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检索条件"任意字段=IEEE International Conference on Progress in Informatics and Computing"

共 16086 条记录，以下是31-40 订阅

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Modeling Effective IoT Task Scheduling in a Simulated Edge and Fog Cloud Environment 17

Modeling Effective IoT Task Scheduling in a Simulated Edge a...

引用

17th ieee international Scientific conference on informatics, informatics 2024

作者： Skrinarova, Jarmila Hamada, Adrian Matej Bel University Faculty of Natural Sciences Banská Bystrica Slovakia University of Žilina Faculty of Management and Informatics Žilina Slovakia

ISBN: (纸本)9798350387674

Applications of modern IoT devices produce a large number of tasks that are often real-time, requiring short response times and eliminating delays. Edge and Fog computing is a suitable platform for processing of IoT applications. Our contribution is the design and implementation of an algorithm that provides pseudo-optimal scheduling of IoT tasks on microcloud computing machines by minimizing the completion time of the last task in the schedule. The algorithm ensures that the deadlines of all scheduled IoT tasks are met, according to their requirements. Our novel approach is to design and implement a scheduling algorithm that aims to achieve both criteria. We designed experiments and used simulations to compare our algorithm with the Earliest Deadline First algorithm. We showed that our new approach presented in the Improved EDF algorithm achieves both criteria (makespan and deadline fulfill rate for each task) significantly better than the standard algorithm. © 2024 ieee.

关键词： Scheduling algorithms

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Proceedings of the 2021 ieee international conference on Communications, computing, Cybersecurity and informatics, CCCI 2021

Proceedings of the 2021 IEEE International Conference on Com...

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2021 ieee international conference on Communications, computing, Cybersecurity and informatics, CCCI 2021

ISBN: (纸本)9781665432085

The proceedings contain 31 papers. The topics discussed include: energy management systems and smart phones: a systematic literature survey;real-time instance segmentation for low-cost mobile robot systems based on computation offloading;fault-tolerant orchestration of bags-of-tasks with application-directed checkpointing in a distributed environment;a traffic prediction assisted routing algorithm for elastic optical networks;Gibbs free-energy prediction method for iron-base alloy materials based on deep learning;XAI-AV: explainable artificial intelligence for trust management in autonomous vehicles;a survey of encrypted malicious traffic detection;and classification of soil images using convolution neural networks.

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Effects of Weighting Schemes on Retrieval Precision 9

Effects of Weighting Schemes on Retrieval Precision

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9th ieee international conference on progress in informatics and computing, PIC 2024

作者： Yamout, Fadi Mouallem, Youssef International University of Beirut Department of Computer Science Beirut Lebanon

ISBN: (纸本)9798350363203

Finding relevant information in a vast and growing amount of data has become significant since the arrival of the internet. An Information Retrieval System is described as searching and retrieving a list of documents, such as web pages or other items, in response to a user query. There are many weighting schemes, such as Boolean, Term Frequency or TF, and Term-Frequency-Inverse-Document-Frequency or TFIDF. In Boolean, a '1' indicates the presence of a term in the document, whereas a '0' indicates its absence. In TF, the term is represented by the number of occurrences in a document. TFIDF is the most popular one. It combines TF with an inverse document frequency IDF;IDF gives less weight to terms that frequently occur in many documents. With reference to the information retrieval system literature, TFIDF gives more accurate results on the computationally expensive expenses. Term Frequency is less expensive than TFIDF, and the least expensive one is the Boolean weighting scheme. In this paper, we investigated the effect of different weighting schemes and found out that, in many cases, Boolean and TF performed the same as TFIDF and, in a few cases, outperformed them. The experiments in this paper were conducted on many datasets of variant sizes and types. The dataset documents were indexed using TFIDF, TF, and Boolean. Then, using the queries that come with the dataset, we computed the precision and recall by comparing the results of different weighting schemes;the queries were also indexed using TFIDF, TF, and Boolean weighting schemes. The objective is to show that the use of the Boolean or TF weighting scheme, which is considered not computationally expensive, instead of TFIDF, which is regarded as very computationally expensive, does not significantly affect the results and, in a few cases, gives better results. © 2024 ieee.

关键词： Information retrieval

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A Scalable Clustered Architecture for Cyber-Physical Systems 21

A Scalable Clustered Architecture for Cyber-Physical Systems

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ieee 21st international conference on Industrial informatics (INDIN)

作者： Cabral, Bernado Costa, Pedro Fonseca, Tiago Ferreira, Luis Lino Pinho, Luis Miguel Ribeiro, Pedro INESC TEC ISEP Porto Portugal EVOLEO Technol Maia Portugal

ISBN: (纸本)9781665493130

Developing distributed and scalable Cyber-Physical Systems (CPS) that can handle large amounts of data at high data rates at the edge, remains a challenging task. Also, the limited availability of open-source solutions makes it difficult for developers and researchers to experiment with and deploy CPSs on a larger scale. This work introduces Edge4CPS, an open-source multi-architecture solution built over Kubernetes that aims to enable an easy to use, efficient and scalable solution for the deployment of applications on edge-like distributed computing clusters. To verify the successful real-world implementation of the introduced architecture, the system was tested in a railway scenario, derived from the Ferrovia 4.0 project, which highlights its functionalities.

关键词： Edge computing Cloud Open-Source Kubernetes Cluster Network

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Proceedings of 2021 ieee 20th international conference on Cognitive informatics and Cognitive computing, ICCI*CC 2021

Proceedings of 2021 IEEE 20th International Conference on Co...

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20th ieee international conference on Cognitive informatics and Cognitive computing, ICCI*CC 2021

ISBN: (纸本)9781665421195

The proceedings contain 37 papers. The topics discussed include: a general theory of adaptivity and homeostasis in the brain and in the body;from data to information granules: an environment of granular computing;the failure of deep neural networks to capture human language’s cognitive core;on the emergence of autonomous systems towards deep thinking machines and general AI driven by abstract intelligence theories and intelligent mathematics;evaluating the cognitively-related productivity of a universal dependency parser;a robust polyscale length complexity measure for stochastic self-affine processes;explainable AI for car crash detection using multivariate time series;mobile AI stroke health app: a novel mobile intelligent edge computing engine based on deep learning models for stroke prediction – research and industry perspective;real-time cognitive evaluation of online learners through automatically generated questions;and dynamic neural network approach to human emotion: an analysis based on sliding time windows.

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Frequency-Domain Assisted Fundus Image Enhancement Network Based on the Mamba Model 9

Frequency-Domain Assisted Fundus Image Enhancement Network B...

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9th ieee international conference on progress in informatics and computing, PIC 2024

作者： Luo, Bin Wen, Yang Ji, Jianhua Cao, Wenming Li, Ping Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing College of Electronic and Information Engineering Shenzhen China College of Electronic and Information Engineering Shenzhen University Shenzhen China The Hong Kong Polytechnic University Department of Computing and School of Design Hong Kong Hong Kong

ISBN: (纸本)9798350363203

Enhancing fundus images is crucial for early diagnosis and monitoring of retinal diseases. Although CNN and Transformer-based methods have made great progress, CNNs struggle with long-range dependencies, and Transformers face challenges with large model sizes and low computational efficiency. To address these issues, Selective Structured State-Space Models (SSMs) offer an efficient solution with linear complexity. However, standard Mamba models still suffer from local pixel information loss and channel redundancy, limiting their performance in fundus image enhancement. We propose MambaFE, which incorporates localized refinement and an advanced channel attention mechanism to resolve challenges such as pixel detail loss and excessive channel information. Considering that retinal structures play a vital role in fundus analysis, retaining these structures during the enhancement process is crucial. As retinal structures are predominantly located within the high-frequency components of fundus images, we introduce a frequency-guided approach to capture reliable representations of these features, ensuring improved retention of essential information during enhancement. Comprehensive experimental findings indicate that the proposed MambaFE outperforms contemporary fundus image enhancement techniques, significantly enhancing the quality of clinically low-quality and synthetically degraded fundus images, while offering robust support for real-world medical applications. © 2024 ieee.

关键词： Pixels

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Predictive Analysis of Machine Learning Frameworks in the review of Multivariate datasets for Crop Yield Prediction 4

Predictive Analysis of Machine Learning Frameworks in the re...

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4th ieee international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Sedimo, Kutlwano Kuthadi, Venumadhav Dinakenyane, Othapile Selvaraj, Rajalakshmi Botswana International University of Science and Technology Department of Computing and Informatics Palapye Botswana

ISBN: (纸本)9798331518981

Crop Yield Prediction (CYP) is crucial for optimizing agricultural practices globally. This study conducts an in-depth review of Machine Learning (ML) techniques applied to multivariate datasets for crop yield forecasting. We examine the performance of models including deep learning, ensemble classifiers, and hybrid approaches, emphasizing their ability to integrate diverse data sources and adapt to varying climatic conditions. Despite significant progress, challenges such as sparse data handling and generalization across regions persist. This paper not only identifies these challenges but also proposes advanced solutions for improving prediction accuracy, offering a roadmap for future research in agricultural ML frameworks. © 2024 ieee.

关键词： Adversarial machine learning

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Task Scheduling Using TVIW-PSO in Cloud computing 3

Task Scheduling Using TVIW-PSO in Cloud Computing

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international conference on Advances in computing, Communication and Applied informatics (ACCAI)

作者： Krishna, B. Siva Rama Krishna, T. Murali Sri, J. Bhavya Priya, B. Vishnu Lakireddy Bali Reddy Coll Engn Comp Sci & Engn Mylavaram 521230 India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

Cloud with increased resource usage is crucial to decrease expenses. Regarding cloud computing, the resource allocation and scheduling tasks playa Important role in the quest for cost-efficiency, maximizing resource utilization within cloud environments is key. This is where optimizing task scheduling comes in, and a promising approach emerges a multi-variant particle swarm optimization this explores PSO's adaptability to task scheduling, highlighting its ability to improve load balancing, reduce execution time, and improve resource allocation in cloud environments, further refining its effectiveness in cloud task scheduling. It's a promising avenue for achieving cost-effective and high-performance cloud operations, paving the way for a more efficient and scalable future.

关键词： Task Scheduling Throughput Makespan Particle Swarm Optimization Time Varying Inertia Cloud computing

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Implementation of Database Search with Quantum computing: Grover's Algorithm vs Linear Search 1

Implementation of Database Search with Quantum Computing: Gr...

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1st ieee international conference on Ambient Intelligence, Knowledge informatics and Industrial Electronics, AIKIIE 2023

作者： Khurana, Sachin Nene, Manisha J Defence Institute of Advanced Technology SoCE&MS Pune India

ISBN: (纸本)9798350316469

The present study aims to examine the real-world implications of Grover's algorithm, specifically focusing on its quadratic speedup in contrast to classical linear search techniques employed for unsorted databases. The objective is to bridge the gap between theoretical potential and practical application in the field of quantum computing. This study conducts a comprehensive analysis of Grover's Algorithm and traditional linear search across databases of varying sizes, uniquely contributing by empirically validating Grover's Algorithm's superior performance and aligning it with theoretical expectations. Significantly, the study expands its analysis to encompass larger databases, further affirming the algorithm's utility in handling growing database sizes. This study significantly contributes to the progress of quantum computing through the practical implementation of theoretical principles as well as enhancing the scientific community's understanding of quantum search algorithms and their relative efficacy. © 2023 ieee.

关键词： Grover's Algorithm Quantum computing Quantum Gates Search Methods

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ArcaDB: A Disaggregated Query Engine for Heterogenous Computational Environments 17

ArcaDB: A Disaggregated Query Engine for Heterogenous Comput...

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ieee 17th international conference on Cloud computing (ieee CLOUD)

作者： Ruiz-Rohena, Kristalys Rodriguez-Martinez, Manuel Univ Puerto Rico Dept Elect & Comp Engn Mayaguez PR 00680 USA Univ Puerto Rico Dept Comp Sci & Engn Mayaguez PR USA

ISBN: (纸本)9798350368543;9798350368536

Modern enterprises rely on data management systems to collect, store, and analyze vast amounts of data related to their operations. Nowadays, clusters and hardware accelerators (e.g., GPUs, TPUs) have become a necessity to scale with the data processing demands in many applications related to social media, bioinformatics, surveillance systems, remote sensing, and medical informatics. Given this new scenario, the architecture of data analytics engines must evolve to take advantage of these new technological trends. In this paper, we present ArcaDB: a disaggregated query engine that leverages container technology to place operators at compute nodes that fit their performance profile. In ArcaDB, a query plan is dispatched to worker nodes that have different computing characteristics. Each operator is annotated with the preferred type of compute node for execution, and ArcaDB ensures that the operator gets picked up by the appropriate workers. We have implemented a prototype version of ArcaDB using Java, Python, and Docker containers. We have also completed a preliminary performance study of this prototype, using images and scientific data. This study shows that ArcaDB can speed up query performance by a factor of 3.5x in comparison with a shared-nothing, symmetric arrangement.

关键词： cloud computing data analytics query engine DBaaS cloud accelerators

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