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检索条件"任意字段=3rd International Workshop on Real-Time Computing Systems and Applications"

共 3672 条记录，以下是1-10 订阅

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12th international workshop on Biological Knowledge Discovery from Data, BIOKDD 2021, 5th international workshop on Cyber-Security and Functional Safety in Cyber-Physical systems, IWCFS 2021, 3rd international workshop on Machine Learning and Knowledge Graphs, MLKgraphs 2021, 1st international workshop on Artificial Intelligence for Clean, Affordable and Reliable Energy Supply, AI-CARES 2021, 1st international workshop on time Ordered Data, Protime2021 and 1st international workshop on AI System Engineering: Math, Modelling and Software, AISys2021 held at 32nd international Conference on Database and Expert systems applications, DEXA 2021

12th International Workshop on Biological Knowledge Discover...

引用

12th international workshop on Biological Knowledge Discovery from Data, BIOKDD 2021, 5th international workshop on Cyber-Security and Functional Safety in Cyber-Physical systems, IWCFS 2021, 3rd international workshop on Machine Learning and Knowledge Graphs, MLKgraphs 2021, 1st international workshop on Artificial Intelligence for Clean, Affordable and Reliable Energy Supply, AI-CARES 2021, 1st international workshop on time Ordered Data, Protime2021 and 1st international workshop on AI System Engineering: Math, Modelling and Software, AISys2021 held at 32nd international Conference on Database and Expert systems applications, DEXA 2021

ISBN: (纸本)9783030871000

The proceedings contain 23 papers. The special focus in this conference is on Database and Expert systems applications. The topics include: Semantic Influence Score: Tracing Beautiful Minds Through Knowledge Diffusion and Derivative Works;robust and Efficient Bio-Inspired Data-Sampling Prototype for time-Series Analysis;membership-Mappings for Data Representation Learning: Measure Theoretic Conceptualization;membership-Mappings for Data Representation Learning: A Bregman Divergence Based Conditionally Deep Autoencoder;data Catalogs: A Systematic Literature Review and Guidelines to Implementation;task-Specific Automation in Deep Learning Processes;approximate Fault Tolerance for Edge Stream Processing;deep Learning Rule for Efficient Changepoint Detection in the Presence of Non-Linear Trends;time Series Pattern Discovery by Deep Learning and Graph Mining;a Conceptual Model for Mitigation of Root Causes of Uncertainty in Cyber-Physical systems;integrating Gene Ontology Based Grouping and Ranking into the Machine Learning Algorithm for Gene Expression Data Analysis;SVM-RCE-R-OPT: Optimization of Scoring Function for SVM-RCE-R;short-Term Renewable Energy Forecasting in Greece Using Prophet Decomposition and Tree-Based Ensembles;a Comparative Study of Deep Learning Approaches for Day-Ahead Load Forecasting of an Electric Car Fleet;Security-Based Safety Hazard Analysis Using FMEA: A DAM Case Study;Privacy Preserving Machine Learning for Malicious URL Detection;remote Attestation of Bare-Metal Microprocessor Software: A Formally Verified Security Monitor;Provenance and Privacy in ProSA: A Guided Interview on Privacy-Aware Provenance;placeholder Constraint Evaluation in Simulation Graphs;Walk Extraction Strategies for Node Embeddings with rdF2Vec in Knowledge Graphs;bridging Semantic Web and Machine Learning: First Results of a Systematic Mapping Study.

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Optimizing LLM Architectures for real-time applications in Full-Stack Development 3

Optimizing LLM Architectures for Real-Time Applications in F...

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3rd international Conference on Automation, computing and Renewable systems, ICACRS 2024

作者： Kannadasan, Tamilarasan Frisco TX United States

ISBN: (纸本)9798331532420

As real-time applications become increasingly integral to full-stack development, the demand for efficient and scalable Large Language Model (LLM) architectures has surged. This study explores optimization strategies for LLMs tailored to meet the stringent performance and latency requirements of real-time environments. We investigate architectural enhancements, including model pruning, quantization, and parallel processing techniques, to reduce computational overhead without compromising accuracy. To validate the proposed approach, we employ the Common Crawl Corpus dataset as a comprehensive case study, leveraging its extensive and diverse textual data to simulate real-world application scenarios. Our experiments demonstrate significant improvements in response times and resource utilization, enabling seamless integration of LLMs into full-stack frameworks. Additionally, we address challenges related to data handling and model adaptability, ensuring that optimized architectures maintain robustness across dynamic workloads. The findings highlight the potential of LLM optimizations to bridge the gap between advanced natural language processing capabilities and the immediate demands of real-time application development. This work provides a foundational framework for developers aiming to harness the power of LLMs in full-stack projects, paving way for more responsive and intelligent web and software solutions. © 2024 IEEE.

关键词： Natural language processing systems

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Refined Face Emotion Recognition using Convolutional Neural Networks with Adaptive Loss Functions for real-time applications 3

Refined Face Emotion Recognition using Convolutional Neural ...

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3rd international Conference on Automation, computing and Renewable systems, ICACRS 2024

作者： Rani, Ritu Gupta, Sheifali Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331532420

This research study proposes a CNN-based method for face emotion recognition. The model is created using many convolutional layers, then pooling and dense layers, so effectively extracting and analyzing both linear and non-linear properties from facial images. The generalization and performance of the model are enhanced by means of data augmentation and adaptive loss functions including the fusion of softmax and center loss that combine High accuracy, precision, recall, and F1-scores are obtained by evaluating the proposed model on both real-time inputs and standard *** results show the model is suited for practical usage since it can manage complicated, noisy, and blocked data. Underlining the efficiency of the CNN model in emotional recognition activities and assuming its uses in security, healthcare, and human-computer interaction, the research opens the path for further improvements in this field. © 2024 IEEE.

关键词： Convolution

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Hybrid AI-Electronic systems for real-time Edge Processing in IoT Networks 3

Hybrid AI-Electronic Systems for Real-Time Edge Processing i...

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

作者： Sweety Prasanna Kiruba, G. Esakki Madura, E. Kumar, Bura Vijay Kavitha, M. Reddy, T. Srinivas Athiraja, A. Bannari Amman Institute of Technology Department of Computer Technology Tamil Nadu Sathyamangalam India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Sathyamangalam India School of Computer Science and Artificial Intelligence Sr University Warangal India Kathir College of Engineering Department of Artificial Intelligence and Data Science Neelambur Coimbatore India Malla Reddy Engineering College Department of Ece Main Campus Maisammaguda Telangana Medchal India Hyderabad India

ISBN: (纸本)9798331515911

real-time data processing is facing many issues as a result of the Internet of Things' rapid expansion, especially at the network's edge where latency, bandwidth, and compute resources are limited. The creation of hybrid AI-electronic systems for real-time edge processing is investigated as a solution to these problems. To improve the effectiveness and precision of data processing in Internet of Things contexts, the research combines machine learning algorithms with sophisticated electronic circuits. The techniques include creating a brand-new hybrid framework that blends edge computing and AI models and focuses for low latency and low power consumption. When compared to conventional edge processing systems, extensive simulations and real-world research show a 25% reduction in processing latency and a 15% gain in data categorization accuracy. According to the findings, the suggested hybrid AI-electronic system can successfully handle the requirements of contemporary IoT networks and offer a scalable real-time edge processing solution. This strategy has potential for use in smart city applications, industrial automation, and other fields in the future. © 2025 IEEE.

关键词： Adversarial machine learning

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A Comprehensive Survey on Unmanned Aerial Vehicles (UAVs): Types, Structural Components, Communication systems, and Operating Platforms 3

A Comprehensive Survey on Unmanned Aerial Vehicles (UAVs): T...

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

作者： Tauseef, Md. Reddy, T. Sai Sankalp Lohith, R. Thanushree, M.P. Sravani, K. Reva University School of Ece Bengaluru India

ISBN: (纸本)9798331515911

Unmanned Aerial Vehicles (UAVs), commonly known as drones, have experienced rapid growth and widespread adoption across military and civilian applications in recent years. This paper offers a comprehensive survey of UAV technologies, focusing on their classifications, structural components, communication systems, and operating platforms. UAVs are categorized based on size, range, and altitude capabilities. Key structural elements such as frames, propulsion systems, sensors, and control mechanisms are examined. The paper also addresses communication requirements, architectures, protocols, and associated challenges. Furthermore, it reviews real time operating systems utilized in UAVs, high-lighting their design considerations. The survey concludes by identifying emerging trends and future research directions in UAV development, providing valuable insights for both newcomers and seasoned researchers in the field. © 2025 IEEE.

关键词： Drones

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Application and Evaluation of real-time Analysis Algorithms in Dynamic Data Processing systems 3

Application and Evaluation of Real-Time Analysis Algorithms ...

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3rd international Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Chen, Si College of Digital Business Liaoning Vocational University of Technology Liaoning Jinzhou China

ISBN: (纸本)9798331508456

Dynamic data processing systems are key technologies in modern data-driven applications, widely used in fields such as the Internet of Things, finance and healthcare. This study explores the application and evaluation of real-time analytical algorithms for dynamic data processing. An efficient algorithmic framework combining adaptive windowing, incremental learning and distributed computing is proposed to achieve low-latency and high-accuracy decision-making capabilities. Through experimental validation in a real data streaming environment, the results show that the proposed algorithm significantly outperforms traditional algorithms with about 35% reduction in latency and 20% improvement in prediction accuracy. The research results in this paper show that the real-time analysis algorithm has a promising application in dealing with dynamic heterogeneous data streams. Meanwhile, the article also discusses the limitations and future optimisation directions of the method, which provides a reference for further research in related fields. © 2025 IEEE.

关键词： Data reduction

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Employing Deep Neural Networks for real-time Anomaly Detection and Mitigation in IoT-Based Smart Grid Cybersecurity systems 3

Employing Deep Neural Networks for Real-Time Anomaly Detecti...

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3rd IEEE international Conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Quraishi, Aadam Rusho, Maher Ali Prasad, Anurag Keshta, Ismail Rivera, Richard Bhatt, Mohammed Wasim Intervention Treatment Institute Research HoustonTX United States University Of Colorado Boulder Department Of Lockheed Martin Engineering Management BoulderCO United States Gurugram India College Of Applied Sciences AlMaarefa University Computer Science And Information Systems Department Riyadh Saudi Arabia Escuela Politécnica Nacional Department Of Informatics And Computer Science Quito Ecuador Model Institute Of Engineering And Technology J&K Jammu India

ISBN: (纸本)9798350318609

This research introduces a novel anomaly detection framework for IoT -based Smart Grid Cybersecurity systems. Leveraging autoencoders, LSTM networks, GANs, SOMs, and transfer learning, our approach achieves superior precision, recall, and execution time compared to existing methods. Visualizations and an ablation study further validate the method's efficiency, emphasizing the critical roles of attention mechanisms and transfer learning. This comprehensive solution addresses the dynamic challenges of smart grid cybersecurity, offering a versatile and adaptive anomaly detection mechanism for real-world applications. This indicates the real-time efficacy of our anomaly detection method. Through our study of ablation and all aspects of computing, we discovered that attention processes and transfer learning facilitate faster problem solving in a dynamic smart grid. Our method is distinct and adaptable enough to address every problem arising from the discovery of anomalies in IoT-driven Smart Grid Cybersecurity systems. © 2024 IEEE.

关键词： Anomaly detection

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Poster: AutoCalNet - Continous real-time Calibration of Networked Mobile Volumetric Cameras 26

Poster: AutoCalNet - Continous Real-Time Calibration of Netw...

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26th international workshop on Mobile computing systems and applications

作者： Laursen, Flemming Pandya, Krutik Melo, Gael Lobo, Jason Zhao, Detim Gold, Lauren Likamwa, Robert Arizona State Univ Tempe AZ 85281 USA

ISBN: (纸本)9798400714030

AutoCalNet enables continuous real-time calibration of mobile 3D cameras by decoupling calibration from content streaming. It leverages a scalable device-edge-cloud network to minimize bandwidth, manage latency, and maintain high precision in calibration data, prioritizing trackable regions and feature points that will facilitate spatiotemporal tracking. This approach provides a flexible, efficient solution for networked camera systems without being constrained by content-specific requirements.

关键词： real-time calibration Mobile 3D cameras Networked camera systems Spatial computing Latency management 5G MEC

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Custom LLM Architectures for Context-Aware User Interactions in Web applications 3

Custom LLM Architectures for Context-Aware User Interactions...

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3rd international Conference on Automation, computing and Renewable systems, ICACRS 2024

作者： Kannadasan, Tamilarasan Frisco TX United States

ISBN: (纸本)9798331532420

The rapid evolution that continuously occurs in web applications makes personalization and intuitiveness within the experiences of users all the more important. The following study deals with the development of an LLM architecture customized for contextually aware interactions within web environments. Using these kinds of datasets, obtained from real user interactions, together with behavioral analytics and contextual metadata coming from a variety of sources such as the Microsoft COCO dataset and Amazon Customer Reviews, we train LLMs to understand user needs through explicit interaction patterns or contextual cues. To this end, we integrate state-of-the-art natural language processing methodologies with contextual embedding strategies that empower models to dynamically adapt responses, enhance user engagement, and smoothen workflows. We perform extensive experimentation and user testing, demonstrating large improvements in user satisfaction and efficiency of interaction over traditional static models. Furthermore, we discuss a number of challenges related to data privacy, model scalability, and real-time processing that need to be resolved to make such custom LLMs practical in a wide range of web application settings. This work underlines how context-aware architectures can bring revolutionary changes to LLMs for user interactions, offering more responsive, intelligent, and user-centric web applications. © 2024 IEEE.

关键词： Spatio-temporal data

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Research on the Application of Artificial Intelligence Technology in Microelectronics Testing and Fault Diagnosis 3

Research on the Application of Artificial Intelligence Techn...

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3rd international Conference on Data Analytics, computing and Artificial Intelligence, ICDACAI 2024

作者： Zhang, Yiqun Liu, Hailin Xu, Feng Weifang Engineering Vocational College Shandong Qingzhou262500 China

ISBN: (纸本)9798331519032

This paper explores the integration of Artificial Intelligence (AI) technologies in the field of microelectronics testing and troubleshooting. Traditional methods for testing and troubleshooting microelectronic devices often face challenges such as high complexity, time-consumption, and the need for expert *** technologies, with their advanced capabilities in machine learning, neural networks, and expert systems, offer promising solutions to these problems. This study provides an overview of the key AI approaches employed in microelectronics, discusses their implementation in various test and diagnostic scenarios, and evaluates their impact in terms of improved accuracy, efficiency, and automation. Through a detailed analysis of case studies and real-world applications, the paper demonstrates how AI-driven approaches are revolutionizing the microelectronics industry, paving the way for more reliable and sophisticated electronic devices. The study concludes with a discussion of the current limitations of AI technologies in the field and suggests potential directions for future research and development. © 2024 IEEE.

关键词： Expert systems

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