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IEEE Advanced information technology, Electronic and Automation Control Conference (IAEAC)

作者： Tonglin Cheng Juan Li Jintao Luo Zhihui Li Faculty of Information Technology Beijing University of Technology Beijing Engineering Research Center for loT Software and Systems

With the rapid development of computer vision and deep learning technologies, object detection plays a crucial role in various fields. Due to the complexity and diversity of underwater environments, underwater vision tasks face great challenges. In this paper, we improved the YOLOv8 detection model for the fish detection task in complex environments. Specifically, we proposed a multi-scale feature fusion method (MFANet) that combines multiscale weighted feature fusion with multiscale attention to efficiently capture multilevel features of fishes in complex environments. We designed a multiscale weighted feature fusion module that combines feature maps at different scales to enhance the model’s representation of the target. By introducing a multi-scale attention mechanism, the model is able to pay more attention to important feature regions. We conducted experiments on real underwater complex environment datasets. The results show that our proposed improved method significantly enhances the performance of the YOLOv8 model in fish detection tasks and lays a good foundation for subsequent underwater detection tasks.

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Content Moderation in Social Media: The Characteristics, Degree, and Efficiency of User Engagement 3

Content Moderation in Social Media: The Characteristics, Deg...

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3rd Asia Symposium on Signal Processing, ASSP 2022

作者： Wang, Kanlun Fu, Zhe Zhou, Lina Zhu, Yunqin The University of North Carolina at Charlotte Business Information Systems and Operations Management Charlotte United States The University of North Carolina at Charlotte Software and Information Systems Charlotte United States University of Science and Technology of China Information Science and Technology Hefei China

ISBN: (纸本)9781665475853

Social media emerge as common platforms for knowledge sharing/exchange in online communities. Meanwhile, they also become a hotbed for the diffusion of misinformation. Content moderation is one of the measures for preventing the distribution of misinformation. Despite the increasing research attention to content moderation, the role of user engagement in content moderation remains significantly understudied. It is unclear how different characteristics and degrees of user engagement in social media might impact the performance of content moderation. In addition, the efficiency of content moderation has not been addressed by prior studies. This study aims to fill these research gaps by investigating the characteristics of user engagement behavior in social media and developing automated models to support content moderation that leverage a state-of-the-art pre-trained model for text analysis. The evaluation results with Reddit data suggest that the directivity and temporal characteristics of user engagement have significant effects on the effectiveness of content moderation. Additionally, leveraging the entire history of user engagement tends to be inefficient or even impractical, yet our findings provide evidence and a guide for improving the efficiency of content moderation using user engagement data without compromising model effectiveness. Our findings have research and practical implications for the moderation and deterrence of misinformation in social media. © 2022 IEEE.

关键词： Efficiency

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COVID-19 Lung CT Image Segmentation: A Comparison of Various U-Net Variants 2nd

COVID-19 Lung CT Image Segmentation: A Comparison of Various...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Alebiosu, David Olayemi Folayan, Adeola Chen, Wei Tolulope, Abejide Ajibade, Samuel-Soma M. School of Engineering and Technology Department of Computing and Information Systems Sunway University Petaling Jaya Malaysia School of Medical and Health Science Monash University Petaling Jaya Malaysia School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China College of Science and Engineering University of Derby Derby United Kingdom Department of Computing and Information Systems Sunway University Petaling Jaya Malaysia Department of Computer Engineering Istanbul Ticaret Universitesi Istanbul Turkey

ISBN: (纸本)9789819639489

Lung segmentation has become a bedrock in the effective diagnosis, and classification of coronavirus (COVID-19) from radiological images such as computed tomography (CT) and X-ray images. Since the coronavirus (COVID-19) discovery, several methods have been employed to segment the COVID-19-infected areas from lung CT images. One of the most popular segmentation methods is the U-Net model. U-Net is a convolutional neural network used for medical image segmentation. U-Net and its variants have become a more reliable architecture used for medical image segmentation. U-Net models have produced outstanding results in segmenting diseases such as COVID-19 from lung CT images. The exceptional results produced by the U-Net model have inspired various researchers to explore the potential of U-Net for various segmentation tasks. This study compares the performances of recently used state-of-the-art U-Net models on lung CT images for tuberculosis segmentation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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Allocation and Migration of Virtual Machines Using Machine Learning

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Computers, Materials & Continua 2022年第2期70卷 3349-3364页

作者： Suruchi Talwani Khaled Alhazmi Jimmy Singla Hasan JAlyamani Ali Kashif Bashir School of Computer Science&Engg. Lovely Professional UniversityPunjabIndia National Center for Robotics and Internet of Things Technology Communication and Information Technology Research InstituteKing Abdulaziz City for Science and Technology(KACST)Riyadh11442Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology in RabighKing Abdulaziz UniversityRabigh21911Saudi Arabia Department of Computing and Mathematics ManchesterMetropolitan UniversityManchesterM156BHUK

Cloud computing promises the advent of a new era of service boosted by means of virtualization *** process of virtualization means creation of virtual infrastructure,devices,servers and computing resources needed to deploy an application *** extensively practiced technology involves selecting an efficient Virtual Machine(VM)to complete the task by transferring applications from Physical Machines(PM)to VM or from VM to *** whole process is very challenging not only in terms of computation but also in terms of energy and *** research paper presents an energy aware VM allocation and migration approach to meet the challenges faced by the growing number of cloud data *** Learning(ML)based Artificial Bee Colony(ABC)is used to rank the VM with respect to the load while considering the energy efficiency as a crucial *** most efficient virtual machines are further selected and thus depending on the dynamics of the load and energy,applications are migrated fromoneVMto *** simulation analysis is performed inMatlab and it shows that this research work results in more reduction in energy consumption as compared to existing studies.

关键词： Cloud computing VM allocation VM migration machine learning

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A Novel Parallel computing Confidentiality Scheme Based on Hindmarsh-Rose Model

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Computers, Materials & Continua 2023年第8期76卷 1325-1341页

作者： Jawad Ahmad Mimonah Al Qathrady Mohammed SAlshehri Yazeed Yasin Ghadi Mujeeb Ur Rehman Syed Aziz Shah School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghEH105DTUK Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi112612United Arab Emirates School of Science Technology and HealthYork St John UniversityYorkYO317EXUK Research Centre for Intelligent Healthcare Coventry UniversityCoventryCV15FBUK

Due to the inherent insecure nature of the Internet,it is crucial to ensure the secure transmission of image data over this ***,given the limitations of computers,it becomes evenmore important to employ efficient and fast image encryption *** 1D chaotic maps offer a practical approach to real-time image encryption,their limited flexibility and increased vulnerability restrict their practical *** this research,we have utilized a 3DHindmarsh-Rosemodel to construct a secure *** randomness of the chaotic map is assessed through standard *** proposed system enhances security by incorporating an increased number of system parameters and a wide range of chaotic parameters,as well as ensuring a uniformdistribution of chaotic signals across the entire value ***,a fast image encryption technique utilizing the new chaotic system is *** novelty of the approach is confirmed through time complexity *** further strengthen the resistance against cryptanalysis attacks and differential attacks,the SHA-256 algorithm is employed for secure key *** results through a number of parameters demonstrate the strong cryptographic performance of the proposed image encryption approach,highlighting its exceptional suitability for secure ***,the security of the proposed scheme has been compared with stateof-the-art image encryption schemes,and all comparison metrics indicate the superior performance of the proposed scheme.

关键词： Hindmarsh-rose model image encryption SHA-256 parallel computing

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Mitigating Smishing: Challenges and Future Work

arXiv

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arXiv 2024年

作者： Faklaris, Cori Department of Software and Information Systems College of Computing and Informatics University of North Carolina at Charlotte CharlotteNC United States

U.S. Federal Trade Commission data for 2020-2022 shows that 23% of all fraud reports include Text as the contact method, with reported losses in the same period rising 279%, to $326 million. These numbers, and estimates of other unreported losses, have spurred our lab and others in industry and academia to direct resources to understanding and mitigating smishing. In this workshop paper, we describe three principal challenges in smishing mitigation that are distinct from traditional phishing: (1) limitations of mobile device affordances, (2) the complexity of cellular network infrastructure, and (3) cognitive and contextual factors in mobile device usage. We describe ideas for mitigating smishing, including a high-level overview of our lab's work to create and test interface designs for text-message recipients. © 2024, CC BY-SA.

关键词： Mobile security

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Analysis of Machine Learning (ML) and Internet of Things (IoT): A Scientometric Review 20

Analysis of Machine Learning (ML) and Internet of Things (Io...

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20th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2024

作者： Ajibade, Samuel-Soma M. Jasser, Muhammed Basheer Legaspino, Rosie Fe B. Gimeno, Anabella R. Ypanto, Leomar A. Baird, Jeem Clyde C. Adediran, Anthonia Oluwatosin Sunway University School Of Engineering And Technology Department Of Computing And Information Systems Selangor Malaysia Cebu Technological University Cebu Moalboal Philippines Federal University Of Uberlandia Faculty Of Architecture And Urban Design Uberlândia Brazil

ISBN: (纸本)9798350382310

The objective of this paper is to critically examine the publication's trends and research landscape on the Internet of Things and Machine Learning (Io T-ML) research through bibliometric analysis (BA). The Elsevier Scopus database was selected to identify, screen, and analyze the published documents on IoT-ML. The publication trends and major stakeholders on the topic were subsequently. BA was performed to examine the co-authorships, keyword occurrences, and citations using VOSviewer software. Lastly, a systematic literature review was performed to summarize the scientific and technological advancements. Results revealed that the publications on IoT-ML largely comprise articles, conference proceedings, and reviews, whereas the publication trends showed an increase from 2 to 105 between 2015 and 2021. The top researchers on IoT-ML are S. Baskar, M. Guizani, and A. Souri, whereas the top institution is the Vellore Institute of technology (India). However, the top country and research funder are;India, and National Natural Science Foundation (China), respectively. BA showed that the research landscape on IoT-ML comprises numerous networks of co-authors and countries working on related themes. The most impactful publication is Mahdavinejad et al. (2018) which has 479 citations, whereas the most prestigious journal is the IEEE Io T journal with 9 published documents in Scopus. Keyword analysis revealed IoT-ML has potential applications in health care, medicine, computer security and data analytics etc. Overall, IoT-ML has significant socio-economic and research impacts, and as such, the field will witness geometric techno-scientific growth and development in the future, particularly in the areas of network security and big data analytics. © 2024 IEEE.

关键词： Machine learning

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LightEnsemble-Guard: An Optimized Ensemble Learning Framework for Securing Resource-Constrained IoT systems

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IEEE Access 2025年 13卷 101764-101781页

作者： Tanveer, Muhammad Usama Munir, Kashif Amjad, Madiha Alyamani, Hasan J. Bermak, Amine Rehman, Atiq Ur Khwaja Fareed University of Engineering and Information Technology Institute of Information Technology Rahim Yar Khan 64200 Pakistan King Abdulaziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Jeddah 21589 Saudi Arabia Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar

The rapid proliferation of real-time Internet of Things (IoT) devices has increased the need for efficient and accurate security mechanisms. Real-time IoT devices are highly vulnerable to cyberattacks due to their continuous connectivity and limited security mechanisms. In this work, we propose LightEnsemble-Guard, an ensemble learning-based approach specifically designed for resource-constrained IoT environments. Our method achieves a high detection accuracy of 99.55%, with strong precision, recall, and F1-score, while maintaining a low computational cost of just 22.23 seconds. To ensure robustness, we conducted 5-fold cross-validation and ROC curve evaluations, confirming the model’s reliability and generalizability. LightEnsemble-Guard integrates three lightweight classifiers LightGBM, XGBoost, and Extra Trees using a majority voting mechanism to improve detection performance on highly imbalanced datasets without burdening system resources. This ensemble strategy ensures an optimal balance between detection performance and computational resource utilization, making it well-suited for IoT networks, where processing power and memory are limited. The results highlight LightEnsemble-Guard as an effective, scalable and lightweight solution for real-time IoT security, significantly outperforming traditional models in both accuracy and computational efficiency. © 2013 IEEE.

关键词： Attack Detection Cybersecurity Ensemble Learning LightEnsemble-Guard Machine Learning

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A New RISC-V CPU for Safety-Critical systems 12

A New RISC-V CPU for Safety-Critical Systems

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12th Mediterranean Conference on Embedded computing, MECO 2023

作者： Mach, Jan Kohutka, Lukas Cicak, Pavel Institute of Computer Engineering and Applied Informatics Slovak University of Technology Bratislava842 16 Slovakia Institute of Informatics Information Systems and Software Engineering Slovak University of Technology Bratislava842 16 Slovakia

ISBN: (纸本)9798350322910

In this paper, we propose a novel hardening technique against Single Event Effects, which enables high-frequency operation and does not cause large power consumption and area overhead. The protection is based on redundancy, separation of the pipeline into two main sections, and is fully applicable at Register Transfer Level. It provides fast detection of faults, simple recovery by a flush of the pipeline, and allows a large prediction unit to be unprotected. We present the protection scheme at the core with the RISC-V instruction set. Simulations confirm that the protection can handle the injected faults. Synthesis shows that protection lowers the maximum frequency by only 3.9%. The area increase is only 108% and can be even lower. Total power consumption increased by 119%. © 2023 IEEE.

关键词： Pipelines

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Digital Twin-Driven Intrusion Detection for IoT and Cyber-Physical System

Digital Twin-Driven Intrusion Detection for IoT and Cyber-Ph...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence systems, IACIS 2024

作者： Ramamoorthy, Saravanan Kandaneri Sindhu, L. Valarmathi, K. Gobinath, C. Umaeswari, P. College of Non-Medicine University: Texila American University Guyana Sona College of Technology Department of Information Technology Salem India S. A. Engineering College Department of Information Technology Chennai India Reva University School of Computing and Information Technology Bengaluru India R.M.K. Engineering College Department of Computer Science and Business Systems Kavaraipettai India

ISBN: (纸本)9798350360660

From smart homes to industrial automation, the efficiency and connectivity of many applications have been greatly improved by the fast expansion of the Internet of Things (IoT) and Cyber-Physical systems (CPS). Sophisticated cyberattacks are becoming more common in this networked ecosystem, thus strong security measures are needed. In order to protect CPS and IoT settings, this study presents a new intrusion detection method that makes use of Digital Twin technology. To build an adaptable and dynamic Intrusion Detection System (IDS), our suggested architecture combines Digital Twins with state-of-the-art machine learning methods. In order to accurately detect and categorize any threats, the IDS constantly learns from data in both the past and the present. When tested on benchmark datasets and in real-world Internet of Things (IoT) settings, the framework outperforms conventional intrusion detection system (IDS) solutions in terms of detection accuracy and false alarm rate reduction. To protect IoT and CPS infrastructures from ever-changing cyber threats, this scalable and resilient security solution is offered by this IDS that is powered by digital twins. We present ICNN-FCID, an Integrated Convolutional Neural Network for Fog computing Environment, a hybrid deep learning intrusion detection model for fog computing environments. © 2024 IEEE.

关键词： Network intrusion

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