Intelligent Method for Ranking the Risks in Sustainable Business Practices

Mahmoud Ibrahim; Mahmoud Ismail

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

Fusion: Practice and Applications | 2023

Regression Analysis and Artificial Neural Network Approach to Predict of Surface Roughness in Milling Process

Surface roughness (Ra) has a significant influence on the fatigue strength, corrosion resistance, and aesthetic appeal of machine components. Ra is hence a crucial manufacturing process parameter. This study predicts Ra of aluminum alloy Al-7024 after milling. Regression analysis and artificial neural network (ANN) modeling approaches are suggested for predicting Ra values. For better surface roughness, the cutting parameter must be set properly. Spindle speed, feed rate, and depth of cut have been chosen as predictors. Through 31 study cases, regression and ANN were used to examine how these parameters affected Ra. The measurement of surface roughness, together with comprehensive Ra analysis and regression analysis. The findings of this investigation indicate that Ra was predicted by both the regression and ANN models. convergent results from model predictions are obtained. This convergence highlights the promising methodology used in this work to forecast Ra in the milling of Al-7024. The findings demonstrated that, in comparison to the regression model, which had an average variation from the actual values of roughly 1%, The surface roughness was accurately predicted by the ANN model.

groups

Zaineb Hameed Neamah mail -

Ahmad Al-Talabi mail -

Asma A. Mohammed Ali mail

link https://doi.org/10.54216/FPA.130103

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2023

Design of High-Performance Intelligent WSN based-IoT using Time Synchronized Channel Hopping and Spatial Correlation Model

Wireless Sensor Network (WSN) is one of the most significant contributors to the Internet of Things (IoT), and it plays a significant role in the lives of individuals. There are three main problems in the design of traditional WSN based-IoT. First problem about data; the WSN transmits a huge volume of data to the IoT for processing. The second problem is the energy; since sensor nodes rely on their limited battery, conserving energy is crucial, and the third problem about efficiency of transmission. This paper presents new WSN based IoT framework that integrate important techniques to solve these problems; To increase the effectiveness of data processing and storing, the intelligent Adaptive Boosting stochastic algorithm is applied. IEEE 802.15.4e time slotted channel hopping (TSCH) protocol is used because it has the benefits such as collision-free transmission and multi-hop transmission.  Data reduction at the Gateway (GW) level of the network is achieved through spatial correlation between sensors with the goal of conserving energy. Principle idea of this new framework is to identify the advantages of integrating the important techniques; intelligent Adaptive Boosting Stochastic diffusion search algorithm, TSCH, and Special correlation model. As a result, the proposed framework can thereby satisfy the need for a long battery life of low-rate applications and at the same time, the need for high throughput for high-rate uses also for testing it in achieved efficient classification of data, the important performance measures are used.

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Hamza M. Ridha Al-Khafaji mail -

Refed Adnan Jaleel mail

link https://doi.org/10.54216/FPA.130104

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2023

Fusion Methodologies of the Assessment of the Effectiveness of Digital Technologies in Commercial Banks

The introduction and active use of modern digital technologies in commercial banks is becoming a modern trend in the banking sector and allows for improved quality of service to customers. At this point, the importance of assessing the effectiveness of the introduction of digital technologies in industries is increasing. Foreign methodologies for assessing the effectiveness of the introduction of digital technologies in various fields were studied, compared, analyzed, and identified. There are a few methodologies for assessing the effectiveness of digital technologies in the banking industry. The novelty of this research is the fusion of methodologies for assessing the development of digital technologies in commercial banks and determining the level of use of digital technologies offered by commercial banks. To increasing the effectiveness of the introduction of digital technologies in commercial banks, measures of a strategy are developed and recommended by the researcher for the effective development of digital technology offers by commercial banks in Uzbekistan.

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Muyassarzoda Fayzieva mail

link https://doi.org/10.54216/FPA.130105

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2023

Students’ Performance Prediction in Higher Education During COVID-19 Pandemic Based on Recurrent Forecasting and Singular Spectrum Analysis

The COVID-19 pandemic is a virus that is changing habits in human life worldwide. The COVID-19 outbreaks in Indonesia have forced educational activities such as teaching and learning to be conducted online. Teaching and learning activities using the online method are familiar, but the effectiveness of this method still needs to be investigated to be applied in all educational systems. This study used the predictive modeling of Recurrent Forecasting (RF) derived from Singular Spectrum Analysis (SSA) to know the online learning method's practicality on the student's academic performance. The fundamental notion of the predictive fusion model is to improve the effectiveness of several forms of forecast models in SSA by employing a fusion method of two parameters, a window length (L), and a number of leading components (r). This study used undergraduate students' grade point averages (GPA) from a public university in Indonesia through online classes during the COVID-19 epidemic. The experiments unveiled that a parameter of L = 14 ( ) yielded the finest prediction using the RF-SSA model with a root mean square error (RMSE) value of 0.20. Such a finding signified the ability of the RF-SSA to project the students' academic performance according to the GPA for the forthcoming semester. Nonetheless, developing the RF-SSA algorithm for greater effectiveness is essential to acquiring more datasets, such as by gathering a bigger group of respondents from several Indonesian universities.

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Kismiantini mail -

Shazlyn M. Shaharudin mail -

Adi Setiawan mail -

Rasyidhani Aditya Rizky mail -

Salsa-Billa Syahida Al-Hasania mail -

Murugan Rajoo mail -

Hairulnizam Mahdin mail -

Salama A Mostafa mail

link https://doi.org/10.54216/FPA.130106

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

Details open_in_new

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

Details open_in_new

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.

groups

Mustafa El-Taie mail -

Aaras Y.Kraidi mail

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

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

Details open_in_new

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.  

groups

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

Details open_in_new

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.

groups

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.

groups

Ahmed Sleem mail

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

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

Details open_in_new

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.

groups

Mahmoud Ibrahim mail -

Mahmoud Ismail mail

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

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

Details open_in_new