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Structural Health Monitoring Data Analysis Using Deep Learning Techniques 24

Structural Health Monitoring Data Analysis Using Deep Learni...

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8th international Conference on Electronic Information Technology and Computer engineering, EITCE 2024

作者： Yang, Linfei Huang, Enzo Faculty of Science and Technology University of Macao China Faculty of Business Administration University of Macao China

ISBN: (纸本)9798400710094

With the rapid development of deep learning technology, its application in the field of Structural Health Monitoring (SHM) represents a significant leap from traditional detection methods. This paper aims to explore how deep learning algorithms enhance the assessment of civil infrastructure integrity by analyzing large datasets from sensors. We first introduce the background of SHM, highlighting the shortcomings of traditional methods in terms of accuracy and efficiency. Next, we employ deep learning models for data analysis, investigating their applications in structural damage detection and prediction. Through a comprehensive analysis of existing literature, we find that deep learning not only overcomes the limitations of manual inspections but also significantly improves the overall performance of SHM by enhancing real-time monitoring capabilities. Furthermore, this paper discusses hybrid systems that combine data-driven models with physical understanding, as well as the distributed intelligence brought about by edge computing. These factors collectively provide more reliable and flexible solutions for future infrastructure management. Our key findings indicate that the application of deep learning can significantly improve the accuracy and efficiency of SHM, enabling a quicker response to the needs of dynamic communities. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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DDoS Detection in SDN Environments: Leveraging Machine Learning Algorithms with Local Interpretable Model-Agnostic Explanations (LIME) for Enhanced Security

DDoS Detection in SDN Environments: Leveraging Machine Lear...

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international Conference on business Intelligence and Data Analytics, BIDA 2024

作者： Karna, Siwani Jahanve, Poluru Reddy Yadukul, S. Bhaskaran, Sreebha Vekkot, Susmitha Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (纸本)9789819777167

SDN, short for Software Defined Networking, represents a network architecture controlled by software applications. distributed Denial-of-Service (DDoS) attacks create a challenge to these SDN environments and lead to many issues like loss of data and network instability, and takes a lot of time to address requests made by human users. It is very crucial to detect such attacks, for users to seamlessly use the network and take advantage of what is available online. This study demonstrates the significance of Machine Learning algorithms in identifying malicious traffic. Employing algorithms Gradient Boosting Classifier (GBC), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Logistic Regression (LR) proves effective in detecting complex and dynamic attacks. The study utilizes Local Interpretable Model-agnostic Explanation (LIME) to analyze the strength of each feature that contributes towards the prediction of classification. The results highlight the superior performance of the Gradient Boosting Classifier, achieving an accuracy of 99.75% and an F1-score of 99.68% in detecting network anomalies and threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Application programs

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A Tamper-Proof Smart Contract Metamodel for Blockchain to Optimise Computational Latency 3rd

A Tamper-Proof Smart Contract Metamodel for Blockchain to Op...

引用

3rd international Conference on computing and Communication Networks, ICCCN 2023

作者： Sengupta, Ratul Srivastava, Ruchika Mishra, Sushruta Abualigah, Laith Kalinga Institute of Industrial Technology Deemed to be University Bhubaneswar India Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al-Albayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Gelugor11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

ISBN: (纸本)9789819726707

In recent years, blockchain technology has garnered considerable interest on account of its peer-to-peer, distributed consensus, and anonymity functionalities. A smart contract functions as a verifiable, self-executing, and self-validating computing layer. This study seeks to identify the key concepts and associated future research goals pertaining to smart contracts based on the blockchain. Smart contracts enable the implementation of programmable assets, including money, and the automation of business logic processes that were previously executed manually. In response to this disclosure, a comprehensive examination of smart contracts employing blockchain technology is undertaken. In this article, we propose an innovative and practical metamodel for ensuring the integrity of protocols within smart contracts facilitated by blockchain technology. This article begins with an explanation of smart contracts’ operational models, functionalities, and applications. Analysis of the results reveals that the utilisation of the model alleviates the workload associated with numerous measurements. It was determined that the mean response latency, computation time, and overhead were each 1.47, 1.19, and 3.04 s. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart contract

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Optimizing Underwater Acoustic Survey Strategies with Autonomous Underwater Vehicles: A Simulation-Based Framework 21st

Optimizing Underwater Acoustic Survey Strategies with Autono...

引用

21st international symposium on distributed computing and Artificial Intelligence, DCAI 2024

作者： Lagudi, Antonio Severino, Umberto Bruno, Fabio Department of Mechanical Energetic and Management Engineering University of Calabria Arcavacata di Rende87036 Italy

ISBN: (纸本)9783031809453

Ocean exploration and inspection missions involving underwater robotic systems have gained increasing popularity in various domains such as marine science, archaeology, and defense in recent years. However, deploying vessels and robotic solutions for such research activities entails significant costs for acquisition and maintenance. This is primarily due to the logistical challenges involved in transporting equipment between locations, which necessitates a substantial financial investment, in addition to expenses associated with crew and research personnel. Effective planning of survey campaigns is crucial to avoid costly missteps arising from poor organization. It ensures that expenditures are justified and that objectives are achieved without unnecessary setbacks or errors. In this context, the utilization of simulation methodologies adds invaluable value in mitigating costs and exploring various solutions before the mission is carried out in the real world. Their utility extends beyond the examination of specific sensors or setups;they are also a valuable tool for planning different campaigns with different mission objectives. In this paper, a simulation framework that integrates an Autonomous Underwater Vehicle with a Multibeam Echo Sounder as the perception sensor is presented. The objective is to streamline acoustic survey mission planning and generate synthetic acoustic image data incorporating the modeling of typical error sources, simplifying the evaluation of output data for real applications. This system can also be utilized as a Software-in-the-loop system for testing and verification of control systems and algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Acoustic imaging

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Adaptive Cyber Defence: Leveraging GANs for Simulating and Mitigating Advanced Network Attacks in IoT Environments 11th

Adaptive Cyber Defence: Leveraging GANs for Simulating and ...

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11th international symposium on Applied computing for Software and Smart Systems, ACSS 2024

作者： Rao, P. Kiran Chatterjee, Subarna Prakash, P. Suman Ramana, K. Seshadri Rajeev Gandhi Memorial College of Engineering and Technology Andra Pradesh Nandyal518501 India Department of CSE MS Ramaiah University of Applied Science Karnataka Bengaluru India Department of CAI G Pullaiah College of Engineering and Technology Andra Pradesh Kurnool India Department of CAI Ravindra College of Engineering for Women Andra Pradesh Kurnoo518001 India

ISBN: (纸本)9789819797615

The progressive dissemination of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) has ushered in a new era of connectivity, with vast applications spanning from medicare to smart city infrastructure. However, this expansion has been paralleled by a corresponding increase in the sophistication and variety of cyber threats targeting these networks. Traditional cyber security measures, designed for a less dynamic threat landscape, are proving increasingly insufficient in protecting against the innovative and varied attack methods now in commonplace. This study introduces an innovative application of Generative Adversarial Networks (GANs) to address this challenge, presenting a novel framework for the simulation and mitigation of advanced network attacks, particularly focusing on distributed Denial of Service (DDoS) and spoofing attacks which pose significant threats in IoT environments. Generative Adversarial Networks (GANs), comprising two neural networks-the generator and the discriminator compete in a game-theoretic scenario, facilitating a deep understanding of attack patterns through the generation of realistic, synthetic cyber-attack scenarios. This research exploits GANs to bridge the gap between the static nature of traditional security protocols and the dynamic, evolving landscape of cyber threats. By training on a comprehensive dataset of known attacks and normal network activities, our proposed model, the Dynamic Adaptive Threat Simulation GAN (DATS-GAN), is capable of producing varied and realistic attack scenarios. These simulations serve a dual purpose: they not only enhance the detection capabilities and responsiveness of current security systems but also provide a basis for the development of new, adaptive security mechanisms capable of dynamically responding to the ever-changing cyber threat landscape. The effectiveness of DATS-GAN is demonstrated through extensive empirical analysis, highlighting significant improvements in the detecti

关键词： Generative adversarial networks

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Edge and Fog computing in Cyber-Physical Systems

Edge and Fog Computing in Cyber-Physical Systems

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Intelligent Control, computing and Communications (IC3), international Conference on

作者： Priyanshi Gangrade Virendra Singh Kushwah Saroj S. Date Archana D. Jagdale Kamal Upreti Vinai K. Singh School of CSE and AI VIT Bhopal University Sehore India Dept. of AI and DS CSMSS Chh. Shahu college of Engineering Chh. Sambhajinagar Maharashtra India Barclays Services Corp New York USA Department of Computer Science Christ University Delhi NCR Ghaziabad India Department of Computer Science & Engineering GL Bajaj Group of Institutions Mathura India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

The benefits of cyber-physical system advances include low latency and high bandwidth data processing in areas such as automotive, healthcare, and business automation. Traditional environments are often located in centralized and remote locations and cannot meet the demand. Edge computing and cloud computing have become fundamental concepts that will bring computing closer to the center of the data. Edge computing can reduce latency and bandwidth consumption by processing data on or near IoT devices. Fog computing adds another layer to this by distributing work and storage across multiple nodes, thus providing a scalable and flexible infrastructure. This article discusses the principles, benefits, and challenges of integrating edge and cloud computing into a CPS environment. It leverages the power of proximity-based edge computing and the centralized capabilities of cloud computing to provide scalable, instantaneous responses to CPS applications or time to optimize services. The demonstration shows a variety of things from smart cities to the use of IoT in healthcare in CPS. The article also covers some specific security and privacy issues and future directions in distributed computing, including the role of AI and 5G, which are supposed to offer additional resources in various applications.

关键词： Cloud computing Automation Smart cities Medical services Bandwidth Cyber-physical systems Data processing Internet of Things Edge computing business

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Collaborative and Integrated Platform to Support distributed Manufacturing System Using a Service-Oriented Approach Based On Cloud computing Paradigm

Collaborative and Integrated Platform to Support Distributed...

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Computational, Communication and Information Technology (ICCCIT), international Conference on

作者： Ankita Nainwal Vishal Sharma Neeraj Varshney Nandini Shirish Boob NV Uma Reddy K. Jyothsna Reddy Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Lovely Professional University Phagwara India Department of Computer Engineering and Applications GLA University Mathura Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu Department of Artificial Intelligence & Machine Learning New Horizon College of Engineering Bangalore Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

Increasing interest in distributed manufacturing systems is a result of the dynamic nature of today's business environment and with that comes the popularity of their use. This has given rise in the interest in distributed manufacturing systems. The indirect consequence of this has led to much higher use of distributed manufacturing systems. A large number of production processes, dispersed across a multitude of locations geographically separate, must be controlled and integrated in order to achieve production agility, flexibility and efficiency of costs. In our most recent project, we developed an integrated, collaborative manufacturing platform, the centerpiece of which was LAYMOD. To address the issues posed by such systems, this research presents a collateral and integrated platform based on a service oriented approach that can operate within the cloud computing paradigm. These systems present problems which the study is conducted in order to solve. Utilising the features of cloud computing, it aims to serve as a platform in solving process coordination and hence improving efficiency and scalability of distant manufacturing processes and potential real time communications among various stake holders. Furthermore, statistical research on the rates of platform module adoption in different settings provides specifics of (a) use patterns of some platform modules and (b) what capabilities are provided by some platform modules. When extrapolated to a wider size, our work is applied towards the creation of collaborative and integrated platforms for a number of the various manufacturing processes which are currently in use.

关键词： Cloud computing Manufacturing processes Scalability Collaboration Production Companies Real-time systems Service-oriented architecture Standards business

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Edge and Fog computing in Cyber-Physical Systems

Edge and Fog Computing in Cyber-Physical Systems

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2025 international Conference on Intelligent Control, computing and Communications, IC3 2025

作者： Gangrade, Priyanshi Kushwah, Virendra Singh Date, Saroj S. Jagdale, Archana D. Upreti, Kamal Singh, Vinai K. School of Cse and Ai Vit Bhopal University Sehore India Csmss Chh. Shahu college of Engineering Dept. of Ai and Ds Maharashtra Chh. Sambhajinagar India Barclays Services Corp 745 Seventh Avenue New York United States Department of Computer Science Christ University Delhi Ncr Ghaziabad India Gl Bajaj Group of Institutions Department of Computer Science & Engineering Mathura India

ISBN: (纸本)9798331527495

关键词： Cyber-Physical Systems (CPS) Data Processing Edge computing Fog computing

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Multi-Agent Systems for Autonomous IoT Network Management Using distributed Reinforcement Learning

Multi-Agent Systems for Autonomous IoT Network Management Us...

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international symposium on Advanced computing and Communication (ISACC)

作者： Neelamegam G Rajaram Venkatesan Ramya SR R.S. Ramya Akshya. J M. Sundarrajan Mani Deepak Choudhry Department of Computer Science and Engineering VSB College of Engineering and Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science Builders Engineering College Tirupur Tamil Nadu India Department of Computer Science and Engineering Dr NGP Institute of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. The framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. The proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. The system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. The proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems.

关键词： Smart cities Reinforcement learning Medical services Throughput Dynamic scheduling Energy efficiency Real-time systems Resource management Optimization Multi-agent systems

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A Study on the Impact of Partitioning on Community Detection in Graph Networks 6th

A Study on the Impact of Partitioning on Community Detec...

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6th international Conference on Computational Intelligence in Communications and business Analytics, CICBA 2024

作者： Joseph, Neethu Vidhya, S. Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9783031813382

The rapid growth of electronic data generation, processing and storage necessitates large-scale data processing in numerous domains like road networks, car ad hoc networks, bitcoin, pictures, brain networks, and citation networks. In these applications, Graph is a natural and potent tool for describing and capturing dependence relationships between items and it is important to detect the communities within these networks for meaningful interpretation, aiding in tasks such as identifying anomalies in road traffic systems or uncovering deceptive activities on social media. This paper delves into the exploration of graph partitioning approaches, employed to segment large graphs into subgraphs and subsequently identify communities within these partitioned graph networks. The investigation focuses on modularity-based algorithms, particularly the Louvain algorithm, and examines the effectiveness of pyMetis and Deep Learning-based methods in enhancing the efficiency of graph partitioning to yield reliable community detection. The experiments showed that pyMetis outperformed the GCN Method in case of graph partitioning and also showed that the computation cost is reduced while doing graph partition and then proceeding with community detection as compared to usual community detection. Also, the non-distributed and distributed methods of community detection are evaluated and the distributed approach using Ray Framework produced better results than non-distributed community detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Data handling

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