Some Special Types of Neutrosophic Domains

Shawqi; Adel

doi:https://doi.org/10.54216/PAMDA.050103

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.

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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

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

This article defines the central tendency fuzzy measures, which include the weighted fuzzy possiblistic mean and the fuzzy probability mean involving octagonal fuzzy numbers. The same is supported by a fuzzy variant of the Black-Scholes option model, in which uncertain pricing parameters such as volatility, interest rate, and stock price are described using octagonal fuzzy numbers.

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K. Meenakshi mail -

Pavithra S. mail -

S. Sathish mail -

Prabakaran N. mail

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

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

On Graded Weakly Jgr-classical Prime Submodules

Let 0 be a group, Υ be a 0-graded commutative ring with unity 1 and M a graded Υ-module. Our goal in this paper, introducing the concept of graded weakly Jgr -classical prime submodule as a generalization of graded weakly classical prime submodule and offering several results pertinent of graded weakly Jgr - classical prime submodules. For instance, we give characterizations of graded weakly Jgr -classical prime submodule. Also, we give some restrictions for graded submodule to be a graded weakly Jgr -classical prime submodule. A proper graded submodule V of M is said to be a graded weakly Jgr -classical prime submodule of M if, whenever 0̸ = abx ∈ V where a, b ∈ h(Υ) and x ∈ h(M), then either ax ∈ V + Jgr (M) or bx ∈ V + Jgr (M), The symbol Jgr (M) indicates the graded Jacobson radical of Υ-module M.

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Malak Alnimer mail -

Khaldoun Al-Zoubi mail

link https://doi.org/10.54216/IJNS.270225

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

Interval-Valued Picture Fuzzy Almost Ideals in Semigroups

An interval-valued neutrosophic set is a type of neutrosophic sets where the membership, indeterminacy, and non-membership degrees are represented by closed intervals within the unit interval [0, 1]. An interval-valued picture fuzzy set is one of special cases of interval-valued neutrosophic sets. In this paper, we apply interval- valued picture fuzzy sets on almost ideals of semigroups. Moreover, we study a relationship between each almost ideal in a semigroup and their interval-valued picture fuzzifications.

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Winita Yonthanthum mail -

Anusorn Simuen mail -

Ronnason Chinram mail

link https://doi.org/10.54216/IJNS.270226

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

Solution of Intuitionistic Fuzzy System of Linear Volterra Integro-differential Equations by a Novel Hybrid Method

Our study addresses the intuitionistic fuzzy system of linear Volterra-integro-differential equations of the second kind. Intuitionistic fuzzy General Transform (I-F-G-transform) method has been used to find the exact solution of these systems. We present two numerical examples for illustrating the applicability of the Intuitionistic fuzzy General integral transform method.

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Guelfen hanane mail

link https://doi.org/10.54216/IJNS.270227

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2026

Cryptanalysis In Block Ciphers: A Comprehensive Review and Future Directions

This paper examines the use of cryptography in block ciphers and assesses their security, with a focus on the Advanced Encryption Standards (AES). The study reviews key cryptanalytic techniques, including differential cryptanalysis (8.3%), linear cryptanalysis (4.2%), and integral cryptanalysis (4.2%). They give their share (in percentage) regarding the relative frequency in the cryptanalysis research literature from 2015 to 2024 according to their literature survey. Side-channel attacks showed the highest practical success rates, with some studies showing up to 50.0% effectiveness. Additionally, the study examines more sophisticated attack techniques such as meet-in-the-middle attacks, quantum-related threats, and biclique cryptanalysis (16.0%).The entire round AES is resistant to a wide range of attack techniques thanks to its strong diffusion and confusion mechanisms and reliable key schedule. The study concludes that cryptanalysis is essential for strengthening encryption schemes against emerging threats, particularly those resulting from quantum computing.

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Lama Al-Ghamdi mail -

Mawada Al-Sari mail -

Monir Abdullah mail -

Ghassan Ahmed Ali mail

link https://doi.org/10.54216/FPA.210211

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2026

Development of an Efficient Cluster-Based Hybrid LEACH+TEEN Protocol for Time-Critical WSN Application

Wireless Sensor Networks (WSNs) play a crucial role in monitoring and data collection for various real-time applications, including environmental surveillance, industrial automation, and smart cities. However, achieving energy efficiency and timely data delivery remains a critical challenge, especially in time-sensitive scenarios. This research presents the development of an efficient cluster-based hybrid routing protocol that combines the strengths of Low-Energy Adaptive Clustering Hierarchy (LEACH) and Threshold-sensitive Energy Efficient Network (TEEN) protocols to address these challenges. The proposed Hybrid LEACH-TEEN protocol dynamically adapts to both periodic and event-driven data transmission needs by integrating LEACH’s randomized cluster-head selection and TEEN’s threshold- based data transmission mechanism. This hybrid approach significantly reduces redundant transmissions and optimizes energy consumption across the network. Extensive simulations were conducted to evaluate the protocol’s performance in terms of network lifetime, stability period, energy consumption, and the number of alive nodes over time. Results demonstrate that the Hybrid protocol outperforms traditional LEACH and TEEN protocols, particularly in time- critical applications, by ensuring prompt response to critical events while maintaining energy-efficient operation. This work contributes to the design of intelligent and adaptive routing strategies for next- generation WSNs.

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Basim Jamil Ali mail -

Mohanad Ali Meteab Al-Obaidi mail

link https://doi.org/10.54216/FPA.210212

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

A Systematic Review on Classification Techniques of Microorganisms: Challenges and Recommendations – Towards Medical Intelligent Systems

Microorganisms are commonly found in our daily living environments and play a crucial role in environmental pollution control, disease prevention, and treatment, as well as food and drug production. To fully utilize the diverse functions of microorganisms, their analysis is essential using Intelligent Systems. Traditional analysis methods can be labor- intensive and time-consuming. As a result, image analysis using Intelligent Systems i.e. machine learning or deep learning have been introduced to improve efficiency. Deep learning networks algorithms such as CNN contain a stack of multi-layer, the first layer and the last are the input and output layers, between them are the hidden layers to extract and learn many features in images, recurrent network algorithms (RNN) combined with convolution neural network (CNN), these networks allow to process a series of images to extract the crucial information from images and also these algorithms help to minimize the size of images and reduce the redundancy in microrganisms images According to previous studies, these algorithms are the most used to classify the images of microorganisms. However, the classification of microorganism images presents several challenges these include the need for robust algorithms due to varying application contexts, the presence of insignificant features, along various analysis tasks that need to be addressed. The research summarizes significant advancements that tackle these challenges through deep learning and machine learning methods. Current obstacles, gaps in knowledge, unresolved issues, limitations, and difficulties in classification techniques are also discussed.

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Marwa T. Albayati mail -

Mohd Ezanee Bin Rusli mail -

Moamin A. Mahmoud mail -

Aws A. Abdulsahib mail -

Mohammed F. Alomari mail -

Sallar S. Murad mail

link https://doi.org/10.54216/JISIoT.180224

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

Prospects for Applied Mathematics and Data Analysis | 2025