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2023 IEEE International Conference on Communications, ICC 2023

作者： Wang, Fei Zhang, Xi Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Networking and Information Systems Laboratory Department of Electrical and Computer Engineering Texas A&m University College StationTX77843 United States

ISBN: (纸本)9781538674628

Intelligent reflecting surface (IRS), unmanned aerial vehicle (UAV), and Terahertz (THz) communications, which are recognized as 6G promising techniques, have attracted significant attentions. We propose UAV energy minimization schemes for IRS/UAV-based mobile-edge-computing (MEC) and traffic-offloading over broadband THz mobile networks. In the networks, multiple UAVs serving as MEC-servers collect data from multiple ground users (GUs) with the assistance of a set of passive IRSs. First, we formulate a UAV energy minimization problem, which jointly optimizes the IRSs' phase shifts, UAVs' trajectories, and system computation and communication resources. For THz communications, since the composite channel power gains of GUs are complicated functions of UAVs' trajectories and IRSs' phase shifts, the formulated optimization problem is non-convex. Then, using the alternating optimization (AO) technique, we decompose this non-convex optimization problem into three sub-problems which then can be iteratively solved. Finally, we validate and evaluate the proposed schemes by numerical analyses, which show that the energy consumption of UAVs can be reduced by around 40% by using IRSs. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Spectrum Sharing in Low-Earth Orbit Satellite Systems Under an Interference Protection Constraint

Spectrum Sharing in Low-Earth Orbit Satellite Systems Under ...

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IEEE International Conference on Communications (ICC)

作者： Eunsun Kim Ian P. Roberts Jeffrey G. Andrews 6G@UT Wireless Networking and Communications Group University of Texas at Austin Department of Electrical and Computer Engineering UCLA Wireless Lab

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

This work investigates the in-band coexistence between two dense low-earth orbit (LEO) satellite communication systems by analyzing two preeminent large-scale constellations, namely Starlink and Kuiper, both which have been granted non-exclusive rights to operate at 20 GHz. Through extensive simulation of Starlink and Kuiper based on their public filings, we examine downlink performance of both systems when Kuiper is obliged to protect Starlink by not inflicting prohibitive interference onto its ground users. We show that Kuiper is capable of reliably satisfying a strict protection constraint at virtually all times by strategically selecting which overhead satellites are used to serve its ground users. In fact, while protecting Starlink users in this way, our results show that Kuiper can remarkably also deliver near-maximal downlink SINR to its own ground users, revealing a feasible route to fruitful coexistence of both systems. For instance, as the constellations orbit the globe, we show that Kuiper is always capable of keeping its inflicted interference at least 12 dB below noise and in doing so sacrifices only about 1 dB in SINR over 80% of the time.

关键词： Satellites Low earth orbit satellites Interference Downlink Orbits Reliability Protection

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Contagion Classification using Deep Learning Approach

Contagion Classification using Deep Learning Approach

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networking and Communications (ICNWC), International Conference on

作者： Sai Prajwal Kacham Angayarkanni S A Rohithshan S Arpan Mishra Vignesh. K Department of Networking and Communication College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

Potatoes are a major crop globally, but diseases can seriously harm their growth and quality. Recently, there's been a lot of interest in using machine learning to identify potato diseases from pictures. In this study, we've developed a way to automatically classify potato diseases using deep learning methods. By training a model with CNN and SVM algorithms on a dataset of potato disease images, we achieved an accuracy rate of 93.95%. This shows that deep learning is effective in identifying potato diseases. Our method could be a big help to farmers and researchers, making it faster and easier to spot and deal with potato diseases, which could lead to better crop yield and quality.

关键词： Deep learning Training Support vector machines Machine learning algorithms Vectors Classification algorithms Convolutional neural networks

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Deep Learning Approaches for Intrusion Detection with Emerging Cybersecurity Challenges

Deep Learning Approaches for Intrusion Detection with Emergi...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Hemalatha, S. Mahalakshmi, M. Vignesh, V. Geethalakshmi, M. Balasubramanian, D. Jose, Anand A. St. Joseph's Institute of Technology Department of Electrical and Electronics Engineering Chennai600119 India Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur Chennai603203 India Karpagam College of Engineering Department of Computer Science and Engineering Coimbatore641032 India Kcg College of Technology Department of Mathematics Chennai600097 India Jaya Engineering College Department of Mca Chennai602024 India Kcg College of Technology Department of Ece Tamil Nadu Chennai600097 India

ISBN: (纸本)9798350313987

Intrusion Detection Systems (IDS) play a pivotal role in preventing computer networks from an ever-evolving landscape of cybersecurity threats. As cyberattacks become increasingly sophisticated and diversified, the need for robust and adaptable intrusion detection models has never been more critical. This study explores the application of Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) in the context of intrusion detection, particularly in addressing emerging cybersecurity challenges. The performance of deep learning (DL)-based IDSs is highly dependent on the dataset used and no benchmark models for intrusion detection have been found. In this work, a recent dataset CIC-DDoS-2019 is used for the detection of DDOS (distributed deny of services). This work underscores the importance of adopting DL techniques, including DNNs, CNNs, and RNNs, for intrusion detection in the face of emerging cybersecurity challenges. It highlights the potential of these neural network architectures to enhance network security, adapt to evolving threats, and mitigate the risks posed by modern cyber adversaries. Moreover, it encourages further research and development in this field to stay ahead of the ever-changing cybersecurity landscape. © 2023 IEEE.

关键词： DDoS attacks Deep learning Distributed denial-of-service (DDoS) Intrusion detection system Machine Learning

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Unmasking Digital Falsehoods: A Comparative Analysis of LLM-Based Misinformation Detection Strategies

Unmasking Digital Falsehoods: A Comparative Analysis of LLM-...

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Advanced Algorithms and Control engineering (ICAACE), International Conference on

作者： Tianyi Huang Jingyuan Yi Peiyang Yu Xiaochuan Xu Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA The Information Networking Institute Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

The proliferation of misinformation on social media has raised significant societal concerns, necessitating robust detection mechanisms. Large Language Models such as GPT-4 and LLaMA2 have been envisioned as possible tools for detecting misinformation based on their advanced natural language understanding and reasoning capabilities. This paper conducts a comparison of LLM-based approaches to detecting misinformation between text-based, multimodal, and agentic approaches. We evaluate the effectiveness of fine-tuned models, zero-shot learning, and systematic fact-checking mechanisms in detecting misinformation across different topic domains like public health, politics, and finance. We also discuss scalability, generalizability, and explainability of the models and recognize key challenges such as hallucination, adversarial attacks on misinformation, and computational resources. Our findings point towards the importance of hybrid approaches that pair structured verification protocols with adaptive learning techniques to enhance detection accuracy and explainability. The paper closes by suggesting potential avenues of future work, including real-time tracking of misinformation, federated learning, and cross-platform detection models.

关键词： Adaptation models Systematics Social networking (online) Computational modeling Large language models Scalability Zero shot learning Real-time systems Fake news Public healthcare

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The Impact of CyRIS in Ascertaining Accurate Implementation of Projects

The Impact of CyRIS in Ascertaining Accurate Implementation ...

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International Conference on Computing and networking Technology (ICCNT)

作者： Ahma Zia Sharifi Md. Abul Ala Walid Ghezal Ahmad Jan Zia Md. Nazmus Salehin Ihsan Ul Haq Safi Iqbal Al Islam Department of Networking and Communication Faculty of Computer Science Nangarhar Univeristy Afghanistan Department of Computer Science and Engineering Khulna University of Engineering & Technology (KUET) Khulna Bangladesh Bundesanstalt für Materialforschung und-prüfung (BAM) Berlin Germany Department of Computer Science and Engineering Bangladesh Army University of Engineering & Technology (BAUET) Natore Bangladesh Department of Networking and Communication Faculty of Computer Science Nangarhar University Afghanistan Department of Computer Science & Engineering Daffodil International University Bangladesh (DIU) Savar Dhaka

This systematic review seeks to evaluate the impact of CyRIS in ascertaining accurate implementation of projects with a primary focus on the merits of its functionalities. Core to the study is the view that CyRIS-driven project implementation reduces the risks of inaccuracy to an acceptable level. Although CyRIS is an extensively researched area in cyber security, a comprehensive review of subcategories, specifically, the accurate implementation of projects using the tool remains understudied. The proposed systematic review of literature provides insights and future directions from selected databases, including ScienceDirect, Springer Journals, SAGE Journals, and Wiley Online Library. Moreover, the limitations and strengths of each article collected are assessed using the PRISMA model to identify risks of bias. The research, which consists of journal articles, conferences, and symposium papers, is analyzed using "Cyber-Range Instantiation Systems and accurate implementation of projects" as the key phrase and "Cyber-Risk Models", "Cyber Range Instantiation", and "Cyber Security Training", and "Accurate Implementation of Projects" as the keywords. The findings of the study will bridge the awareness gap in using CyRIS-led Cyber-Risk Models to execute accurate training scenarios, improve weaknesses of implementation, simulate real-time attack scenarios, and highlight existing gaps as well as prospects for future research.

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Cryptographic Ledger for Cyber Security in Smart Grid

Cryptographic Ledger for Cyber Security in Smart Grid

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International Conference on Edge Computing and Applications (ICECAA)

作者： Elanchezhiyan E K. Kalaiselvi D. Banumathy Department of Networking and Communications Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Department of Computer Science and Engineering Paavai Engineering College Namakkal Tamilnadu

In addition to the physical security of energy networks, cyber security is essential to protecting these systems as well. Cyber threats can stem from malicious hackers who have infiltrated the networks to gain unauthorized access to sensitive data, or from vulnerabilities within the systems themselves. It is increasingly important that smart grid companies invest in cyber security solutions, such as strong passwords, two-factor authentication, encryption, and regular software updates, to counteract these threats. Additionally, it is beneficial for organizations to create incident response plans that are tailored to their specific needs, and which define the chain of command and actions to take in the case of an incident. To details the usage of smart grids in various domains and places in an effective way and explains the efficient way of consuming power for smart ventilators by monitoring and providing cyber security against cyber-attacks. The distributed power from various regions is collected from the less predominant places and supplied to the smart ventilators through smart inverters.

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Analysis of Classification and Stage-wise Prediction Techniques of Melanoma

Analysis of Classification and Stage-wise Prediction Techniq...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Sudha D R. Naresh Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Tamilnadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamilnadu India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Melanoma prediction is a skin cancer, and one in seventeen people is affected by this disease. Identifying and classifying early stages is an important research area in this field. Different research has been conducted in this field. This disease spreads to every body part, so early prediction and classification are very important for medical diagnosis. This paper presented a brief survey of melanoma prediction, classification and early-stage analysis and predictions. In particular, machine- and deep-learning-based techniques are used to analyze and predict melanoma. In this paper, different dataset details and metrics for evaluation are also presented in the assessment and implementation of models. Finally, the future direction of the research is also given for further study.

关键词： Measurement Surveys Technological innovation Explainable AI Distributed databases Melanoma Predictive models Real-time systems Medical diagnosis Diseases

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Enhanced Route Optimization: Incorporating Road Safety Factors for Optimal Path Selection 4

Enhanced Route Optimization: Incorporating Road Safety Facto...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Subash, R. Prathaban, Banu Priya Ganeshkumar, N. Gopinathan, S. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India Srm Institute of Science and Technology School of Computing Department of Networking and Communications Chennai India Gitam School of Technology Gandhi Institute of Technology and Management Department of Computer Science and Engineering Karnataka Bengaluru562163 India Sri Sai Ram Institute of Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331508845

Efficient route optimization is a crucial concern in modern transportation systems, influencing travel time, resource consumption, and user satisfaction. By empowering users to select their priority parameters, such as safety or efficiency, this project enables travellers to make eco-conscious choices and select routes that minimize their environmental footprint. Real-time data integration ensures users can bypass traffic jams and accidents, reducing emissions and fuel consumption. Furthermore, the focus on safety factors helps prevent accidents, thereby fostering safer road environments. Existing systems often fall short, providing limited customization and overlooking safety and sustainability priorities. To bridge these gaps, this project introduces an advanced route planning system that empowers users to select their priority parameters, integrates real-time data for up-to- the-minute updates, and prioritizes road safety. It introduces a pioneering approach that enhances the Dijkstra Algorithm by integrating real-time accident rate and travel time data into the route optimization process. By considering these additional factors, the algorithm produces routes that not only minimize distance but also prioritize safety and time efficiency in dynamic real-world conditions. The algorithm's multi-factor optimization considers distance, accident rates, and travel times, enabling the creation of routes that balance these criteria and offer improved decision-making for users through a user- friendly interface. The algorithm's dynamic adaptability, multi- factor optimization, and practical implementation showcase its potential to revolutionize how routes are planned, leading to safer, faster, and more informed travel experiences. © 2024 IEEE.

关键词： Travel time

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A Dual-Modal Fusion Framework for Detection of Mild Cognitive Impairment Based on Autobiographical Memory

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IEEE Journal of Biomedical and Health Informatics 2025年第6期PP卷 PP页

作者： Chang, Ho-Ling Wai, Thiri Liao, Yu-Shan Lin, Sheng-Ya Chang, Yu-Ling Fu, Li-Chen National Taiwan University G.I. of Networking and Multimedia Taipei Taiwan National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan National Taiwan University Department of Psychology Taipei Taiwan

This paper introduces a dual-modal early cognitive impairment detection system based on autobiographical memory (AM) tests, and our approach is to automatically extract pre-defined acoustic features and self-designed embeddings to enhance linguistic representation of the spontaneous speech data. By integrating dual-modal data, we effectively enrich the features that aid in model learning, especially addressing the subtle symptoms exhibited by individuals with mild cognitive impairment (MCI), an intermediate stage between healthy individuals and those with Alzheimer's disease (AD). To account for spontaneous speech's unstructured and implicit nature, two additional embeddings, namely, speaker embedding and conversation embedding, are introduced to augment the information available for model learning, thus enriching the feature set for improving the model accuracy. The proposed dual-modal approach is tested on a self-collected Chinese spontaneous speech dataset due to the limited unstructured speech open-access dataset for MCI detection. The system's effectiveness is evaluated through a series of experiments, including ablation studies, to determine the impact of each module on overall performance. The proposed system achieved an average accuracy of 78% in detecting MCI, demonstrating its comparative effectiveness. Enhancements in our system are achieved by integrating a directional encoder tailored to capture temporal information across sequential visits. This addition leads to a 3% increase in detection accuracy within a subset of participants who have undergone multiple AM test sessions. Implementing such a longitudinal approach in analyzing unstructured speech data for MCI detection taps into a relatively underexplored area of research, offering new insights. © 2025 IEEE.

关键词： Neurodegenerative diseases

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