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Reversible data hiding in encrypted images with block-based bit-plane reallocation

Multimedia Tools and Applications 2024年第37期83卷 84911-84932页

作者： Liu, Li Chen, Chaofan Wu, Yingchun Chang, Chin-Chen Wang, Anhong College of Electronic Information and Engineering Taiyuan University of Science and Technology Taiyuan030024 China Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan

As cloud storage and multimedia communication continue to evolve, the preservation of image privacy is becoming increasingly important. Reversible data hiding in encrypted images (RDHEI) is an effective method for enhancing the protection of personal privacy. To improve the embedding capacity, this paper introduces a novel scheme for RDHEI using block-based bit-plane reallocation. First, an effective optimized prediction method is utilized to predict the value of each pixel, followed by the calculation of prediction errors (PEs). Then, block-based bit-planes of PEs are extracted, and ten different bit-plane types are categorized to facilitate bit-plane reallocation, thereby freeing up redundant space. Finally, an adaptive encryption procedure is employed to encrypt the reallocated bit-plane, excluding the flag-bits. Experimental results demonstrate that the proposed scheme achieves average embedding rates (ER) of 3.584 bpp, 3.501 bpp, and 2.985 bpp on the three experimental datasets, all of which are higher than those of other state-of-the-art RDHEI schemes while ensuring both reversibility and security. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cryptography

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Secure data authentication and access control protocol for industrial healthcare system

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Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 4853-4864页

作者： Gupta, Daya Sagar Mazumdar, Nabajyoti Nag, Amitava Singh, Jyoti Prakash Department of Computer Science and Engineering Rajiv Gandhi Institute of Petroleum Technology Jais UP Amethi229304 India Department of Information Technology Indian Institute of Information Technology Allahabad Prayagraj India Department of Computer Science and Engineering Central Institute of Technology Kokrajhar BTR Kokrajhar India Department of Computer Science and Engineering National Institute of Technology Patna Patna India

Because of recent COVID-19 epidemic, the Internet-of-Medical-Things (IoMT) has acquired a significant impetus to diagnose patients remotely, regulate medical equipment, and track quarantined patients via smart electronic devices installed at the patient’s end. Nevertheless, the IoMT confronts various security and privacy issues, such as entity authentication, confidentiality, and integrity of health-related data, among others, rendering this technology vulnerable to different attacks. To address these concerns, a number of security procedures based on traditional cryptographic approaches, such as discrete logarithm and integer factorization problems, have been developed. All of these protocols, however, are vulnerable to quantum attacks. This paper, in this context, presents a data authentication and access control protocol for IoMT systems that can withstand quantum attacks. A comprehensive formal security assessment demonstrates that the proposed algorithm can endure both current and future threats. In terms of data computing, transmission, and key storage overheads, it also surpasses other related techniques. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Authentication

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AI Based Automated Food Recognition Using CNN for Fitness Mobile Application

AI Based Automated Food Recognition Using CNN for Fitness Mo...

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2023 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2023

作者： Mahmood, Atif Mahmood, Rafay Azzuhri, Saaidal Razalli Azizul Hasan, Zati Hakim Mat Kiah, Miss Laiha University of Central Punjab Faculty of Information Technology and Computer Science Lahore Pakistan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System & Technology Kuala Lumpur50603 Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Department of Artificial Intelligence Kuala Lumpur50603 Malaysia

ISBN: (纸本)9798350347494

Eating a healthy, balanced diet with regular exercise is important for maintaining physical and mental health. Healthy diet and exercise promise a healthier lifestyle. Other than that, people competing in different sports need a customized, strict workout and diet plan. Trainees offer their best at gymnasium by following a strict workout plan recommended by their coaches. However, there is no way till now to determine if the trainees are taking the prescribed diet strictly. Therefore, an optimized application with the feature of real time scanning of the prescribed meal can be helpful to overcome this problem. For this purpose, we have used Convolutional Neural Networks (CNN) along with Image Processing. Using ResNet50 with activation functions of ReLU and Softmax, our trained CNN model and the proposed architecture achieved an accuracy of 82.70 percent. We used CNN pre-trained models such as InceptionV3 with LeakyReLU and Softmax, InceptionV3 with ReLU and Softmax, VGG16 with ReLU and Softmax, VGG16 with ReLU and Softmax, and ResNet50 with ReLU and Softmax, so the achieved accuracy, respectively, is 78%, 71%, 58%, 56%, and 82.7%. Therefore, the proposed model has the potential to achieve high performance on real time food scanning which can be used in online fitness platforms(mobile applications and websites) and fitness gadgets. © 2023 IEEE.

关键词： Convolutional neural networks

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Label-Free Biomolecule Detection with InP/AlGaAs Charge Plasma Dielectric-Modulated Vertical TFET Using TaN as Metal Gate

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IEEE Access 2025年 13卷 89667-89684页

作者： Elshafie, Hashim Alqahtani, Abdulrahman Saad Mubarakali, Azath Adarsh Rag, S. Venkatesh, M. Parthasarathy, P. King Khalid University College of Computer Science Department of Computer Engineering Abha61421 Saudi Arabia University of Bisha College of Computing and Information Technology Bisha67714 Saudi Arabia King Khalid University College of Computer Science Department of Informatics and Computer Systems Abha61421 Saudi Arabia Manipal Institute of Technology Department of Data Science and Computer Applications Manipal Academy of Higher Education Manipal576104 India CMR Institute of Technology Department of Electronics and Communication Engineering Karnataka Bengaluru560037 India

This study introduces a label-free biosensing method for biomolecule detection utilizing an InP/AlGaAs charge plasma dielectric-modulated vertical tunnel field-effect transistor (InP/AlGaAs VTFET) featuring TaN as the metal gate. The device comprises a nanocavity beneath the gate metal adjacent to the tunnelling junction, where biomolecules engage with the dielectric material, resulting in fluctuations in the drain current. A twin metal gate architecture with laterally split dielectrics is employed to eliminate short-channel effects. The biosensor has exceptional sensitivity, attaining a peak drain current sensitivity of 2.5 × 10 cm2 for biomolecules such as albumin (k = 7). The gadget proficiently identifies biomolecules with varying dielectric constants and charge distributions, enabling adaptable label-free detection of diverse targets. The study examines the influence of dielectric constant on critical metrics such as Energy Band Diagrams, Drain Current, Drain Sensitivity, and subthreshold swing (SS). The overlap between the source and pocket regions, along with the introduction of an auxiliary gate, optimizes the electrical characteristics. Simulation results show that the proposed InP/AlGaAs VTFET achieves a maximum sensitivity of 3.5 × 103, outperforming configurations without overlap (2 × 103), highlights the potential of proposed InP/AlGaAs VTFET for scalable, high-sensitivity, label-free biomolecule detection. © 2013 IEEE.

关键词： Junction gate field effect transistors

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FedPark: A Federated Learning Crowdsensing Solution for On-street Parking Availability

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IEEE Access 2025年 13卷 99717-99733页

作者： Attiah, Afraa Alahmadi, Shatha Hakeem, Abeer Mohaisen, Linda Almakky, Abeer Alhebshi, Reemah M. King Abdulaziz University Faculty of Computing and Information Technology Department of Information Technology Jeddah 21589 Saudi Arabia King Abdulaziz University Faculty of Computing and Information Technology Department of Computer Science Jeddah 21589 Saudi Arabia

Locating available on-street parking in busy urban areas is often frustrating and time-consuming, particularly during peak hours. Inefficient parking searches contribute to traffic congestion, increased fuel consumption, and higher carbon emissions, making real-time parking availability information essential for smart urban mobility. This paper presents FedPark, a privacy-preserving crowdsensing system that leverages drivers’ smartphone sensors to monitor parking space occupancy. FedPark employs a Federated Learning (FL) algorithm, enabling the creation of a global model that aggregates insights from multiple local models maintained on individual drivers’ devices. Each local model learns from a user’s driving patterns without sharing personal data, ensuring enhanced privacy protection. Instead of transmitting raw data, only model updates are shared, reducing communication overhead and security risks. FedPark is evaluated under different urban conditions such as different numbers of drivers and destinations. The evaluation demonstrates that FedPark achieves a high level of accuracy at 98.31%, recall of 98.31%, precision of 98.34%, and an F1-score of 98.31% comparable to centralized machine learning models, including XGBoost, Random Forest, CatBoost, CNN, and ensemble classifiers while preserving driver privacy. © 2013 IEEE.

关键词： Crowdsensing federated learning machine learning on-street parking privacy protection urban mobility

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Emotion detection and semantic trends during COVID-19 social isolation using artificial intelligence techniques

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16985-16993页

作者： Jelodar, Hamed Orji, Rita Matween, Stan Weerasinghe, Swarna Oyebode, Oladapo Wang, Yongli Faculty of Computer Science Dalhousie University Halifax Canada Institute of Computer Science Polish Academy of Sciences Warsaw Poland Department of Community Health and Epidemiology Faculty of Medicine Dalhousie University Halifax Canada School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China

Taking advantage of social media platforms, such as Twitter, this paper provides an effective framework for emotion detection among those who are quarantined. Early detection of emotional feelings and their trends helps to implement timely intervention strategies. Given the limitations of medical diagnosis of early emotional change signs during the quarantine period, artificial intelligence (AI) models provide effective mechanisms in uncovering early signs, symptoms and escalating trends. Novelty of the approach presented herein is a multitask methodological framework of text data processing, implemented as a pipeline for meaningful emotion-semantic analysis and trend detection. Regarding the significant applications of this research work, the experimental results revealed that our AI-based emotion-semantic aspects can effectively uncover people’s emotional reactions during a pandemic, especially when abiding to the stay-at-home preventive measure. Moreover, the research can be applied to uncover reactions to similar public health policies that affect people’s well-being. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： COVID-19

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Variance-Reduced Deep Actor-Critic with an Optimally Subsampled Actor Recursion

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3607-3623页

作者： Mandal, Lakshmi Diddigi, Raghuram Bharadwaj Bhatnagar, Shalabh Indian Institute of Science Department of Computer Science and Automation Karnataka Bangalore560012 India International Institute of Information Technology Karnataka Bangalore560100 India

Reinforcement learning (RL) algorithms combined with deep learning architectures have achieved tremendous success in many practical applications. However, the policies obtained by many deep reinforcement learning (DRL) algorithms are seen to suffer from high variance making them less useful in safety-critical applications. In general, it is desirable to have algorithms that give a low iterate-variance while providing a high long-term reward. In this work, we consider the actor-critic (AC) paradigm, where the critic is responsible for evaluating the policy while the feedback from the critic is used by the actor for updating the policy. The updates of both the critic and the actor in the standard AC procedure are run concurrently until convergence. It has been previously observed that updating the actor once after every L>1 steps of the critic reduces the iterate variance. In this article, we address the question of what optimal L-value to use in the recursions and propose a data-driven L-update rule that runs concurrently with the AC algorithm with the objective being to minimize the variance of the infinite horizon discounted reward. This update is based on a random search (discrete) parameter optimization procedure that incorporates smoothed functional (SF) estimates. We prove the convergence of our proposed multitimescale scheme to the optimal L and optimal policy pair. Subsequently, through numerical evaluations on benchmark RL tasks, we demonstrate the advantages of our proposed algorithm over multiple state-of-the-art algorithms in the literature. © 2020 IEEE.

关键词： Optimization

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Enhanced Dynamic S-Box Design Based on Adaptive Heuristic Evolution and Multi-Stage Nonlinearity for Securing IoT-Based Remote Health Monitoring Systems

Enhanced Dynamic S-Box Design Based on Adaptive Heuristic Ev...

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Dube, Anand Prakash Yadav, Raghav Shuats Department of Computer Science & Information Technology Prayagraj India

ISBN: (纸本)9798331508432

This paper presents an enhanced method for designing dynamic S-Boxes, integrating adaptive heuristic evolution with multi-stage nonlinearity enhancements, tailored to improve cryptographic security in IoT-based remote health monitoring systems. The unprecedented rise in patient numbers during the COVID-19 pandemic highlighted the need for continuous health monitoring, enabled by IoT devices. Given the sensitivity and volume of health data transmitted, ensuring robust data security has become essential. The proposed S-Box design introduces an adaptive mechanism that adjusts the permutation and substitution processes based on real-time cryptographic performance feedback, optimizing the S-Box structure dynamically. By incorporating multi-stage nonlinearity, the method increases cryptographic complexity, effectively countering linear and differential cryptanalysis. The hybrid approach combines modular operations, affine transformations, and enhanced multiplicative inversion, generating a broad spectrum of robust, key-dependent S-Boxes. The method is further enhanced by introducing lightweight block encryption to secure sensitive health data in resource-constrained IoT environments. This approach also extends cryptographic evaluations to include dynamic avalanche criteria and an enhanced bit independence metric, ensuring superior resilience against attacks. Experimental results demonstrate the effectiveness of the K-star classification method in predicting critical health conditions, achieving an accuracy of 95%, with lightweight encryption providing secure data transmission. Comparative studies validate the method's superior performance over contemporary S-Box designs, emphasizing its potential for integration in high-security cryptographic frameworks, particularly for secure communication channels and image encryption in healthcare IoT systems. © 2024 IEEE.

关键词： Affine transforms

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Exploring Knowledge Landscapes: Clustering and Topic Modeling of Sebha University Scientific Publications 4

Exploring Knowledge Landscapes: Clustering and Topic Modelin...

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4th IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2024

作者： Abdelhamed, Roqyia Essgaer, Mansour Sebha University Faculty of Science Department of Computer Sciences Sebha Libya Sebha University Faculty of Information Technology Department of Information Technology Sebha Libya

ISBN: (纸本)9798350372632

The continuous and exponential growth in scientific publications poses a challenge in manually sorting and searching for specific topics within a university's publications. Recent advancements in artificial intelligence, natural language processing, and clustering methods, particularly the K-Means, have shown satisfactory performance in handling large volumes of textual data. Furthermore, topic modeling methods, notably praised for their effectiveness in organizing textual documents. This study focuses on evaluating the performance of the K-Means and Latent Dirichlet Allocation in clustering Sebha University scientific publications. Different term weighting is employed namely term frequency inverse document frequency, and Word2Vec to represent the tokens. K-Means demonstrated notable performance when coupled with Word2vec, as indicated by metrics such as the Davies-Bouldin Index, Silhouette Score, Calinski-Harabasz Index, and Inertia. The best performance was observed when using term frequency inverse document frequency with the Entropy metric. The evaluation metrics for K-Means did not unanimously agree on the optimal number of clusters. In contrast, Latent Dirichlet Allocation consistently performed well, with all measurements agreeing on the best results with the smallest number of clusters. Among the various combinations tested, Latent Dirichlet Allocation paired with Word2vec produced the best results across metrics, demonstrating superior cohesion strength through the topic coherence metric and minimizing noise aggregates based on the entropy metric. © 2024 IEEE.

关键词： Statistics

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Oppositional Red Fox Optimization Based Task Scheduling Scheme for Cloud Environment

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computer Systems science & Engineering 2023年第4期45卷 483-495页

作者： B.Chellapraba D.Manohari K.Periyakaruppan M.S.Kavitha Department of Information Technology Karpagam Institute of TechnologyCoimbatore641032TamilnaduIndia Department of Computer Science and Engineering St.Joseph’s Institute of TechnologyChennai600119India Department of Computer Science&Engineering SNS College of EngineeringCoimbatore641107India Department of Computer Science&Engineering SNS College of TechnologyCoimbatore641035India

Owing to massive technological developments in Internet of Things(IoT)and cloud environment,cloud computing(CC)offers a highly flexible heterogeneous resource pool over the network,and clients could exploit various resources on *** IoT-enabled models are restricted to resources and require crisp response,minimum latency,and maximum bandwidth,which are outside the *** was handled as a resource-rich solution to aforementioned *** high delay reduces the performance of the IoT enabled cloud platform,efficient utilization of task scheduling(TS)reduces the energy usage of the cloud infrastructure and increases the income of service provider via minimizing processing time of user ***,this article concentration on the design of an oppositional red fox optimization based task scheduling scheme(ORFOTSS)for IoT enabled cloud *** presented ORFO-TSS model resolves the problem of allocating resources from the IoT based cloud *** achieves the makespan by performing optimum TS procedures with various aspects of incoming *** designing of ORFO-TSS method includes the idea of oppositional based learning(OBL)as to traditional RFO approach in enhancing their efficiency.A wide-ranging experimental analysis was applied on the CloudSim *** experimental outcome highlighted the efficacy of the ORFO-TSS technique over existing approaches.

关键词： Metaheuristics task scheduling cloud computing internet of things makespan red fox optimizer

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