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检索条件"任意字段=IEEE International Conference on Distributed Computing Systems"

共 150545 条记录，以下是4401-4410 订阅

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GVMP: A Novel Internet of Things Application Module Placement Strategy for Heterogeneous Infrastructure using iFogSim 15

GVMP: A Novel Internet of Things Application Module Placemen...

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

作者： Panda, Sanjaya Kumar Tyagi, Divyanshu Rout, Rashmi Ranjan Nit Warangal Department of Cse Telangana Warangal506004 India

ISBN: (纸本)9798350370249

The growing usage of Internet of Things (IoT) devices in smart city applications has resulted in a surge in data volume. However, a centralized cloud server for IoT applications is practically infeasible because of high latency and network congestion. Fog computing offers a solution by processing data near the network edge through distributed network devices. Nonetheless, selecting a fog device capable of placing, hosting and processing an application module is quite challenging and a nondeterministic polynomial (NP)-hard problem. Therefore, an efficient application module placement strategy is needed to minimize latency and network usage in an IoT-fog-cloudbased environment. This paper proposes the gateway validation module placement (GVMP) strategy for effectively deploying application modules in IoT-fog-cloud-based environments. The proposed strategy places modules on heterogeneous fog devices in the environments. The performance of the GVMP strategy is evaluated using the iFogSim tool and compared with the edgeward module placement (EWMP) strategy. The simulation results show that the GVMP strategy significantly reduces average application latency and network usage compared to the EWMP strategy. © 2024 ieee.

关键词： Average Application Latency Cloud computing Edge computing Fog computing Internet of Things Module Placement Network usage

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FedHZone: Federated Learning Based Hazardous Zone Detection Using Internet of Drone Things

FedHZone: Federated Learning Based Hazardous Zone Detection ...

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2024 ieee international conference on Communications, computing, Cybersecurity and Informatics, CCCI 2024

作者： Biswas, Payel De, Debashis Obaidat, Mohammad S. Hazra, Arnab Sadoun, Balqies Jogesh Chandra Chaudhuri College Department of Computer Science Kolkata India Maulana Abul Kalam Azad University of Technology Centre of Mobile Cloud Computing Department of Computer Science and Engineering West Bengal WB Nadia India School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China School of Computing SRM University Department of Computational Intelligence SRM Nagar TN Kattankulathur603203 India School of Engineering The Amity University UP Noida201301 India Dr. Sudhir Chandra sur Institute of Technology & Sports Complex Department of Computer Science and Engineering Kolkata India College of Engineering Al Balqa ' Applied University Al-Salt19117 Jordan

ISBN: (纸本)9798350349832

Air pollution can affect human health, so it is necessary to predict the air quality index (AQI) in advance. In this work, air quality data collected by the Internet of Drone Things (IoDT) is predicted and analyzed to detect highly polluted zones using a distributed privacy-preserving federated learning (FL) approach. Air quality data for a particular zone is collected and then fed into a Long short-Term memory (LSTM) based model to predict the pollution level in local training. Global aggregation is done by an IoDT-mounted central server to aggregate the received models from the local IoDT level. Privacy-preserving FedHZone predicts and analyses the AQI values from the past time series dataset in a multi-drone environment. This work detects the most hazardous zone in a larger region of Jaipur, Rajasthan using real-Time data. The result shows FedHZone with stacked LSTM having ''2%-7% better accuracy than the vanilla LSTM-based model. © 2024 ieee.

关键词： Federated learning

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Parallel computing Using CUDA and MultiThreading in Background Removal Process 3

Parallel Computing Using CUDA and MultiThreading in Backgrou...

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3rd international conference on Digital Transformation and Applications, ICDXA 2024

作者： Tew, Yiqi Lee, Wee Yan Tam, Ga Men Tunku Abdul Rahman University of Management and Technology Faculty of Computing and Information Technology Kuala Lumpur Malaysia

ISBN: (纸本)9798350373424

Parallel processing involves challenges on data dependency. In this work, we presented an investigation of performance into image background removal using the Rembg algorithms incorporating parallel computing techniques including Multithreading and Compute Unified Device Architecture (CUDA). In the background subtraction algorithm, each pixel of the image is compared with the pixels around it. Thus, it will be difficult to parallelise the system if the pixel is being accessed at the same time;otherwise, it will lead to data corruption or inconsistent results. CUDA and Multithreading are utilised in 2, 4 and 8 threads on two different hardware specification machines. Results show the proposed work achieves a maximum of 5.3092 and 5.7913 speed up value in CUDA and Multithreading respectively. In a nutshell, dynamic thread allocation and advanced load balancing algorithms that adapt to changing workloads in real-time can ensure that the computational workload is distributed evenly among threads and GPU cores. © 2024 ieee.

关键词： Pixels

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Data-Driven Retention Strategies: Exploring the Efficacy of Composite Deep Learning for Customer Churn Prediction in Telecommunications 2

Data-Driven Retention Strategies: Exploring the Efficacy of ...

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2nd international conference on distributed computing and Optimization Techniques, ICDCOT 2024

作者： Habelalmateen, Mohammed I. Bala Dhandayuthapani, V. Malathy, V. Pramodhini, R. Ramakrishna, D. The Islamic University Najaf Iraq College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Branch Department of It Oman Sr University Department of Ece Warangal India Nitte Meenakshi Institute of Technology Dept of Ece Bengaluru India Electronics and Communication Engineering Department Telangana Hyderabad India

ISBN: (纸本)9798350382952

The customer churn, otherwise known as subscriber loss, inclusive among one of the key challenges of the telecommunication industry (TCI). Forecasting churn gives rise to telecoms step-saving retentional strategies, serving as the means preventing losing revenue. To this research applies of the innovative customer churn prediction (CCP) model employing data transformation approaches and feature selection schemes. The work entail analyzing six data transformation methods (Log, Rank, Box-Cox, Z-score, Discretization, and weight-of-evidence) and eight classifiers such that each data conversion technique is applied or tested on multiple types of classifiers such as K-Nearest Neighbors (KNN), Naive Bayes (NB), Logistic Regression (LR), Random Forest (RF) The univariate way of feature selection is used to determine the features which have major effect and the proposal grid search is also adopted for hyperparameter optimisation. Experiments are conducted on four selected telecommunication industry (TCI) datasets. The evaluation of the model's performance involves using metrics such as accuracy and recall which are understood to be better than existing tools. © 2024 ieee.

关键词： Telecommunication industry

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A Simple Approach to Zero Crossing Detection of Voltage and its Application in distributed Power Supply 4

A Simple Approach to Zero Crossing Detection of Voltage and ...

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4th international conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Rukonuzzaman, M. Mahboob, Monon Department of Electrical and Electronic Engineering 408/1 Kuratoli Dhaka1229 Bangladesh

ISBN: (纸本)9798331530938

Turning on and off the relays at or near the zero-crossing point of the supply voltage is essential for their reliable and safe operation, as well as for minimizing disturbances in current and voltage waveforms. This paper presents two innovative software approaches for detecting zero crossings in distributed power supply systems, which are crucial for enhancing voltage regulation and minimizing disturbances in voltage and current waveforms. By enabling precise relay operations at zero crossing points, these methods not only improve system stability but also protect relays from potential malfunctions. The versatility of these approaches is highlighted by their compatibility with a universal voltage range of 85-265 V and frequencies of 50 Hz or 60 Hz, requiring minimal hardware for implementation. Additionally, the methods facilitate frequency measurement, which allows for the assessment of other critical parameters such as voltage, current, power, power factor, and energy consumption. The paper also introduces a frequency measurement technique capable of detecting islanding in grid-connected systems, further enhancing the reliability of power supply systems. Validation of these methods using the PSIM® simulator demonstrates their effectiveness and ease of implementation, as they can be executed through straightforward coding. One of the approaches also takes into account the aging process of the components. © 2025 ieee.

关键词： distributed power generation

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RenderFusion: Balancing Local and Remote Rendering for Interactive 3D Scenes 22

RenderFusion: Balancing Local and Remote Rendering for Inter...

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22nd ieee international Symposium on Mixed and Augmented Reality (ISMAR)

作者： Lu, Edward Bharadwaj, Sagar Dasari, Mallesham Smith, Connor Seshan, Srinivasan Rowe, Anthony Carnegie Mellon Univ Pittsburgh PA 15213 USA NVIDIA Santa Clara CA USA Mag Leap Inc Plantation FL USA Bosch Res Sunnyvale CA USA

ISBN: (纸本)9798350328387

Many modern-day XR devices (e.g. mobile headsets, phones, etc.) lack the computing resources required to render complex 3D scenes in real-time. Typically, to render a high-resolution scene on a lightweight XR device, 3D designers arduously decimate and fine-tune the objects. As an alternative, remote rendering systems can utilize powerful nearby servers to stream rendering results to a client. While this is a promising solution, it can introduce a variety of latency and reliability issues, especially under variable network conditions. In this paper, we present a distributed rendering system that combines both remote rendering and on-device, "local" rendering to add robustness to network fluctuations and device workloads. To maximize user QoE, our approach dynamically swaps an object's rendering medium, adjusting for client workload, low frame rates, and several perceptual characteristics. To model these characteristics, we perform a study under simulated conditions to measure how users perceive latency and complexity differences between objects in a scene. Using the results of the study, we then provide an algorithm for choosing the optimal object rendering medium, based on rendering complexity as well as network and latency models, ensuring that a target frame rate will be met. Finally, we evaluate this algorithm on a prototype implementation that can provide cross-platform split rendering using web technologies.

关键词： Computer systems organization-Architectures-distributed architectures computing methodologies-Computer graphics-Graphics systems and interfaces-Mixed / augmented reality

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Early Experiences of Noise-Sensitivity Performance Analysis of a distributed Deep Learning Framework

Early Experiences of Noise-Sensitivity Performance Analysis ...

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ieee international conference on Cluster computing (CLUSTER)

作者： Rojas, Elvis Knobloch, Michael Daoud, Nour Meneses, Esteban Mohr, Bernd Costa Rica Inst Technol Cartago Costa Rica Natl Univ Costa Rica Heredia Costa Rica Costa Rica Natl High Technol Ctr San Jose Costa Rica Forschungszentrum Julich GmbH Julich Supercomp Ctr Julich Germany

ISBN: (数字)9781665498562

ISBN: (纸本)9781665498562

Deep Learning (DL) applications are used to solve complex problems efficiently. These applications require complex neural network models composed of millions of parameters and huge amounts of data for proper training. This is only possible by parallelizing the necessary computations by so-called distributed deep learning (DDL) frameworks over many GPUs distributed over multiple nodes of a HPC cluster. These frameworks mostly utilize the compute power of the GPUs and use only a small portion of the available compute power of the CPUs in the nodes for I/O and inter-process communication, leaving many CPU cores idle and unused. The more powerful the base CPU in the cluster nodes, the more compute resources are wasted. In this paper, we investigate how much of this unutilized compute resources could be used for executing other applications without lowering the performance of the DDL frameworks. In our experiments, we executed a noise-generation application, which generates a very-high memory, network or I/O load, in parallel with DDL frameworks, and use HPC profiling and tracing techniques to determine whether and how the generated noise is affecting the performance of the DDL frameworks. Early results indicate that it might be possible to utilize the idle cores for jobs of other users without affecting the performance of the DDL applications in a negative way.

关键词： distributed Deep Learning Performance Analysis Noisy Environments

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Enhancing Medical VQA with Self-Attention Based Multi-Model Approach 15

Enhancing Medical VQA with Self-Attention Based Multi-Model ...

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

作者： Sakthivel, V. Mohan, Bharathi G. Elakkiya, R. Department of Computer Science Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science BITS Pilani Dubai Campus Dubai United Arab Emirates

ISBN: (纸本)9798350370249

In the medical and healthcare fields, the integration of clinical images and question-answering systems is believed to be a powerful tool that has the capacity to change the pattern of diagnosing. The proposed model introduces an innovative approach using CNN and LSTM with self-attention for medical Visual Question Answering (VQA). Our approach serves the role of feature extraction and question embedding. The achieved results reveal the efficiency of the model in interpreting the VQA-RAD dataset, which mainly consists of radiology images. The baseline model obtained an accuracy of 70.71% without self-attention and 71.97% with self-attention. Additionally, the DenseNet Pretrained Model with self-attention scored an accuracy of 74.89%, and without self-attention, it achieved an accuracy of 73.22%, showing reliable performance against existing solutions. This paper contributes to the development of medical VQA systems and proposes avenues for future research and adjustments in the applications of healthcare systems. ©2024 ieee.

关键词： Diagnosis

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Perception System of Autonomous Vehicles: Sophisticated Attacks, Defense Strategies, and Future Directions 15

Perception System of Autonomous Vehicles: Sophisticated Atta...

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

作者： Neethu, M.R. Remyakrishnan, P. Mukesh, Sa Barath Kumar, J. Royal, Ala Manas Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore641112 India Amrita Vishwa Vidyapeetham TIFAC-CORE in Cyber Security Amrita School of Engineering Coimbatore641112 India

ISBN: (纸本)9798350370249

The advent of autonomous vehicles marks a significant milestone in the evolution of automotive technologies. Central to the operation of autonomous vehicles is the perception system that interprets the surroundings and makes decisions. The security of perception systems against malicious attacks is a critical concern. This work thoroughly analyzes the vulnerabilities inherent in the perception systems of autonomous vehicles, examining the multifaceted threats posed by adversaries. The crucial need for ongoing research in AV security is underscored and a framework for future investigations to develop more secure and trustworthy autonomous vehicles is provided. The analysis emphasizes the importance of a proactive approach to security, advocating for integrating advanced defense mechanisms as a fundamental component of autonomous vehicle design and development and future research challenges. © 2024 ieee.

关键词： Autonomous Vehicles Camera Lidar Perception systems Security attacks

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CloudAuc: An Innovative Approach Utilizing Paul Milgrom's SMRA Auction Procedure to Distribute Cloud Resources among Various Clients

CloudAuc: An Innovative Approach Utilizing Paul Milgrom's SM...

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2024 ieee international conference on Information Technology, Electronics and Intelligent Communication systems, ICITEICS 2024

作者： Turjya, Sapthak Mohajon Singh, Raj Sarkar, Pritam Swain, Sujata Bandyopadhyay, Anjan Computer Science and Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9798350382693

Cloud computing is a revolutionary computing paradigm that has transmuted the way data is handled/processed and storage is managed by bringing computational activities closer to data in centralized data repositories in remote data centers. Besides these advantages, efficiently controlling various resources in cloud architectures generates numerous issues because of their diverse and dynamic nature. This research article focuses on Nobel laureate Paul Milgrom's auction procedure, recognizing it as a feasible resolution to these issues. Paul Milgrom in his auction procedure proposes an efficient method-ology for allocating resources, encouraging competition and clarity. Especially, the SMRA (Simultaneously multiple-round auction) facilitates efficient resource allotment by permitting clients to perform combination-based bidding over numerous iterations. Via iterative alterations steered by price updations, clients can revise their proposed bids to correctly coincide with their exact requirements. This research article proposes a novel CloudAuc procedure, founded upon Paul Milgrom's bidding methodology, as a creative strategy for enhancing resource allotment in cloud architectures. By promoting truthful bidding and responsively enhancing resource allotment, CloudAuc strives to enhance precision, clarity, and resilience in cloud architectures, thereby promoting resource allotment procedures in centralized frameworks. © 2024 ieee.

关键词： Cloud computing architecture

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