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Detection of Misinformation Related to Pandemic Diseases using Machine Learning Techniques in Social Media Platforms

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EAI Endorsed Transactions on Pervasive Health and Technology 2024年 10卷

作者： Naeem, J. Gul, O.M. Parlak, I.B. Karpouzis, K. Salman, Y.B. Kadry, S.N. Department of Computer Engineering Bahcesehir University Istanbul Turkey Informatics Institute Istanbul Technical University Istanbul Turkey Department of Computer Engineering Galatasaray University Istanbul Turkey Panteion University of Social and Political Sciences Greece Department of Software Engineering Bahcesehir University Istanbul Turkey Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon MEU Research Unit Middle East University Amman11831 Jordan

INTRODUCTION: The advent of the COVID-19 pandemic has brought with it not only a global health crisis but also an infodemic characterized by the rampant spread of misinformation on social media platforms. OBJECTIVES: In response to the urgent need for effective misinformation detection, this study presents a comprehensive approach harnessing machine learning and deep learning techniques, culminating in ensemble methods, to combat the proliferation of COVID-19 misinformation on Facebook, Twitter, Instagram, and YouTube. METHODS: Drawing from a rich dataset comprising user comments on these platforms, encompassing diverse COVID-19-related discussions, our research applies Support Vector Machine (SVM), Decision tree, logistic regression, and neural networks to perform in depth analysis and classification of comments into two categories: positive and negative information. The innovation of our approach lies in the final phase, where we employ ensemble methods to consolidate the strengths of various machine learning and deep learning algorithms. This ensemble approach significantly improves the model’s overall accuracy and adaptability. RESULTS: Experimental results underscore the efficacy of our methodology, showcasing marked improvements in detection performance compared to individual models. After applying ensemble learning, we achieve an accuracy of 91% for Facebook data, 79% for Instagram data, 80% for Twitter data and 95% for YouTube data. CONCLUSION: Our system not only aids in curbing the dissemination of COVID-19 misinformation but also provides a robust framework for addressing misinformation across various contexts on social media platforms. © 2024 J. Naeem et al., licensed to EAI.

关键词： COVID-19

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Electrically Programmable Non-Volatile Silicon Photonic Content Addressable Memory (CAM) cell

Electrically Programmable Non-Volatile Silicon Photonic Cont...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Lian, Chuanyu Sun, Hongyi Huang, Yi-Siou Vitale, Steven A. Hu, Juejun Takeuchi, Ichiro Youngblood, Nathan Vagionas, Christos Ríos Ocampo, Carlos A. Department of Materials Science & Engineering University of Maryland College ParkMD20742 United States Institute for Research in Electronics and Applied Physics University of Maryland College ParkMD20742 United States Advanced Materials and Microsystems Group MIT Lincoln Laboratory LexingtonMA02139 United States Department of Materials Science & Engineering MIT CambridgeMA02139 United States Quantum Materials Center Department of Physics University of Maryland College ParkMD20742 United States Electrical & Computer Engineering Department The University of Pittsburgh PittsburghPA15213 United States Department of Informatics Center for Interdisciplinary Research and Innovation Aristotle University of Thessaloniki Thessaloniki Greece

We experimentally demonstrate the first electrically programmable, non-volatile silicon photonic content addressable memory cell using Sb2Se3 phase change material on microring resonators, opening the path for light-based search operations in zero-power look-up tables. © Optica Publishing Group 2024

关键词： Silicon photonics

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Hybrid BiGRU-CNN Model for Load Forecasting in Smart Grids with High Renewable Energy Integration

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IET Generation, Transmission and Distribution 2025年第1期19卷

作者： Ullah, Kaleem Shakir, Daniyal Abid, Usama Alahmari, Saad Aslam, Sheraz Ullah, Zahid US-Pakistan Centre for Advanced Studies in Energy University of Engineering and Technology Peshawar Peshawar Pakistan Sr. Business Analyst S&P Global Islamabad Islamabad Pakistan Department of Computer Science and Information Technology School of Computing Engineering and Mathematical Sciences La Trobe University MelbourneVIC Australia Department of Computer Science Applied College Northern Border University Arar Saudi Arabia Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Department of Computer Science CTL eurocolege Limassol Cyprus Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Lombardia Italy

Integrating renewable energy sources into smart grids increases supply and demand management because renewable energy sources are intermittent and variable. To overcome this type of challenge, short-term load forecasting (STLF) is essential for managing energy, demand-side flexibility, and the stability of smart grids with renewable energy integration. This paper presents a new model called BiGRU-CNN to improve the operation of STLF in smart grids. The BiGRU-CNN model integrates bidirectional gated recurrent units (BiGRUs) to model temporal dependencies and convolutional neural networks (CNNs) to extract spatial patterns from energy consumption data. The newly developed BiGRU captures past and future contexts through bidirectional processing, and the CNN component extracts high-level features to enhance the accuracy of load demand prediction. The BiGRU-CNN model is compared with two other hybrid models, CNN-LSTM and CNN-GRU, on real-world data from American electric power (AEP) and ISONE datasets. Simulation results show that the proposed BiGRU-CNN outperforms other models, with single-step forecasting yielding root mean square error (RMSE) of 121.43 (AEP) and 123.57 (ISONE), mean absolute error (MAE) of 90.95 (AEP) and 62.97 (ISONE), and mean absolute percentage error (MAPE) of 0.61% (AEP) and 0.41% (ISONE). For multi-step forecasting, the model yields RMSE of 680.02 (AEP) and 581.12 (ISONE), MAE of 481.12 (AEP) and 411.20 (ISONE), and MAPE of 3.27% (AEP) and 2.91% (ISONE). The results show that the BiGRU-CNN model can generate accurate and reliable STLF, which is useful in managing massive renewable energy-integrated smart grids. © 2025 The Author(s). IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Convolutional neural networks

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Multimodal clinical benchmark for emergency care (MC-BEC): a comprehensive benchmark for evaluating foundation models in emergency medicine 23

Multimodal clinical benchmark for emergency care (MC-BEC): a...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Emma Chen Aman Kansal Julie Chen Boyang Tom Jin Julia Rachel Reisler David A Kim Pranav Rajpurkar Department of Biomedical Informatics Harvard Medical School and Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University Department of Computer Science Stanford University Department of Emergency Medicine Stanford University School of Medicine Department of Biomedical Informatics Harvard Medical School

We propose the Multimodal Clinical Benchmark for Emergency Care (MC-BEC), a comprehensive benchmark for evaluating foundation models in Emergency Medicine using a dataset of 100K+ continuously monitored Emergency department visits from 2020-2022. MC-BEC focuses on clinically relevant prediction tasks at timescales from minutes to days, including predicting patient decompensation, disposition, and emergency department (ED) revisit, and includes a standardized evaluation framework with train-test splits and evaluation metrics. The multimodal dataset includes a wide range of detailed clinical data, including triage information, prior diagnoses and medications, continuously measured vital signs, electrocardiogram and photoplethysmograph waveforms, orders placed and medications administered throughout the visit, free-text reports of imaging studies, and information on ED diagnosis, disposition, and subsequent revisits. We provide performance baselines for each prediction task to enable the evaluation of multimodal, multitask models. We believe that MC-BEC will encourage researchers to develop more effective, generalizable, and accessible foundation models for multimodal clinical data.

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Electrically Programmable Non-Volatile Silicon Photonic Content Addressable Memory (CAM) cell

Electrically Programmable Non-Volatile Silicon Photonic Cont...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Silicon photonics

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Trustworthy and Load-Balancing Routing Scheme for Satellite Services with Multi-Agent DRL

Trustworthy and Load-Balancing Routing Scheme for Satellite ...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Jiaxin Song Ying Ju Lei Liu Qingqi Pei Celimuge Wu Mian Ahmad Jan Shahid Mumtaz School of Telecommunications Engineering Xidian University Xi'an China Graduate School of Informatics and Engineering the University of Electro-Communications Chofu Japan Department of Computer Science Abdul Wali Khan University Mardan Mardan Pakistan Department of Applied Informatics Silesian University of Technology Akademicka Poland Engineering Department Nottingham Trent University Nottingham UK

Massive computing tasks of various applications have been generated in 6G space-air-ground integrated networks, and need to be transmitted securely and reliably. Nevertheless, the mobility of satellites and the untrusted nodes bring new challenges to the routing scheme design in low earth orbit (LEO) satellite networks. To improve the system trust and elevate the service quality, this paper proposes a fully distributed trustworthy load-balancing routing scheme for satellite services with a multi-agent dueling double deep Q network (D3QN)-based learning algorithm. Our scheme organizes multiple agents to generate hop-by-hop routes and makes decisions based on the trust value of the nodes, which has good scalability to deploy on various satellite constellations and can meet the trust requirements of the services. Besides, we add a variable delay constraint into the load minimization objective to meet various delay-sensitive satellite quality of service (QoS) requirements. We demonstrate that the proposed scheme dramatically reduces the link queue utilization rate and enhances the system capability of handling delay-sensitive services. The packet loss rate of our scheme is 24% lower than that of the benchmark scheme when the system has 30% malicious nodes.

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Promoting Accurate Image Reconstruction via Synthetic Noise for Unsupervised Screw Anomaly Detection and Location

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Chen, Jian Wen Ju Lin, Wan Li, Kuan Ming Mrozek, Dariusz Hung, Che Lun National Tsing Hua University Department of Computer Science Hsinchu300044 Taiwan National Kaohsiung University of Science and Technology Department of Mold and Die Kaohsiung80778 Taiwan National Taiwan University Department of Mechanical Engineering Taipei106319 Taiwan Silesian University of Technology Department of Applied Informatics Gliwice44-100 Poland National Yang Ming Chiao Tung University Institute of Biomedical Informatics Taipei11221 Taiwan

With residuals-based unsupervised models, defects can be detected using only normal samples, boosting both efficiency and practicality in industrial anomaly detection (AD) applications. While these approaches offer benefits, they also present challenges in detecting anomalies in cylindrical objects, such as imprecise image reconstruction and camera blind spots. As a result, this article introduces ScrewNet, an unsupervised AD framework designed specifically for cylindrical objects like screws. To improve the accuracy of reconstruction in the AD network, the synthetic noise module and pair training approach are employed during the training stage. The synthetic noise module, utilizing one of the CycleGAN generators, produces defects that match real-world distributions, thereby enabling the AD network to extract valuable features while preserving image structure. The pair training approach is designed to enable the AD network to achieve accurate reconstruction using similar background input-output pairs. To tackle the inspection blind spots problem, an efficient, cost-effective inspection system using multiple mirrors is developed for the inference stage. This approach enables effective AD across the entire 360° surface of the screw. In experiments, three different types of screw datasets containing both damaged and undamaged screws are provided. Numerous experiments demonstrate the superiority of our approach over several state-of-the-art (SOTA) unsupervised AD networks. For instance, ScrewNet outperforms baselines by 4 dB in average peak signal-to-noise ratio (PSNR) for image reconstruction quality and by 32% in average area under the receiver operating characteristic curve (AUROC) for pixel-level anomaly segmentation. In addition, image-level detection achieves 98.3% accuracy across the three screw datasets. The code is available at https://***/vitowen9580/ScrewNet. © 2025 IEEE.

关键词： Image reconstruction

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Towards Swarm Intelligence Ontology for Formal Description of Metaheuristics Algorithms

Towards Swarm Intelligence Ontology for Formal Description o...

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Computing, Networking, Telecommunications & engineering Sciences Applications (CoNTESA), International Conference on

作者： Domagoj Tuličić Marko Horvat Sandra Lovrenčić Department of Computer System and Networks University of Applied Science in Information Technology Zagreb Croatia Department of Applied Computing University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia Department of Theoretical and Applied Foundations of Information Sciences University of Zagreb Faculty of Organization and Informatics Varaždin Croatia

Swarm intelligence is a class of nature-inspired metaheuristic algorithms, that is specifically derived from biological systems in nature with an emphasis on their social interactions. These algorithms have been primarily developed to solve complex combinatorial optimization problems and are extremely popular among researchers in metaheuristic algorithms. This is evident from the numerous published scientific papers describing algorithms inspired by biological systems, ranging from insects such as ants and bees to the largest animals on the planet like whales. All these algorithms share some common concepts and relationships that can be explicitly identified, named, and formalized. These shared concepts and relationships can be organized and represented through the construction of an ontology. The goal of this paper is to present a formal ontology that standardizes vocabulary, as well as a set of rules enabling precise communication and reasoning about these algorithms. Ultimately, this will improve our understanding, analysis, research processes, and utilization of these computational approaches.

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Secure Terahertz Indoor Communications Using Blockage Feature-Based Artificial Noise in 6G

Secure Terahertz Indoor Communications Using Blockage Featur...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Suheng Tian Ying Ju Lei Liu Qingqi Pei Ning Zhang Celimuge Wu Shahid Mumtaz School of Telecommunications Engineering Xidian University Xi’an China Department of Electrical and Computer Engineering University of Windsor Windsor Canada Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan Department of Applied Informatics Silesian University of Technology Akademicka Gliwice Poland Departement of Engineering Nottingham Trent University Nottingham UK

Terahertz communication with abundant spectrum resources is envisioned as the key technology of 6G. Despite its narrow beam, terahertz transmission is still vulnerable to eaves-dropping attacks in indoor scenarios. In this paper, we propose a blockage feature-based artificial noise scheme to safeguard the indoor network in the presence of multiple access points (APs), users, and eavesdroppers. Those APs with blocked links to the typical user are selected to emit artificial noise to deteriorate the reception of eavesdroppers. Thus, communication security is ensured without escalating the instability of legitimate connections caused by the small coverage nature of terahertz beams. By comprehensively considering the propagation characteristics of terahertz, such as the three dimensions narrow beam and the human blocking effect, we derive the theoretical expressions of the connection outage probability, the secrecy outage probability, and the average number of perfect links per unit area. Numerical results demonstrate that the proposed scheme outperforms the traditional schemes in terms of connection stability and secrecy performance.

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SiHoneypot: A Digital Twin-Based Honeypot for Autonomous Vehicles

SiHoneypot: A Digital Twin-Based Honeypot for Autonomous Veh...

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International Conference on Modern Circuits and Systems Technologies (MOCAST)

作者： Athanasios Liatifis Charis Eleftheriadis Zisis Mpatzos Ioannis Nanos Thomas Lagkas Sotirios Goudos Vasileios Argyriou Konstantinos E. Psannis Ioannis D. Moscholios Panagiotis Sarigiannidis Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Sidroco Holdings Ltd Nicosia Cyprus Department of Computer Science International Hellenic University Kavala Greece Department of Physics Aristotle University of Thessaloniki Thessaloniki Greece Department of Networks and Digital Media Kingston University London Surrey UK Department of Applied Informatics University of Macedonia Thessaloniki Greece Department of Informatics and Telecommunications University of Peloponnese Tripolis Greece

ISBN: (数字)9798350385427

ISBN: (纸本)9798350385434

Autonomous Vehicles (AVs) stand as the vanguard of the automotive industry's evolution, offering a multitude of advantages in terms of transportation efficiency and applications of critical importance. Notably, their interconnection with various smart devices, such as smartphones and associated services, is achieved effortlessly. However, these merits are counterbalanced by significant security risks pertaining to human safety and the potential exposure of personal data. This work introduces SiHoneypot, an innovative honeypot system rigorously crafted to address security challenges intrinsic to AVs. SiHoneypot leverages Digital Twins and incorporates state-of-the-art trends in software deployment, providing a faithful emulation of Autonomous Vehicle systems. Demonstrating its efficacy as a strategic decoy, SiHoneypot affords sufficient time for other security systems to enact responsive measures. Experimental results underscore the minimal resources required for the deployment of SiHoneypot, emphasizing its operational efficiency and resource optimization. Moreover, the inherent extensibility and versatility of SiHoney-pot's architecture are showcased, illustrating its adaptability to evolving security challenges within the dynamic landscape of autonomous vehicular technologies.

关键词： Emulation Transportation Market research Time measurement Software Sensors Digital twins

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