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A dynamic model of social media ad information diffusion in uncertain environment

Soft computing 2024年 1-16页

作者： Jin, Meiling Ning, Yufu Liu, Fengming Li, Zhen Zheng, Haoran Gao, Yichang Zhou, Jian School of Management Shanghai University Shanghai200444 China School of Information Engineering Shandong Youth University of Political Science Jinan250103 China Smart Healthcare Big Data Engineering and Ubiquitous Computing Characteristic Laboratory in Universities of Shandong Jinan250103 China Business School Shandong Normal University Jinan250014 China School of Logistics and Maritime Studies Bahrain Polytechnic Isa Town Bahrain School of Economics Shanghai University Shanghai200444 China Confucius Institute University of Bahrain Zallaq Bahrain

In the social media environment, ad (advertise) information diffusion can effectively increase the ad promotion effect and promote product marketing. Therefore, it is essential to study the mechanism of ad information diffusion in social media. This study aims to help cope with the complexity and uncertainty in social media ad information diffusion systems by identifying the causal relationships behind ad information diffusion behavior and clarifying the feedback mechanisms of system factors, and then testing the validity of the system model through simulation. Specifically, this study innovatively combines system dynamics and uncertainty theory to construct a dynamic model of ad information diffusion system. Particularly, the uncertainty effect of environmental noise on the ad information diffusion system is considered and portrayed as a Liu process. This study can explore the diffusion mechanism of ad information more precisely, so as to better serve the ad promotion industry. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： System theory

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A Secured and Optimized Broadcast Authentication Scheme for the Internet of Medical Things 4

A Secured and Optimized Broadcast Authentication Scheme for ...

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4th IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Riaz, Syed Waleed Khan, Fazlullah Ullah, Insaf Alshawi, Bandar Alturki, Ryan Wedyan, Mohammad Abdul Wali Khan University Mardan Computer Science Department KPK Pakistan School of Computer Science University of Nottingham Ningbo China Faculty of Science and Engineering Zhejiang Ningbo315104 China Institute for Analytics and Data Science University of Essex ColchesterCO4 3SQ United Kingdom College of Computing Umm Al-Qura University Department of Computer and Network Engineering Makkah Saudi Arabia College of Computing Umm Al-Qura University Department of Software Engineering Makkah Saudi Arabia Faculty of Information Technology and Computer Sciences Department of Computer Sciences Irbid21163 Jordan

ISBN: (纸本)9798350395914

This Internet of Medical Things (IoMT), facilitates the medical stop regarding real-time monitoring of patients, medical emergency management, remote surgery, patient information management, medical equipment, drug monitoring, etc. However, IoMT devices communicate with each other in an open environment that makes them vulnerable to a wide range of malicious threats from malicious entities. To protect the devices and their associated data, we designed a broadcast authentication scheme for IoMT devices using identity-based public key cryptography that exploits the lightweight features of the Hyper-Elliptic Curve (HEC). We then performed the security analysis based on the Random Oracle Model (ROM), in which we have proved that the proposed scheme is unforgeable under the hardiness of the hyperelliptic curve discrete logarithm problem. The proposed scheme is analyzed in terms of computational and communication overhead and the experimental results justify the superiority of the proposed work in comparison to the existing schemes. © 2024 IEEE.

关键词： Authentication

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Learnable Broad Learning for Semi-Supervised Specific Emitter Identification in the Internet of Everything

Learnable Broad Learning for Semi-Supervised Specific Emitte...

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IEEE Conference on Wireless Communications and Networking

作者： Yibin Zhang Yang Peng Qin Wang Yun Lin Guan Gui Dusit Niyato Fumiyuki Adachi College of Telecommunications and Information Engineering NJUPT Nanjing China College of Information and Communication Engineering Harbin Engineering University Harbin China College of Computing and Data Science Nanyang Technological University Singapore International Research Institute of Disaster Science Tohoku University Sendai Japan

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Specific emitter identification (SEI) is crucial in the Internet of Everything (IoE). Over the past decade, deep learning (DL) and broad learning (BL)-enabled SEI technologies have emerged. Recently, many researchers have begun exploring semi-supervised learning techniques to address the semi-supervised SEI (SS-SEI) problem with limited labeled RF signals. However, existing SS-SEI solutions often prioritize identification performance, leading to high computational overheads and lacking iterability. To overcome these challenges, this paper proposes a novel SS-SEI solution based on a learnable broad learning network (LBL). Initially, a pretrained DL-based SEI model is downloaded to the edge device. Meanwhile, an updatable BL-based SEI method is deployed locally on the edge device to identify unlabelled signals. When the LBL solution is operational, edge devices capture real-time unlabelled RF signals. The pretrained DL-based SEI method and the locally BL-based SEI method jointly identify these RF signals. The identification results and the new real-time RF signals are then used to update the weights of the BL-based SEI method at the edge devices. The LBL SS-SEI solution is validated using an open-source, large-scale, real-world automatic dependent surveillance-broadcast (ADS-B) dataset. Experimental results demonstrate that the proposed LBL solution offers significant advantages regarding SS-SEI performance.

关键词： Performance evaluation Deep learning Computational modeling RF signals Semisupervised learning Feature extraction Real-time systems Object recognition Internet of Things Servers

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Empowering autonomous IoT devices in blockchain through gasless transactions

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Blockchain: Research and Applications 2025年第2期6卷

作者： Madhwal, Yash Yanovich, Yury Korotkevich, Aleksandra Parshina, Daria Seropian, Nshteh Gavrilov, Stepan Nikolaev, Alex Balachander, S. Murugan, A. Skolkovo Institute of Science and Technology Moscow121205 Russia Faculty of Computer Science HSE University Moscow109028 Russia Faculty of Information and Communication Technologies ITMO University St. Petersburg197101 Russia Moscow Institute of Physics and Technology Moscow141701 Russia Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur603203 India

The article introduces a proof-of-concept (PoC) that demonstrates the management of Internet of Things (IoT) devices' infrastructure via smart contracts, facilitating their interaction with the blockchain through gasless transactions. The focus is empowering IoT devices to autonomously sign transactions using their verified private keys, eliminating the necessity for external wallets and enabling blockchain interaction using Biconomy without incurring gas fees. In this PoC, managers can validate IoT devices, permitting them to transmit transactions securely without being able to manipulate measurements or risking losing crypto assets in case of hardware malfunctions. This innovative method ensures that devices with minimal funds can access sensor data and communicate with a smart contract on the blockchain to update information utilizing account abstraction. Detailed workflow and simulation results are provided to showcase the practicality and advantages of this approach in scenarios demanding seamless automated blockchain engagement through IoT devices. The PoC code is openly accessible on GitHub, enhancing the transparency and accessibility of our research outcomes. © 2025 The Author(s)

关键词： Account abstraction Blockchain Gasless transaction Internet of Things Mobile application Smart contract

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Deep Convolutional Neural Network Framework for Brain Tumor Classification using MRI Images 2

Deep Convolutional Neural Network Framework for Brain Tumor ...

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2nd International Conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Sakthi, U. Thangaraj, K. Tamizhselvi, A. Kirubakaran, M.K. Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu TN Chennai603203 India Department of Information Technology Sona College of Technology Junction Main Road TN Salem636005 India Department of Information Technology St. Joseph's College of Engineering TN Chennai600119 India Department of Artificial Intelligence and Data Science St. Joseph's Institute of Technology TN Chennai600119 India

ISBN: (纸本)9798350340235

Brain cancer is one of the high-risk diseases and increases the death rate in all countries, affecting both men and women. The early diagnosis and severity of brain cancer leads to better medical treatment and save people's lives. The machine learning procedure has been applied for early detection and treatment of brain cancer in the biomedical field by classifying them into low-risk and high-risk groups. In cancer research, the predictive and classification model has been developed using Deep Convolutional Neural Network (DCNN) algorithms for accurate decision making. The Magnetic Resonance Image (MRI) classification technique DCNN is advanced to detect and match feature points of training and test images. The DCNN classifier based on the outcome of feature points then classifies images. The key notion of this proposed research effort is to implement and execute the proposed DCNN algorithm on cancer patient datasets for risk level classification. The brain cancer affected patient details are collected from UCI machine learning data repository for experimental analysis. In this research study, the DCNN algorithm is proposed and it gives better accuracy and faster than the KNN, CNN and SVM. © 2023 IEEE.

关键词： Brain Cancer Convolutional Neural Convolutional Network Deep Learning Medical Resonance Image Classification

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All-in-one Weather-degraded Image Restoration via Adaptive Degradation-aware Self-prompting Model

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IEEE Transactions on Multimedia 2025年

作者： Wen, Yuanbo Gao, Tao Li, Ziqi Zhang, Jing Zhang, Kaihao Chen, Ting Chang'an University School of Information Engineering Xi'an China Chang'an University School of Data Science and Artificial Intelligence Xi'an China Australian National University School of Computing CanberraACT Australia Harbin Institute of Technology School of Computer Science and Technology Shenzhen China

Existing approaches for all-in-one weather-degraded image restoration suffer from inefficiencies in leveraging degradation-aware priors, resulting in sub-optimal performance in adapting to different weather conditions. To this end, we develop an adaptive degradation-aware self-prompting model (ADSM) for all-in-one weather-degraded image restoration. Specifically, our model employs the contrastive language-image pre-training model (CLIP) to facilitate the training of our proposed latent prompt generators (LPGs), which represent three types of latent prompts to characterize the degradation type, degradation property and image caption. Moreover, we integrate the acquired degradation-aware prompts into the time embedding of diffusion model to improve degradation perception. Meanwhile, we employ the latent caption prompt to guide the reverse sampling process using the cross-attention mechanism, thereby guiding the accurate image reconstruction. Furthermore, to accelerate the reverse sampling procedure of diffusion model and address the limitations of frequency perception, we introduce a wavelet-oriented noise estimating network (WNE-Net). Extensive experiments conducted on eight publicly available datasets demonstrate the effectiveness of our proposed approach in both task-specific and all-in-one applications. © 1999-2012 IEEE.

关键词： Restoration

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Intelligent Network Optimisation for Beyond 5G Networks Considering Packet Drop Rate 25

Intelligent Network Optimisation for Beyond 5G Networks Cons...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Mahmoud, Haitham Aneiba, Adel He, Ziming Tong, Fei Guo, Liucheng Asyhari, Taufiq Wang, Ziwei Gao, Zhen College Of Computing Birmingham City University Birmingham United Kingdom Samsung Cambridge Solution Centre System Lsi Samsung Electronics Cambridge United Kingdom London United Kingdom Monash University Data Science Department Tangerang Indonesia School Of Engineering Lancaster University Lancaster United Kingdom School Of Information And Electronics Beijing Institute Of Technology Beijing China

ISBN: (纸本)9798350340266

To meet the growing expectations for fast and dependable connectivity, Novel approaches such as reinforcement learning-based resource allocation and network slicing are essential to consider. Enhancing network intelligence through the use of deep reinforcement learning and machine learning may increase capacity, lower latency and congestion, improve energy efficiency, and open up new revenue opportunities and business models. However, the current body of research falls short in terms of thoroughly exploring network slicing datasets and taking packet drop likelihood into account when allocating resources. With reference to current benchmarks, this paper presents a network slicing strategy and illustrates its efficacy using seven machine learning algorithms (ANN, SVM, RF, DT, GNG, LDA, and RT). To prioritize packets that run the risk of being dropped, a priority algorithm is also developed. To improve network performance, a resource allocation technique is used that is based on the mathematical study of packet drop rate. Utilizing deep reinforcement learning and genetic algorithms, the distribution of tasks across Cloud and Edge resources is further improved according to network slice characteristics. In spite of high network traffic, this guarantees constant service availability. © 2024 IEEE.

关键词： Resource allocation

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HistoMSC: Density and topology analysis for AI-based visual annotation of histopathology whole slide images

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Computers in Biology and Medicine 2025年 190卷 109991-109991页

作者： Ahmad, Zahoor Al-Thelaya, Khaled Alzubaidi, Mahmood Joad, Faaiz Gilal, Nauman Ullah Mifsud, William Boughorbel, Sabri Pintore, Giovanni Gobbetti, Enrico Schneider, Jens Agus, Marco Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar Kite Pharma Santa MonicaCA United States Visual and Data-intensive Computing CRS4 Italy Qatar Computing Research Institute Hamad Bin Khalifa University Qatar Foundation Doha Qatar

We introduce an end-to-end framework for the automated visual annotation of histopathology whole slide images. Our method integrates deep learning models to achieve precise localization and classification of cell nuclei with spatial data aggregation to extend classes of sparsely distributed nuclei across the entire slide. We introduce a novel and cost-effective approach to localization, leveraging a U-Net architecture and a ResNet-50 backbone. The performance is boosted through color normalization techniques, helping achieve robustness under color variations resulting from diverse scanners and staining reagents. The framework is complemented by a YOLO detection architecture, augmented with generative methods. For classification, we use context patches around each nucleus, fed to various deep architectures. Sparse nuclei-level annotations are then aggregated using kernel density estimation, followed by color-coding and isocontouring. This reduces visual clutter and provides per-pixel probabilities with respect to pathology taxonomies. Finally, we use Morse–Smale theory to generate abstract annotations, highlighting extrema in the density functions and potential spatial interactions in the form of abstract graphs. Thus, our visualization allows for exploration at scales ranging from individual nuclei to the macro-scale. We tested the effectiveness of our framework in an assessment by six pathologists using various neoplastic cases. Our results demonstrate the robustness and usefulness of the proposed framework in aiding histopathologists in their analysis and interpretation of whole slide images. © 2025 The Authors

关键词： Image annotation

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Combined Effect of Concept Drift and Class Imbalance on Model Performance During Stream Classification

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Computers, Materials & Continua 2023年第4期75卷 1827-1845页

作者： Abdul Sattar Palli Jafreezal Jaafar Manzoor Ahmed Hashmani Heitor Murilo Gomes Aeshah Alsughayyir Abdul Rehman Gilal Department of Computer and Information Sciences Universiti Teknologi PETRONAS(UTP)Seri Iskandar32610Malaysia Centre for Research in Data Science UTPPerak32610Malaysia High Performance Cloud Computing Centre(HPC3) UTPPerak32610Malaysia School of Engineering and Computer Science Victoria University of WellingtonWellington6012New Zealand AI Institute University of Waikato WellingtonHamilton3240New Zealand Anti-Narcotics Force Ministry of Narcotics ControlIslamabad46000Pakistan College of Computer Science and Engineering Taibah UniversityMadinah42353Saudi Arabia

Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes overtime, leading to class imbalance and concept drift issues. Both these issuescause model performance degradation. Most of the current work has beenfocused on developing an ensemble strategy by training a new classifier on thelatest data to resolve the issue. These techniques suffer while training the newclassifier if the data is imbalanced. Also, the class imbalance ratio may changegreatly from one input stream to another, making the problem more *** existing solutions proposed for addressing the combined issue of classimbalance and concept drift are lacking in understating of correlation of oneproblem with the other. This work studies the association between conceptdrift and class imbalance ratio and then demonstrates how changes in classimbalance ratio along with concept drift affect the classifier’s *** analyzed the effect of both the issues on minority and majority classesindividually. To do this, we conducted experiments on benchmark datasetsusing state-of-the-art classifiers especially designed for data stream ***, recall, F1 score, and geometric mean were used to measure theperformance. Our findings show that when both class imbalance and conceptdrift problems occur together the performance can decrease up to 15%. Ourresults also show that the increase in the imbalance ratio can cause a 10% to15% decrease in the precision scores of both minority and majority *** study findings may help in designing intelligent and adaptive solutionsthat can cope with the challenges of non-stationary data streams like conceptdrift and class imbalance.

关键词： Classification data streams class imbalance concept drift class imbalance ratio

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Multivariate Time Series Imputation Based on Masked Autoencoding with Transformer 24

Multivariate Time Series Imputation Based on Masked Autoenco...

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24th IEEE International Conference on High Performance computing and Communications, 8th IEEE International Conference on data science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Wang, Yongsheng Xu, Hao Xu, Zhiwei Gao, Jing Wu, Yuhao Zhang, Zhe Inner Mongolia University of Technology College of Data Science and Application Hohhot China Chinese Academy of Sciences Institute of Computing Technology Beijing China Inner Mongolia Agricultural University College of Computer and Information Engineering Hohhot China

ISBN: (纸本)9798350319934

In this work, we propose a self-supervised learning model based on the transformer framework, using it to impute missing data in multivariate time series. Unlike selfsupervised learning models in NLP and CV fields, it always trains a huge pre-trained model, which can fine-tune the model well in other datasets in the same field. Our Multivariate Time Series Based on the Maksed AutoEncoding model (MTAE) uses the position encoding and mask matrix that are more suitable for missing time series while keeping the general framework of the transformer model unchanged, which can take standard time series information into account. Also, our change can note the change in time delay caused by missing data. Finally, we validate the imputation results of MTAE on two different time series datasets and obtain better evaluations than other machine learning models with a missing dataset rate of 90%. © 2022 IEEE.

关键词： Time series

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