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• OVERVIEW AND ANALYSIS OF THREE-DIMENSIONAL PACKAGING FOR MARINE CARGO TRANSPORTATION

  V.V. Kureichik, Y.V. Balyasova, V.V. Bova

  2025-02-16

  Abstract ▼

  This article describes the problem of three-dimensional packaging of goods in various types of containers
  during maritime cargo transportation. Maritime cargo transportation plays a significant role in
  international trade, is carried out in specific and non-standard conditions, is characterized by increased
  humidity, contact with sea salt, vibration, temperature interference and is carried out by container ships
  transporting various categories of goods in containers selected taking into account the specifics of the
  cargo being transported, which ensures reliability and safety. Of particular importance is the presence of
  protection of goods from a variety of negative and man-made environmental factors, which confirms the
  importance of properly designed marine cargo packaging, ensuring the preservation of goods, equipment,
  raw materials, or materials throughout the entire time of transportation by sea, as well as reliable fastening
  on deck or inside cargo compartments, excluding the possibility of damage to cargo, through exposure
  vibration and static loads. The article describes the task of three-dimensional packaging in containers
  for marine cargo transportation. Criteria and constraints are considered, and a modified combined
  multi-criteria objective function is constructed. Its value should tend to 1, which corresponds to 100%
  filling of voids. Also, the paper provides a brief overview and analysis of methods and algorithms for finding
  solutions to the problem of three-dimensional packaging, their features, advantages and disadvantages
  are revealed. Taking into account the analysis, it is noted that metaheuristic methods and search algorithms
  are effective for solving the NP-complex problem of three-dimensional packaging, as they allow
  obtaining sets of quasi-optimal solutions in polynomial time.

• A BIOINSPIRED APPROACH TO SOLVING THE PROBLEM OF 3D PACKAGING

  V.I. Danilchenko , V.V. Bova , М. М. Semenova , S.V. Ignateva , М. B. Shayliev

  2026-02-27

  Abstract ▼

  This article examines one of the most important combinatorial optimization problems – three-dimensional packaging. Optimizing three-dimensional packaging reduces costs and improves logistics efficiency, making it relevant for industry. This paper analyzes classical approaches such as greedy algorithms and dynamic programming, as well as widely used methods, including evolutionary algorithms and local search. An analysis of existing methods, including greedy search, dynamic programming, evolutionary algorithms, and local search, revealed their key characteristics and identified suitable areas of application. In the context of this analysis, an overview of the key methods that dominated during certain historical periods is presented. The analysis includes consideration of the application conditions of various methods, their effectiveness for specific types of problems, as well as their advantages and limitations.
  A multi-level search algorithm is presented that combines the advantages of traditional and modern optimization methods. This multi-level algorithm improves the accuracy of the packaging problem solution through dynamic parameter adjustment. A software package for solving the three-dimensional packaging optimization problem using bioinspired algorithms has been developed. A computational experiment was conducted on test examples (benchmarks). The packing quality obtained using the developed combined bioinspired algorithm is, on average, 7% higher than the packing results obtained using known algorithms, while the solution time is 7% to 25% shorter, demonstrating the effectiveness of the proposed approach. A series of tests and experiments allowed us to refine theoretical estimates of the time complexity of packing algorithms. In the best case, the time complexity of the algorithms is O(n²), and in the worst case, O(n³).

• INTELLIGENT METHODS OF PARAMETRIC FORECASTING AND OPTIMIZATION OF UAV TRAJECTORIES

  V.I. Danilchenko , V.V. Bova

  263-276

  2025-12-30

  Abstract ▼

  This paper examines the problem of intelligent parametric forecasting and trajectory optimization for unmanned aircraft systems (UAS) using evolutionary algorithms and machine learning methods. The relevance of the study stems from the multi-criteria and high complexity of UAS trajectory generation processes, as well as the need for accurate and timely assessment of its flight parameters. This is particularly important for ensuring the reliability, safety, and efficient performance of flight missions in UAS operating conditions, including scenarios related to the operation of critical infrastructure facilities. The objective of the study is to improve the accuracy of trajectory parameter diagnostics and the reliability of parametric forecasting of UAS trajectories under conditions of uncertainty and the multi-criteria nature of the problem. The paper proposes a hybrid approach incorporating a genetic algorithm (GA), a particle swarm algorithm (PSO), and an XGBoost machine learning model that provides adaptive assessment of the quality of the generated solutions. A computational software package has been implemented, including selection, recombination, mutation, and elite inheritance mechanisms, as well as a machine learning module for validating route trajectories and associated parameters. A computational experiment was conducted, which compared the effectiveness of GA and PSO under various operating scenarios. Testing was performed on industry-specific datasets with varying numbers of iterations. The computational experiment revealed the advantage of the genetic algorithm, namely, a 14–17% improvement in the quality of design solutions. The results of the study demonstrate high adaptability and practical applicability in modeling, parametric forecasting, and routing tasks, and also indicate the potential for integration with intelligent UAS navigation and monitoring systems. The article's materials are of practical interest to specialists in the field of UAS development and operation, as well as to researchers working on multi-criteria route planning, parametric forecasting, and improving the reliability of UAS operations.

• ESTIMATING THE EFFECTIVENESS OF THE METHOD FOR SEARCHING THE ASSOCIATIVE RULES FOR THE TASKS OF PROCESSING BIG DATA

  V. V. Bova, E.V. Kuliev, S.N. Scheglov

  2020-07-20

  Abstract ▼

  The modern databases have significant volume and consist of large masses of information.
  One of the popular methods of knowledge identification in terms of tasks of analysis and processing
  of large data volumes is composed of the algorithms for searching the associative rules.
  The paper solves the problem of building the bases of associative rules for the analysis of the unstructured
  large data volumes on the basis of searching different regularities considering the importance
  of their characteristics. The authors propose the method for synthesizing the bases and
  building the transaction database to calculate the threshold values of support and application of
  criteria of estimating implicit associations. This allows us to extract repeated and implicit associative
  rules. To improve the computational effectiveness of extracting the associative rules, the paper
  applies the genetic algorithm for optimization of input parameters of the characteristic searching
  space. The developed method shortens the time of rules extraction, reduces the number of generated
  common rules, and avoid the resource-consuming procedure of pre-processing the synthesized
  rule base. The authors developed the program and algorithmic module to carry out the experimental
  research of the proposed method for synthesizing the associative rules on the basis of filtering
  the input parameters of the search model for solving the tasks of processing the unstructured
  data. The experiments conducted on the test transaction bases allow us to clarify the theoretical
  estimations of time complexity of the proposed method that used the genetic algorithm to calculate
  the weighed support of the set of rules considering the assessment of a priori informative content
  of the characteristics included in the dataset. The time complexity of the developed method is estimated
  as  О(I2). The comparative analysis is performed using the test data of the Retail Data
  with the algorithms Apriori and Frequent Pattern-Growth. The results have proven the effectiveness
  of the search method on big sets of transactions. The method allows us to reduce the cardinal
  of an irredundant set of extracted associative rules in more than 40% in comparison with the popular
  algorithms. The experiments have shown that the method can be effective for the tasks of
  knowledge discovery in terms of processing large volumes of data.

• IMPLICIT THREATS IDENTIFICATION BASED ON ANALYSIS OF USER ACTIVITY ON THE INTERNET SPACE

  V. V. Bova , D. Y. Zaporozhets, Y.A. Kravchenko , E. V. Kuliev , V. V. Kureichik , N. A. Lyz

  2020-10-11

  Abstract ▼

  The article is devoted to the problem of identifying implicit information threats of a user's
  search activity in the internet space based on an analysis of his activity in the course of this interaction.
  The use of knowledge stored in the Internet space for the implementation of criminal intentions
  poses a threat to the whole society. Identifying malicious intent in the users’ actions of the
  global information network is not always a trivial task. The proven technologies for analyzing the
  context of user interests fail in the case of cautious and competent actions of attackers who do not
  explicitly demonstrate the goal they are pursuing. The paper analyzes the threats associated with
  certain scenarios for the implementation of search procedures that manifest themselves in search
  activities. Criteria of inefficient and effective search scenarios estimation are described. Among
  the signs indicating the possibility of a threat, the following main ones are highlighted: avoiding
  solving the problem in aimless navigation or attractive resources, superficial search, lack of
  meaningful immersion in solving the search problem, and chaotic actions during the search.
  To determine the presence of adverse signs, a system of indicators is built. The features of an effective
  scenario for organizing a search in the Internet space are formulated, options for the presence
  of implicit threats for a similar situation are described.An approach for identification the
  described threats is presented taking into account the specified criteria for evaluating various
  scenarios of user behavior in the global information space. A machine learning algorithm has
  been developed to identify problem scenarios by comparing with key behavioral patterns. The
  software implementation of the subsystem for identifying information threats has been created,
  experimental studies have been conducted to confirm the effectiveness of the subsystem. Experimental
  studies were carried out on the basis of processing open data from social networks, as well
  as using analysis of user search activity in the university corporate information environment.

• DEEP LEARNING METHODS FOR NATURAL LANGUAGE TEXT PROCESSING

  V.V. Kureichik, S.I. Rodzin, V.V. Bova

  2022-05-26

  Abstract ▼

  The analysis of approaches based on deep learning (DL) to natural language processing
  (NLP) tasks is presented. The study covers various NLP tasks implemented using artificial neural
  networks (ANNs), convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
  These architectures allow solving a wide range of natural language processing tasks that previously
  could not be effectively solved: sentence modeling, semantic role labeling, named entity
  recognition, answers to questions, text categorization, machine translation. Along with the advantages
  of using CNN to solve NLP problems, there are problems associated with a large number
  of variable network parameters and the choice of its architecture. We propose an evolutionary
  algorithm for optimizing the architecture of convolutional neural networks. The algorithm initializes
  a random population of a small number of agents (no more than 5) and uses the fitness function
  to get estimates of each agent in the population. Then a tournament selection is carried out
  between all agents and a crossover operator is applied between the selected agents. The algorithm
  has such an advantage as the small size of the network population, it uses several types of CNN
  layers: convolutional layer, maximum pooling layer (subdiscretization), medium pooling layer and
  fully connected layer. The algorithm was tested on a local computer with an ASUS Cerberus Ge-
  Force ® GTX 1050 Ti OC Edition 4 GB GDDR5, 8 GB of RAM and an Intel(R) Core(TM) i5-4670
  processor. The experimental results showed that the proposed neuroevolutionary approach is able
  to quickly find an optimized CNN architecture for a given data set with an acceptable accuracy
  value. It took about 1 hour to complete the algorithm execution. The popular TensorFlow framework
  was used to create and train CNN. To evaluate the algorithm, public datasets were used:
  MNIST and MNIST-RB. The kits contained black-and-white images of handwritten letters and
  numbers with 50,000 training samples and 10,000 test samples.

• METHODS AND ALGORITHMS FOR TEXT DATA CLUSTERING (REVIEW)

  V.V. Bova, Y.A. Kravchenko, S.I. Rodzin

  2022-11-01

  Abstract ▼

  The article deals with one of the important tasks of artificial intelligence – machine processing
  of natural language. The solution of this problem based on cluster analysis makes it possible
  to identify, formalize and integrate large amounts of linguistic expert information under conditions
  of information uncertainty and weak structure of the original text resources obtained from
  various subject areas. Cluster analysis is a powerful tool for exploratory analysis of text data,
  which allows for an objective classification of any objects that are characterized by a number of
  features and have hidden patterns. A review and analysis of modern modified algorithms for agglomerative
  clustering CURE, ROCK, CHAMELEON, non-hierarchical clustering PAM, CLARA
  and the affine transformation algorithm used at various stages of text data clustering, the effectiveness
  of which is verified by experimental studies, is carried out. The paper substantiates the
  requirements for choosing the most efficient clustering method for solving the problem of increasing the efficiency of intellectual processing of linguistic expert information. Also, the paper considers
  methods for visualizing clustering results for interpreting the cluster structure and dependencies
  on a set of text data elements and graphical means of their presentation in the form of
  dendograms, scatterplots, VOS similarity diagrams, and intensity maps. To compare the quality of
  the algorithms, internal and external performance metrics were used: "V-measure", "Adjusted
  Rand index", "Silhouette". Based on the experiments, it was found that it is necessary to use a
  hybrid approach, in which, for the initial selection of the number of clusters and the distribution of
  their centers, use a hierarchical approach based on sequential combining and averaging the characteristics
  of the closest data of a limited sample, when it is not possible to put forward a hypothesis
  about the initial number of clusters. Next, connect iterative clustering algorithms that provide
  high stability with respect to noise features and the presence of outliers. Hybridization increases
  the efficiency of clustering algorithms. The research results showed that in order to increase the
  computational efficiency and overcome the sensitivity when initializing the parameters of clustering
  algorithms, it is necessary to use metaheuristic approaches to optimize the parameters of the
  learning model and search for a global optimal solution.

• MULTILEVEL APPROACH TO TWO-DIMENSIONAL PACKING PROBLEM FOR GEOMETRIC FIGURES OF COMPLEX SHAPES

  V.V. Kureichik, V.V. Bova, А.Y. Нalenkov

  2023-12-11

  Abstract ▼

  The paper considers one of the important combinatorial optimization problems, namely the
  two-dimensional packing problem for geometric figures of complex shapes. It belongs to the class
  of NP-complex and difficult optimization problems. In this paper, the formulation of the twodimensional
  packing problem is given and described, and a combinatorial objective function that
  takes into account all constraints is introduced. Due to the complexity of this problem, a multilevel
  approach is proposed, which consists in dividing the two-dimensional packing problem into 4
  subproblems and solving each subproblem sequentially in a strict order. At the same time, for each
  of the subtasks a unique set of objects that are not repeated in the other subtasks is defined. To
  implement the multilevel approach, the authors developed a combined bioinspired algorithm
  based on the methods of genetic search and bioinspired optimization. This approach allows to
  significantly reduce the time of obtaining the result, partially solve the problem of preliminary
  convergence of algorithms and obtain sets of quasi-optimal solutions in polynomial time. A software
  package has been developed and algorithms for automated two-dimensional packing based
  on the combined bioinspired algorithm have been implemented. A computational experiment on
  test cases (benchmarks) has been carried out. The packing quality obtained on the basis of the
  developed combined bioinspired algorithm, on average, by 2% exceeds the packing results obtained
  using known algorithms at comparable solution time, which indicates the effectiveness of the proposed approach. The conducted series of tests and experiments allowed us to refine the
  theoretical estimates of the time complexity of the packing algorithms. In the best case the time
  complexity of the algorithms is O(n2), in the worst case - O(n3).

• METHOD FOR SEARCHING SEQUENTIAL PATTERNS OF USER'S BEHAVIOR ON THE INTERNET

  V.V. Kureychik, V. V. Bova, Y.A. Kravchenko

  2020-11-22

  Abstract ▼

  One of the important tasks of data mining is to isolate patterns and detect related events in
  sequential data based on the analysis of sequential patterns. The article examines the possibility of
  using sequential patterns to analyze the events of search and cognitive activity of users when interacting
  with Internet resources of an open information and educational environment. Searching
  for sequential patterns is a complex computational task whose goal is to retrieve all frequent sequences
  representing potential relationships within elements from a transactional database of
  sequences of search activity events with a given minimum support. To solve it, the article proposes
  a method for searching for patterns in sequences of events to detect hidden patterns that indicate
  possible levels of vulnerability when performing information search tasks in the Internet space.
  A mathematical model of user behavior in a search session based on the theory of sequential patterns
  is described. To improve the computational efficiency of the method, a modified algorithm
  for generating sequential patterns has been developed, at the first stage of which AprioriAll is
  performed, which forms frequent candidate sequences of all possible lengths, and at the second
  stage, a genetic algorithm for optimizing the input parameters of the feature space of the generated
  set to search for maximum patterns. A series of computational experiments were carried out on
  test data from the MSNBC corpus, the SPMF open source data mining library. The comparative
  analysis was carried out with the VMSP and GSP algorithms. The research results confirmed the
  efficiency of the search for maximum sequential patterns by the proposed algorithm in terms of the
  execution time and the number of extracted patterns. The results of the experimental studies of the
  method showed that to increase the stability and accuracy of the work, the sample size obtained as
  a result of the GA operation will reduce the required number of scans of the pattern database,
  providing acceptable computational costs comparable to the VMSP algorithm and the GSP algorithm
  that exceeds the search time for sequential patterns. an average of more than 150 %.