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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (Science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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A Novel Federated Learning Framework for Healthcare Applications Using Wearable Devices 4

A Novel Federated Learning Framework for Healthcare Applicat...

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4th IEEE International Conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Natarajan, Rajesh Krishna, Sujatha Ranjith, Christodoss Prasanna University of Technology and Applied Sciences Al-Aqr 324 College of Computing and Information Sciences Information Technology Department Shinas Oman Messiah University Computer Science Department MechanicsburgPA United States

ISBN: (纸本)9798331518882

A key necessity in managing signals for processing and generating useful information in the healthcare domain is the use of many algorithms. Traditional computing methods have been successful but may not be effective in the future as secure privacy matters. This has led to the increasing popularity of machine learning technologies. However, we present a novel device for devices and sensors for federated learning (FL), which is aimed at establishing a new, safer framework. This pattern uses machine learning with a lot of emphasis on the availability of the devices. In such a case, models for the detection, monitoring, and management of diseases can be created. The FL approach is also believed to be applicable to several healthcare contexts. © 2025 IEEE.

关键词： Federated learning

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Implementing an Intelligent Irrigation System in Oman Using Machine Learning Technique

Implementing an Intelligent Irrigation System in Oman Using ...

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Device Intelligence, computing and Communication Technologies, (DICCT), International Conference on

作者： Vimal Kumar Stephen Sanjiv Sharma Zaina Rashid Salim Al-Jufaili Senthil Jayapal College of Computing and Information Sciences University of Technology And Applied Sciences Ibra Oman

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

In this study, machine-learning technique is used to collect environmental data in real time and measures other aspects like soil moisture, temperature, water level, humidity. Water use efficiency has recently increased because to new agricultural techniques, sophisticated sensors as Micro Controller, and Wireless Sensor Network (WSN). Irrigation Fault Detection method based on XGBoost (IFD-XGBoost) Algorithm used as proposed method as robust to missing values imputing them naturally during tree splitting. The Suggested method is optimized for speed, using parallel and distributed computing to make predictions faster compare to other Regression Algorithm and avoid over fitting. Using Min-Max Normalization, features with different sizes and units can affect the efficacy of several machine learning algorithms. By warping the statistical relationship between features, removal of noisy data and error detection can reduce the machine learning models' efficiency. Lasso Regression is a linear model that combines regularization and feature selection. Additionally, less data will be kept in the centralized cloud as a result. Using the IFD-XGBoost Algorithm's ability to handle missing data, learn from past errors, and adjust parameters, our proposed approach would be the best model to implement. It outperform from other algorithm in Predictive accuracy, making it reliable for calculating water needs and forecasting weather. Investigating the implementation of several machine learning models that can offer the best real-world irrigation decision management is the main goal of this article. The comparative analysis to evaluate the IFD-XGBoost method yielded a minimum RMSE of 0.3524, MAE of 0.1832, MAPE of 4.24 with the highest accuracy of 99.2%, precision of 96.2% and AUC-ROC of 95%, indicates when combined, these metrics provide insightful data about each model's ability to anticipate future developments in irrigation systems.

关键词： Temperature sensors Temperature measurement Irrigation Wireless sensor networks Machine learning algorithms Soil moisture Machine learning Humidity Prediction algorithms Data models

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DLGPDT: Using Dynamic Population Sizing for Enhanced Hotel Performance Prediction in Oman 4th

DLGPDT: Using Dynamic Population Sizing for Enhanced Hotel ...

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4th International Conference on Intelligent Systems and Pattern Recognition, ISPR 2024

作者： Al Jassim, R. S. AbdulWahhab Mansoory, Shqran Al Jetly, Karan Maqbali, Hilal Al Al-Balushi, Muna College of Computing and Information Sciences University of Technology and Applied Sciences Muscat Oman

ISBN: (纸本)9783031821493

This paper presents a study on assessing hotel performance in Oman using a proposed machine-learning technique that integrates evolutionary algorithms in its architecture. The research investigates the issue of population diversity within a previously based genetic programming mechanism. It explores an adaptive population size adjustment technique, proposing enhancements to sustain and enhance population diversity in the genetic programming process. The results illustrate the superiority of this new mechanism over conventional approaches in terms of robustness, accuracy, and stability, particularly evident when applied to tourism datasets. These findings underscore the method’s potential as a practical solution for addressing challenges in machine learning in the tourism sector. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Leisure industry

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RT-PPS: Real-time privacy-preserving scheme for cloud-hosted IoT data

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Journal of High Speed Networks 2025年第1期31卷 71-89页

作者： Elhoseny, Mohamed Riad, Khaled College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates Faculty of Computers and Information Mansoura University Mansoura35516 Egypt Computer Science Department College of Computer Sciences & Information Technology King Faisal University Al-Ahsa 31982 Saudi Arabia Mathematics Department Faculty of Science Zagazig University Zagazig44519 Egypt

The Internet of Things (IoT) is a rapidly growing network of devices that can communicate with each other and with cloud-based services. These devices generate vast amounts of data that can be used to provide valuable insights into user behavior, environmental conditions, and other important factors. However, as this data is collected and processed by cloud-hosted services, there is a growing concern about privacy and security. Without adequate protection, sensitive information could be exposed to hackers or other malicious actors, putting both individuals and organizations at risk. To address this challenge, real-time privacy-preserving techniques can be used to protect IoT data without compromising its value. This paper introduces an efficient Real-time privacy-preserving scheme (RT-PPS) for cloud-hosted IoT data. RT-PPS employs multi-authority attribute-based encryption on a hybrid cloud environment to keep data secure and private, while still allowing it to be processed and analyzed by cloud-hosted services. RT-PPS has efficient response time and resource consumption, which gives it the ability to handle a huge number of concurrent users at the same time without notable delay. The proposed RT-PPS has been validated through extensive experimental evaluation on a variety of configurations. Moreover, the proposed scheme has been computationally compared with the state-of-the-artwork. RT-PPS has shown excellent performance, effectiveness, and efficiency. The RT-PPS encryption time for a 1 GB dataset while considering 1024 slices is approximately 1000 ms. Also, the RT-PPS decryption time for a 1 GB ciphertext while considering 1024 slices are approximately 235 ms. Finally, RT-PPS is proven secure against any polynomial-time attacks and their variations that have at most a negligible advantage in the introduced security model. Moreover compared to most of the state-of-the-artwork, RT-PPS reduced the ciphertext size and lowered the computations in the encryption, key g

关键词： Ciphertext

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Adaptive Multi-Scale Local Correction for Semi-Supervised 3D Medical Image Segmentation

Adaptive Multi-Scale Local Correction for Semi-Supervised 3D...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Xinqiang Lu, Wenhuan Zheng, Ke Xu, Junhai College of Intelligence and Computing Tianjin University Tianjin300350 China School of Software and Communication Tianjin Sino-German University of Applied Sciences Tianjin300350 China

ISBN: (纸本)9798350368741

In recent years, semi-supervised 3D medical image segmentation has gained significant attention. However, current methods often struggle with multi-scale voxel differences and overlook the importance of loss weight balancing. To address these issues, we propose an adaptive multi-scale local correction method (AMLC). Our key contributions are: (1) a multi-scale local correction module to accurately capture differences between 3D voxel blocks at various scales;(2) an adaptive weighting adjustment module that dynamically balances losses, improving robustness and training. Experiments show that AMLC significantly outperforms existing methods on the public dataset, demonstrating its effectiveness. © 2025 IEEE.

关键词： adaptive weight adjustment medical image segmentation Multi-scale local correction

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Innovating Economic Models for Smart Green Cities Through Artificial Intelligence Techniques

Innovating Economic Models for Smart Green Cities Through Ar...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Nasim Al Balushi College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The main challenges resulting from urbanization include poverty, environmental degradation, and unsustainable resource extraction. Urbanization is not only one of the most significant human activities but also an inevitable trend in the 21st century. With over 80% of the global population expected to reside in cities by the end of this century, sustainable urbanization will likely play a critical role in reducing consumption and emissions. This paper explores the integration of artificial intelligence (AI) into economic models to aid in the development of smart, green cities. AI technologies, leveraging data analytics, machine learning, and predictive modeling, can optimize resource allocation and inform urban planning decisions to enhance the sustainability of urban environments. AI enables the implementation of smart grids, traffic management systems, and the automation of public sector activities, all of which contribute to reducing energy consumption and minimizing environmental impact. By combining AI with the Internet of Things (IoT) and other modern technologies, cities can achieve proactive urban planning, foster public participation, and improve resource management. This study highlights the potential of AI in driving climate neutrality and promoting sustainable economic growth in future urban landscapes.

关键词： Economics Biological system modeling Urban areas Green products Urban planning Data models Resource management Internet of Things Artificial intelligence Sustainable development

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Design and Application of Teaching System Using Artificial Intelligence Technology

Design and Application of Teaching System Using Artificial I...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Vimal Kumar Stephen Ramesh Palanisamy Anand Muthukumarappan Kumar Kaliyamoorthy College of Computing and Information Sciences University of Technology and Applied Sciences Ibra Sultanate of Oman

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

In recent years, a large number of academic institutions have examined and experimented with teaching system evaluations with varying degrees of success. Techniques and tools for assessing the quality of the teaching system in general higher education institutions are required in order to comprehend the quality of learning and the teaching system in universities and colleges, to enhance school circumstances, and to develop the AI of teaching management in Oman. Important elements of the contemporary education institute and teaching system are lesson planning, implementation, test preparation, in-class audiovisual aid use, classroom instruction and practice, and assessment. Because of this, the success of instruction greatly depends on the design of the system of instruction. This study presents an IFH-DAdaBoost algorithm, which is based on artificial intelligence (AI), as a solution to the current issue of the skills fostered by universities and colleges not always aligning with societal needs regarding creativity, practices in engineering, and international competitiveness. The real-world teaching effect shows that modern students prefer and give better evaluation ratings to AI designs. The experiment results also show the stability, accuracy of the suggested method, which overcomes the design constraints of the traditional teaching system. Thus, by enhancing their training in practical abilities, an AI-driven teaching system would significantly improve students' overall quality and innovative capability.

关键词： Training Technological innovation Ethics Accuracy Education Learning (artificial intelligence) Planning Artificial intelligence System analysis and design Numerical stability

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Adaptive MOOC - An Approach to Identify Learners' Preferences to Enhance Their Engagement

Adaptive MOOC - An Approach to Identify Learners' Preference...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Mangesh Wanjari Amal Al Abri Someshvar Vashish College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

Massive Open Online Courses have completely revolutionized the delivery of education, affording new levels of access to learners of all walks of life around the world. However, one of the greatest challenges facing educators and the providers of platforms on which these courses are delivered is how to keep learners engaged in the courses and motivate them to complete the courses they are registered into. This paper proposes a novel method of improving learner engagement in a course by determining and appealing to individual preferences in content delivery. The proposed framework draws on learner log data from the LMS (Moodle) to cluster students according to their interactions with different types of course material. Based on the preferred content types for each cluster, we will be able to recommend the most suitable materials to the individual learners and enhance overall engagement and motivation towards active participation in the course. It also includes the results of a questionnaire circulated to these students, in order to cross-check the results of the clustering analysis and provide important insights with respect to the effectiveness of the proposed approach.

关键词： Technological innovation Learning management systems Ethics Electronic learning Adaptive systems Education Artificial intelligence

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Artificial intelligence-inspired comprehensive framework for desertification prediction

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International Journal of Cognitive computing in Engineering 2025年 6卷 462-476页

作者： Alsubai, Shtwai Mosleh, Mogeeb A.A. Al-Hadhrami, Suheer A. Bhatia, Munish College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Alkharj Saudi Arabia Faculty of Engineering & Computing University of Science & Technology Aden Yemen Faculty of Engineering and Information Technology Taiz Univeristy Taiz Yemen Department of Computer Engineering College of Engineering Al Mukalla Hadhramout Yemen Department of Computer Applications National Institute of Technology Kurukshetra India

Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area even more difficult. This research presents an enhanced framework utilizing the Internet of Things (IoT) for ongoing monitoring, data gathering, and analysis to evaluate desertification patterns. The framework utilizes Bayesian Belief Networks (BBN) to categorize IoT data, while a low-latency processing method on edge computing platforms enables effective detection of desertification trends. The classified data is subsequently analyzed using an Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA) for forecasting decisions. Using cloud computing infrastructure, the ANN-GA model examines intricate data connections to forecast desertification risk elements. Moreover, the Autoregressive Integrated Moving Average (ARIMA) model is employed to predict desertification over varied time intervals. Experimental simulations illustrate the effectiveness of the suggested framework, attaining enhanced performance in essential metrics: Temporal Delay (103.68 s), Classification Efficacy—Sensitivity (96.44 %), Precision (95.56 %), Specificity (96.97 %), and F-Measure (96.69 %)—Predictive Efficiency—Accuracy (97.76 %) and Root Mean Square Error (RMSE) (1.95 %)—along with Reliability (93.73 %) and Stability (75 %). The results of classification effectiveness and prediction performance emphasize the framework's ability to detect high-risk zones and predict the severity of desertification. This innovative method improves the comprehension of desertification processes and encourages sustainable land management practices, reducing the socio-economic impacts of desertification and bolstering at-risk ecosystems. The results of the study hold considerable importance for enhancing regional efforts in combating desertification, ensuring food security, and formulatin

关键词： Life on land Climate action Desertification Internet of things Artificial neural network

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