Numer czasopisma
Computer Science
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ISSN: 1508-2806
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Data wydania
2024
Rocznik
Vol. 25
Numer
No. 1
Prawa dostępu
Dostęp: otwarty dostęp
Uwagi:
Prawa: CC BY 4.0 
Uznanie autorstwa 4.0 Międzynarodowe (CC BY 4.0)
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Rocznik czasopisma (rel.)
Rocznik czasopisma
Computer Science
Vol. 25 (2024)
Artykuły numeru (rel.)
Artykuł
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A survey on syntactic pattern recognition methods in bioinformatics
(Wydawnictwa AGH, 2024) Flasiński, Mariusz
Formal tools and models of syntactic pattern recognition which are used in bioinformatics are introduced and characterized in the paper. They include, among others: stochastic (string) grammars and automata, hidden Markov models, programmed grammars, attributed grammars, stochastic tree grammars, Tree Adjoining Grammars (TAGs), algebraic dynamic programming, NLC- and NCE-type graph grammars, and algebraic graph transformation systems. The survey of applications of these formal tools and models in bioinformatics is presented.
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Using deep neural networks to improve the precision of fast-sampled particle timing detectors
(Wydawnictwa AGH, 2024) Kocot, Mateusz; Misan, Krzysztof; Avati, Valentina; Bossini, Edoardo; Grzanka, Leszek; Minafra, Nicola
Measurements from particle timing detectors are often affected by the time walk effect caused by statistical fluctuations in the charge deposited by passing particles. The constant fraction discriminator (CFD) algorithm is frequently used to mitigate this effect both in test setups and in running experiments, such as the CMS-PPS system at the CERN’s LHC. The CFD is simple and effective but does not leverage all voltage samples in a time series. Its performance could be enhanced with deep neural networks, which are commonly used for time series analysis, including computing the particle arrival time. We evaluated various neural network architectures using data acquired at the test beam facility in the DESY-II synchrotron, where a precise MCP (MicroChannel Plate) detector was installed in addition to PPS diamond timing detectors. MCP measurements were used as a reference to train the networks and compare the results with the standard CFD method. Ultimately, we improved the timing precision by 8% to 23%, depending on the detector’s readout channel. The best results were obtained using a UNet-based model, which outperformed classical convolutional networks and the multilayer perceptron.
Artykuł
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Generalizing clustering inferences with ml augmentation of ordinal survey data
(Wydawnictwa AGH, 2024) Kumar, Bhupendera; Kumar, Rajeev
In this paper, we attempt to generalize the ability to achieve quality inferences of survey data for a larger population through data augmentation and unification. Data augmentation techniques have proven effective in enhancing models’ performance by expanding the dataset’s size. We employ ML data augmentation, unification, and clustering techniques. First, we augment the limited survey data size using data augmentation technique(s). Second, we carry out data unification, followed by clustering for inferencing. We took two benchmark survey datasets to demonstrate the effectiveness of augmentation and unification. The first dataset contains information on aspiring student entrepreneurs’ characteristics, while the second dataset comprises survey data related to breast cancer. We compare the inferences drawn from the original survey data with those derived from the transformed data using the proposed scheme. The results of this study indicate that the machine learning approach, data augmentation with the unification of data followed by clustering, can be beneficial for generalizing the inferences drawn from the survey data.
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Efficient selection methods in evolutionary algorithms
(Wydawnictwa AGH, 2024) Stańczak, Jarosław
Evolutionary algorithms mimic some elements of the theory of evolution. The survival of individuals and the ability to produce offspring play significant roles in the process of natural evolution. This process is called natural selection. This mechanism is responsible for eliminating weaker members of the population and provides the opportunity for the development of stronger individuals. The evolutionary algorithm, an instance of evolution in the computer environment, also requires a selection method – a computerized version of natural selection. Widely used standard selection methods applied in evolutionary algorithms are usually derived from nature and prefer competition, randomness, and some kind of “fight” among individuals. But the computer environment is quite different from nature. Computer populations of individuals are typically small, making them susceptible to premature convergence towards local extremes. To mitigate this drawback, computer selection methods must incorporate features distinct from those of natural selection. In the computer selection methods randomness, fight, and competition should be controlled or influenced to operate to the desired extent. This work proposes several new methods of individual selection, including various forms of mixed selection, interval selection, and taboo selection. The advantages of incorporating them into the evolutionary algorithm are also demonstrated, using examples based on searching for the maximum ?-clique problem and traditional Traveling Salesman Problem (TSP) in comparison with traditionally considered highly efficient tournament selection, deemed ineffective proportional (roulette) selection, and other classical methods.
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The most current solutions using virtual-reality-based methods in cardiac surgery – a survey
(Wydawnictwa AGH, 2024) Mikolajewski, Dariusz; Bryniarska, Anna; Wilczek, Piotr Michal; Myslicka, Maria; Sudol, Adam; Tenczynski, Dominik; Kostro, Michal; Rekawek, Dominika; Tichy, Rafal; Gasz, Rafal; Pelc, Mariusz; Zygarlicki, Jaroslaw; Koziol, Michal; Martinek, Radek; Kahankova Vilimkova, Radana; Dominik Vilimek; Kawala-Sterniuk, Aleksandra
There is a widespread belief that VR technologies can provide controlled, multisensory, interactive 3D stimulus environments that engage patients in interventions and measure, record and motivate required human performance. In order to investigate state-of-the-art and associated occupations we provided a careful review of 6 leading medical and technical bibliometric databases. Despite the apparent popularity of the topic of VR use in cardiac surgery, only 47 articles published between 2002 and 2022 met the inclusion criteria. Based on them, VR-based solutions in cardiac surgery are useful both, for medical specialists and for the patients themselves. The new lifestyle required from cardiac surgery patients is easier to implement thanks to VR-based educational and motivational tools. However, it is necessary to develop the above-mentioned tools and compare their effectiveness with Augmented Reality (AR). For the aforementioned reasons, interdisciplinary collaboration between scientists, clinicians and engineers is necessary.
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Machine learning based event reconstruction for the MUonE experiment
(Wydawnictwa AGH, 2024) Zdybał, Miłosz; Kucharczyk, Marcin; Wolter, Marcin
A proof-of-concept solution based on the machine learning techniques has been implemented and tested within the MUonE experiment designed to search for New Physics in the sector of anomalous magnetic moment of a muon. The results of the DNN based algorithm are comparable to the classical reconstruction, reducing enormously the execution time for the pattern recognition phase. The present implementation meets the conditions of classical reconstruction, providing an advantageous basis for further studies.