Vehicle to Vehicle communication (V2V) is the foundation of intelligent transportation systems, but due to high mobility and frequent topology changes, reliable and secure routing is still a challenge, and it is further exacerbated when vehicles are potentially malicious. The existing trust-aware routing protocols, e.g., Trusted Context-aware Opportunistic Routing (TCOR), rely heavily on heuristic and deterministic trust aggregation mechanisms, which are less effective in achieving optimal tradeoff between trust and routing efficiency as vehicular environments change dynamically. In order to overcome these limitations, we model the trust-aware V2V routing as a multi-objective optimization problem and design a new routing scheme based on a Quantum Inspired Trust-Aware Opportunistic Routing (Q-TAOR). The proposed method adoptedly choose secure forwarding paths in the face of malicious by taking into account trust maximization and routing efficiency. An effective quantum-inspired probabilistic representation is employed to extend the solution search space and generate reliable routes more efficiently via convergence yet does not depend on static decision rules. Therefore, the routing scheme integrates both direct and indirect trust observations and embeds optimization within a robust path selection process under highly dynamic scenarios. Results obtained using OMNeT++ show the effectiveness of the proposed approach under realistic vehicular mobility and attack circumstances. Simulation outcomes are valid for the proposed quantum-inspired trust-aware routing algorithm to be optimal for secure V2V communication: when attacker nodes are produced, highest-performing packet delivery ratio and robustness in comparison to TCOR, TCOR-Rec and conventional routing protocols.

Sustainable grazing management requires balancing livestock productivity with ecosystem preservation, yet existing monitoring systems integrate heterogeneous data from IoT sensors, satellite imagery, and field surveys without a unified semantic layer, limiting holistic decision support. This paper proposes ONTOGRAZING, an ontology-based monitoring architecture for sustainable grazing management. Using the Uschold and King ontology engineering framework, domain knowledge was collected through surveys involving 23 livestock farmers and 4 agro-pastoral institutions in Cameroon, complemented by a systematic literature review. Seven core concepts and fourteen semantic relationships were modeled in OWL using Protégé. A five-module monitoring architecture composed of Query Reformulator, Data Integrator, Source Monitoring, Alert, and Storage modules was designed around the ontology. ONTOGRAZING was evaluated using the HermiT 1.4.3.456 reasoner and SPARQL queries. The ontology contains 47 classes, 14 object properties, and 9 data properties, and passed all consistency checks. Comparative analysis demonstrates that ONTOGRAZING is the first ontology to jointly cover forage management, dietary preferences, pasture composition, ecological–economic trade-offs, and land-use regulations. These results highlight the potential of ontology-based integration to improve interoperability and semantic decision support in agro-pastoral systems, while future work will focus on full prototype implementation and integration with real-world IoT platforms and agricultural databa.

A Modified Group Method of Data Handling (MGMDH) is a component of a synthetic method of constructing multivariate polynomial regression given by a redundant representation. The MGMDH is used to construct multivariate linear regression given by a redundant representation in the case when a decomposition method, which is also a component of the synthetic method, allowed to estimate with a given accuracy the values of unknown coefficients for nonlinear terms of a multivariate polynomial regression. As statistical studies have shown, the MGMDH efficiently finds the correct structure of a multivariate linear regression when the realizations of a random factor in the tests are an order of magnitude smaller than the modules of the corresponding values of the regression to be determined. Only in this case, the use of the regularity criterion in the MGMDH almost always allows finding the correct structure of a multivariate linear regression given by a redundant representation. In this paper, the MGMDH is adapted for the case when during the tests the modules of the random factor implementations and the values of the regression to be determined take values of the same order, which significantly increases the efficiency of using the MGMDH for constructing multivariate linear regressions given by redundant representations. It is obvious that the adapted MGMDH for multivariate linear regressions given by redundant representations presented in this work is easily transformed using the standardization operation for the general problem of constructing multivariate regression given by a redundant representation in the case when the unknown coefficients are linear in the regression structure.

Brain tumor detection and classification from MRI images is a challenging task. Early and accurate diagnosis are essential for selecting appropriate treatment plans and improving patient outcomes. Despite significant advances in deep learning for medical image recognition, comprehensive comparative analyses of brain tumor classification models, particularly regarding ensemble optimization, remain limited. This paper uses four state-of-the-art deep learning frameworks, namely EfficientNetB4, MobileNetV3, MobileNetV2, and EfficientNetB0, to classify brain MRI images into four categories: Glioma, Meningioma, Pituitary tumor, and Normal. It employs a two-phase transfer learning approach, followed by 5-fold cross-validation on 875 MRI images. A unified experimental framework is employed, incorporating a two-phase transfer learning approach, consistent preprocessing, and a rigorous evaluation protocol with 5-fold cross-validation and an independent test set to prevent data leakage. Both full and selective ensemble strategies are examined to improve the robustness and stability. The models are evaluated using accuracy, precision, recall, F-1 score, confusion matrices, and accuracy curves, and statistical validation using McNemar’s test. MobileNetV3 achieves the highest test accuracy of 98.76%, followed by EfficientNetB4 (97.89%) and EfficientNetB0 (93.48%). MobileNetV2 performs significantly worse, with an accuracy of less than 80%. The selective ensemble technique (which uses the best models) attains the highest accuracy of 92.97%, compared to the full ensemble (84.40%), which improves prediction robustness but does not surpass the best individual model in peak accuracy. Overall, it can be concluded that MobileNetV3 is the most suitable architecture for brain tumor classification, delivering high accuracy with minimal computational complexity. The selective ensemble approach also enhances performance, maintaining computational efficiency, emphasizing the importance of informed model selection in neuro-oncological image analysis and clinical decision-support systems.

Connected autonomous vehicles (CAVs) are reshaping mobility but remain vulnerable to technical, organizational, and regulatory risks. This study develops a hybrid multi criteria decision-making framework that integrates CRITID for objective weighting, Fuzzy BWM for expert uncertainty modeling, and VIKOR Swarm for adaptive compromise ranking. To enhance realism, four scenarios were constructed: scalability focused (A1), compliance & reliability focused (A2), resilient high performance ecosystem (A3), and organizational vulnerability focused (A4). Results show that Scenario A3 consistently outperforms others, achieving the lowest group utility shortfall, smallest individual regret, and most favorable compromise measure. Shapley Value sensitivity analysis confirmed cybersecurity and scalability as dominant criteria, while expert AI validation reinforced the robustness of A3’s ranking. Monte Carlo simulations further demonstrated stability underweight perturbations, with A3 retaining its top position in over 80% of runs. The study contributes a transparent, reproducible, and scenario based methodology for vehicular risk assessment, bridging technical and organizational dimensions. Limitations include reliance on static scenario design and expert elicitation, suggesting future work should incorporate dynamic data streams and edge AI for real time risk recalibration.

Requirements change management is one of the core business analyst's activities, directly affecting change impact analysis, stakeholder communication, and the long-term system maintainability. While research on this topic examines in detail change processes, tracking methods, and change type classification, the problem of systematically documenting requirements changes remains underexplored. Existing research lacks a unified classification of change documentation approaches and context-sensitive recommendations for their selection, which limits their effectiveness in managing requirements.
To address this gap, this study develops a context-oriented framework for selecting approaches to requirements change documentation. The framework integrates three components: a conceptual model based on the Baseline–Delta–Target State triad, a taxonomy of documentation approaches, and a context-driven selection mechanism grounded in empirical evidence. A systematic literature review was combined with an analysis of the survey of 324 practicing business analysts from Ukrainian and international companies. Statistically significant associations between selected project context attributes and documentation practices were identified using the Chi-square test of independence and Cramer's V, while additional dimensions were supported through evidence from the literature.
The framework incorporates six documentation approaches: Full Target State, Delta-only, Target-driven Delta, Delta-driven Target, Parallel Use, and Hybrid Cycle. Four contextual dimensions emerge as key factors: project, environment, resources, and stakeholders. To support context-based selection of the change documentation approach, a matrix was developed that integrates the identified dependencies.
The results position requirements change documentation as a context-sensitive knowledge management mechanism rather than a universal procedural standard.

The rapid advancements in deep learning and classifier fusion techniques offer promising solutions to enhance the accuracy and robustness of biometric authentication systems in this paper we propose the integration of these methodologies, specifically in multimodal biometric systems that utilize face and fingerprint recognition. The research investigates various deep learning architectures, highlighting their effectiveness in processing diverse biometric datasets. Additionally, it examines classifier fusion techniques, which combine multiple classifiers to improve person identification performance. A significant focus of this research is on spoofing and anti-spoofing measures. Biometric systems, especially those involving facial and fingerprint recognition, are vulnerable to spoofing attacks such as the use of photographs, videos, or artificial fingerprints to impersonate legitimate users. We developed various anti-spoofing strategies that are integrated into the biometric authentication process to mitigate these risks. These include techniques like texture analysis, motion analysis, and liveness detection, which help differentiate between genuine biometric traits and spoofed samples. We benchmarked a comparative analysis of deep learning models and classifier fusion, demonstrated their strengths, weaknesses, and best practices. Additionally, performance evaluations focus on key metrics such as accuracy, computational efficiency, scalability, and the system’s ability to resist spoofing attacks. Ultimately, the paper emphasizes the potential of these advanced techniques to revolutionize biometric systems, with a particular focus on future research directions for optimizing these methodologies, particularly in the context of improving robustness against spoofing and enhancing the overall security of biometric authentication systems. overall system Equal Error Rate (EER), the True Acceptance Rate at a specified False Acceptance Rate (e.g., TAR @ 0.1% FAR), and the accuracy of the anti-spoofing module.

The present research aims to introduce a brand new theoretical framework for solving multi-dimensional fractional partial differential equations (FPDEs) by developing a novel integral transform tool called the Double Mohand-Generalized ARA Transform (DM-GART). The DM-GART is a triple-integral operator that applies the Mohand transform twice—once in each spatial variable x and y and the ARA transform once in the temporal variable t; the adjective “Double” refers specifically to the double spatial application of the Mohand transform. The theoretical properties and existence/uniqueness results of this newly developed integral transform are rigorously established in a Banach fixed-point theorem setting. The newly developed integral transform tool is then synergistically combined with the Adomian Decomposition Method (ADM) to produce a novel technique called the Coupled Double Mohand-Generalized ARA Decomposition Method (CDM-GADM). The CDM-GADM is applied for solving generalised fractional biological population equations. The technique is assessed by comparing exact solutions with N-term series solutions for N = 4, 6, and 8. From the results obtained in Tables 3–10, it can be noted that with an increase in the terms from N = 4 to N = 8, the absolute errors decrease several orders of magnitude; the absolute errors for N = 8 are as low as 10⁻¹⁰ for α = 1.0 at smaller values of time. The results are obtained in the form of convergent series characterized by the Mittag-Leffler function, validating the efficiency of the proposed method. A tolerance of ε = 10⁻⁶ is used as the practical stopping criterion.

Recently, the academic recommendation system represents the process of suggesting suitable institutions, courses, or learning pathways for students based on their performances and interests. Yet, the conventional systems didn’t concentrate on temporal dynamic pattern analysis within the Indian higher education institutions, leading to less effective or static academic recommendations. Thus, an academic recommendation system is proposed for Indian higher education institutions using Few-Shot PairNorm-Apical Dendrite Graph Attention Networks (FSPN-ADGAT) by considering temporal dynamic pattern analysis. Primarily, the student data undergoes pre-processing. Further, student performance analysis is done, followed by feature extraction. Now, the institutional course data undergoes pre-processing, followed by contextual embedding of text using Adapter Layers-Bidirectional Encoder Representations from Transformers (AL-BERT). Similarly, by using SRC, course similarity is analyzed between the pre-processed course data and extracted features. Similarly, the temporal dynamic pattern analysis is done from the pre-processed course data using Student-t Likelihood-based Bayesian Change Point (SL-BCP) and indicator extraction. Now, based on the analyzed course similarity, extracted features, contextual embedding output, analyzed temporal dynamic patterns, and extracted indicators, the node and matrix construction is performed. Lastly, the academic recommendation using FSPN-ADGAT provides personalized course suggestions to the students. Therefore, the proposed FSPN-ADGAT attained a lower Mean Absolute Error (MAE) of 0.171 than the conventional techniques.

Over the past seven years, significant changes have occurred in both the development paradigms and the practical use of software systems of varying complexity. These changes are largely driven by the rapid adoption of online artificial intelligence technologies based on large-scale language models. Such models are currently actively used in software development tasks, including source code generation and test plan creation, thereby integrating across various stages of the software development lifecycle. This article examines a classic research object—namely, the process of developing a system requirements specification—and proposes an approach to its formal verification using the ChatGPT online service. First, a detailed mathematical formalization of the research object is presented, followed by a structured model for preparing system requirements in projects using ChatGPT at various stages of development. Next, the proposed approach is illustrated using a real IT project example, demonstrating the sequential stages of requirements preparation in a modern development environment. The article defines the main categories of system requirements and discusses their representation in project documentation. To support the analysis, relevant tabular data and UML diagrams are provided. Furthermore, the study describes a methodology for formal requirements verification through prompt-based interaction with the ChatGPT system. The scientific novelty of this work lies in the application of requirements verification by modeling the expected behavior of the future software system using ChatGPT. Future research directions include incorporating a fifth category of requirements – business rules – using ChatGPT, which will enable modeling the behavior of the software system in real business processes.