Abstract : The article presents an analysis of the capabilities and challenges associated with the use of artificial intelligence (AI) in medical diagnostics. Key applications of AI, such as text information processing, medical image analysis, and clinical outcome prediction, are examined. The study indicates that diagnostic automation enhances the speed and accuracy of medical decisions, yet there are significant challenges, including interpretability, data confidentiality, and potential ethical issues. Prospects for further AI development in medicine are identified, including model improvements and specialist training for working with new technologies. This article highlights the importance of AI in medicine and the need for a careful approach to its integration.

Abstract : The article explores the possibilities and challenges of using blockchain technology in the financial sector. It describes the main applications of blockchain, such as international transfers, identity verification, and business process automation through smart contracts. The study highlights blockchain's advantages, including decentralization, increased transparency, and reduced operational costs. Major challenges, such as scalability, security, and regulatory compliance, are identified as limiting factors for the widespread adoption of this technology in traditional financial structures. In conclusion, the article emphasizes that successful blockchain integration in the financial sector requires the development of security standards and adaptation to legal requirements.

Abstract : This article examines modern big data processing methods applied to consumer behavior analysis. The primary focus is on machine learning and deep learning techniques for consumer segmentation, text data analysis, and result visualization. Advantages of using these methods in marketing research, such as increased prediction accuracy and cost optimization, are discussed. The challenges associated with data quality and the need for skilled professionals are also highlighted. It is concluded that applying big data processing methods allows companies to adapt marketing strategies and enhance customer service, providing a long-term competitive advantage.

Abstract : The article explores the potential of Internet of Things (IoT) technologies for optimizing industrial processes. Key IoT advantages, such as real-time data monitoring, enhanced equipment condition control, and predictive maintenance, are described. Emphasis is placed on predictive maintenance using machine learning methods to forecast failures and schedule preventive actions. Additionally, energy efficiency improvements and inventory management optimization enabled by IoT are discussed. The conclusion highlights IoT's importance for industrial digital transformation and improving business competitiveness.

Abstract : The article examines modern software development methods for high-load systems (HLS), including microservice architecture and asynchronous programming. The advantages of these approaches, such as increased performance, fault tolerance, and the scalability of individual system components, are described. Examples of asynchronous operations for parallel data processing are provided, allowing for reduced response time and improved system stability. In conclusion, the significance of these methods for creating resilient HLS capable of handling large data volumes and intense workloads is discussed.

Abstract : The article examines machine learning (ML) methods for predicting cyberattacks and enhancing cybersecurity. The focus is on classification algorithms and anomaly detection techniques that identify both known and unknown types of attacks. Examples of applying classification models, such as random forests, and anomaly detection methods, such as isolation forests, for network activity analysis are provided. The prospects for using deep learning and reinforcement learning methods to analyze time series and dynamic data are also discussed. The conclusion emphasizes the importance of these approaches for maintaining the resilience of information systems amid constantly evolving cyber threats.

Abstract : The article examines the potential of blockchain platforms for managing and optimizing supply chains. Key benefits of blockchain, such as transparency, reliability, and process automation through smart contracts, are discussed. Successful applications of blockchain by companies like Maersk and Walmart for tracking shipments and ensuring product safety are presented. A Solidity smart contract example demonstrates the automation of logistics operations. The conclusion highlights the significance of blockchain for managing logistics processes, optimizing costs, and securing supply chains.

Abstract : The article explores the potential of neural networks (NNs) for analyzing Internet of Things (IoT) data and predicting technical failures in industrial equipment. NNs analyze parameters such as vibrations, temperature, and pressure to detect anomalies and forecast possible faults. Key stages of data preparation and NN architectures, including recurrent and convolutional networks, are described. Examples of successful applications in companies like Siemens, GE, and Hitachi demonstrate the effectiveness of predictive analytics based on IoT data. The conclusion emphasizes the importance of IoT and NNs in enhancing reliability and reducing costs.

Abstract : The article explores anomaly detection algorithms for heterogeneous data sets. It describes key methods, including statistical approaches, machine learning algorithms, and deep learning techniques such as Isolation Forest, autoencoders, and recurrent neural networks (RNNs), as well as time series methods. An example using Isolation Forest to detect anomalous data points is provided. The article emphasizes the relevance of these algorithms in failure prediction and security enhancement tasks. The conclusion highlights the importance of combining different approaches to achieve high accuracy and efficiency.

Abstract : This article explores modern data encryption methods in cloud systems using machine learning (ML) algorithms. The main approaches include adaptive encryption models that adjust data protection processes based on the data type and volume. Special attention is given to ML-based anomaly detection methods, enabling prompt identification of potential threats and preventing data leaks. The use of hybrid encryption methods, combining symmetric and asymmetric encryption, ensures high security levels and optimized computational costs. Emphasis is placed on ML’s role in enhancing encryption key distribution and risk prediction processes. Practical recommendations for implementing proposed solutions aimed at improving cloud system reliability are presented. The analysis shows that the use of ML in cloud data encryption significantly enhances data security, minimizes risks, and boosts cloud technology performance.