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Content Caching Algorithms in Drone-Aided Ad Hoc Networks

computers, Materials & Continua 2024年第12期81卷 4727-4742页

作者： Yong Beom Park Jian Kim Beom Kyu Suh Ismatov Akobir Ki-Il Kim Department of Computer Science and Engineering Chungnam National UniversityDaejeon34134Republic of Korea

Content delivery networks(CDNs)lead to fast content distribution through content caching at specific CDN servers near end ***,existing CDNs based on infrastructure cannot be employed in special cases,such as military ***,a temporary CDN without an existing infrastructure is *** achieve this goal,we introduce a new CDN for drone-aided ad hoc networks,whereby multiple drones form ad hoc networks and quickly store specific content according to new caching *** the typical CDN server,the content-caching algorithm in the proposed architecture considers the limited storage capacity of the *** present three content distribution algorithms that consider the constraints and mobility of *** main contribution of content caching for drone-aided ad hoc networks is to keep partial segments rather than whole content as well as move the drone near to area with a high volume of *** proposed scheme is evaluated to demonstrate its feasibility in terms of content acquisition time and utilization in several practical scenarios through ***,acquisition time in CDN to support drone movement is improved by approximately 50%and 40%rather than one in the proposed naive greedy approach as a function of content request interval and size,respectively.

关键词： Content delivery networks content caching unmanned aerial networks Ad hoc networks partial segment duplicated contents drone

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Innovative Design for Mutual Coupling Reduction in Dual-Element Array Antennas for ISM Applications Using Whale Optimization Algorithm

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Progress in Electromagnetics Research C 2025年 152卷 121-129页

作者： Atashpanjeh, Elham Rezaei, Pejman Department of Electrical and Computer Engineering Semnan University Semnan35131-19111 Iran

This paper introduces a decoupled dual-element array antenna designed to address the challenges of mutual coupling between elements. To tackle this issue, a neutralization line is strategically incorporated to suppress leaky surface currents, while ensuring that the antenna’s central frequency and radiation pattern remain intact. The dimensions of the neutralization line are carefully optimized using the Whale Optimization Algorithm (WOA) to achieve the best possible performance, focusing on minimizing mutual coupling and enhancing gain. By placing the neutralization line nearby between the two elements, surface currents are efficiently redirected back to the radiating element, preventing leakage to neighboring elements. This approach also results in a more compact structure. The proposed antenna, with overall dimensions of 50 mm × 30 mm × 1.6 mm, is simulated using analytic software. It achieves an impressive 27 dB reduction in mutual coupling and delivers an ultra-wide bandwidth of 1.2 GHz within the Industrial Scientific and Medical (ISM) band at an operating frequency of 2.4 GHz, with a measured maximum gain of −5.19 dB. The structure was fabricated, and experimental results closely matched the simulations, confirming the design’s effectiveness. By leveraging the WOA optimization method, the geometry of the neutralization line was fine-tuned to maximize performance, significantly improving inter-element decoupling. This design approach is simple yet effective and can be readily extended to other antenna array configurations, demonstrating strong potential for compact and efficient ISM band applications. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Directional patterns (antenna)

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Secure beamforming and deployment design for rate-splitting multiple access-based UAV communications

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Science China(Information Sciences) 2025年第1期68卷 342-356页

作者： Bing WANG Xiaofeng TAO Shujun HAN Huici WU Kai YANG Zhu HAN National Engineering Research Center of Mobile Network Technologies Beijing University of Posts and Telecommunications Peng Cheng Laboratory Department of Electrical and Computer Engineering University of Houston

Rate-splitting multiple access(RSMA) has recently gained attention as a potential robust multiple access(MA)scheme for upcoming wireless networks. Given its ability to efficiently utilize wireless resources and design interference management strategies, it can be applied to unmanned aerial vehicle(UAV) networks to provide convenient services for large-scale access ground users. However, due to the line-of-sight(LoS) broadcast nature of UAV transmission, information is susceptible to eavesdropping in RSMA-based UAV networks. Moreover, the superposition of signals at the receiver in such networks becomes complicated. To cope with the challenge, we propose a two-user multi-input single-output(MISO) RSMA-based UAV secure transmission framework in downlink communication networks. In a passive eavesdropping scenario, our goal is to maximize the sum secrecy rate by optimizing the transmit beamforming and deployment location of the UAV-base station(UAV-BS),while considering quality-of-service(QoS) constraints, maximum transmit power, and flight space limitations. To address the non-convexity of the proposed problem, the optimization problem is first decoupled into two subproblems. Then, the successive convex approximation(SCA) method is employed to solve each subproblem using different propositions. In addition, an alternating optimization(AO)-based location RSMA(L-RSMA) beamforming algorithm is developed to implement joint optimization to obtain the suboptimal solution. Numerical results demonstrate that(1) the proposed L-RSMA scheme yields a28.97% higher sum secrecy rate than the baseline L-space division multiple access(SDMA) scheme;(2) the proposed L-RSMA scheme improves the security performance by 42.61% compared to the L-non-orthogonal multiple access(NOMA) scheme.

关键词： rate-splitting multiple access UAV communication precoding optimization deployment location physical layer security

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Enhanced segmentation of optic disc and cup using attention-based U-Net with dense dilated series convolutions

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Neural Computing and Applications 2025年第9期37卷 6831-6847页

作者： Kumar, G. Bharadwaja Kumar, Soham Department of Computer Science and Engineering VIT Chennai TamilNadu Chennai600127 India

Delineating the boundaries of the optic disc and cup regions is a critical pre-requisite for glaucoma screening because it allows for precise measurement of key parameters, such as cup-to-disc ratio, which is a critical indicator of optic nerve head damage, a hallmark of glaucoma progression. Accurate segmentation enables early detection and monitoring of the disease, aiding in timely intervention to prevent vision loss. The main contribution of this research work is to develop an automated process to isolate and demarcate the optic disc and cup from retinal fundus images. To prevent the blood vessels from interfering with the segmentation process, a novel method is used for vessel mask generation and vessel inpainting. Most of the research works have used based encoder–decoder models like U-Net architecture or handcrafted feature extraction techniques such as hough transform, fuzzy clustering, etc. The proposed model has made significant modifications to the U-Net model. (1) Dual attention mechanism at every layer of decoder and (2) dense dilated series convolutions as skip connections to generate higher level feature map. The proposed model achieved benchmark accuracies - Dice score of 95.95% and IoU score of 92.22% for optic disc segmentation averaged over fivefold. For the task of outlining the optic cup region, it attained a Dice score of 88.7% and IoU of 79.72%. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Image segmentation

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Explainable AI for Pancreatic Cancer Prediction and Survival Prognosis: An Interpretable Deep Learning and Machine Learning Approach

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Informatica (Slovenia) 2024年第4期48卷 623-640页

作者： Srinidhi, B. Bhargavi, M.S. Department of Computer Science and Engineering Bangalore Institute of Technology Bengaluru India

Pancreatic cancer's devastating impact and low survival rates call for improved detection methods. While Artificial Intelligence has shown remarkable progress, its increasing complexity has led to "black box" models, hindering their acceptance in critical fields like healthcare. To address this, Explainable Artificial Intelligence (XAI) has gained traction, aiming to create transparent AI systems. In this study, we propose a comprehensive approach that combines the power of Deep Learning for pancreatic cancer detection using Computed Tomography (CT) images and Machine Learning (ML) for survival prognosis based on clinical data. By leveraging CT images with Deep learning models such as Convolutional Neural Networks, VGG-16 and DenseNet-201, effective diagnosis of Pancreatic Cancer is achieved and comprehensive insights into the tumor's spatial characteristics are obtained. DenseNet-201 outperformed the other models in terms of accuracy and interpretability with a predictive accuracy of 95%. The integration of ML techniques such as Stochastic Gradient Descent, Naïve Bayes and Extra Tree classifiers with clinical data predicts the chances of survival, providing vital information for treatment planning and personalized care. To validate the model's accuracy and interpretability, a comprehensive XAI validation is conducted using state-of-the-art techniques like Local Interpretable Model-agnostic Explanations and Shapley Additive Explanations. These methods provide localized explanations for predictions, allowing clinicians to understand risk and survival chances. This study holds immense potential to aid healthcare professionals in diagnosis, prognosis, and personalized treatment strategies, contributing to enhanced patient outcomes in the fight against pancreatic cancer. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Stochastic systems

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Comprehensive overview of Alzheimer's disease utilizing Machine Learning approaches

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Multimedia Tools and Applications 2024年第37期83卷 85277-85329页

作者： Kumar, Rahul Azad, Chandrashekhar Department of Computer Science and Engineering National Institute of Technology Jamshedpur India

Alzheimer's disease is a common and complex brain disorder that primarily affects the elderly. Because it is progressing and has few effective therapies, it requires a thorough understanding of the condition;our study offers a comprehensive analysis of AD with a dual approach that combines both bibliometric and experimental analyses. The bibliometric analysis applies statistical and mathematical techniques to figure out the states of AD research, including publishing trends, prestigious journals, and collaborative networks. Concurrently, the experimental examination explores current advancements, focusing on Machine Learning, Deep Learning, and Metaheuristic approaches, tackling complex issues resulting from varied datasets. The experimental work is fascinating because it uses twenty classifiers and two datasets, initially without feature selection and then with seven feature selection techniques. This thorough investigation focuses on developments in disease processes, therapeutic approaches, and diagnostic tool development. This research offers a multidimensional overview of Alzheimer's disease by combining bibliometric and experimental methods, addressing problems and highlighting the shortcomings of earlier studies. By helping academics, policymakers, and healthcare professionals navigate the complexities of AD, this novel methodology advances a more thorough understanding of the Alzheimer's disease domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Speech processing

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Blockchain enabled MediVault for healthcare system

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Multimedia Tools and Applications 2025年第15期84卷 14805-14825页

作者： Chaurasia, Brijesh Kumar Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

The management of healthcare data has significantly benefited from the use of cloud-assisted MediVault for healthcare systems, which can offer patients efficient and convenient digital storage services for storing their medical records. Nevertheless, there are security risks associated with the current digital healthcare data, as malicious parties may work with cloud storage service providers to alter patient records or to directly disclose health record content to other adversaries for monetary advantage. In this paper, a blockchain-enabled MediVault for healthcare systems is proposed not only to provide safe storage of healthcare data in digital form but also secure access for authenticated entities such as patients, doctors, and pharmacists. In this MediVault, we introduced NFT generation and storage over the cloud using Interplanetary File System (IPFS) and FireBase as per user adaptability, Ehtereum Blockchain for immutability, and encryption and decryption using asymmetric keys for confidentiality. The empirical results and the performance evaluation demonstrate that the proposed MediVault is secure, simple, and efficient with a limited computation overhead. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Learning and Optimization over Robust Networked Systems

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Performance Evaluation Review 2025年第3期52卷 23-26页

作者： Lin, I-Cheng Department of Electrical and Computer Engineering Carnegie Mellon University United States

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Deep feature-support vector machine based hybrid model for multi-crop leaf disease identification in Corn, Rice, and Wheat

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Multimedia Tools and Applications 2025年第8期84卷 4751-4771页

作者： Bhola, Amit Kumar, Prabhat Department of Computer Science and Engineering National Institute of Technology Patna Bihar India

Corn, Rice, and Wheat serve as primary staple foods globally, playing a pivotal role in the economies of numerous countries. Despite their paramount importance, these cereal crops face susceptibility to various diseases, posing a significant threat to production. Timely and accurate disease diagnosis is imperative to mitigate these risks. In response, a range of Machine Learning (ML) and Deep Learning (DL) models have been devised to automate the identification of diseases impacting cereal crops. While DL excels with abundant data, traditional ML may outperform DL with limited data. This study proposes a hybrid model integrating deep transfer learning and ML techniques for identifying crop diseases. The model utilizes pre-trained DenseNet201 weights to extract features, which are subsequently fed into a Support Vector Machine (SVM) for identification. Experimental results show that the hybrid model outperforms several benchmark counterparts. The DenseNet201 deep feature combined with SVM exhibits superior performance, achieving an impressive accuracy score of 87.23% with just 20.2M parameters. The model underwent comprehensive performance assessment using precision, recall, F1 score, and overall accuracy metrics. It effectively classifies healthy and diseased categories for each crop, achieving accuracies of 99.82%, 98.75%, and 84.15% for Corn, Wheat, and Rice, respectively. Moreover, the model’s remarkable performance and lightweight nature make it a practical and efficient real-time crop disease detection solution, emphasizing its relevance and significance for farmers and researchers in the agricultural community. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Support vector machines

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L3Buddy: a location-aware academic content-recommendation system through machine learning based cache techniques

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Multimedia Tools and Applications 2025年第5期84卷 2633-2649页

作者： Guddadmani, Ashwini Chougale, Supriya Gokanvi, Megha Tapale, Manisha Pattar, Santosh Department of Computer Science and Engineering KLE Technological University Belagavi Campus India

At present, recommendation systems have become pivotal in personalized education learning management systems, where there is a growing need for location-based suggestions. Our problem addresses the inefficiency of current systems in integrating real-time location data, leading to latency and irrelevant content delivery. We propose L3Buddy, a context-aware system leveraging machine learning for location-specific video recommendations. This approach enhances accuracy and reduces latency, improving the user experience by delivering timely, relevant content. Our results demonstrate significant improvements in both recommendation precision and system responsiveness. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Adversarial machine learning

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