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

Alshaimaa A.

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

International Journal of BIM and Engineering Science | 2023

Proposal for a Project Management Office "PMO" in the Public Establishment of Housing in Syria

In today's rapidly changing world where change is the master of the situation, the rapid development of project management practices in the business world has become a necessity rather than an option. Organizations operate in a competitive environment and for this reason they look for what distinguishes them from their competitors, improves the success of projects, (PM offices or PMOs) gives this feature, hence, organizations have adopted PMOs in many industries including the construction industry. This study highlighted the importance of applying modern project management methodologies in Syria in order to keep pace with the global market, especially through adopting Project Management Offices (PMO) in its construction industry as (PMO) is the ideal approach in managing projects efficiently and successfully. The Public Establishment of Housing in Syria has been selected as a case study because of the importance of the housing sector in Syria, which was affected by the Syrian crisis and the recent earthquake, which led to an increase in the demand for housing. A framework has been proposed and developed for PMO implementation and operation in the Public Establishment of Housing in Syria. This proposed Project Management Office aims to improve the reality of its project management by ensuring that these projects are completed in a timely manner, within budget limits, and to the required quality standards. The methodology used in this research included two axes: the first is a study of the literature for a deep understanding of PMO, and the second is interviews with housing organization employees to identify current practices in project management in the organization and what problems they suffer from, and then suggest the appropriate type of project management office for it.

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Sosaina Alchoufi mail -

Muhammad Shaaban mail

link https://doi.org/10.54216/IJBES.070102

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

Details open_in_new

Journal of Neutrosophic and Fuzzy Systems | 2023

Single-valued Plithogenic entropy measurement

In the last decade many researchers paid attention towards uncertainty measurement in given attributes arises unconsciously. These types of uncertainty measurement become more complex in case of dynamic changes in multi-variable, multi-attributes or Plithogenic attributes. It creates major problem at time of knowledge discovery and processing tasks. To resolve this issue, a method is proposed in this paper for precise measurement of randomness arises unconsciously for single valued Plithogenic attribute. In addition the selection of some important Plithogenic attributes at user defined granulation is discussed with an illustrative example.

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Prem Kumar Singh mail

link https://doi.org/10.54216/JNFS.070102

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2023

MADM Strategy Application of Bipolar Single Valued Heptapartitioned Neutrosophic Set

The fundamental goal of this study is to propose the concept of a bipolar single-valued heptapartitioned neutrosophic set (BSVHNS). We also outline the fundamental of BSVHNS traits and illustrate a few sample theorems. We define the fundamentals of the properties of the accuracy and scoring functions for the BSVHNS. The bipolar single-valued heptapartitioned mean in neutrosophic arithmetic (BSVHMNA) operator and the bipolar single-valued heptapartitioned mean in neutrosophic geometric (BSVHMNG) operator are defined and their fundamental properties are established in this article. We develop two Multi-Attribute Decision Making (MADM) strategies in the context of the BSVHNS environment: One is BSVHNS-MADM strategy which is on the BSVHMNA operator and another one is BSVHNS-MADM strategy which is on the BSVHMNG operator. Finally, we demonstrate the effectiveness of the developed procedures using a numerical example drawn from the actual world.

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Myvizhi M. mail -

Ahmed M. Ali mail -

Ahmed Abdelhafeez mail -

Haitham Rizk Fadlallah mail

link https://doi.org/10.54216/IJNS.210419

Volume & Issue

Vol. Volume 21 / Iss. Issue 4

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2023

Ranking and Evaluation Risks of Human Error Factors in Uncertain and Imprecision Information

Reasons for Human Error, the term "risk" is used to describe the many potential causes of human mistakes. Capabilities, organizational culture, job complexity, and environmental variables are just a few of the many aspects that fall under this category. Accidents, improvements in safety, and gains in productivity may all benefit from a better understanding of and approach to minimizing human error. This paper highlights the necessity for comprehensive methods and actions to limit the effect of human error by providing an overview of the primary human error components and their implications for risk management. Due to various criteria, the concept of multi-criteria decision-making (MCDM) is used to deal with various criteria. This paper used the MCDM tools to rank and evaluate the risks of human error factors. The DEMATEL method is a MCDM tool is used to compute the weights of these factors and rank the risks. The DEMATEL method is integrated with the neutrosophic set to deal with uncertain information. This paper used the single-valued neutrosophic set with three values (truth, indeterminacy, and falsity) values. The twenty risks are identified in this paper and ranked.   

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Shimaa S. Mohamed mail -

Ahmed Abdel-Monem mail

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

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

Details open_in_new

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.

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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.  

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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.

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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. 

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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.

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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