Traditional software effort estimation methods, such as term frequency–inverse document frequency (TF-IDF), are widely used due to their simplicity and interpretability. However, they struggle with limited datasets, fail to capture intricate semantics, and suffer from dimensionality, sparsity, and computational inefficiency. This study used pre-trained word embeddings, including FastText and GPT-2, to improve estimation accuracy in such cases. Seven pre-trained models were evaluated for their ability to effectively represent textual data, addressing the fundamental limitations of TF-IDF through contextualized embeddings. The results show that combining FastText embeddings with support vector machines (SVMs) consistently outperforms traditional approaches, reducing the mean absolute error (MAE) by 5–18% while achieving accuracy comparable to deep learning models like GPT-2. This approach demonstrated the adaptability of pre-trained embeddings for small datasets, balancing semantic richness with computational efficiency. The proposed method optimized project planning and resource allocation while enhancing software development through accurate story point prediction while safeguarding privacy and security through data anonymization. Future research will explore task-specific embeddings tailored to software engineering domains and investigate how dataset characteristics, such as cultural variations, influence model performance, ensuring the development of adaptable, robust, and secure machine learning models for diverse contexts.

Web attacks often target web applications because they can be accessed over a network and often have vulnerabilities. The success of an intrusion detection system (IDS) in detecting web attacks depends on an effective traffic classification system. Several previous studies have utilized machine learning classification methods to create an efficient IDS with various datasets for different types of attacks. This paper utilizes the Canadian Institute for Cyber Security’s (CIC-IDS2017) IDS dataset to assess web attacks. Importantly, the dataset contains 80 attributes of recent assaults, as reported in the 2016 McAfee report. Three machine learning algorithms have been evaluated in this research, namely random forests (RF), k-nearest neighbor (KNN), and naive bayes (NB). The primary goal of this research is to propose an effective machine learning algorithm for the IDS web attacks model. The evaluation compares the performance of three algorithms (RF, KNN, and NB) based on their accuracy and precision in detecting anomalous traffic. The results indicate that the RF outperformed the NB and KNN in terms of average accuracy achieved during the training phase. During the testing phase, the KNN algorithm outperformed others, achieving an average accuracy of 99.4916%. However, RF and KNN achieved 100% average precision and recall rates compared to other algorithms. Finally, the RF and KNN algorithms have been identified as the most effective for detecting IDS web attacks.

Scaling Agile in industrial software projects is a process control problem that must balance governance, scalability, and adaptability while keeping decisions auditable. We present a hybrid fuzzy multi-criteria decision-making (MCDM) framework that combines Fuzzy Analytic Hierarchy Process (FAHP) for uncertainty-aware weighting with a tunable VIKOR–PROMETHEE ranking stage. Weighting and ranking are kept distinct to support traceability and parameter sensitivity. A three-layer hierarchy organizes twenty-two criteria across organizational, project, group, and framework levels. In a single-enterprise validation with two independent expert panels (n = 10 practitioners), the tuned hybrid achieved lower rank error than single-method baselines (mean absolute error, $\mathrm{MAE}$ = 1.03; Spearman $\rho$ = 0.53) using pre-specified thresholds and a transparent $\alpha +\beta$ = 1 control. The procedure is practical for process governance: elicit priorities, derive fuzzy weights, apply the hybrid ranking, and verify stability with sensitivity analysis. The framework operationalizes modeling, optimization, control, and monitoring of scaling decisions, making trade-offs explicit and reproducible in industrial settings.

In the context of the digital transformation of the economy, cost management is acquiring a new dimension, emphasizing the integration of automat-ed and analytical solutions into corporate decision-making. The purpose of this study is to develop a structured approach to evaluating the effectiveness of cost optimization through the application of digital technologies within business environments. The relevance of the topic stems from the growing need to enhance transparency, speed, and accuracy in managerial processes amid volatile markets and increasingly complex organizational structures. The need for the research arises from the lack of comprehensive and adaptable models for assessing the actual impact of digital tools on cost structures across various industries. The methodology involves the development of a system of key performance indicators (KPIs) that reflect the degree of reduction in direct and indirect expenses following the implementation of digital solutions. The study employs methods such as factor analysis, calculation of relative deviations, and comparative evaluation using “before-and-after implementation” models across different cost categories. The research perspectives include further adaptation of assessment models to specific industry contexts and the refinement of analytical indicators to support integrated digital cost transformation.

In the context of digitalization and the growing dependence of business on electronic communications, data protection is becoming a key element of corporate security. The purpose of this article is to analyze modern cybersecurity threats in the field of business communication and to determine effective methods of information protection. The relevance of the study is due to the increase in the number of cyberattacks aimed at intercepting business correspondence, leaking confidential data and compromising corporate communication channels. The research methodology includes a systemic analysis of scientific literature, a comparative review of cybersecurity solutions, as well as a case analysis of the practices of leading companies. The analysis identified the most vulnerable points in business communications - email, instant messengers and cloud platforms. Encryption technologies, two-factor authentication and the use of secure communication protocols are considered. The results of the study confirm the need for an integrated approach to information security in business, including not only technical measures, but also personnel training. Practical application consists in the formation of recommendations for the development of a corporate cybersecurity strategy that ensures resistance to threats in the communication space.

Pervasive services use non-compatible protocols (SDPs, SOAP, REST, UPnP), which are difficult to interoperate with. In this paper, the new broker architecture, ReSHAPE, solving this service heterogeneity, is introduced. ReSHAPE is the only system to combine centralized knowledge of service and native protocol interaction functionality, and coordinated by the broker. This helps reduce the overhead of translation used in intermediate techniques as well as reducing the client-side overhead of dealing with real multi-protocols. ReSHAPE will allow an efficient direct communication using different types of services by leveraging a protocol-agnostic gateway and specialized adapters. Experimental measurements with Web Services, UPnP and RMI adapters show that there is little operational overhead in local and distributed applications. ReSHAPE proposes a scalable and extendable framework to provide a significant improvement in the service interoperability and user experience in complex smart environments. The future direction is to add more protocol adapter support and add more optimization and security capabilities.