Optimizing Customer Relationship Management through Business Intelligence for Sustainable Business Practices

Shereen; Mahmoud M.; Heba; Mahmoud

doi:https://doi.org/10.54216/AJBOR.030105

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

Volume & Issue

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

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American Journal of Business and Operations Research | 2020

Strategic Management for Credit Risk in Supply Chain Networks: A Novel Framework

This study addresses the imperative for robust credit risk management strategies by proposing a novel framework tailored for supply chain networks. It aims to bridge existing gaps in credit risk assessment methodologies by amalgamating empirical insights, advanced computational techniques, and comprehensive data analytics. Leveraging a comprehensive dataset encompassing diverse attributes crucial for credit risk assessment, this study employs a meticulous methodology. It integrates machine learning algorithms, notably LightGBM, and exploratory data analysis techniques to preprocess data, examine missing values, assess variable correlations, and construct a predictive model. The empirical journey reveals insightful findings, emphasizing missing value patterns, variable interrelationships, and model performance. Precision-recall and ROC curves depict the model's ability to discern default and non-default cases, showcasing its efficacy in credit risk assessment within supply chain contexts. Our study contributes a foundational framework for strategic credit risk management within supply chain networks, offering actionable insights for stakeholders. While acknowledging limitations and the need for ongoing model refinement, this research sets the stage for future explorations and transformative practices in adaptive risk management strategies for interconnected supply chain networks.

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Abedallah Z. Abualkishik mail -

Rasha Almajed mail

link https://doi.org/10.54216/AJBOR.010106

Volume & Issue

Vol. Volume 1 / Iss. Issue 1

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American Journal of Business and Operations Research | 2021

Enhancing Market Price Decision-Making in Fintech through A Busines¬s Intelligence Technique

The surge of Fintech data and its implications on informed decision-making within the transportation sector have spurred the need for advanced analytical frameworks. This study addresses the challenge of leveraging Fintech data's temporal dynamics to enhance predictive capabilities and decision-making. The methodologies encompass an AutoEncoder (AE) for spatial feature extraction and an Improved Gated Recurrent Unit (IGRU) to capture temporal dependencies. Additionally, the Huber loss function optimizes model parameters, particularly in handling outliers. Integrating these techniques, our study explores Fintech data's spatial and temporal patterns, contributing insights for transportation planners and Fintech industries. Results demonstrate the efficacy of AE in learning spatial features, while IGRU effectively captures temporal dependencies, enabling the prediction of Fintech data with enhanced accuracy. The application of Huber loss ensures robustness by mitigating outlier influence. By the study's end, the model's predictive capabilities foster informed decision-making, offering opportunities to enhance Fintech data quality, reduce congestion, and bolster road safety. Overall, this research underscores the significance of advanced machine learning methodologies in decoding Fintech data's intricacies, laying a foundation for data-driven decision-making in the transportation and Fintech sectors.

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Mahmoud Ismail mail

link https://doi.org/10.54216/AJBOR.020204

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

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American Journal of Business and Operations Research | 2021

A Strategic Business Intelligence Framework for Sustainable Asset Management in Finance

Amidst the evolving landscape of finance, integrating sustainability principles into asset management stands as a pivotal pursuit for fostering long-term value creation. This research addresses the symbiotic relationship between business intelligence methodologies and sustainable asset management within the domain of finance. Leveraging advanced machine learning techniques including logistic regression, XGBoost, and CatBoost, this study delves into the exploration of sustainable finance practices and their implications for optimized asset management strategies. The study analyzes and models Asset data, aiming to understand the multifaceted dynamics and interdependencies shaping sustainable asset management decisions. Logistic regression serves as a foundation to model the relationships between variables, while XGBoost and CatBoost handle the complexities of categorical attributes, predicting outcomes related to sustainability metrics and financial performance indicators within the asset portfolio.  Through comprehensive analyses and visualizations, this research illuminates critical insights into the influential factors driving sustainable asset management decisions. The findings underscore the significance of leveraging data-driven methodologies to optimize asset management strategies aligned with environmental, social, and governance considerations.

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Mahmoud M. Ismail mail

link https://doi.org/10.54216/AJBOR.020205

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

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American Journal of Business and Operations Research | 2021

Optimizing Customer Relationship Management through Business Intelligence for Sustainable Business Practices

Amidst the dynamic landscape of contemporary business, the integration of Business Intelligence (BI) with Customer Relationship Management (CRM) emerges as a crucial paradigm for fostering sustainable business practices. This research investigates the synergy between BI-driven CRM strategies and sustainable operations, addressing the imperative to optimize customer relationships for sustainable business growth. Leveraging models such as BG/NBD, and Gamma Gamma, and employing K-means clustering techniques, this study seeks to decode the intricate relationship between these strategies. The BG/NBD model facilitates predictions of Customer Lifetime Value (CLTV), while the Gamma Gamma model estimates the Expected Average Profit, enabling a comprehensive understanding of customer behavior. Utilizing K-means clustering aids in customer segmentation, offering insights for targeted strategies. Visualization analyses, including the Elbow Method and Silhouette Plot, guide optimal cluster determination and cluster quality assessment. Ultimately, this research underscores the potential of BI-infused CRM approaches not only to drive profitability and enhance customer relationships but also to champion sustainable business practices. The findings provide a robust framework for businesses to craft and implement BI-enhanced CRM strategies, steering them toward sustainable growth while fostering customer-centricity and profitability in modern business environments.

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Shereen Zaki mail -

Mahmoud M. Ismail mail -

Heba Rashad mail -

Mahmoud Ibrahim mail

link https://doi.org/10.54216/AJBOR.030105

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

Details open_in_new