An improved Approach for Sustainable Risk Management Practices in Modern Businesses

Abedallah Z.; Rasha

doi:https://doi.org/10.54216/JSDGT.040101

International Journal of Advances in Applied Computational Intelligence | 2023

Metaheuristic Optimization for Enhancing Cyber Security Index Prediction: A DTO+FGW Approach with MLP Integration

In the realm of cybersecurity, the evaluation and enhancement of cyber resilience are paramount to safeguarding nations and organizations against evolving digital threats. This paper introduces a novel approach that integrates the Dipper Throated Algorithm (DTO) and the Grey Wolf Optimizer (GWO) to fortify the analysis of Cyber Security Indexes. These indexes encompass vital metrics, including the Cybersecurity Exposure Index (CEI), Global Cyber Security Index (GCI), National Cyber Security Index (NCSI), and Digital Development Level (DDL). Leveraging the adaptive nature of DTO and the collaborative hunting strategies of GWO, the proposed DTO+GWO algorithm aims to optimize the evaluation of cyber readiness, exposure levels, and global commitments to cybersecurity. The Cyber Security Indexes dataset, featuring indicators from 193 countries, serves as the testing ground. This study contributes to advancing cyber threat assessment methodologies, fostering a proactive stance in the face of cyber risks globally. Through rigorous optimization, the DTO+GWO algorithm exhibits promising potential to elevate the precision and efficacy of cybersecurity evaluations. The optimization results demonstrate a notable achievement, with an RMSE of 0.0090, reflecting the algorithm's enhanced performance in fine-tuning the assessment of cybersecurity indexes.

groups

Ahmed Mohamed Zaki mail -

Abdelaziz A. Abdelhamid mail -

Abdelhameed Ibrahim mail -

Marwa M. Eid mail -

El-Sayed M. El-Kenawy mail

link https://doi.org/10.54216/IJAACI.040202

Volume & Issue

Vol. Volume 4 / Iss. Issue 2

Details open_in_new

Journal of Neutrosophic and Fuzzy Systems | 2023

New Notion for Neutrosophic Soft Normed Linear Spaces

An idea about neutrosophic soft normed space with linear tends to every soft points set throughout the field with scalar  that can be examined using a different approach in this study. Additionally, the terms Cauchy and Convergence are defined. A few theorems are proved that is related to these ideas.

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Jeyaraman M. mail -

Muthuraj R. mail -

Nachammal K. mail -

Iswariya S. mail

link https://doi.org/10.54216/JNFS.070105

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

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Journal of Neutrosophic and Fuzzy Systems | 2023

Common Fixed Point Theorems For Weakly Compatible Mappings In Complex Valued M− Fuzzy Metric Spaces

In this paper, we introduce the notion of complex valuedM- fuzzy metric spaces. We are proving a common fixed point theorem for weakly compatible mappings satisfying common E.A. Like property in complex valued M- fuzzy metric space. Our results improve and extend the results of Singh et al.12

groups

M. Jeyaraman mail -

M. Pandiselvi mail

link https://doi.org/10.54216/JNFS.070106

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024

Quaternion Framework of Neutrosophic Information with its Distance Measures and Decision-Making Model

Neutrosophic sets can be used to model uncertain data in real-world applications. To increase the use of complex neutrosophic sets, the space of quaternion numbers is investigated in this work. Analysts in complex contexts can benefit from the knowledge and direction that quaternion neutrosophic sets can offer by modeling complicated systems and capturing the interactions between various factors. Division algebras are used in some applications, such as particular formulations of class field theory, but they are generally far less important than quaternion numbers. Three-dimensional information with imaginary membership, imaginary indeterminacy, and imaginary non-membership functions is represented using quaternion neutrosophic sets. Intriguing quaternion numbers give us useful results when we analyze complicated data. Some basic characteristics of the derived concepts are examined. Novel quaternion-based operations and the analysis of order relations and logic operations are also explored based on neutrosophic set theory. For modeling uncertainty in quaternion-based systems, quaternion neutrosophic sets are helpful. Other fuzzy sets are unable to adequately capture the sophisticated fuzzy information that they can represent, such as uncertainty in both size and direction. The capacity to define fuzzy distance and similarity metrics is one of its intriguing qualities. We also present two quaternion distance measures and evaluate their properties. We use quaternion representations and measurements in a neutrosophic framework for decision-making models, and the results are excellent. Additionally, it shows readers how to construct the connections between traits and alternatives that are used in decision-making issues. An example is provided at the end to help illustrate the suggested strategy and provide additional context. Finally, we employ a different distance metric that is illustrated in the reliability section to validate the developed methodologies. It is possible to address the findings of studies on the application of quaternion neutrosophic sets for addressing various types of uncertainty in optimization problems related to the design and management of complex systems.

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Muhammad Kamran mail -

Nadeem Salamat mail -

Shahzaib Ashraf mail -

Ahmed M. Hassan mail -

Walid Karamti mail

link https://doi.org/10.54216/IJNS.230220

Volume & Issue

Vol. Volume 23 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2024

Representation of Symbolic 2-Plithogenic Matrices by Symbolic 2-Plithogenic Linear Transformations

The main purpose of this article is to study about the representation symbolic 2-plithogenic matrices by linear transformations between symbolic 2-plithogenic vector spaces, where it proves that every symbolic 2- bplithogenic matrix can be represented uniquely by a linear transformation between symbolic 2-plithogenic vector spaces. Also, this work proves that any linear function between symbolic 2-plithogenic vector spaces must be an AH-linear transformation.

groups

P. Prabakaran mail -

Bol´ıvar V. Jadan mail -

Rita Azucena D. V´asquez mail -

Marcos Lalama Flores mail

link https://doi.org/10.54216/IJNS.230221

Volume & Issue

Vol. Volume 23 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2024

On Schur Complement in k-Kernel Symmetric Neutrosophic and Intuitionistic Fuzzy Matrices

The present study provides the necessary and sufficient criteria for the k-Kernel symmetry (KS) of a Schur complement (SC) in a k-KS Neutrosophic Fuzzy matrices (NFM) and Intuitionistic Fuzzy Matrices (IFM). Equivalent characterizations of KS and k-KS NFM and IFM are presented in this work. We provide a few fundamental examples about KS NFM and IFM. It is demonstrated that while k-symmetric implies k-KS, but the converse need not be true. A few fundamental characteristics of k-KS IFM and NFM are obtained.

groups

G. Marimuthu mail -

S. Chanthirababu mail

link https://doi.org/10.54216/IJNS.230222

Volume & Issue

Vol. Volume 23 / Iss. Issue 2

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2023

The Use of Bayesian Techniques with Binary and Vector Data

This research provides a conceptual framework and examples for applying Bayesian techniques to binary and vector data.  For the binary data, for observations take on one of two possible values, Bayesian logistic regression and Bayesian networks are techniques,  applicable Bayesian logistic regression places priors on the coefficients and derives the posterior using the likelihoods under a logistic model. Bayesian networks represent dependencies between binary variables graphically and perform inference using conditional probability tables. For vector data, where observations are multi-dimensional, Bayesian linear regression places priors on the regression coefficients and finds posterior using the likelihoods under linear model. Gaussian process regression models the relationship between inputs and outputs as a draw from a   Gaussian process prior and computes the posterior process given observed data. The research provides the conceptual framework underlying Bayesian analysis, including key concepts such as prior and posterior distributions. It highlights the advantages of Bayesian methods like the ability to incorporate domain knowledge and model uncertainty. Numerical examples demonstrate how Bayesian techniques can be applied to binary and vector data classification tasks. The abstract summarizes the core ideas and contributions of the research on this topic.

groups

Shaymaa Riyadh Thanoon mail

link https://doi.org/10.54216/GJMSA.0100104

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2024

Partial orderings, Characterizations and Generalization of k-idempotent Neutrosophic fuzzy matrices

In this article, First, we study the different orderings for k-idempotent Neutrosophic fuzzy matrices (NFM). With this idea, we also discover some properties for the k- Neutrosophic fuzzy matrices and demonstrate the connection between the generalized inverse and different orderings. We also go through some properties for the T-ordering, T- reverse ordering, minus, and space ordering in k-idempotent Neutrosophic fuzzy matrices using the g-inverses with numerical examples is given. Minus ordering is a partial ordering in the set of all regular fuzzy matrices. We have introduced ordering on k− idempotent fuzzy matrices and developed the theory of fuzzy matrix partial ordering. The minus ordering and k−space ordering are identical for k− idempotent matrices. Next, we introduce and study the concept of k–Idempotent Neutrosophic fuzzy matrix as a generalization of idempotent NFM via permutations. It is shown that a kidempotent NFM reduces to an idempotent NFM if and only if PK = KP. The Conditions for power symmetric NFM to be k-idempotent are derived and some related results are given.

groups

M. Anandhkumar mail -

T. Harikrishnan mail -

S. M. Chithra mail -

V. Kamalakannan mail -

B. Kanimozhi mail

link https://doi.org/10.54216/IJNS.230223

Volume & Issue

Vol. Volume 23 / Iss. Issue 2

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Journal of Sustainable Development and Green Technology | 2024

An Integrated Business Intelligence Framework for Sustainable Risk Mitigation

This research paper examines the critical juncture of business intelligence and sustainable risk management in response to the increasing challenges faced by modern businesses. Our study recognizes that organizations must navigate uncertainties while prioritizing sustainability. It focuses on analyzing credit risk data. We present a comprehensive examination of predictive performance using Logistic Regression, Decision Tree, and K-Nearest Neighbors classifiers augmented by the Synthetic Minority Over-sampling Technique (SMOTE) for class rebalancing. The empirical findings presented through detailed tables and figures reveal intricate relationships and patterns within the data. This research also contributes to the broader discourse on responsible business practices by highlighting the integration of business intelligence in sustainable risk mitigation. Moreover, comparative analysis of machine learning algorithms under various resampling techniques further strengthens the framework’s reliability.

groups

Harith Yas mail

link https://doi.org/10.54216/JSDGT.030205

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

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Journal of Sustainable Development and Green Technology | 2024

An improved Approach for Sustainable Risk Management Practices in Modern Businesses

The complexity of the business environment, which is shaped by dynamic economic, environmental, and social factors, makes it more important for companies to develop sustainable risk management practices. This research proposes a novel approach that combines traditional methods with modern machine learning techniques in response to the complex challenges faced by contemporary businesses in balancing risk and sustainability. Our study uses a public bank loan default dataset as a case study to address missing data systematically through robust imputation mechanisms and transform categorical variables using feature encoding. Spearman correlation analysis helps us understand complex variable relationships and guides subsequent feature selection. The decision tree classifier, a powerful machine learning algorithm known for its interpretability, is applied to identify key factors contributing to risk assessment. The hierarchical structure of the decision tree not only reveals important variables but also provides an explicit representation of the decision-making process. ROC curve analysis shows how well our predictive model can differentiate potential loan defaults.

groups

Abedallah Z. Abualkishik mail -

Rasha Almajed mail

link https://doi.org/10.54216/JSDGT.040101

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new