FSM Vakıf Üniversitesi Araştırma ve Akademik Performans Sistemi
DSpace@FSM, FSM Vakıf Üniversitesi’nin bilimsel araştırma ve akademik performansını izleme, analiz etme ve raporlama süreçlerini tek çatı altında buluşturan bütünleşik bilgi sistemidir.
Güncel Gönderiler
Öğe Türü: Öğe , AI and The Future of Finance: Global Finance at a Crossroads(CRC Press, 2026) Bagis, BilalThe AI (artificial intelligence) revolution is fast transforming the global economy and the finance world. Accordingly, AI is likely to take over a wide range of financial roles in the financial world and many other related roles in adjacent sectors. AI sort of new innovations may even create much more complicated models and new instruments that provide a competitive edge. AI adoption in finance is expected to enhance automation, optimization, financial forecasting, analytical perspectives, fraud detection as well as enabling informed decision-making processes. Yet the accompanying ethical, risk and security concerns, stability-related issues as well as regulatory and risk management-related challenges also need to be addressed. New means and alternative approaches, regulatory arrangements are needed to mitigate risks related to AI adoption in finance. Risk and security concerns, transparency and accountability issues, new regulatory frameworks as well as other critical roles assumed by AI in shaping finance are all central to this AI-driven future of finance.Öğe Türü: Öğe , AI-Driven Decision-Making in Finance(CRC Press, 2026) Bagis, BilalThis book explores the deep impact of AI and digital transformation on finance, its practices and decision intelligence processes. The book and its practical, analytical chapters present further insights regarding ways to effectively utilize AI, potentially even beyond the existing theoretical and practical knowledge. Regulatory, ethical, security, and moral challenges and concerns associated with the use of AI in finance are thoroughly analyzed. Readers will take away a conceptual and practical understanding of AI and digitalization as well as real-world practical case studies, so that they can be applied appropriately in a workplace setting. It aims to differentiate itself by focusing more specifically on the practical realities of AI and digital transformation in decision-making processes within finance, offering actionable guidance and real-world case study or implications, which are less emphasized in most other competitors. The book is appropriate for experts and professionals interested in deployment of AI, digitalization and their implementation in finance. No doubt, academics, professionals, students, and finance experts will find this book an informative practice resource. The book would suit anyone interested in AI and its broader applications in finance or its uses in decision-making processes.Öğe Türü: Öğe , Preface(CRC Press, 2026) Bagis, BilalThis book explores the deep impact of AI and digital transformation on finance, its practices and decision intelligence processes. The book and its practical, analytical chapters present further insights regarding ways to effectively utilize AI, potentially even beyond the existing theoretical and practical knowledge. Regulatory, ethical, security, and moral challenges and concerns associated with the use of AI in finance are thoroughly analyzed.Öğe Türü: Öğe , A Longitudinal Study on Continuities and Shifts in Community Interpreting Services in Türkiye: The Case of The Refugees Association (Mülteciler Derneği)(University of Ljubljana Press, 2026) Erdoğan, Özgür BülentThis study examines continuities and changes in community interpreting practices in Türkiye, one of the world’s largest refugee-hosting countries, through a longitudinal ethnographic approach. Drawing on fieldwork from 2017 to 2018, and a revisit in 2024 to Mülteciler Derneği [Refugees Association] in Istanbul’s Sultanbeyli district, it examines how community interpreters of Syrian origin negotiate changing institutional settings, shifting migration policies and rising negative public sentiment. Using one-on-one interviews, focus group interviews, and discourse analysis, the research investigates interpreters’ emotional labour, coping strategies, and professional identity formation in a politically charged environment. Informed by reflexive ethnography and intersectionality, it highlights how interpreters’ experiences are shaped by multiple social determinants, including gender, disability and migration status. The findings reveal both structural continuities, such as centralized interpreter coordination, and new challenges, including workforce reduction and emotional strain. By situating interpreting within its broader sociopolitical context, the study offers a temporally grounded understanding of interpreter agency and resilience in humanitarian work.Öğe Türü: Öğe , Adaptive Task-Oriented Locomotion Control of a 2D Planar Robotic Fish Model Using Deep Reinforcement Learning and Sensory-Feedback CPG Network(MDPI, 2026) Koca, Gonca Özmen; Korkmaz, Deniz; Bal, Cafer; Ay, Mustafa; Akpolat, Zühtü HakanAutonomous locomotion in robotic fish requires task-dependent control capabilities under changing environmental conditions. This paper proposes a hierarchical simulation-based control framework for a two-joint robotic fish in a two-dimensional (2D) planar environment. This framework integrates the twin delayed deep deterministic policy gradient (TD3) algorithm with a sensory-feedback central pattern generator (CPG). A nonlinear planar dynamic model is designed as the learning environment, and a CPG network generates rhythmic undulatory swimming. The CPG network generates smooth locomotor patterns, while the TD3 policy performs high-level neuromotor modulation for task-dependent behavior. In the target-reaching benchmark, TD3–CPG achieves a 100.0% success rate with a Wilson 95% confidence interval (CI) of [96.30%, 100.00%], outperforming benchmark models. The proposed controller is also evaluated with obstacle avoidance in target reaching and station keeping under current disturbances. In circular obstacle avoidance, TD3–CPG achieves a 98.0% success rate and a 98.0% safe-pass rate, whereas the multiple rectangular obstacle scenarios yield an overall success rate of 91.7% over 96 trials. In station keeping, the controller achieves stay ratios of 87.57 ± 12.81% under constant current and 96.88 ± 10.79% under gust current, while keeping the mean target distance below the 0.25 m station keeping radius in both cases. Within the adopted 2D planar simulation environment, the obtained results demonstrate that the proposed method exhibits task-dependent maneuvering performance within the evaluated scenarios.
