Contents of Volume 34 (2024)

1/2024 2/2024

4/2024

  • [1] Purkrábková Z., Langr M., Hrubeš P., Brabec M. (CZ)
    Data Governance in Traffic Data: Anomaly Detection with Generalized Additive Models, pp. 203-218

      Full text         https://doi.org/10.14311/NNW.2024.34.011

    Abstract: The primary objective of the presented research is to enhance an existing data quality control application by integrating advanced anomaly detection mechanisms based on generalized additive models. This approach targets time- series traffic data, where traditional methods may fall short in identifying complex, non-linear patterns of anomalies. In collaboration with Simplity s.r.o., we are extending their current data quality assessment tool to incorporate generalized additive models, providing a more robust and dynamic solution for monitoring and ensuring the reliability of traffic datasets. The integration of these models aims to improve the accuracy of anomaly detection, leading to more effective data management in transport systems and contributing to higher standards of data quality in the field of traffic informatics.

  • [2] Çevik H., Přibyl O., Samandar S. , (CZ, USA) ,
    Understanding Travel Behavior: A Deep Neural Network and SHAP Approach to Mode Choice Determinants. 219-241

      Full text         https://doi.org/10.14311/NNW.2024.34.012

    Abstract: Understanding individual travel behavior is crucial for developing effective travel demand management strategies and informed transportation policies. This study investigates the factors influencing individuals’ mode choices by analyzing data from a comprehensive travel survey. We employ a deep neural network model to explore the relationships between survey variables and respondents’ transportation mode preferences, focusing on both observable and latent factors. The SHAP method is applied to interpret the model’s outputs, providing global and local explanations that offer detailed insights into the contribution of each variable to mode choice decisions. By identifying the key determinants of mode selection and uncovering the complex interactions between these factors, this research provides valuable insights for designing targeted policies that can better address transportation needs and influence sustainable travel behavior.

  • [3] Dostál R., Dostálová A., Johanidesová A., Kocourek J., Kremlík V. , (CZ)
    Parking Capacity Implementation Evaluation Tool. 243-261

      Full text         https://doi.org/10.14311/NNW.2024.34.013

    Abstract: This paper presents a novel tool for optimising residential parking allocation in urban environments using linear programming techniques. The tool addresses the growing challenge of parking space management in cities by quantifying parking utilisation and accessibility. It employs a unique application of the transport problem from Graph Theory to allocate parking supply to household demand while considering real-world constraints such as walking distances and infrastructure limitations. The methodology involves the pre-processing of supply, demand, and distance matrix data, followed by an optimization process that minimises total walking distance and penalises unmet demand. The tool’s effectiveness is demonstrated through an experiment in the Czech town of Slany, showcasing its ability to evaluate current parking situations and assess the impact of potential changes in parking supply. Key outputs include the percentage of satisfied demand, utilization rates of parking supply, and detailed allocation maps. This approach provides urban planners and policymakers with valuable insights for developing efficient and sustainable parking solutions, while also highlighting areas for further research in data preparation and model refinement.

3/2024

  • [1] Coloma-Salazar M.-E., Arzola-Ruiz J., Marrero-Fornaris C.-E., Socha V., Asgher U, (Cuba, CZ, Pakistan) , ,
    A Combinatorial Approach for Optimizing Transportation System: Multi-Objective Decision-Making Framework, pp. 135-168

      Full text         https://doi.org/10.14311/NNW.2024.34.008

    Abstract: This study presents a comprehensive multi-objective transportation model aimed at optimizing complex vehicle routing problems, which are nondeterministic polynomial time NP-hard due to spatial, temporal, and capacity constraints. In this study, the multi-objective transportation model integrates decisionmaker preferences with hybrid optimization techniques, including the approximatecombinatorial method, ant colony optimization and evolutionary algorithms. it seeks to minimize transportation costs, time, and emissions while accounting for real-world constraints such as fleet composition, customer demand, and servicelevel agreements. The techniques like multi-criteria decision-making methods are employed to refine the solution set, balancing objectives like cost, time, environmental impact, and service level. The novel optimization model is applied to a fuel distribution case study involving 18 customers and a heterogeneous fleet, where it optimizes vehicle routes to meet delivery requirements efficiently. The multiobjective transportation framework generates multiple feasible solutions, which are further narrowed down using decision-making frameworks to ensure alignment with organizational goals and decision-maker preferences. The integration of quantitative optimization techniques with qualitative decision-making processes makes this model robust and scalable, offering a practical tool for enhancing operational efficiency in transportation systems. This approach effectively addresses real-world logistics challenges, demonstrating significant improvements in route efficiency, cost savings, and environmental sustainability.

  • [2] Mocková D., Teichmann D., Sekničková J., Kuncová M., (CZ)
    Asymmetric Orienteering Problem with Profitable Penalty, pp. 169-188

      Full text         https://doi.org/10.14311/NNW.2024.34.009

    Abstract: This paper solves a modified version of the asymmetric traveling salesman problem with the possibility of omitting certain nodes and with a defined time limit for the total travel time, also referred to as the asymmetric orienteering problem (AOP). This problem belongs to the class of NP-hard problems. A proposed mathematical model maximizes the total score gained from visiting nodes within a predefined time limit. The possibility of exceeding the time limit, which results in a penalty to the total score, is also considered. The profitable penalty is examined, i.e., whether accepting the penalty can be advantageous for increasing the total score. The problem is demonstrated in a case study from the ski adventure race, organized in the Jizera Mountains in the Czech Republic.

  • [3] Svítek M., (CZ, SK) ,
    Integrated Information Assessment Using Geometric Algebra, pp. 189-201

      Full text         https://doi.org/10.14311/NNW.2024.34.010

    Abstract: The goal of the paper is to introduce a universal approach for calculating integrated information assessment (IIA) in complex systems by utilizing the geometric product from geometric algebra (GA). Traditional models of consciousness try to explain how neural networks and cognitive processes give rise to a unified conscious experience. Quantum mechanics (QM) could provide a framework for understanding this integration by suggesting that conscious experience arises from entangled states across different system parts. Thanks to the high redundancy of neural networks, it is possible to realize different variants of cognitive processes in parallel and switch between them as needed. This opens up the possibility of hypothetically creating non-separable (not necessarily non-local) entangled models without requiring a quantum environment. The described IIA algorithm is derived from the assessment of entanglement in QM systems using GA. The results are shown on a set of illustrative examples.

2/2024

  • [1] Yi D., Kim I., Bu S., (Korea)
    Variations of Training Process in Vanilla Recurrent Neural Network Framework, pp. 73-87

      Full text         https://doi.org/10.14311/NNW.2024.34.005

    Abstract: Recurrent neural networks (RNNs) are capable of learning features and long term dependencies from sequential and time series data and show outstanding performance in sequential modeling tasks. However, training process in RNNs is troubled by issues in learning processes such as slow inference, vanishing gradients and difficulties in capturing long term dependencies. In this paper, we introduce a new learning technique to update the weight set as we change the input sequence which is shifted by certain amount of time in training process, instead of using a traditional way to calculate one set of the weights and bias in training time series with sequences shifted by certain amount of time series. We also consider an algorithm for an evaluation process. In the traditional way, the evaluation process is executed by using final weights and biases calculated in the training process. Instead, during the testing process, the weights and biases are iteratively updated in each sequence as done in the training process. Several numerical experiments demonstrate the efficiency of the proposed techniques.

  • [2] Uglickich E., Nagy I , (CZ)
    3D Local Crime Type Models Based on Crime Hotspot Detection, pp. 89-110

      Full text         https://doi.org/10.14311/NNW.2024.34.006

    Abstract: This paper deals with the analysis of the relationship between locations and types of crime observed in the Czech Republic. Cluster analysis of crime data based on the recursive Bayesian mixture estimation algorithm is used to identify crime hotspots and estimate local models of crime type. The experiments report that the 2D configuration of the algorithm allows the detection of crime hotspots online. The 3D configuration provides 29% more accurate crime type models than 2D clustering and alternative data mining algorithms. For the data set used, it was determined in which crime hotspots the most serious and most frequent types of crime can be expected to occur with the highest probability. The limitation of the study is the artificial support of the 3D clusters by the fully continuous data vector with the recoded values of the crime type. The potential use of the algorithm is expected in online web applications for sharing information on criminal offenses managed by the Police of the Czech Republic with the public and local government entities in the Czech Republic.

  • [3] Kovanda J., Hozman J. , (CZ)
    Equations of Motion of Mechanical Systems with Switchable Constraints, pp. 111-134

      Full text         https://doi.org/10.14311/NNW.2024.34.007

    Abstract: The article deals with a numerical and analytical approach to solving the equations of motion, which enables to treat the considered problems with the change of system structure or number of degrees of freedom without interrupting the numerical integration process. The described methodology allows effectively incorporate switchable constraints in the systems in accordance with their flexible structures. The crucial idea is based on the formulation of the resulting differentialalgebraic equations into a saddle point system, where the switchable constraints are represented by a sign matrix with variable rank. In connection with this property, a pseudoinversion is applied to eliminate algebraic variables and transform the problem to the first order system of ordinary differential equations. Moreover, the time independent case leads to linear autonomous systems with non-diagonalizable matrices, as is proved. The relevant numerical scheme is based on Runge-Kutta methods, that correspond to the power series of the resulting matrix exponential for time independent problems. The methodology presented is illustrated on the idealized two-mass oscillator with a switchable constraint. The numerical experiments performed range from initial stages, through simple transient cases to damped intentional control. The advanced applications can be found in robotics, active and controlled systems, and in the simulations of complex systems in biology and related areas. Moreover, the methodology can also be applied in the simulation of transport systems, especially in relation to vehicle technology, a quarter car suspension system, a vibration control mechanism, a torsion system with a clutch, and machine balancing and storage should to be highlighted.

1/2024

  • [1] Sudharson D., Gomathi R., Selvam L., (India)
    Software Reliability Analysis by Using the Bidirectional Attention Based Zeiler-Fergus Convolutional Neural Network, pp. 1-25

      Full text         https://doi.org/10.14311/NNW.2024.34.001

    Abstract: Software quality assurance relies heavily on software reliability as one of its primary metrics. Numerous studies have been conducted to identify the software reliability. Improved software dependability may be studied using a triangular approach that includes software modeling, measurement, and improvement. Each of these steps is critical to the development of a solid software system. Improved accuracy in calculating dependability is critical to managing the quality of software. It has been discovered that deep learning algorithms are excellent methods of assessing many aspects of software dependability. Software systems contain distinct characteristics that can be addressed using deep learning techniques. In this study, a deep-learning-based bidirectional attention-based Zeiler-Fergus convolutional neural network (BA-ZFCNN) technique has been suggested to assess software dependability. In the beginning, the data were standardized by using the scalable error splash method. This approach was then used to extract the software fault-related characteristics using hypertuned evolutionary salp swarm optimization (HESSO). Finally, the Zeiler-Fergus convolutional neural network based on bidirectional attention (BA-ZFCNN) may be used to assess software dependability. The suggested method is used to forecast how many defects or failures there are in a software product. AR1 software defect data is widely used to test the effectiveness of deep learning and traditional machine learning methods. The experimental results reveal that the proposed method’s accuracy (96.7%) is higher than the current techniques’ accuracy.

  • [2] Beneš V., Svítek M., Michalíková A., Melicherčík M. , (CZ, SK) ,
    Situation Model of the Transport, Transport Emissions and Meteorological Conditions, pp. 27-36

      Full text         https://doi.org/10.14311/NNW.2024.34.002

    Abstract: Air pollution in cities and the possibilities of reducing this pollution represent one of the most important factors that today’s society has to deal with. This paper focuses on a systemic approach to traffic emissions with their relation to meteorological conditions, analyzing the effect of weather on the quantity and dispersion of traffic emissions in a city. Using fuzzy inference systems (FIS) the model for predicting changes in emissions depending on various conditions is developed. The proposed model is based on traffic, meteorology and emission data measured in Prague, Czech Republic. The main objective of the work is to provide insight into how urban planners and policymakers can plan and manage urban transportation more efficiently with environmental protection in mind.

  • [3] Halim A.A., Mustafa W.A., Nasir A.S.A., Ismail S., Alquran H., (Malaysia, Jordan) ,
    Nucleus Cell Segmentation on Pap Smear Image Using Bradley Modification Algorithm, pp. 37-51

      Full text         https://doi.org/10.14311/NNW.2024.34.003

    Abstract: Early detection of cervical cancer can help patients obtain the best treatment through various means. In general, computer-aided diagnosis has a high impact on the accuracy, reliability, and convenience of cervical cancer. However, several limitations have been faced through the design process in detecting or classifying the cells, such as variation of image features and low-image resolution. Moreover, shape indifference is one of the limitations in terms of image processing scope. The metrics used to measure the size and shape of the cells have not been developed to distinguish the differences between the shape of the objects. This paper focused on the detection and segmentation of the nucleus cell region in Pap smear images based on Bradley local thresholding. The proposed method evolved several steps, such as color adjustment, k-means, and a Bradley modification algorithm. Based on image quality assessment (IQA), the numerical evaluation results indicate that the proposed approach has segmented a full area of the nucleus cell region significantly and efficiently compared to the original Bradley algorithm. We obtained F-measure (98.62%), sensitivity (99.13%), and accuracy (97.96%). It has also been proven that the proposed method can effectively address the issue of low contrast and black noise. Hence, the proposed method differs from the previous research in terms of color disproportion adjustment and the modification of Bradley’s algorithm for Pap smear image convenience.

  • [4] Saranya K., Pandiyan P.M., Hema C.R. , (India)
    Certain Investigations on Feature Selection Technique Using Artificial Immune Systems for EEG Color Visualization Classification, pp. 53-71

      Full text         https://doi.org/10.14311/NNW.2024.34.004

    Abstract: This paper aims to extract and select the significant features of electroencephalogram (EEG) signals and classify the visual stimulation of distinct colors. In this work, a novel method for selecting distinct colors using EEG signals called affinity artificial immune and Daubechies wavelet time-based learning (AAIDWTL) is proposed. Initially, the EEG signals were collected in a controlled environment and an in-built band-pass filter was applied to remove the artifacts. The filtered signals were converted into frequency domain signals using least squarebased short-term Fourier transform. After that, by utilizing Daubechies wavelet statistical time-based feature extraction model the time domain features were extracted. Followed by, computationally efficient features were selected using an affinity artificial immune-based feature selection model. The selected features were classified using a polynomial kernel multiclass classification-based machine learning algorithm and achieved an accuracy of 97.5% when compared with other methods like linear discriminant analysis (LDA) which obtained only 92%. Furthermore, while utilizing the proposed method classification time was considerably less when compared to LDA. The experimental result shows that the proposed color stimulation of the EEG signals method achieved greater improvement in terms of both classification time and classification accuracy with a minimum false positive rate.