Synergistic Navigation Control for Mobile Robots: Integrating Type-2 Fuzzy Logic and Neural Networks.

International Journal of Neutrosophic Science | 2024

Enhanced Academic Stress-Coping Skills Assessment in College Students: A Comparative Study of Neutrosophic Distance Measure and Proposed Cubic Pythagorean Fuzzy Hypersoft TOPSIS Method

In multi-criteria decision-making scenarios involving real numbers, interval numbers, and a combination of membership and non-membership grades, accurate decision-making is crucial yet challenging. The integration of diverse grade values into a single value poses a significant challenge for decision-makers. To address this issue, this study introduces the concept of a cubic Pythagorean fuzzy hypersoft set, facilitating information aggregation without ambiguity. The characteristics of correlation coefficients and aggregation operators are emphasized, underscoring their importance in decision-making processes. An algorithm based on correlation coefficients (CC) is proposed for the TOPSIS method, which ranks preferences based on their similarity to the ideal solution, applied here to examine how college students cope with academic stress. Furthermore, the efficiency of the proposed method is demonstrated through a comparative study, wherein the correlation coefficient in the TOPSIS method is contrasted with existing distance measures (DMs). Results indicate the superiority of CC in the TOPSIS method over DMs. In addition to comparing the proposed method with existing distance measures, the efficacy of the proposed approach is further demonstrated through a comparative analysis with established neutrosophic distance measures. This comprehensive evaluation highlights the robustness and versatility of the proposed method in addressing the complexities of multi-criteria decision-making scenarios, particularly in assessing stress management strategies among college students, thus providing valuable contributions to decision-making contexts. This study contributes to enhancing decision-making processes, particularly in evaluating stress management strategies among college students, thereby offering valuable insights for academic contexts.

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E. Prabu mail -

M. Gopala Krishnan mail -

A. Bobin mail

link https://doi.org/10.54216/IJNS.230405

Volume & Issue

Vol. Volume 23 / Iss. Issue 4

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International Journal of Neutrosophic Science | 2024

The Box and Muller Technique for Generating Neutrosophic Random Variables Follow a Normal Distribution

The focus of operations research is the existence of a problem that requires making an appropriate decision that helps reduce risk and achieves a good level of performance. Operations research methods depend on formulating realistic issues through mathematical models consisting of a goal function and constraints, and the optimal solution is the ideal decision, despite the multiplicity of these methods. However, we encounter many complex issues that cannot be represented mathematically, or many issues that cannot be studied directly. Here comes the importance of the simulation process in all branches of science, as it depends on applying the study to systems similar to real systems and then projecting this. The results if they fit on the real system. So simulation is the process of building, testing, and running models that simulate complex phenomena or systems using specific mathematical models. The simulation process depends on generating a series of random numbers subject to a regular probability distribution in the field [0, 1], and then converting these random numbers into random variables subject to the distribution law. Probability, according to which the system to be simulated operates, using appropriate techniques for both the probability density function and the cumulative distribution function. Classical studies have provided many techniques that are used during the simulation process, and to keep pace with the great scientific development witnessed by our contemporary world, we found that a new vision must be presented for this. Techniques A vision based on the concepts of neutrosophics, the science founded by the American mathematical philosopher Florentin Smarandache. The year 1995, in which new concepts of probabilities and probability distributions are used, as we presented in previous research some techniques from a neutrosophic perspective, and as an extension of what we presented previously, we present in this research a neutrosophic vision of the Box and Muller technique used to generate random variables that follow a normal distribution.

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Maissam Jdid mail -

Florentin Smarandache mail

link https://doi.org/10.54216/IJNS.230406

Volume & Issue

Vol. Volume 23 / Iss. Issue 4

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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.

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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

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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

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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.

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Ahmad Raza Khan mail -

Abdul Khader Jilani mail

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

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

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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.

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Monalin Pal mail -

Rubini P. mail

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

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

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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.

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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

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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.

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B. Shanthini mail -

N. Subalakshmi mail

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

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

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International Journal of Neutrosophic Science |

SOME NEW RESULTS ON DIFFERENCE PARACOMPACT NOTION IN TOPOLOGICAL SPACES

An interesting area of research in topology is D-paracompact spaces. It is a significant type of topological space that retain compactness while benefiting from paracompactness and is considered as generalization of compact spaces. The concept of D-paracompactness was introduced and its basic characteristics were examined by the author in [17]. In this research, we introduce and improve this concept further by using a special type of covering and the difference sets (called as D-sets), which contains new and impactful properties. As a result, we obtained several new properties and results. We discuss the concept, characteristics, and theorems that related of D-paracompact space. We also studied different characterizations of D-paracompact spaces and discussed how they relate to other topological characteristics. We also give numerous instances of D-paracompact spaces, highlighting their applicability in different topological spaces

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Fusion: Practice and Applications |

Synergistic Navigation Control for Mobile Robots: Integrating Type-2 Fuzzy Logic and Neural Networks.

Abstract: Intelligent mobile robots operate in environments characterized by various uncertain-ties, necessitating effective navigation strategies to accomplish tasks such as path tracking and obstacle avoidance. This research employs a omni drive mobile robot to autonomously reach predefined targets in diverse scenarios within static and dynamic environments. The study evaluates two distinct controllers, a fuzzy logic controller and a neural network controller, em-ployed to guide the mobile robot safely towards its destination while mitigating collision risks with obstacles. These controllers regulate the mobile robot linear and angular velocities, ensuring adaptive navigation in real-time. Experimental results underscore the efficacy and adaptability of each controller, particularly in addressing uncertainty challenges inherent in mobile robot nav-igation. Through systematic evaluation and comparison, insights are gained into the relative performance and suitability of fuzzy logic and neural network controllers in enhancing mobile robot autonomy and robustness. This research contributes to advancing the understanding of navigation techniques in mobile robotics, facilitating the development of more efficient and re-liable autonomous systems for real-world applications.

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