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检索条件"任意字段=3rd International Conference on High Performance Computing in Science and Engineering, HPCSE 2017"

共 1550 条记录，以下是1-10 订阅

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3rd international conference on high performance computing in science and engineering, hpcse 2017

3rd International Conference on High Performance Computing i...

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3rd international conference on high performance computing in science and engineering, hpcse 2017

ISBN: (纸本)9783319971353

The proceedings contain 15 papers. The special focus in this conference is on high performance computing in science and engineering. The topics include: MERIC and RADAR generator: Tools for energy evaluation and runtime tuning of HPC applications;Disc vs. annulus: On the bleaching pattern optimization for FRAP experiments;modeling and simulation of microalgae growth in a Couette-Taylor bioreactor;karhunen-Loéve decomposition of isotropic Gaussian random fields using a tensor approximation of autocovariance kernel;a Bayesian approach to the identification problem with given material interfaces in the darcy flow;applications of trace estimation techniques;fourier method for approximating eigenvalues of indefinite stekloff operator;proportionality-based gradient methods with applications in contact mechanics;Schur complement-schwarz DD preconditioners for non-stationary darcy flow problems;relating computed and exact entities in methods based on lanczos tridiagonalization;software tool for cranial orthosis design;Implementation of BM3D filter on intel xeon phi for rendering in blender cycles;Investigating convergence of linear SVM implemented in PermonSVM employing MPRGP algorithm.

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Proceedings of SEHPCCSE 2015: 3rd international Workshop on Software engineering for high performance computing in Computational science and engineering - Held in conjunction with SC 2015: The international conference for high performance computing, Networking, Storage and Analysis

Proceedings of SEHPCCSE 2015: 3rd International Workshop on ...

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3rd international Workshop on Software engineering for high performance computing in Computational science and engineering, SEHPCCSE 2015 - co-located with Supercomputing 2015

ISBN: (纸本)9781450340120

The proceedings contain 5 papers. The topics discussed include: simulation software engineering: experiences and challenges;testing and debugging exascale applications by mocking MPI;developing a high performance quantum chemistry program with a dynamic scripting language;lighthouse: an automated solver selection tool;and nix based fully automated workflows and ecosystem to guarantee scientific result reproducibility across software environments and systems.

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AI-Powered Advertisement Design: A PIL-Based Approach for Quality and performance Analysis 3

AI-Powered Advertisement Design: A PIL-Based Approach for Qu...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Chilupuri, Harshitha Ramesh, G. Praveen, J. Gude, Venkataramaiah Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Artificial Intelligence and Machine Learning Hyderabad India Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad500090 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Electrical and Electronics Engineering Telangana Hyderabad500 090 India Gp Technologies Llc United States

ISBN: (纸本)9798331515911

The paper presents the development of a Data science solution that applies generative AI models in order to create high-quality images to be used in visual marketing and advertising. Using the Python library PIL (Pillow) along with machine learning frameworks, the solution combines predefined product images with the selected backgrounds, enhancing image synthesis techniques for more appealing and contextually relevant visuals. This leads to improved efficiency and effectiveness in the creation of content by automatically placing product images onto the background images used in marketing campaigns. The approach demonstrated a 98.06% SSIM (Structural similarity Index) score, indicating significant visual quality improvement. Initial results suggest a 15% enhancement in visual appeal compared to traditional image creation methods. © 2025 IEEE.

关键词： Data science

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Detection of DDoS Attacks in Fog computing using Interpretable Quantum-based 2Q Learning Approach 3

Detection of DDoS Attacks in Fog Computing using Interpretab...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Sohan Jayram Reddy, K. Bhargavi, K. Siddaganga Institute of Technology Department of Computer Science and Engineering Karnataka Tumakuru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

It is seen that there is a rapid increase in Internet of Things (IoT) devices, and fog computing has emerged as an efficient and viable solution to reduce latency by performing computations closer to the edge. However, this nature of fog computing makes it vulnerable to Distributed Denial of Service (DDoS) attacks, which can disrupt services. DDoS causes major impact over the fog performance which include resource saturation, increased latency, reduced resource availability, decrease in processing capacity of the fog nodes, and many more. There have been various works proposed to detect DDoS attacks in fog computing, but they lack scalability and have high response times. In this paper, a novel Quantum-based-2QLearning (Q2QL) framework is designed to efficiently detect and mitigate DDoS attacks in fog computing. This framework using quantum-enhanced reinforcement learning reduces overestimation bias and improve speed of convergence. Q2QL framework also achieves high detection accuracy and reduces response time. The results show that the proposed framework performs well across critical metrics, including latency, attack detection time, and overall system performance. © 2025 IEEE.

关键词： Fog computing

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Epilepsy Seizure Detection based on EEG Signals using Machine Learning 3

Epilepsy Seizure Detection based on EEG Signals using Machin...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Goel, Aadya School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India

ISBN: (纸本)9798331515911

Seizure detection is a critical aspect of epilepsy management, and accurate identification can significantly improve patient outcomes. This study explores the application of machine learning techniques, specifically the Random Forest classifier, for the detection of seizures using electroencephalogram (EEG) data. By employing a systematic approach that includes feature extraction and channel reduction, we enhanced the model's performance. Relevant features were extracted from EEG signals, including time-domain statistics and frequency-domain characteristics, which are vital for effective classification. The model was evaluated using standard classification metrics such as precision, recall, F1-score, and accuracy, yielding high performance, particularly in identifying non-seizure events. These findings highlight the potential of ensemble learning methods for real-time seizure monitoring and underscore the importance of this research in developing reliable diagnostic tools. Overall, our results demonstrate that machine learning approaches can significantly contribute to improved patient outcomes in epilepsy management, paving the way for future advancements in clinical applicability. © 2025 IEEE.

关键词： Electroencephalography

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Cloud-Enabled Bio-Inspired Optimization: A Scalable Solution for Complex Real-World Problems 3

Cloud-Enabled Bio-Inspired Optimization: A Scalable Solution...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Shreya Sakthi, U.R. Sruthi, S. Shruti, S.G. Rajganesh, N. Department of Computer Science and Engineering Sri Venkateswara College of Engineering Sriperumbudur India

ISBN: (纸本)9798331527549

Bio-inspired optimization algorithms, encompassing swarm intelligence, neural networks, fuzzy logic, and evolutionary algorithms, are powerful tools for addressing complex real-world optimization problems. However, their effectiveness is often constrained by the computational demands of training and execution, which surpass the capabilities of traditional computing platforms—particularly when dealing with massive datasets or intricate optimization challenges. These limitations restrict the practical applicability of such algorithms. This work introduces a framework that leverages cloud computing as a scalable and efficient platform to overcome these computational barriers. By integrating bio-inspired optimization techniques with the elastic and high-performance resources of cloud environments, this research demonstrates significant improvements in both the scope and efficiency of these algorithms. Experimental results validate the proposed approach, showcasing enhanced performance, reduced execution time, and the ability to solve larger and more complex real-world problems, thereby unlocking the full potential of bio-inspired optimization in practical applications. © 2025 IEEE.

关键词： Cloud platforms

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Maximizing Throughput and Minimizing Latency: Evolutionary Algorithms for Optimizing Wireless Cellular Networks 3

Maximizing Throughput and Minimizing Latency: Evolutionary A...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Pechetti, Jyoshitha Yadav, Sneha Tarunya, G. Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

In the rapidly evolving world of wireless cellular network, optimizing key parameters like data throughput and latency is of critical importance for ensuring high quality communication services. The proposed presents a novel approach to multi-objective optimization to enhance overall network performance using Genetic Algorithms (GA), Pareto, and Particle Swarm Optimization (PSO). By employing optimization algorithms, a balance is sought in terms of trade-off of other parameters while maximizing data throughput and minimizing latency. Simulation layers indicate the comparative study further solidifies the efficacy of the value of evolutionary algorithms in optimizing wireless networks to satisfy the growing needs of high speed and less latency services in today’s communication landscape. © 2025 IEEE.

关键词： Optimization algorithms

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computing Power Detection Based on Smart Contract 3

Computing Power Detection Based on Smart Contract

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3rd international conference on Electrical, Electronics, and Information engineering, EEIE 2024

作者： Liu, Shaowei Liu, Xue Long, Chengnian School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Jiangsu Automation Research Institute Lianyungang China

ISBN: (纸本)9781510688179

As blockchain technology is widely applied across various fields, the performance requirements of blockchain systems, such as throughput and transaction latency, are increasing. In this context, blockchain systems have gradually evolved to adopt a two-phase consensus protocol that involves election and block generation. By electing a small number of nodes from a large-scale network to form a committee, a more efficient internal consensus protocol can be executed, thereby improving the overall performance of the blockchain system. This paper proposes a node performance detection method based on smart contracts, which sets performance thresholds for committee nodes without compromising the decentralization of public chains. This ensures high performance, low latency, and enhances the reliability of the consensus protocol. © The Authors.

关键词： Blockchain Consensus Election performance Detection Smart Contract

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AEGIS: Adaptive Ensemble-based Guard for Intrusion Systems 3

AEGIS: Adaptive Ensemble-based Guard for Intrusion Systems

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Kumar, Niharika Prasanna Rajgoli, Sarah Rukhmuddin R V Institute of Technology and Management Dept of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798331515911

The evolving sophistication of cyber threats demands an adaptive, ensemble-based approach to Intrusion Detection Systems (IDS). This paper introduces AEGIS, an advanced framework designed to enhance real-time threat detection through a multi-layered ensemble architecture. AEGIS incorporates novel features like adaptive thresholding, temporal-aware feature selection, and concept drift monitoring to maintain robust performance amidst dynamic and polymorphic attacks. With a combination of Random Forest classifiers and gradient boosting, AEGIS achieves 99.97% detection accuracy, while minimizing false positives. The system's resilience is further fortified by adversarial testing, time-based cross-validation, and ensemble diversity management, ensuring high adaptability to evolving network behaviors. Our results demonstrate that AEGIS not only surpasses traditional IDS frameworks in detection accuracy but also introduces significant advancements in real-time anomaly detection, scalability, and computational efficiency. The proposed framework provides a pathway for the development of future-proof IDS architectures capable of defending against increasingly sophisticated threats. © 2025 IEEE.

关键词： Network intrusion

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Quantum spiking neural networks for image classification 3

Quantum spiking neural networks for image classification

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3rd international conference on Algorithms, Network, and Communication Technology, ICANCT 2024

作者： Liu, Shilong Gu, Yongjian Faculty of Information Science and Engineering Ocean University of China Qingdao266100 China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

As the field of artificial intelligence experiences the emergence of various large-scale models, the computing power requirements are increasing. Simultaneously, Moore’s Law is approaching its limits, which intensifies the need to address how to perform high-dimensional complex data computations efficiently and with minimal energy consumption. Quantum computing and neuromorphic computing, with their unique features, offer potential solutions to this contemporary challenge. In this study, we aim to integrate quantum computing with neuromorphic computing, and develop a quantum spiking neural networks architecture. We proposed three measurement strategy to reducing operational costs, making it more friendly to near-term quantum hardware. We evaluated the performance of these three models on the standard MNIST dataset for classification tasks and found that they maintained a high level of accuracy despite a reduced number of parameters and a substantial decrease in the number of quantum circuit shots. © 2025 SPIE.

关键词： Quantum computers

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