Evaluating and Managing Sustainability Performance of Supply Chain and Business Process Management: An Integrated and Applied Approach

Ather Abdulrahman

doi:https://doi.org/10.54216/JSDGT.030202

Fusion: Practice and Applications | 2023

A Review of Glowworm Swarm Optimization Meta-Heuristic Swarm Intelligence and its Fusion in Various Applications

Natural phenomena inspire the meta-heuristic algorithm to carry out the aim of reaching the optimal solution. Glowworm swarm optimization (GSO) is an original swarm intelligence algorithm for optimization, which mimic the glow behavior of glowworm that can effectively capture the maximum multimodal function. GSO is part of the meta-heuristic algorithm used to solve the optimization problem. This algorithm solves many problems in optimization, especially in science, engineering, and network. Therefore, this paper review exposes the GSO method in solving the problem in any industry area. This study focuses on the basic flow of GSO, the modification of GSO, and the hybridization of GSO by conducting the previous study of the researcher. Based on this study, the GSO application in the engineering industry gets the highest score of 15% among other sectors.

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Muhammad A. S. Mohd Shahrom mail -

Nurezayana Zainal mail -

Mohamad F. Ab. Aziz mail -

Salama A. Mostafa mail

link https://doi.org/10.54216/FPA.130107

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2023

Blockchain Meets Edge Intelligence for Smart Cities Sustainability: An Insightful Review and Prospective Analysis

The convergence of blockchain technology and edge intelligence has emerged as a transformative force in the realm of smart cities, offering unprecedented opportunities to enhance sustainability. This paper presents a comprehensive review and prospective analysis of this convergence, shedding light on its potential to revolutionize urban environments. We commence with an exploration of the fundamental components of blockchain technology, emphasizing its core principles of decentralization, immutability, and consensus mechanisms. Simultaneously, we trace the historical evolution of blockchain from its origins with Bitcoin to its broad applications in diverse domains. In the context of edge intelligence, we examine how this paradigm shift decentralizes data processing, enabling real-time decision-making and enhancing data security. We elucidate its key components, such as edge devices and analytics algorithms, while highlighting its critical role in reshaping the urban landscape. The crux of this paper lies in the convergence of blockchain and edge intelligence, where we explore its profound implications for the sustainability of smart cities. From efficient energy management and waste reduction to improved transportation and green infrastructure, this convergence empowers smart cities to optimize resource usage, reduce environmental impact, and enhance the quality of life for their residents. Through a prospective analysis, we anticipate emerging trends and innovations that will shape the future of smart city sustainability, including AI integration, 5G connectivity, and circular economy initiatives. As smart cities continue to evolve, they stand at the forefront of addressing urbanization challenges while fostering sustainable, inclusive, and prosperous urban environments.

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Mustafa El-Taie mail -

Aaras Y.Kraidi mail

link https://doi.org/10.54216/JCIM.120105

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

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International Journal of Wireless and Ad Hoc Communication | 2023

Innovations at the Nexus of Sustainability and Industry 4.0: Data-Driven Approach for Preemptive Equipment Management in Smart Factories

  The convergence of Industry 4.0 and sustainability has brought forth a new era of manufacturing, where data-driven approaches play a pivotal role in achieving operational efficiency while minimizing environmental impact. This paper presents an innovative framework for sustainable smart manufacturing through data-driven predictive maintenance planning. By integrating advanced analytics and machine learning, we propose a preemptive equipment management approach that not only optimizes production processes but also fosters environmental responsibility. Our methodology combines the power of Long Short-Term Memory (LSTM) networks for pattern modeling and the Sea Lion Optimization Algorithm for feature selection. We demonstrate the effectiveness of our approach through a comprehensive empirical analysis conducted on a real case study, where the results indicate significant improvements over baseline studies, as evidenced by reduced Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), along with higher R-squared (R2) values. Our findings emphasize the synergy between technological innovation and sustainability imperatives, positioning our approach as a catalyst for reshaping modern manufacturing practices.  

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Ahmed Hatip mail -

Karla Zayood mail -

Rabah Scharif mail

link https://doi.org/10.54216/IJWAC.070104

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

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Journal of Sustainable Development and Green Technology | 2023

Green IoT Protection: Sustainability-Driven Machine Intelligence for Malware Defense

As the Internet of Things (IoT) continues to expand, the security of connected devices becomes a paramount concern. Malicious actors exploit vulnerabilities in these devices, leading to severe consequences such as data breaches, privacy infringements, and service disruptions. Traditional security measures struggle to keep pace with the evolving threat landscape, necessitating advanced solutions. In this paper, we present a pioneering approach to fortify the security of IoT environments against malware through the integration of advanced machine intelligence techniques. Our work addresses this critical concern by introducing a comprehensive Machine Intelligence Strategy designed to detect and classify malware in IoT ecosystem. Leveraging Support Vector Machines (SVM) with different kernel choices, our strategy offers a multi-faceted defense mechanism. Through extensive experimentation and evaluation on public dataset of malware images, we demonstrate the efficacy of our strategy in fortifying the guardianship of connected devices, fostering a safer and more resilient IoT ecosystem. Beyond technical contributions, our research fosters a deeper understanding of the symbiotic relationship between machine intelligence and IoT security, propelling advancements in safeguarding the ever-expanding landscape of interconnected devices.

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Ayman H. Abdel-aziem mail -

Tamer H. M. Soliman mail

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

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

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Journal of Sustainable Development and Green Technology | 2023

Towards Sustainable Smart Cities: Exploring the Synergy of Blockchain and Edge Intelligence - A Review and Outlook

The evolution of smart cities represents a pivotal transformation in urban development, driven by the integration of cutting-edge technologies. Among these, blockchain and edge intelligence have emerged as pivotal forces shaping the future of smart cities. This paper presents a comprehensive review and outlook on the potential synergy between blockchain and edge intelligence, highlighting their transformative impact on sustainable smart city development.  In our analysis, we delve into the key components and technologies associated with smart cities, emphasizing their goals of sustainability, efficiency, and improved quality of life. We introduce the concepts of blockchain and edge intelligence, elucidating their applications across various industries and urban domains. Moreover, we identify gaps in the existing literature and underscore the critical need for further research in the synergy of these technologies in smart cities.  Our exploration extends to the significance of the study, emphasizing the timeliness of this research amidst growing interest in sustainable smart cities. We discuss the potential benefits and implications of this technological convergence for urban planning, technology adoption, and sustainability. This paper envisions smart cities that prioritize sustainability, circular economies, and data privacy, while fostering innovation and collaboration among public and private stakeholders. As we look to the future, we anticipate that this convergence will pave the way for more resilient, sustainable, and inclusive smart cities, and we outline potential areas for further research and development in this exciting field.

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Ahmed Sleem mail

link https://doi.org/10.54216/JSDGT.030101

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

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Journal of Sustainable Development and Green Technology | 2023

Intelligent Method for Ranking the Risks in Sustainable Business Practices

In an era marked by increasing global interconnectivity and multifaceted risks, the imperative for effective risk management in international business administration has never been more pronounced. This paper presents a novel and sustainable approach to ranking risks within this dynamic landscape. Leveraging the power of the Multinomial Naive Bayes classifier, our method empowers organizations to systematically assess and prioritize risks while embracing sustainability principles. Through meticulous experimentation and analysis, we demonstrate the method's efficacy and its capacity to enhance decision-making processes for businesses operating on an international scale. Our experiments validate the method's robustness and applicability, contributing to the fields of international business administration and risk management. The findings underscores the critical importance of intelligent, data-driven risk assessment and mitigation in an interconnected world. It not only contributes to the fields of international business administration and risk management but also offers a blueprint for harmonizing economic success with environmental and social responsibility.

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Mahmoud Ibrahim mail -

Mahmoud Ismail mail

link https://doi.org/10.54216/JSDGT.030102

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

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Journal of Sustainable Development and Green Technology | 2023

A Strategy for Sustainable Administration Policies: Analysis and Evaluation

As businesses expand their global footprint in an era marked by growing environmental and ethical concerns, the administration of international operations has become a focal point for fostering sustainability and responsible corporate behavior. This paper delves into the intricate realm of sustainable administration in international business, with a particular focus on strategies employed by organizations to align their practices with environmental, social, and ethical considerations. Leveraging advanced data analytical methods, including k-means clustering, and guided by the insights of the Elbow Method, our research provides a comprehensive analysis of sustainable administration strategies. Drawing from Prudential Life Insurance as a case study, we explore how multinational corporations navigate the complexities of sustainability, particularly in the face of global challenges. Through rigorous examination and empirical findings, our study offers actionable insights for businesses aiming to strike a balance between competitiveness and responsible global citizenship. In an increasingly interconnected world, this research contributes to the ongoing dialogue on sustainable business practices, underlining the significance of sustainable administration in shaping a more resilient and equitable global economy.

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Ahmed M. Ali mail -

Ahmed Abdelhafeez mail

link https://doi.org/10.54216/JSDGT.030103

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

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Journal of Sustainable Development and Green Technology | 2023

Methodologically improved model of international competitiveness

It is known that the study of economic processes includes micro, macro, and an international level analysis of certain individual economic entities, that is, firms and industries (branches). While the process of studying and making decisions on the international economic behavior of specific individual subjects in order to maximize the satisfaction of unlimited needs in the context of limited resources is a research object and subject of the field of "international economy". The modern economic theories researched in the scientific article require wider use of mathematical instruments in the study of quantitative aspects of economic processes. One of the more widely used models in practice is the economic-mathematical model. An economic-mathematical model is a formalized classification of economic processes or phenomena, the composition of which is formed depending on the objective or subjective characteristics arising from the research purpose. Economic-mathematical models expressed quantitative aspects of economic processes through functions, equations, or inequalities. Modern economic theories, mathematical models, and functions (equalities or inequalities) used in the implementation of micro, macro, or international economic analysis of gross income increase in the article indicate the scientific basis, expediency, and relevance of the chosen topic.

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Asomiddin Soatovich Yusupov mail

link https://doi.org/10.54216/JSDGT.030104

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

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Journal of Sustainable Development and Green Technology | 2023

Smart Supply Chain management for Sustianble Business Administrations

The integration of smart supply chain technologies has emerged as a catalyst for reshaping the landscape of sustainable business administrations. This paper presents a comprehensive investigation into the dynamic relationship between smart supply chains and sustainability, examining their intricate interplay and the transformative potential they hold for modern supply chain management. Leveraging an ensemble of three machine learning models—Decision Trees, Support Vector Machines, and Logistic Regression—we analyze extensive datasets encompassing supply chain operations. Our findings demonstrate that the strategic deployment of smart technologies enhances predictive accuracy, informs data-driven decision-making, and optimizes supply chain processes. This research underscores the pivotal role of smart supply chains in achieving sustainability objectives. By fusing predictive accuracy with data-driven decision-making, our research underscores the pivotal role of smart supply chains in achieving sustainable business practices. The insights presented herein offer not only academic contributions but also actionable guidance for businesses navigating the intricacies of modern supply chain management.

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Ather Abdulrahman Ageeli mail

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

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

Details open_in_new

Journal of Sustainable Development and Green Technology | 2023

Evaluating and Managing Sustainability Performance of Supply Chain and Business Process Management: An Integrated and Applied Approach

As global supply chains become increasingly complex and environmentally conscious, the imperative for Sustainability-Driven Decision-Making (SDDM) gains paramount importance. This paper delves into the transformative potential of machine learning in reshaping sustainability practices within supply chains. Leveraging a diverse dataset encompassing provisioning, production, sales, and commercial distribution across clothing, sports, and electronic supplies, we employ a range of machine learning algorithms, including Logistic Regression, Gaussian Naive Bayes, Support Vector Machines, k-Nearest Neighbors, Linear Discriminant Analysis, Random Forest, Extra Trees, XGBoost, and Decision Trees. Our analysis spans critical dimensions of supply chain management, from fraud detection to late delivery prediction, and illuminates the pivotal role of these algorithms in improving sustainability outcomes. Through empirical experimentation, we identify optimal models for each task, revealing their strengths and limitations. Additionally, we visualize feature importance, offering insights into the factors shaping sustainability within supply chains. Our research underscores the symbiotic relationship between data-driven decision-making and sustainable practices, paving the way for more responsible, efficient, and resilient supply chains. As businesses seek to navigate an evolving landscape, the fusion of machine learning and sustainability emerges as a compelling paradigm, fostering a future where supply chains not only optimize operations but also contribute to global sustainability goals.

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Ather Abdulrahman Ageeli mail

link https://doi.org/10.54216/JSDGT.030202

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new