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Reversible Data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

IEEE Internet of Things Journal 2025年第13期12卷 23933-23945页

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： Cryptography Encryption Security Data mining Adaptive coding Correlation Cloud computing Polynomials Image coding Streaming media

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Blockchain-Enabled Mitigation Strategies for Distributed Denial of Service Attacks in IoT Sensor Networks:An Experimental Approach

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computers, Materials & Continua 2024年第12期81卷 3679-3705页

作者： Kithmini Godewatte Arachchige Mohsin Murtaza Chi-Tsun Cheng Bader M.Albahlal Cheng-Chi Lee Department of Engineering Technologies School of ScienceComputing and Engineering TechnologiesSwinburne UniversityMelbourne3122Australia School of Engineering Science Technology Engineering and Mathematics CollegeRMIT UniversityMelbourneVIC 3001Australia Department of Information Technology College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Library and Information Science Fu Jen Catholic UniversityNew Taipei City24205TaiwanChina Department of Computer Science and Information Engineering Asia UniversityTaichung City41354TaiwanChina

Information security has emerged as a crucial consideration over the past decade due to escalating cyber security threats,with Internet of Things(IoT)security gaining particular attention due to its role in data communication across various ***,IoT devices,typically low-powered,are susceptible to cyber ***,blockchain has emerged as a robust solution to secure these devices due to its decentralised ***,the fusion of blockchain and IoT technologies is challenging due to performance bottlenecks,network scalability limitations,and blockchain-specific security ***,on the other hand,is a recently emerged information security solution that has great potential to secure low-powered IoT *** study aims to identify blockchain-specific vulnerabilities through changes in network behaviour,addressing a significant research gap and aiming to mitigate future cybersecurity *** blockchain and IoT technologies presents challenges,including performance bottlenecks,network scalability issues,and unique security *** paper analyses potential security weaknesses in blockchain and their impact on network *** developed a real IoT test system utilising three prevalent blockchain applications to conduct *** results indicate that Distributed Denial of Service(DDoS)attacks on low-powered,blockchain-enabled IoT sensor networks cause measurable anomalies in network and device performance,specifically:(1)an average increase in CPU core usage to 34.32%,(2)a reduction in hash rates by up to 66%,(3)an increase in batch timeout by up to 14.28%,and(4)an increase in block latency by up to 11.1%.These findings suggest potential strategies to counter future DDoS attacks on IoT networks.

关键词： Blockchain cyber intrusions DDoS IoT low powered sensors vulnerabilities wireless network

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Simulation of Social Media-Driven Bubble Formation in Financial Markets using an Agent-Based Model with Hierarchical Influence Network 36

Simulation of Social Media-Driven Bubble Formation in Financ...

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36th European Modeling and Simulation Symposium, EMSS 2024, Held at the 21st International Multidisciplinary Modeling and Simulation Multiconference, I3M 2024

作者： Bohorquez, Gonzalo Cartlidge, John School of Computer Science University of Bristol Bristol United Kingdom School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom

ISBN: (纸本)9791281988026

We propose that a tree-like hierarchical structure represents a simple and effective way to model the emergent behaviour of financial markets, especially markets where there exists a pronounced intersection between social media influences and investor behaviour. To explore this hypothesis, we introduce an agent-based model of financial markets, where trading agents are embedded in a hierarchical network of communities, and communities influence the strategies and opinions of traders. Empirical analysis of the model shows that its behaviour conforms to several stylized facts observed in real financial markets;and the model is able to realistically simulate the effects that social media-driven phenomena, such as echo chambers and pump-and-dump schemes, have on financial markets. © 2024 The Authors.

关键词： Marketplaces

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Automated heart disease prediction using improved explainable learning-based technique

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Neural Computing and Applications 2024年第26期36卷 16289-16318页

作者： Bizimana, Pierre Claver Zhang, Zuping Hounye, Alphonse Houssou Asim, Muhammad Hammad, Mohamed El-Latif, Ahmed A. Abd School of Computer Science and Engineering Central South University Changsha410083 China School of Mathematics and Statistics Central South University Changsha410008 China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt Information Technology Department Faculty of Computers and Information Menoufia University Shebin El-Koom Egypt

Heart disease (HD) stands as a major global health challenge, being a predominant cause of death and demanding intricate and costly detection methods. The widespread impact of heart failure, contributing to increased rates of morbidity and mortality, underscores the urgency for accurate and timely prediction and diagnosis. This is crucial for effective prevention, early detection, and treatment, thereby reducing the threat to individual health. However, the early and precise prediction of HD remains a significant challenge. The complexity of medical data poses a considerable challenge for healthcare professionals, who are required to interpret and utilize this information swiftly for effective intervention. Addressing this gap, our study introduces a novel Improved Explainable Learning-Based Technique (IELBT) for HD prediction. This technique harnesses a strategic combination of feature selection, Venn diagrams, data normalization methods, optimized parameters, and machine learning algorithms, specifically tailored for predicting HD. We evaluated the performance of our model using the Alizadeh Sani HD dataset, aiming to accurately detect the presence or absence of the condition. Our results demonstrate that the IELBT, employing a support vector machine with a robust scaling approach, optimal parameterization, and a data split ratio of 70:30, achieves an impressive accuracy rate of 96.00%. Beyond achieving high accuracy, the IELBT outperforms similar models in existing literature and provides significant interpretability and explanation, essential elements in the field of medical diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

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Semi-analytical finite element method applied for characterizing micropolar fibrous composites

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Applied mathematics and Mechanics(English Edition) 2024年第12期45卷 2147-2164页

作者： J.A.OTERO Y.ESPINOSA-ALMEYDA R.RODRIGUEZ-RAMOS J.MERODIO School of Engineering and Sciences Monterrey Institute of TechnologyEstado de Mexico 52926Mexico Institute of Engineering and Technology Autonomous University of Ciudad JuarezCiudad JuarezChihuahua 32310Mexico Postgraduate Program in Computational Modeling in Science and Technology Federal Fluminense UniversityRıo de Janeiro 27255-125Brazil Faculty of Mathematics and Computer Science University of HavanaLa Habana 10400Cuba Department of Mathematics Applied to ICT School of Informatics Systems EngineeringTechnical University of MadridMadrid 28031Spain

A semi-analytical finite element method(SAFEM),based on the two-scale asymptotic homogenization method(AHM)and the finite element method(FEM),is implemented to obtain the effective properties of two-phase fiber-reinforced composites(FRCs).The fibers are periodically distributed and unidirectionally aligned in a homogeneous *** framework addresses the static linear elastic micropolar problem through partial differential equations,subject to boundary conditions and perfect interface contact *** mathematical formulation of the local problems and the effective coefficients are presented by the *** local problems obtained from the AHM are solved by the FEM,which is denoted as the *** numerical results are provided,and the accuracy of the solutions is analyzed,indicating that the formulas and results obtained with the SAFEM may serve as the reference points for validating the outcomes of experimental and numerical computations.

关键词： semi-analytical approach fiber-reinforced composite(FRC) effective property finite element method(FEM) asymptotic homogenization method(AHM) micropolar elasticity

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MedBot - An NLP based ChatBot for Diabetes Prediction 15

MedBot - An NLP based ChatBot for Diabetes Prediction

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sreekar, K. Aswin, V. Narayanan, Vishnu Thangavel, Senthil Kumar Somasundaram, K. Shanmugam, Selvanayaki Kolandapalayam Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Ashland University Department of Mathematics and Computer Science OH United States

ISBN: (纸本)9798350370249

In this age of technology, with digital devices being accessible by almost everyone, have made our lives much better. With the advent of Machine Learning and advanced algorithms at our tips, the applications are immensurate. Such an application can be found in the health-care sector. The need for better and accessible health-care is of immense precedence. In this paper, we employed Machine Learning algorithms like Logistic Regression and other state of the art techniques to predict diabetes, given patient data. Diabetes is an incessant condition that is affecting millions worldwide, every year. Early detection of diabetes and prompt diagnosis is crucial. We have addressed that using a chat-bot to predict diabetes, which we believe is a portable and accessible solution for first-level assessment. We have employed PIMA Indian Diabetes Dataset into our chat-bot, providing interactive medical advice, and enhancing patient engagement, using Telegram API, resulting in an accuracy of 77%. © 2024 IEEE.

关键词： Electronic health record

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AIR-SCAN: Aircraft Identification and Recognition using Deep Learning Scanning 11

AIR-SCAN: Aircraft Identification and Recognition using Deep...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Kumar, Aman Singh, Soumyendra Mohan G, Bharathi Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Mathematics India

ISBN: (纸本)9798350350357

The research work explores advanced techniques in object detection frameworks, specifically focusing on Faster R-CNN and YOLOv8, to enhance the accuracy of military aircraft detection. Leveraging the multi-stage Faster R-CNN architecture, we investigate the impact of adjusting anchor ratios, systematically evaluating their influence on the model's ability to detect objects with diverse shapes. Departing from the conventional anchor-based approach, YOLOv8 introduces anchor-free detection, eliminating predefined anchors and increasing robustness to object scale variations. Our experimentation includes anchor ratio configurations and explores the innovative components of YOLOv8, such as the CSPDarknet53 backbone and the C2f module. It aims to contribute insights into optimizing military aircraft detection accuracy, catering to the dynamic and critical nature of aerial threats. By providing a comparative analysis of Faster R-CNN and YOLOv8, we shed light on their strengths and weaknesses, offering valuable considerations for deployment in military contexts. The paper's findings contribute to the continual advancement of computer vision applications in national security, emphasizing the importance of automated detection for efficient and timely threat identification. © 2024 IEEE.

关键词： National security

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A Multi-Task Framework Using Mamba for Identity, Age, and Gender Classification from Hand Images 15

A Multi-Task Framework Using Mamba for Identity, Age, and Ge...

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15th International Conference on Information and Knowledge Technology, IKT 2024

作者： Rezasoltani, Amirabbas Hosseini, Alireza Toosi, Ramin Akhaee, Mohammad Ali School of Mathematics Statistics and Computer Science College of Science University of Tehran Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Adak Vira Iranian Rahjoo Company Tehran Iran

ISBN: (纸本)9798331522254

Biometric authentication is crucial for secure access, surpassing traditional password-based methods vulnerable to breaches. Non-intrusive techniques like hand-based biometrics offer unique advantages, using physiological traits to identify individuals and predict soft biometrics such as age and gender. However, most current systems are designed with a single-purpose focus. In this field, Convolutional Neural Networks (CNNs) are typically utilized but struggle to capture long-range dependencies in images. Transformers, while more effective at handling such relationships, come with high computational costs. To overcome these challenges, this study introduces a novel multi-task learning framework that predicts identity, age, and gender simultaneously. The framework integrates the efficient long-range dependency modeling of Mamba, utilizing Visual State-Space Models (VSS) to capture both local and global patterns with reduced computational complexity. Experiments on the 11k Hands dataset demonstrate superior or competitive performance compared to existing methods. © 2024 IEEE.

关键词： Convolutional neural networks

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Recent Application and Progress of Metal Halide Perovskite Photodetector on Flexible Substrates

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Chinese Journal of Chemistry 2023年第24期41卷 3689-3702页

作者： Shuigen Li Xiangyu Xie Fan Huang Lili Shu Langyu Liu Chasheng Wang Yong Yang Rong Li School of New Energy Science and Engineering Xinyu UniversityXinyuJiangxi338004 China Department of Engineering Mechanics School of Aerospace EngineeringHuazhong University of Science and TechnologyWuhanHubei430074 China School of Mathematics and Computer Science Xinyu UniversityXinyuJiangxi338004 China Orthopedics of People's Hospital of Xinyu XinyuJiangxi338004 China

In recent years,flexible photodetectors(FPDs)have received increasing attention due to their applications in electronic eyes,flexible sensing,terminal devices,and wearable *** addition,metallic halide perovskite materials are considered as future materials for FPDs due to their compatibility with flexible substrates,low cost,simple synthesis methods,and superior optoelectronic *** review provides a comprehensive overview of the relevant cutting-edge research in the field of flexible perovskite photodetectors(FPPDs)from 2020 to ***,the evaluation criteria for FPPDs are discussed and the development of perovskite stability criteria is emphatically ***,the synthesis methods and device construction processes of metal halide perovskite materials commonly used by researchers in the past three years are *** include single crystals and low-dimensional ***,we have elaborated on the research of self-powered FPPD and its contributions in wearability,terminals,and ***,a summary of developments and possibilities in the field of FPPDs from 2020 to 2022 is provided.

关键词： 2D/3D perovskites Perovskite phases Nanostructures photodetectors Self-powered Heterojunction

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An Automated Identification Approach for Partial Discharge Detection Using Density-Based Clustering Without User Inputs

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 310-320页

作者： Rahman, Md Anisur Ang, Li-Minn Seng, Kah Phooi Charles Sturt University School of Computing Mathematics & Engineering BathurstNSW2795 Australia University of the Sunshine Coast School of Science and Engineering Sippy DownsQLD4556 Australia Xi'an Jiaotong Liverpool University School of AI & Advanced Computing Suzhou215123 China Queensland University of Technology School of Computer Science BrisbaneQLD4000 Australia

A recently published state-of-the-art density-based clustering technique called ICFSFDP for partial discharge (PD) detection requires various sensitive user-defined input parameters. This article presents a parameter-free clustering technique called PDAutoClust for PD detection. The PDAutoClust algorithm can produce high-quality clusters for PD datasets without requiring user-defined input parameters. PDAutoClust produces high-quality clustering results by utilizing a vein-based density clustering approach. The vein of a cluster is produced by using multivariate kernel density estimation and a unique neighborhood set. We compared the performance of PDAutoClust against ICFSFDP and seven other state-of-the-art density-based and non-density-based clustering techniques by using four PD datasets in terms of adjusted rand index, normalized mutual information, F1-score, and purity. Another contribution of the article is a novel merging technique used with PDAutoClust to merge small non-viable clusters that a clustering technique may produce. PDAutoClust produces the final clusters for a dataset by merging the non-viable clusters that a clustering technique may produce. We also evaluate the performance of PDAutoClust with merging technique versus PDAutoClust without merging technique using four datasets. Simulation results for PDAutoClust with the merging technique show good performance compared to ICFSFDP and seven other state-of-the-art clustering techniques. We also performed an ablation study to demonstrate the importance of the steps involved in PDAutoClust. © 2020 IEEE.

关键词： Merging

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