Neutrosophic set (NS) and logic are powerful mathematical approaches for managing different uncertainties. Amongst different approaches for examining NS statistics, rough set theory (RST) offers a valuable instrument in the domain of NS statistics, and masses of researchers have been motivated by NS combination of RST. Recently, there have been no wide-ranging statistics and literature reviews of the universal RST and its applications. The Financial Crisis Prediction mechanism leverages cutting-edge computation methods to predict possible disruptions or economic downturns. By investigating past fiscal information, marketplace gauges, and macroeconomic features, the typical recognizes primary caution indications of imminent disasters. This practical method helps financial institutions, policymakers, and investors in applying pre-emptive procedures to alleviate fiscal marketplaces and threats. In this paper, we develop a Financial Crisis Prediction Model using Neutrosophic Fusion of Rough Set Theory (FCPM-NFRST) methodology. The suggested FCPM-NFRST method for financial crises incorporates numerous forward-thinking systems to improve predictive performance. It is initiated by the Firefly Algorithm (FFA) based feature selection to detect the fittest fiscal gauges. Consequently, the Neutrosophic Fusion of RST (NFRST) is exploited for strong cataloguing and successful management of vagueness and roughness in economic information. Lastly, the Whale Optimization Algorithm (WOA) is exploited for parameter fine-tuning, enhancing the system's accuracy. Investigational study displays that the FCPM-NFRST ensemble mechanism is more robust and superior than its complements. Accordingly, this study powerfully suggests that the suggested FCPM-NFRST method is very competitive than conventional and other existing algorithms.
Read MoreDoi: https://doi.org/10.54216/IJNS.250211
Vol. 25 Issue. 2 PP. 129-140, (2025)
Neutrosophic set (NS) and Neutrosophic logic (NL) play a major part in approximation theory. They are generalizations of intuitionistic fuzzy sets and logic correspondingly. Rough NS (RNS) combines the concepts of RS and NL to deal with vagueness, uncertainty, and imprecision in information. By integrating truth, indeterminacy, and false degrees, RNS provides a more solid basis for analyzing and classifying complicated data. Particularly, this makes it powerful in applications where incompleteness and ambiguity of data are ubiquitous. Smart cities are a current trend to contain information and communication technologies (ICTs) in the progression of great urban cities. It would be beneficial in defining the city's movement by monitoring the regular flow of traffic jams and visitors. One important characteristic of smart cities is Crowd management, which assists in safety and enjoyable experiences for the residents and visitors. Since the crowd density (CD) classification method encounters tasks including inter-scene, non-uniform density, and intra-scene deviations, occlusion and convolutional neural networks (CNNs) approaches were beneficial. This work focuses on the design of Automated Crowd Density Recognition using the Rough Neutrosophic Set for Smart Cities (ACDR-RNSSC) method in urban management. The presented ACDR-RNSSC method focuses on identifying various types of crowd densities in smart cities. Firstly, the ACDR-RNSSC method utilizes the ResNet50 method for feature extraction. Second, the classification is done using RNS. RNS is utilized for its ability to manage the vagueness and uncertainty in crowd density statistics. Lastly, the parameter is fine-tuned using the Fruit Fly Optimization Algorithm (FOA). This ensures that the model attains high robustness and accuracy in forecasting crowd density. The empirical analysis of the ACDR-RNSSC method is examined under benchmark crowd dataset and the outcomes are tested using various metrics. This study states the improvement of the ACDR-RNSSC method over existing techniques.
Read MoreDoi: https://doi.org/10.54216/IJNS.250210
Vol. 25 Issue. 2 PP. 117-128, (2025)
The objective of this paper is to build the Split-Complex version of Diffie-Hellman key Exchange Algorithm, where we use the mathematical foundations of Split-Complex Number Theory and Integers, such as congruencies, raising a split-complex integer to a power of split-complex integer to build novel algorithms for key Exchange depending of famous Diffie-Hellman algorithm. Additionally, we present the proposed version of the Diffie-Hellman algorithm based on neutrosophic number theory. Also, we analyze the complexity of the novel algorithms with many examples that explain their applied validity.
Read MoreDoi: https://doi.org/10.54216/IJNS.250201
Vol. 25 Issue. 2 PP. 01-10, (2025)
Indian agriculture aims at achieving sustainable development, which increases crop production per square unit without damaging the ecosystem and natural resources. Timely and prompt diagnosis and analysis of plant diseases are very beneficial in increasing food crop productivity and plant health and decreasing plant diseases. Plant disease specialists are not accessible in distant regions therefore there is an urgent need for reliable, automatic low-cost, and approachable solutions to detect plant disease without the expert’s opinion and laboratory inspection. Classical machine learning (ML)-based image classification techniques and Deep learning (DL)-based computer vision (CV) approaches such as Convolutional Neural Networks (CNN) was employed to detect plant disease. Neutrosophic set (NS), a generality of fuzzy set (FS) and intuitionistic FS (IFS), presented to characterize inconsistent, uncertain, imprecise, and incomplete data in realistic conditions. Besides, interval NS (INSs) was exactly proposed to resolve the problems with a collection of numbers in the actual entity. On the other hand, there are high levels of operational reliability for INSs, along with the decision-making method and INS aggregation operators. This study presents an Efficient Plant Disease Detection using the Possibility Neutrosophic Hypersoft Set Approach (EPDD-pNSHSS) method. The suggested EPDD-pNSHSS method uses the DL method for the recognition and classification of plant diseases. Initially, the EPDD-pNSHSS method takes place the Median filtering (MF) through the preprocessing to progress image superiority and eliminate noise. In the meantime, the possibility neutrosophic hypersoft set (pNSHSS) classifier is utilized for the detection of diseased and healthy leaf images. To optimize the detection accuracy of the pNSHSS mechanism, the whale optimization algorithm (WOA) is employed for adjusting the hyperparameter value of the DSAE technique. Wide-ranging experiments are implemented to exhibit the supremacy of the EPDD-pNSHSS method. The empirical findings showcased the development of the EPDD-pNSHSS method over other existing techniques.
Read MoreDoi: https://doi.org/10.54216/IJNS.250202
Vol. 25 Issue. 2 PP. 11-21, (2025)
A neutrosophic set (NS) is an advanced computational technique that accesses uncertain information via three membership functions. A soft expert set (SES) is derived from the hypothesis of a “soft set” with computer technology. Currently, this method is utilized in various domains such as intelligent systems, measurement theory, probability theory, cybernetics, game theory, and so on. Internet user faces a myriad of risks with the development of malware worldwide. The most prominent type of malware, Ransomware, encrypts confidential data without releasing the files until the user makes a ransom payment. Internet of Things (IoT) framework is a wide region of Internet-related devices with further computation capacities with storage capabilities that can be damaged by malware creators. Ransomware is a cruel and new malware on Internet with increasing attack levels. Ransomware encrypts the whole information to make users incapable of accessing important information and their files. In this article, we propose a Complex Proportional Assessment Based Neutrosophic Approach for Ransomware Detection in Cybersecurity (CPABNA-RDCS) methodology in IoT environment. The objective of the CPABNA-RDCS approach is to identify and categorize the ransomware to accomplish cybersecurity in the IoT network. Primarily, the CPABNA-RDCS method exploits min-max normalization for scaling the input dataset into relevant format. Meanwhile, the ransomware classification takes place via Complex Proportional Assessment Based Neutrosophic (CPABN) method. Finally, grey wolf optimizer (GWO) is employed for optimum hyperparameter choice of the CPABN system. The experimental results of the CPABNA-RDCS method are inspected on benchmark data. The simulation analysis emphasized the developments of the CPABNA-RDCS method over other existing techniques.
Read MoreDoi: https://doi.org/10.54216/IJNS.250203
Vol. 25 Issue. 2 PP. 22-32, (2025)
Cyber-attacks involve a large number of malicious events including phishing, malware attacks, ransomware, social engineering, etc. Automatic cyber-attack recognition and classification are obtained by different technologies and techniques, including artificial intelligence (AI), data analytics, machine learning (ML), deep learning (DL), and other forward-thinking approaches. As a generality of the fuzzy set (FS) and intuitionistic FS (IFS), the Neutrosophic set (NS) can handle incongruous, uncertain, and indeterminacy data where the indeterminate is explicitly measured, and the degree of truth, indeterminacy, and false functions are liberated. It may successfully define inconsistent, uncertain, and incomplete data and overcome certain limitations of the present techniques in representing uncertain decision data. The indeterministic portion of uncertain information, presented in the NS concept, has been instrumented in proper decision-making that is impossible by the IFS concept. Cyber threat detection and classification is a crucial research area that develops intelligent systems that can identify and categorize a variety of cyber-attacks in real time. This article develops an Integrating Machine Learning with Two-Person Intuitionistic Neutrosophic Soft Games for Cyber threat Detection in Blockchain Environment (IMLTPIN-CDBE) system. The main aim of the IMLTPIN-CDBE methodology lies in the automatic recognition of the cyber-threat BC platform. The initial phase of data normalization using a min-max scalar is conducted in the IMLTPIN-CDBE method. Moreover, the two-person intuitionistic neutrosophic soft games (TPINSSG) technique is applied for cyberattack recognition. Finally, the grasshopper optimization algorithm (GOA) technique is applied for fine-tuning the hyperparameter included in the TPINSSG classifiers. A sequence of experiments has been conducted on the ransomware database to exhibit the great performance of the IMLTPIN-CDBE method. The empirical findings show the supremacy of the IMLTPIN-CDBE method over other current approaches.
Read MoreDoi: https://doi.org/10.54216/IJNS.250204
Vol. 25 Issue. 2 PP. 33-43, (2025)
Sentiment Analysis (SA) is a crucial task for analyzing online content over languages for processes such as content moderation and opinion mining. However advanced NLP modeling approaches frequently need an abundance of training datasets to accomplish their outcomes. SA is a classification task where the polarity of text dataset is detected, viz., to analyze a document or sentence expressing a positive, negative, or neutral sentiment. Deep learning (DL) becomes predominant in resolving Natural Language Processing (NLP) tasks. On the other hand, this technique requires a significantly enormous quantity of annotated corpus, which is not easier to attain, particularly under these lower resource settings. Neutrosophic Net-RBF Neural Network (NNRBFNN) combines the principle of neutrosophic logic (NL) with RBF-NNs for handling data indeterminacy and uncertainty. This combined strategy optimizes conventional NNs by incorporating the possibility of addressing incomplete and imprecise data, augmenting decision-making in challenging circumstances. This paper introduces a Neutrosophic Net-RBF Neural Network with Sentiment Analysis on a Low Resource Language (NNRBFNN-SALRL) model. To accomplish this, the NNRBFNN-SALRL method undertakes data pre-processing to transform the input dataset into a helpful format, and Term Frequency Inverse Document Frequency (TF-IDF) technique is utilized for the process of word embedding. For the classification method, the NNRBFNN model is used. To optimize the recognition outcomes of the NNRBFNN method, the hyperparameter tuning technique can be done using the Bayesian Optimization Algorithm (BOA). Wide-ranging experiments were conducted to validate the superior outcomes of the NNRBFNN-SALRL method. The empirical findings indicated that the NNRBFNN-SALRL method emphasized betterment over other approaches.
Read MoreDoi: https://doi.org/10.54216/IJNS.250205
Vol. 25 Issue. 2 PP. 44-56, (2025)
A neutrosophic is a strong framework to characterize novel mathematical structures. This framework is more suitable and flexible set side by side to fuzzy sets and intuitionistic fuzzy sets. In this work, we focus on some famous mathematical spaces like Ls,p (u)when we work on displaying a feature the immediate and contrary theorems of unrestrained functions in the spaceLs,p (u)are considered. Also, some characteristics of modification symmetric and modulus of neutrosophic smoothness have been discussed. Moreover, the identical among approximate tools such as the neutrosophic K-functional and neutrosophic modulus of softness.
Read MoreDoi: https://doi.org/10.54216/IJNS.250206
Vol. 25 Issue. 2 PP. 57-63, (2025)
The objective of this paper is to extend the concept of standard soft sets to pentapartitioned neutrosophic vague soft sets (PNVSSs) by applying soft set theory to pentapartitioned neutrosophic vague sets (PNVSs)to make them stronger and more usable. We additionally describe its null, absolute, and fundamental operations, such as complement, subset, equality, union, and intersection, using examples. In addition, we defined the Pentaprtitioned Neutrosophic Vague multiset and the Possibility Pentaprtitioned Neutrosophic Vague sets (PPNVSs). We also look at several related properties and the proofs for them. Finally, this concept is applied to a decision-making problem, and its viability is demonstrated using an example. Related properties and the proofs for them. Finally, this concept is applied to a decision-making problem, and its viability is demonstrated using an example.
Read MoreDoi: https://doi.org/10.54216/IJNS.250207
Vol. 25 Issue. 2 PP. 64-83, (2025)
In this paper, we present sentiment analysis on Twitter data by employing Neutrosophic Sentiment Analysis (NSA). NSA captures sentiments by considering three aspects: truth, falsehood, and indeterminacy, offering a more nuanced understanding of sentiment in tweets. To enhance this analysis, we integrate the results from Neutrosophic logic (NL) sentiment analysis into a Bi-directional Long Short-Term Memory (LSTM) model. This integration takes use of NL's capacity to manage uncertainty and indeterminacy in social media material, as well as the Bi-directional LSTM's capability to capture temporal relationships in sequential data. Our combined NL-Bidirectional LSTM technique attempts to increase the precision of forecasting, particularly when it comes to predicting stock market patterns based on Twitter sentiment. Through comprehensive evaluation, we demonstrate the effectiveness of this approach, highlighting its potential to address the inherent uncertainties and indeterminacies in social media data and thereby provide more reliable predictions for stock market movements.
Read MoreDoi: https://doi.org/10.54216/IJNS.250208
Vol. 25 Issue. 2 PP. 84-92, (2025)
We construct and analyze the concept of complex cubic anti neutrosophic subbisemiring (ComCANSBS). We analyze the important properties and homomorphic aspects of ComCANSBS. For bisemirings, we propose the ComCANSBS level sets. A complex neutrosophic subset of bisemiring Ⓢ is represented by the symbol Γ if and only if each non-empty level set R(℘,κ), where R) = |ℜâŠ¤Γ ·eiθ z}|{ℑâŠ¤Γ ,z}|{ℜ ×’Γ ·eiθz}|{ℑ ×’Γ ,z}|{ℜΓ ·eiθz}|{ℑΓ ,ℜâŠ¤Γ ·eiθℑâŠ¤Γ ,ℜ ×’Γ · eiθℑ ×’Γ,ℜΓ · eiθℑΓ ) is a ComCANSBS of Ⓢ. Let Υ be a ComCANSBS of bisemiring Ⓢ. If and only if Υ is a ComCANSBS of Ⓢ × â“ˆ, then Γ is a ComCANSBS of bisemiring Ⓢ. Let Γ be the strongest complex anti neutrosophic relation of bisemiring Ⓢ. We show that homomorphic images of all ComCANSBSs are ComCANSBSs, and homomorphic pre-images of all ComCANSBSs are ComCANSBSs. There are examples given to illustrate our results.
Read MoreDoi: https://doi.org/10.54216/IJNS.250209
Vol. 25 Issue. 2 PP. 93-116, (2025)
Quality control (QC) charts are essential for ensuring industry process stability, but imprecise data make traditional methods unuseful in such a case. Neutrosophic control charts are available to handle the imprecise data. This article learns fuzzy logic as an approach of handling uncertainty more suitably than neutrosophic approaches. Fuzzy QC charts make use of fuzzy numbers, membership functions and fuzzy control limits and as such are more realistic compared to conventional charts. The study introduces a Fuzzy Adaptive Exponentially Weighted Moving Average (FAEWMA) chart, specifically designed for univariate data in a fuzzy atmosphere. The FAEWMA chart, incorporating α-cuts, is engineered to detect shifts in process means, showcasing its effectiveness through both theoretical development and practical applications. This approach improves decision-making in process control and represents a significant advancement over traditional QC methods.
Read MoreDoi: https://doi.org/10.54216/IJNS.250212
Vol. 25 Issue. 2 PP. 141-154, (2025)
In this paper, we have defined the concept of two-fold maximal units in finite two-fold neutrosophic rings modulo integers, where a sufficient and necessary condition for such class of generalized units will be provided. We characterize all maximal units in the following two-fold neutrosophic rings(Z_n (I))_(f_I ) for nE{2,3,4,5}.
Read MoreDoi: https://doi.org/10.54216/IJNS.250213
Vol. 25 Issue. 2 PP. 155-164, (2025)
This paper presents a modified homotopy perturbation method (HPM), which aimed at solving systems of ordinary differential equations (ODEs). The MHPM, which combines the HPM, Laplace transform, and Padé approximants, offers an alternative approach to address the challenges associated with solving such problems. By employing this method, it becomes feasible to overcome these challenges and obtain a dependable approximation for the exact solution. The effectiveness and applicability of the proposed scheme are demonstrated through preliminary results derived from illustrative examples, all of which correspond to exact solutions.
Read MoreDoi: https://doi.org/10.54216/IJNS.250214
Vol. 25 Issue. 2 PP. 165-175, (2025)
This study delves into the innovative use of sentiment analysis in conjunction with neutrosophic time series to forecast stock market trends in various contexts. By meticulously analyzing financial news and social media data, sentiment scores are derived and subsequently integrated into a neutrosophic time series model. This model is uniquely adept at managing uncertainty and indeterminacy, providing a robust framework for prediction. The findings indicate that this integrated approach significantly enhances predictive accuracy and reliability over traditional time series models. This research presents a novel methodology for tackling the intrinsic unpredictability of stock markets, offering a more reliable tool for investors and analysts across diverse financial environments. Additionally, by incorporating sentiment scores from a wide range of sources, the model captures a comprehensive view of market sentiment, reflecting the collective mood and opinions of investors. This comprehensive approach ensures that the predictions are not only accurate but also reflective of real-time market dynamics. Finally, this work highlights the possibility of merging sentiment analysis with sophisticated modeling approaches to change stock market prediction, as well as providing a promising avenue for future financial forecasting research.
Read MoreDoi: https://doi.org/10.54216/IJNS.250215
Vol. 25 Issue. 2 PP. 176-182, (2025)
An irreversible k-threshold conversion process on a graph G=(V,E) is an iterative process that studies the spread of a one way change (from state 0 to 1) on V(G). The process begins by choosing a set S_0⊆V. For each step t(t=1,2,…,), S_t is obtained from S_(t-1) by adjoining all vertices that have at least k neighbors in S_(t-1). We call S_0 the seed set of the k-threshold conversion process and if S_t=V(G) for some t≥0, then S_0 is called an irreversible k-threshold conversion set (IkCS) of G. The k-threshold conversion number of G (denoted by (c_k (G)) is the minimum cardinality of all the IkCSs of G. In this paper, we study IkCSs of toroidal grids and the tensor product of two paths. We determine c_2 (C_3×C_n ) and we present upper and lower bounds for c_2 (C_m×C_n) for m,n≥3. We also determine c_2 (P_2×P_n ),c_2 (P_3×P_n ) and present an upper bound for c_2 (P_m×P_n) when m,n>3. Then we determine c_3 (P_m×P_n) for m=2,3,4 and arbitrary n. Finally, we determine c_4 (P_m×P_n) for arbitrary m,n. . Also, we study the same concepts over some neutrosophic graphs with suggestions for future neutrosophic and fuzzy generalizations.
Read MoreDoi: https://doi.org/10.54216/IJNS.250216
Vol. 25 Issue. 2 PP. 183-196, (2025)
The main goal of this work is to study the effect of applying Lagrange's polynomials on finding the numerical solutions of many different neutrosophic boundary value problems, where we use those polynomials to solve three different neutrosophic boundary value problems numerically, and we present many numerical tables to compare the accuracy of the solutions obtained by Lagrange's polynomials with other famous methods such as Adomian's method.
Read MoreDoi: https://doi.org/10.54216/IJNS.250217
Vol. 25 Issue. 2 PP. 197-205, (2025)
The BE-Algebra was presented by Kim in 2007. After that, several authors studied this type of logic concept in algebra. In this paper, we introduce more properties and remarks of BE-Algebra. Note that (A,*,1) is called BE-algebra if ∀ a ∈A, b, c ∈A: collect a*a=1, a*1=1, 1*a=a and a*(b*c)=b*(a*c). In addition, a Neutrosophic BE-filter FI subset of the Neutrosophic BE-algebra is Neutrosophic BE-algebra AI is Neutrosophic BE-subalgebra AI. Some new results and the criterion to determine some properties of BE-algebra and several relationships with another algebra namely Hibert algebras (H-algebra).
Read MoreDoi: https://doi.org/10.54216/IJNS.250218
Vol. 25 Issue. 2 PP. 206-211, (2025)
We introduce the concept of complex cubic Q neutrosophic subbisemiring (CCQNSBS) is a new extension of cubic Q neutrosophic subbisemiring. We examine the characteristics and homomorphic features of CCQNSBS. We communicate the CCQNSBS level sets for bisemirings. A cubic complex Q neutrosophic subset G if and only if each non-empty level set R is a ComCQNSBS of S. We show that the intersection of all CCQNSBSs yields a CCQNSBS ofS. If S1, S2, …,Sn be the finite collection of CCQNSBSs of respectively. Then S1* S2* …* Sn is a CCQNSBS of S1* S2* …* Sn. If F : S1 --- S2 is a homomorphism, then F is a subbisemiring of CCQNSBS of S2. Examples are provided to show how our findings are used.
Read MoreDoi: https://doi.org/10.54216/IJNS.250219
Vol. 25 Issue. 2 PP. 212-232, (2025)
The notion of the complex Q bipolar neutrosophic subbisemiring (CQBNSBS) is a fundamental notion to be considered for tackling tricky and intricate information. Here, in this study, we want to expand the notion of CQBNSBS by giving a general algebraic structure for tackling bipolar complex fuzzy (BCF) data by fusing the conception of CQBNSBS and subbisemiring. Keeping in view the importance of fuzzy algebraic structures, in this manuscript, we develop the concept of CQBNSBS. We analyze the important properties and homomorphic aspects of CQBNSBS. For bisemirings, we propose the CQBNSBS level sets. We also develop the notions of homomorphic images of all CQBNSBSs is also CQBNSBS and homomorphic pre-images of all CQBNSBSs is also CQBNSBS. Examples are provided to demonstrate our findings.
Read MoreDoi: https://doi.org/10.54216/IJNS.250220
Vol. 25 Issue. 2 PP. 233-253, (2025)
In this paper, we have put forward the SH-homo and 0−preserving SH-homo to compare two SH groupoids along with few examples. Some properties of SH-homo and 0−preserving SH-homo are explored. Also, we proved the SH-homo between two SuperHyper BCI-Algebra is 0−preserving SH-homo. Finally, the category of SuperHyper Groupoid, SuperHyper BCI-Algebra and Neutrosophic SuperHyper BCI-Algebra were investigated.
Read MoreDoi: https://doi.org/10.54216/IJNS.250221
Vol. 25 Issue. 2 PP. 254-262, (2025)
Our study focusses on the concept of the kernel with in neutrosophic crisp sets (NCS_s) and its relationship with the separation axioms of NCTS, coinciding, and shedding light on the properties that characterize them.
Read MoreDoi: https://doi.org/10.54216/IJNS.250222
Vol. 25 Issue. 2 PP. 263-267, (2025)
Neutrosophic Logic is an offspring study region in which every intention is projected to hold the proportion of indeterminacy in a subset I, the percentage of truth in a subset T, and the percentage of falsity in subset F. Neutrosophic set (NS) has been effectively used for indeterminate data processing, and establishes benefits to handle with the indeterminacy information of data and is quite a method stimulated for classification application and data analysis. NS delivers an effective and precise method to describe imbalanced data as per the features of the data. Recently, the usage of the Internet of Things (IoT) has enlarged rapidly, and cyber security effects have enlarged beside it. On the state-of-the-art of cyber security is Artificial Intelligence (AI), which employed for the progress of intricate techniques to defense systems and networks, containing IoT systems. Though, cyber-attackers have determined how to develop AI and have started to utilize adversarial AI for accomplishing cybersecurity threats. Therefore, this study designs a new Interval-Valued Neutrosophic Set using Optimization Algorithm-Based Intrusion Detection System (IVNSOA-IDS) technique in IoT cybersecurity. The key objective of the IVNSOA-IDS method rests in the automatic identification of intrusion detection in IoT cybersecurity. In the IVNSOA-IDS technique, data pre-processing is executed to convert the raw data into a compatible format. Besides, the interval-valued neutrosophic set (IVNS) model has been utilized for the automated identification of intrusion detection. Finally, an improved whale optimization algorithm (IWOA) is employed for the better hyperparameter tuning of the IVNS classifier. To demonstrate the enhanced performance of the IVNSOA-IDS technique, an extensive of simulations take place and the performances are inspected under distinct aspects. The experimental outcome reported the advancement of the IVNSOA-IDS methodology under various metrics.
Read MoreDoi: https://doi.org/10.54216/IJNS.250223
Vol. 25 Issue. 2 PP. 268-278, (2025)
Neutrosophic set (NS) is a prevailing logic aimed at facilitating the understanding of inconsistent and indeterminate data; several kinds of complete or incomplete data can be described as interval-valued NS (IVNS). This study presents aggregation operator for IVNSs and prolongs the generalized weighted aggregation (GWA) operations to congruently work with IVNS information. Also, these results are formulated as IVNSs that are represented by indeterminate, truth, and false degrees. The tremendous growth of financial innovation offers a several convenience to people’s lives and production and brings many security risks to financial technology. To avoid financial risk, an improved way is to construct an accurate warning mechanism before the financial risk takes place, not to solve this matter after the risk outbreak. Recently, deep learning (DL) has delivered outstanding results in the natural language processing and image recognition areas. Thus, researcher used DL techniques for the financial risk prediction and obtained satisfactory results. This study develops a new Pythagorean Neutrosophic Normal Interval-Valued Weighted Averaging for Financial Risk Prediction (PNNIVWA-FRP) method using sustainable development. The objective of the PNNIVWA-FRP method is to have two dissimilar stages of processes. Initially, financial data are classified by the PNSNIVWA technique. This method is used for its highest proficiency in managing imprecision and uncertainty in financial data, containing incomplete and ambiguous data. Second, the classified parameter is fine-tuned by means of Glowworm Swarm Optimization (GSO) technique. Based on the luminescent communication of glowworms, GSO is proficient at navigating multidimensional, complex search spaces for identifying better solutions. The empirical findings on benchmark dataset demonstrate the effectiveness of the PNNIVWA-FRP method, showcasing significant development in prediction results than classical approaches.
Read MoreDoi: https://doi.org/10.54216/IJNS.250224
Vol. 25 Issue. 2 PP. 279-289, (2025)
An interval neutrosophic set (INS) is an example of a NS, which is simplified from the theory of fuzzy set (FS), classical set, paradoxist set, intuitionistic FS, paraconsistent set, interval-valued FS, interval-valued intuitionistic FS, and tautological set. The association of an element to an INS is stated by 3 values such as t, i, and f. These values signify memberships of truth, indeterminacy, and false, correspondingly. Bankruptcy prediction is also called a corporate failure or bankruptcy prediction, which is a major focus in the area of finance and accounting, as the condition of a business is extremely substantial to its partners, shareholders, investors, creditors, even its suppliers, and buyers. Practitioners and researchers were reserved for emerging models and approaches to forecast the bankruptcy of companies more rapidly and precisely. With the excessive growth of contemporary information technology, it has developed to use machine learning (ML) or deep learning (DL) techniques to perform the prediction, from the preliminary study of economic statements. This study introduces an Optimized Bankruptcy Prediction using Feature Selection with m-Polar Neutrosophic Topological Spaces (OBPFS-MPNTS) method. The projected OBPFS-MPNTS system uses the parameter tuning and DL method to forecast the presence of bankruptcy. To achieve this, the OBPFS-MPNTS approach uses min-max normalization to convert input data into a uniform format. The OBPFS-MPNTS method begins with a grey wolf optimization (GWO) for selecting feature subsets. In addition, the OBPFS-MPNTS algorithm applies the m-polar neutrosophic topological space (MPNTS) system for bankruptcy prediction. To upsurge the performance of the MPNTS system, the whale optimizer algorithm (WOA) is employed. The experimentation outcome study of the OBPFS-MPNTS system is verified on a benchmark database and the outcomes pointed out the developments of the OBPFS-MPNTS algorithm over other current methodologies.
Read MoreDoi: https://doi.org/10.54216/IJNS.250225
Vol. 25 Issue. 2 PP. , (2025)
As a generalization of fuzzy set (FS) and intuitionistic FS (IFS), neutrosophic sets (NS) were proposed to signify imprecise, uncertain, inconsistent and imperfect data present in real-time. Moreover, the interval NS (INSs) were developed just to find out the problems with an array of statistics in the actual unit interval. Then, there are least consistent processes for INSs, along with the decision-making process and INS aggregation operator. The vital operations are presented on n-valued interval NSs like intersection, union, multiplication, addition, scalar division, scalar multiplication, false-favorite and truth favorite. Bankruptcy prediction was a major concern in the areas of finance and management science that appealed to the attention of practitioners and researchers. With the great progress of up-to-date information technology, it has been developed to utilize machine learning (ML) or deep learning (DL) techniques to perform the prediction, from the primary analysis of financial statements. If ML methods have adequate interpretability, they might be employed as effectual analytical methods in bankruptcy calculation. This manuscript presents a Bankruptcy Prediction using Cutting-Edge N-Valued Interval Neutrosophic Sets (BP-CENVINS) mechanism. The projected BP-CENVINS method is a complicated approach to bankruptcy forecast that affects radical data preprocessing, classification, and hyper parameter optimization approaches. Initially, the Z-score normalization regularizes the fiscal details to increase the comparability and stability throughout the information. Next, it employs the CENVINS for the classification, skillfully detecting the subtle communication amongst variables to differentiate between creditworthy and bankrupt organizations. Finally, the Grasshopper Optimization Algorithm (GOA) is applied for parameter tuning to improve the predictive outcomes of the CENVINS classifiers, systematically purifying design parameters to achieve finest efficiency. An extensive experiments is made to illustrate the betterment of the BP-CENVINS technique. The simulation outcomes of the BP-CENVINS method have exhibited better performances than other existing methodologies.
Read MoreDoi: https://doi.org/10.54216/IJNS.250226
Vol. 25 Issue. 2 PP. 303-312, (2025)
In this study, a new notion of m-polar neutrosophic set (MPNS) and m-polar neutrosophic topology is introduced. To achieve this goal, first, we explore numerous representations of the concept of MPNS and deliberate its definitive characteristics. Some operations on MPNS were established. A score function is proposed for comparing the MPN numbers (MPNNs). Next, an MPN topology is introduced and closure, frontier, interior, and exterior for MPNS are defined with representative examples. Depression is a popular mental health problem that disturbs a broad range of individuals worldwide. Generally, people who undergo from this attitude have problems like mood swings, low concentration, suicide, and dementia. A social media platform such as Twitter enables to interact and share videos and photos that express their moods. Hence, the studies on social media content present an overview of personal sentiments, such as depression. Research has been undertaken on depression recognition in English and less in Arabic. The recognition of depression from Arabic social media falls after owing to the lack of resources and techniques and the available difficulty of the Arabic language. This article presents a novel Applied Linguistics with m-Polar Neutrosophic Set Mood Change and Depression on Social Media (MPNS-MCDSM) technique on Arabic Text Analysis. To accomplish this, the MPNS-MCDSM method undertakes a data pre-processing stage to convert the input dataset into a beneficial format. In addition, the Glove word embedding method is applied to the feature extraction from the preprocessed dataset. For the classification process, the m-Polar Neutrosophic Set (MPNS) classifier can be applied. Finally, the Whale Optimization Algorithm (WOA) is applied for optimum adjustment of the hyperparameters related to the MPNS classifier. The simulation outcomes of the MPNS-MCDSM technique are verified on the benchmark dataset. The experimental result analysis of the MPNS-MCDSM technique shows its promising solution over other existing approaches.
Read MoreDoi: https://doi.org/10.54216/IJNS.250227
Vol. 25 Issue. 2 PP. 313-324, (2025)
In this paper, we introduce the notion of $\flat,\ell$-neutrosophic subsemigroup (NSS), neutrosophic left ideal(NLI), neutrosophic right ideal(NRI), neutrosophic ideal (NI), neutrosophic bi-ideal(NBI), $(\epsilon, \epsilon \vee q)$-neutrosophic ideal, neutrosophic bi-ideal of an ordered $\Gamma$-semigroups and discuss some of their properties. The concept of $\flat,\ell$-neutrosophic ideal is a new extension of neutrosophic ideal over ordered $\Gamma$-semigroups $\mathcal{Z}$. A non-empty subset $\xi_{\flat}$ is a $(\flat, \ell)$-NSS (NLI, NRI, NBI, (1,2)-ideal) of $\mathcal{Z}$. Then the lower level set $\Delta_{\flat}$ is an subsemigroup $(LI, RI, BI, (1,2)-ideal)$ of $\mathcal{Z}$, where $\Delta_{\flat}=\{\varrho\in \mathcal{Z}|\Delta(\varrho)> \flat\}$, $\Psi_{\flat}=\{\varrho\in \mathcal{Z} |\Delta(\varrho)> \flat\}$ and $\mho_{\flat}=\{\varrho\in \mathcal{Z}|\Delta(\varrho)< \flat\}$. A subset $\xi=[\Delta,\Psi,\mho]$ is a $(\flat, \ell)- NSS[NLI,NRI,NBI,(1, 2)-ideal]$ of $\mathcal{Z}$ if and only if each non-empty level subset $\xi_{t}$ is a subsemigroup $[LI,RI,BI,(1,2)-ideal]$ of $\mathcal{Z}$ for all $t\in(\flat, \ell]$. Every $(\epsilon, \epsilon \vee q)$NBI of $\mathcal{Z}$ is a $(\flat,\ell)$NBI of $\mathcal{Z}$, but converse need not be true and examples are provided to illustrate our results.
Read MoreDoi: https://doi.org/10.54216/IJNS.250228
Vol. 25 Issue. 2 PP. 325-337, (2025)
Quadripartitioned neutrosophic set is an extension of neutrosophic set and n-valued neutrosophic logic for solving real-world issues. In order to demonstrate the validity of the suggested idea, this paper's major goal is to provide several quadripentapartition neutrosophic probability distributions with numerical examples. Neutosophic probability has up till now been obtained from traditional statistical distributions, with less contributions to the statistical distribution's creation. With the help of numerical examples, we introduced the quadripartition neutrosophic binomial distribution, the quadripartitioned Poisson distribution, and the quadripartitioned Poisson distribution as a limiting case of the neutrosophic binomial distribution. We also proposed the quadripartitioned exponential distribution and the quadripartitioned uniform distribution. This paper paves the door for addressing problems that adhere to the classical distributions while still include inaccurately stated data.
Read MoreDoi: https://doi.org/10.54216/IJNS.250229
Vol. 25 Issue. 2 PP. 338-352, (2025)
This article proposed a novel method to categorize the best student in all progressive studies by using the single valued Neutrosophic soft set-in variable sense. An ambivalence set of multi-observer data which is related to analyse the students, taken as input for categorizing the best student identification. Neutrosophic soft set is an immense application to find out the choice-making problem in the Neutrosophic area. The creation of an analogous table has shaped the classification investigation. It helps to put up things, people into groups according to their quality, ability, performance etc., in Neutrosophic environment.
Read MoreDoi: https://doi.org/10.54216/IJNS.250230
Vol. 25 Issue. 2 PP. 353-358, (2025)
We construct and analyze the concept of complex cubic neutrosophic subbisemiring (ComCNSBS). We analyze the important properties and homomorphic aspects of ComCNSBS. For bisemirings, we propose the ComCNSBS level sets. A complex neutrosophic subset of bisemiring S is represented by the symbol G if and only if each non-empty level set R(p,x), where R is a ComCNSBS of S. We show that homomorphic images of all ComCNSBSs are ComCNSBSs, and homomorphic pre-images of all ComCNSBSs are ComCNSBSs. There are examples given to illustrate our results.
Read MoreDoi: https://doi.org/10.54216/IJNS.250231
Vol. 25 Issue. 2 PP. 259-277, (2025)