The Role of Neutrosophic Logic in Enhancing Trust and Reliability in Internet of Things Architectures

Remya P. George; Nazia Ahmad; Rubina Liyakat Khan; Sajithunisa Hussain; Samandarboy Sulaymanov; Ambuj Kumar Agarwal

doi:https://doi.org/10.54216/IJNS.270117

Journal of Intelligent Systems and Internet of Things | 2026

Symptom-Based Detection of COVID‑19 Cases Using Machine Learning Algorithms

Mammals are susceptible to the lethal disease called coronavirus. This virus often infects humans through the aerial precipitation of any fluid released from the bodily part of the affected entity. This viral variant is deadlier than other sudden viruses. Given the ongoing thread which COVID-19 on health systems in the worldwide, there is a rising interest in development a mechanism that effective in terms of cost and classification. A mechanism for categorizing and scrutinizing the estimations derived from this virus' symptoms is proposed in this paper. The precision of various machine-learning classifiers is calculated in this study in order to determine the optimal classifier for COVID-19 identification. Because the COVID-19 dataset has the greatest precision of 100%, it was classified using AdaBoost and Bagging. Additionally, precision, recall, and F-score measures together with the ROC were deployed for evaluating detection performance to ensure the approach is capable and successful.

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Hussein Ibrahim Hussein mail -

Lateef Abd Zaid Qudr mail -

Weal Hasan Ali Almohammed mail

link https://doi.org/10.54216/JISIoT.180126

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2026

Improvement to the Gradient Projection Method Used to Find the Optimal Solution for Neutrosophic Nonlinear Models Constrained by Equality Constraints

A mathematical model consists of decision variables, a goal function, and constraints. The region of possible solutions for a nonlinear mathematical model is the set of vectors whose components satisfy all constraints. The optimal solution is the vector whose components satisfy all constraints, and at which the function reaches an optimal value (maximum or minimum). Nonlinear programming constitutes an important and fundamental part of operations research and is more comprehensive than linear programming. Its applications have spread across all branches of science, including engineering, physics, chemistry, management, economics, and military fields, among others. Nonlinear programming can also be used in forecasting, estimation, applied statistics, and determining the costs resulting from the production, purchase, and storage of goods. Given this importance, and in order to obtain a more accurate solution that takes into account all the changes that the system under study may be exposed to, we have previously presented a neutrosophic study of nonlinear models and some of the methods used to find the optimal solution. In addition to what we have previously done, in a research we present an improvement to the gradient projection method used to find the optimal solution for nonlinear models constrained by equal constraints, enabling us to obtain the optimal solution in fewer steps. We will then apply it to find the solution. Optimization of nonlinear neutrosophic models.

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Maissam Jdid mail -

Florentin Smarandache mail

link https://doi.org/10.54216/IJNS.270116

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2026

Component Reusability Optimization Based on Restructuring Monolithic Code

In modern software engineering, monolithic code structures are increasingly incompatible with the flexibility demanded by today’s platforms. These tightly coupled systems pose challenges for scalability, integration, and secure deployment. This paper presents a method for restructuring monolithic Java classes into optimized, reusable software components. We analyze each class using 19 object-oriented metrics from the CKJM suite, evaluating cohesion and coupling properties. Using our proposed framework—Good Global Optimization Dynamic Weighted Metrics (GGODWM)—we cluster interrelated classes and transform them into high-level components suitable for microservice environments. These components are evaluated within a Component Base Redesign Structure (CBRS) environment to measure reusability. Our experimental results show a 52% improvement in cohesion and coupling balance, outperforming traditional Turbo_MQ-based metrics. By enhancing component modularity and reducing interdependencies, the proposed approach contributes to more secure and maintainable code, thus supporting cybersecurity goals such as reduced attack surface and easier vulnerability management.

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Zeyd Saeed mail -

Mustafa Ismael Khudair mail -

Ahmed Khader Ali Ibrahim mail -

Rahman Nahi Abid mail

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

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

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Fusion: Practice and Applications | 2026

Hybrid Adaptive Swarm Enhanced Vision Transformer for Accurate Corn Leaf Disease Prediction

Early and precise detection of corn leaf diseases is important for maintaining crop yield and quality. This work suggests a new end-to-end system Hybrid Adaptive Swarm-enhanced Vision Transformer (HAS-ViT) to overcome the limitations of current techniques such as poor accuracy, high computational expense, and overfitting and inefficient feature extraction. The suggested framework combines a three-stage pipeline such as segmentation, classification and optimization to overcome the issues. First, Adaptive Gradient Masking with Color Entropy (AGM-CE) is a novel segmentation technique that isolates diseased areas through an integration of local color entropy and gradient energy in the LAB color space. This guarantees accurate area selection and removal of the background. Then, a transformer model is constructed named Vision Transformer with Enhanced Visual Attention (ViT-EVA). It integrates depthwise attention layers as well as lesion-aware region concentration, enhancing separation of disease classes and model simplification. Finally, Adaptive Bio-Inspired Gradient Tuning (ABGT) optimizer integrates the Bat Algorithm, AdamW and gradient sign flipping for effective learning and convergence. The mechanism speeds up convergence, prevents local minima and maintains exploration exploitation trade-offs at training. The performance of proposed work is measured on a corn disease dataset and performs at 98.1% accuracy and 0.12 loss than conventional and current transformer-based models.

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Nilam Sachin Patil mail -

E. Kannan mail

link https://doi.org/10.54216/FPA.210116

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

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Fusion: Practice and Applications | 2026

Automated Rheumatoid Arthritis Diagnosis and Grading with KL-Grading Deepnet-X

Arthritis significantly affects mobility and quality of life due to joint inflammation and dysfunction. Its most common type, rheumatoid arthritis (RA), primarily influences multiple joints and tissues, especially in women aged 30–50. Common symptoms include pain, swelling, and stiffness. The growing prevalence of RA, projected to reach 44 million globally by 2045, underscores the need for advanced diagnostic methods. MRI offers detailed visualization of joint structures, essential for accurate diagnosis. However, current grading systems like OARSI and Kellgren-Lawrence are subjective and prone to variability. This study introduces the KL Grading DeepNetX framework, a deep learning-based model for automated RA grading and classification. The approach integrates image preprocessing and segmentation to extract key features such as joint space narrowing and cartilage thickness. Comparative analysis shows that KL Grading DeepNetX outperforms traditional methods with high precision, sensitivity, specificity, and F1-score. This framework enables earlier, more accurate and efficient detection of arthritis using knee MRI images.

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Govindan Rajesh mail -

Nandagopal Malarvizhi mail

link https://doi.org/10.54216/FPA.210117

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

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Fusion: Practice and Applications | 2026

JPEG-Resistant DCT Steganography for Secure Communication

In this work, the researchers presented an ingenious new way to conceal secret messages within images, a practice called steganography. This technique embedded secret messages within images undetectably. To embed the secret data, it applies a mathematical trick called Discrete Cosine Transform (DCT) that is commonly used to compress image files to hide the secret data in areas of the image that are not too complex or too simple. The algorithm adaptively selected embedding locations based on image texture to the appearance of the image, choosing the most appropriate places to hide the secret and the picture to appear normal. This new method of hiding data is more magical and less detectable than older methods, which modify the smallest details of an image (so-called Least Significant Bit techniques). It examines the patterns of the image such as whether it is smooth or has many details and selects obscure, secure locations to conceal the message. They tried this with 1,000 images, and in each image, they embedded a small message (a paragraph of text). The pictures came out great afterwards with just minor adjustments that most people would not have noticed. 95% of the buried messages could be dragged out flawlessly even after the images had been reduced in size with the JPEG. An artificial intelligence-based high-tech detection tool only detected the hidden data half the time 52%, a significant improvement over the older techniques where it located 85 percent or 65% of the secrets.

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Israa Abdulkadhim Jabbar Al Ali mail -

Zainab A. Abdulazeez mail -

Rawaa.M.aljubouri mail

link https://doi.org/10.54216/FPA.210118

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

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Fusion: Practice and Applications | 2026

Using Deep Learning Strategy to Implement AI Tools Fusion in Academics

The advancement of artificial intelligence (AI) in the field of education system has revolutionized traditional education paradigms. The ability of language models to process human language has revolutionized the field of artificial intelligence. The fusion of deep learning and cognitive science is getting attention in the academic system. The absence of structured policies and lack of AI fusion strategies in academics disrupt traditional teaching classrooms resulting in misuse and resistance in adoption of AI. This marks the importance of preparation of AI policies for effective implementation of AI tools in teaching and learning. This paper highlights the importance of framing the guidelines for organized and practical implementation of AI fusion in academics. This study bridges the gap by developing a standardized framework to transform normal classrooms into dynamic data driven platforms promoting professional development for teachers and empowering students with digital literacy and autonomous learning. The study examines predictive performance using deep learning strategies to extract key features of teaching, learning and cognitive and predicts the impact of AI in sustainable teaching.   The highest importance scores range from 0.89 to 0.94, which indicates the importance of selected key features in models’ predictions. The highest mean score of 4.5 of the model establishes satisfaction of teachers and students with policy objectives. The results of the study indicate that integration of deep learning cognitive strategy along with clear policies framework help in achieving higher adoption and performances rates of AI in sustainable classrooms when compared with traditional teaching strategies with minimal AI-integration.

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Moosa Ahmed Hassan Bait Ali Sulaiman mail -

Anita Venugopal mail

link https://doi.org/10.54216/FPA.210119

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

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Prospects for Applied Mathematics and Data Analysis | 2025

A Brief Study on Fuzzy Off-Group Theory

Uncertainty-handling frameworks such as fuzzy sets, rough sets, intuitionistic fuzzy sets, neutrosophic sets, Picture Fuzzy Sets, hyperneutrosophic sets, and plithogenic sets have attracted sustained research interest. These frameworks have been widely applied across various mathematical disciplines, including graph theory, topology, algebra, and group theory. More recently, the concept of the offset has emerged as a powerful and promising generalization of conventional uncertainty models. In this paper, we introduce a novel algebraic structure called the Fuzzy Off-Group and conduct an in-depth study of its fundamental mathematical properties. We hope that this framework will further advance research in group theory and uncertainty modeling with offsets, and that it will open up new avenues for application.

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Takaaki Fujita mail -

Arif Mehmood Khattak mail -

Arkan A. Ghaib mail

link https://doi.org/10.54216/PAMDA.050101

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

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Prospects for Applied Mathematics and Data Analysis | 2025

On the Properties and Illustrative Examples of Soft SuperHypergraphs and Rough SuperHypergraphs

In graph theory, a hypergraph generalizes a classical graph by allowing each hyperedge to join any number of vertices, thereby modeling relationships beyond simple pairwise connections.[1] A superhypergraph takes this further by applying recursive powerset constructions to its hyperedge set, creating hierarchical and self-referential network layers.[2] A soft graph defines a family of subgraphs parameterized over a fixed universe of vertices and edges, while a rough graph uses lower and upper approximations to capture uncertainty in graph structure. In this paper, we revisit Soft SuperHypergraphs and Rough SuperHypergraphs—originally introduced in [3]—which integrate the flexibility of soft and rough graph frameworks with the layered com- plexity of superhypergraphs. We provide precise definitions, illustrative examples, and a detailed analysis of their fundamental properties, demonstrating their potential for modeling hierarchical and uncertain network systems.

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Takaaki Fujita mail -

Atiqe Ur Rahman mail -

Arkan A. Ghaib mail -

Talal Ali Al-Hawary mail -

Arif Mehmood Khattak mail

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

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2026

The Role of Neutrosophic Logic in Enhancing Trust and Reliability in Internet of Things Architectures

A vast amount of Internet of Things (IoT) devices deployment has created huge issues about trust management and reliability guarantees in heterogeneous, dynamic and often uncertain ecosystems. Available probabilistic or fuzzy-logic-based models do not hold water to deal with indeterminacy and contending data in distributed IoT networks. The current paper proposes a brand new framework to model trust and reliability in IoT systems by implementing Neutrosophic Logic to build quantification and strengthen trust and reliability in IoT systems. Incorporating the semantic understanding of data and node behavior in uncertainty using three dissimilar elements to represent trust: truth, indeterminacy and falsity, the model commands a wider range of semantics in the relationship of data and nodes during the phase of uncertainty. A mathematical solution is established to measure trust scores and reliability indexes based on Neutrosophic membership functions. Further, a new dynamic trust assessment and anomaly detection algorithm is presented based on a multi-layered decision-making process. This simulation and case- study definition shows the effectiveness of the proposed framework in having less false positives, better reliability estimation, and the solid optimization of decision support in a very uncertain environment of IoT. The work therefore further develops the process of Neutrosophic systems integration with IoT and its setting up of basis of more intelligent, context-aware and robust trust management systems.

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Remya P. George mail -

Nazia Ahmad mail -

Rubina Liyakat Khan mail -

Sajithunisa Hussain mail -

Samandarboy Sulaymanov mail -

Ambuj Kumar Agarwal mail

link https://doi.org/10.54216/IJNS.270117

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

Details open_in_new