units operating at full power, (2) all units operating within specified limits with unrestricted utility power exchange, and (3) microgrid operation using only non-zero-emission energy sources. This third scenario highlights the algorithm's efficacy in a challenging context not covered in prior research. Simulation results from these scenarios are compared with traditional Sine Cosine Algorithm (SCA) and other recent optimization methods using three test examples. The innovation of CSASCA lies in its chaotic self-adaptive mechanisms, which significantly enhance optimization performance. The integration of these mechanisms results in superior solutions for operation cost, emissions, and execution time. Specifically, CSASCA achieves optimal values of 590.45 €ct for cost and 337.28 kg for emissions in the first scenario, 98.203 €ct for cost and 406.204 kg for emissions in the second scenario, and 95.38 €ct for cost and 982.173 kg for emissions in the third scenario. Overall, CSASCA outperforms traditional SCA by offering enhanced exploration, improved convergence, effective constraint handling, and reduced parameter sensitivity, making it a powerful tool for solving multi-objective optimization problems like microgrid scheduling.

• Klíčová slova
• Energy management, Micro-grid (MG), Multi-objective optimization, Photovoltaic (PV), Renewable energy sources (RESs), Sine cosine algorithm, Wind turbine (WT),
• Publikační typ
• časopisecké články MeSH