Intelligent Data Mining Approach for Advanced Risk Analysis in Financial Sectors

Khyati; Gopal

doi:https://doi.org/10.54216/AJBOR.010205

Prospects for Applied Mathematics and Data Analysis | 2023

Neutrosophic Treatment of Duality Linear Models and the Binary Simplex Algorithm

One of the most important theories in linear programming is the dualistic theory and its basic idea is that for every linear model has dual linear model, so that solving the original linear model gives a solution to the dual model. Therefore, when we solving the linear programming model, we actually obtain solutions for two linear models. In this research, we present a study of the models. The neutrosophic dual and the binary simplex algorithm, which works to find the optimal solution for the original and dual models at the same time. The importance of this algorithm is evident in that it is relied upon in several operations research topics, such as integer programming algorithms, some nonlinear programming algorithms, and sensitivity analysis in linear programming...

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Maissam Jdid mail -

Florentin Smarandache mail

link https://doi.org/10.54216/PAMDA.020202

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2024

Some properties of neutrosophic nil radicals of neutrosophic ideals in rings

In this paper, we consider the notion of neutrosophic nil radicals of neutrosophic ideals in commutative rings and some properties of such nil radicals. Finally, we study the properties of semiprime neutrosophic ideals of rings.

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A. Priya mail -

P. Maragatha Meenakshi mail -

Aiyared Iampan mail -

N. Rajesh mail

link https://doi.org/10.54216/IJNS.230108

Volume & Issue

Vol. Volume 23 / Iss. Issue 1

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Fusion: Practice and Applications | 2024

An Efficient Hybrid Optimizer for Resource Reuse in a Cloud Environment

In a cloud context, merging complimentary numerous virtual machines (VMs) on an existing physical machine (PM) is the primary method for optimizing physical resources. One well-known area of research concentrates on making better use of VM migration resources when taking into account the dynamically changing resource demands of VMs. Finding the ideal balance between the complexity and performance of the VM migration optimization is the problem here. On the one hand, effective resource reuse is achieved through VM migration planning, and on the other, VM migration frequency is decreased to improve migration efficiency. On the other hand, a cloud data centre’s enormous PM and VM population typically makes migration planning more challenging, which impedes the VM migration decision-making process. By reducing the number of VM migration options to make VM migration planning easier and address these issues, this study recommend a hybrid Ant Colony and Genetic Algorithm (AGO) resource pool architecture. Then, establishing this model as a base, we develop the hybrid resource-reuse optimization method, which maximizes resource utilization with a minimal number of VM migrations. Finally, we evaluate hybrid AGO using simulation testing and real-world trials on a working cloud platform. Compared to similar methods, the findings show that hybrid AGO increases average resource utilization by 15%, reduces the use of PMs by 15%, and decreases the average number of migrations by 30%.

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V. S. Lavanya mail -

D. Mythrayee mail

link https://doi.org/10.54216/FPA.140107

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2023

Properties of Fuzzy Semi-Open Sets with Regards to Fuzzy Ideals

This paper introduces a broadened concept of fuzzy semi-open sets by framing them in the context of fuzzy ideals. Where many of their elementary properties will be presented in terms of theorems and lemmas. Also, many related examples about the validity of semi-open sets and their relationships with fuzzy ideals will be provided and discussed.

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Hassan A. Al-Hadi Ahmed mail

link https://doi.org/10.54216/IJNS.220411

Volume & Issue

Vol. Volume 22 / Iss. Issue 4

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International Journal of Neutrosophic Science | 2024

Some Properties of Fuzzy Semi-Open Sets in Fuzzy Bi-Spaces

The idea of fuzzy “semi-open sets” within the framework of fuzzy fields was proposed in the theory of fuzzy topology. This investigation delves deeper into the concept, specifically examining fuzzy “semi-open sets” concerning ω̃1 (ω̃2) with respect to ω̃2 (ω̃1). Additionally, we explore pairs of fuzzy “semi-open sets” in the context of a fuzzy bi-space and analyse their implications on results applicable in bi-topological spaces.

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Hassan A. Al-Hadi Ahmed mail

link https://doi.org/10.54216/IJNS.230109

Volume & Issue

Vol. Volume 23 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things |

Manta Ray Foraging Optimization Algorithm with Deep Learning Assisted Automated Phishing URL Detection Model

In current scenario, phishing attacks are vital threats to cyberspace security. Phishing is one of the common types of scams that attract individuals to access mischievous URLs (Uniform Resource Locators) as well as their personal data like IDs, passwords, and others. Many intelligent attacks have been launched to cheat users by retrieving a trustworthy website or any online platform in order to get data. Phishing URL classification is one of the crucial cybersecurity tasks intended to classify and moderate malevolent web addresses considered to cheat consumers by revealing sensitive data. Numerous researchers in cyberspace are interested in generating intelligent techniques as well as offering security services on a phishing website that grows more clever and malicious daily. Therefore, this study introduces a manta ray foraging optimization with deep learning-based phishing website detection (MRFODL-PWD) technique. The major intention of the MRFODL-PWD technique is to recognize and classify the presence of legitimate or phishing URLs. In the presented MRFODL-PWD technique, several stages of pre-processing to transfer data into a useful setup, and BERT is applied for feature extraction. Moreover, deep belief network (DBN) model can be used for automated phishing URL detection. Furthermore, the MRFO algorithm selects the hyperparameter values of the DBN model. An extensive comparison study stated that the MRFODL-PWD technique accomplishes enhanced phishing URL detection results over other models.

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SubashiniKsuba.pooja@gmail.com mail

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American Journal of Business and Operations Research | 2020

Assessment of Sintering Flue Gas Management Using Multi-Criteria Decision-Making Methodology

To evaluate and promote ecologically responsible practices in the sintering business, conducting a sustainability evaluation of sintering flue gas is essential. An important step in making iron and steel, sintering releases flue gas emissions that, if not controlled, may harm the environment. Reducing emissions, improving energy efficiency, managing waste, using water, utilizing resources, monitoring community effects, complying with regulations, conducting a life cycle assessment, and continuously improving are all part of the assessment's extensive scope. When these aspects are considered, stakeholders may better understand the economic, social, and environmental effects of sintering flue gas management. This paper used the multi-criteria decision-making (MCDM) methodology to evaluate the criteria. We used the DEMATEL method as an MCDM method. The DEMATEL is used to build the relation between the criteria. We collect ten criteria in this study. We compute the criteria weights to show this study’s best and worst criterion. The DEMATEL method is used to draw the effect diagram between criteria.

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Muzafer Saracevic mail -

Nan Wang mail -

Elma Elfic Zukorlic mail -

Suad Becirovic mail

link https://doi.org/10.54216/AJBOR.010204

Volume & Issue

Vol. Volume 1 / Iss. Issue 2

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American Journal of Business and Operations Research | 2019

A Sustainable Approach for Assessing Safety Management in Subterranean Infrastructure excavation Using Multi-Criteria Decision-Making

Subterranean infrastructure excavation necessitates stringent safety assessment methodologies due to its complex nature. This study addresses this imperative by presenting an integrated framework based on the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique. This methodology amalgamates multi-source information fusion and DEMATEL-driven multi-criteria decision-making techniques. The approach evaluates safety parameters within subterranean infrastructure excavation by synthesizing expert insights, on-site measured data, and predefined criteria. Through a systematic construction of judgment matrices, our approach offers a standardized means to assess observed values against established safety benchmarks.  The collaborative synthesis of expert assessments and empirical data not only informs the comprehensive relation matrix, highlighting intricate interdependencies among key factors but also fosters a structured pathway for evaluating safety. This integrated methodology, adaptable across diverse excavation scenarios, equips stakeholders with a holistic understanding of safety factors within subterranean construction. Facilitating informed decision-making, enables the optimization of safety protocols and interventions, thereby enhancing overall safety standards within such critical infrastructure projects.

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Irina V. Pustokhina mail -

Denis A. Pustokhin mail

link https://doi.org/10.54216/AJBOR.000104

Volume & Issue

Vol. Volume 0 / Iss. Issue 1

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American Journal of Business and Operations Research | 2019

A Decision Support Tools Using Multi-Criteria Decision-Making Approach for Financial Performance Analysis in a Competitive Global Economy

Stakeholders may gauge a company's financial well-being, profitability, and efficiency via a financial performance review. An outline of the main points of evaluating financial performance is given in this abstract. Revenue growth, profitability, liquidity, cash flow, return on investment, debt management, asset efficiency, market value, return on equity, and comparative analysis against industry peers are all the evaluation's financial criteria and metrics. The market value, debt levels, liquidity, profitability, cash flow management, revenue-generating capabilities, and the firm's financial condition may be better understood by looking at these metrics. We proposed a methodology to evaluate the financial performance in the competitive global economy. We gather the criteria to be analyzed. So, we used the concept of multi-criteria decision-making (MCDM) to deal with various and conflicting criteria. We compute the weights of the criteria by the mean value. Then, we used the criteria weights as input into the MCDM method. We used the VIKOR method to rank the various companies in this study. We collected ten criteria and 20 companies to be organized. We conducted the sensitivity analysis in two parts and changed the weights of criteria under ten different cases. In the second case, we change the parameter in the VIKOR method with a value between 0.1 and 1. The results of the two cases show the results are stable and the proposed model performs well.

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Ahmed M. Ali mail -

Ahmed Abdelhafeez Ibrahim mail

link https://doi.org/10.54216/AJBOR.000105

Volume & Issue

Vol. Volume 0 / Iss. Issue 1

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American Journal of Business and Operations Research | 2020

Intelligent Data Mining Approach for Advanced Risk Analysis in Financial Sectors

The dynamics of financial risk assessment in banking necessitate robust methodologies that harness the potential of intelligent data mining. In this study, we propose an applied approach that integrates sophisticated data mining techniques, notably XGBoost, within the context of banking data. Addressing the limitations of conventional risk assessment methodologies, our research emphasizes the need for a more precise and nuanced approach to identifying potential risks inherent in financial portfolios. Leveraging exploratory data analytics, meticulous preprocessing, and advanced modeling techniques, our methodology meticulously unraveled the intricate landscape of financial data. Through the application of XGBoost and comparative analysis against Support Vector Regression (SVR) and Random Forest (RF) models, this study elucidates the superiority of XGBoost in accurately predicting financial risk. Moreover, distributional analysis of socio-demographic attributes and loan amounts unveiled significant insights into risk determinants. The results underscore the pivotal role of intelligent data mining in refining risk assessment strategies within banking sectors. The comparative analysis, distributional insights, and superior predictive performance of XGBoost collectively emphasize the potential for advanced data mining techniques to revolutionize risk evaluation methodologies, empowering informed decision-making processes in navigating financial complexities.

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Khyati Chaudhary mail -

Gopal Chaudhary mail

link https://doi.org/10.54216/AJBOR.010205

Volume & Issue

Vol. Volume 1 / Iss. Issue 2

Details open_in_new