An Empirical Evaluation of the Stock Market Using Fuzzy Variant Black and Scholes Model Involving Central Fuzzy Measures

K.; Pavithra; S.; Prabakaran

doi:https://doi.org/10.54216/IJNS.270224

International Journal of Neutrosophic Science | 2026

Navigating Bipolar Indeterminacy: Bipolar IndetermSoft Sets and Bipolar IndetermHyperSoft Sets for Knowledge Representation

A variety of mathematical frameworks—such as fuzzy sets, intuitionistic fuzzy sets, neutrosophic sets, soft sets, rough sets, and plithogenic sets—have been developed to model uncertainty, with wide applications in decision making, data analysis, and artificial intelligence. Within soft set theory, extensions like hypersoft sets, indeterm-soft sets, indeterm-hypersoft sets, bipolar soft sets, and bipolar hypersoft sets have further enhanced its expressive power. In this paper, we introduce two new constructs: bipolar indeterm-soft sets and bipolar indeterm-hypersoft sets. We provide their formal definitions, establish key algebraic properties, and demonstrate how they naturally combine bipolar evaluation with inherent indeterminacy. These models offer a versatile toolkit for capturing complex forms of uncertainty and lay the groundwork for future theoretical advances and practical applications in soft set theory.

groups

Takaaki Fujita mail -

Florentin Smarandache mail

link https://doi.org/10.54216/IJNS.270218

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

Neutrosophic Approach in Route-Optimization of Traveling Salesman Problems

The Travelling Salesman Problem (TSP) possesses a significant challenge in optimization, complicated by real-world uncertainties such as fluctuating traffic conditions, weather variability and inconsistent travel durations. Traditional mathematical formulation fails to adequately incorporate these uncertainties, thus limiting their effectiveness. This paper introduces a modified approach to solving the TSP by employing Single-Valued Triangular Neutrosophic Sets (SVTNS), which effectively manages the indeterminate and ambiguous data. The proposed methodology to transform the neutrosophic fuzzy data into crisp numbers using a specifically modified score function. A stepwise procedure is introduced, encompassing crisp conversion, range evaluation and iterative optimization processes to attain an optimal and practically viable solution. The proposed methodology is validated through numerical computation to demonstrate its efficiency in determining the minimal crisp travelling costs and optimizing travelling schedules under the various weighting scenarios. This research advances the applicability of neutrosophic sets in decision-making to provide a reliable framework to address the uncertainties inherent in practical travelling Salesman issues.

groups

Udit Sharma mail -

Tarun Kumar mail -

Jahnvi mail -

Kailash Dhanuk mail -

M. K. Sharma mail

link https://doi.org/10.54216/IJNS.270219

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2026

Vector Search in Large Language Models: Experimental Evaluation with MongoDB Atlas

The growth of Large Language Models (LLMs) applications has intensified the demand for efficient vector database solutions capable of handling high-dimensional semantic search operations. Contemporary information retrieval systems face significant challenges in processing complex queries across vast knowledge repositories while maintaining contextual accuracy and computational efficiency. This research investigates the optimization potential of vector search implementations in LLMs through comprehensive evaluation using MongoDB Atlas as the primary vector database platform. Traditional keyword-based retrieval methods fail to capture semantic relationships and contextual nuances essential for accurate information extraction in modern AI applications. Vector-based query optimization enables semantic similarity matching, allowing systems to access contextually relevant data or information even when exact keyword matches are absent. But it significantly improving response quality and user experience. The study addresses critical performance bottlenecks in production-scale vector search deployments, where query latency and retrieval accuracy directly impact system usability. Through systematic comparison of traditional text-embedding-ada-002 against the advanced text-embedding-3-small model, we demonstrate substantial performance enhancements across multiple evaluation metrics. Results establish text-embedding-3-small as superior for semantic search applications, while GPT-4o-mini demonstrates optimal faithfulness performance (0.9067) for accuracy-critical deployments.

groups

Deepak mail -

Savita Sheoran mail

link https://doi.org/10.54216/JCIM.170211

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

Separation Axioms in Neutrosophic Bipolar Fuzzy Topological Space

The purpose of this research is to introduce the notion of neutrosophic bipolar Ti – spaces (i = 0, 1, 2, 3, 4) via neutrosophic bipolar fuzzy topological spaces, and investigate their different properties. By defining neutrosophic bipolar Ti – spaces (i = 0, 1, 2, 3, 4), some interesting results on neutrosophic bipolar separation axioms via neutrosophic bipolar fuzzy topological spaces are proved.

groups

S. Gowri mail -

V. M. Vijayalakshmi mail

link https://doi.org/10.54216/IJNS.270220

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

Neutrosophic Prediction of Consumer Decisions Using the RBF Neural Network Method

The utilization of neutrosophic concept to forecast patron purchase conduct has been thoroughly tested in preceding research using various fashions. This study examines the number one elements affecting clients' selections to shop for mobile phones, dividing them into 4 separate ranges consistent with their purchasing behaviours. The tiers, from the first to the fourth layer, characterize exclusive ranges of customer hobby and participation. The main intention is to create an efficient neutrosophic predictive version that examines purchaser conduct thru pertinent traits that signify their opportunity of buying. We utilize the Neutrosophic Radial Basis Function (NRBF) model for neutrosophic class to do that. The results indicate a minimal blunders fee and improved neutrosophic category accuracy, mainly in contrast to the BIC version, which exhibited lower accuracy. NRBF exhibited a sturdy location below the curve (AUC) rating, underscoring the model's efficacy. These findings provide big insights into consumer preferences and decision-making methods, enhancing procedures for market analysis and cantered advertising initiatives.

groups

Omar Fawzi Salih Al-Rawi mail -

Ahmed Naziyah alkhateeb mail -

Siti Salwani Yaacob mail

link https://doi.org/10.54216/IJNS.270221

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

DNA Sequence Identification via Biologically Guided Feature Engineering and Hybrid ML–LSTM Networks

The promoter is the part of DNA, which is responsible of initiating RNA polymerase transcription of a gene. The location of this part of DNA is upstream the transcription start site. According to researches, the genetic promotors contribute majorly in many human diseases such as cancer, diabetes and Huntington’s disease. Therefore, promotor detection corresponds as a very crucial task. In this study, a hypered detection system, which integrates biologically developed feature extraction with traditional machine learning (ML) algorithms in addition to use Long Short-Term Memory (LSTM) network as a deep learning approach, has been proposed. The dataset used includes 106 nucleotide sequences. Results obtained from the study show that the perfect performance across all metrics (accuracy, sensitivity, specificity, precision, and F1-score) has been achieved when Naive Bayes used as a classifier, which reach 100% and AUC=1.The confusion matrix analyses and ROC curve confirm that LSTM model achieved 100% training accuracy and 84.38% test accuracy. The architecture and performance of the proposed model make it applicable in IoT-based intelligent genomic and healthcare systems, which enabling real-time and remote promoter detection.

groups

Marwa Mawfaq Mohamedsheet Al-Hatab mail -

Maysaloon abed qasim mail -

Sinan S. Mohammed Sheet mail

link https://doi.org/10.54216/JISIoT.180222

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Network-Aware Vehicle Detection and Tracking Using Hybrid Deep Learning and Simulated GPS in UAV Systems

The proposed study analyses a hybrid deep learning method to monitor a vehicle with drones with augmented simulated GPS data to increase awareness and localization accuracy. The system combines both the high detection speed of a real-time YOLOv5 with the high recognition accuracy of task-driven Faster R-CNN, which makes the performance of the system quite balanced, fully applicable to the application of aerial surveillance enforcement. The results will mimic realistic monitoring conditions since synthetic aerial scenes were produced in which vehicle density is randomly distributed and simulated geolocation data. Both models were applied in the processing of each scene and the resultant images were combined by a voting scheme. The hybrid system had an accuracy of 1.00, recalls 0.90, and F1 score of 0.95- it performed higher than the Faster R-CNN alone (F1 score:0.89) and higher in different conditions. The novelty of the proposed research is based on the fact that the invention combines the methods of dual-modality object detection (visual + spatial) and the use of a GPS base, which allows not only visual object detection but also object positioning. As opposed to the approaches previously used, based on single-modality models and without consideration of the data on geolocation, the framework achieves the integration of object recognition and useful mapping. The suggested system is lighttrack, economically feasible, and it is conveniently deployable to present scalable real-time traffic tracking, smart city planning, and aerial autonomy surveillance.

groups

Mohanad Ali Meteab Al-Obaidi mail -

Shajan Mohammed Mahdi mail -

Mustafa R. Al-Saadi mail -

Yasmin Makki Mohialden mail -

Saba Abdulbaqi Salman mail

link https://doi.org/10.54216/JISIoT.180223

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

2D-DCT and Quantization Accelerator for video codecs on MPSoC FPGA using OpenCL framework and Neutrosophy

Video codecs based on lossy compression techniques take advantage of removing redundant data in spatial and frequency domains. The various modes of intra- and inter-predictions help to reduce the redundant information in the spatial domain in standard video codecs like AVC, HEVC, and VVC. Further, the removal of redundant information in the frequency domain is achieved by adaptive quantization of transformed frames obtained after DCT-II or DST transformation techniques. In traditional video codec standards, adaptive quantization matrices are derived using the Human Visual System (HVS) model and display resolution parameters, which adjust the quantization step size to preserve perceptually significant pixel information in transformed blocks. The Neutrosophic (NS)-based approach introduces a more refined mechanism for generating the quantization matrix, utilizing Neutrosophic set membership values (true, indeterminate, and false) assigned to each region or frequency component of the transformed block. These values reflect the certainty of pixel relevance, enabling a more adaptive, perceptually driven quantization process. The proposed method incorporates NS logic in combination with the Human Visual System (HVS) model and display resolution parameters. By blending these factors, the quantization step size is optimally tuned to enhance visual quality. The HLS implementation of the transformation and quantization technique suitable for video codec acceleration using the OpenCL framework is adopted in our work. The design was implemented and tested on the Xilinx ZCU-104 board using a standard test sequence from the JCTVC and UVG datasets of various resolutions and diversified content. The testing showed an optimized resource utilization of 60.36%, with notable metrics indicating perceptually good results. The objective metrics showed an improvement of 3.77% in PSNR and 1.83% in SSIM compared to standard HVS-based quantization.

groups

Sumalatha S. mail -

Rajeswari mail

link https://doi.org/10.54216/IJNS.270222

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

Novel Approach to Solve a Neutrosophic Transportation Problem

The transportation problem is a linear programming challenge focused on allocating resources efficiently across multiple locations while minimizing costs. Widely used in operations research, the transportation problem has numerous practical applications. Traditional approaches often struggle with imprecise data, which membership grades and fuzzy set theory can be used to address. Fuzzy sets concept provides a valuable framework for analysing transportation models under uncertainty. Neutrosophic sets have gained significant attention as a powerful tool for handling incomplete, ambiguous, and inconsistent data. Their ability to manage indeterminacy has made them increasingly popular in decision-making research, leading to extensive studies on their applications. This paper explores the use of imprecise parameters to improve transportation problem solution methods, emphasizing the versatility and advancements of neutrosophic sets. While various techniques exist for interpreting neutrosophic sets, certain limitations and field-specific requirements persist. In this study, trapezoidal fuzzy neutrosophic numbers make up fundamental components with respect to transportation problem. The proposed mathematical operations, algorithmic process, and framework achieve a 95% confidence level in clarifying uncertainties compared to the results with other methods. The effectiveness has been demonstrated with a numerical example for this approach, with comparisons to existing methods highlighting its advantages.

groups

Melita Vinoliah E. mail -

Krishnaveni G. mail -

Balaganesan M. mail -

Sudha G. mail -

Chiranjibe Jana mail -

Nikola Ivković mail

link https://doi.org/10.54216/IJNS.270223

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026