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Yayın Cargo company recommendation study based on probabilistic linguistic term set(Bitlis Eren Üniversitesi, 2023-12-28) Çoban, Veysel; Aksezer, Sezgin ÇağlarThe global economic structure is the main reason for changes in consumption habits and consumer behavior. Developing information technologies direct producers and consumers to e-commerce. Cargo services are an important link in the chain in the fast and effective operation of e-commerce. The growth in e-commerce has a driving force in the development of cargo services and cargo companies. Cargo companies can survive in global competition by being preferred by customers and increasing their number of customers. The change in the number of customers occurs by communicating the satisfaction or dissatisfaction with the cargo company to potential customers. This study deals with the preference levels of cargo companies serving in Turkey according to customer suggestions. The data obtained from the survey evaluations are processed and recommendation ranking calculations are made for cargo companies. Probabilistic Linguistic Term Sets (PLTS) are used to eliminate customer ambiguities in survey evaluations. Alternative cargo company recommendations are ranked based on the customers' past service experiences from cargo companies. Aras Cargo, MNG Cargo, PTT Cargo, Surat Cargo, UPS Cargo, Yurtiçi Cargo companies are evaluated according to price, personnel, speed, reliability and network attributes. The maximum deviation optimization method based on the Lagrangian function is used to calculate the weights of the cargo companies' attributes. The probabilistic linguistic cosine similarity method compares cargo companies pairwise under attributes and a similarity matrix is obtained for six cargo companies. The similarity matrix defines the alternative cargo company recommendation ranking based on customers' past experiences. UPS, SURAT and MNG cargo companies stand out as the most prioritized companies according to the evaluation results. The effects of attribute weights are observed by designing six different scenarios and it is observed that the differentiating attribute weights affect the recommendation ranking. Spearman correlation coefficient evaluation based on recommendation rankings indicates a high relationship between attributes.Yayın Automating cyber risk assessment with public LLMs: an expert-validated framework and comparative analysis(Institute of Electrical and Electronics Engineers Inc., 2026-03-26) Ünal, Nezih Mahmut; Çeliktaş, BarışTraditional cyber risk assessment methodologies face a critical dilemma: they are either quantitative yet static and context-agnostic (e.g., CVSS), or context-aware yet highly labor-intensive and subjective (e.g., NIST SP 800-30). Consequently, organizations struggle to scale risk assessment to match the pace of evolving threats. This paper presents an automated, context-aware risk assessment framework that leverages the reasoning capabilities of publicly available Large Language Models (LLMs) to operationalize expert knowledge. Rather than positioning the LLM as the final decision-maker, the framework decouples semantic interpretation from risk scoring authority through a transparent, deterministic Dynamic Metric Engine. Unlike complex closed box machine learning models, our approach anchors the AI's reasoning to this expert-validated metric schema, with weights derived using the Rank Order Centroid (ROC) method from a survey of 101 cybersecurity professionals. We evaluated the framework through a comparative study involving 15 diverse real-world vulnerability scenarios (C1-C15) and three supplementary sensitivity stress tests (C16-C18). The validation scenarios were independently assessed by a cohort of ten senior human experts and two state-of-the-art LLM agents (GPT-4o and Gemini 2.0 Flash). The results show that the LLM-driven agents achieve scoring consistency closely aligned with the human median (Pearson r ranging from 0.9390 to 0.9717, Spearman ρ from 0.8472 to 0.9276) against a highly reliable expert baseline (Cronbach's α =0.996), while reducing the assessment cycle time by more than 100× (averaging under 4 seconds per case vs. a human average of 6 minutes). Furthermore, a dedicated context sensitivity analysis (C13-C15) indicates that the framework adapts risk scores based on organizational context (e.g., SME vs. Critical Infrastructure) for identical technical vulnerabilities. Importantly, the system is designed not merely to replicate expert intuition, but to enforce bounded, policy-consistent risk evaluation under predefined governance constraints. Overall, these findings suggest that commercially available LLMs, when constrained by expert-validated metric schemas, can support reproducible, transparent, and real-time risk assessments.
