Integrating Neutrosophic Logic with Bi-directional LSTM Model for Predicting Stock Market Movements

S.S.; Vediyappan; Mana; Broumi

doi:https://doi.org/10.54216/IJNS.250208

Prospects for Applied Mathematics and Data Analysis | 2023

The Computation of Particular Roots of Nonlinear Complex Equations of the Form: (an√is K + (x+10y) n√is)n = c

Solving polynomial equations involves finding their roots. In this respect, this idea dominates the minds of many mathematicians about how to find those roots. The Abel Ruffini theorem emphasizes that there is no general formula involving only the coefficients of a polynomial equation of degree five or higher that allows us to compute its solutions using radicals and its associate to the Galois Theory. The mathematical need for solving polynomial equations represents the motivation for the development of systems of numbers from Natural numbers to Complex numbers throughout the history of mathematics. Complex numbers play a central role in this context. The Fundamental Theorem of Algebra tell us that every nonconstant polynomial equation with complex coefficients has at least one complex root. While the Galois group associated with a polynomial captivates its symmetries and determines whether it is solvable by radicals. From a mathematical standpoint, it is customary to visualize polynomials in the form:P_n (x)=a_n x n+a_(n-1) x (n-1)+---+a_1 x 1+a_0, Where the set of coefficients {a_n, a_(n-1),---,a_0}ECand P_n (x)EC[x]. We have reconceptualized the polynomial generated by the formula (ax+y)^n=c in our previous work and computing radicals of more degree 5. In this article, we present a natural procedure formula that will lead us to find a solution for a class of polynomials nonlinear Complex numbers with degree 𝑛 associated with the equation:(ansquris K + (x+10y) nsquris)n = c as a particular class of Complex Polynomials.

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Adel Al-odhari mail -

Shaker AL -Assadi mail

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

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2025

A Novel Authentication Mechanism with Efficient Mathematic Based Technique

The security of any device or data on it is greatly dependent on the authentication and session handling. Using an MFA-based OTP method, the most popular web apps, such as communication mail, social media platforms, and financial transactions, manage spoofing attempts and attempt to keep them to a minimum. There is statistical evidence that indicates that between April 2020 and March 2022, this well-known OTP mechanism lost 1434.75 crore rupees, further weakening its hold on security. This unusual situation is driving research toward authentication methods that rely solely on itself without external aid. In order to improve security, self-dependent authentication methods (passwords, combinations of image clicks, etc.) have not been streamlined or made sufficiently dynamic. By comparing state-of-the-art methods, the suggested work, Mathematic Based Technique (MBT), will enhance the dynamic behaviour of passwords and optimize to give greater security. In the event of an eavesdropping assault, the Mathematic Based Technique (MBT) will make it difficult for hackers to pull the efforts to crack the password with the probability with permutation value is equal to O (7810). Mathematical proof of the result is provided, and it is compared to the six best state-of-the-art mechanisms which are now in use, those are Picasso Pass (PP) which uses layered mechanism, Dynamic Password Protocol (DPP) which uses date and time in it, Dynamic Pattern Image (DPI) which resembles mobile pattern authentication, Dynamic Array Pin (DAP) which uses area based pin or a pre-defined pin, and Bag of Password (BP) which uses image.

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Balajee R. M. mail -

Suresh Kallam mail -

M. K. Jayanthi Kannan mail

link https://doi.org/10.54216/JCIM.150107

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2025

On Two Novel Generalized Versions of Diffie-Hellman Key Exchange Algorithm Based on Neutrosophic and Split-Complex Integers and their Complexity Analysis

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.

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Dima Alrwashdeh mail -

Talat Alkhouli mail -

Ahmed Soiman Rashed Alhawiti mail -

Ali Allouf mail -

Hussein Edduweh mail -

Abdallah Al-Husban mail

link https://doi.org/10.54216/IJNS.250201

Volume & Issue

Vol. Volume 25 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2025

An Efficient Plant Disease Detection: Possibility Neutrosophic Hypersoft Set Approach with Whale Optimization Algorithm

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.

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Abdalla Ibrahim Abdalla Musa mail -

Mohammed Abdullah Al-Hagery mail

link https://doi.org/10.54216/IJNS.250202

Volume & Issue

Vol. Volume 25 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2025

Complex Proportional Assessment Based Neutrosophic Approach for Ransomware Detection in Cybersecurity IoT System

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.

groups

Louai A. Maghrabi mail

link https://doi.org/10.54216/IJNS.250203

Volume & Issue

Vol. Volume 25 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2025

Integrating Machine Learning with Two-Person Intuitionistic Neutrosophic Soft Games for Cyberthreat Detection in Blockchain Environment

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.

groups

Abdalla Ibrahim Abdalla Musa mail -

Mohammed Abdullah Al-Hagery mail

link https://doi.org/10.54216/IJNS.250204

Volume & Issue

Vol. Volume 25 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Neutrosophic Net-RBF Neural Networks with Bayesian Optimization Based Sentiment Analysis on Low Resource Language

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.

groups

Abdalla Ibrahim Abdalla Musa mail -

Mohammed Abdullah Al-Hagery mail

link https://doi.org/10.54216/IJNS.250205

Volume & Issue

Vol. Volume 25 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Direct and converse approximation theorems in neutrosophic L_(δ,p) (U) space

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.

groups

Alaa Adnan Auad mail -

Mohammed A. Hilal mail

link https://doi.org/10.54216/IJNS.250206

Volume & Issue

Vol. Volume 25 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Pentapartitioned Neutrosophic Vague Soft Sets and its Applications

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.

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Manal Al-labadi mail -

Shuker Khalil mail -

Radhika V. R. mail -

Mohana K. mail

link https://doi.org/10.54216/IJNS.250207

Volume & Issue

Vol. Volume 25 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Integrating Neutrosophic Logic with Bi-directional LSTM Model for Predicting Stock Market Movements

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.

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S.S. Saravanaraj mail -

Vediyappan Govindan mail -

Mana Donganont mail -

Broumi Said mail

link https://doi.org/10.54216/IJNS.250208

Volume & Issue

Vol. Volume 25 / Iss. Issue 2

Details open_in_new