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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.

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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.

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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.

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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.

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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.

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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
International Journal of Artificial Intelligence and Education Technology | 2023

Bibliographic analysis: Teaching with social media tools

This bibliographic analysis explores the use of social media tools in education and their impact on teaching and learning experiences. The analysis reviews research literature to provide evidence of the positive effects of social media tools on various aspects of education, including student engagement, critical thinking skills, academic achievement, and learning outcomes. YouTube videos are highlighted as particularly effective in promoting critical thinking skills and improving students' comprehension and retention of information. Additionally, social media platforms like Facebook are discussed as effective learning management systems, facilitating communication and collaboration among students and teachers. The flipped classroom model, which incorporates social media tools, is also examined for its positive effects on student learning. However, the successful integration of social media tools in education depends on factors such as instructional design, teacher training, and copyright considerations. Further research is needed to explore the long-term effects and potential challenges associated with the use of social media tools in education. Overall, this analysis emphasizes the opportunities that social media tools offer educators to enhance teaching and learning experiences, promote student engagement and collaboration, and improve learning outcomes. It underscores the importance for educators to stay informed about the latest research and best practices in utilizing social media tools effectively in educational settings as technology continues to evolve.

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Hojiyeva Iroda Avezovna mail
link https://doi.org/10.54216/IJAIET.020101

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

Details open_in_new
Financial Technology and Innovation | 2023

The Regulatory Landscape of Green Finance: An Improved Approach for Market Development

Green finance has emerged as a pivotal solution to address environmental challenges and foster sustainable development. This paper explores the regulatory landscape of green finance, focusing on the opportunities it presents and the barriers that hinder its market development. The paper begins with an introduction to the significance of green finance and its role in achieving environmental sustainability. It then provides an overview of green finance, including its definition, scope, and key stakeholders. An improved regulatory approach for green finance is thoroughly presented, covering global initiatives, national and regional policies, and the roles of key regulatory bodies. Next, we delve into the opportunities within the regulatory landscape, such as incentives, policy frameworks, and supportive measures for green financial products. However, challenges arise, including regulatory gaps, lack of standardized definitions, and legal risks for green investments. Case studies illustrate successful regulatory models, while highlighting the challenges faced by specific countries or regions. Finally, we offer a set of recommendations to strengthen regulatory frameworks, enhance transparency and disclosure requirements, and promote international cooperation and knowledge sharing, thus advancing the transformative potential of green finance.

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Ayman H. Abdel-aziem mail -
Tamer H. M. Soliman mail
link https://doi.org/10.54216/FinTech-I.020103

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

Details open_in_new
Financial Technology and Innovation | 2023

An Optimization Model for Assessment Resilience Engineering in Social Technical Organizations as a safety Management Paradigm

 An innovative safety administration paradigm, resilience engineering (RSE), is becoming more popular in today's sociotechnical organizations. It is thought that the properties of complicated social and technical structures better align with RSE. Especially when it comes to measuring and modeling, RSE is much more difficult due to its various criteria character and the inclusion of both qualitative and quantitative latent components. Using the extant neutrosophic TODIM (Portuguese of interactive and multi-criteria decision-making) approach, this study seeks to create a neutrosophic mixed multi-criteria decision-making (MCDM) framework for assessing and evaluating resilience. Several indicators of resilience were defined as part of the first assessment methodology. After that, the neutrosophic TODIM technique was used to assign relative importance to the various resilience indicators and to rate the effectiveness of the various operational units. As an illustration of the model's efficacy, we conducted a risk assessment of a gas refinery, a prototypical sociotechnical structure.

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Mahmoud M. Ibrahim mail -
Mahmoud M. Ismail mail -
Shereen Zaki mail -
Heba R. Abdelhady mail
link https://doi.org/10.54216/FinTech-I.020104

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

Details open_in_new