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5th international symposium on Intelligent Computing systems

作者： Rizo-Blanco, Ismel Martin-Gonzalez, Anabel Brito-Loeza, Carlos Legarda-Saenz, Ricardo Espinosa-Romero, Arturo Lafon-Hughes, Laura Univ Autonoma Yucatan Fac Matemat Computat Learning & Imaging Res Merida 97119 Mexico Univ Republ Ctr Univ Reg Litoral Norte CENUR Salto Uruguay

ISBN: (纸本)9783031829307;9783031829314

The comet assay is a versatile method used to determine the DNA damage in individual cells. The cells processed by this technique preserve the stable genetic material in the head of the comet and the unstable portion of DNA in the tail. The analysis of the resulting microscopic images from this test must be carried out by an expert, who must precisely determine the extent of DNA liberated from the head of the comet, since it is related to the level of damage. Since this is a time-consuming and very specialized task, the objective of this research is to develop a computational system, based on the use of a convolutional neural network, for the automatic classification of cells processed by comet assay according to the level of DNA damage they present.

关键词： Comet Assay Convolutional Neural Network Cell Classification Deep Learning

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Multi-Objective Optimization Scheme for Energy systems in Distribution networks Based on Cloud-Edge-Device Collaborative Framework 5

Multi-Objective Optimization Scheme for Energy Systems in Di...

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5th IEEE international Conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Liu, Jingzhi Zhang, Jianming Yang, Hongyi Bao, Zhenghong Zhan, Fei Liu, Zhidong State Grid Qinghai Electric Power Research Institute Xining Qinghai China State Grid Qinghai Electric Power Company Xining Qinghai China

ISBN: (纸本)9798331533694

As energy systems evolve, single-system economic optimization no longer meets the growing multi-energy demands of users. To address this problem, this paper proposes a multi-objective optimization method for distribution network energy systems based on cloud-edge-device collaborative (CEDC) architecture. First, a CEDC system for the energy system of the distribution network is constructed in response to energy scheduling requirements, and edge-device clusters are used to achieve distributed management of energy system units. Second, based on the energy scheduling requirements, corresponding energy scheduling commands are generated for the target energy systems. These energy scheduling commands are decomposed into at least one computational task, then the computational tasks are assigned to the edge cluster control units of the corresponding target energy systems. Then, a multi-objective optimization scheme is designed, which jointly minimizes the total power supply cost of a single energy system and the total delay time across multiple energy systems. Finally, the scheme is solved by the particle swarm optimization (PSO) algorithm introducing the simulated annealing. Simulation results show that the proposed algorith can minimize the total energy cost and overall system delay. © 2025 IEEE.

关键词： Power distribution networks

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Enhancing Peer-to-Peer Network Security using 3D Convolutional Autoencoder based Anomaly Detection 1

Enhancing Peer-to-Peer Network Security using 3D Convolution...

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1st international Conference on Intelligent systems and Computational networks, ICISCN 2025

作者： Rana Veer Samara Sihman Bharattej, R. Ajznblasm, Zaid Satish, E.G. Adlin Sylvia, A. Devi, M. Saint Leo University Department of Computer Science FL United States The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331529246

In this era, Peer-to-Peer (P2P) networks have developed as a revolutionized network for data distribution and alternate to traditional client-server network. The features of P2P networks allow a distributed group of users to stake resources in the form of digital content or information. However, P2P networks lack in controlling access and it has limited security. In this research, Three Dimensional-Convolutional Autoencoder (3D-CA) is proposed to improve the security in P2P network. Initially, the data is obtained from University of New South Wales Network Behavior 2015 (UNSW-NB15) dataset and then preprocessed with data cleaning to ensure data quality. After that, the P2P autoencoder architecture is employed to learn the normal patterns which employs the detection in P2P network. Finally, the proposed 3D-CA is trained to detect the anomalies based on reconstruction error between input node and reconstructed pattern. From the results, the proposed 3D-CA model attained better outcomes compared to existing K-means algorithm in terms of accuracy, precision as well as F1-score by achieving 0.764, 0.90 and 0.80 respectively. © 2025 IEEE.

关键词： Peer to peer networks

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EPPDL: An efficient privacy-preserving distributed ledger for digital asset transfer in Web3.0

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FUTURE GENERATION computer systems-THE international JOURNAL OF ESCIENCE 2025年 167卷

作者： Ma, Lichuan Zhou, Lu Huang, Hang Qu, Youyang Liu, Xuefeng Xidian Univ State Key Lab Integrated Serv Networks 2 South Taibai Rd Xian 710071 Shaanxi Peoples R China Shaanxi Key Lab Blockchain & Secure Comp 2 South Taibai Rd Xian 710071 Shaanxi Peoples R China CSIRO Data61 Res Way Clayton Vic 3168 Australia

Nowadays, anew generation of decentralized internet framework, coined as Web3.0, is emerging. However, due to the insufficient computing power on the user side and the know-your-customer regulatory requirements, it is unrealistic to fully achieve decentralization in Web3.0 currently. The service provider-intermediated architecture seems more practical by including federated service providers. At the same time, in order to fully stimulate users to create and share contents in the era of Web3.0, the importance of digital assets, e.g., digit tokens and cryptocurrencies, is increasing. Asa result, whether digital asset transfer can be securely and efficiently accommodated determines the further development of Web3.0. Blockchain is believed to be one effective solution to guarantee the security of digital asset transfer. However, existing works either target fully decentralized scenarios or fail to settle digital asset transfers with high efficiency. Thus in this paper, a formal yet novel service provider-intermediated architecture is firstly proposed to closely align with the practical requirements of Web3.0. Then, an efficient privacy-preserving distributed ledger construction protocol, coined as EPPDL, is proposed to safeguard digital asset transfer among users registered at different service providers. Concrete security analysis proves that the proposed EPPDL is secure against different types of adversaries, while comprehensive experiments verify its efficiency and effectiveness.

关键词： Web3.0 Privacy preserving distributed ledger Blockchain

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Enhancing Electric Vehicle Charging systems with distributed Edge Intelligence 16

Enhancing Electric Vehicle Charging Systems with Distributed...

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16th international Conference on Ambient systems, networks and Technologies networks, ANT 2025 / 8th international Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Rahmani, Rahim Stojcevska, Biljana Department of Computer and Systems Sciences Stockholm University Sweden American University of Europe Skopje North Macedonia

The rapid proliferation of electric vehicles (EVs) and the corresponding expansion of charging systems, driven by global regulatory mandates, present significant challenges to the electric grid. These systems also generate massive volumes of heterogeneous, distributed data, necessitating advanced edge-cloud computing solutions. Deploying distributed Edge Intelligence (DEI) offers a transformative approach by contextualizing raw data, minimizing dependency on centralized resources, enhancing task performance through experience-based learning, predicting outcomes under uncertainties, optimizing data routing, and enabling self-organization of networked devices. Traditional centralized machine learning approaches are often unsuitable due to high communication costs, low reliability, legal constraints, and scalability limitations. In this context, Federated Learning (FL) emerges as a promising solution, enabling distributed machine learning models to be trained locally on decentralized data while preserving privacy and reducing communication overhead. This paper provides a comprehensive analysis of the key challenges associated with EV charging systems, including scalability, security, and privacy concerns. It also explores the integration of blockchain technology with FL at distributed edges to enhance system efficiency, reliability, and privacy. By addressing these challenges, the study outlines a framework for sustainable EV adoption and efficient charging infrastructure deployment in the context of evolving technological and regulatory landscapes. © 2025 The Author(s).

关键词： Blockchain Charging systems distributed Edge Intelligence Electric Vechicle smart contract

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Analyzing Data Traffic in IoT networks: A Novel Graph-Based Architecture 5th

Analyzing Data Traffic in IoT Networks: A Novel Graph-Based ...

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5th international Conference on Computing and Network Communications, CoCoNet 2023

作者： George, Gemini Department of Computer Applications St. George’s College Aruvithura Erattupetta Kerala Kottayam686122 India

ISBN: (纸本)9789819745395

The rapid deployment of IoT networks in different industrial services has caused the emanation of a huge volume of data from sensors and monitors. The efficient analysis and compact representation of the big data generated are critical to managing its transfer, storage, and latency. In this work, we propose a novel graphical model that has the flexibility to adapt to different architectural features of distributed IoT systems with low-power sensors. Further, we consider the traffic load minimization problem in the network and map the problems of network coverage and clustering onto the graph to find an optimal distributed network structure that minimizes the traffic load. The proposed approaches are tested under different network conditions, and the simulation results emphasize the improved performance of the method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph based architecture IoT Network construction Sensor network

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GenesisRM: : A state-driven approach to resource management for distributed JVM web applications

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FUTURE GENERATION computer systems-THE international JOURNAL OF ESCIENCE 2025年 163卷

作者： Xu, Dishi Liu, Fagui Wang, Bin Tang, Xuhao Zeng, Dinghao Gao, Huaiji Chen, Runbin Wu, Qingbo South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Pengcheng Lab Dept New Networks Shenzhen 518066 Peoples R China Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China

Reducing resource waste while maintaining end-to-end latency service-level objective (SLO) by simultaneously managing CPU bandwidth, memory allocation, and pod number of web applications running on Java virtual machine (JVM) is challenging. The challenges stem from the complexity of the multi-type resource allocation optimization problem, the high sensitivity of JVM performance to resource scaling actions, and the lack of low-level resource scaling mechanisms. We present GenesisRM, a resource management framework with a novel state-driven architecture. Specifically, we design a state control model for JVM web applications that encompasses seven pod states. This model serves as an abstraction layer, decoupling the centralized resource management system into a global state manager and distributed pod managers. The state manager controls the state transitions of the pods based on the overall workload, while the pod managers dynamically allocate resources for each pod according to the state and local workload. Then, we propose a multi-frequency control model with two predictive state controllers and a reactive state controller to manage the state of pods based on the state control model. In addition, GenesisRM brings new mechanisms to scale JVM pods' low-level resources without damaging their performance. We evaluate our work using a real-world JVM web application benchmark in three different scale server clusters of Pengcheng Laboratory Developer Cloud, and the 21- day experimental results show that GenesisRM saves 31.70% CPU and 17.60% memory compared to the best-performing state-of-the-art solutions while guaranteeing the SLO imposed on end-to-end latency.

关键词： Cloud computing Resource management Autoscaling Workload prediction Quality of service JVM

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A Mathematical Model for Data Traffic Management in Next-Generation IoT networks 5th

A Mathematical Model for Data Traffic Management in Next-G...

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5th international Conference on Computing and Network Communications, CoCoNet 2023

作者： George, Gemini Department of Computer Applications St. George’s College Aruvithura Erattupetta Kerala Kottayam686122 India

ISBN: (纸本)9789819745395

The rapid deployment of IoT networks in different industrial services has caused the emanation of a huge volume of data from sensors and monitors. The efficient analysis and compact representation of the big data generated is critical to managing its transfer, storage, and latency. In this work, we propose a novel graphical model that has the flexibility to adapt to different architectural features of distributed IoT systems with low-power sensors. Further, we consider the traffic load minimization problem in the network and map the problems of network coverage and clustering onto the graph to find an optimal distributed network structure that minimizes the traffic load. The proposed approaches are tested under different network conditions, and the simulation results emphasize the improved performance of the method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Internet of things

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An Efficient Device Placement Method for distributed Training of Multi-branch Neural Network-Based Remote Sensing Interpretation 25th

An Efficient Device Placement Method for Distributed Traini...

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25th international Conference on Web Information systems Engineering, WISE 2024

作者： Long, Ao Wang, Yuewei Huang, Xiaohui Han, Wei Fan, Runyu Chen, Yunliang Li, Jianxin School of Computer Science China University of Geosciences Wuhan China School of Information Technology Deakin University Melbourne Australia

ISBN: (纸本)9789819605699

Multi-branch neural networks are widely used in remote sensing intelligent interpretation because they can fuse multi-modal remote sensing data to improve interpretation accuracy. Meanwhile, in the current "big data era" of remote sensing, the application of distributed deep learning has been steadily growing to improve the training efficiency of remote sensing interpretation models. However, prevailing distributed training systems necessitate users to manually devise a parallelization plan or automatically generate one from a restricted space of model parallelism configurations. At the same time, these techniques are not suitable for extending the application of multi-branch neural network-based remote sensing interpretation on computing devices. In this paper, considering both the data and the model parallelism, we design and implement a fast and near-optimal device placement method for automatically placing branches in multi-branch neural networks across multiple devices. The core idea is to consider the trade-off between the computation and the inter-device communication to minimize the total training time. Through careful formulation of the problem as an integer program, our method can offer optimal placement and scheduling of the branches. We conduct experiments to deploy and evaluate the proposed method on two multi-branch networks. The results indicate that the method finds non-trivial device placements outperforming baselines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mixed-integer linear programming

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DFASCN: A distributed flocking approach for UAV swarm collective navigation

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FUTURE GENERATION computer systems-THE international JOURNAL OF ESCIENCE 2025年 171卷

作者： Li, Yibing Zhang, Zitang Huang, Yujie Sun, Qian Dong, Qianhui Harbin Engn Univ Sch Informat & Commun Engn Harbin 150001 Peoples R China Harbin Engn Univ Sch Phys & Optoelect Engn Harbin 150001 Peoples R China

In recent years, the application domains of unmanned swarms have been continuously expanding. ing swarm navigation methods predominantly rely on communication networks for frequently information exchange to achieve stable navigation behavior. However, this reliance presents challenges in achieving coordinated cooperative behavior in communication-restricted and obstacle-rich environments. To the task efficiency of swarms in such mission settings, we propose a distributed flocking framework guide unmanned aerial vehicle (UAV) swarms in navigating from a starting point to a target in unknown environments. Our approach begins by employing Boyd's OODA loop (Observe, Orient, Decide, Act), combined with a locally limited perception model, to develop an interactive decision-making process between individual UAVs and their external environment. We classify the roles of different UAV platforms within the swarm, enhancing cooperative flight efficiency through the guiding behavior of critical nodes. Each UAV utilizes dynamic adjustment mechanism for control parameters, allowing adaptive modifications based on local states. Additionally, each UAV is equipped with a model predictive control (MPC) controller, which provides feasible control inputs to ensure robust and reliable operation in complex and dynamic scenarios. To evaluate the adaptability of our method, we conducted simulations across various task environments with differing obstacle densities and numbers of UAVs. The results validate the algorithm's effectiveness and scalability, highlighting its robustness and potential applicability to real-world scenarios.

关键词： UAV swarm Collective navigation Predictive control

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