Optical Character Recognition System for Digit Recognition Using Deep Learning

Mona; Marwa M.

doi:https://doi.org/10.54216/JAIM.060102

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.

groups

Abedallah Z. Abualkishik mail -

Rasha Almajed mail

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

Volume & Issue

Vol. Volume 1 / Iss. Issue 1

Details open_in_new

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

Details open_in_new

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

Details open_in_new

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

American Journal of Business and Operations Research | 2021

Strategic Integration of Business Intelligence for Sustainable Portfolio Management in the Industry 4.0 Era

The advent of Industry 4.0 has propelled a transformative shift in business paradigms, prompting the strategic integration of business intelligence (BI) for sustainable portfolio management. This study addresses the need to discern optimal strategies in clustering investor portfolios within this dynamic landscape. Leveraging the Gap Statistic Algorithm and Silhouette Coefficient, a systematic methodology was employed to cluster investors based on diverse portfolio attributes, including asset allocation, risk profiles, and historical performance metrics. A feature correlation map elucidated attribute interdependencies, while summary statistics provided a comprehensive snapshot of the investor dataset. Results from the Gap Statistic Algorithm revealed an optimal cluster count, guiding the segmentation of investors into distinct clusters. Subsequent validation using the Silhouette Coefficient affirmed the coherence and quality of the clusters derived. The findings underscore the efficacy of BI-driven approaches in effectively clustering investors based on portfolio characteristics within Industry 4.0, facilitating nuanced insights into investor behaviors and preferences. Conclusively, this research illuminates pathways for informed decision-making in sustainable portfolio management, emphasizing the pivotal role of BI tools in optimizing investor segmentation strategies for contemporary industrial landscapes.

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Ahmed M. Ali mail -

Ahmed Abdelhafeez mail -

Shimaa S. Mohamed mail

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

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2024

⃗ȷρ Neutrosophic F Subgroup Over a Finite Group

Neutrosophic set has been developed as a mathematical method for procuring indeterminate and incomplete information. Neutrosophic fuzzy set is a powerful generic system that has been recently developed. In several areas, including data and information analysis, data science, information and decision, have successfully applied neutrosophic concept. Not just that but also the important problems we experience in variety of fields, such as computing, life science, social development, and technical work are represented by neutrosophic fuzzy sets. In this paper, we have presented the idea of an implication-based (ȷρ) neutrosophic fuzzy (F) subgroup over a finite group and a ȷρ neutrosophic F normal subgroup over a finite group. Further, we have established a few fundamental properties of a ȷρ neutrosophic F subgroup over a finite group and ȷρ neutrosophic F normal subgroup over a finite group.

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V. Dhanya mail -

M. Selvarathi mail -

M. Ambika mail

link https://doi.org/10.54216/IJNS.230113

Volume & Issue

Vol. Volume 23 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024

A Neutrosophic Decision-Making Methods of the Key Aspects for Supply Chain Management in International Business Administrations

The importance of supply chain management in the field of international business administration is investigated in this study. Global businesses rely heavily on effective supply chain management, which coordinates the international transfer of materials, data, and money. The paper illuminates the critical nature of supply chain management on a worldwide scale. Distance, cultural differences, legal constraints, and logistics are only some of the problems and complexity of international supply chain management that are explored in this article. Topics covered include supplier selection and management, demand forecasting, inventory control, transportation, and distribution network design, as well as other techniques used by businesses to improve their worldwide supply chains. The study also discusses how international supply networks are affected by globalization, free trade agreements, and geopolitical considerations. Organizational strategies for overcoming hurdles such as tariffs, quotas, and political instability in international commerce are discussed. This paper used the neutrosophic sets (NSs) to deal with uncertainty in assessment factors of supply chains in international business. The NS is integrated with the DEMATEL method. The neutrosophic DEMATEL is used to show relationships between factors.

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Ather Abdulrahman Ageeli mail

link https://doi.org/10.54216/IJNS.230114

Volume & Issue

Vol. Volume 23 / Iss. Issue 1

Details open_in_new

Prospects for Applied Mathematics and Data Analysis | 2023

A few little steps beyond Knuth’s Boolean Logic Table with Neutrosophic Logic: A Paradigm Shift in Uncertain Computation

The present article delves into the extension of Knuth’s fundamental Boolean logic table to accommodate the complexities of indeterminate truth values through the integration of neutrosophic logic (Smarandache & Christianto, 2008). Neutrosophic logic, rooted in Florentin Smarandache’s groundbreaking work on Neutrosophic Logic (cf. Smarandache, 2005, and his other works), introduces an additional truth value, ‘indeterminate,’ enabling a more comprehensive framework to analyze uncertainties inherent in computational systems. By bridging the gap between traditional boolean operations and the indeterminacy present in various real-world scenarios, this extension redefines logic tables, introducing neutrosophic operators that capture nuances beyond the binary realm. Through a thorough exploration of neutrosophic logic's principles and its implications in computational paradigms, this study proposes a novel approach to logic design that accommodates uncertain, imprecise, and incomplete information. This paradigm shift in logic tables not only broadens the spectrum of computing methodologies but also holds promise in fields such as decision-making systems and data analytics. This article amalgamates insights from over twelve key references encompassing seminal works in boolean logic, neutrosophic logic, and their applications in diverse scientific and computational domains, aiming to pave the way for a more robust and adaptable logic framework in computation.

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Florentin Smarandache mail -

Victor Christianto mail

link https://doi.org/10.54216/PAMDA.020201

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

Journal of Sustainable Development and Green Technology | 2023

Global Socio-Economic Problems and Approaches to Their Resolution

This article explores the causes, classification, and description of global problems, as well as ways to solve them. It also covers global development, the Millennium Development Goals, and sustainable development goals.

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Abdurakhmonov F. Abdufarmonovich mail

link https://doi.org/10.54216/JSDGT.030203

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2023

Optical Character Recognition System for Digit Recognition Using Deep Learning

Because it is so difficult to distinguish handwritten digits, digit identification is one of the most critical applications in computer vision. This is one of the reasons why it is so tough. The field of handwritten character recognition is one in which a great deal of application of numerous deep learning models has occurred. The startling parallels that can be drawn between deep learning and the brain are primarily responsible for its meteoric rise in popularity. In this study, the Artificial Neural Network and the Convolutional Neural Network, two of the most used Deep Learning algorithms, were investigated with an eye toward the recognition process's feature extraction and classification phases. With the assistance of the categorical cross-entropy loss and the ADAM optimizer, the models were trained on the MNIST dataset. Backpropagation and gradient descent are the two methods utilized during the training process of neural networks that contain reLU activations and carry out automatic feature extraction. In computer vision, one of the most common and widely used classifiers is the Convolution Neural Network, sometimes referred to as ConvNets or Convolutional neural networks. This network is used for the recognition and categorization of images.

groups

Mona Awad mail -

Marwa M. Eid mail

link https://doi.org/10.54216/JAIM.060102

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new