Exploring the Influences of Metaverse on Education Based on the Neutrosophic Appraiser Model

Sara Fawaz; Ibrahim; Mona

doi:https://doi.org/10.54216/IJNS.230112

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

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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

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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

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American Journal of Business and Operations Research | 2019

Forecasting Business Demand to Enhance Supply Chain Financial Optimization: A Predictive Modeling Approach

Predictive modeling plays a pivotal role in enhancing supply chain financial optimization by accurately forecasting business demand. This study investigates the efficacy of employing Gradient Boosting Decision Trees (GBDT) as a predictive modeling technique for precisely forecasting business demand within the context of supply chain management. Leveraging a comprehensive analysis of historical business sales data, this research scrutinizes the effectiveness of GBDT in capturing intricate demand patterns and fluctuations. Through a meticulous methodology, encompassing iterative GBDT modeling, the study demonstrates the model's ability to iteratively refine predictions, resulting in enhanced accuracy in forecasting business sales. Visual representations showcasing temporal trends, volatility, and decomposition of sales data provide critical insights into demand dynamics, serving as foundational elements for improved predictive models. The comparative analysis between predicted and actual sales data highlights the predictive capabilities of the GBDT approach, offering valuable insights for optimizing supply chain financial management. While presenting promising results, ongoing research aims to further enhance GBDT's predictive power by refining algorithms and exploring additional influential factors in demand variability. This research contributes to the advancement of predictive modeling techniques within supply chain financial optimization, aiding businesses in strategic decision-making and resource allocation.

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Noura Metawa mail

link https://doi.org/10.54216/AJBOR.000201

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Vol. Volume 0 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2024

Neutrosophic Treatment of the Modified Simplex Algorithm to find the Optimal Solution for Linear Models

Science is the basis for managing the affairs of life and human activities, and living without knowledge is a form of wandering and a kind of loss. Using scientific methods helps us understand the foundations of choice, decision-making, and adopting the right solutions when solutions abound and options are numerous. Operational research is considered the best that scientific development has provided because its methods depend on the application of scientific methods in solving complex issues and the optimal use of available resources in various fields, private and governmental work in peace and war, in politics and economics, in planning and implementation, and in various aspects of life. Its basic essence is to use the data provided for the issue under study to build a mathematical model that is the optimal solution. It is the basis on which decision makers rely in managing institutions and companies, and when operations research methods meet with the neutrosophic teacher, we get ideal solutions that take into account all the circumstances and fluctuations that may occur in the work environment over time. One of the most important operations research methods is the linear programming method. Which prompted us to reformulate the linear models, the graphical method, and the simplex method, which are used to obtain the optimal solution for linear models using the concepts of neutrosophic science. In this research, and as a continuation of what we presented previously, we will reformulate the modified simplex algorithm that was presented to address the difficulty that we were facing when applying the direct simplex algorithm. It is the large number of calculations required to be performed in each step of the solution, which requires a lot of time and effort.

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Maissam Jdid mail -

Florentin Smarandache mail

link https://doi.org/10.54216/IJNS.230110

Volume & Issue

Vol. Volume 23 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2024

Synergizing Neutrosophy and Randomized Blocks Design: Development and Analytical Insights

The design of the experiment is a strategy for effectively examining the relationship between input design parameters and process output and developing a greater understanding. A randomized block design is an experimental design that has two primary factors and is widely used in agriculture, environment, biological, animal, and food sciences, where experimental material is heterogeneous and precise. In a randomised block design, one or more observations may lose their true significance due to an accident, poor handling, pest infestations in agricultural trials, or other factors. It is prudent to treat this value as missing and estimate it. In today’s practical situations, uncertainty and inaccuracies are inevitable in most research areas. It is important to handle such data, which can lead to inaccurate and unreliable results. Neutrosophy is the branch of philosophy that provides an efficient method to study impreciseness among the data. Some of the common sources of Neutrosophy in randomised block design are incorrect blocking factor selection, measurement error, subjective factors, and natural variability. It is paramount to handle the Neutrosophy in a randomised block design; otherwise, it may lead to various problems, like a high risk of false positives. In this paper, the Neutrosophic Randomised Block Design (NRBD) is introduced to tackle data impreciseness. The study also, outlines a methodology for estimating missing observations in NRBD and presents its analysis. Additionally, the study compares the efficiency of NRBD to that of the Neutrosophic Completely Randomised Design (NCRD).

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Srishti Kumari mail -

Azarudheen S. mail

link https://doi.org/10.54216/IJNS.230111

Volume & Issue

Vol. Volume 23 / Iss. Issue 1

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Fusion: Practice and Applications | 2024

Effectual Augmentation of Glaucoma Prediction in Retinal Fundus Images using Hybrid Level Fusion of Image Pre-Processing Techniques

Glaucoma is a condition where the eyes of human beings are infected due to retinal damage which could result in loss of vision. It generally occurs due to prolonged pressure on the eye and affects the optic nerve if not treated at the earliest stage. However, it is hard for even experts to detect it at the earlier stage. Hence numerous image processing techniques were applied to identify Glaucoma in retinal eyes. The profound purpose of the work is to propose a pre-processing console to remove outliers in the Glaucoma retinal Fundus images using Denoising techniques of pre-processing to enhance the prediction using image pre-processing and computer vision techniques. The model was created with three stages including applying the denoising model using the Median Filtering for Edge Preservation, Contrast Limited Adaptive Histogram Equalization (CLAHE) and optimizing by eliminating irrelevant features using the Black Widow Optimization model and finally evaluating the performance of denoising techniques using accuracy-based predictions. The results showed that after performing a combination of denoising and optimizing techniques, the image quality was enhanced with 97% outperforming the existing models.  

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Anita Madona M. mail -

Paneer Arokiaraj S. mail

link https://doi.org/10.54216/FPA.140108

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

A Novel Approach for Communication-related to suicidal detection on Twitter using multi-class data

Suicide is a significant issue for public health worldwide since suicide is not something that happens randomly but is influenced by social and environmental variables as well. At the same time, effective early diagnosis and treatment may lead to several positive health and behavioural results. Suicide persists undiagnosed and untreated for many reasons, including denial of sickness and cultural and social disgrace. Through the ubiquity of social media, by expressing opinions, thoughts and everyday struggles with mental health on social media, millions of people are sharing their online identity. As opposed to typical retrospective research that uses self-reported surveys and questionnaires, this study assesses the validity of identifying suicidal symptoms using Twitter tweets that were gathered over more than a year, using a variety of online web-blogging sites as points of reference. For recognizing tweets expressing suicidal thoughts, three sets of characteristics are employed for training the dataset employing base and ensemble classifiers. The Rotation Forest (RF) approach is the preferred baseline, and the Maximum Probability Voting Decision approach is used in seven different labelled classes relating to suicide communication and class demonstrating suicidal thoughts. With the suicidal ideation class scoring 0.76 and the suicidal contents for all seven classes scoring 0.82, this revised model was able to attain an F-measure. To increase awareness of the vocabulary made use of on Twitter to express suicidal thoughts, the findings are summarized by highlighting the predictive principal component of suicide communication in classrooms.

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Rajesh Kumar mail -

N. Venkatram mail

link https://doi.org/10.54216/FPA.140109

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024

Exploring the Influences of Metaverse on Education Based on the Neutrosophic Appraiser Model

The growth of information technology over the course of human history has resulted in an update to traditional schooling. The Metaverse is an innovative concept for social work that incorporates many different types of technology. These technologies include big data, interactivity, artificial intelligence (AI), game design, internet computing, the Internet of Things (IoTs), and blockchain. It is reasonable to anticipate that the utilization of Metaverse will contribute to the advancement of educational practices. However, the structures of the Metaverse in educational settings are not yet developed to the point where they are ready for use. When it comes to schooling and the Metaverse, there are a lot of questions that need answering. Considering this, the purpose of this research is to provide a comprehensive analysis of the use of Metaverse in educational settings. This article provides an in-depth study of the use of the Metaverse in education, with a particular emphasis on contemporary technology, obstacles, and possibilities, as well as potential future paths. First, we provide a concise introduction to the use of the Metaverse in education, as well as an explanation of the rationale for including it. After that, we look at a few crucial aspects of the Metaverse's use in the educational sector, such as the individual's capacity to create their own personalized learning and teaching environments. The next step is appraising determined alternatives and criteria which related to utilize metaverse in education environment. Hence, entropy is supported with SingleValue Neutrosophic Sets (SVNSs) to analyze and valuation of criteria’s weights. Then Combined Compromise Solution (CoCoSo) is utilized under authority of SVNSs to rank alternatives related to deploying metaverse in educations. The results demonstrated that alternative 1 is the optimal otherwise alternative 3 is worst.

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Sara Fawaz AL-baker mail -

Ibrahim Elhenawy mail -

Mona Mohamed mail

link https://doi.org/10.54216/IJNS.230112

Volume & Issue

Vol. Volume 23 / Iss. Issue 1

Details open_in_new