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Blockchain-based knowledge-aware semantic communications for remote driving image transmission

Digital Communications and Networks 2025年第2期11卷 317-325页

作者： Yangfei Lin Tutomu Murase Yusheng Ji Wugedele Bao Lei Zhong Jie Li Department of Computer and Network Engineering University of Electro-Communications Information Technology Center Nagoya University National Institute of Informatics(NII) Hohhot Minzu College Toyota Motor Corporation Department of Computer Science and Engineering Shanghai Jiaotong University

Remote driving, an emergent technology enabling remote operations of vehicles, presents a significant challenge in transmitting large volumes of image data to a central server. This requirement outpaces the capacity of traditional communication methods. To tackle this, we propose a novel framework using semantic communications, through a region of interest semantic segmentation method, to reduce the communication costs by transmitting meaningful semantic information rather than bit-wise data. To solve the knowledge base inconsistencies inherent in semantic communications, we introduce a blockchain-based edge-assisted system for managing diverse and geographically varied semantic segmentation knowledge bases. This system not only ensures the security of data through the tamper-resistant nature of blockchain but also leverages edge computing for efficient management. Additionally, the implementation of blockchain sharding handles differentiated knowledge bases for various tasks, thus boosting overall blockchain efficiency. Experimental results show a great reduction in latency by sharding and an increase in model accuracy, confirming our framework's effectiveness.

关键词： Semantic communication Remote driving Semantic segmentation Blockchain Knowledge base management

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Optic-GAN: a generalized data augmentation model to enhance the diabetic retinopathy detection

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International Journal of Information Technology (Singapore) 2025年第4期17卷 2251-2269页

作者： Kapoor, Preeti Arora, Shaveta Department of Computer Science and Engineering The NorthCap University Gurugram India

Diabetic retinopathy is a severe eye condition that can lead to vision loss at severe stages necessitating the early detection. Automating the detection reduces the labor and facilitates timely intervention. Deep learning models have advanced detection capabilities but often suffer from overfitting due to limited or imbalanced labeled fundus data. Most of the existing models still depend on conventional augmentation techniques which fail to provide a controlled realistic fundus image generation. To address these limitations, this paper presents Optic-GAN, a strategic, memory- and time-efficient generative model for dataset expansion. Optic-GAN balances the MESSIDOR I and II datasets, achieving a low Fréchet inception distance score and improving model accuracy from 89.56 to 96.06%. Optic-GAN stands out by being independent of the dataset structures, unlike existing models, enabling broader, more effective application across diverse contexts. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Augmentation Cycle GAN Diabetic retinopathy detection Fréchet inception distance Fundus images Generative A.I InceptionV3 Medical images Optic-GAN ResNet50V2 XceptionNet

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Enhancing Skin Disease Diagnosis: Light GBM-DMS Algorithm for Accurate Image Classification

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Cognitive Computation 2025年第3期17卷 1-18页

作者： Kalpana, B. Senthilselvi, A. Muruganandam, S. Kumar, S. Vinodh Department of Information Technology RMD Engineering College Tamil Nadu Kavaraipettai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai India Department of Computer Science and Business Systems Panimalar Engineering College Poonamallee Chennai Tamil Nadu India Department of Computer Science and Engineering Rajalakshmi Engineering College Tamil Nadu Thandalam Chennai India

Globally, skin diseases are emerging as the most common health problem. It initiates depressive disorder, and it also causes physical health distress. It rarely led to skin cancer in extreme cases. Diagnosing skin disease is subjective and time-consuming when it is performed by health experts manually. Hence, automatic skin disease prediction is required by both dermatologists and patients to enhance and accelerate treatment plans. Even though many machine-learning techniques are available to detect skin diseases, the timely prediction and accurate classification of skin lesions are still in demand. Hence, the Discrete Mycorrhizal Search Algorithm is used to optimize the novel LightGBM to meet this objective. Firstly, intrusions like noise elimination and hair removal are performed and then the image resizing takes place by utilizing the preprocessing pipelines. Subsequent to data pre-processing, the prime features are extracted using the convolutional self-attention based L2 regularization, thus minimizing the data complexity. At last, the extracted features are shifted to the final categorization phase which is done by the novel approach for effective classification of skin diseases. Two skin disease classification images, including datasets, the Skin Lesions Dermatoscopic Image dataset and Skin Cancer MNIST HAM10000, are selected to validate the classification performance. Comprehensive analyses are performed with significant measures that showed the proposed model attained better performances of 98.6%, 0.95, and 10.43 s from accuracy, intersection of union, and execution time, respectively. The stability and robustness under different environmental conditions for clinical applications in skin disease detection and classification tasks are analyzed for making decisions in real-time scenarios. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Dermatitis

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Enhanced Triple Layered Approach for Mitigating Security Risks in Cloud

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computers, Materials & Continua 2025年第4期83卷 719-738页

作者： Tajinder Kumar Purushottam Sharma Xiaochun Cheng Sachin Lalar Shubham Kumar Sandhya Bansal Computer Science&Engineering Department Jai Parkash Mukand Lal Innovative Engineering&Technology InstituteRadaur135133HaryanaIndia School of Computer Science&Engineering Galgotias UniversityGreater Noida203201Uttar PradeshIndia Computer Science Department Bay Campus Fabian WaySwansea UniversitySwanseaSA18ENUK Department of Engineering and Technology Gurugram UniversityGurugram122003India Presidency School of Computer Science Presidency UniversityBangalore560089India Maharishi Markendeshwar Engineering College Maharishi Markandeshwar(Deemed to be)UniversityMullanaAmbala133203India

With cloud computing,large chunks of data can be handled at a small ***,there are some reservations regarding the security and privacy of cloud data *** solving these issues and enhancing cloud computing security,this research provides a Three-Layered Security Access model(TLSA)aligned to an intrusion detection mechanism,access control mechanism,and data encryption *** TLSA underlines the need for the protection of sensitive *** proposed approach starts with Layer 1 data encryption using the Advanced Encryption Standard(AES).For data transfer and storage,this encryption guarantees the data’s authenticity and ***,the solution employs the AES encryption algorithm to secure essential data before storing them in the Cloud to minimize unauthorized ***-based access control(RBAC)implements the second strategic level,which ensures specific personnel access certain data and *** RBAC,each user is allowed a specific role and *** implies that permitted users can access some data stored in the *** layer assists in filtering granular access to data,reducing the risk that undesired data will be discovered during the *** 3 deals with intrusion detection systems(IDS),which detect and quickly deal with malicious actions and intrusion *** proposed TLSA security model of e-commerce includes conventional levels of security,such as encryption and access control,and encloses an insight intrusion detection *** method offers integrated solutions for most typical security issues of cloud computing,including data secrecy,method of access,and *** extensive performance test was carried out to confirm the efficiency of the proposed three-tier security *** have been made with state-of-art techniques,including DES,RSA,and DUAL-RSA,keeping into account Accuracy,QILV,F-Measure,Sensitivity,MSE,PSNR,SSIM,and computation time,encryption time,and decryption *** proposed

关键词： Cloud security:data encryption AES access control intrusion detection systems(IDS) role-based access control(RBAC)

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A blockchain-based solution to combating identity crime and credit card application fraud using data mining algorithms

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2025年第2期9卷 96-104页

作者： Shakadwipi, Amol Jagdish Jain, Dinesh Chandra Nagini, S. Department of Computer Science and Engineering Oriental University Indore India

Fraud, specifically identity theft and credit card fraud, poses significant threats not only to financial institutions but also to their users. In response to this growing problem, we present an innovative approach that integrates self-sovereign identity management based on blockchain and complex data analysis. Our comprehensive solution is designed to revolutionize identity verification in credit card application processes by significantly enhancing security and reducing vulnerability to identity fraud. The system that will be developed from our solution will help users obtain self-sovereign identity credentials through blockchain technology or distributed ledger technology, granting them full control over their personal data. This approach has been proven to drastically reduce the likelihood of identity theft, and it does not require centralization of data. Besides, the use of blockchain technology ensures more credible records of identification, as they are transparent and immutable. At P&L, we combine smart data mining with blockchain-based identity solutions as our primary strategy. These algorithms detect patterns and anomalies related to identity theft in massive datasets. The technology can quickly flag suspicious activity and verify identity claims in real-time by continuously comparing recent user activity with historical data. © (2025), (Society of Sytematic Innovation). All rights reserved.

关键词： Blockchain Credit Card Applications Fraudulent Identity Management Identity Theft Identity Verification Suspicious Activities Vulnerability

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Event Management System Using Spatial and Event Attribute Information

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SN computer science 2025年第3期6卷 1-9页

作者： Setia, Sonia Anjli, Km Bisht, Upasana Jyoti Raj, Dharm Department of Computer Science & Engineering Sharda School of Engineering & Technology Sharda University Uttar Pradesh Greater Noida India Department of Computer Science Institute of Innovation in Technology & Management Delhi Janakpuri India Department of Computer Science and Engineering ITS Engineering College Greater Noida India

The event management mechanism matches messages that have been subscribed to and events that have been published. To identify the subscriptions that correspond to the occurrence inside the category, it must first run a clustering operation on the subscription message data and determine the category to which the event belongs. In this work, we offer an on-the-fly publish-subscribe technique that can handle on-the-fly subscription changes and category modifications in a distributed environment, as well as the synchronization of spatial information and event attribute information. Additionally, without any prior information, clustering and matching processes can be carried out rapidly. The publish-subscribe method is implemented on a distributed system, and a load-balancing solution is proposed for the algorithm on the distributed system. We then constructed our cluster and used real data to empirically evaluate the suggested publish-subscribe technique. Experiments show that incorporating both similarity and relevance with frequency, compared to focusing on similarity alone, significantly decreases the number of categories which need to be matched for events. This leads to a 15.5% improvement in the speed of matching events and subscriptions. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Clustering algorithms Distributed systems Publish-and-subscribe systems

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A Comprehensive Data Description for LoRaWAN Path Loss Measurements in an Indoor Office Setting: Effects of Environmental Factors

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IEEE Access 2025年 13卷 83148-83170页

作者： Obiri, Nahshon Mokua Laerhoven, Kristof Van University of Siegen Department of Electrical Engineering and Computer Science Germany

This paper presents a comprehensive dataset of LoRaWAN technology path loss measurements collected in an indoor office environment, focusing on quantifying the effects of environmental factors on signal propagation. Utilizing a network of six strategically placed LoRaWAN end devices (EDs) and a single indoor gateway (GW) at the University of Siegen's Hölderlinstraße Campus in the City of Siegen, Germany, we systematically measured signal strength indicators such as the Received Signal Strength Indicator (RSSI) and the Signal-to-Noise Ratio (SNR) under various environmental conditions, including temperature, relative humidity, carbon dioxide (CO₂) concentration, barometric pressure, and particulate matter levels (PM_2.5). Our empirical analysis confirms that transient phenomena such as reflections, scattering, interference, occupancy patterns (induced by environmental parameter variations), and furniture rearrangements can alter signal attenuation by as much as 10.58 dB, highlighting the dynamic nature of indoor propagation. As an example of how this dataset can be utilized, we tested and evaluated a refined Log-Distance Path Loss and Shadowing Model that integrates both structural obstructions (Multiple Walls) and Environmental Parameters (LDPLSM-MW-EP). Compared to a baseline model that considers only Multiple Walls (LDPLSMMW), the enhanced approach reduced the root mean square error (RMSE) from 10.58 dB to 8.04 dB and increased the coefficient of determination (R2) from 0.6917 to 0.8222. By capturing the extra effects of environmental conditions and occupancy dynamics, this improved model provides valuable insights for optimizing power usage and prolonging device battery life, enhancing network reliability in indoor Internet of Things (IoT) deployments, among other applications. The dataset, accessible upon reasonable request, offers a solid foundation for future research and development in indoor wireless communication. © 2013 IEEE.

关键词： dataset environmental factors indoor propagation internet of things (IoT) LoRaWAN low-power wide area network (LPWAN) path loss

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AG Codes Have No List-Decoding Friends: Approaching the Generalized Singleton Bound Requires Exponential Alphabets

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IEEE Transactions on Information Theory 2025年第4期71卷 2443-2451页

作者： Alrabiah, Omar Guruswami, Venkatesan Li, Ray University of California at Berkeley Department of Electrical Engineering and Computer Science BerkeleyCA94709 United States University of California at Berkeley Department of Electrical Engineering and Computer Science Department of Mathematics Simons Institute for the Theory of Computing BerkeleyCA94709 United States Santa Clara University Department of Mathematics and Computer Science Santa ClaraCA95053 United States

A simple, recently observed generalization of the classical Singleton bound to list-decoding asserts that rate R codes are not list-decodable using list-size L beyond an error fraction L/L+1 (1-R) (the Singleton bound being the case of L=1 , i.e., unique decoding). We prove that in order to approach this bound for any fixed L > 1 , one needs exponential alphabets. Specifically, for every L > 1 and R ∈ (0,1) , if a rate R code can be list-of-L decoded up to error fraction L/L+1 (1-R - ∈ ) , then its alphabet must have size at least exp (ΩL,R(1\ ∈)) . This is in sharp contrast to the situation for unique decoding where certain families of rate R algebraic-geometry (AG) codes over an alphabet of size O(1\ ∈2) are unique-decodable up to error fraction (1-R-\ ∈ )2 . Our bounds hold even for subconstant ∈ ≥ 1/n , implying that any code exactly achieving the L-th generalized Singleton bound requires alphabet size 2ΩL,R(n) . Previously this was only known only for L=2 under the additional assumptions that the code is both linear and MDS. Our lower bound is tight up to constant factors in the exponent—with high probability random codes (or, as shown recently, even random linear codes) over exp(OL(1\ ∈)) -sized alphabets, can be list-of-L decoded up to error fraction L/L+1 (1-R -∈ ) . © 1963-2012 IEEE.

关键词： Errors

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Orbit-Orbit Interaction in Spatiotemporal Optical Vortex

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engineering 2025年第2期45卷 44-51页

作者： Jian Chen Jie Zhao Xi Shen Dewei Mo Cheng-Wei Qiu Qiwen Zhan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Shanghai Key Laboratory of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

While spin-orbit interaction has been extensively studied,few investigations have reported on the interaction between orbital angular momenta(OAMs).In this work,we study a new type of orbit-orbit coupling between the longitudinal OAM and the transverse OAM carried by a three-dimensional(3D)spatiotemporal optical vortex(STOV)in the process of tight *** 3D STOV possesses orthogonal OAMs in the x-y,t-x,and y-t planes,and is preconditioned to overcome the spatiotemporal astigmatism effect.x,y,and t are the axes in the spatiotemporal *** corresponding focused wavepacket is calculated by employing the Debye diffraction theory,showing that a phase singularity ring is generated by the interactions among the transverse and longitudinal vortices in the highly confined *** Fourier-transform decomposition of the Debye integral is employed to analyze the mechanism of the orbit-orbit *** is the first revelation of coupling between the longitudinal OAM and the transverse OAM,paving the way for potential applications in optical trapping,laser machining,nonlinear light-matter interactions,and more.

关键词： Spatiotemporal optical vortex Orbital angular momentum Momentum interaction Highly confined wavepacket Diffraction

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An Intelligent Smart Dynamic Feature Analysis Based Approach by Utilizing Deep Learning to Improve the Breast Cancer Detection

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IEEE Access 2025年 13卷 81643-81665页

作者： Shim, Seong-O Hussain, Lal Alsolami, Eesa Alkinani, Monagi H. University of Jeddah College of Computer Science and Engineering Department of Computer and Network Engineering Jeddah23890 Saudi Arabia The University of Azad Jammu and Kashmir Department of Computer Science and Information Technology King Abdullah Campus Chatter Kalas Muzaffarabad13100 Pakistan The University of Azad Jammu and Kashmir Neelum Campus Department of Computer Science Athmuqam13230 Pakistan University of Jeddah College of Computer Science and Engineering Department of Cybersecurity Jeddah23890 Saudi Arabia University of Jeddah College of Computer Science and Engineering Department of Computer Science and AI Jeddah23890 Saudi Arabia

Breast cancer remains one of the important global health concerns with high rates of mortality, highlighting the significance of more sophisticated diagnostic methods. Conventional methods, generally comprised of costly imaging and invasive biopsies, are of high burdens. Motivated by the limitations, the present study comes up with an innovative automated solution for the identification of breast cancer using deep learning analysis of mammograms. Moving away from the traditional approaches with inherent pre-processing and feature extraction constraints, this research focuses on a two-pronged improvement strategy: improved mammogram quality and highly optimized deep learning architecture. Specifically, we present a new Optimized InceptionResNetV2 model significantly optimized through the thoughtful addition of large data augmentation to increase robustness, LeakyReLU activation to facilitate gradient flow and accelerate learning, and MeanDropout regularization to mitigate overfitting and improve generalization. The model was also trained using Quantization aware training (QAT) to enable efficient deployment on low-resource devices without significant performance degradation. The performance on our proposed approach for the massive mammogram dataset reflects an evident improvement in detection performance over traditional techniques. Our InceptionResNetV2 optimized achieved state-of-the-art accuracy with outstanding measures of 98.06% sensitivity, 97.05%, positive predictive value (PPV) and specificity of 99.60%, negative predictive value (NPV) of 86.83%, 97.94% accuracy, F1-score of 96.90%, Matthew’s correlation coefficient (MCC) of 90.67%, and AUC of 0.9939. The benefits of proposed system are that it can deliver a more efficient, precise, and possibly cost-effective diagnostic tool for breast cancer. Through synergistic integration of architectural optimization, sophisticated regularization methods, and deployment-aware training, our proposed system enables earlier

关键词： Diseases

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