Computational Intelligence for Automatic Detection Cardiac Arrhythmia from ECG Signals: Taxonomy and Open Issues

Reem; Fuad; Milan

doi:https://doi.org/10.54216/IJAACI.020202

International Journal of Neutrosophic Science | 2023

Choosing Suppliers for Healthcare Supply Chains under Neutrosophic Multi-Criteria Decision-Making Method

Given the importance of suppliers to the overall viability and effectiveness of a supply chain, the assessment and choice of suppliers is a popular topic of research. Thus, in today's global climate, businesses must develop a methodical approach to evaluating and selecting the most suitable supplier based on their standards. To meet this need, businesses might turn to multi-criteria decision-making (MCDM) techniques, since deciding on a reliable provider is fundamentally an MCDM challenge. Although many examples of using these strategies for supplier assessment and choice can be found in the published literature, not enough research has been conducted on their effectiveness in the healthcare industry. In the healthcare industry, hospitals must also think about supplier-related choices to lessen hazards and threads that impact their efficacy. The VIKOR technique, which is focused on the development of a compromising answer within the context of options and assessment standards, yields excellent outcomes in situations requiring the consideration of numerous factors simultaneously. Numerous studies have shown that neutrosophic numbers actively aid in the process of making decisions. Suppliers in healthcare alternatives selection is a challenge that has been solved by the presented technique.

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Iruma Alfonso Gonzalez mail -

Guido Guida Acevedo mail -

Flor B. Morocho Quinchuela mail

link https://doi.org/10.54216/IJNS.210209

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2023

The impact of economic growth and fiscal policy on poverty rate in Uzbekistan: application of neutrosophic theory and time series approaches

The aim of this paper is to analyze how economic growth and fiscal policy impact on poverty rate in Uzbekistan. To reach this aim neutrosophic-AHP method was applied together with two time series models, namely Autoregressive Distributed Lags (ARDL) and Vector Autoregression (VAR) models. The statistical data of Uzbekistan over the period of 2000-2021 was used. Neutrosophic-AHP served as a basis for time series analysis. In accordance with AIC and BIC criteria, VAR was chosen as the most adequate model. Results of VAR model showed that a poverty rate has a delay effect on two years, when the increase of the second lag of poverty difference by one unit also increased current poverty difference by 0.501 units. Also, it was revealed that economic growth affects poverty adversely with delay in one lag, whereas taxes – with two lags. Interesting situation occurred with government expenditures, which impacted negatively on poverty after a one-year lag, but positively after two lags.

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Aziza Usmanova mail

link https://doi.org/10.54216/IJNS.210210

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2023

Colorectal Cancer Prediction Using Machine Learning and Neutrosophic MCDM Methodology: A Case Study

The third most common disease worldwide, colorectal cancer (CRC) is responsible for around 10% of annual cancer diagnoses. The success of personalized treatment hinges on the ability to recognize biomarkers linked with CRC longevity and forecast the prognosis of CRC patients. The goal of this research is to provide a novel approach to doing multi-attribute colorectal cancer analysis by using machine learning algorithms with multi-criteria decision-making (MCDM) methods and neutrosophic set (NS). The NS is used to overcome the uncertainty in the dataset. This paper used the neutrosophic AHP method to get the weights of features in the used dataset. Then the machine learning algorithms are used to give analysis and prediction of colorectal cancer. The decision tree (DT) and support vector machine (SVM) is used to analyze and predict colorectal cancer. The dataset has nine features like age, gender, dukes stage, location, and Disease-free survival. This paper shows the analysis of the dataset and the correlation among the features.

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Juan Viteri Rodríguez mail -

Julio Rea Martínez mail -

Freddy F. Jumbo Salazar mail

link https://doi.org/10.54216/IJNS.210211

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2023

Neutrosophic-based Machine Learning Techniques for Analysis and Diagnosis the Breast Cancer

Approximately one in eight women will get breast cancer in their lifetime. Because of the risks associated with radiation exposure, various women choose to avoid getting detected with breast cancer. Non-invasive breast cancer detection methods have limitations concerning the safety of radiation exposure and the accuracy with which tumors in the breast are diagnosed. Machine learning methods are commonly used to diagnose breast cancer. This paper applied three different machine learning methods like KNN, Naïve Bayes, and ID3. These methods are applied to the Wisconsin Breast Cancer dataset. In the process of categorization, data with unbalanced classes is problematic because methods are more probable to categorize fresh observations to the majority class since the likelihood of cases forming the plurality class is considerably high. So neutrosophic set is used to overcome the vague and uncertain data. This paper used single-valued neutrosophic numbers to evaluate the criteria. This paper used ROC and accuracy to evaluate the methods. The KNN has a 96.7%, Naïve Bayes has a 95.2%, and ID3 has a 95.3% accuracy.

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Rosita Elizabeth O. Torres mail -

Jhonny Rodríguez Gutiérrez mail -

Alex G. Lara Jacome mail

link https://doi.org/10.54216/IJNS.210115

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2023

Assessment and prediction of Chronic Kidney using an improved neutrosophic artificial intelligence model

CKD, or chronic kidney failure, is characterized by a gradual decline in kidney operation over time and may be linked to a wide range of medical conditions. Initial detection and therapy are the best tools for combating chronic kidney disease, although they often only delay the development of renal failure. The eGFR-based CKD grading system is useful for risk stratification, patient monitoring, and treatment strategy development. Personalized care and treatment planning will be possible if this research is successful in predicting how soon a CKD individual will need to begin dialysis. The machine learning methods used to predict CKD. But the dataset contains uncertain information, so the neutrosophic set is used to overcome this issue. This paper suggests a framework including the neutrosophic DEMATEL and machine learning method to predict CKD. The neutrosophic DEMATEL method is used to give weights to all variables of the dataset. Then conduct the preprocessing data to eliminate the variables with the least weight. The three machine learning methods used in this paper are Gradient Boosting (GB), Ada Boosting (AB), and Random Forest (RF). The results show the accuracy of the three algorithms. The AB has a 99.166% accuracy, and it is the highest accuracy in this paper followed by the GB has 98.3%, then RF has 92.85%.

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Neyda Hernández Bandera mail -

Jenny M. Moya Arizaga mail -

Enrique Rodríguez Reyes mail

link https://doi.org/10.54216/IJNS.210116

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2023

Neutrosophic Multi-criteria Decision-making Methodology for Evaluation chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD), is a debilitating lung condition that may lead to several other serious health problems and even death if left untreated. The ability to diagnose illnesses quickly and affordably is crucial. First and foremost, helping physicians determine how severe COPD cases are is crucial for placing patients in the appropriate institutions. Based on system engineering principles and real-world clinical practice, this article develops a COPD severity evaluation indicator system followed by suggests a neutrosophic distance from the average solution (EDAS) approach to making decisions in a linguistically uncertain setting. The alternatives are ranked by how far they are from the average answer on every factor using the EDAS technique. Distance-based multi-criteria decision-making techniques are analogous to this approach. It expedites the decision-making process by streamlining the computation of distances to an agreed solution. The EDAS method is used to compute the weights of criteria and then rank the alternatives under the neutrosophic model. The neutrosophic set is used in this paper to solve the uncertain information in the process of this evaluation. The EDAS method is applied in various criteria and alternatives and the results are discussed. 

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Neyda Hernández Bandera mail -

Jenny Maribel M. Arizaga mail -

Enrique Rodríguez Reyes mail

link https://doi.org/10.54216/IJNS.210117

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2023

Dietary Strategies for People with Celiac Disease by Neutrosophic Entropy Approach

  Celiac disease is an autoimmune illness that causes damage to the small intestine and, in some cases, the bones as well. Histological analysis of duodenal biopsies obtained during upper digestive endoscopy is required for a diagnosis. The production of antibodies may be detected by immunological testing by taking a blood sample. Histology takes a long time, and endoscopy is intrusive. This paper used the MCDM method to compute the objective the celiac disease.  In statistical distribution theory, entropy is often employed as a proxy for the uncertainty, unpredictability, or chaos of experimental results. The literature's entropy approaches provide a numeric measure of a random variable's information but struggle to handle data with interval values. The results of an experiment with an unknown outcome are often presented in interval form. The entropy method is used to compute the weights of the criteria. The neutrosophic sets were used to overcome the uncertain information in this study. This paper used six criteria and nine alternatives. The results are shown in this study.  

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Ximena Trujillo Romero mail -

Alvaro P. Moina Veloz mail -

Daniela A. Cobo Álvarez mail

link https://doi.org/10.54216/IJNS.210118

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2023

Cybersecurity Detection Model using Machine Learning Techniques

The use of machine learning methods in cybersecurity is only one of many examples of how this once-emerging innovation has entered the mainstream. Anomaly-based identification of common assaults on vital infrastructures is only one instance of the various applications of malware analysis. Scholars are using machine learning-based identification in numerous cybersecurity solutions since signature-based approaches are inadequate at identifying zero-day threats or even modest modifications of established assaults. In this work, we introduce the machine-learning models-based security framework to detect cyber-attacks. This paper used three machine learning models Logistic Regression, Random Forest, and K-Nearest Neighbor This framework not only reduces the computational difficulty of the framework by minimizing the feature parameters, but it also performs well in terms of accuracy in forecasting unknown scenarios in the tests. Finally, we ran trials using cybersecurity datasets to measure the machine learning model's performance using metrics including precision, recall, and accuracy.

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Mustafa El-Taie mail -

Aaras Y.Kraidi mail

link https://doi.org/10.54216/JCIM.120104

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2022

Triangular Neutrosophic Multi-Criteria Decision Making AHP Method for Solar Power Site Selection

The depletion of fossil fuel reserves, rising fuel costs, and heightened awareness of ecological problems are just a few of the recent developments that have contributed to a greater reliance on renewable energy alternatives. There is a growing need to evaluate appropriate locations in order to make the most efficient use of renewable energy alternatives. This research looks at the parameters that determine how well-spaced solar farms can be in Egypt. So, the multi-criteria decision-making (MCDM) methodology is used to deal with these criteria. The MCDM is a hybrid with the neutrosophic set to deal with vague information. This paper presented the neutrosophic AHP method to select the best location for solar power (SP). The AHP method is selected to compute the weights of factors in an easy and efficient way. This paper collected the criteria from previous work, then evaluated by the experts. The case study in Egypt is presented to select the best location for SP. The sensitivity analysis is presented to show the rank of locations when changing the weights of factors. 

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Abedallah Z. Abualkishik mail -

Rasha Almajed mail

link https://doi.org/10.54216/IJAACI.020201

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2022

Computational Intelligence for Automatic Detection Cardiac Arrhythmia from ECG Signals: Taxonomy and Open Issues

Cardiac arrhythmia is a medical disorder, in which the heart beats sporadically or irregularly leading to serious health consequences if left untreated. Early detection of arrhythmias is essential for timely intervention and management of the condition. Recently, there has been a growing interest in using computational intelligence techniques to automatically detect arrhythmias from electrocardiogram (ECG) signals. This approach offers the potential to improve the accuracy and efficiency of arrhythmia detection, as well as reduce the workload on healthcare professionals. This work reviews the current state-of-the-art ML methods for detecting arrhythmias including deep neural networks, support vector machines, and random forests. We will also discuss the challenges associated with using these techniques, such as the need for large and diverse datasets, and the interpretation of model outputs. We also highlight the open research that require further research and development to fully realize the potential of these algorithms in clinical practice.

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Reem Atassi mail -

Fuad Alhosban mail -

Milan Dordevic mail

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

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new