Vol. 6 No. 10 (2024): Volume 06 Issue 10

The relevance of the research topic is due to the increasing complexity of construction projects, stricter requirements for deadlines, quality of work, as well as increased competition in the relevant industry. In the current conditions, traditional management methods often turn out to be ineffective, which leads to deadlines, budget overruns, and a significant decrease in the quality of construction.

The purpose of the study is to analyze and systematize ideas about modern models of productivity improvement in construction project management, as well as to assess their potential impact on key performance indicators of projects. Special attention is paid to the formulation of the author's view of the advantages and limitations of specific models.

The study revealed contradictions between the need to introduce innovative approaches to working with projects and the conservative practices that have developed in the industry. In addition, it is advisable to point out the discrepancy between the pace of development of digital technologies and the speed of their adaptation in the construction sector.

It is concluded that integrated models combining elements of flexible methodologies, digital developments, and Lean approaches are the most effective. The role of digital twins, predictive analytics, and blockchain in improving productivity in this area is particularly emphasized.

The article examines the strength and deformability of concrete reinforced with basalt fiber and includes experimental results. Based on the results of the conducted experiments, optimal parameters were identified.

In recent years, the fintech industry has experienced rapid growth, driven by technological advancements and evolving consumer expectations. Fintech companies offer innovative financial services, such as digital banking, investment platforms, and payment solutions, catering to the needs of a tech-savvy customer base. However, as competition intensifies, customer retention has emerged as a critical challenge for these companies. According to a study by Ransom (2021), acquiring a new customer can cost five times more than retaining an existing one, making it imperative for fintech organizations to focus on strategies that enhance customer loyalty. The financial technology (fintech) sector has experienced unprecedented growth in recent years, fundamentally transforming how individuals and businesses access and manage financial services. Characterized by the integration of technology with financial services, fintech encompasses a wide array of offerings, including digital banking, peer-to-peer lending, robo-advisory services, and payment processing. As of 2023, the global fintech market was valued at approximately $309 billion and is projected to reach around $1.5 trillion by 2030, according to a report by Fortune Business Insights. This remarkable growth is largely attributed to advancements in digital technology, increasing smartphone penetration, and a growing consumer preference for online financial solutions. Moreover, the COVID-19 pandemic accelerated the adoption of digital financial services, as consumers sought contactless transactions and remote banking options.

ZENODO DOI:- https://doi.org/10.5281/zenodo.14008362

Real estate management plays a key role in promoting energy efficient solutions when renting out properties. The purpose of the study is to analyze the impact of management companies on the introduction of energy-efficient technologies to increase the competitiveness of facilities and reduce operating costs. The methodology is based on the analysis of data on the application of modern energy-efficient solutions, including lighting, heating and automation systems in buildings in the Czech Republic. The results showed that the use of such technologies helps to reduce utility costs by 20-40% and increases the attractiveness of facilities for tenants. In conclusion, property management aimed at energy efficiency ensures the achievement of sustainable development and economic benefits for owners and tenants. These measures increase the market value of the properties and extend the lease terms, which strengthens the position in the real estate rental market.

ZENODO DOI:- https://doi.org/10.5281/zenodo.14000245

Rising energy prices and stricter environmental regulations are exacerbating the problem of inefficient heat consumption in the commercial sector. Central heating systems of office buildings, shopping malls, and industrial complexes are often characterized by excessive thermal energy consumption, which leads to significant economic losses and a negative impact on the natural environment. The purpose of the study is to study the directions and trends of optimization in the field under consideration (taking into account modern technological capabilities, and economic factors).

There are disagreements in the professional community about the priority of measures to improve energy efficiency: some experts prefer improving the thermal insulation of buildings, others — the introduction of intelligent control systems, and others — the integration of renewable energy sources. The present study demonstrates that the greatest effect is achieved with a systematic approach combining various initiatives.

The conclusion is formulated that in the current conditions and the future, a comprehensive application of modern thermal insulation materials, highly efficient heating equipment, intelligent management mechanisms, and alternative energy sources is required.

The article is of interest to engineers-designers of heating systems, energy managers of commercial facilities, specialists in energy efficiency of buildings, and heads of companies interested in optimizing operating costs and improving the environmental friendliness of their real estate.

ZENODO DOI:- https://doi.org/10.5281/zenodo.14000315

This study explores the application of sentiment analysis in the banking sector, focusing on customer feedback to enhance service quality and customer experiences. We collected a comprehensive dataset of approximately 100,000 entries from diverse sources, including customer satisfaction surveys, social media platforms, and direct feedback. A robust preprocessing pipeline was employed to address challenges associated with unstructured data, informal language, and mixed sentiments. We evaluated several machine learning and natural language processing models, including Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Random Forest, Long Short-Term Memory (LSTM), and BERT (Bidirectional Encoder Representations from Transformers), using metrics such as accuracy, precision, recall, F1 score, AUC-ROC, and training time. The results revealed that advanced models, particularly BERT, achieved superior performance with an accuracy of 88% and an F1 score of 0.86, demonstrating an exceptional ability to capture nuanced sentiments. This study underscores the importance of employing sophisticated sentiment analysis techniques in banking to derive actionable insights from customer feedback. The findings suggest that leveraging advanced models can significantly improve service quality and customer satisfaction, while also presenting avenues for future research into real-time sentiment analysis and its integration with customer relationship management systems.

ZENODO DOI:- https://doi.org/10.5281/zenodo.13981553

This study assessed the performance of four machine learning algorithms—Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN)—for predicting airline security threats using a dataset of 100,000 entries with 30 features. The models were evaluated based on accuracy, precision, recall, F1-Score, and AUC-ROC. The Neural Network achieved the highest performance, with an accuracy of 88%, precision of 86%, recall of 85%, F1-Score of 85.5%, and AUC-ROC of 0.90, demonstrating superior capability in capturing complex, non-linear patterns. The Random Forest model followed, with an accuracy of 85%, precision of 83%, recall of 82%, F1-Score of 82.5%, and AUC-ROC of 0.87, offering a robust and generalizable solution. The SVM model attained an accuracy of 81%, precision of 80%, recall of 78%, F1-Score of 79%, and AUC-ROC of 0.84, showing effective binary classification but with higher computational costs. The Decision Tree model, while interpretable, had the lowest performance metrics: accuracy of 78%, precision of 76%, recall of 72%, F1-Score of 74%, and AUC-ROC of 0.79. The results indicate that Neural Networks and Random Forests are the most effective models for airline security threat detection, with Neural Networks providing the highest overall accuracy and AUC-ROC.

ZENODO DOI:- https://doi.org/10.5281/zenodo.13981482

This study explores the evolution and current state of business intelligence (BI) tools and their strategic role in driving business growth. The research utilizes a combination of market analysis, industry case studies, and theoretical frameworks, including the DIKW hierarchy and Resource-Based View, to examine BI adoption trends. The results highlight the importance of data quality, cross-functional collaboration, and user adoption in maximizing BI effectiveness. Key findings indicate that cloud-based and self-service BI tools significantly improve data-driven decision-making, while challenges remain in data governance and integration. To address these challenges, organizations must implement robust data policies and empower users through training and self-service capabilities. The study concludes that integrating BI tools into digital transformation initiatives provides a competitive edge, enabling strategic planning, operational efficiency, and innovation. This research offers new insights into how organizations can leverage BI tools for sustained growth and enhanced decision-making.

In the context of modern DevOps, application security and the implementation of the principle of least privilege (PoLP) are becoming critical elements aimed at minimizing risks and improving the sustainability of IT systems. This article analyzes approaches to integrating security measures at all stages of the software development lifecycle, starting from the early phases, which reduces the likelihood of vulnerabilities. Special attention is paid to the principle of least privilege, which restricts access by users and system components to only the necessary rights, thereby increasing security and preventing unauthorized access. Strategies for minimizing permissions, ensuring infrastructure protection, and automating security checks in CI/CD pipelines are considered. The challenges associated with implementing these principles are also discussed, and ways to overcome them are proposed to improve the security and stability of software solutions.

ZENODO DOI:- https://doi.org/10.5281/zenodo.13959998

This paper examines the use of unsupervised machine learning and vector models in the design and optimization of retail channels for telecommunications services. Unsupervised machine learning allows you to analyze and identify hidden patterns in large volumes of untagged data, which is especially important in a dynamically changing consumer market. Vector models, in turn, provide high accuracy of demand forecasting and inventory management, contributing to an increase in the efficiency of trading channels. The synergy of these technologies allows companies to improve customer experience, optimize operational processes and increase competitiveness in the market. The main focus of the work is on data processing methods, including correlation analysis, the use of the support vector machine (SVM) method and its adaptation to solve problems related to predicting customer behavior and optimizing logistics processes.

The digitalization of agricultural production has led to the widespread use of mobile technologies for monitoring and managing agricultural processes. Applications and devices such as drones and satellites allow you to receive real-time data on soil conditions, humidity, plant growth, and pest threats. This data helps farmers to make quick decisions on irrigation, fertilization, and crop forecasting. Modern mobile solutions such as AgroMonitor integrate with various data sources and provide accurate information, which increases the efficiency of resource management and reduces production risks. The introduction of these technologies not only helps to increase yields but also ensures the sustainable development of agricultural operations, minimizing the impact of external factors. Thus, digital tools are becoming an important element of the modernization of the agro-industrial sector, contributing to its competitiveness at the global level.

This study investigates the effectiveness of various machine learning models in predicting product demand based on customer satisfaction data. Four models—Linear Regression, Random Forest, Gradient Boosting, and Support Vector Machine (SVM)—were evaluated using performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R² score. The results indicate that Gradient Boosting achieved the highest accuracy, with an MAE of 2.56, MSE of 12.75, RMSE of 3.57, and R² score of 0.82, effectively capturing the complex, non-linear relationships inherent in customer satisfaction factors. Random Forest also demonstrated strong performance, while Linear Regression and SVM showed limitations in handling intricate datasets. These findings underscore the importance of utilizing advanced machine learning techniques for accurate demand forecasting, highlighting the critical role of customer satisfaction data in enhancing predictive capabilities. The insights gained from this research can guide organizations in optimizing inventory management and improving customer satisfaction in a rapidly evolving market.

zenodo DOI:- https://doi.org/10.5281/zenodo.13908001

The use of machine learning methods to optimize big data sorting algorithms has become an urgent research topic due to the growing volume of information and the requirements for their rapid processing. Machine learning provides opportunities to automate and improve traditional sorting methods, allowing you to reduce the cost of computing resources and time. This is achieved by analyzing the characteristics of the data and preprocessing them using classification and regression. The main advantages of using machine learning in sorting big data include improving the accuracy and adaptability of algorithms to different types of data, which is especially important for areas with large amounts of information, such as finance, medicine and logistics. Progressive machine learning algorithms such as supporting vectors, decision trees, and gradient boosting demonstrate high efficiency and potential for further development and integration.

This study investigates the application of sentiment analysis to customer feedback in the banking sector, utilizing natural language processing (NLP) techniques and machine learning models to classify customer sentiments into positive, neutral, and negative categories. Feedback was sourced from online platforms, including bank websites, social media, and third-party review sites. Data preprocessing steps, such as tokenization, stemming, and feature extraction using TF-IDF, were employed to prepare the text for analysis. Various machine learning algorithms, including Logistic Regression, Random Forest, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Naïve Bayes, were implemented and evaluated using metrics such as accuracy, precision, recall, and F1-score. The results show that LSTM outperformed all models with a 91% accuracy, followed closely by SVM at 89%. These findings demonstrate the potential of advanced machine learning techniques in accurately classifying sentiments and provide valuable insights into customer satisfaction and areas for improvement within the banking sector. Future work aims to further optimize models for better classification of neutral feedback and explore more advanced deep learning models, such as BERT.

zenodo DOI:- https://doi.org/10.5281/zenodo.13908078

This study explores the application of machine learning algorithms for detecting anomalies in GitHub data to enhance the evaluation of technological projects. The research aims to develop a robust methodology for identifying data anomalies, such as artificial activity spikes, that can distort project assessments. Methods such as Isolation Forest, One-Class SVM, and advanced deep learning techniques like autoencoders and GANs are employed to analyze and identify irregular patterns in GitHub repositories. The findings demonstrate that these algorithms effectively detect both obvious and subtle anomalies, offering reliable insights into project authenticity. The proposed conceptual model integrates these methods into a scalable system, enhancing transparency and accuracy in technological project evaluation. The novelty of this work lies in its comprehensive approach to analyzing GitHub data, combining traditional and deep learning techniques to improve the reliability of assessments, making it a significant contribution to the field.

Spike-wave discharges (SWD) are crucial biomarkers in the diagnosis and monitoring of neurological disorders such as epilepsy. Accurate classification of SWD is essential for effective clinical interventions and improving patient outcomes. This study presents a novel approach for classifying spike-wave discharges using the Short-Time Fourier Transform (STFT). By leveraging STFT's capability to analyze non-stationary signals, we extract time-frequency features from EEG recordings to accurately distinguish SWD from other brain activities. The extracted features are then classified using machine learning algorithms, providing high accuracy in identifying SWD events. Performance evaluation demonstrates that the proposed STFT-based method offers significant improvements in classification accuracy and computational efficiency compared to traditional time-domain analysis. The study's findings highlight the potential of STFT in real-time applications for automated seizure detection, contributing to advancements in neurological disorder diagnostics.

In the modern world of the fintech industry, tax changes are one of the key problems, especially with fixed loans. This study examines the Tax Buffer mechanism, designed to effectively manage tax obligations that vary depending on the jurisdiction and stages of delivery of goods. The main task of the mechanism is to automatically recalculate taxes to minimize the risk of errors and reduce the burden on the accounting and legal departments of the company. The implementation of this solution allows you to reduce the number of manual operations, reduce transaction costs and improve the customer experience by eliminating the need to notify users of every change in the amount of taxes. The results of the implementation of the mechanism have shown its high efficiency: a significant reduction in the number of errors and financial disputes, as well as an increase in operational efficiency. The Tax Buffer mechanism is an important innovation that helps to increase the resilience of fintech companies to changes in tax legislation.

This paper discusses methods for preventing SQL (Structured Query Language) injections in identity and access control (IAM) systems. SQL injections represent one of the most serious threats to web security, allowing attackers to gain unauthorized access to and modify data. The main security methods include filtering input data, using prepared statements and parameterization, implementing stored procedures, restricting access rights, and regularly updating software. Effective privilege management and database activity monitoring also play a key role in preventing attacks. The introduction of these measures helps protect confidential information, ensures reliable authentication and authorization, and maintains data integrity. The paper highlights the importance of an integrated approach to database security in the face of growing cyber threats.