检索结果-内蒙古大学图书馆

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Predicting Lifespan of Ground-to-Air Multipath Components in mmWave UAV Channels

Predicting Lifespan of Ground-to-Air Multipath Components in...

引用

IEEE Conference on Wireless Communications and Networking

作者： Wahab Khawaja Rune H. Jacobsen Sajid Hussain Ismail Guvenc Dept. Electrical and Computer Engineering Aarhus University Aarhus Denmark Dept. Computer Systems Engineering Mirpur Univ. Science and Technology Mirpur AJK Pakistan Dept. Electrical and Computer Engineering North Carolina State University Raleigh NC USA

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In mobile ground-to-air (GA) propagation channels, the birth and death of multipath components (MPCs) are frequently observed, and the wide-sense stationary uncorrelated scattering (WSSUS) assumption does not always hold. Several methods exist for tracking the birth and death of MPCs, however, to the best of knowledge of authors, there is no existing literature that addresses the prediction of the lifespan of the MPCs in non-WSSUS GA propagation channels. In this work, we consider the GA channel as non-WSSUS and individual MPCs across receiver positions are represented as time series based on the Euclidean distance between channel parameters of the MPCs. These time series representations, referred to as path bins, are analyzed using a semi-Markov chain model. The channel parameter variations and dependencies between path bins are used to predict the lifespan of path bins using weighted sum method, machine learning classifiers, and deep neural networks. For comparison, the birth and death of path bins are also modeled using a Poisson distribution and a Markov chain. Simulation results demonstrate that deep neural networks offer highly accurate predictions for the lifespan (including death) of MPC path bins in the considered GA propagation scenario.

关键词： Analytical models Simulation Time series analysis Scattering Artificial neural networks Machine learning Receivers Euclidean distance Autonomous aerial vehicles Millimeter wave communication

来源：评论

学校读者我要写书评

暂无评论

Self Organized Anchor Group based Routing Protocol for IoT Initial Setup 17

Self Organized Anchor Group based Routing Protocol for IoT I...

引用

17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Gupta, Naveen Kumar Aggarwal, Apeksha Yadav, Rama Shankar Kumar Nagaria, Rajendra Dr B R Ambedkar National Institute of Technology Jalandhar Center of Energy and Environment Dept. of Information Technology Jalandhar India Jaypee Institute of Information Technology Dept. of CSE and IT Noida India Motilal Nehru National Institute of Technology Allahabad Department of Computer Science and Engineering Prayagraj India Motilal Nehru National Institute of Technology Allahabad Department of Electronics and Comm. Engineering Prayagraj India

ISBN: (纸本)9798331531195

Geographical routing approach relies on local geographic information and performs multi-hop routing with the help of local decisions. The nodes are represented by 3D coordinates as some real IoT networks may be deployed in high buildings, hilly areas, underground or underwater. The geographical routing protocols follow the greedy path and suffer from void node problem (VNP). The traditional recovery policies follow a longer path around the boundary nodes of the void region which may go out of energy quickly. Thus, this paper proposes the concept of anchor group and presents an efficient anchor group routing protocol (EAGR) for IoT networks. The anchor group is treated as a sub-destination region where a group of nodes behaves like sub-destinations, which bypass the boundary nodes of the void to minimize the path length and divide the network traffic, in case of void in the network. The paper is also focused on considering the residual energy in the contention-based next-hop selection process. The experimental work has been carried out in the OMNET++ simulator's INET framework and observed that the proposed method is outperforming existing protocols. Moreover, it has reported a longer network lifetime and is able to survive longer than other existing geographical routing protocols on high load. © 2025 IEEE.

关键词： Routing protocols

来源：评论

学校读者我要写书评

暂无评论

Enhancing 6G-Satellite Network Integration with Artificial Intelligence: A Future Communication Paradigm 22

Enhancing 6G-Satellite Network Integration with Artificial I...

引用

22nd International Learning and technology Conference, L and T 2025

作者： Salah, Ibrahim Hussein, Aziza I. Wageh Lotfy, Mostafa Mabrook, Mohamed Mourad Almaaqal University College of Engineering Control and Computer Department Basrah61001 Iraq Effat University College of Engineering Energy and Technology Research Center Saudi Arabia Faculty of Technology and Education Beni-Suef University Dept. of Process Control Technology Beni-Suef Egypt Beni-Suef University Faculty of Navigation Science & Space Technology Space Communication Dept. Beni-Suef Egypt

ISBN: (纸本)9798331532215

The integration of 6G networks and satellite communications is set to revolutionize global connectivity, offering seamless coverage across terrestrial and non-terrestrial environments. Artificial Intelligence (AI) is essential for improving this integration, addressing challenges such as dynamic resource management, latency reduction, and network optimization. AI techniques like machine learning, deep learning, and reinforcement learning offer innovative solutions to handle the complexities of 6G-satellite networks. These advancements promise to significantly improve network efficiency, enhance data transmission reliability, and ensure seamless connectivity across different areas. Potential use cases include smart cities, autonomous vehicles, and Internet of Things (IoT) applications, where AI-driven 6G-satellite integration will be crucial. The proposed AI-enhanced 6G-satellite framework not only addresses current challenges but also lays the groundwork for a resilient, scalable, and globally interconnected communication infrastructure, offering a promising future. © 2025 IEEE.

关键词： Satellite communication systems

来源：评论

学校读者我要写书评

暂无评论

Decoding Emotions: Facial Expression Recognition and Semantic Rating Prediction with Deep Learning

Decoding Emotions: Facial Expression Recognition and Semanti...

引用

Devices for Integrated Circuit (DevIC)

作者： Soumya Suvra Khan Diganta Sengupta Anupam Ghosh Atal Chaudhuri Dept. of Computer Science and Engineering Meghnad Saha Institute of Technology Kolkata India Dept. of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of CSE and Department of AIML Netaji Subhash Engineering College Kolkata India Dept. of Computer Science and Engineering Sister Nivedita University Kolkata India

ISBN: (数字)9798350391107

ISBN: (纸本)9798350391114

This work comprises of three studies. We use the Japanese Female Facial Expression (JAFFE) dataset, comprising of 213 images of 10 Japanese female subjects, depicting six primary emotions: Happy, Sad, Surprised, Angry, Disgust, and Fear, to conduct three different studies. In the first study, we developed a Convolutional Neural Network (CNN) model to classify major human facial expressions using only the image data. To facilitate this, we constructed a dataset in CSV format containing two columns: the image file names and t heir corresponding expressions, which were encoded based on the naming convention of the dataset. Our model achieved a testing accuracy of 97.08%, demonstrating its effectiveness in recognizing facial expressions. In our second and third studies, we focused on predicting individual ratings for the expressions using the semantic ratings provided in the accompanying text file. These ratings were derived from psychological evaluations on a 5-point scale for the six emotions. We developed a multi-class regression CNN model to quantify these ratings. The second study included all six expressions and yielded a testing accuracy of 75.82%, while the third study, which excluded the expression of Fear, achieved a higher testing accuracy of 86.06%. These findings suggest that Fear may be processed differently from the other basic facial expressions, consistent with observations in psychological studies. This work highlights the potential of using deep learning techniques to quantify and understand the subtle distinctions of human facial expressions.

关键词： Deep learning Performance evaluation Emotion recognition Accuracy Face recognition Semantics Psychology Predictive models Convolutional neural networks Testing

来源：评论

学校读者我要写书评

暂无评论

Maximizing Performance in Wireless Mobile Ad-Hoc Networks Through Unconventional Routing Strategies

引用

SN computer Science 2025年第2期6卷 1-13页

作者： Singh, Satyanand Sreelakshmy, R. Bhagyalakshmi, L. Shekhar, Himanshu Thanuja, Annamaraju Suman, Sanjay Kumar School of Electrical & Electronics Engineering Fiji National University Suva Fiji Dept. of ECE Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Tamil Nadu Chennai India Rajalakshmi Engineering College Tamil Nadu Chennai India Dept. of ECE Hindustan Institute of Technology and Science Tamil Nadu Chennai India Dept. of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Dept. of ECE and Dean R & D St. Martin’s Engineering College Telangana Secunderabad India

A Mobile Ad-Hoc Network (MANET) comprises wireless mobile nodes without a fixed infrastructure or central control. Nodes communicate within range, with distant nodes acting as intermediaries. Frequent connection failures occur due to node mobility. This work aims to enhance a novel MANET routing algorithm and assess its impact on selected multipath protocols. Prioritizing paths with higher residual energy optimizes battery usage, minimizing route lifetimes and communication delays. Energy and link quality considerations led to the development of an Energy and Link aware Ad hoc On-Demand Multipath Distance Vector (AOMDV) protocol, outperforming traditional AOMDV in energy efficiency. Additionally, a Reliable Link AOMDV protocol predicts connection failures based on received signal strength, pre-emptively switching paths to maintain data transfer reliability, ultimately improving packet transfer efficiency and productivity. These enhancements in route discovery contribute to overall routing quality improvement. Compared to AOMDV, EQL-simulation AOMDV’s results show that it uses 87% less energy when selecting a reliable link. The novel finding is that enhanced AOMDV protocols prioritizing energy and link quality improve energy efficiency and reliability in MANETs, reducing communication delays and maintaining stable data transfer through pre-emptive path switching. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： AOMDV EQL-AOMDV MANET REL-AOMDV

来源：评论

学校读者我要写书评

暂无评论

Crude Oil Price Forecasting using Multi-Feed Data: A Comparative Study

Crude Oil Price Forecasting using Multi-Feed Data: A Compara...

引用

2025 International Conference on Emerging Technologies for Intelligent Systems, ETIS 2025

作者： Paul, Sruthi N Chaco, Anu Mary Bhattacharjee, Biplab National Institute of Technology Dept. of Computer Science and Engineering Calicut India Jindal Global Business School O.P. Jindal Global University Sonepat India

ISBN: (纸本)9798331507541

Crude oil price forecasting has a significant role in advancing the Sustainable Development Goals by enabling more informed decision-making in energy management and resource allocation. The accurate forecasting of crude oil prices is always a challenging task due to the inherently volatile nature of the price data. Additionally, the non-linearity and non-stationarity of the data make the price forecasting further complicated. In such cases, machine learning models can outperform the traditional statistical models. The machine learning models have the potential to analyse complex, non-linear, and non-stationary time series data and generate more precise forecasting results. In this work, we implemented the machine learning-based crude oil price forecasting model using multi-feed data. Along with the historic crude oil price data, we used financial markets' data and Google Trends data, which give additional information about the price fluctuations. We can analyse user sentiment information using Google Trends data, which also influences crude oil prices. We developed three forecasting models using Random Forest, XGBoost, and Linear Regression models. The comparative study reveals that the Random Forest model outperforms the other two models in terms of accuracy. © 2025 IEEE.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Smart Plant Irrigation System with Arduino Uno and Soil Moisture Sensor 2

Smart Plant Irrigation System with Arduino Uno and Soil Mois...

引用

2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Patni, Jagdish Chandra Surya Prakash, Vuppu Vardhan Reddy, Gajjala Sri Snehith Santhosh, Anjana Reddy, Yammanuru Chaitanya Bhaskarrao Bahadure, Nilesh Srivastava, Akhilesh Kumar Dept. of CSE Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Science and Engineering School of Technology GSFC University Gujarat Vadodara India ABES Engineering College Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798331530389

Automatic Smart Plant Watering System is a smarter solution that works on Arduino Uno, which can automatically operate for plant watering at optimal moisture conditions of soil. It checks the moisture with the help of a soil moisture sensor and, once the level of moisture goes down below a threshold, initiates a water pump. This user-friendly system is also energy efficient, making it suitable for busy or travel users, further contributing to sustainability plant care, thereby making it ideal for residential and nursery settings while emphasizing the promising use of smart agriculture and IoT technologies. This paper focuses on developing a smart water framework designed to efficiently manage water distribution based on real-time soil and environmental conditions. © 2025 IEEE.

关键词： Soil moisture

来源：评论

学校读者我要写书评

暂无评论

An Optimized Steganographic Technique for 24-Bit Digital Images using Slab Partitioning

An Optimized Steganographic Technique for 24-Bit Digital Ima...

引用

International Conference on ent Computing and Control Systems (ICICCS)

作者： Pinaki Pratim Acharjya Jayeeta Majumdar Subhabrata Barman Jayashri Deb Sinha Ramsundar Ghorai Arpan Ghosh Dept. of Computer Science and Engineering Haldia Institute of Technology Haldia West Bengal India Dept. of Computer Science and Engineering (Data Science) Haldia Institute of Technology Haldia West Bengal India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Based on image slabs and sheets, a novel kind of image steganography method for digital images has been proposed in this research work. Using the binary representation of the pixel, this approach splits the cover image into 24 fixed-size sheets. The secret message's binary counterpart is then incorporated into the cover image. It looks through each sheet's rows and columns to locate the one that most closely matches the secret message's binary counterpart. The row or column's position and how it differs from the message it stores in the LSB slab. Compared to previous ways, the proposed technique somewhat modifies the cover image.

关键词： Image quality Steganography Visualization Digital images Control systems Robustness Slabs

来源：评论

学校读者我要写书评

暂无评论

Hybrid Convolutional Neural Network and Random Forest Model for Predicting Water Level Fluctuations in Kaptai Reservoir to Enhance Water Resource Management

Hybrid Convolutional Neural Network and Random Forest Model ...

引用

International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Anik Sarkar Md Farhan Shahriyar Abdullah Md Raihan Chy Mohammed Abdul Al Arafat Tanzin Md Jisan Mashrafi Abrar Fahim Department of Civil Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh Dept. of Computer Science and Engineering World University of Bangladesh Dhaka Bangladesh Dept. of Computer Science and Engineering Southern University Bangladesh Chittagong Bangladesh Dept. of Computer Science and Engineering Brac University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

The Kaptai Reservoir, the country’s largest artificial freshwater body plays a vital role in hydroelectric power generation, flood control and agricultural support. However, its water levels are subject to fluctuations influenced by both climatic variations and increasing human consumption. Despite its crucial environmental impact there is a lack of research on the Kaptai Reservoir water body. The escalating effects of climate change present significant challenges to water resource management particularly in vulnerable regions such as Bangladesh. This paper presents a machine learning-based approach utilizing Convolutional Neural Networks (CNN) for feature extraction and Random Forest Regression (RFR) for Predicting water level fluctuations in the Kaptai Reservoir. Historical climate data from 2013 to 2022 including rainfall, temperature, and humidity were used to train the model. The CNN model effectively captured both temporal and spatial relationships in the water level time series while the RFR model demonstrated high prediction accuracy with a Root Mean Square Error (RMSE) of 0.5267 and a Mean Absolute Error (MAE) of 0.3128. These results highlight the model’s strong performance and its potential for real-time water management and decision-making aimed at mitigating the impacts of climate change on the reservoir. The findings offer valuable insights for policymakers working to ensure the long-term sustainability and resilience of water resources in Bangladesh.

关键词： Climate change Temperature distribution Fluctuations Time series analysis Predictive models Reservoirs Convolutional neural networks Root mean square Random forests Resilience

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：