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Automation and Computation (AUTOCOM), International Conference on

作者： G.Anne Nisha Sreetha P Seetha A K. Manickam Sharath Kumar Jagannathan Sekar S D Department of Informatics Kyoto Computer Gakuin Kyoto Japan Department of Biomedical Engineering KPR Institute of Engineering and Technology Tamil Nadu India Department of Information Technology S. A. Engineering College Thiruverkadu Chennai India Department of Mathematics Sona College of Technology Tamil Nadu India Data Science Institute Saint Peter's University New Jersery Department of Mechanical Engineering R.M.K.Engineering College Chennai Tamilnadu India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Operational efficiency is one of the most important factors affecting customer satisfaction directly in e-commerce. Still, constant addressing of timely delivery, path simplification, and reduction of delays remains challenging problems. Deep Random Forest (DRF) is a new approach presented in this work to examine and forecast significant logistics metrics including delivery times and satisfaction scores. Development of this approach targeted to solve these issues. The model makes use of e-commerce past performance including order volumes, customer comments, and delivery times. Findings: The suggested approach achieves 10% increase in user satisfaction, 12% cost reduction, and 15% faster delivery times than baseline methods. This is accomplished by means of DRF, applied to examine trends and carry out predictive modifications. Results and Methodology: These results outperform those of the benchmarks established by Gradient Boosting and XGBoost, which displayed respective rises in satisfaction of 9% and 8%. Apart from improving the quality of service, the use of DRF not only strengthened user loyalty but also contributed to improve the transportation efficiency. This approach exposes the transforming opportunities of artificial intelligence in logistics management for e-commerce systems. This approach guarantees operations with a customer-centric concentration benefiting from measurable advantages.

关键词： Accuracy Costs Transportation Boosting Real-time systems Computational efficiency Electronic commerce Random forests Optimization Logistics

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Dual Dielectric Metasurface for Simultaneous Sensing and Reconfigurable Reflections

Dual Dielectric Metasurface for Simultaneous Sensing and Rec...

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European Conference on Antennas and Propagation, EuCAP

作者： Mahesh Birari Deepak Singh Nagarkoti Anestis Katsounaros Hruday Kumar Reddy Mudireddy Jagannath Malik George C. Alexandropoulos EpsilonR UK Texas Instruments USA Space Innovation Solutions Greece Indian Institute of Technology Patna India Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece Department of Electrical and Computer Engineering University of Illinois Chicago USA

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

This paper presents a novel dual-functional hybrid Reconfigurable Intelligent Surface (RIS) for simultaneous sensing and reconfigurable reflections. We design a novel hybrid unit cell featuring dual elements, which share the same phase center, to support both intended functionalities, with the antenna being miniaturized via a high dielectric material approach. The hybrid unit cell has a size of one eighth of the wavelength forming the foundation of an innovative metasurface that incorporates a sub-wavelength reflecting array of split-ring unit cells integrated with a load-tuning matrix. In particular, two interleaved sensing arrays of half-wavelength spacing, orthogonal polarization, and quarter-wavelength offset are embedded within the proposed dual-functional RIS, each tasked to sense the channel parameters towards one of the end communication nodes wishing to profit from the surface's reconfigurable reflections. Our full-wave simulations, indicatively centered around the frequency of 5.5 GHz, showcase the promising performance of both designed hybrid unit cells and reflective split-ring ones.

关键词： Wireless communication Human-robot interaction Reconfigurable intelligent surfaces Metasurfaces Reflector antennas Reflection Sensors Permeability Substrates Load modeling

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Provably Secure Efficient Key-Exchange Protocol for Intelligent Supply Line Surveillance in Smart Grids

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IEEE Internet of Things Journal 2025年

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J.F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan 250353 China

Intelligent supply line surveillance is critical for modern smart grids. Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the smart grid. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage, and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the Random or Real (ROR) model. Furthermore, informal security analysis demonstrates the protocol's robustness in rigorously resisting various attacks, including physical tampering, impersonation, ephemeral secret leakage and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol's superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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Enhancing Graph Representation Learning with Localized Topological Features

arXiv

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arXiv 2025年

作者： Yan, Zuoyu Zhao, Qi Ye, Ze Ma, Tengfei Gao, Liangcai Tang, Zhi Wang, Yusu Chen, Chao Wangxuan Institute of Computer Technology Peking University China Computer Science and Engineering Department University of California San Diego United States Department of Biomedical Informatics Stony Brook University United States Halıcıoğlu Data Science Institute University of California San Diego United States Weill Cornell Medicine Cornell University United States

Representation learning on graphs is a fundamental problem that can be crucial in various tasks. Graph neural networks, the dominant approach for graph representation learning, are limited in their representation power. Therefore, it can be beneficial to explicitly extract and incorporate high-order topological and geometric information into these models. In this paper, we propose a principled approach to extract the rich connectivity information of graphs based on the theory of persistent homology. Our method utilizes the topological features to enhance the representation learning of graph neural networks and achieve state-of-the-art performance on various node classification and link prediction benchmarks. We also explore the option of end-to-end learning of the topological features, i.e., treating topological computation as a differentiable operator during learning. Our theoretical analysis and empirical study provide insights and potential guidelines for employing topological features in graph learning tasks. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Minimum Mean Squared Error Holographic Beamforming for Sum-Rate Maximization

arXiv

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arXiv 2025年

作者： Sheemar, Chandan Kumar Khan, Wali Ullah Alexandropoulos, George C. Ahmed, Manzoor Chatzinotas, Symeon SnT department The University of Luxembourg Luxembourg Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens16122 Greece School of Computer and Information Science Institute for AI Industrial Technology Research Hubei Engineering University Xiaogan City432000 China

This paper studies the problem of hybrid holographic beamforming for sum-rate maximization in a communication system assisted by a reconfigurable holographic surface. Existing methodologies predominantly rely on gradient-based or approximation techniques necessitating iterative optimization for each update of the holographic response, which imposes substantial computational overhead. To address these limitations, we establish a mathematical relationship between the mean squared error (MSE) criterion and the holographic response of the RHS to enable alternating optimization based on the minimum MSE (MMSE). Our analysis demonstrates that this relationship exhibits a quadratic dependency on each element of the holographic beamformer. Exploiting this property, we derive closed-form optimal expressions for updating the holographic beamforming weights. Our complexity analysis indicates that the proposed approach exhibits only linear complexity in terms of the RHS size, thus, ensuring scalability for large-scale deployments. The presented simulation results validate the effectiveness of our MMSE-based holographic approach, providing useful insights. © 2025, CC BY.

关键词： Iterative methods

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ACHealthChain blockchain framework for access control and privacy preservation in healthcare

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Scientific Reports 2025年第1期15卷 1-25页

作者： Tawfik, Ahmed M. Al-Ahwal, Ayman Eldien, Adly S. Tag Zayed, Hala H. Computer Science Department Faculty of Computers and Artificial Intelligence Benha Egypt Communication and Electronics Department Pyramids-Institute for Engineering and Technology Giza Egypt Electrical Engineering Department Faculty of Engineering at Shoubra Benha Egypt Faculty of Engineering Egypt University of Informatics (EUI) Cairo Egypt

Ensuring privacy and confidentiality in healthcare data management remains a critical challenge. Traditional centralized access control mechanisms are susceptible to security breaches, including unauthorized access, data leakage, and single points of failure, as well as privacy violations such as patient record exposure and improper data sharing. To address these issues, this paper proposes ACHealthChain, a blockchain-based framework leveraging Hyperledger Fabric for decentralized and transparent access control. The framework integrates the InterPlanetary File System (IPFS) for decentralized storage and ensures privacy through Hyperledger Fabric channels. ACHealthChain features PolicyChain for fine-grained access control and revocation, structuring patient health data into separate subchains for EHRs and diagnoses with permissioned access. Additionally, LogChain enhances auditing and accountability. A series of experiments evaluate ACHealthChain’s performance and scalability, considering metrics such as throughput, latency, and resource utilization. Results demonstrate that ACHealthChain improves throughput by 19.7% and reduces latency by 87%, outperforming existing frameworks built on the same platform. The scalability analysis further confirms the framework’s capability to handle increasing workloads within an expanding blockchain network. ACHealthChain presents a promising solution for secure and efficient healthcare data sharing with potential real-world applications. © The Author(s) 2025.

关键词： Access control Blockchain Electronic health records Privacy Security

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Empowering Consumer Healthcare Through Sensor-Rich Devices using Federated Learning for Secure Resource Recommendation

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IEEE Transactions on Consumer Electronics 2025年

作者： Fathima, Afifa Salsabil Basha, Syed Muzamil Ahmed, Syed Thouheed Khan, Surbhi Bhatia Asiri, Fatima Basheer, Shakila Shukla, Madhu School of Computer Science and Engineering REVA University Karnataka Bengaluru India School of Computing and Information Technology REVA University Karnataka Bengaluru India Department of Data Science School of Science Engineering and Environment University of Salford Manchester United Kingdom Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India College of computer science Informatics and computer systems department King Khalid University Saudi Arabia Department of Information Systems College of computer and Information science Princess Nourah bint Abdulrahman University P.O. BOX 84428 Riyadh11671 Saudi Arabia the Department of CSE - AI ML & DS Marwadi University Gujarat Rajkot360003 India

When implementing zero-trust edge computing, offloading computational tasks and data access through traditional model training and usage approaches can lead to increased latency. Since the traditional methods often involve extensive communication with a central server, creating additional network hopping stations/nodes resulting in increased latency. The challenge is bound to allocate a befitting resource at a given consumer demand. In this proposed system, a federated learning model based data offloading and consumer medical resource recommendation of IoT is discussed and validated. The user/consumer group and local training models are aligned with edge servers for data preprocessing and customization with a series of resources demand creation and coordination. The consumer resource allocating priorities are fine-grained with the proposed blockchain based priority analyzer for recommendation and allocation. The computational parameter such as resource pool, average waiting time, energy consumption and transmission trust delays are observed and validated. The proposed framework fetches consumer resources logs and synchronizes the centralized training model for effective scheduling and allocation of resources with an accuracy of 94.92% under the 5G operating spectrum. The technique has demonstrated minimal latency in offloading the data request demand and resource allocation at the cloud servers. © 2025 institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Federated learning

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Privacy-preserving authentication protocol for user personal device security in Brain–computer Interface

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computer Standards and Interfaces 2025年 94卷

作者： Prajapat, Sunil Rana, Aryan Kumar, Pankaj Das, Ashok Kumar Susilo, Willy Srinivasa Ramanujan Department of Mathematics Central University of Himachal Pradesh Dharamshala176215 India Department of Computer Engineering AI Security Research Center Gachon University Korea Republic of Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad500 032 India Department of Computer Science and Engineering College of Informatics Korea University 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of Institute of Cybersecurity and Cryptology University of Wollongong WollongongNSW2522 Australia

Brain–computer Interface (BCI) technology has emerged as a transformative tool, particularly for individuals with severe motor disabilities. Non-invasive BCI systems, leveraging Electroencephalography (EEG), offer a direct interface between users and external devices, bypassing the need for muscular control. However, ensuring the security and privacy of users’ neural data remains a critical challenge. In this paper, we propose a novel privacy-preserving authentication scheme for EEG-based BCI systems, utilizing elliptic curve cryptography (ECC). Our scheme balances robust security with computational efficiency, making it suitable for resource-constrained environments. Since we are addressing security in a resource-constrained environment, such as EEG in BCI, we have constructed a lightweight authentication algorithm to meet the stringent requirements of minimal computational resources and energy consumption. The security analysis and performance evaluation of the authentication protocol show that our scheme is resistant to various attacks, such as replay, offline password guessing, privilege insider, user impersonation, and smart card stolen attacks. It offers mutual authentication and key agreement, requiring only 1632 bits of communication cost and 15.67139 ms of computational cost for the entire login authentication and key agreement phase. Our study lays a solid foundation for future investigation of innovative solutions for BCI security. © 2025 Elsevier B.V.

关键词： Authentication Protocol

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NFT-based Data Provenance for AI Transparency in Enterprise Information Systems

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Procedia computer Science 2025年 256卷 565-572页

作者： Yiannis Verginadis Orestis Almpanoudis Dimitris Apostolou Marcela T. de Oliveira Gregoris Mentzas Department of Business Administration Athens University of Economics and Business Patission 76 10434 Athens Greece Institute of Communications and Computer Systems Iroon Polytechniou 9 15780 Zografou Greece Department of Informatics University of Piraeus 18534 Piraeus Greece Department of Engineering Systems and Services Delft University of Technology (TU Delft) 2600 AA Delft The Netherlands

Enterprise Information Systems have a long-established and crucial role for modern organizations, as they enable seamless integration and management of critical business processes, ensuring efficiency in operations, data accuracy, and enhanced decision-making capabilities. One of their most interesting emerging technologies refer to the use of Artificial Intelligence as they may seamlessly automate routine tasks, offer predictive analytics, and provide deep insights, ultimately leading to intelligent data-driven decisions and improved operational efficiency. Of course, this direction of work is accompanied by some important challenges that come from the opacity of certain AI models and their potential biases due to low-quality training data used. In this paper, we argue that such challenges can be mitigated by a novel framework able to integrate, in a transparent manner, quality-related metadata on datasets used for training the AI-enabled emerging technologies in the field of EIS systems. These metadata are minted as Non-Fungible Tokens (NFTs) over the blockchain.

关键词： NFT-based Data Provenance AI Transparency in EIS

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LEARNING SURROGATE EQUATIONS FOR THE ANALYSIS OF AN AGENT-BASED CANCER MODEL

arXiv

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arXiv 2025年

作者： Burrage, Kevin Burrage, Pamela Kreikemeyer, Justin N. Uhrmacher, Adelinde M. Weerasinghe, Hasitha N. School of Mathematical Sciences Queensland University of Technology BrisbaneQLD Australia Department of Computer Science University of Oxford United Kingdom Modelling and Simulation Institute for Visual and Analytic Computing Faculty of Informatics and Electrical Engineering University of Rostock Rostock Germany

In this paper, we adapt a two species agent-based cancer model that describes the interaction between cancer cells and healthy cells on a uniform grid to include the interaction with a third species – namely immune cells. We run six different scenarios to explore the competition between cancer and immune cells and the initial concentration of the immune cells on cancer dynamics. We then use coupled equation learning to construct a population-based reaction model for each scenario. We show how they can be unified into a single surrogate population-based reaction model, whose underlying three coupled ordinary differential equations are much easier to analyse than the original agent-based model. As an example, by finding the single steady state of the cancer concentration, we are able to find a linear relationship between this concentration and the initial concentration of the immune cells. This then enables us to estimate suitable values for the competition and initial concentration to reduce the cancer substantially without performing additional complex and expensive simulations from an agent-based stochastic model. The work shows the importance of performing equation learning from agent-based stochastic data for gaining key insights about the behaviour of complex cellular dynamics. © 2025, CC BY.

关键词： Lung cancer

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