Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651

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Now showing 1 - 6 of 6
  • Article
    Citation - WoS: 2
    Citation - Scopus: 3
    Age-Related Decline in Source and Associative Memory
    (Springer Heidelberg, 2025) Sumer, Erdi; Kaynak, Hande
    This review explores the multifaceted nature of age-related decline in source memory and associative memory. The review highlights the potential effects of age-related decline in these types of memory. By integrating insights from behavioral, cognitive, and neuroscientific research, it examines how encoding, retrieval, and neural mechanisms influence this decline. Understanding these processes is critical to alleviate memory decline in older adults. Directing attention to source information during encoding, employing unitization techniques to strengthen memory associations, and utilizing metacognitive strategies to focus on relevant details show promise in enhancing memory retrieval for older adults. However, the review acknowledges limitations in processing resources and executive function, necessitating a nuanced approach to the complexities of age-related decline. In conclusion, this review underscores the importance of understanding the complexities of age-related source and associative memory decline and the potential benefits of specific cognitive strategies. It emphasizes the need for continued research on age-related memory function to improve the quality of life for aging populations.
  • Article
    Citation - WoS: 5
    Citation - Scopus: 7
    Ranking Surgical Skills Using an Attention-Enhanced Siamese Network With Piecewise Aggregated Kinematic Data
    (Springer Heidelberg, 2022) Gilgien, Matthias; Ozdemir, Suat; Ogul, Burcin Buket
    Purpose Surgical skill assessment using computerized methods is considered to be a promising direction in objective performance evaluation and expert training. In a typical architecture for computerized skill assessment, a classification system is asked to assign a query action to a predefined category that determines the surgical skill level. Since such systems are still trained by manual, potentially inconsistent annotations, an attempt to categorize the skill level can be biased by potentially scarce or skew training data. Methods We approach the skill assessment problem as a pairwise ranking task where we compare two input actions to identify better surgical performance. We propose a model that takes two kinematic motion data acquired from robot-assisted surgery sensors and report the probability of a query sample having a better skill than a reference one. The model is an attention-enhanced Siamese Long Short-Term Memory Network fed by piecewise aggregate approximation of kinematic data. Results The proposed model can achieve higher accuracy than existing models for pairwise ranking in a common dataset. It can also outperform existing regression models when applied in their experimental setup. The model is further shown to be accurate in individual progress monitoring with a new dataset, which will serve as a strong baseline. Conclusion This relative assessment approach may overcome the limitations of having consistent annotations to define skill levels and provide a more interpretable means for objective skill assessment. Moreover, the model allows monitoring the skill development of individuals by comparing two activities at different time points.
  • Article
    Citation - WoS: 21
    Citation - Scopus: 22
    Design of Neuro-Swarming Computational Solver for the Fractional Bagley-Torvik Mathematical Model
    (Springer Heidelberg, 2022) Sabir, Zulqurnain; Raja, Muhammad Asif Zahoor; Baleanu, Dumitru; Guirao, Juan L. G.
    This study is to introduce a novel design and implementation of a neuro-swarming computational numerical procedure for numerical treatment of the fractional Bagley-Torvik mathematical model (FBTMM). The optimization procedures based on the global search with particle swarm optimization (PSO) and local search via active-set approach (ASA), while Mayer wavelet kernel-based activation function used in neural network (MWNNs) modeling, i.e., MWNN-PSOASA, to solve the FBTMM. The efficiency of the proposed stochastic solver MWNN-GAASA is utilized to solve three different variants based on the fractional order of the FBTMM. For the meticulousness of the stochastic solver MWNN-PSOASA, the obtained and exact solutions are compared for each variant of the FBTMM with reasonable accuracy. For the reliability of the stochastic solver MWNN-PSOASA, the statistical investigations are provided based on the stability, robustness, accuracy and convergence metrics.
  • Article
    Citation - WoS: 20
    Citation - Scopus: 20
    Bipolar Intuitionistic Fuzzy Graph Based Decision-Making Model To Identify Flood Vulnerable Region
    (Springer Heidelberg, 2023) Augustin, Felix; Narayanamoorthy, Samayan; Ahmadian, Ali; Balaenu, Dumitru; Kang, Daekook; Nithyanandham, Deva
    Bipolar intuitionistic fuzzy graphs (BIFG) are an extension of fuzzy graphs that can effectively capture uncertain or imprecise information in various applications. In graph theory, the covering, matching, and domination problems are benchmark concepts applied to various domains. These concepts may not be defined precisely using a crisp graph when the vertices and edges are more uncertain. Therefore, this study defines the covering, matching and domination concepts in bipolar intuitionistic fuzzy graphs (BIFG) using effective edges with certain important results. To define these concepts when the effective edges are absent, some novel approaches are discussed. To illustrate the domination concepts, the applications in disaster management and location selection problems are discussed. Further, a BIFG-based decision-making model is designed to identify the flood-vulnerable zones in Chennai, where the city's most and least vulnerable zones are identified. From the proposed model, Kodambakkam (Z(10)) is the most susceptible zone in Chennai. Finally, a comparative analysis is done with the existing techniques to show the efficiency of the model.
  • Article
    Citation - WoS: 8
    Citation - Scopus: 9
    Intermodal Humanitarian Logistics Using Unit Load Devices
    (Springer Heidelberg, 2022) Kavlak, Hasan; Ertem, Mustafa Alp; Satir, Benhur
    Intermodal freight transportation facilitates today's global trade. The benefits of intermodal freight transportation have been studied and are more observable in commercial logistics; however, the potential benefits of humanitarian logistics have not been thoroughly investigated. This research aims to present a resilient transportation framework by modeling intermodal transportation utilizing interoperable loading devices during disaster responses. We developed an integer programming model based on a time-space network by considering route and vehicle availabilities that are allowed to change with time. We consider vehicles with varying capacities in three transportation modes (i.e., ground, maritime, and air). The contribution of this study is threefold: (1) Two compatible unit load devices are proposed for humanitarian logistics; (2) a mathematical model that includes integer variable representation for vehicle fleets in different transportation modes is developed; and (3) intermodal transportation is compared with single-mode transportation using a real-life dataset. Our main results are as follows: In terms of cost, intermodal transportation is effective when demand occurs in consecutive periods and response time is short. Inventory is held more in intermodal transportation when it is cost-effective to use transportation modes with large capacities. Thus, the benefits of the responsiveness of intermodal transportation outweigh the costs of mode interchange and inventory holding for sudden-onset disasters where quick responses are needed within a short time.
  • Article
    Citation - WoS: 24
    Citation - Scopus: 27
    Bi-Objective Adaptive Large Neighborhood Search Algorithm for the Healthcare Waste Periodic Location Inventory Routing Problem
    (Springer Heidelberg, 2022) Aydemir-Karadag, Ayyuce
    There has been an unexpected increase in the amount of healthcare waste during the COVID-19 pandemic. Managing healthcare waste is vital, as improper practices in the waste system can lead to the further spread of the virus. To develop effective and sustainable waste management systems, decisions in all processes from the source of the waste to its disposal should be evaluated together. Strategic decisions involve locating waste processing centers, while operational decisions deal with waste collection. Although the periodic collection of waste is used in practice, it has not been studied in the relevant literature. This paper integrates the periodic inventory routing problem with location decisions for designing healthcare waste management systems and presents a bi-objective mixed-integer nonlinear programming model that minimizes operating costs and risk simultaneously. Due to the complexity of the problem, a two-step approach is proposed. The first stage provides a mixed-integer linear model that generates visiting schedules to source nodes. The second stage offers a Bi-Objective Adaptive Large Neighborhood Search Algorithm (BOALNS) that processes the remaining decisions considered in the problem. The performance of the algorithm is tested on several hypothetical problem instances. Computational analyses are conducted by comparing BOALNS with its other two versions, Adaptive Large Neighborhood Search Algorithm and Bi-Objective Large Neighborhood Search Algorithm (BOLNS). The computational experiments demonstrate that our proposed algorithm is superior to these algorithms in several performance evaluation metrics. Also, it is observed that the adaptive search engine increases the capability of BOALNS to achieve high-quality Pareto-optimal solutions.