Relevant bibliographies by topics / Neutrosophic soft inverse images

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Academic literature on the topic 'Neutrosophic soft inverse images'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Neutrosophic soft inverse images"

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Al-Sharqi, Faisal Al, Abd Ghafur Ahmad, and Ashraf Al Al-Quran. "Mapping on Interval Complex Neutrosophic Soft Sets." International Journal of Neutrosophic Science 19, no. 4 (2022): 77–85. http://dx.doi.org/10.54216/ijns.190406.

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The neutrosophic idea is a fertile environment adaptable to different mathematical tools. This paper aims to introduce the mapping of complex interval neutrosophic soft sets (MI-CNSSs). Further, the images and inverse images of complex interval neutrosophic soft sets and their properties are explored. These will be supported by concrete examples, and this paper will present some theorems about complex interval neutrosophic soft images and inverse images.
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Al-Quran, Ashraf, Nasruddin Hassan, and Shawkat Alkhazaleh. "Fuzzy Parameterized Complex Neutrosophic Soft Expert Set for Decision under Uncertainty." Symmetry 11, no. 3 (2019): 382. http://dx.doi.org/10.3390/sym11030382.

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In the definition of the complex neutrosophic soft expert set (CNSES), parameters set is a classical set, and the parameters have the same degree of importance, which is considered as 1. This poses a limitation in modeling of some problems. This paper introduces the concept of fuzzy parameterized complex neutrosophic soft expert set (FP-CNSES) to handle this issue by assigning a degree of importance to each of the problem parameters. We further develop FP-CNSES by establishing the concept of weighted fuzzy parameterized complex neutrosophic soft expert set (WFP-CNSES) based on the idea that ea
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R., R., KR Balasubramanian, and A. Vadivel. "Interval Valued Neutrosophic Subalgebra in INK-Algebra." International Journal of Neutrosophic Science 24, no. 1 (2024): 51–64. http://dx.doi.org/10.54216/ijns.240105.

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This work presents the concept of interval-valued neutrosophic INK-subalgebras, also known as IV N INKsubalg’s, which are the level and strong level neutrosophic INK-subalgebras. Next, we establish and validate a few theorems that establish the connection between these concepts and neutrosophic INK-subalgebras. We define the images and inverse images of IV N INK-subalgebras and study the transformations of the homomorphic images and inverse images of interval valud neutrosophic (briefly, IV N) INK-subalgebra into IV N INK-subalgebras.
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Kharal, Athar, and B. Ahmad. "Mappings on Fuzzy Soft Classes." Advances in Fuzzy Systems 2009 (2009): 1–6. http://dx.doi.org/10.1155/2009/407890.

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We define the concept of a mapping on classes of fuzzy soft sets and study the properties of fuzzy soft images and fuzzy soft inverse images of fuzzy soft sets, and support them with examples and counterexamples.
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KHARAL, ATHAR, and B. AHMAD. "MAPPINGS ON SOFT CLASSES." New Mathematics and Natural Computation 07, no. 03 (2011): 471–81. http://dx.doi.org/10.1142/s1793005711002025.

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In this paper, we define the notion of a mapping on soft classes and study several properties of images and inverse images of soft sets supported by examples and counterexamples. Finally, these notions have been applied to the problem of medical diagnosis in medical expert systems.
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Prasad, Archana Kumari, Mahima Thakur, Jyoti Pandey Bajpai, and Samajh Singh Thakur. "MAPPINGS ON FERMATEAN FUZZY SOFT CLASSES." South East Asian J. of Mathematics and Mathematical Sciences 20, no. 01 (2024): 197–210. http://dx.doi.org/10.56827/seajmms.2024.2001.15.

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This paper defined mappings on Fermatean fuzzy soft classes and established some results related to images and inverse images of Fermatean fuzzy soft sets under these mappings. Further Fermatean fuzzy soft continuous , Fermatean fuzzy soft open and Fermatean fuzzy soft closed mappings in Fermatean fuzzy soft topological spaces are created and established some theorems related their properties and characterizations.
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Zorlutun, İdris. "Soft set-valued mappings and their application in decision making problems." Filomat 35, no. 5 (2021): 1725–33. http://dx.doi.org/10.2298/fil2105725z.

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In this paper, we introduce the notion of a set-valued mapping on soft classes and study several properties of images and inverse images of soft sets supported by examples and counterexamples. Finally, these notions have been applied in decision making problems.
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Al-Quran, Ashraf, and Shawkat Alkhazaleh. "Relations between the Complex Neutrosophic Sets with Their Applications in Decision Making." Axioms 7, no. 3 (2018): 64. http://dx.doi.org/10.3390/axioms7030064.

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The basic aim of soft computing is to trade precision for a tractableness and reduction in solution cost by pushing the limits of tolerance for imprecision and uncertainty. This paper introduces a novel soft computing technique called complex neutrosophic relation (CNR) to evaluate the degree of interaction between two complex neutrosophic sets (CNSs). CNSs are used to represent two-dimensional information that are imprecise, uncertain, incomplete and indeterminate. The Cartesian product of CNSs and subsequently the complex neutrosophic relation is formally defined. This relation is generalise
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Imène, Im�, and Afef Selmi. "Integrating Neutrosophic Vague N-Soft Sets with Chimp Optimization Algorithm for Sentiment Analysis on Social Media." International Journal of Neutrosophic Science 25, no. 1 (2025): 51–63. http://dx.doi.org/10.54216/ijns.250104.

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The swift development in social media through the internet produces vast data in a real-time scenario that has startling effects on large datasets. It generated the high-level use of sentiments and emotions in social networking media. Sentiment analysis (SA) using a neutrosophic set presents a new technique to handle the integral ambiguity and uncertainty in text datasets. Different from classical approaches, which categorize sentiment as positive, negative, or neutral, the neutrosophic set allows for the comparison analysis of truth-, indeterminacy-, and falsie-membership functions for all th
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Al-Saidi, Muslem, Áron Ballagi, Oday Ali Hassen, and Saad M. Saad. "Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm." Computers 13, no. 4 (2024): 106. http://dx.doi.org/10.3390/computers13040106.

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In recent years, Sign Language Recognition (SLR) has become an additional topic of discussion in the human–computer interface (HCI) field. The most significant difficulty confronting SLR recognition is finding algorithms that will scale effectively with a growing vocabulary size and a limited supply of training data for signer-independent applications. Due to its sensitivity to shape information, automated SLR based on hidden Markov models (HMMs) cannot characterize the confusing distributions of the observations in gesture features with sufficiently precise parameters. In order to simulate un
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Book chapters on the topic "Neutrosophic soft inverse images"

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Debnath, Somen. "Inverse Single-Valued Neutrosophic Soft Set and Its Application." In Handbook of Research on Advances and Applications of Fuzzy Sets and Logic. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-7979-4.ch032.

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Single-valued neutrosophic set (SVNS) can be viewed as an instance of the neutrosophic set (NS) proposed by Smarandache in 2005 to solve real decision-making problems that contain imperfect knowledge of the domain. Embedding the idea of SVNS with the soft set (SS), a new mathematical structure known as a single-valued neutrosophic soft set (SVNSS) is introduced to model the vague concept parametrically. In this chapter, the authors introduce a new soft model, namely inverse single-valued neutrosophic soft set (ISVNSS), that can be perceived as another mathematical model which is used to solve
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Sastry, A. S. C. S., P. V. V. Kishore, Ch Raghava Prasad, and M. V. D. Prasad. "Denoising Ultrasound Medical Images." In Medical Imaging. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0571-6.ch031.

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Medical ultrasound imaging has revolutioned the diagnostics of human body in the last few decades. The major drawback of ultrasound medical images is speckle noise. Speckle noise in ultrasound images is because of multiple reflections of ultrasound waves from hard tissues. Speckle noise degrades the medical ultrasound images lessening the visible quality of the image. The aim of this paper is to improve the image quality of ultrasound medical images by applying block based hard and soft thresholding on wavelet coefficients. Medical ultrasound image transformation to wavelet domain uses debauch
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Kumarasamy, Umamaheswari, G. V. Shrichandran, and A. Vedanth Srivatson. "Diffuse Optical Tomography System in Soft Tissue Tumor Detection." In Digital Image Processing - Advances and Applications [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.98708.

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Topical review of recent trends in Modeling and Regularization methods of Diffuse Optical Tomography (DOT) system promotes the optimization of the forward and inverse modeling methods which provides a 3D cauterization at a faster rate of 40frames/second with the help of a laser torch as a hand-held device. Analytical, Numerical and Statistical methods are reviewed for forward and inverse models in an optical imaging modality. The advancement in computational methods is discussed for forward and inverse models along with Optimization techniques using Artificial Neural Networks (ANN), Genetic Al
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Chao E.Y., Barrance P., Genda E., Iwasaki N., Kato S., and Faust A. "Virtual Reality (VR) Techniques in Orthopaedic Research and Practice." In Studies in Health Technology and Informatics. IOS Press, 1997. https://doi.org/10.3233/978-1-60750-883-0-107.

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Modeling the musculoskeletal joint system using biomechanical analysis and computer graphics techniques allows us to visualize normal, diseased and reconstructed joint function. This model can be used to study the loading of bones and joints under theoretical and simulated activities. In this study, intact cadavers were imaged using MRI, CT scanning and cryo-sectioning techniques. Using sequential pixel information of bone and soft tissue boundaries collected from digital camera images, MRI and CT scans, the volumetric models of the musculoskeletal joint system are reconstructed. “De
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Conference papers on the topic "Neutrosophic soft inverse images"

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Kavitha, M., and P. Murugadas. "Generalized inverse of centrosymmetric and K-centrosymmetric fuzzy neutrosophic soft matrices." In INTERNATIONAL CONFERENCE ON ADVANCED AND APPLIED MATHEMATICAL SCIENCES (ICAAMS2022). AIP Publishing, 2024. https://doi.org/10.1063/5.0220248.

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Pogue, B. W., and M. S. Patterson. "Forward and Inverse Calculations for Near-Infrared Imaging Using a Multigrid Finite Difference Method." In Advances in Optical Imaging and Photon Migration. Optica Publishing Group, 2022. http://dx.doi.org/10.1364/aoipm.1994.ncpdir.176.

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A multigrid finite difference method for solving the diffusion is used to solve the forward optical imaging problem. Simulations of near-infrared imaging are presented for some simple test objects, using typical soft tissue interaction coefficients. The forward calculation used here may provide a fast method of reconstructing diagnostic optical images.
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Webb, Kevin J., Jong C. Ye, Charles A. Bouman, and Rick P. Millane. "Nonlinear Multigrid Optimization for Soft Tissue Imaging Using a Bayesian Optical Diffusion Approach." In In Vivo optical Imaging at the NIH. Optica Publishing Group, 1999. http://dx.doi.org/10.1364/ivoi.1999.dis194.

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Optical diffusion tomography involves the reconstruction of an object cross section from measurements of scattered and attenuated light. While Bayesian approaches are well suited to this difficult nonlinear inverse problem, the resulting optimization problem is very computationally expensive. We propose a nonlinear multigrid technique for computing the maximum a posteriori (MAP) reconstruction in the optical diffusion tomography problem. The multigrid approach improves reconstruction quality, by avoiding local minima, and it dramatically reduces computation. Each iteration of the algorithm alt
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