A new class of NeutroOpen, NeutroClosed, AntiOpen and AntiClosed sets in NeutroTopological and AntiTopological spaces

Jeevan K.; G. Chandra

doi:https://doi.org/10.54216/IJNS.200206

Journal of Neutrosophic and Fuzzy Systems | 2023

Sentiment Analysis for Social Media Tweets Using Single-Valued Neutrosophic Sets and Fuzzy Sets

In the last ten years, exciting work at the intersection of several academic disciplines has been done in the areas of view mining and sentiment analysis. The sheer amount of social media text that is now accessible for sentiment analysis has expanded by a factor of multiples with the development of social media networks, resulting in the creation of a formidable corpus. An examination of the sentiments included within tweets has been performed to measure the general public's perspective on breaking news, as well as a variety of laws, regulations, individuals, and political movements. In the assessment of the sentiment of Twitter data, fuzzy logic (FL) was used, but neutrosophy, which makes consideration the idea of indeterminacy, was not applied. Fuzzy logic (FL) was utilized since neutrosophy was not utilized to analyze tweets. In this study, we present the idea of single valued-neutrosophic sets (SVNSs) that may have positive, indeterminate, and negative memberships. We used the sanders dataset to apply the proposed methodology. The fuzzy set (FS) has the indeterminacy value opposite the NS. FS has two only degrees, truth, and falsity. This paper shows the difference between the NS and FS in the sample of data.

groups

Gopal Chaudhary mail -

Amena Mahmoud mail -

J. A. Lobo Marques mail

link https://doi.org/10.54216/JNFS.050205

Volume & Issue

Vol. Volume 5 / Iss. Issue 2

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2023

A Short Note on Some Observable Outputs of Some Linear Discrete Time Systems

  In this paper, we study the problem of controllable and observable output of the type:     On the other hand, we present some new result about the formula of this linear discrete-time system and about controllable out put  

groups

Khadija Ben Othman mail

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

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2023

On The Fuzzy Weak Complex Vector Spaces

The objective of this paper is to present for the first time the concept of fuzzy weak complex vector spaces depending on the ring of fuzzy weak complex numbers. Also, we examine some elementary algebraic properties of fuzzy weak complex vector spaces in terms of theorems. On the other hand, we illustrate many related examples to explain the novelty of these spaces.

groups

Ahmed Hatip mail

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

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2023

Q-Complex Neutrosophic Set

Most complex problems in the real-world typically involve uncertain,incomplete and indeterminate two-dimensional data i.e. information pertaining to the attributes and the periodicity of the problem parameters. To meet the demand for models that has the ability to handle these information with these characteristics, the introduction of neutrosophic sets (NSs) was followed by their extension to the complex neutrosophic sets (CNSs). In this paper, we introduce the concept of Q- complex neutrosophic set (Q-CNS) by extending the ranges of the membership functions in Q-neutrosophic set (Q-NS) from [0,1] to the unit circle in the complex plane. Q-CNS plays a key role in the decision making theory, where the extra information provided by the elements of the Q-set serve in modeling of some decision making problems. Based on this new concept we define the basic theoretical operations such as complement, equality, subset, union, intersection, Q-complex neutrosophic product and Cartesian product. Some related examples are also given to enhance the understanding of the proposed concepts. The basic properties of these operators are also verified with supporting proofs.

groups

Ashraf Al-Quran mail -

Abd Ghafur Ahmad mail -

Faisal Al-Sharqi mail -

Abdalwali Lutfi mail

link https://doi.org/10.54216/IJNS.200201

Volume & Issue

Vol. Volume 20 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2022

A Novel Intrusion Detection Framework (IDF) using Machine Learning Methods

An intrusion detection system is a critical security feature that analyses network traffic in order to avoid serious unauthorized access to network resources. For securing networks against potential breaches, effective intrusion detection is critical. In this paper, a novel Intrusion Detection Framework (IDF) is proposed. The three modules that comprise the suggested IDF are: (i) Data Pre-processing Module (DPM), (ii) Feature Selection Module (FSM), and Classification Module (CM). DPM collects and processes network traffic in order to prepare data for training and testing. The FSM seeks to identify the key elements for recognizing DPM intrusion attempts. An Improved Particle Swarm Optimization is used (IPSO). IPSO is a hybrid method that uses both filter and wrapper approaches to generate accurate and relevant information for the classification step that follows. Primary Selection Phase (PSP) and Completed Selection Phase (CSP) are the two consecutive feature selection phases in IPSO. PSP employs a filtering approaches to quickly identify the most significant features for detecting intrusion threats while eliminating those that are redundant or ineffective. In CSP, the next level of IPSO, this behavior reduces the computing cost. For accurate feature selection, CSP uses Binary Particle Swarm Optimization (Bi-PSO) as a wrapper approach. Based on the most effective features identified by FSM, The CM aims to identify intrusion attempts with the minimal processing time. Therefore, a K-Nearest Neighbor KNN classifier has been deployed. As a result, based on the significant features identified by the IPSO technique, KNN can accurately detect intrusion attacks with the least amount of processing time. The experimental results have shown that the proposed IDF outperforms other recent techniques using UNSW_NB-15 dataset. The accuracy, precision, recall, F1score, and processing time of the experimental outcomes of our findings were assessed. Our results were competitive with an accuracy of 99.8%, precision of 99.94%, recall of 99.85%, F1-score of 99.89%, and excursion time of 59.15s when compared to the findings of the current works.

groups

Shereen H. Ali mail

link https://doi.org/10.54216/JCIM.100103

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2023

n-Refined Indeterminacy of Some Modules

This article presents the notion of n-refined neutrosophic modules such as cyclic, simple, and finitely generated modules. n-refined neutrosophic is a generalization of neutrosophic properties. This paper presents new relations among n-refined neutrosophic modules. Finally, several examples and properties have been studied about the relations between these modules.

groups

M. Abdallah Salih mail -

D. Alawi Jarwan mail -

M. Mohammed Abed mail

link https://doi.org/10.54216/IJNS.200202

Volume & Issue

Vol. Volume 20 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2023

A New Modified Logistic Distribution: Properties and Applications in Uncertainty Data Modeling

The logistic distribution is widely used to model various types of applied data. The modified logistic distribution under neutrosophic statistics is introduced in this work. The neutrosophic logistic distribution (NLD) and its engineering applications are mainly emphasized. An appealing characteristic of the suggested NLD is that it is useful to many widely utilized survival assessment metrics, including the reliability function, hazard function, and survival function. Applications of some mathematical and statistical properties of the suggested model are discussed. Numerical investigations on simulated data are used to validate the theoretical findings experimentally. From an application point of view, it is inferred that the proposed distribution fits data with imprecise, hazy, and fuzzy information better than the usual model. In addition, the maximum likelihood (ML) technique for the proposed model is discussed under the neutrosophic inference framework. Eventually, some illustrative examples related to system reliability are provided to clarify further the implementation of the neutrosophic probabilistic model in real-world problems.

groups

A. M. Mohamed Ibrahim mail -

Zahid Khan mail -

Fuad S. Al-Duais mail

link https://doi.org/10.54216/IJNS.200203

Volume & Issue

Vol. Volume 20 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2023

Comparison Slice Inverse Regression Method with Machine Learning Techniques in Multivariate Data

In this study, the research aims to use some methods that deal with several independent variables with a dependent variable, where two methods were used to deal with, which is the method of slice inverse regression (SIR), which is considered a non-classical method, and two methods of machine learning, which is (TLBO, PSO), which is most popular of the teaching methods machine learning, the work of (SIR), (TLBO, PSO) is based on making reduced linear combinations of a partial set of the original explanatory variables, which may suffer from the problem of heterogeneity and the problem of multicollinearity between most of the explanatory variables. These new combinations of linear compounds resulting from the two methods will reduce the largest number of explanatory variables to reach one or more new dimensions called the effective dimension. The root mean square error criterion will be used to compare the two methods to indicate the preference of the methods.

groups

Omar A. abd Alwahab mail

link https://doi.org/10.54216/IJNS.200204

Volume & Issue

Vol. Volume 20 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2023

An integrated AHP MCDM based Type-2 Neutrosophic Model for Assessing the Effect of Security in Fog-based IoT Framework

The term "Internet of Things" (IoT) refers to a network of connected, intelligent devices that are responsible for the collecting and dissemination of data. Because technology automates the tasks we do daily, our lives have become simpler as a result. However, with a typical architecture for the cloud and the Internet of Things, real-time data processing is not always practicable. This is particularly true for latency-sensitive apps. This eventually resulted in the development of fog computing. On the one hand, the fog layer may perform computations and data processing at the very edge of the network, which enables it to provide results more quickly. On the other hand, this pushes the attack surface closer to the machines themselves, which is a security risk. Because of this, the sensitive data that is stored on the layer is now susceptible to assaults. Therefore, considering the security of the fog-IoT is of the utmost significance. A system or platform's level of security is determined by a number of different elements. When it comes to conducting an accurate risk assessment, the sequence in which these considerations are considered is of the utmost importance. Because of this, determining the level of security offered by fog and IoT devices becomes a Multi-Criteria Decision-Making (MCDM) dilemma. This article presents a two-stage hybrid multi-criteria decision-making model that is based on type-2 neutrosophic numbers (T2NNs). The goal of this article is to give scientists and practitioners a decision-making tool that is both easy and versatile. The initial step of this process is determining the weights of criteria by the AHP method in the T2NN environment. Second, the T2NN-based Multi-Attributive Border Approximation area Comparison (MABAC) method is used to rank the various fog security based on IoT. Both of these methods are described in more detail below. With the help of the comparison study, the high reliability and robustness of the combined AHP and MABAC based type-2 neutrosophic model have been proven.

groups

Mohammad D. Alshehri mail

link https://doi.org/10.54216/IJNS.200205

Volume & Issue

Vol. Volume 20 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2023

A new class of NeutroOpen, NeutroClosed, AntiOpen and AntiClosed sets in NeutroTopological and AntiTopological spaces

A lot of research has been done on the types of open and closed sets in general topological spaces and also in general bitopological spaces. Types of sets like pre-open sets and pre-closed sets, semi-open sets and semi-closed sets, Alpha-open sets, and Alpha-closed sets, regular open sets and regular closed sets, g-open sets and g-closed sets, and many more have been defined and studied. In the current study, an attempt has been made to define and give examples of a new category of open and closed sets, namely, NeutroOpen and NeutroClosed sets. Further, the concept of neutron-topology is used to define NeutroPreOpen and NeutroPreClosed sets, NeutroSemiOpen and NeutroSemiClosed sets, NeutroAlphaOpen and NeutroAlphaClosed sets, NeutroRegularOpen and NeutroRegularClosed sets, NeutroBetaOpen, and NeutroBetaClosed sets, and several examples have been given to illustrate each of the new classes of sets. Also, the concept of AntiTopology has been used to define another class of sets, namely, AntiOpen and AntiClosed sets of the above five classes of sets, namely, regular-open/closed; semi-open/closed, Alpha-open/closed, Beta-open/closed pre-open/closed sets. Further, a new class of subsets is identified which are named as NeutroTauOpen and NeutroTauClosed sets. Similar subsets in anti-topological spaces are named as AntiTauOpen and AntiTauClosed sets.

groups

Jeevan K. Khaklary mail -

G. Chandra Ray mail

link https://doi.org/10.54216/IJNS.200206

Volume & Issue

Vol. Volume 20 / Iss. Issue 2

Details open_in_new