392 research outputs found

    Multi-Criteria Decision Making Model For Hotel Selection Problem Under Complex Dual Hesitant Fuzzy Information

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    ABSTRACTThe notion of the “complex dual hesitant fuzzy set (CDHFS)” is the combination of the “dual hesitant fuzzy set (DHFS)” and the “complex fuzzy set (CFS).” It is characterized by two degrees, namely the membership and nonmembership, in the form of a finite subset on a unit disc in the complex plane. CDHFS is useful for dealing with real-world problems involving uncertain or hard-to-predict information. Also, to approximate smoothly, the Einstein operators are well-known aggregation operators, while prioritized operators are effective tools for prioritization among criteria. Therefore, the goal of this study is to develop some prioritized aggregation operators under the CDHFS environment; namely the complex dual hesitant fuzzy prioritized averaging (CDHFPA) operator, the complex dual hesitant fuzzy prioritized geometric (CDHFPG) operator, the complex dual hesitant fuzzy Einstein prioritized averaging (CDHFEPA) operator, and complex dual hesitant fuzzy Einstein prioritized geometric (CDHFEPG) operator. Some properties of the proposed operators are investigated in detail. In addition, a multi-criteria decision-making (MCDM) method based on the proposed operators with the complex dual hesitant fuzzy setting is developed. Moreover, a numerical example is given for the application and effectiveness of the developed MCDM approach. A comparison study is also done with existing methods to show that the proposed MCDM method is better and more reliable. The study finds that if the expert’s preference is used to choose the right aggregation operators, the decision maker will have access to a wide range of compromise solutions

    Box-plots of the error rates produced by random forest, using top 10 features selected by different feature selection methods for TumorC dataset.

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    Box-plots of the error rates produced by random forest, using top 10 features selected by different feature selection methods for TumorC dataset.</p

    Tissue characterization of benign cardiac tumors by cardiac magnetic resonance imaging, a review of core imaging protocol and benign cardiac tumors

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    Generally, cardiac masses are initially suspected on routine echocardiography. Cardiac magnetic resonance (CMR) imaging is further performed to differentiate tumors from pseudo-tumors and to characterize the cardiac masses based on their appearance on T1/T2-weighted images, detection of perfusion and demonstration of gadolinium-based contrast agent uptake on early and late gadolinium enhancement images. Further evaluation of cardiac masses by CMR is critical because unnecessary surgery can be avoided by better tissue characterization. Different cardiac tissues have different T1 and T2 relaxation times, principally owing to different internal biochemical environments surrounding the protons. In CMR, the signal intensity from a particular tissue depends on its T1 and T2 relaxation times and its proton density. CMR uses this principle to differentiate between various tissue types by weighting images based on their T1 or T2 relaxation times. Generally, tumor cells are larger, edematous, and have associated inflammatory reactions. Higher free water content of the neoplastic cells and other changes in tissue composition lead to prolonged T1/T2 relaxation times and thus an inherent contrast between tumors and normal tissue exists. Overall, these biochemical changes create an environment where different cardiac masses produce different signal intensity on their T1- weighted and T2- weighted images that help to discriminate between them. In this review article, we have provided a detailed description of the core CMR imaging protocol for evaluation of cardiac masses. We have also discussed the basic features of benign cardiac tumors as well as the role of CMR in evaluation and further tissue characterization of these tumors

    Efficient resource prediction framework for software-defined heterogeneous radio environmental infrastructures

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    Artificial Intelligence (AI) is defining the future of next-generation infrastructures as proactive and data-driven systems. AI-empowered radio systems are replacing the existing command and control radio networks due to their intelligence and capabilities to adapt to the radio environmental infrastructures that include intelligent networks, smart cities and AV/VR enabled factory premises or localities. An efficient resource prediction framework (ERPF) is proposed to provide proactive knowledge about the availability of radio resources in such software-defined heterogeneous radio environmental infrastructures (SD-HREIs). That prior information enables the coexistence of radio users in SD-HREIs. In a proposed framework, the radio activity is measured in both the unlicensed bands that include 2.4 and 5 GHz, respectively. The clustering algorithms k- means and DBSCAN are implemented to segregate the already measured radioactivity as signal (radio occupancy) and noise (radio opportunity). Machine learning techniques CNN and LRN are then trained and tested using the segregated data to predict the radio occupancy and radio opportunity in SD-HREIs. Finally, the performance of CNN and LRN is validated using the cross-validation metrics

    The Influence of the Hawrami Dialect on Mawlawi Tawagozi’s Poems

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    The Goran dialect is crucial to our literary history as the Kurdish nation. It has also remarkably impacted the works of some famous authors and poets of our Kurdish nation. Mawlawi Tawagozi wrote his poems (Poetry) in Hawrami, a branch of the Gorani dialect. In the present study, the questions regarding the factors that have driven this Sorani dialect poet to adopt Hawrami in writing his poetry have been answered. Moreover, according to the scientific sources as well as analyzing his poems, the prominent factors which resulted in the supremacy of the Hawrami dialect in the poetry of the poet mentioned above are explained. In the practical part of this study, the factors that fascinate this poet with the Hawrami dialect are described and exemplified. The following questions in this study are answered: Why has this dialect been so significant, and has this poet written his entire poetry in it? Was it his love for that dialect, the beauty and charm of the Hawraman area and the love for Naqshabandi Sufism, or maybe due to his love for Tawela and Byara religiously It was Islam Sheiks? Did the ruling and policy of the Ardalan Emirate (Vassaldom) have any role?

    Bar-plots of error rates of the proposed and the other classical methods on various subsets of genes for Srbct dataset.

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    Bar-plots of error rates of the proposed and the other classical methods on various subsets of genes for Srbct dataset.</p

    The State of AI-Empowered Backscatter Communications: A Comprehensive Survey

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    The Internet of Things (IoT) is undergoing significant advancements, driven by the emergence of Backscatter Communication (BC) and Artificial Intelligence (AI). BC is an energy-saving and cost-effective communication method where passive backscatter devices communicate by modulating ambient Radio-Frequency (RF) carriers. AI has the potential to transform our way of communicating and interacting and represents a powerful tool for enabling the next generation of IoT devices and networks. By integrating AI with BC, we can create new opportunities for energy-efficient and low-cost communication and open the door to a range of innovative applications that were previously not possible. This paper brings these two technologies together to investigate the current state of AI-powered BC. We begin with an introduction to BC and an overview of the AI algorithms employed in BC. Then, we delve into the recent advances in AI-based BC, covering key areas such as backscatter signal detection, channel estimation, and jammer control to ensure security, mitigate interference, and improve throughput and latency. We also explore the exciting frontiers of AI in BC using B5G/6G technologies, including backscatter-assisted relay and cognitive communication networks, backscatter-assisted MEC networks, and BC with RIS, UAV, and vehicular networks. Finally, we highlight the challenges and present new research opportunities in AI-powered BC. This survey provides a comprehensive overview of the potential of AI-powered BC and its insightful impact on the future of IoT

    Bar-plots of error rates of the proposed and the other classical methods on various subsets of genes for Lungcancer dataset.

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    Bar-plots of error rates of the proposed and the other classical methods on various subsets of genes for Lungcancer dataset.</p

    Several Zagreb indices of power graphs of finite non-abelian groups

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    Molecular topology can be described by using topological indices. These are quantitative measures of the essential structural features of a proposed molecule calculated from its molecular structure. It is a numerical value obtained from a molecular configuration that reflects the significant physical characteristics of the suggested molecule. Numerous physical properties, chemical reactivity, and biological activity are correlated with the chemical composition using an algebraic number. The power graph P(G) of a finite group G is a graph whose vertex set is G and in which two distinct vertices are connected by an edge when one element is an integral power of the other. This article investigates a wide range of degree-based topological descriptors for power graphs of various finite groups. We find numerous Zagreb indices (given in Table 1) of power graphs of finite non-cyclic and cyclic groups, dihedral, and generalized quaternion groups

    Novel Dual Partitioned Maclaurin Symmetric Mean Operators for the Selection of Computer Network Security System With Complex Intuitionistic Fuzzy Setting

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    Intuitionistic fuzzy sets (IFSs) are key concepts in ambiguity and uncertainty. However, IFSs deal only with anticipation, not periodicity. To do so, complex intuitionistic fuzzy sets (CIFS) can handle uncertainties and periodicity simultaneously. Also, the Maclaurin symmetric mean (MSM) operator is a better tool for dealing with the criteria&#x2019;s interrelationships. As a result, this article presents a multi-criteria decision-making (MCDM) approach in the CIFS setting, which draws inspiration from the CIFS and MSM operators. We develop complex intuitionistic fuzzy partitioned dual Maclaurin symmetric mean (CIFPDMSM) operators and their weighted form by considering that all the criteria can be arranged into some groups. The proposed operators deal not only with interrelationships between criteria but also with partitioned relationships among criteria. Some properties of the proposed operators are discussed in detail. Further, an MCDM approach is developed based on the proposed operators in the CIFS environment. To show the effectiveness and application of the developed method, we also present a numerical example for selecting computer network security systems. Finally, the method is compared with the existing technique to demonstrate the proposed method&#x2019;s applicability and feasibility
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