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

R.; A.

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

Fusion: Practice and Applications | 2025

Multi-Step Financial Stock Index Forecasting Model Using Convolutional Neural Network with Gated Recurrent Unit Approach

Prediction of time series is a vital issue related to an extensive array of financial, and social applications, and engineering. The main challenge arises from the intricacy due to the temporal assets of time series and the unavoidable weakening function of analytical systems. Therefore, it is usually problematic to precisely forecast values, particularly in a multi-step ahead situation. Multi-step financial stock price forecast over a lasting perspective is vital for predicting its instability, letting economic organizations charge and evade derivatives, and banks to measure the hazard. Recently, Deep learning systems have been capable to perceive and analyze intricate patterns and connections in the data automatically and haste up the trading procedure. This manuscript designs and develops a Multi-Step Financial Stock Index Forecasting Model Using a Convolutional Neural Network with Gated Recurrent Unit (MFSIFM-CNNGRU) model. The proposed MFSIFM-CNNGRU model relies on enhancing the predicting model for the financial stock index. To accomplish that, the data normalization stage is initially performed by employing z-score normalization to convert input data into a suitable format. Next, the proposed MFSIFM-CNNGRU model designs a hybrid of convolutional neural network and gated recurrent unit (CNN-GRU) technique for the prediction model. Eventually, the hyperparameter selection of the CNN-GRU model can be implemented by the design of the improved whale optimization algorithm (IWOA). The efficiency of the MFSIFM-CNNGRU method has been validated by comprehensive studies using the benchmark dataset. The numerical result shows that the MFSIFM-CNNGRU method has better performance and scalability under various measures over the recent techniques

groups

Denis Shakhov mail -

Inomjon Yusubov mail -

Sanat Yakubov mail -

Aleksey Ilyin mail -

Emil Hajiyev mail -

Tatyana Khorolskaya mail

link https://doi.org/10.54216/FPA.180208

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

A Smartphone-based Real-time Medication Adherence Monitoring App to Support Full Medication Self-Management among Elderly Faculty Members with Chronic Illness

There has been a widespread misconception that the role of physicians in healthcare systems is limited to accurate diagnosis and prescription writing. This poor vision is based on the assumption that the patient will fully adhere to the written medical prescription, which rarely happens in reality, because most patients disregard their physicians’ instructions for purposeful reasons like financial hardship or inadvertent causes like forgetfulness. In the contemporary university community, which blends in-person instruction with distance learning, the duties of University faculty members go beyond simple research and teaching to include other responsibilities that would place more burdens and stress on them, which could have a detrimental effect on their lives and cause their medical treatment regimens to fall flat totally. With the development of artificial intelligence techniques and the increasing use of mobile devices, it's easier to develop intelligent apps that cover every part of our everyday routine, including the medical sector, as it's now possible to remotely diagnose, treat and monitor patients’ adherence to prescribed medication plans without the need for direct human involvement. This paper combines artificial intelligence techniques and mobile technology to build a healthier university community by providing an effective smart medication reminder mobile app that supports the principle of medication self-management to improve adherence of medication in-take among patient faculty members at Mansoura University who are undergoing long-term therapy. The evaluation plan of the proposed smart medication reminder mobile app was implemented at two primary levels. The proposal’s acceptability was tested at the initial level by a team comprising both mobile app developers and medical professionals. The proposal’s feasibility was tested on a random sample of patient faculty members from Mansoura University in the second level. The outcomes of the first evaluation level showed that, the services provided by the proposal were highly gained satisfaction of the evaluation team, which means it is suitable for wider use in University environments. While, the outcomes of the second evaluation level revealed that the percentage of taking meds improved among the sample of patient faculty members after using the proposal more than before, which means that it is a useful tool to enhance medication adherence of patient faculty members, especially the elderly with chronic medical disorders.

groups

W. K. ElSaid mail -

Mona Esmat mail -

Nahed Amasha mail

link https://doi.org/10.54216/FPA.180209

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Melanoma Skin Cancer Detection Using Deep Learning Methods and Binary GWO Algorithm

Melanoma is one of the most aggressive types of skin cancer, and its early detection is critical to improving survival rates and treatment outcomes for patients. Conventional diagnostic methods often suffer from high computational costs and low accuracy, primarily due to inadequate feature selection and classification strategies. The goal of this research is to combine state-of-the-art deep learning techniques with optimization algorithms to develop a precise and efficient predictive system for melanoma detection. In this work, we propose a novel framework that integrates Convolutional Neural Networks (CNNs) for image classification and a binary Grey Wolf Optimization (GWO) algorithm for feature selection. The binary GWO algorithm identifies the most relevant features from dermatological images, eliminating redundancy and reducing the computational burden. The CNN is then trained on the refined feature subset to enhance classification efficiency. Extensive experiments on publicly available skin lesion datasets demonstrate that the proposed model significantly outperforms traditional machine learning models. Improvements in sensitivity, specificity, and overall classification accuracy highlight the effectiveness of combining deep learning with optimization techniques. Our results show that deep learning and optimization methods, such as the binary GWO algorithm, can be successfully applied to melanoma diagnosis. This strategy not only improves detection efficiency and accuracy but also supports early diagnosis and treatment planning, leading to better patient outcomes. By leveraging the binary GWO algorithm to optimize the feature selection process and CNNs for image classification, the proposed approach reduces computational costs while increasing classification accuracy. When trained and evaluated on publicly available skin lesion datasets, the model demonstrates significant improvements in sensitivity, specificity, and overall accuracy compared to conventional machine learning models.

groups

Mohammed Yousif mail -

Noor M Jassam mail -

Ahmad Salim mail -

Hussein Ali Bardan mail -

Ahmed Farhan Mutlak mail -

Anas D. Sallibi mail -

Abdalrahman Fatikhan Ataalla mail

link https://doi.org/10.54216/FPA.180211

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Quantum Assisted Blockchain Security Model Using Artificial Intelligence to Reduce Quantum Attacks

Presently, smart sensors ensure commercial decisions where integrated electronic systems can be securely organized using blockchain and quantum computing because of their unique characteristics and features. In the current scenario, large-scale quantum computers can be built in which most current cryptographic systems can be hacked. Since digital and quantum computers can conduct computations simultaneously, a quantum tool for blockchain framework design is required. Based on these concerns in this research, an enhanced quantum-assisted blockchain security model using the artificial intelligence (EQ-BSM-AI) technique has been proposed. This model validates cryptosystems and blockchain technologies to determine their vulnerability to quantum attacks. Further, in this model, quantum assisted edge computing technique has been used to model the Human-centric Internet of Things (HIoT) system by introducing a quantum key generation process. Based on the post-quantum blockchain (PQB), a secured cryptosystem that is highly resistant to quantum computer attacks has been introduced in this research. This quantum channel with multiple inputs and outputs (MIMO) is designed for a quantum-based communication system to make this model more efficient and withstand errors. In EQ-BSM-AI, an improved quantum encryption algorithm (IQEA) stores the keys for encryption with a generalized probability accumulation model. For the current quantum computers and communications, our proposed system resulted in an improved sampling error reduction of 12.4%, enhanced efficiency of quantum entanglement of 96.3%, information randomness of 93.9%, correlation analysis of 93.2%, and increased resistance to quantum computing attacks of 90.8% when compared with other existing approaches.

groups

Ammar AbdRaba Sakran mail -

Ruwaida Mohammed Yas mail -

Ali Fadhil Rashid mail -

Massila Kamalrudin mail -

Mustafa Musa mail

link https://doi.org/10.54216/FPA.180210

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Binary Arithmetic Optimization Algorithm Using a New Transfer Function for Fusion Modeling

Organizations use fusion data modeling to integrate multiple data sources and build precise representations that achieve better organizational clarity. One recent method that has proven effective in many benchmark tests is the arithmetic optimization algorithm (AOA). AOA applies basic distribution behavior to arithmetic operations such as multiplication, division, addition, and subtraction. This paper focuses on the innovative application of AOA in addressing the feature selection problem. The binary version of this algorithm (BAOA) is introduced to solve problems of binary nature. The main part of this version is the transfer function that converts a continuous search space into a discrete search space. Therefore, a new Fountain-shaped transfer function is proposed to enhance global exploration and local exploitation in the BAOA algorithm. The performance of the proposed Fountain-shaped transfer function has been compared with V-shaped and S-shaped transfer functions. Based on ten public datasets, the performance of the proposed transfer function is validated. The Experimental results show the superiority of the proposed Fountain-shaped transfer function not only in getting high classification accuracy with few selected features but also requires inexpensive computational costs.

groups

Zaynab Ayham Almishlih mail -

Omar Saber Qasim mail -

Zakariya Yahya Algamal mail

link https://doi.org/10.54216/FPA.180212

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

On Modules Related to Homomorphism Their Kernel Equal Zero in Neutrosophic Theory

Neutrosophic set is a modern branch as a generalization of fuzzy concept.  Zadeh in 1965 presented fuzzy concept and later he introduced more applications in more subjects of mathematics.  On of the type branch of mathematics is fuzzy algebra. In this work, we present and clarify several results of several modules, which has zero-kernel, and zero homomorphism in neutrosophic theory. The aim modules are mnonoform and small monoform modules.  Several concepts have been studied in this paper like Quasi-dedekind and uniform modules.  We proved that if ( ( )) is a module over neutrosophic ring ( ). If ) is a directed sum of simple submodules an  is monoform, then ) is monoform module.  Also, if  𝒯) is a semi simple ring and  𝒯) is a  𝒯)-module, so  𝒯) is small and satisfies all conditions of monoform with Q-dedekind property. On the other hand, let be an R-module. is a neutrosophic modules and generated by  and . So, is a weak neutrosophic. Finally, we presented more results, examples and properties about the topic with new results in neutrosophic algebra.

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Firas N. Hameed mail -

Fawzi N. Hammad mail -

Majid Mohammed Abed mail

link https://doi.org/10.54216/IJNS.250427

Volume & Issue

Vol. Volume 25 / Iss. Issue 4

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Integrating Cybersecurity into Renewable Energy Development: A Data-Driven Decision Tree Approach for Environmental Protection

The global shift towards renewable energy sources is vital for environmental protection and sustainable development. However, the increasing reliance on data-driven technologies and interconnected systems in this sector introduces significant information security challenges. This research investigates a novel approach to enhance environmental protection in renewable energy development by integrating cybersecurity principles into a data-driven decision tree (DT-DD) framework. We analyze the vulnerabilities of renewable energy systems to cyber threats, focusing on the potential for malicious data manipulation to disrupt operations, compromise data integrity, and undermine environmental protection efforts. Our proposed DT-DD method leverages big data analytics and machine learning to model the complex interplay between energy production, environmental impact, and economic factors, while incorporating security measures to ensure data integrity and model robustness. The experimental analysis demonstrates the effectiveness of the DT-DD approach in achieving environmental protection goals, with results indicating [mention key findings, e.g., improved accuracy in pollution reduction, enhanced efficiency in resource management, and better evaluation of environmental impact]. Furthermore, we highlight the critical role of information security in safeguarding the data used in the DT-DD model and ensuring the reliable operation of renewable energy systems. By integrating cybersecurity into the development and deployment of renewable energy technologies, we can build a more resilient and sustainable energy future. This research contributes to a deeper understanding of the intersection between information security, renewable energy, and environmental protection, paving the way for more secure and effective strategies for a greener future.

groups

Israa Shihab Ahmed mail -

Ahmed Luay Ahmed mail -

Massila Kamalrudin mail -

Mustafa Musa mail

link https://doi.org/10.54216/JCIM.150225

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

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