CSIT - 2025 рік

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https://elar.khmnu.edu.ua/handle/123456789/18099

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Перегляд CSIT - 2025 рік за Ключові слова "artificial intelligence"

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  • Ескіз
    Документ
    Artificial intelligence approach to identifying propaganda techniques and objects, taking into account ethical and legal aspects
    (Хмельницький національний університет, 2025) Molchanova, Maryna; Dutt, Pawan Kumar
    The article explores the ethical and legal aspects of applying artificial intelligence (AI) technologies to detect propaganda techniques in textual content. The study presents a multi-level approach to identifying signs of propaganda in textual data, recognizing common rhetorical strategies of influence, and establishing semantic links between the detected techniques and their respective targets. The consistent use of neural network models is justified, as it ensures both classification accuracy and transparency of the obtained results through the application of local interpretability methods. The paper presents experimental results based on a corpus of Ukrainian-language news texts and informational messages from social media platforms. The proposed approach demonstrated alignment between the model's predictions and independent expert assessments, confirming its potential applicability in conditions with limited human oversight. Special attention is given to the compliance of the proposed system with existing regulatory frameworks, including constraints on automated decision-making, the user's right to explanation, and the prevention of discriminatory effects resulting from biased training data. The study addresses risks associated with misclassification, potential impacts on freedom of expression, and the accountability of developers in cases where the system is applied in automated content moderation scenarios. The integration of interpretability tools into neural network analysis is proposed as a core design principle to ensure adherence to ethical AI standards. Based on the obtained findings, the study concludes that the development of such systems requires the simultaneous consideration of technical effectiveness, legal compliance, and social responsibility, which are essential conditions for their safe implementation in the practice of analyzing public communications.
  • Ескіз
    Документ
    Legal and ethical bases for creating representative datasets to detecting manifestations of cyberbullying in text content
    (Хмельницький національний університет, 2025) Sobko, Olena; Chochia, Archil
    The article is devoted to developing the method for creating of representative text data datasets for detecting manifestations of cyberbullying in text content, considering ethical and legal principles. The primary focus is ensuring fair and equal representation of different demographic groups in text samples, which is critical for creating non-discriminatory and socially responsible artificial intelligence models. Emphasis is placed on compliance with key ethical principles – preventing harm, avoiding bias, and ensuring representativeness – and provisions of international law, particularly the General Data Protection Regulation. Proposed method for creating of representative text data datasets for detecting manifestations of cyberbullying in text content, taking into account ethical principles, which includes the following stages: preliminary processing of text data, analysis of distributions according to ethical aspects (age, gender, religion etc.), and representative adjustment through multi-criteria optimization. Machine learning models are trained on prepared balanced samples using appropriate reference datasets to classify text samples according to ethical criteria. The comparison is based on official demographic data for Ukraine, which ensures the reliability of the assessment of deviations. As a result of applying the developed method, a representative sample was created with a deviation of the proportions of ethical groups from the target values within 0.00-0.04%. The statistical metrics obtained confirmed the effectiveness of the selected models and demonstrated a high degree of compliance with the ethical responsibility requirements of the results. The analysis showed that the initial datasets contained imbalances, which were successfully eliminated through multi-criteria optimization and data augmentation. The developed approach can be integrated into preparing training samples for ethically oriented artificial intelligence systems that perform automated detection of cyberbullying manifestations in text content, reducing the risks of reproducing social biases and increasing trust in algorithmic decisions.
  • Ескіз
    Документ
    Method and cyber-physical system for forecasting and optimizing electricity consumption in residential districts
    (Хмельницький національний університет, 2025) Pysmeniuk, Volodymyr; Levashenko, Vitaly
    The development of cyber-physical systems combined with machine learning algorithms opens new opportunities for forecasting and optimizing electricity consumption in residential districts. This study examined existing technologies and solutions for energy consumption management, identifying their advantages and disadvantages. The analysis showed that modern commercial systems are primarily designed either for industrial use or individual consumption, lacking a comprehensive approach for residential districts. The proposed forecasting and optimization method is based on hybrid machine learning algorithms. For energy consumption forecasting, a combination of recurrent neural networks (RNN) and XGBoost was used, allowing for the consideration of both temporal dependencies and nonlinear factors. For energy consumption optimization, a combination of genetic algorithms (GA) and particle swarm optimization (PSO) was implemented, ensuring efficiency in finding optimal solutions. The developed cyber-physical system includes sensors for data collection, microcontrollers (Raspberry Pi) for data processing, and intelligent systems for controlling electrical appliances. This enables real-time energy consumption analysis and management, improving the energy efficiency of residential districts. Experimental results confirmed the effectiveness of the proposed approach, demonstrating high accuracy in energy consumption forecasting and the potential for reducing electricity costs through optimized usage. The proposed method has significant potential for scaling and implementation in large residential complexes, contributing to sustainable development and reducing the load on energy grids. Thus, the results of this study can be used for further improvement of energy management systems, promoting efficient electricity use, reducing consumer costs, and minimizing the environmental impact of energy systems.