Neutrosophic AHP Method with Machine Learning Algorithms to The Priority of Maintenance in the Facility of Healthcare

Nihal N.; Ibrahim

doi:https://doi.org/10.54216/JNFS.010208

Journal of Intelligent Systems and Internet of Things | 2023

An Approach Based on Decision-Making Algorithms for Qos-Aware Iot Services Composition

Because there is now so many Internet of Things–based service providers globally, it will be hard to choose an Internet of Things service that is appropriate for the demand from the huge pool of Internet of Things services that are already available and display comparable characteristics. When making an acceptable choice, one can take into account the quality-of-service, or QoS, factors that characterize a certain service. In this article, we consider the Internet of Things to be the combination of its three3 potential parts, which are things, a connectivity unit, and a computational object. A definition of an IoT may contain the quality of service metrics for every one of these elements. We suggest a methodology that creates utilizes multi-criteria decision-making (MCDM) as a known approach using the MABAC method for the goal of carrying out the choice process where the quality of service parameters of different components of the internet of things act as criteria. Together, the data and our demonstration of the efficiency of the suggested strategy form a coherent whole.

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Abdullah Ali Salamai mail

link https://doi.org/10.54216/JISIoT.080101

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

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Journal of Neutrosophic and Fuzzy Systems | 2023

A Study of a Support Vector Machine Algorithm with an Orthogonal Legendre Kernel According to Neutrosophic logic and Inverse Lagrangian Interpolation

The decision-making process is greatly affected by the data collection stage. If the data collection process is not well controlled, i.e. there is some data lost due to the poor quality of the devices used or the lack of accuracy in the data entry process...etc., this will affect the work of the SVM algorithm, which is considered one of the best. Most of the workbooks suffer from the problems of missing and anomalous data. In this paper, we propose a method to treat the missing and anomalous data by reshaping the data set defined by the classical method into the neutrosophical data set by calculating the amount of true T, false F, and neutrality I in the neutrosophical set using inverse Lagrangian interpolation. We noticed the superiority of our proposed method for processing missing data over the method of [21], then we trained a support vector machine algorithm with orthogonal legender kernel on a breast cancer dataset taken from the Statistics Department of Al-Bayrouni Hospital in Damascus, where the proposed algorithm achieved a classification accuracy of 97%. The reason we chose a support vector machine classifier with an orthogonal legender kernel has two goals: the first is to eliminate the repetition of support vectors in the feature space. The second is to solve the problem of non-linear data distribution.

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Mohammed Alshikho mail -

Maissam Jdid mail -

Said Broumi mail

link https://doi.org/10.54216/JNFS.050105

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

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Journal of Neutrosophic and Fuzzy Systems |

Algebraic Approach to Neutrosophic Confidence Intervals

In this paper, confidence intervals for neutrosophic statistical populations were driven in many cases. Firstly, confidence intervals for one neutrosophic normal population parameters were driven including population’s mean which was driven under the assumption that variance is known, then it was driven under the assumption that variance is unknown and estimated based on the sample. Confidence interval for the neutrosophic variance was also driven based on sample’s estimates. Secondly, confidence intervals for two neutrosophic normal populations were driven including confidence intervals for means differences when variance are known or unknown, also confidence intervals for variances ratio for two populations were driven. All theorems and calculations were done using the AH-Isometry. Suitable numerical examples were presented and solved successfully.

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International Journal of Neutrosophic Science |

Delta open sets in Neutrosophic Hypersoft Topological Spaces

In this paper, neutrosophic hypersoft δ-open sets are introduced by defining the neutrosophic hypersoft regular open sets, pre-open sets, δ-interior and δ-closure. Under the guidance of these definitions, neutrosophic hypersoft δ semi-open sets and δ pre-open sets are also introduced. Further, we have deduced inuitionistic hypersoft topology and fuzzy hypersoft topology from the neutrosophic hypersoft topology. Moreover, we discuss about the relations between neutrosophic hypersoft δ-open sets, δ semi-open sets, δ pre-open sets, semi-open sets and pre-open sets and their properties with examples.

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International Journal of Neutrosophic Science |

δ-open sets in Neutrosophic Hypersoft Topological Spaces

In this paper, neutrosophic hypersoft δ-open sets are introduced by defining the neutrosophic hypersoft regular open sets, pre-open sets, δ-interior and δ-closure. Under the guidance of these definitions, neutrosophic hypersoft δ semi-open sets and δ pre-open sets are also introduced. Further, we have deduced inuitionistic hypersoft topology and fuzzy hypersoft topology from the neutrosophic hypersoft topology. Moreover, we discuss about the relations between neutrosophic hypersoft δ-open sets, δ semi-open sets, δ pre-open sets, semi-open sets and pre-open sets and their properties with examples.

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Volume & Issue

Vol. Volume 20 / Iss. Issue 4

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Journal of Neutrosophic and Fuzzy Systems | 2021

Human brain tumors detection using neutrosophic c-means clustering algorithm

For the last several decades, detecting human brain tumors has evolved into one of the most difficult problems in the field of medical research. In the realm of medical image processing, the categorization of brain tumors is a difficult job to do. In this research, we offer a model for the detection of human brain tumors in magnetic resonance imaging (MRI) images that makes use of the template-depend neutrosophic c-means and is compared with the fuzzy C means method. This model is referred to as the NCM method. In this suggested method, well first of all, the pattern K-means method is used to initialize segmentation markedly through the ideal choice of a template, depending on the gray-level intensity of the image; besides which, the revised membership is calculated by the ranges from the closest centroid to cluster pieces of data by using neutrosophic C-means (NCM) method while it approaches its perfect outcomes; and at last, the NCM clustering method is used for sensing tumor positron emission tomography (PET) imaging The findings of the simulation reveal that the suggested method can produce improved identification of pathological and normal cells in the human brain despite a little separation in the intensity of the grey level.

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Nihal N. Mostafa mail

link https://doi.org/10.54216/JNFS.010106

Volume & Issue

Vol. Volume 1 / Iss. Issue 1

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Journal of Neutrosophic and Fuzzy Systems | 2021

Neutrosophic-based machine learning context for the trustworthiness of devices in the internet of things

The industrial sector is among the most suited sectors that may considerably advantage from the implementation of the ideas and technology of the Industrial Internet of Things (IIoT), and it is one of the most competitive industries in the world. The increased use of automated processes in manufacturing sectors results in a wide variety of applications based on IIoT. These applications call for the efficient integration of a wide variety of different systems and the execution of smooth operations across all machines. The issue of integration and smooth operation presents IIoT as a new subject of study in smart manufacturing. This carries with it several problems, including those on security, accountability, confidence, and dependability. As part of the Industrial Internet of Things (IIoT), many devices will be linked to one another and interact with one another through wireless and internet infrastructure. When this kind of situation plays out, the reliability of the IIoT devices becomes a key component in the process of preventing injection by hostile machines. As a result, an intelligent computer model is required to effectively cluster and categorize the level of trustworthiness possessed by the IIoT devices. In this article, we describe a trust model for the Internet of Things (IIoT) that is based on the neutrosophic TOPSIS and is utilized by IIoT apps to determine the trust score of IIoT devices. The reliability of devices is evaluated by the model that was constructed using the historical knowledge, chronological knowledge, and network behavior information that is received from IIoT devices. In addition to that, the model suggests KNN, and a Decision tree to categorize the attributes that were collected.

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Abdullah Ali Salamai mail

link https://doi.org/10.54216/JNFS.010107

Volume & Issue

Vol. Volume 1 / Iss. Issue 1

Details open_in_new

Journal of Neutrosophic and Fuzzy Systems | 2023

Algebraic Approach to Neutrosophic Confidence Intervals

In this paper, confidence intervals for neutrosophic statistical populations were driven in many cases. Firstly, confidence intervals for one neutrosophic normal population parameters were driven including population’s mean which was driven under the assumption that variance is known, then it was driven under the assumption that variance is unknown and estimated based on the sample. Confidence interval for the neutrosophic variance was also driven based on sample’s estimates. Secondly, confidence intervals for two neutrosophic normal populations were driven including confidence intervals for means differences when variance are known or unknown, also confidence intervals for variances ratio for two populations were driven. All theorems and calculations were done using the AH-Isometry. Suitable numerical examples were presented and solved successfully.

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Abdulrahman Astambli mail -

Mohamed Bisher Zeina mail -

Yasin Karmouta mail

link https://doi.org/10.54216/JNFS.050201

Volume & Issue

Vol. Volume 5 / Iss. Issue 2

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Journal of Neutrosophic and Fuzzy Systems | 2021

An Integrated Neutrosophic MCDM Methodology for Material Selection

Choice of materials is difficult since it requires considering several factors, assigning relative importance to those criteria, and ultimately choosing the most relevant criterion. Finally, it is important to set the criteria in a way that takes into account both the known material attributes and the needs of the application. Therefore, MCDM techniques may be used to the process of selecting materials. A possibility of incomplete dilemmas arises due to the decision maker's language inputs. Therefore, the inputs might be supplied as fuzzy numbers in order to circumvent the issue. Considering that a neutrosophic set is a metaphor to overcome uncertainty of human perceptions. To assess this recruitment process in a neutrosophic setting, this paper employs a neutrosophic-based version of the MCDM tool TOPSIS to determine which alternative materials should be used for the center console of an electric car.

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Ahmed Abdelmonem mail -

Mahmoud M.Ismail mail

link https://doi.org/10.54216/JNFS.010108

Volume & Issue

Vol. Volume 1 / Iss. Issue 1

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Journal of Neutrosophic and Fuzzy Systems | 2021

Neutrosophic AHP Method with Machine Learning Algorithms to The Priority of Maintenance in the Facility of Healthcare

The creation of decision-support techniques that can be used in the planned preservation and recertification ordering of healthcare facility investments is regarded as an assignment of extremely high difficulty due to the multitude of ambiguity and levels of individuality that is accessible in a decision-making procedure of this nature. This research employs a mixture of Neutrosophic logic and the Analytical Hierarchical Process (AHP) to generate a trustworthy score of hospital structure facilities depending on their varying levels of evaluation and achievement deficiencies. This is done to reduce the partiality that is related to expert-driven choices and to make the rankings more objective. This is additionally merged with the innovative use of machine learning techniques in this field, specifically: Random Forest, and Naive Bayes, to automate the process of setting priorities and making it reproducible, thereby reducing the essential for extra professional decisions.

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Nihal N. Mostafa mail -

Ibrahim Elhenawy mail

link https://doi.org/10.54216/JNFS.010208

Volume & Issue

Vol. Volume 1 / Iss. Issue 2

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