ML-kNN-H: A Multi-Label Classification Model based on Hoeffding’s Inequality

Mashail; Manal; Omaima

doi:https://doi.org/10.54216/FPA.190226

International Journal of Neutrosophic Science | 2025

Modified Compact Finite Difference Methods for Solving Fuzzy Time Fractional Wave Equation in Double Parametric Form of Fuzzy Number

Fuzzy fractional partial differential equations have become a powerful approach to handle uncertainty or imprecision in real-world modeling problems. In this article, two compact finite difference schemes, the compact Crank-Nicolson and the compact center time center space methods, were developed and used to obtain a numerical solution for fuzzy time fractional wave equations in the double parametric form. The principles of fuzzy set theory are utilized to perform a fuzzy analysis and formulate the proposed numerical schemes. The Caputo formula is used to define the time-fractional derivative considered. The stability of the proposed schemes is analyzed by means of the Von Neumann method. To illustrate the practicality of the numerical methods, a specific numerical instance was performed. The outcomes were showcased through tables and figures, revealing the efficacy of the schemes in terms of accuracy and their ability to decrease computational expenses.

groups

Maryam Almutairi mail -

Norazrizal Aswad bin Abdul Rahman mail

link https://doi.org/10.54216/IJNS.260214

Volume & Issue

Vol. Volume 26 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Towards Sustainable Economy: Boosting Financial Credit Risk Forecasting Using Bipolar Single-Valued Neutrosophic Graph Sets Approach

A neutrosophic set (NS) contains 3 modules such as the degree of truth (T), degree of falsity (F), and degree of indeterminacy (I). While fuzzy graphs (FG) occasionally fall short of providing optimum outcomes, the NS and neutrosophic graphs (NG) provide a strong substitute, which efficiently handles the uncertainties related to indeterminate and inconsistent data in real-life scenarios. Conversely, bipolar neutrosophic methods, which account for both negative and positive effects, deliver a more flexible and applicable technique. Financial crisis prediction (FCP) is inherent in the detection of major social and economic impacts that crises of financial might hold on a global measure. It generally outcomes in vast financial losses, redundancy, and losses in values of assets that lead to significantly affected individuals and businesses. In recent times, the credit risk prediction methods have aided businesses in resolving whether to award credit to users who applied. This paper presents the Financial Credit Risk Forecasting Using Bipolar Single-Valued Neutrosophic Graph Sets Approach (FCRF-BSVNGSA) method. The main intention of the FCRF-BSVNGSA method is to develop an effective method for financial credit risk prediction using advanced methods. At first, the data normalization stage utilizes Z-score normalization for converting the input data into a beneficial format. Furthermore, for the financial credit risk classification process, the proposed FCRF-BSVNGSA model employs the bipolar single-valued neutrosophic graphs (BSVNG) approach. Finally, the multi‐objective hippopotamus optimization (MOHO) approach fine-tunes the hyperparameter values of the BSVNG model optimally and results in superior classification performance. An extensive simulation of the FCRF-BSVNGSA approach is performed under the Statlog (German Credit Data) dataset. The experimental validation of the FCRF-BSVNGSA approach portrayed a superior accuracy value of 95.59% over exisitng techniques.

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Elvir Akhmetshin mail -

Ilyos Abdullayev mail -

Aleksey Ilyin mail -

Denis Shakhov mail -

Tatyana Khorolskaya mail

link https://doi.org/10.54216/IJNS.260215

Volume & Issue

Vol. Volume 26 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Parameter Estimation in Multiple Linear Regression: A Neutrosophic Perspective with the Simple Averaging Method (SAM)

Regression modeling is a significant statistical tool aimed at quantifying and understanding the nature of relations between the predictor and response variables. The routine parameter estimation procedures, like OLS and ML, are based heavily on the assumption of normality in data, which will not be the case for most real-world data scenarios. The paper presents a Neutrosophic approach for the estimation of parameters in multiple linear regression models, making use of the Neutrosophic principles to treat uncertainties, indeterminacies, and inconsistencies in actual data, a proposed method is called the Simple Averaging Method, or SAM. This is a robust alternative to traditional methods and provides reliable results even if the assumptions of normality are not held. SAM performance is tested using real-time crime data in the USA and demonstrates its capabilities to deal with complex datasets. The comparative analysis between the OLS model and the same model is done via RMSE and MAD metrics. The results show that SAM significantly outperforms OLS with an RMSE of 34.37598 in contrast to 58.05248 for OLS. Graphical analysis further confirms SAM's performance over and above OLS. Critical issues of regression modeling with incorporation of neutrosophic logic cover their critical challenges, especially when standard assumptions are violated.

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Kesavulu Poola mail -

V. Pavankumari mail -

J. Anil Kumar mail -

Akkyam Vani mail -

Asif Alisha S. mail -

A. Srinivasulu mail

link https://doi.org/10.54216/IJNS.260216

Volume & Issue

Vol. Volume 26 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

£ukasiewicz Intuitionistic Fuzzy Filters in Hoops and its Application in Medical Diagnosis

The new theory of £ukasiewicz įntuitionistic ꞙuzzy set and £ukasiewicz įntuitionistic ꞙuzzy ꞙilter is introduced. Some properties of £ukasiewicz įntuitionistic ꞙuzzy ꞙilter is presented. It is explored that under what circumstances, the £ukasiewicz įntuitionistic ꞙuzzy set can be a £ukasiewicz įntuitionistic ꞙuzzy ꞙilter. An algorithm for diagnosing disease is developed and provided with demonstration.

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N. Abirami mail -

M. Mary Jansirani mail

link https://doi.org/10.54216/IJNS.260217

Volume & Issue

Vol. Volume 26 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Clean Graphs over Rings of Order P^2

Assume R is a commutative ring with unity. The clean graph CL(R) is defined in which every vertex has the form (a, v), where a is an idempotent in R and v is a unit. In CL(R), two distinct vertices (a1, v1) and (a2, v2) are adjacent if a1a2 = a2a1 = 0 or v1v2 = v2v1 = 1. In this paper, we show that the clean graph CL(R) over a ring of order p2 can be defined only if R is one of the rings: Zp2 ,Zp &oplus;Zp,Zp(+)Zp and GF(p2). Then, we study the spectrum, the biclique partition number, and the eigensharp property for the these clean graphs.

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Heba Adel Abdelkarim mail -

Edris Rawashdeh mail -

Eman Rawshdeh mail

link https://doi.org/10.54216/IJNS.260218

Volume & Issue

Vol. Volume 26 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Neutrosophic Analysis for the Future of Artificial Intelligence in Language Education

The neutrosophic set, a mathematical framework that accounts for truth, indeterminacy, and falsity, plays a crucial role in enhancing artificial intelligence (AI)-driven language education. By integrating neutrosophic logic, AI systems can better handle linguistic ambiguities, dynamically adapt learning materials, and offer more precise and personalized feedback. This paper explores the application of neutrosophic theory in intelligent tutoring systems (ITS), natural language processing (NLP), and AI-assisted feedback mechanisms, all within an uncertainty-based framework. Through the incorporation of neutrosophic models, AI can more effectively assess learner responses by considering elements of truth, uncertainty, and falsehood, leading to more adaptive and context-aware language instruction. Furthermore, the study highlights how AI, powered by neutrosophic logic, contributes to breaking language barriers, increasing accessibility, and fostering inclusive learning environments. Ethical concerns, bias mitigation, and data privacy challenges in AI-driven language learning are also addressed, emphasizing the need for responsible AI implementation. Finally, the paper underscores the synergistic balance between AI and human educators, advocating for adaptive AI frameworks that enhance linguistic comprehension while ensuring pedagogical integrity. Future research directions focus on leveraging neutrosophic logic to further improve AI's reliability, adaptability, and overall effectiveness in personalized language education.

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Hilal Abdul-Raziq Sadiq mail -

Shakirova Zulfiya Normahamatovna mail -

Mullasadikova Nigora Muramanovna mail -

Madayeva Mu‘tabarxon Amanullayevna mail -

Askarov Abdurashid Murodjonovich mail

link https://doi.org/10.54216/IJNS.260219

Volume & Issue

Vol. Volume 26 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

A Descent Conjugate Gradient Method for Large Scale Unconstrained Optimization Problems with Application

In recent years, there has been a surge of attention to the Conjugate Gradient Method (CGM) and its applications. This is because the algorithm of CGM does not require the computation of the second derivative or an approximation during the iteration process. In this study, a four-term descent CGM is proposed by utilizing the famous Polak–Ribiere–Polyak (PRP) conjugate gradient formula. The direction of the proposed method achieves the descent property without line search consideration. In addition, the convergence properties are met to generate the stationary points. Findings from numerical experiments on unconstrained optimization and robotic motion control problems demonstrate that the novel approach outperforms some existing methods including the famous CG-Descent conjugate gradient method.

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Ahmad Alhawarat mail -

Sultanah Masmali mail -

Ibrahim M. Sulaiman mail -

Issam A. R. Moghrabi mail -

Norazura Ahmad mail -

Shahrina Ismail mail

link https://doi.org/10.54216/IJNS.260220

Volume & Issue

Vol. Volume 26 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Study Neutrosophic Quasi-Frobenius by Local and Artinian Rings

In this paper, we study the relationships between the Neutrosophic quasi-Frobenius rings and the Neutrosophic of local rings and Artinian rings. In addition, we present study the relationship between the Neutrosophic quasi-Frobenius ring and some concepts such as Neutrosophic semisimple ring, Neutrosophic module injective and Neutrosophic Noetherian ring. Finally, we introduce some mathematical formulas with an commutative, coherent and Neutrosophic perfect ring, through which we obtain the Neutrosophic quasi-Frobenius ring.

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Omar A. Khashan mail -

Majid M. Abed mail

link https://doi.org/10.54216/IJNS.260222

Volume & Issue

Vol. Volume 26 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Multi Chronic Disease Prediction by Fine Tuning Random Forest using Social Group Optimization

Accurate disease prediction is essential for enabling preventive healthcare and reducing the burden of chronic illnesses. This study introduces an innovative multi-disease prediction framework leveraging machine learning and optimization techniques to address limitations in precision and scope present in prior research. Specifically, we focus on predicting five major diseases—diabetes, heart disease, kidney disease, liver disease, and breast cancer—by employing the Social Group Optimization (SGO) algorithm to fine-tune the Random Forest (RF) classifier's hyperparameters.The proposed SGO-optimized RF model optimizes seven critical parameters - n_estimators, max_depth, min_samples_split, min_samples_leaf, max_features, bootstrap, and criterion simultaneously, significantly enhancing the model's performance. Our approach, applied to five disease datasets, achieves notable accuracy improvements: 98.25 When tested on diverse datasets, the model achieves exceptional accuracy: 98.25% for breast cancer, 84.62% for liver disease, 93.44% for heart disease, 82.47% for diabetes, and 100% for chronic kidney disease. On average, the SGO-optimized RF outperforms existing methods with a 2.3% accuracy improvement across datasets. This research highlights the transformative potential of SGO-based optimization in advancing the accuracy and reliability of predictive models. The results demonstrate the framework's applicability in clinical scenarios, providing precise and actionable insights that support early diagnosis and improve patient outcomes.

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Sudhirvarma Sagiraju mail -

Jnyana Ranjan Mohanty mail -

Anima Naik mail

link https://doi.org/10.54216/FPA.190225

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

ML-kNN-H: A Multi-Label Classification Model based on Hoeffding’s Inequality

Multi-label data stream classification plays a crucial role in various applications, including recommendation systems, real-time monitoring systems, smart cities, social media analysis, and healthcare. Its ability to classify constantly generated, potentially unbounded data at a high rate is of utmost importance. Besides accommodating multiple labels, data streams may evolve due to concept drift and bias toward particular classes due to class imbalance. This research introduces the multi-label classification model based on Hoeffding inequality (ML-kNN-H). The proposed model aims to process multi-label data streams, handle concept drift, and class imbalance. ML-kNN-H removes instances introducing errors based on a dynamic value computed from the Hoeffding inequality instead of a fixed value, thereby enhancing the model's efficiency and applicability to different types of data streams. Several experiments have been conducted to assess the model's performance in the presence of concept drift (abrupt and gradual drift) and class imbalance. Particularly, it has been evaluated against six kNN multi-label classifiers on ten datasets: synthetic and real world. The results indicate that ML-kNN-H outperformed the other classifiers on benchmark datasets in terms of Subset Accuracy, Accuracy, Hamming Score, and F-score, except in running time. Statistical analysis has also been utilized to measure the significance of the ML-kNN-H compared to the state-of-the-art classifiers.

groups

Mashail Althabiti mail -

Manal Abdullah mail -

Omaima Almatrafi mail

link https://doi.org/10.54216/FPA.190226

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

Details open_in_new