Application of Neutrosophic Stratified Ranked Set Sampling: An Efficient Sampling Technique in the Estimation of Average Relative Humidity in USA

Vishwajeet; Rajesh; Anamika

doi:https://doi.org/10.54216/IJNS.260312

Pure Mathematics for Theoretical Computer Science | 2025

LS-Extending Fuzzy Modules

The main aim of this paper is extend the notion of S-extending fz-modules into LS-extending fz-modules and study this new notion. This lead us introduce and study other notions such as: purely semisimple, purely extending and purely y-extending fz-modules. Moreover, the relationships LS-extending fz-module with the various types.

groups

Hassan K. Marhon mail

link https://doi.org/10.54216/PMTCS.050102

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

A New class Closed Sets in Fuzzy Neutrosophic Topology

The goal of this study is to introduce fuzzy neutrosophic -closed sets, a novel notion of collections in fuzzy neutrosophic topology. In this research, we use certain novel concepts, theories, and hypotheses to explore and analyze other innovative characteristics of these classes. In order to make clear the connections among the new research of -closed sets and other sets, a collection of instances is given and explored.

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Yaseen S. R. mail -

Asmaa Ghasoob Raoof mail -

Shadia Majeed noori mail

link https://doi.org/10.54216/IJNS.260311

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Enhanced Image Encryption through Combined Arnold and Three Other Chaos Techniques

In an era where digital technologies dominate all aspects of life, image encryption has emerged as a fundamental pillar of data protection and securing sensitive information. With the rise of sophisticated cyber threats and attacks, the search for innovative and stronger encryption methods has become an urgent necessity. This research proposes an enhanced image encryption scheme combining the Arnold map, 2D Henon map, memristor elements, and exponential nonlinearity chaos techniques to address vulnerabilities in conventional encryption methods. The hybrid approach ensures robustness against statistical, differential, and brute-force attacks. Experimental results demonstrate superior performance with unified histogram distribution, including near-ideal information entropy (7.99941), infinite peak signal-to-noise ratio (PSNR), and high resistance to differential attacks (NPCR = 99.61%, UACI = 35.08%). A keyspace of  and key sensitivity correlation difference rate (CDR) of 99.61% further validate security. Comparative analysis with recent studies confirms the proposed method’s superiority in encryption strength and computational performance. Consequently, the results of the proposed method making it a promising option for high-security image protection applications.

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Sameeh Abdulghafour Jassim mail -

Alaa Abulqahar Jihad mail -

Mohammed I. Khalaf mail

link https://doi.org/10.54216/JCIM.160206

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

An Integrated Cryptographic Approach Using Elliptic Curve Cryptography, Triple Data Encryption Standard and Hash-based Message Authentication Code

Security of digital communication becomes of prime importance due to the fast growing cybersecurity attacks. Classical encryption algorithms frequently drop down in offering the vital level of security required to safeguard critical information. The advances in cryptography methods are very important to solve this issue and ensure integrity and privacy.  This paper focuses on the weaknesses of the current methods through investigating mixing multiple encryption methods. The research explores whether combining Hash-based Message Authentication Code (HMAC), Elliptic Curve Cryptography (ECC), and Triple Data Encryption Standard (3DES) can provide upgrade to security for end-to-end encryption.  The chief objective is to improve and evaluate a powerful encryption framework that make use the strengths of HMAC, ECC and 3DES. This is done by showing how mixing these algorithms together can improve security and reliability levels to safeguard digital communications. An extensive analysis is performed by using several metrics. These involve ciphering and deciphering speed, key generation, NIST test and Avalanche effect. The results show that these combinations increase significantly security level of digital communication. It shows better performance than traditional cryptography in both security and speed. Combining HMAC, ECC and 3DES provide practical solution to increase security level in end-to- end encryption. It improves the vulnerabilities in traditional cryptography by building multi-layer security framework. It is concluded that the proposed framework is powerful and a candidate for developing and has strong resistance against cyber threats.

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Farah Tawfiq Abdul Hussien mail -

Sura khalid salsal mail

link https://doi.org/10.54216/JCIM.160207

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Denoising and Compressing Color Images Using New Wavelet Efficiency

Compressing color images (such as JPEG 2000) with wavelet transforms that are used in image parsing into approximate and detailed coefficients in the Multi Resolution Analyses (MRA) stage, such as Symlet 2, Coiflet 2, and Daubechies 2. The rapid development that occurs in modern life and the development of technology and artificial intelligence has increased the need to find an advanced and fast technology in image transfer, which requires reducing the space used by very large image data through the compression process that images need during transfer and transmission. Therefore, the need to accomplish this work has been necessitated by finding a new method and purely mathematical methods with equations and transformations that will be performed on Hermite polynomials to obtain the discrete Hermite wavelets (DHWT) to meet the great challenge in the field of images due to the mathematical properties that characterize these waves to be ready to perform the image analysis process known in the field of images (MRA), which is summarized in entering the color image to analyze the color image into two types of coefficients, which are detail coefficients and convergence coefficients due to the high level and low level, respectively, to divide the image into four blocks, which are Low Low, High Low, Low High and High High  to then remove the noise and then compress, A suitable algorithm was created in MATLAB to read the program for this tool as in common waves (Symlet 2, Coiflet 2, and Daubechies 2) to obtain good results with new wavelet. The results obtained and through comparisons with basic wavelet work such as Haar and Daubechies etc. to obtain the values of the most important image quality parameters and the experiment was carried out on a sample of JPEG 2000 The tables in this work show the results that will be obtained that prove the efficiency of the proposed model after calculating the image quality parameters Mean Square Error (MSE), Peak Signal of Noise Ratio (PSNR), Bit Per Pixel (BPP) and Compassion Ratio (CR). 

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Hanaa Mahdi Habeb mail -

Zainab Galib Salman Alrashid mail -

Saad Ismael Ibrahim mail -

Asma A. Abdulrahman mail -

Hassan Mohamed Muhi-Aldeen mail

link https://doi.org/10.54216/JCIM.160208

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Internet of Medical Things Powered by Machine Learning for Real-Time Diabetes Prediction

Diabetes is a common chronic illness that requires ongoing patient monitoring to diagnose the condition in a timely manner. With the significant advancements of the Internet of Medical Things (IoMT) sector in recent years, it is feasible now to monitor the patient's information continuously. There are many studies that used IoMT and machine learning (ML) techniques to diagnose diabetes but so far, the accuracy of the performance is still below the required level. Therefore, this study proposes a common framework for IoMT, cloud, and ML techniques to diagnose diabetes in real-time. IoMT devices continuously collect vital information of diabetic patients such as glucose and insulin levels. Then, this data is transmitted using various communication technologies to be stored in the cloud for diagnosis. Finally, to improve diagnostic accuracy, voting ensemble strategy-based method has been proposed that combines predictions from three base ML techniques (Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF)). The proposed voting model achieved promising results in diagnosing diabetes with an accurate rate of up to 98.0%, outperforming the base classifiers in this and previous studies.

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Qusay Saihood mail -

Inas H Kareem mail -

Omar Ayad Ismael mail -

Saad I. Mohammed mail -

Ahmed NO Algburi mail

link https://doi.org/10.54216/JISIoT.170108

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Brain Tumor Diagnosis Using Pre-Trained Conventional Neural Network Model

Diagnosis of brain tumors from MRI scans is a vital concern in medical imaging that contributes to the&ensp;need for fast and accurate deep learning models. In this study, it is proposed a Hybrid CNN-ViT Feature Extraction framework that utilizes the local&ensp;spatial feature extraction capability of Convolutional Neural Networks (CNNs) and long-range dependency capturing ability of Vision Transformers (ViTs). The method&ensp;starts with a set of advanced preprocessing techniques such as contrast limited adaptive histogram equalization (CLAHE) and data augmentation based on generative adversarial networks (GAN) to help increase image quality and balance the dataset. First,&ensp;trained by a CNN-based backbone is EfficientNet to obtain low- and mid-level spatial features, the hybrid model is proposed. These feature maps are further converted into patches and input to a Vision Transformer &ensp;(ViT) encoder, where self-attention functions to refine global feature representations. The proposed method utilized concatenation and attention-based mechanism for feature fusion, which ensured the&ensp;discriminative classification of features from both CNN and ViT. Finally, a fully connected layer with the softmax classifier predicts the presence of tumor&ensp;and its kind. Extensive experiments have been&ensp;conducted on benchmark brain MRI datasets, which show that the Hybrid CNN-ViT model significantly outperforms traditional CNN-based models and achieves higher accuracy, precision, recall, and F1-score. The study demonstrates&ensp;the successful application of hybrid deep learning techniques for robust and generalizable brain tumor classification. The novelty of this&ensp;research lies in integrating spatial information with context attention in enhancing AI-based medical diagnostics.

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Shokhan M. Al-Barzinji mail -

Mohammed Q. Jawad mail -

Othman Mohammed Jasim mail -

Zaid Sami Mohsen mail -

Omar Falah Al-Jumaili mail

link https://doi.org/10.54216/JISIoT.170109

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

SecureRS-CBIR: A Privacy-Preserving Deep Learning Framework for Content-Based Remote Sensing Image Retrieval

Recent advancements in Remote Sensing (RS) have created challenges in data storage, retrieval, and privacy. Existing Content-Based Image Retrieval (CBIR) systems are useful but often face limitations related to hypersensitivity towards remote sensing data in the cloud, scalability, and security. This article presents SecureRS-CBIR, a privacy-preserving framework for remote sensing image retrieval combining deep learning with multi-level encryption. The system uses three CNN models (VGG16, ResNet50, and DenseNet121) for feature extraction and implements encryption through image division, texture extraction, subblock shuffling, and color encryption. Experiments on the Aerial Image Dataset show VGG16 achieving 96% validation accuracy, with ResNet50 and DenseNet121 at 95% and 94% respectively. DenseNet121 excelled at DenseResidential classification (41/42 correct) with minor confusion between Beach and Desert categories. The framework successfully balances security with retrieval efficiency, maintaining privacy through robust encryption while enabling accurate content-based searches, providing a scalable solution for secure image retrieval in cloud environments. This work offers a new approach for remote sensing image retrieval by enabling efficient searching in large-scale datasets while addressing privacy concerns in cloud environments, thereby contributing to the relevant literature.

groups

Ahmed Sabah Ahmed AL-Jumaili mail -

Huda Kadhim Tayyeh mail

link https://doi.org/10.54216/JISIoT.170110

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

VSG parallel power distribution control strategy by adaptive virtual impedance

As electric power develops, stable distribution of output power has become a key issue, and more and more power distribution strategies have been proposed. However, most of them are single distribution strategies with large errors and low credibility, which makes it difficult to maintain the stability of motor output distribution power in the actual situation. Therefore, by characteristics of adaptive virtual impedance to reduce small signals influence in the circuit and parallel power stability of virtual synchronous machine virtual synchronous generator control strategy, this research establishes a parallel power model of virtual synchronous generator, selects the changes of voltage and current as the measurement standard of the system, and sets up simulation experiments to determine whether to add adaptive virtual impedance to design a control strategy that can stably distribute output power. Results showed that it can keep output ratio of active power and reactive power within range of 2:1, and voltage difference at the output terminal is 0, and the current is 0.8A, which meets the requirements of circulating current. In a word, the control strategy of virtual synchronous generator designed in this research has high accuracy and strong stability. Compared with previous control strategies, the control strategy of parallel power distribution can ensure the stability of output power in the actual situation. This achievement has certain application prospects in the field of motor power distribution.

groups

Jianfeng Wang mail -

Nurulazlina Ramli mail -

Noor Hafizah Abdul Aziz mail

link https://doi.org/10.54216/JISIoT.170111

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

Application of Neutrosophic Stratified Ranked Set Sampling: An Efficient Sampling Technique in the Estimation of Average Relative Humidity in USA

The study examined the shortcomings of conventional statistical techniques in managing unclear or ambiguous data and emphasized the necessity of implementing neutrosophic statistical techniques as a more enhanced remedy. Advanced techniques like neutrosophic statistics (NS) were developed since traditional statistical methods are unable to handle the uncertainty present in ambiguous data. In order to tackle this problem, the study suggested an innovative and novel sampling method called "neutrosophic stratified ranked set sampling (NSRSS)" in addition to specialized neutrosophic estimators for precisely predicting the population mean in the proximity of uncertainty. This novel strategy adjusted ranked set sampling (RSS) techniques to allow the special features of neutrosophic data. Furthermore, the study improved the precision of estimating the population mean in uncertain situations by introducing neutrosophic estimators that use subsidiary information inside the structure of stratified ranked set sampling (SRSS). The work provided theoretical insights into the performance of these estimators by presenting comprehensive formulations of bias and mean squared error (MSE). To illustrate the efficacy of the suggested techniques, the study includes simulation studies, numerical examples conducted using the computer language R. Evaluations utilizing MSE, and percentage relative efficiency (PRE) demonstrated the higher accuracy of the suggested estimators over conventional alternatives. The findings demonstrated the NSRSS's applicability, particularly for predicting population means in situations where heterogeneity and uncertainty are prevalent. Furthermore, it was demonstrated that the estimators and technique produced interval-based findings, which provided a more accurate depiction of the uncertainty related to population parameters. The reliability of the estimators in estimating population means was greatly improved by this interval estimation in combination with a lower MSE. A significant vacuum in the field of statistical research is filled by the study's introduction of estimators and a customized sampling approach made especially for neutrosophic data. This research significantly advances statistical theory and practice by extending traditional statistical approaches to efficiently handle ambiguous data, especially for applications where exact data is few, heterogeneous, or uncertain. The empirical validation through numerical illustrations and simulations conducted in R further solidifies the practicality and robustness of the proposed techniques, reinforcing their applicability to real-world scenarios.

groups

Vishwajeet Singh mail -

Rajesh Singh mail -

Anamika Kumari mail

link https://doi.org/10.54216/IJNS.260312

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new