Several Results on Some Kinds of Continuity via Fuzzy Neutrosophic β^(^m)-Closed Sets

Nawras N.; Fatimah M.

doi:https://doi.org/10.54216/IJNS.260213

International Journal of Neutrosophic Science | 2025

On Ranking Algorithms for SV NT′s and NV NT′s

Ranking algorithms are very important tools in decision making. There are two ranking algorithms for nvalued neutrosophic tuplets (Single-Valued MultiNeutrosophic tuplets): The S-ranking algorithm of Single- Valued MultiNeutrosophic tuplets, which is introduced by F.Smarandache in 2023, and the N-ranking algorithm of n-valued neutrosophic tuplets, Which is introduced by V. L. Nayagam and Bharanidharan R. in 2023. In this paper we show (by examples) that these two ranking algorithms are not a total ordering for the set of n-valued neutrosophic tuplets. These algorithms do not taking into account the number of sources, which is a very important factor in neutrosophic n-valued refined sets theory. We introduce two ranking algorithms: The integrated S-ranking algorithm of Single-Valued MultiNeutrosophic tuplets, and the integrated N-ranking algorithm of n-valued neutrosophic tuplets. These algorithms are improvements of the S-ranking algorithm of Single-Valued MultiNeutrosophic tuplets, and the N-ranking algorithm of n-valued neutrosophic tuplets, respectively, and taking the number of sources into account. We construct different examples to show that each step in the integrated ranking algorithms is necessary to make them a total ordering for the set of all n-valued neutrosophic tuplets.

groups

Murad M. Arar mail

link https://doi.org/10.54216/IJNS.260205

Volume & Issue

Vol. Volume 26 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Lagrange’s theorem based on neutrosophic sets

This paper explores the fundamental concepts of sub-level subgroups, element orders, normalizers, and centralizers within the framework of neutrosophic group theory. Additionally, it examines quotient groups and the index of a subgroup, extending classical algebraic structures to a neutrosophic setting. Finally, a generalized formulation of Lagrange’s theorem is presented, demonstrating its applicability in the neutrosophic environment and highlighting its implications for uncertain and indeterminate group structures.

groups

Aiyared Iampan mail -

C. Sivakumar mail -

Neelamegarajan Rajesh mail

link https://doi.org/10.54216/IJNS.260206

Volume & Issue

Vol. Volume 26 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

A Study On Neutrosophic UP-algebra

In this paper we apply the neutrosophic set on the concept of the UP-algebra to obtain some types of neurosophic sets satisfies certain conditions which are called neutrosophic Up-subalgebras. Several types of these neutrosophic Up-subalgebras are introduced and their properties are investigated. Also, illustrative examples are given when they are needed.

groups

Hamdiya S. Hasan mail -

Muwafaq M. Salih mail -

Alias B. Khalaf mail

link https://doi.org/10.54216/IJNS.260207

Volume & Issue

Vol. Volume 26 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Neutrosophic model-driven decision support system for international market selection based on Montecarlo simulation and a novel neutrosophic AHP score function

This article presents a tool for international market selection (IMS) that integrates Neutrosophic Analytic Hierarchy Process (Neutrosophic AHP) and Monte Carlo simulation to reduce uncertainty in export decision-making. The methodology begins with a comprehensive literature review identifying five key criteria and twenty-three sub-criteria for IMS, supported by the insights of five notable authors in the field. Using Neutrosophic AHP, the weights of each criterion and sub-criterion are calculated and incorporated into a mathematical model designed for market selection. Data are collected from globally renowned sources and adjusted to probability distributions, enabling scenario simulation through Monte Carlo. The developed algorithm evaluates 193 countries, generating a ranking of potential destinations based on the determined weights and obtained information. The tool is validated by testing hundreds of products from 4,290 tariff lines under the SA 2012 version, confirming its applicability across diverse commercial contexts. The results highlight the tool's ability to accurately and adaptively identify viable export markets, offering a robust model for strategic decision-making in business internationalisation.

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Rojas-Gualdrón, Rafael mail -

Lozano-Suarez, Lina mail -

Polo-Triana, Sonia mail

link https://doi.org/10.54216/IJNS.260208

Volume & Issue

Vol. Volume 26 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Applications of Neutrosophic N-Structures in Ternary Semirings: A Study on Neutrosophic Ternary N-Subsemirings

In this paper, we apply neutrosophic N-structures in ternary semirings. We consider ternary neutrosophic N-subsemirings of ternary semirings. We investigate the conditions for neutrosophic N-structures to be neutrosophic ternary N-subsemirings. In addition, we show the relation between ternary subsemirins and neutrosophic ternary N-subsemirins. Finally, we showed that the homomorphic preimage of the neutrosophic ternary N-subsemirings is a neutrosophic ternary N-subsemirings and the onto homomorphic image of the neutrosophic ternary N-subsemiring is also a neutrosophic ternary N-subsemirings.

groups

Thammarat Panityakul mail -

Ronnason Chinram mail

link https://doi.org/10.54216/IJNS.260209

Volume & Issue

Vol. Volume 26 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Robust Plant Disease Recognition Using a Neutrosophic-Enhanced, RBF-Based Stacked Ensemble of ConvNeXt and Classical CNN Models

Accurate and timely recognition of plant diseases is crucial to prevent crop loss and ensure global food security. This paper presents a robust ensemble-based framework that combines six classical and state-of-the-art deep convolutional neural networks (DCNNs), including a ConvNeXt architecture, and integrates Neutrosophic Science to better handle uncertainty in leaf images. The proposed approach features three main components: (1) transfer learning with pre-trained DCNNs, (2) a model-averaging strategy to stabilise individual predictions, and (3) a stacked ensemble design that employs a radial basis function (RBF) meta-learner to refine the classification outputs. Experiments on the Plant Village dataset, comprising 54,305 segmented images of 38 plant diseases, included 10-fold cross-validation. The results show that the final stacking ensemble achieved near-perfect performance with 99.97% accuracy and an F1 score of 99.55% on an unseen test set of 27,160 images. Compared with standalone models, the ensemble demonstrated greater robustness in distinguishing visually similar diseases, benefiting from the complementary strengths of multiple DCNN architectures. The Neutrosophic component further enhances reliability by modelling uncertainties due to noise, occlusions, and environmental variations. Although a higher computational overhead and modest misclassifications remain, especially in certain visually overlapping classes, this study demonstrates the effectiveness of an ensembledriven, uncertainty-aware strategy. These findings hold considerable promise for real-world agricultural applications, where rapid and accurate disease diagnosis is paramount.

groups

Emre Ozbilge mail -

Ebru Ozbilge mail

link https://doi.org/10.54216/IJNS.260210

Volume & Issue

Vol. Volume 26 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Multi-Step Neutrosophic Cognitive Map Based Decision Making Framework for Short-Term Financial Stock Market Price Trend Prediction

Neutrosophic cognitive maps are expansion of fuzzy cognitive maps, containing indetermination in causal relations. Fuzzy cognitive maps do not require an indeterminate relationship, making it less adequate for real-time applications. A logic in which every proposition is projected to have the truth percentage in subset T and the falsity percentage in subset F is named Neutrosophic Logic. This logic is also considered the general form of Intuitionistic fuzzy logic. Stock price prediction is a main topic in economics and finance, which has promoted the priority of investigators in recent years to improve improved predictive methods. Predicting price and tendency of the stock market denote indispensable features of finance and investment. Many scientists have presented their ideas to predict the market price to make money while trading utilizing different methods like statistical and technical analysis. This manuscript proposes a Neutrosophic Cognitive Map-Based Short-Term Financial Stock Market Price Trend Prediction (NCM-SFSMPTP) model. The main goal of NCM-SFSMPTP technique relies on improving the accurate approach for stock market price trend prediction. At first, the min-max normalization methodology is utilized in the data normalization phase to standardize and scale data for consistency, comparability, and efficient processing. For the classification process, the neutrosophic cognitive map (NCM) technique is employed. Finally, the improved arithmetic optimization algorithm (IAOA)-based hyper-parameter selection is implemented to enhance the classification outcomes of the NCM system. The performance validation of the NCM-SFSMPTP methodology is verified under the Apple Stock Price Trend and Indicators dataset and the outcomes are determined regarding to several measures. The experimental validation of the NCM-SFSMPTP method illustrated a superior accuracy value of 94.79% over existing models in stock market price trend prediction process.

groups

Alexander Chupin mail -

Alisher Sherov mail -

Tukhtabek Rakhimov mail -

Emil Hajiyev mail -

Hafis Hajiyev mail

link https://doi.org/10.54216/IJNS.260211

Volume & Issue

Vol. Volume 26 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Principal L-fuzzy ideals and filters on a trellis

In this paper, we study the notion of principal (crisp) fuzzy ideals (resp. filters) on the setting of trellises (or weakly associative lattices as called by several authors). More specifically, we introduce the notions of L-fuzzy ideals and L-fuzzy filters on a given trellis and provide basic characterizations of these notions based on their weakly associative meet and join operations. We pay particular attention to the kind of principal L-fuzzy ideals (resp. filters) on a given trellis, which are more complicated in the absence of the (associativity) transitivity property.

groups

Sarra Boudaoud mail -

Lemnaouar Zedam mail -

Soheyb Milles mail

link https://doi.org/10.54216/IJNS.260212

Volume & Issue

Vol. Volume 26 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Deep Learning-based sensitive data detection with optimization-enabled secure encryption model for data privacy preservation in IoT

The express expansion of the Internet of Things (IoT) has led to an exponential increase for data being generated and transmitted from various connected devices. This poses significant challenges in terms of data privacy and security, as unauthorized access to such sensitive information can have severe consequences like identity theft or financial fraud. This research proposes a model for sensitive data detection and protection in IoT, based on deep learning and optimization-enabled secure encryption. By combining deep learning-based sensitive data detection and optimization-enabled secure encryption, this model offers a comprehensive solution to preserve data privacy in IoT. The proposed model uses a novel and secure encryption algorithm, ensuring the privacy of the data. An algorithm, Improved Skill Optimization Algorithm (ISOA), which enhances the performance of existing optimization algorithms by incorporating the concept of Double Exponential Smoothing (DES), is proposed for the secure key generation for the data encryption. Data Encryption Standard (DES) is a block cipher algorithm that encrypts and decrypts data using a 56-bit key and 64-bit blocks. The proposed model provides a robust solution for data privacy preservation in IoT networks, which is crucial for protecting sensitive information from unauthorized access and data breaches. The proposed algorithm's performance analysis is evaluated using metrics, like computation time, memory, and fitness function. Results indicate that proposed ISOA based encryption model succeeded a greater performance, with a memory of 0.5170 MB, computational time of 1126.47 sec and fitness value of 1.3630.

groups

Mathias Agbeko mail -

Disha Handa mail

link https://doi.org/10.54216/JISIoT.160211

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025