Fixed Points Results in Algebra Fuzzy Metric Space with an Application to Integral Equations

Raghad I.; Jaafer Hmood; Hussein S.

doi:https://doi.org/10.54216/IJNS.250433

Journal of Intelligent Systems and Internet of Things | 2025

Leveraging Artificial Intelligence for Assessing Metering Faults in Electric Power Systems

Accurate energy metering is essential for reliable power system operation, fair billing, and effective monitoring of electricity consumption. However, detecting faults in electric energy meters remains challenging because conventional inspection practices, including manual testing, operational sampling, and user-reported verification, are time-consuming, labor-intensive, and often limited in dynamic field conditions. This study proposes a deep learning-assisted prediction model (DLPM) for identifying abnormal metering behavior and improving the assessment of energy meter faults in electric power systems. The proposed model learns the relationship between expected and observed meter trajectories, enabling it to detect significant deviations that may indicate measurement errors or operational faults. By automating the analysis of metering discrepancies, the DLPM provides a more consistent and data-driven alternative to traditional fault diagnosis methods. The model supports accurate deviation estimation, improves abnormality recognition, and assists in identifying potential causes of smart meter malfunction. Simulation results demonstrate that the proposed DLPM achieves strong predictive performance, with 99.2% accuracy, 97.8% overall performance, and 98.9% efficiency. In addition, the model records an average consumption deviation of 10.3% and a root mean square error of 11.2%, indicating its effectiveness in supporting intelligent meter fault assessment. These findings suggest that deep learning can enhance the reliability, automation, and diagnostic capability of smart metering systems in modern electric power networks.

groups

Huda W. Ahmed mail -

Asma Khazaal Abdulsahib mail -

Massila Kamalrudin mail -

Mustafa Musa mail

link https://doi.org/10.54216/JISIoT.150207

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Optimizing Traffic Flow and Enhancing Security in Cooperative Intelligent Transportation Systems Using NGSIM

Cooperative Intelligent Transportation Systems (C-ITS) cannot work effectively if they do not have both efficient traffic management and solid security. We put forward in this paper an original framework that takes advantage of the Next Generation Simulation (NGSIM) dataset to improve traffic flow and system security by identifying False Data Injection Attacks (FDIA). By applying leading machine learning algorithms to authentic traffic data, we generate models that support improved vehicle coordination as well as provide assistance with security vulnerabilities in C-ITS systems. We are concentrating our method on the optimization of traffic dynamics by making intelligent decisions, while keeping the system secure from malicious cyber attacks. Analyses of the NGSIM data revealed that our proposed approaches produced important advancements in traffic flow efficiency and the accuracy of anomaly detection. Results prove that our framework minimizes congestion and concurrently enhances the reliability and security of collaborative vehicle systems. This investigation proposes a practical approach for fusing traffic optimization with cybersecurity, improving smart city evolution and the future of autonomous vehicles and vehicle connectivity.

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Sultan Ahmed Almalki mail -

Tami Abdulrahman Alghamdi mail -

Azan Hamad Alkhorem mail

link https://doi.org/10.54216/FPA.180213

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

RBHAP-HLB framework with high data privacy for secured EHR storage

For data security and integrity, the sharing of Electronic Health Records (EHRs) utilizing blockchain is becoming a vital vision. However, blockchain and storage wielded in prevailing studies arises security and scalability issues. To overcome these issues, this paper proposes a novel Quadratic Interpolation-based Brownian Motion-Double Elliptic Curve Cryptography (QI-BM-DECC)-centric EHR securing in Hyper-Ledger Blockchain (HLB) with Inter-Planetary File System (IPFS). Primarily, the patient and doctor are registered on the hospital website; then, the keys and QR codes are generated for the patient. After that, the patient login with the credential details, QR code, and the purpose of login. The patient did the online consultation booking after successful login; then, the consultation is done grounded on the time scheduled by the doctor. Afterward, the patient securely uploads the EHR on the HLB with IPFS utilizing QI-BM-DECC. Meanwhile, an attribute-centric hashed access policy is created with the selected attributes. After that, utilizing the Mean Public keys- Digital Signature Algorithm (MP-DSA) approach, the hashed access policy is signed. When a doctor request for EHR access, the signature is verified and the access request is sent to the patient. Now, the doctor downloads the EHR from IPFS after being accepted by the patient. The experiential outcomes exhibited the proposed technique’s dominance over the other mechanisms.

groups

R. Saranya mail -

A. Murugan mail

link https://doi.org/10.54216/FPA.180214

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

An Efficient Learning Approach to Imbalanced Multinomial Classification

The presented methodology provides an innovative way to answer a question that is rarely observed in academic literature: How can complex data issues like multiple class imbalance be solved using the available models in a simple and efficient way? In this approach, observations are modeled without additional preprocessing. Several classification models including Random Forest (RF), Support Vector Machines (SVM), and Decision Tree (DT) are utilized for conducting the classification analysis. The parameters of these models and the cross-validation function are adjusted to each individual set of observations. This approach has not been researched in depth. We test it about class imbalance in the target variable. Our results demonstrate the benefits of the proposed method.  First, parameter tuning of ML models can be an effective strategy to handle class imbalance. Second, random shuffling prior to cross validation can be a key to resolving the bias coming from multiclass imbalance. Another important finding is that the best results can be achieved when random shuffling, cross validation and parameter tuning are combined. These findings are key to handling class imbalance in classification. Therefore, this research extends the opportunities to handle class imbalance in a simple, quick, and effective way in cases without adding additional complexity to the model.

groups

Ani Petkova mail -

Borislava Toleva mail -

Ivan Ivanov mail

link https://doi.org/10.54216/FPA.180215

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Survey of Research Opportunities that use Artificial Intelligence in Image Steganography

Steganography conceals ”secrets” within an convenient and expedient multimedia carrier. The carrier could be text (i.e., not plain text), images, audio and/or video files (i.e., carrier channels). The fact that concealed information is contained in the otherwise ordinary and mundane carrier file is known only by the sender-receiver pair. Only they share the existence of the secret. Images are the most popular (i.e., multimedia) carriers because of their inherent property that enables better obfuscation. Content adaptive image steganography is a new trend in the field for messaging secrets inside unsuspected image file transfers. As the name suggests, the embedding locations are altered adaptively depending on the image content that optimizes the decision of choosing a location inside the carrier so that an embedding is not discernible (i.e., additive distortion is minimized). Herein, we critique the various approaches used for content-adaptive image steganography which can be broadly categorized as CNNbased, GAN-based, along with minimizing additive distortion function-based. We provide a brief historical account toward better anticipating the future research opportunities in terms of properties, and evaluation metrics. A summary table of these past and future directions is provided. Moreover, we highlight trends along with their concomitant advantages and disadvantages toward identifying opportunity gaps.

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Ayyah Abdulhafidh Mahmoud Fadhl mail -

Bander Ali Saleh Al-rimy mail -

Sultan Ahmed Almalki mail -

Tami Abdulrahman Alghamdi mail -

Azan Hamad Alkhorem mail -

Frederick T. Sheldon mail

link https://doi.org/10.54216/FPA.180216

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

On the Numerical Approximation and Optimization Techniques for Solving an Inverse Cauchy Problem of Viscous-Burgers’ Equation

This paper deals with some inverse problems for nonlinear time-dependent PDEs in one spatial dimension, we investigate an inverse Cauchy problem that is settled by the nonlinear viscous Burgers equation. The viscous Burgers equation is a partial differential equation that is encountered in fluid dynamics studies, particularly in the domain of upward flow. The simplified model of the viscous Burgers equation explains the behavior of incompressible viscous fluid. The inverse Burgers problem belongs to a class of problems called ill-posed problems, which implies that there may be multiple sets of initial and/or boundary conditions that result in the same solution of the Burgers equation. To obtain robust and reliable solutions, it is essential to use regularization and cross-validation methods. However, it is often difficult to solve analytically, so numerical approaches are developed to overcome this difficulty. Domain decomposition (DDM) was used with alternative iterative methods. We performed a numerical reconstruction of the velocity and normal stress tensor that were vanished on an inaccessible part of the boundary using the over-prescribed noisy data obtained on the other accessible part of the boundary.

groups

Mohammed A. Hilal mail -

Faris M. Alwan mail -

Alaa Adnan Auad mail

link https://doi.org/10.54216/IJNS.250428

Volume & Issue

Vol. Volume 25 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2025

Subclass of uniformly starlike functions associated with a linear operator whose coefficients are the reciprocal Gamma function

This study, aims to consider the coefficients of the reciprocal Gamma function in order introduce a linear operator by the means of Hadamard product. Thus, we define a new subclass of uniformly starlike functions of order 𝛼, Γ−1(𝛼). Further, we obtain coefficient estimates, distortion theorems, convex linear combinations and radii of close-to-convexity, starlikeness and convexity for functions 𝑓∈Γ−1(𝛼). In addition, we investigate the inclusion conditions for the Hadamard product and the Integral transform.

groups

Jamal Salah mail

link https://doi.org/10.54216/IJNS.250429

Volume & Issue

Vol. Volume 25 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2025

Exploring Critical Path Solving Methods under Neutrosophic

Over the past few decades, the traditional critical path method and its various generalizations have become the most popular technique for managing complex projects. It plays a crucial role in differentiating between critical and non-critical tasks to enhance project schedules. For the first time in the literature, our proposed model implements two algorithms for the study of the critical path method, each addressing an advanced framework in the form of a single-valued triangular neutrosophic. The proposed algorithm 1 utilizes Python to extended Dijkstra’s algorithm under the neutrosophic framework, while the proposed algorithm 2 employs linear programming for optimality checks, which is solved using LINGO. Our comparison with previous research on the critical path method shows that the proposed algorithms are better at dealing with uncertainty, making project schedules more reliable and flexible. The findings lead to the proposed algorithm framework, combined with Python and LINGO, to enhance decision-making and improve the accuracy and efficiency of critical path identification in complex project environments.

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M. Navya Pratyusha mail -

Ranjan Kumar mail

link https://doi.org/10.54216/IJNS.250430

Volume & Issue

Vol. Volume 25 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2025

New Higher-Order Implicit Method for Approximating Solutions of Boundary-Value Problems

This paper is devoted to introducing a novel numerical approach for approximating solutions to Boundary Value Problems (BVPs). Such an approach will be carried out by using a new version of the shooting method, which would convert the BVP into a linear system of two initial value problems. This system can then be solved by the so-called Obreschkoff approach. The numerical solution of the main BVP will ultimately be a linear combination of the solutions of the two system of equations. Two physical applications will be presented in order to confirm that the suggested numerical technique is valid.

groups

Iqbal M. Batiha mail -

Mohammad W. Alomari mail -

Iqbal H. Jebril mail -

Thabet Abdeljawad mail -

Nidal Anakira mail -

Shaher Momani mail

link https://doi.org/10.54216/IJNS.250432

Volume & Issue

Vol. Volume 25 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2025