Enhanced support vector machine-based intelligent classification
of trusted nodes in WBAN for Resilient Infrastructure

International Journal of Neutrosophic Science | 2024

Innovative Perspective on Neutrosophic Cubic Z-Algebras

This study explores an innovative perspective on neutrosophic cubic Z-algebras, delving into the theoretical framework within mathematical structures. Through a comprehensive analysis, we uncover unique insights that contribute to the advancement of algebraic methodologies, particularly in handling uncertainties represented by neutrosophic elements. This work aims to present the idea of neutrosophic cubic sets in Z-algebras, as well as the usage of false membership function, truth, and indeterminacy in Z-algebras. Further, the results on -union, -intersection, -union, and -intersection of neutrosophic cubic Z-subalgebras are provided. This paper also discusses homomorphisms of Z-algebras and its associated characteristics.

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G. Nisha Devi mail -

P. Hemavathi mail -

R. Vinodkumar mail -

P. Muralikrishna mail -

Aiyared Iampan mail

link https://doi.org/10.54216/IJNS.230307

Volume & Issue

Vol. Volume 23 / Iss. Issue 3

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International Journal of Neutrosophic Science | 2024

Utilization of neutrosophic Kuhn-Tucker’s optimality conditions for Solving Pythagorean fuzzy Two-Level Linear Programming Problems

This article considers a bi-level linear programming with single valued trapezoidal fuzzy neutrosophic cost coefficient matrix and Pythagorean fuzzy parameters in the set of constraints both in the right and left sides. Based on the score functions of the neutrosophic numbers and Pythagorean fuzzy numbers, the model is changed to the corresponding crisp bi-level linear programming (BLP) problem. This problem is designated as a Pythagorean fuzzy bi-level linear programming (PFBLP) problem under neutrosophic environment. Kuhn-Tucker's conditions for optimality are necessary and sufficient for the existence of the optimal solution to a BLP problem. Using the suggested methodology, the problem is formulated as a single-objective non-linear programming problem with several variables and constraints. Two typical numerical examples are examined to illustrate the proposed approach.

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Hamiden Abd El- Wahed Khalifa mail -

Ashraf Al-Quran mail -

Faisal Al-Sharqi mail -

Binyamin Yusoff mail -

Khadiga W. Nahar Tajer mail -

Abeer T. Faisal mail -

Ali M. Alorsan Bany Awad mail

link https://doi.org/10.54216/IJNS.230308

Volume & Issue

Vol. Volume 23 / Iss. Issue 3

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International Journal of Neutrosophic Science | 2024

Group decision-making algorithm based on AOs settings for single-valued neutrosophic fuzzy soft expert environment

The incorporation of expert opinions and handling of data uncertainty are addressed by Al-Alkhazaleh through the introduction of a soft expert set. This extension of the soft set framework aims to enhance the analysis and decision-making processes by incorporating expert knowledge. On the other hand, the utilization of single neutrosophic sets (SVNSs) and fuzzy sets (FSs) has been introduced as models to effectively handle uncertain data. In this work, the authors propose a model that combines the essential characteristics of fuzzy sets (FSs) and single neutrosophic sets (SVNSs) within expert systems. Consequently, this model aims to offer decision-makers increased flexibility when interpreting uncertain information, empowering them in the decision-making process. From a scientific point of view, the process of evaluating this high-performance SVNFSES disappears. Therefore, in this paper, we initiated a new approach known as single-valued neutrosophic fuzzy soft expert sets (SVNFSESs) as a new development in a fuzzy soft computing environment. We investigate some fundamental operations on SVNFSESS along with their basic properties. Also, we investigate AND and OR operations between two SVNFSESS as well as several numerical examples to clarify the above fundamental operations. Finally, we have given an aggregation operator (AO) for SVNFSESs to construct a new algorithm to demonstrate the method’s effectiveness in handling some real-life applications.

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Faisal Al-Sharqi mail -

Ashraf Al-Quran mail -

Badria A. Ali Yousif mail -

Abeer T. Faisal mail -

Hamiden Abd El- Wahed Khalifa mail -

Mona Aladil mail -

Abd Elazeem M. Abd Elazeem mail

link https://doi.org/10.54216/IJNS.230309

Volume & Issue

Vol. Volume 23 / Iss. Issue 3

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International Journal of Advances in Applied Computational Intelligence | 2023

Analyzing the Impact of Distributed Data and Knowledge-Based Systems on Knowledge Management: A Systematic Mapping Study

Knowledge management is a critical aspect of modern organizations seeking to harness their collective intelligence and remain competitive in a dynamic business environment. With the advent of distributed data and knowledge-based systems, the landscape of knowledge management is undergoing significant transformations. This systematic mapping study aims to provide a comprehensive overview of the current state of knowledge management within the context of distributed environments and knowledge-based systems, shedding light on the key trends and challenges that shape this evolving field. Our study employs a systematic mapping methodology to analyze a wide array of scholarly articles, conference papers, and research reports. Through a structured review process, we identify and categorize relevant publications, facilitating a holistic understanding of the relationships between distributed data and knowledge-based systems in the realm of knowledge management. By mapping the existing literature, we uncover emerging themes, gaps, and areas of interest in this interdisciplinary domain. Key findings reveal the increasing role of distributed systems in enhancing knowledge sharing, collaboration, and decision-making processes. However, challenges related to data security, interoperability, and system integration also surface as important considerations. The systematic mapping study not only offers insights into the current state of knowledge management but also provides a foundation for future research directions and practical implications for organizations striving to optimize knowledge utilization in distributed settings. In conclusion, this research contributes to a deeper understanding of how distributed data and knowledge-based systems are shaping knowledge management practices and provides a roadmap for scholars and practitioners alike to navigate this evolving terrain.

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Rakhimova Gulnoza mail

link https://doi.org/10.54216/IJAACI.040205

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

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Financial Technology and Innovation | 2023

Evaluating Customer and Employee Satisfaction at Luna Park: A Sentiment Analysis of Online Reviews and Internal Feedback

This study aims to evaluate customer and employee satisfaction at Luna Park through sentiment analysis of online reviews and internal feedback. Utilizing advanced sentiment analysis tools, the research systematically categorizes and interprets the opinions expressed in both customer reviews available online and feedback provided by employees. This dual approach provides a comprehensive view of satisfaction levels from two critical perspectives. The findings reveal key areas of strength and opportunities for improvement in Luna Park's operations. The study concludes with actionable recommendations for enhancing both customer and employee experiences, underscoring the importance of integrating insights from diverse feedback sources for effective organizational management.  

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Marjona Allaberganova mail

link https://doi.org/10.54216/FinTech-I.030201

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

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Fusion: Practice and Applications |

Improving Shape Transformations for RGB Cameras Using Photometric Stereo

computer vision tasks. However, these cameras often produce depth maps with limited object details, noise, and missing information. These limitations can adversely affect the quality of 3D reconstruction and the accuracy of camera trajectory estimation. Additionally, existing depth refinement methods struggle to distinguish shape from complex albedo, leading to visible artifacts in the refined depth maps. In this paper, we address these challenges by proposing two novel methods based on the theory of photometric stereo. The first method, the RGB ratio model, tackles the nonlinearity problem present in previous approaches and provides a closed-form solution. The second method, the robust multi-light model, overcomes the limitations of existing depth refinement methods by accurately estimating shape from imperfect depth data without relying on regularization. Furthermore, we demonstrate the effectiveness of combining these methods with image super-resolution to obtain high-quality, high-resolution depth maps. Through quantitative and qualitative experiments, we validate the robustness and effectiveness of our techniques in improving shape transformations for RGB cameras.

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Fusion: Practice and Applications |

Improving Shape Transformations for RGB Cameras Using Photometric Stereo

computer vision tasks. However, these cameras often produce depth maps with limited object details, noise, and missing information. These limitations can adversely affect the quality of 3D reconstruction and the accuracy of camera trajectory estimation. Additionally, existing depth refinement methods struggle to distinguish shape from complex albedo, leading to visible artifacts in the refined depth maps. In this paper, we address these challenges by proposing two novel methods based on the theory of photometric stereo. The first method, the RGB ratio model, tackles the nonlinearity problem present in previous approaches and provides a closed-form solution. The second method, the robust multi-light model, overcomes the limitations of existing depth refinement methods by accurately estimating shape from imperfect depth data without relying on regularization. Furthermore, we demonstrate the effectiveness of combining these methods with image super-resolution to obtain high-quality, high-resolution depth maps. Through quantitative and qualitative experiments, we validate the robustness and effectiveness of our techniques in improving shape transformations for RGB cameras.

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Fusion: Practice and Applications |

Improving Shape Transformations for RGB Cameras Using Photometric Stereo

The emergence of low-cost red, green, and blue (RGB) cameras has significantly impacted various computer vision tasks. However, these cameras often produce depth maps with limited object details, noise, and missing information. These limitations can adversely affect the quality of 3D reconstruction and the accuracy of camera trajectory estimation. Additionally, existing depth refinement methods struggle to distinguish shape from complex albedo, leading to visible artifacts in the refined depth maps. In this paper, we address these challenges by proposing two novel methods based on the theory of photometric stereo. The first method, the RGB ratio model, tackles the nonlinearity problem present in previous approaches and provides a closed-form solution. The second method, the robust multi-light model, overcomes the limitations of existing depth refinement methods by accurately estimating shape from imperfect depth data without relying on regularization. Furthermore, we demonstrate the effectiveness of combining these methods with image super-resolution to obtain high-quality, high-resolution depth maps. Through quantitative and qualitative experiments, we validate the robustness and effectiveness of our techniques in improving shape transformations for RGB cameras.

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Fusion: Practice and Applications |

Enhanced support vector machine-based intelligent classification of trusted nodes in WBAN for Resilient Infrastructure

In various medical settings, ranging from hospitals to mental health care facilities and even homes, the Wireless Body Area Network (WBAN) assumes a critical role in enhancing the real-time monitoring of patients' overall health. The significance of the WBAN has grown recently due to its fundamental utility and its broad array of applications within the medical domain. As the data being transmitted across the WBAN infrastructure pertains to sensitive patient information, ensuring its security remains a matter of paramount importance. The establishment of a strong security framework holds immense necessity for any WBAN network to ensure the secure exchange of data between sensor nodes and other WBAN networks. This document introduces the Extended Support Vector Machine (ESVM) as an approach to differentiate trusted nodes within WBAN networks. This differentiation is accomplished through a classification method that reinforces the security dimensions of these networks. By employing Kernel-based Independent Component Analysis (K-ICA), relevant features are extracted from the data. The results of conducted tests unequivocally demonstrate that, when compared to various methods used previously, the proposed ESVM technique outperforms all of them in terms of its capacity to accurately classify trusted WBAN nodes in process innovation.

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Fusion: Practice and Applications |

Enhanced support vector machine-based intelligent classification of trusted nodes in WBAN for Resilient Infrastructure

In various medical settings, ranging from hospitals to mental health care facilities and even homes, the Wireless Body Area Network (WBAN) assumes a critical role in enhancing the real-time monitoring of patients' overall health. The significance of the WBAN has grown recently due to its fundamental utility and its broad array of applications within the medical domain. As the data being transmitted across the WBAN infrastructure pertains to sensitive patient information, ensuring its security remains a matter of paramount importance. The establishment of a strong security framework holds immense necessity for any WBAN network to ensure the secure exchange of data between sensor nodes and other WBAN networks. This document introduces the Extended Support Vector Machine (ESVM) as an approach to differentiate trusted nodes within WBAN networks. This differentiation is accomplished through a classification method that reinforces the security dimensions of these networks. By employing Kernel-based Independent Component Analysis (K-ICA), relevant features are extracted from the data. The results of conducted tests unequivocally demonstrate that, when compared to various methods used previously, the proposed ESVM technique outperforms all of them in terms of its capacity to accurately classify trusted WBAN nodes in process innovation.

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