Stable Neutrosophic Crisp Topological Space

Doaa Nihad; L. A. A.

doi:https://doi.org/10.54216/IJNS.230411

International Journal of Neutrosophic Science | 2024

Group decision-making based on distance measures settings for single-valued neutrosophic fuzzy soft expert environment

A soft expert set is a concept that combines elements of soft sets and expert systems. It aims to incorporate expert knowledge and uncertainty-handling capabilities into the analysis and decision-making processes. On the other hand, the idea of single neutrosophic sets (SVNSs) and fuzzy sets (FSs) are imported models for handling the uncertainty data. In this work, the authors combine the critical features of FSs and SVNSs under expert systems in one model. Accordingly, this model worked to provide decision-makers with more flexibility in the process of interpreting uncertain information. From a scientific point of view, the process of evaluating this high-performance SVNFSES disappears. Therefore, in this paper, we initiated a new approach known as single-valued neutrosophic fuzzy soft expert sets (SVNFSESs) as a new development in a fuzzy soft computing environment. We investigate some fundamental operations on SVNFSESS along with their basic properties. Also, we investigate AND and OR operations between two SVNFSESS as well as several numerical examples to clarify the above fundamental operations. Finally, we have given distance measures (DM) between two SVNFSESs to construct a new algorithm that is used to demonstrate the effectiveness of the method in handling some real-life applications.

groups

Faisal Al-Sharqi mail -

Ashraf Al-Quran mail -

Agaeb Mahal Alanzi mail -

Hamiden Abd El- Wahed Khalifa mail -

Rawan A. shlaka mail -

Ali Mohammad A. Bany Awad mail -

Heba Ghareb Gomaa mail

link https://doi.org/10.54216/IJNS.230407

Volume & Issue

Vol. Volume 23 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2024

Neutrosophic Logic Empowered Machine Learning Algorithm with Salp Swarm Optimization for Biomedical Image Analysis

Leukemia recognition and classification contain the identification of dissimilar kinds of leukemia, a group of blood cancers that affects the bone marrow and blood. A classical model containing microscopic analysis of blood smears to classify abnormal cells analytic of leukemia. Leukemia recognition employing a united technique of neutrosophic logic and deep learning (DL) signifies a new and complete approach to handling uncertainty and difficulty in medical data. Neutrosophic logic permits the representation of unstated or imperfect data, which is general in medical analyses. DL mainly convolutional neural networks (CNN) or recurrent neural networks (RNN), which can mechanically remove difficult patterns from medicinal imageries, improving the accuracy of leukemia recognition. The neutrosophic logic module accommodates the characteristic uncertainty in medicinal data, offering a formalism to manage imperfect or inaccurate data linked with the analysis procedure. The combination of these dual techniques generates a robust structure which capable of leveraging both the control of DL in image analysis and the flexibility of neutrosophic logic in dealing with uncertainties, contributing to more trustworthy and interpretable leukemia recognition methods.  This study develops a new Salp Swarm Algorithm with a Neutrosophic Logic SVM (SSA-NSVM) model for Leukemia Detection and Classification. The SSA-NSVM technique mainly exploits Neutrosophic Logic (NL) concepts with the DL model for the detection of leukemia. To attain this, the SSA-NSVM model uses bilateral filtering (BF) based image pre-processing. In addition, the SSA-NSVM approach applies a modified densely connected networks (DenseNet) technique for learning complex and intrinsic feature patterns. Besides, the hyperparameter range of the modified DenseNet system takes place utilizing a SSA. At last, the NSVM technique is employed for the detection and identification of leukemia. The performance validation of the SSA-NSVM algorithm is verified utilizing a benchmark medicinal image dataset. The simulation values emphasized that the SSA-NSVM model reaches better detection outcomes than other existing approaches.

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Adwan A. Alanazi mail -

Abdelgalal O. I. Abaker mail -

Sayed Abdel-Khalek mail -

Fahad Mohammed Alhomayani mail -

M. Aripov mail

link https://doi.org/10.54216/IJNS.230408

Volume & Issue

Vol. Volume 23 / Iss. Issue 4

Details open_in_new

Fusion: Practice and Applications | 2024

Privacy-Enhanced Heart Disease Prediction in Cloud-Based Healthcare Systems: A Deep Learning Approach with Blockchain-Based Transmission

The increasing adoption of cloud computing in healthcare presents immense opportunities for disease prediction, while raising critical privacy concerns. This study proposes a novel privacy-preserving scheme that leverages advanced cryptographic techniques, blockchain technology and deep learning approach within a cloud platform, to ensure secure data handling and accurate disease prediction. The proposed methodology encompasses authentication, encryption, blockchain-based transmission, and a deep learning-based heart disease prediction system (HDPS). Through rigorous authentication protocols and two-level security mechanisms, patient data is securely encrypted using RSA and Blowfish encryption before storage in the cloud. Blockchain technology facilitates secure data transmission, ensuring integrity and traceability. At the receiver end, data decryption precedes input into the HDPS, comprising artificial neural networks (ANN), convolutional neural networks (CNN), and recurrent neural networks (RNN). The HDPS incorporates data preprocessing, feature extraction, feature selection, and a deep learning-based prediction model, achieving remarkable accuracy (0.9941) in heart disease prediction. Implemented in MATLAB, this approach offers a robust framework for privacy-preserving heart disease prediction in cloud-based healthcare systems.

groups

Ahmad Raza Khan mail -

Abdul Khader Jilani mail

link https://doi.org/10.54216/FPA.150109

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

An Intelligent Fusion-based Behavioral Trait Prediction for Autistic Spectrum Disorder with Artificial Intelligence

Autism spectrum disorder (ASD) is a neurological and developmental condition impacting individuals' interactions with others, communication, learning, and behavior. While autism can be identified at any point in life, it is characterized as a "developmental disorder" due to the typical onset of symptoms within the initial two years of life. As individuals with ASD transition from childhood to adolescence and young adulthood, they might face challenges in establishing and having friendships, communicating with both peers and adults, and understanding the expected behaviors in education or work. The current study introduces a novel approach for suggesting the right behavioral strategy to assist Autistic Spectrum Disorder with the help of supervised BERT (Bidirectional Encoder Representations from Transformers). Our model achieved an accuracy of 88% with the help of BERT to predict the right behavioral trait. This research demonstrates cost-effectiveness and efficiency in offering recommendations for ASD, making it suitable for applications requiring near real-time outcomes.

groups

Monalin Pal mail -

Rubini P. mail

link https://doi.org/10.54216/FPA.150110

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Prospects for Applied Mathematics and Data Analysis | 2023

Studying the isotherm of the complementary Schaefer Ignaczak thermodynamical process of the first plane state of elastic strains for the unbounded micropolar body-Fourier Schaefer-Ignaczak formulas

This paper concerns the Ignaczak stress-temperature distribution [2] of the homogenous isotropic 2D micropolar thermodynamical in the first plane state of elastic strain, which discussed by Eringen [9] and Nowacki [8]. In [1] we provide this problem with new analytical method called Schaefer-Ignaczak method. In the paper, we do the following; We prove that the complementary Schaefer-Ignaczak process is an isothermal process for infinite 2D (E-N:5) [6,8], with no stresses and temperature at infinity, and then we find the related Fourier Schaefer-Ignaczak formulas [1] for the classical and complementary behavior of a two-dimensional infinite body (E-N:5), which is a micropolar body.

groups

Kheder Manhal Al-Saleh mail -

Mountajab Al-Hasan mail -

Monir Makhlouf mail

link https://doi.org/10.54216/PAMDA.030101

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

Detecting Positive and Negative Deviations in Cross-Domain Product Reviews using Adaptive Stochastic Deep Networks

The analysis of sentiment in product reviews across diverse platforms such as e-commerce website and social media presents a challenging task due to the inherent differences in user behaviour and review formats. This research introduces an innovative methodology for detecting positive and negative deviations in cross-domain product reviews using Adaptive Stochastic Deep Networks (ASDN) tailored for multi-platform sentiment analysis. ASDNs possess mechanisms that enable dynamic adaptation to changes in data distributions, domain shifts, or varying complexities within the input data. The proposed framework aims to capture refined variations in sentiment expression across disparate platforms by incorporating adaptive stochasticity within deep neural networks. By adapting dynamically to changes in review styles, language use, and sentiment patterns unique to each platform, the ASDN architecture facilitates the identification of nuanced sentiment shifts. Through extensive experimentation on comprehensive datasets spanning Amazon, Facebook, and Instagram, the efficacy of the ASDN model in detecting positive and negative sentiment deviations across diverse platforms is demonstrated. This research contributes to advancing the understanding of sentiment dynamics across distinct social platforms and e-commerce sites, paving the way for more robust and adaptable models in cross-domain sentiment analysis.

groups

B. Shanthini mail -

N. Subalakshmi mail

link https://doi.org/10.54216/FPA.150111

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

Improving Shape Transformations for RGB Cameras Using Photometric Stereo

The emergence of low-cost red, green, and blue (RGB) cameras has significantly impacted various computer vision tasks. However, these cameras often produce depth maps with limited object details, noise, and missing information. These limitations can adversely affect the quality of 3D reconstruction and the accuracy of camera trajectory estimation. Additionally, existing depth refinement methods struggle to distinguish shape from complex albedo, leading to visible artifacts in the refined depth maps. In this paper, we address these challenges by proposing two novel methods based on the theory of photometric stereo. The first method, the RGB ratio model, tackles the nonlinearity problem present in previous approaches and provides a closed-form solution. The second method, the robust multi-light model, overcomes the limitations of existing depth refinement methods by accurately estimating shape from imperfect depth data without relying on regularization. Furthermore, we demonstrate the effectiveness of combining these methods with image super-resolution to obtain high-quality, high-resolution depth maps. Through quantitative and qualitative experiments, we validate the robustness and effectiveness of our techniques in improving shape transformations for RGB cameras.

groups

H. I. Wahhab mail -

A. N. Alanssari mail -

Ahmed L. Khalaf mail -

Ravi Sekhar mail -

Pritesh Shah mail -

Jamal F. Tawfeq mail

link https://doi.org/10.54216/FPA.150112

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024

Extending the concepts of complex interval valued neutrosophic subbisemiring of bisemiring

The objective of this paper is to investigate the innovative concept of complex neutrosophic subbisemiring. The novelty of the complex neutrosophic subbisemiring lies in its wide range of truth, indeterminacy, and false function values. It goes beyond the range of [0,1] in the complex plane in contrast to the traditional range [0,1]. Therefore, these three functions can be described mathematically using a complex number in the complex neutrosophic subbisemiring. We develop and analyze the concept of complex interval-valued neutrosophic subbisemiring (CIVNSBS). Moreover, we study homomorphic characteristics and important properties of CIVNSBS. We propose the level sets of CIVNSBS and complex interval valued neutrosophic normal  subbisemiring (CIVNNSBS) of bisemirings. Moreover, we introduce CIVNNSBS of bisemiring. Let ¡ be a complex neutrosophic subset of bisemiring S. Then is a CIVNSBS of S if and only if all non empty level set  is a subbisemiring, where . Let ¡be a CIVNSBS of bisemiring S and V be the strongest complex neutrosophic relation of bisemiring S. Then  ¡ is a CIVNSBS of bisemiring S if and only if V is a CIVNSBS of . We illustrate that homomorphic images of every CIVNSBS is a CIVNSBS and homomorphic pre-images of every CIVNSBS is a CIVNSBS. Examples are provided to illustrate our results.

groups

M. Palanikumar mail -

Nasreen Kausar mail -

Emre Ozbilge mail -

Ebru Ozbilge mail

link https://doi.org/10.54216/IJNS.230409

Volume & Issue

Vol. Volume 23 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2024

Selection process real-life application for new type complex neutrosophic sets using various aggregation operators

A new approach to multiple attribute decision-making (MADM) is presented in this article, which is based on (ȷ1, ȷ2, ȷ3) complex neutrosophic sets (CNS). We are extending the CNS in this way. Complex neutrosophic weighted averaging (CNWA), complex neutrosophic weighted geometric (CNWG), complex generalized neutrosophic weighted averaging (CGNWA), and complex generalized neutrosophic weighted geometric (CGNWG). An algorithm utilizing these operators was presented during our discussion. Extensive score and accuracy values are illustrated numerically. We will also discuss idempotency, boundedness, commutativity, and monotonicity of complex neutrosophic sets as part of this communication. You can find the best option faster, easier, and more conveniently with them. Therefore, complex (ȷ1, ȷ2, ȷ3) is more closely associated with more precise conclusions. A fascinating and intriguing finding was also revealed by the study.

groups

Ibraheem Abu Falahah mail -

Abdallah Al-Husban mail -

Lejo J. Manavalan mail -

Jamil J. Jaber mail -

M. Palanikumar mail -

G. Balaji mail

link https://doi.org/10.54216/IJNS.230410

Volume & Issue

Vol. Volume 23 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2024