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检索条件"任意字段=Conference on Photonic Devices and Algorithms for Computing V"

共 1067 条记录，以下是81-90 订阅

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The Robustness of Spiking Neural Networks in Communication and its Application towards Network Efficiency in Federated Learning

The Robustness of Spiking Neural Networks in Communication a...

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2024 IEEE International Performance, computing, and Communications conference, IPCCC 2024

作者： Nguyen, Manh v. Zhao, Liang Deng, Bobin Severa, William Xu, Honghui Wu, Shaoen Kennesaw State University College of Computing and Software Engineering GA United States Sandia National Laboratories Department of Cognitive & Emerging Computing NM United States

ISBN: (纸本)9798350367942

Spiking Neural Networks (SNNs) have recently gained significant interest in on-chip learning in embedded devices and emerged as an energy-efficient alternative to conventional Artificial Neural Networks (ANNs). However, to extend SNNs to a Federated Learning (FL) setting involving collaborative model training, the communication between the local devices and the remote server remains the bottleneck, which is often restricted and costly. In this paper, we first explore the inherent robustness of SNNs under noisy communication in FL. Building upon this foundation, we propose a novel Federated Learning with Top-κ Sparsification (FLTS) algorithm to reduce the bandwidth usage for FL training. We discover that the proposed scheme with SNNs allows more bandwidth savings compared to ANNs without impacting the model's accuracy. Additionally, the number of parameters to be communicated can be reduced to as low as 6% of the size of the original model. We further improve the communication efficiency by enabling dynamic parameter compression during model training. Extensive experiment results demonstrate that our proposed algorithms significantly outperform the baselines in terms of communication cost and model accuracy and are promising for practical network-efficient FL with SNNs. © 2024 IEEE.

关键词： Federated learning

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Investigation of Task Scheduling in Cloud computing by using Imperialist Competitive and Crow Search algorithms 3

Investigation of Task Scheduling in Cloud Computing by using...

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3rd International conference on Evolutionary computing and Mobile Sustainable Networks, ICECMSN 2023

作者： Ravi, Jayavadivel Rajkumar, N. viji, C. Loganathan, D. Sushma, K.S.N. Stalin, M. Department of Computer Science & Engineering Alliance College of Engineering and Design Alliance University Karnataka Bangalore India Department of Information Science & Engineering Cambridge Institute of Technology Karnataka Bangalore India School of Computer Science and Engineering REVA University Karnataka Bangalore India

Cloud Storage is a complex method that is a method of processing and data of a cloud built by duplication of thousands of related devices in a complex manner. The main function of the data processing server is to show how many users are being investigated and provide accurate, efficient and efficient information. Important Algorithm Editing Players in the Cloud Defines the virtual machine (vM) required for this purpose. The role of editing the algorithm reduces the effect of the schedule. Naturally affected algorithms have recently been used to quickly comply with the recent traditional algorithms. Given many consumers of many cloud computing services, many researchers may have a serious explanation that many researchers take and discuss the complex themes of NP. Some sites use imperialist algorithms (ICA) and birds. The purpose of the proposed project is to develop intelligent scientific algorithms that focus on the integration of ICA and CSA to obtain data. CSA is concentrated on the corner of food habits. The crow is looking for his friends to get enough food for today's food. This will help the CSA find suitable vMs for these machines and complete the equipment. Cloud Sim is used to calculate CSA output with minmin and ant algorithms. The simulation results show that the CSA is extra powerful than the MinMin and Ant procedures. © 2023 Elsevier B.v.. All rights reserved.

关键词： Cloud computing

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20th International Symposium on Applied Reconfigurable computing, ARC 2024

20th International Symposium on Applied Reconfigurable Compu...

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20th International Symposium on Applied Reconfigurable computing, ARC 2024

ISBN: (纸本)9783031556722

The proceedings contain 21 papers. The special focus in this conference is on Applied Reconfigurable computing. The topics include: Deep Quantization of Graph Neural Networks with Run-Time Hardware-Aware Training;Reconfigurable Edge Hardware for Intelligent IDS: Systematic Approach;Bridging the Gap in ECG Classification: Integrating Self-supervised Learning with Human-in-the-Loop Amid Medical Equipment Hardware Constraints;Enabling FPGA and AI Engine Tasks in the HPX Programming Framework for Heterogeneous High-Performance computing;LiDAR 3D Object Detection in FPGA with Low Bitwidth Quantization;cryptographic Security Through a Hardware Root of Trust;analysis of Clock Tree Buffer Degradation Caused by Radiation;NEUROSEC: FPGA-Based Neuromorphic Audio Security;Secure eFPGA Configuration: A System-Level Approach;Graphtoy: Fast Software Simulation of Applications for AMD’s AI Engines;A DSL and MLIR Dialect for Streaming and vectorisation;Analysis of Process variation Within Clock Regions of AMD-Xilinx UltraScale+ devices;high Performance Connected Components Accelerator for Image Processing in the Edge;Spectral-Blaze: A High-Performance FFT-Based CNN Accelerator;trusted computing Architectures for IoT devices;PROACT - Physical Attack Resistance of Cryptographic algorithms and Circuits with Reduced Time to Market;A Flexible Mixed-Mesh FPGA Cluster Architecture for High Speed computing;A Safety-Critical, RISC-v SoC Integrated and ASIC-Ready Classic McEliece Accelerator;Exploiting FPGAs and Spiking Neural Networks at the Micro-Edge: The EdgeAI Approach.

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Enhancing DDoS Attack Detection: A Hybrid SvM-Decision Tree Ensemble Approach 15

Enhancing DDoS Attack Detection: A Hybrid SVM-Decision Tree ...

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15th International conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Muduli, Debendra Bhatta, Sandesh Shookdeb, Shantanu Adhikari, Aayush Sapkota, Aaron Chaturvedi, Prabhat C. V. Raman Global University Dept. of Computer Science and Eng. Bhubaneswar India

ISBN: (纸本)9798350370249

A DDoS attack overwhelms a network or server with an excessive amount of traffic, disrupting its normal operation and rendering it inaccessible to legitimate users. DDoS attacks involve coordinated efforts from multiple compromised devices, known as botnets, flooding the target with traffic simultaneously. This flood of data exhausts the target's resources, causing service disruptions. Machine learning aids in DDoS attack detection by analyzing network traffic patterns. It learns to differentiate between normal and malicious activity by studying features such as packet rates, traffic volume, and protocol anomalies. By training on labeled datasets containing both benign and attack traffic, machine learning algorithms can identify deviations indicative of DDoS attacks, thereby enhancing network security. The hybrid machine learning model combines the precision of Support vector Machines with the decision-making clarity of Decision Trees to effectively combat DDoS attacks. This innovative model achieves an impressive accuracy rate of 99.98% against the CICDDoS2019 dataset, demonstrating its exceptional performance in detecting various DDoS attack vectors. © 2024 IEEE.

关键词： Support vector machines

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A Perspective Analysis of Optical Biosensors in Machine Learning Applications 18

A Perspective Analysis of Optical Biosensors in Machine Lear...

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18th INDIAcom;11th International conference on computing for Sustainable Global Development, INDIACom 2024

作者： Kumar, B. Naveen Panduranga Rao, M.v. Sharan, Preeta varun, K.R. Kallam, Suresh Kumaresan, M. Faculty of Engineering and Technology Department of Computer Science and Engineering Bengaluru India The Oxford College of Engineering Department of Electronics and Communication Engineering Bengaluru India The Oxford College of Engineering Department of Mechanical Engineering Bengaluru India

ISBN: (纸本)9789380544519

Optical biosensors, a novel invention at the intersection of biotechnology and optics, are revolutionizing the field of biomedical diagnostics. Optical biosensors act by identifying variations in light's wavelength, phase, polarization, or intensity caused by interactions between a sensing element and the target analyte. Optical biosensors outperform other sensor types in sensing applications due to their quick, accurate, and extremely sensitive results. When evaluating optical sensors, total power, core power, efficient area, and indices are the most crucial variables to consider. Recent years have seen notable advancements in machine learning (ML), a branch of artificial intelligence (AI). This review investigates the complementary nature of optical biosensors and machine learning methods. Here, the potential utility of ML for optical biosensors is explored in detail. The advantages and drawbacks of the most popular machine learning methods are listed based on an analysis of the optical biosensing data. It highlights the possible effects of machine learning on improving the sensitivity, selectivity, and adaptability of optical biosensing devices, and it summarizes current advancements in the field. Finally, the study considers the challenges and limitations that associated with current optical biosensing techniques. © 2024 Bharati vidyapeeth, New Delhi.

关键词： Machine learning optical biosensor photonic crystal surface plasmon resonance surface-enhanced Raman scattering

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Navigating Grid Dynamics with v2G Technology and Naive Bayes Optimization in IoT Systems 3

Navigating Grid Dynamics with V2G Technology and Naive Bayes...

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3rd International conference for Innovation in Technology, INOCON 2024

作者： Arulprakash, M. Raman, Ramakrishnan Gokhale, Atul A. Saravanan, K. Ishwarya, M.v. Sujatha, S. Srm Institute of Science and Technology Department of Computing Technologies Chennai Tamil Nadu Kattankulathur India Maharashtra Pune India Symbiosis School of Culinary Arts Maharashtra Pune India R.M.D. Engineering College Department of Information Technology Tamil Nadu Chennai India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350381931

vehicle-to-Grid (v2G) technology and Naive Bayes optimization, which might help IoT devices, navigate grid dynamics. Electric vehicles (Evs) with bidirectional charging capability may provide grid assistance using their energy storage capacity. These cars can take and return energy from the grid using v2G technology, stabilizing the system. This paper proposes a unique technique to intelligently control energy flow between the grid and Evs using v2G technology and Naive Bayes optimization algorithms. Using previous data on energy consumption, grid circumstances, and vehicle charging behavior, the Naive Bayes algorithm makes real-time predictions and decisions. In IoT-enabled smart grids, this connection improves energy management efficiency and reliability. The implementation methods and experimental findings of the suggested strategy are discussed in the study. The system grid stability, energy usage, and grid dynamics management are also assessed. The results show that v2G technology and Naive Bayes optimization in IoT systems can intelligently navigate grid dynamics, creating a more sustainable and resilient energy infrastructure. To smart grid expertise and shows how v2G-enabled IoT devices may optimize energy distribution and ensure grid stability in the face of dynamic energy needs. © 2024 IEEE.

关键词： Digital storage

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Advanced Lightweight Encryption Algorithm for Android (IoT) devices

Advanced Lightweight Encryption Algorithm for Android (IoT) ...

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2022 IEEE International conference on Advances in computing, Communication and Applied Informatics, ACCAI 2022

作者： Chandrashekhar, R.v. visumathi, J. Anandaraj, A. Petersoosai Vel Tech Rangarajan Dr. Sagunthala RD Institute of Science and Technology Chennai India

ISBN: (纸本)9781665495295

Security and Controls with Data privacy in Internet of Things (IoT) devices is not only a present and future technology that is projected to connect a multitude of devices, but it is also a critical survival factor for IoT to thrive. As the quantity of communications increases, massive amounts of data are expected to be generated, posing a threat to both physical device and data security. In the Internet of Things architecture, small and low-powered devices are widespread. Due to their complexity, traditional encryption methods and algorithms are computationally expensive, requiring numerous rounds to encrypt and decode, squandering the limited energy available on devices. A simpler cryptographic method, on the other hand, may compromise the intended confidentiality and integrity. This study examines two lightweight encryption algorithms for Android devices: AES and RSA. On the other hand, the traditional AES approach generates preset encryption keys that the sender and receiver share. As a result, the key may be obtained quickly. In this paper, we present an improved AES approach for generating dynamic keys. © 2022 IEEE.

关键词： Cryptography

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Energy Aware Task Scheduling in IoT Based Fog Environment 21

Energy Aware Task Scheduling in IoT Based Fog Environment

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21st OITS International conference on Information Technology, OCIT 2023

作者： Tripathy, Niva Sahoo, Sampa Nayak, Suvendu Chandan C. V. Raman Global University Driems Autonomous College Department of Computer Sc. & Engg Bhubaneswar India C. V. Raman Global University Department of Computer Sc. & Engg Bhubaneswar India Silicon Institute of Technology Department of Computer Sc. & Engg Bhubaneswar India

ISBN: (纸本)9798350358230

Fog computing has become an effective paradigm to overcome the drawbacks of centralized cloud computing for quick rise of data-intensive applications and the spread of Internet of Things (IoT) devices. Fog computing makes use of edge devices called fog nodes to bring processing and storage capabilities closer to the data source, hence lowering latency and bandwidth usage. The energy consumption of fog nodes is still a major worry, especially in regions with limited resources. In order to reduce energy usage and still achieve application performance requirements, effective work offloading solutions are needed. Offloading task with consideration for energy consumption between fog nodes and the cloud is the aim of energy-Aware task distribution. Fog node computational capabilities, energy profiles, communication costs, task characteristics, and performance needs are just a few of the variables that must be taken into account during the decision-making process. Task distributions can be adaptively modified by dynamic task offloading algorithms in response to changes in the fog environment that occur in real time. The proposed energy-Aware task offloading techniques aim to provide a comprehensive solution for fog computing environments, enabling energy-efficient operation, improved system performance, and enhanced sustainability. We have evaluated our proposed method through experimental simulations and the results shows that our method has a significant effect on task offloading in fog computing environment on reducing the execution time and energy consumption. © 2023 IEEE.

关键词： Fog

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A Comparative Analysis of IoT Data Using Machine Learning algorithms 15th

A Comparative Analysis of IoT Data Using Machine Learning Al...

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15th International conference on Soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Nagashree, B.A. Satheesh Kumar, S. Muthukumaran, v. Munrathnam, Meram Angeline Kavitha, M. Department of Computer Science and Applications School of CSA REVA University Bangalore India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Mathematics Rajiv Gandhi University of Knowledge Technologies R.K. Valley Kadapa District Andhra Pradesh Idupulapaya516330 India Department of Mathematics Voorhees College Vellore632001 India

ISBN: (纸本)9783031889912

IoT (Internet of Things) is revolutionizing the commercial environment by acquiring vast amounts of data from various sources. However, processing and analyzing this data is challenging due to the large volume of IoT devices. AI-powered IoT creates intelligent machines that mimic human intelligence, aiding decision-making with minimal human input. Both individuals and professionals’ benefit from this combination, as IoT is concerned with communicating online, but AI allows machines to learn from data and experience. This study demonstrates the analysis of IoT data using machine learning algorithms and compares the accuracy of various machine learning algorithms used. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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MEET: Advancing Mobility-Enhanced Edge Intelligence for Efficient and Sustainable 6G Networks 7

MEET: Advancing Mobility-Enhanced Edge Intelligence for Effi...

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7th International conference on Contemporary computing and Informatics, IC3I 2024

作者： vanteru, Madhu Kumar Dasari, Kavitha Asha, v. Singh, Navdeep Abdul-Zahra, Dalael Saad Ramprakash, R. Balaji Institute of Technology and Science Department of Ece India Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Computer Applications Bangalore India Lovely Professional University Phagwara India Hilla University College Babylon Iraq Anurag University Department of Ece Telangana Hyderabad India

ISBN: (纸本)9798350350067

A machine learning technique known as federated learning, or collaborative learning, allows edge devices to cooperatively integrate to a global model while retaining local copies of their data. The integration of edge AI, IoT, and communication technologies can be greatly improved via federated learning. Because of the abundance of devices, apps, and services available in the modern digital era, users have higher expectations and demands for quality of service (QoS) from network service providers. As a result, extensive deployment of base stations (BSs) overall edge servers (ESs) is required, which raises the cost of deployment and operation - particularly energy expenditures. In order to add flexibility to the creation of the traffic along with mobility management algorithms, the current work makes use of fuzzy logic. Enhancing the quality of service (QoS) of current mobility management solutions and making optimum use of available network resources are the main goals of the research project. In this study, we examine a novel architecture for the efficient and environmentally friendly 6G systems dubbed Mobility-Enhanced Edge inTelligence (MEET), that takes advantage of the detection, communication, computation, and automatic features of intelligent linked vehicles. © 2024 IEEE.

关键词： Federated learning

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