A MCDM Methodology to Analysis Strategies and Factors of Lean Production in Sustainability Development

Samah I. Abdel Aal; Ahmed Abdel-Monem

doi:https://doi.org/10.54216/JSDGT.020204

International Journal of Advances in Applied Computational Intelligence | 2023

Neutrosophic MCDM Model for Assessment Factors of Wearable Technological Devices to Reduce Risks and Increase Safety: Case Study in Education

This study investigates the feasibility of using wearable technologies in education to improve safety. This article explores how wearables may be used to improve school safety and wellness, as well as their advantages, disadvantages, and future potential. The article covers a wide range of wearable gadgets and their respective safety-related features, from smartwatches to location trackers to panic buttons and biometric sensors. Privacy issues, data security, user acceptability, and ethical considerations are only some of the problems and hazards discussed in this research on wearables in education. This study the neutrosophic set to deal with uncertain data. The neutrosophic set is integrated with the multi-criteria decision-making (MCDM) CRITIC method. The CRITIC method is used to compute the weights of factors and rank it. There are 15 factors used in this study. The case study is applied in the education field. Educators, technologists, and legislators all need to work together to guarantee the safe and effective use of wearable devices in schools, as shown by the study's findings. The article reiterates the importance of wearables and their potential to enhance safety measures in education before making the case for more studies, pilot programmers, and policy development to fully realize their promise.

groups

Ahmed Abdelhafeez mail -

Myvizhi M. mail

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

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2023

The Digital Revolution in Trade Finance: Exploring The Impact of Smart Blockchain-Based Letters of Credit On E-business Transactions

  This paper aims to explore the impact of Smart Blockchain-based Letters of Credit (BTLOC) on business transactions in the realm of trade finance. The involvement of a third party in business transactions often leads to complications such as process heterogeneity, increased complexity, information security risks, and higher costs. To address these challenges, this research proposes an innovative solution for activities dependent on third-party participation, specifically in the context of global trading. To provide a comprehensive understanding of this solution, the study employs business process modeling in a transaction scenario, offering a deeper insight into its mechanics. The implementation of platforms built upon blockchain technology (BT), and smart contracts has the potential to significantly reshape and streamline business procedures, thereby benefiting participants engaged in global trade. This research primarily focuses on investigating the theoretical aspects and feasibility of incorporating BT into global trade, considering a paradigm shift in the field. A novel BTLOC is introduced as a key element of the research, enabling the examination of its practicality. Additionally, we explore the applications of BTLOC in real case study of international Trading and explore its potential integration into trade finance processes. Through a multi-case analysis, this research contributes to the understanding of the paradigm shift facilitated by BT. The findings shed light on the future potential applications of blockchain in finance and serve as an illustrative example of the extended capabilities associated with financial processes.  

groups

Zenat Mohamed mail -

Mahmoud M. Ismail mail -

Shereen Zaki mail

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

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2023

Deep Learning Framework of Convolutional Neural Network (CNN) and Attention CNN for Early Diagnosis of Alzheimer's Disease

One of the biggest killers in the industrialized world is Alzheimer's disease (AD). Although computer-aided techniques have shown promising outcomes in laboratory experiments, they have yet to be used in a clinical setting. Recently, deep neural networks have gained traction, particularly for image processing tasks. There has been a dramatic increase in the number of publications written on the topic of identifying AD using deep learning since 2017. It has been observed that deep networks are more efficient than standard machine learning methods for detecting AD. It remains difficult to identify AD because distinguishing between comparable brain signals during categorization needs an extremely discriminative depiction of features. This paper proposed a deep neural network method for prediction the AD. Low-level computer vision has been a hotspot for research into deep convolutional neural networks (CNNs). Studies often focus on enhancing performance through the use of very deep CNNs. Yet, as one goes deeper, the effect of the shallow layers on the deeper ones gradually diminishes. Prompted by reality. This paper compared with the CNN and attention CNN models. The proposed model applied in the AD dataset which contains 5121 images for the train set. The results showed the attention CNN model is better than the CNN model in accuracy, precision, recall, loss, and AUC.

groups

Hoda K. Mohamed mail -

Ahmed Abdelhafeez mail -

Nariman A. Khalil mail

link https://doi.org/10.54216/IJAACI.030201

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2023

Extreme Gradient Boosting (XGBoost) and Support Vector Machine (SVM) models for Hepatitis C Prediction

Hepatitis C Virus (HCV) is a worldwide epidemic. The World Health Organization estimates that annually between 3 and 4 million instances of HCV are recorded. People with HCV would benefit from knowing their illness stage earlier thanks to accurate and timely prognoses. Different noninvasive blood biochemical indicators and patient clinical data have been utilized to determine the disease phase. As a substitute for the invasive and sometimes harmful liver biopsy, machine learning approaches have shown useful in diagnosing each phase of this chronic liver disease. To accurately estimate HCV using sparse weather information, this work offers two machine learning (ML) methods: The Support Vector Machine (SVM) and a simple tree-based ensemble approach called Extreme Gradient Boosting (XGBoost). The two models are applied to real-world data on HCV. The dataset contains 13 variables and 615 cases. The results showed the SVM achieved more accuracy than the XGBoost. The SVM gets 93.5% accuracy and XGBoost gets 90.23% accuracy. 

groups

Alber S. Aziz mail -

Haitham Rizk Fadlallah mail

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

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2023

A Multi-Layer Perceptron (MLP) Neural Networks for Stellar Classification: A Review of Methods and Results

The remarkable capacity of artificial intelligence (AI) to analyze enormous quantities of information and create precise forecasts has led to its growing prominence in the field of scientific Astrophysics. Stellar categorization is the process by which stars are sorted according to the characteristics revealed by their spectra. To analyze the star's electromagnetic radiation, a diffraction or prism screen separates it into a spectrum with an assortment of hues and spectral lines used to categorize the star. Star wavelengths are an extremely important piece of data for space-based photography studies. Employing data from over 100,000 cases and a variety of AI models, this study demonstrates how to categorize stellar properties as either a Galaxy or a Star. This paper used the multi-layer perceptron (MLP) neural network (NN) for stellar classification. The MLP is applied in 18 features. This paper showed the correlation between these features. This paper achieved 97% accuracy from the MLP model. This study compared various optimizers to show the best optimizer. The Adagrad optimizer is the best optimizer due to getting the highest validation accuracy.

groups

Ayman H. Abdel-aziem mail -

Tamer H. M. Soliman mail

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

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2023

Linear Regression and K Nearest Neighbors Machine Learning Models for Person Fat Forecasting

Predicting a person's person fat percentage is an important part of keeping tabs on their health and fitness. An accurate assessment of person fat allows for the development of individualized programmer for health and wellbeing, the promotion of illness prevention, and the evaluation of the efficacy of weight management initiatives. This study reviews the current state of the art in person fat prediction approaches, which includes the use of machine learning algorithms. Obesity is a chronic condition characterized by high levels of person fat and is linked to several health issues. Since several methods exist for estimating person fat percentage to evaluate obesity, these assessments are usually expensive and need specialized equipment. Therefore, determining obesity and its associated disorders requires an accurate estimate of person fat proportion according to readily available person measures. This paper presented a machine-learning model for forecasting person fat. This problem is a regression, so this paper used two regression models to deal with the regression dataset. This paper used linear regression (LR) and k nearest neighbors (KNN). The two models were applied to real datasets. The dataset has 252 records. The results showed the LR has the highest score than the KNN model.

groups

Alshaimaa A. Tantawy mail

link https://doi.org/10.54216/IJAACI.030204

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2023

Wind Turbine Prediction using Deep Learning and Long Short Term Memory (LSTM)

Accurate forecasting is essential for the long-term success of adding wind energy to the national power system. In this study, we look at forecasting wind turbine using a LSTM deep learning model. To forecast potential outcomes for a time series, it is sufficient to initially obtain pertinent details from past data. While many methods struggle with understanding the long-term dependencies encoded in data sets, LSTM options, an instance of the strategy in deep learning, show potential for efficiently overcoming this challenge. An overview of LSTM's architecture and forward propagation method is provided initially. LSTM network is applied to the wind turbine prediction dataset. This dataset has 9 features and 6575 records.  There are four performance matrices used to test the model. The four matrices are mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE). MAPE obtained the least error.

groups

Myvizhi M. mail -

Ahmed Abdel-Monem mail

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

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new

Journal of Sustainable Development and Green Technology | 2023

Towards Sustainable Supply Chain Management: A MCDM Framework

This paper proposes a novel Multi-Criteria Decision Making (MCDM) framework for sustainable supply chain management. The framework addresses the challenges of evaluating and selecting suppliers based on sustainability criteria, optimizing logistics operations, and making sustainable decisions within the supply chain. Through a comprehensive case study, the effectiveness of the proposed framework is demonstrated. The results show that the framework provides a structured and systematic approach for evaluating supplier sustainability performance and supporting decision-making. The framework integrates established MCDM techniques, such as Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), while accommodating the unique requirements of supply chain sustainability. The findings highlight the advantages of the proposed framework, including its ability to handle uncertainties, incorporate multiple criteria, and facilitate informed decision-making for sustainable supply chain management.

groups

Zenat Ahmed mail -

Amal F. Abdel-Gawad mail -

Mahmoud M. Ismail mail

link https://doi.org/10.54216/JSDGT.020202

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

Journal of Sustainable Development and Green Technology | 2023

Rethinking Waste Management: A Holistic Sustainability Framework for a Circular Economy

As societies face increasing environmental challenges, the need for holistic sustainability frameworks in waste management becomes imperative. This paper presents a comprehensive approach to rethinking waste management within the context of a circular economy. The study begins by examining the limitations of current waste management practices, highlighting the urgency to transition towards sustainable solutions. Emphasizing the importance of a circular economy, the paper discusses the potential benefits of adopting circular principles in waste management systems. The primary objective of this research is to propose a holistic sustainability framework that integrates key components for effective waste management, including waste reduction, recycling, resource recovery, and stakeholder engagement. The framework incorporates established methodologies such as Life Cycle Assessment (LCA), Material Flow Analysis (MFA), and Multi-Criteria Decision Analysis (MCDA) to guide decision-making processes. The framework is validated through a case study on Tokyo, Japan, assessing the applicability and effectiveness of the proposed approach in a real-world context. The findings highlight the significance of implementing source separation programs and promoting composting to reduce the organic waste fraction in Tokyo. Such measures can divert organic waste from landfills and transform it into a valuable resource. The validated framework provides insights into developing a holistic sustainability framework for waste management, contributing to the advancement of sustainable practices in achieving a circular economy.

groups

Ahmed Abdel-Rahim EI-Douh mail -

Ahmed Abdelhafeez mail

link https://doi.org/10.54216/JSDGT.020203

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

Journal of Sustainable Development and Green Technology | 2023

A MCDM Methodology to Analysis Strategies and Factors of Lean Production in Sustainability Development

Increases in sustainability performance and the adoption of innovative strategies for continuous improvement were driven by the need for production companies to compete in an increasingly globalized economy. Better operational performances are achieved when sustainable Production is included in industrial processes because wastes, costs, and environmental effects are reduced, and ergonomic requirements are met. To improve their performance and maintain a leading position in the market, several companies have turned to sustainable production practices. The study's goal is to improve the implementation of traditional Lean Production (LP) by creating an integrated Single valued neutrosophic Potentially All Pairwise RanKings of all possible Alternatives (PAPRIKA) method. The PAPRIKA is extended under a neutrosophic set. PAPRIKA is used to compute the weights of criteria by comparing the criteria. Also, the PAPRIKA method is used to rank and select the best strategy. The application is performed on the steps of the PAPRIKA method.

groups

Samah I. Abdel Aal mail -

Ahmed Abdel-Monem mail

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

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new