Modeling Extreme Healthcare Costs Using the Neutrosophic Cauchy Distribution

Afrah Al; Adnan

doi:https://doi.org/10.54216/IJNS.250422

Fusion: Practice and Applications | 2025

Enhanced Clustering Techniques for Marine Ad-Hoc Networks Using Dynamic PFCM

Ship Ad Hoc Networks (SANETs) are an integral part of modern maritime communication and shipping, characterized by dynamic topology and heavy traffic. Accurate node localization in SANETs is of great importance to ensure effective communication, security, and operational decisions. Traditional clustering algorithms, such as Fuzzy C-Means (FCM) and Possibilistic Fuzzy C-Means (PFCM), struggle with the dynamic and collaborative nature of SANETs, being sensitive to noise, outliers, and node distribution of rapidly changing. In this paper, a new clustering algorithm, the Dynamic Weighted Gradient-Based Possibilistic using Fuzzy C-Means (DWGB-PFCM), is specially designed to address the limitations of traditional methods in dynamic SANETs. The DWGB-PFCM contains dynamic weighted distances, flexible membership and uniqueness functions, and enhanced objective functions to improve robustness, adaptability, and efficiency of the cluster. Detailed data processing from the National Buoy Data Center (NDBC) combines spatial environmental parameters such as wind speed, atmospheric pressure, and wave characteristics to simulate real-world ocean challenges. Experimental results show that DWGB-PFCM outperforms traditional methods and separation measurements, with PFCM improving by 15.8%, decreasing by 22.2% in separation entropy, and decreasing by 32.1% in RMSE. In addition, DWGB-PFCM achieves a 15.0% improvement in computational efficiency over FCM. This research lays the foundation for further innovations in clustering algorithms designed for dynamic environments.

groups

Ghufran Abdulameer mail -

Yossra H. Ali mail

link https://doi.org/10.54216/FPA.180118

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Fusion Data Framework for Enhanced Outlier Detection Integrating Statistical and Machine Learning Techniques for Retail Analytics

This paper aims at presenting an overview of the most popular outlier detection methods that can be used in the retail sector to solve such important problems as fraud, inventory issues, and untypical customer behavior. The techniques discussed in this paper include the conventional statistical methods such as Z-score, Mahalanobis Distance, and Elliptic Envelope and the advanced machine learning methods such as Local Outlier Factor (LOF), Isolation Forest, and DBSCAN. Each method is discussed in detail and the advantages and disadvantages of each are evaluated in relation to different retail scenarios. The primary contribution of this study is the new approach to use Artificial Neural Networks (ANN) for tuning contamination parameters in the Elliptic Envelope model, which makes the anomaly detection more accurate and efficient. Furthermore, the study also depicts the application of min-max scaling for normalizing the features where it helps in reducing the effect of outliers and thus improves the model performance. The results show that the integration of the statistical and machine learning methods is very useful for the real-time detection of anomalies particularly in the ever-changing environment of the retail industry. This research presents a practical insight and new methodological approaches that may be useful for researchers and practitioners who develop outlier detection systems. The outcomes of this study have the potential of enhancing data fusion quality, workflow, and decision-making in the context of retailing.

groups

Botirjon Karimov mail -

Murodjon Sultanov mail -

Jasurbek Nematullaev mail

link https://doi.org/10.54216/FPA.180119

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

AI-Powered Election Insights: Predicting the 2024 Trump vs. Kamala Election Showdown with Machine Learning

The United States presidential elections receive a substantial attention not only from American voters, but also from news agencies, politicians, and international governments due to the local and global impact of the outcome. Therefore, different parties strive to predict the election’s results ahead of time, and opinion polls remain the predominant prediction method despite their bias and flaws. Online political communication has immensely evolved in recent years, especially on social media websites like Reddit, which has become a key platform in political discourse offering a valuable resource for studying public opinions on key issues. This study aims to utilize advanced machine learning methods to predict the outcome of the upcoming 2024 U.S. presidential election with a focus on the two primary candidates, former President Trump and Vice President Harris. Employing deep learning techniques to analyze more than 25 thousand online posts on Reddit, the results indicate that on the national level, Harris has more favorable sentiment in comparison to Trump among online users. However, analyzing the data associated with the battleground states, our model predicts that Trump has an edge over Harris, which may result in Trump winning the majority of the electoral votes in these states. This study underscores the importance of integrating social media data with machine learning capabilities for enhanced data-driven forecasts in upcoming elections and major public events.

groups

Yazan Alnsour mail -

Mohammad Alsharo mail -

Malik AL-Essa mail -

Aseel Smerat mail

link https://doi.org/10.54216/JISIoT.150116

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

An Intelligent Decision Support Systems for Financial Fraud Detection Using Pythagorean Neutrosophic Bonferroni Mean Approach with Machine Learning Models

Neutrosophy has developed as a generalization to fuzzy logic and is being employed in the research field in many areas such as set theory, logic, and others. Neutrosophic Logic is one of the neonate study regions and its intention is assessed to have the percentage of truth in a subset T, the percentage of falsity in a subset F, and the percentage of indeterminacy in a subset I. Recently, financial fraud has become a highly major issue, which results in severe consequences across firm sectors and affects people’s everyday lives. Therefore, financial fraud recognition is critical for the prevention of the regularly overwhelming effects of financial fraud. It includes differentiating fraudulent financial data from accurate data and permitting decision-makers to progress suitable plans to reduce the effect of fraud. Over the past few years, Artificial intelligence (AI), mainly machine learning (ML) systems, turned out to be the highest thriving model in fraud detection. This study presents a novel Intelligent Decision Support System for Financial Fraud Detection Using Pythagorean Neutrosophic Bonferroni Mean (IDSSFFD-PNBM) model. The main intention of the IDSSFFD-PNBM algorithm is to enrich the detection model for financial fraud using advanced optimization models. Initially, the z-score normalization is applied in the data normalization stage for converting input data into a beneficial format. Besides, the proposed IDSSFFD-PNBM designs a grasshopper optimization algorithm (GOA) for the selection of feature processes to enhance the efficiency and performance of the model. For the detection and classification procedure, the pythagorean neutrosophic bonferroni mean (PNBM) model has been employed. Additionally, the firefly optimization algorithm (FFOA)-based hyperparameter range method has been done to heighten the recognition outcomes of the PNBM system. The experimental evaluation of the IDSSFFD-PNBM technique takes place using a benchmark dataset. The experimental results indicated an enhanced performance of the IDSSFFD-PNBM technique compared to recent approaches

groups

Umidjon Matyakubov mail -

Ranokhon Sharofutdinova mail -

Aleksey Ilyin mail -

Rustem Shichiyakh mail -

K. Shankar mail -

E. Laxmi Lydia mail

link https://doi.org/10.54216/IJNS.250418

Volume & Issue

Vol. Volume 25 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2025

Geometric Properties of Neutrosophic 𝓆 -Poisson distribution Series through 𝕻𝕞ℵ Operator

This paper investigates the 𝔓𝕞&alefsym; operator, constructed from the Neutrosophic 𝓆-Poisson distribution series. The study examines this operator within the realm of geometric function theory, focusing on key characteristics such as coefficient bounds, growth and distortion behavior, and the determination of convexity and star likeness radii. Additionally, the paper explores the weighted and arithmetic means of functions associated with this operator and analyzes its closure properties under the Hadamard product.

groups

Layla Esmet Jalil mail -

Mohammad El-Ityan mail -

Rafid Habib Buti mail

link https://doi.org/10.54216/IJNS.250419

Volume & Issue

Vol. Volume 25 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2025

Applying Block Method for the Numerical Solutions of the Second Order n-Refined Neutrosophic ODE for n=2, 3

In this paper, we study the applications of block method to find the numerical solutions of some neutrosophic differential problems, where we discuss the approximated n-refined neutrosophic solutions and absolute n-refined neutrosophic errors in two special cases for n=2, and n=3. In addition, we list the numerical tables of our results.

groups

Ahmad A. Abubaker mail -

wael mahmoud mohammad salameh mail -

Sara A. Khalil mail -

Ibraheem Abu Falahah mail -

Ahmed Atallah Alsaraireh mail -

Abdallah Al-Husban mail

link https://doi.org/10.54216/IJNS.250420

Volume & Issue

Vol. Volume 25 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2025

Analyzing and Interpretation of Kernel Neutrosophic Set Based Machine Learning Model for Cost Estimation of Multi Product Supply Chain Management Systems

Neutrosophic set (NS) is a novel devise to handle uncertainty considering the memberships of truth T, indeterminacy I, and falsity F satisfying. It is employed to illustrate the indefinite data more appropriately and precisely than an intuitionistic fuzzy set. The search for cost information over the supply chain is very significant for controlling costs that aid in enhancing and beginning activities in organizations in the value chain. In today’s intricate supply networks, sharing data among suppliers and buyers is important for sustainable competitive benefit. Particularly, for both business partners, cost information is extremely appropriate in buying conditions. As per experimental analyses in literature, artificial neural networks (ANNs) are probable to have a great latent to expose cost structures by machine learning (ML). This study presents a novel Interpretation of Kernel Regression Neutrosophic Set using Enhanced Coati Optimization for Cost Estimation Model (KRNSECO-CEM). The main goal of the presented KRNSECO-CEM technique is to analyze and interpret the multi-product of Supply Chain Management Systems. At first, the KRNSECO-CEM approach applies Z-score normalization to pre-process the input data. For the regression process, the kernel regression based neutrosophic set (KRNS) model can be used. Eventually, the enhanced coati optimization algorithm (ECOA) has been applied for the fine-tuning of the best hyperparameter of the KRNS model. The experimental evaluation of the KRNSECO-CEM algorithm can be tested on a benchmark dataset. The extensive outcomes highlighted the significant solution of the KRNSECO-CEM approach over other recent approaches

groups

Olga Loseva mail -

Bakhtiyar Ruzmetov mail -

Ildar Begishev mail -

Denis Shakhov mail -

Elena Klochko mail -

Elvir Akhmetshin mail

link https://doi.org/10.54216/IJNS.250421

Volume & Issue

Vol. Volume 25 / Iss. Issue 4

Details open_in_new

Fusion: Practice and Applications | 2025

Integrating Deep Learning Architecture with Pufferfish Optimization Algorithm for Real-Time Deepfake Video Detection and Classification Model

Deepfake is a technology employed in making definite videos, which are operated utilizing an artificial intelligence (AI) model named deep learning (DL). Deepfake videos were normally videos that cover activities grabbed by definite people but with another individual's face. Substitute of people appearances in videos utilizing the DL model. The technology of Deepfake permits humans to operate videos and images utilizing DL. The outcomes from deepfakes are challenging to differentiate utilizing normal vision. It is a combination of the words DL and fake, and it mostly denotes material shaped by deep neural networks (DNNs), which is a subclass of machine learning (ML). Deepfake denotes numerous modifications of face models, and integrates innovative technologies, with computer vision and DL. The detection of a deepfake model can be assumed as a dual classification procedure that can be categorized as the original or deepfake class. It works by removing features from the videos or images that is employed to distinguish between original and deepfake content. Therefore, this study proposes Leveraging Pufferfish Optimization and Deep Belief Network for an Enhanced Deepfake Video Detection (LPODBN-EDVD) technique. The LPODBN-EDVD technique intends to detect fake videos utilizing the DL model. In the presented LPODBN-EDVD technique, the data preprocessing stages include splitting the video into frames, face detection, and face cropping. For the process of feature extraction, the EfficientNet model is exploited. Besides, the deep belief network (DBN) classifier can be executed for deepfake video detection. Finally, the pufferfish optimization algorithm (POA) is employed for the optimal hyperparameter selection of the DBN classifier. A wide range of simulations was involved in exhibiting the promising results of the LPODBN-EDVD method. The experimental analysis pointed out the enhanced performance of the LPODBN-EDVD technique compared to recent approaches

groups

Sameer Nooh mail

link https://doi.org/10.54216/FPA.180120

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

Practical Applications of Neutrosophic Logic in Enhancing the Accuracy of Economic Forecasting Models and Supporting Decision-Making in Banks

Using three machine knowledge models that utilise Neutrosophic Logic (NL)—Linear Regression, Random Forest, and Gradient Increasing—this study studies the possibilities of refining financial result forecast. The cognitive behind this is that NL recovers the prediction power of these models across dissimilar organisations by accounting for the inherent uncertainty, unpredictability, and lack of sureness in financial numbers. In this study, the models' presentation is evaluated using a variety of financial factors, including interest rates and stock prices. F1 score, recall, correctness, and exactness are some of the metrics used by this drive. When likened to other models, NL with Gradient Cumulative consistently outperforms them in terms of correctness and robustness. You might think of Abu Dhabi Islamic Bank and the National Bank of Bahrain as two such examples. Companies like Emirates Islamic Bank reap some benefits from Chance Forest's combination of cheap computation with precision, but only to a lower degree. Complex datasets used by businesses like Al Rajhi Bank are beyond the capabilities of Linear Reversion, even when combined with NL. By proving that cooperative techniques combined with NL positively reduce financial data volatility, our results lay the groundwork for improved financial forecasting and decision-making. The exercise has demonstrated that NL has great potential to enhance financial prediction models, which could have future applications in investment planning and risk organization.

groups

Khaled A. Hassan Mohmmed mail -

Hiba Awad Alla Ali Hussin mail -

Nadia Bushra Mohammed Ali mail -

Abdelsamie Eltayeb Tayfor mail

link https://doi.org/10.54216/IJNS.250424

Volume & Issue

Vol. Volume 25 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2025

Modeling Extreme Healthcare Costs Using the Neutrosophic Cauchy Distribution

Real data modelling of extreme events, such as rainfall, temperature, financial costs is very important in neutrosophic statistical methods. The Cauchy distribution is one of statistical models used for modelling such extreme events in natural processes. In cases of imprecise data which most often involve vague, incomplete and ambiguous information, standard statistical methods cannot fully describe the spectrum of uncertainty. In this study, we have considered a new Cauchy distribution under neutrosophic context to deal with uncertain data. The proposed neutrosophic Cauchy distribution (NCD) may analysis extreme events data involving incomplete observations. We provide basic mathematical characteristics and important statistical functions of the Cauchy model under neutrosophic framework. A complete procedure of random numbers generation using neutrosophic quantile function is discussed. The unknown parameters of the proposed are estimated using the maximum likelihood approach. Numerical results show that the proposed model adequately fits the data involving extreme and imprecise values. The performance and flexibility of the model are also supported by an application to a real data set.

groups

Afrah Al Bossly mail -

Adnan Amin mail

link https://doi.org/10.54216/IJNS.250422

Volume & Issue

Vol. Volume 25 / Iss. Issue 4

Details open_in_new