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共 1778 条记录，以下是231-240 订阅

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4th Congress on Intelligent Systems, CIS 2023

4th Congress on Intelligent Systems, CIS 2023

引用

4th Congress on Intelligent Systems, CIS 2023

ISBN: (纸本)9789819990399

the proceedings contain 35 papers. the special focus in this conference is on Intelligent Systems. the topics include: Track learning Agent Using Multi-objective Reinforcement learning;performance Assessment of Gaussian Filter-Based Image Fusion Algorithm;a Cognitive Comparative Analysis of Geometric Shape-Based Cryptosystem;a Lesion Feature Engineering Technique Based on Gaussian Mixture Model to Detect Cervical Cancer;energy Optimization of Electronic Vehicle Using Blockchain Method;pattern recognition: An Outline of Literature Review that Taps into machine learning to Achieve Sustainable Development Goals;novel Approach for Stock Prediction Using Technical Analysis and Sentiment Analysis;visualizing and Exploring the Dynamics of Optimization via Circular Swap Mutations in Constraint-Based Problem Spaces;An Approach to Increase the Lifetime of Traditional LEACH Protocol Using CHME-LEACH and CHP-LEACH;denseMammoNet: An Approach for Breast Cancer Classification in Mammograms;aspect-Based Sentiment Classification Using Supervised Classifiers and Polarity Prediction Using Sentiment Analyzer for Mobile Phone Tweets;microbial Metabolites and Recent Advancement;design of a 3D-Printed Accessible and Affordable Robotic Arm and a User-Friendly Graphical User Interface;profit Maximization of a Wind-Integrated System by V2G Method;detection of Partially Occluded Area in Images Using Image Segmentation Technique;Application of IP Network Modeling Platforms for Cyber-Attack Research;enhancing Information Integrity: machine learning Methods for Fake News Detection;Optimum Selection of Virtual machine in Cloud Using Improved ACO;data Imputation Using Artificial Neural Network for a Reservoir System;depth Multi-modal Integration of Image and Clinical data Using Fusion of Decision Method for Enhanced Kidney Disease Prediction in Medical Cloud;an Efficient Prediction of Obstructive Sleep Apnea Using Hybrid Convolutional Neural Network.

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conference Committees

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pattern recognition and machine learning (PRML), IEEE international conference on

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Incorporating Large Language Models into Named Entity recognition: Opportunities and Challenges 4

Incorporating Large Language Models into Named Entity Recogn...

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4th international conference on Computer, Big data and Artificial Intelligence, ICCBD+AI 2023

作者： Luo, Zhizhao Yang, Yuting Qi, Rui Fang, Zhi Guo, Yikai Wang, Youchen Beijing Institute of Computer Technology and Application Beijing China China Life Property & Casualty Insurance Company Limited Changsha China

ISBN: (纸本)9798350373233

Named entity recognition (NER) is a critical task in natural language processing, involving the identification and classification of entities in text. Traditional approaches to NER, such as rule-based methods and supervised machine learning, faced limitations in adapting to diverse domains and languages due to the need for large labeled datasets. Large language models (LLMs) such as GPT3, which undergo vast pretraining, demonstrate the promising ability to substantially improve NER beyond supervised baselines as evidenced by over a 100% increase in F1 score on domain-specific evaluation *** survey comprehensively examines the pivotal roles and methodologies of LLMs in NER. It provides a thorough analysis of two key techniques: in-context learning and fine-tuning. In-context learning enables quick NER task resolution without major model changes, while fine-tuning offers significant improvements, especially in domain-specific applications. the survey also covers essential resources, including NER corpora, LLM types, and methods of fine-tuning. It addresses challenges related to computational resources and model size. the survey concludes by outlining potential future research directions, emphasizing LLMs' contributions to advancing NER technologies. © 2023 IEEE.

关键词： Supervised learning

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Physics-Aware machine learning for Dynamic, data-Driven Radar Target recognition 4th

Physics-Aware Machine Learning for Dynamic, Data-Driven Rada...

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4th international conference on Dynamic data Driven Applications Systems, DDDAS 2022

作者： Gurbuz, Sevgi Zubeyde Department of Electrical and Computer Engineering The University of Alabama TuscaloosaAL35487 United States

ISBN: (纸本)9783031526695

Despite advances in Artificial Intelligence and machine learning (AI/ML) for automatic target recognition (ATR) using surveillance radar, there remain significant challenges to robust and accurate perception in operational environments. Physics-aware ML is an emerging field that strives to integrate physics-based models with data-driven deep learning (DL) to reap the benefits of both approaches. Physics-based models allow for the prediction of the expected radar return given any sensor position, observation angle and environmental scene. However, no model is perfect and the dynamic nature of the sensing environment ensures that there will always be some part of the signal that is unknown, which can be modeled as noise, bias or error uncertainty. Physics-aware machine learning combines the strengths of DL and physics-based modeling to optimize trade-offs between prior versus new knowledge, models vs. data, uncertainty, complexity, and computation time, for greater accuracy and robustness. this paper addresses the challenge of designing physics-aware synthetic data generation techniques for training deep models for ATR. In particular, physics-based methods for data synthesis, the limitations of current generative adversarial network (GAN)-based methods, new ways domain knowledge may be integrated for new GAN architectures and domain adaptation of signatures from different, but related sources of RF data, are presented. the use of a physics-aware loss term with a multi-branch GAN (MBGAN) resulted in a 9% improvement in classification accuracy over that attained with the use of real data alone, and a 6% improvement over that given using data generated by a Wasserstein GAN with gradient penalty. the implications for DL-based ATR in Dynamic data-Driven Application Systems (DDDAS) due to fully-adaptive transmissions are discussed. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Automatic target recognition

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Research on traffic sign recognition algorithms for autonomous vehicles in smart city traffic systems 4

Research on traffic sign recognition algorithms for autonomo...

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4th international conference on Computer Vision and pattern Analysis, ICCPA 2024

作者： Wang, Chuanfei School of Government Beijing Normal University Beijing100875 China

ISBN: (纸本)9781510682528

With the continuous advancement of smart city construction, autonomous driving technology is playing an increasingly important role in urban traffic systems. this study aims to explore the development and optimization of traffic sign recognition algorithms for autonomous vehicles in smart city traffic environments. By comprehensively analyzing current traffic sign recognition technologies, this paper proposes a traffic sign recognition system based on the YOLOv5 algorithm and utilizes the open-source COCO dataset for model training and testing. the images were preprocessed and annotated, employing CSPDarknet53 as the backbone network, which effectively extracts image features through multiple convolutional layers and residual blocks. A deep learning model was trained, capable of recognizing and classifying various traffic signs such as stop, speed limit, and turn signs. the research results indicate that the model demonstrates high average precision (AP) on the COCO dataset, effectively identifying traffic signs of different sizes and angles, even in complex backgrounds, with high robustness. Compared to traditional methods, the recognition accuracy has improved by 15%, and it has a significant advantage in real-time processing. © 2024 SPIE.

关键词： Object detection Detection and tracking algorithms Unmanned vehicles Performance modeling Feature extraction Image processing data modeling Image segmentation Mathematical optimization

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4th Congress on Intelligent Systems, CIS 2023

4th Congress on Intelligent Systems, CIS 2023

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4th Congress on Intelligent Systems, CIS 2023

ISBN: (纸本)9789819990368

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4th international conference on Digital Technologies and Applications, ICDTA 2024

4th International Conference on Digital Technologies and App...

引用

4th international conference on Digital Technologies and Applications, ICDTA 2024

ISBN: (纸本)9783031686740

the proceedings contain 53 papers. the special focus in this conference is on Digital Technologies and Applications. the topics include: Efficient KPIs Analysis: Harnessing the Power of Excel and VBA Programming for data Visualization and Analysis;A Deep learning-Powered TinyML Model for Gesture-Based Air Handwriting Simple Arabic Letters recognition;applications of machine and Deep learning in Funding Decision: A Review;a Deep learning Approach to Potato Late Blight Detection: Developing and Evaluating a Lightweight Single-Stage Detection Model;deep Neural Networks for Automated Metadata Extraction;performance Optimization in Agro-Vision by Assessing the Impact of data Normalization and Standardization on Deep learning Models;digital Revolution and Emerging Opportunities of the Blockchain in Finance;design, and Optimization Using Genetic Algorithms of a Dual-Band Microstrip Antenna Based on Defective Ground Structure for Wireless Power Transmission Applications;analysis and Design of Power Management System for Wireless Power Transfer Circuits;Multiband Antenna Design Using Giuseppe Peano’s Fractal with Graphene for thz Applications;blockchain Applications in Emergency Logistics: A Bibliometric Study and Future Research Horizons;Towards Efficient Computation Offloading in Multi-user MEC Environments: A Game-theoretic Approach;the Relationship Between Differentiation Strategy and the Achievement of Competitive Advantage;pre-service Teachers’ Perceived Level of Digital Literacy: A Quantitative Study from a Developing Country;navigating Between Conditions and Convictions: Investigating the Influence of Socio-geographical Factors on Interest and Attitudes Toward Artificial Intelligence Among Secondary School Teachers;mapping the Evolution of Open Innovation and Digital Transformation: A Bibliometric Analysis;digital Innovations for Good: A Bibliometric Study of Social Entrepreneurship Digitalization.

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Wheat disease recognition: A Multi-Model Analysis with Customized CNN and Transfer learning enhanced by Explainable AI techniques with LIME and Grad-CAM 4

Wheat disease recognition: A Multi-Model Analysis with Custo...

引用

4th international conference on Intelligent Technologies, CONIT 2024

作者： Sneha Ruchitha, H.K. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Artificial Intelligence and Data Science Karnataka Bangalore India

ISBN: (纸本)9798350349900

the integration of deep neural learning and machine learning techniques into crop classification introduces an innovative dimension to agricultural progress. Concurrently, an image-based deep neural learning accession is employed to address the recognition and stratification of disease-affected wheat plants. the study's core intent is to refine methods for diagnosing and categorizing wheat plant diseases, aligning with the broader aim of enhancing agricultural sustainability. the study unveils the potential of deep neural network models, prominently convolutional neural networks (CNNs), renowned for their effectiveness in image analysis tasks. A comprehensive assessment evaluates five distinct CNN models - VGG19, ResNet50, AlexNet, Inception V3, and DenseNet121 - using a publicly accessible dataset for agricultural research referred to as the Large Wheat Disease Classification dataset (LWDCD2020) of 3814 wheat leaf images with four classes such as leaf rust, wheat loose smut, crown and root rot, which are fungal diseases and healthy plant images. the evaluation, focused on accuracy, reveals nuanced differences in performance. VGG19 emerges as the frontrunner with an impressive 9 6% accuracy, followed closely by ResNet50, AlexNet, Inception V3, and DenseNet121. these nuances offer practitioners valuable insights for selecting optimal solutions. the study's emphasis on transparency and clarity extends to the realm of explainable AI techniques. Prominent methods, Grad-CAM and LIME, are employed to shed light on the decision-making processes of deep neural networks (DNNs) models. In this context, Grad-CAM excels in providing more accurate and insightful explanations compared to LIME. these findings underscore the potential of deep neural networks (DNNs) in wheat disease classification and prediction. Beyond conventional performance metrics, the study underscores the significance of explainable AI methods in clarifying model predictions. © 2024 IEEE.

关键词： Convolutional neural networks

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4th international conference on Digital Technologies and Applications, ICDTA 2024

4th International Conference on Digital Technologies and App...

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4th international conference on Digital Technologies and Applications, ICDTA 2024

ISBN: (纸本)9783031686528

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Deep learning Models for Inference on Compressed Signals with Known or Unknown Measurement Matrix 27th

Deep Learning Models for Inference on Compressed Signals w...

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27th international conference on pattern recognition, ICPR 2024

作者： Yu, Huiyuan Cheng, Maggie Illinois Institute of Technology ChicagoIL60616 United States

ISBN: (纸本)9783031781278

In this paper we propose two sets of compressed learning methods. First, for the setting where the matrix that is used to compress the signal is known, then for the setting where the matrix is unknown. With a known measurement matrix, compressed learning can benefit from the success of compressed sensing by utilizing the intermediate results of signal recovery. In particular, we use a compressed sensing algorithm Blocked Successive Regression (BSR) to generate enhanced features, then apply some widely used machine learning models (such as multilayer perceptron and convolutional neural network) on the enhanced features. It is observed that the machine learning results are significantly improved. With an unknown measurement matrix, deep models are developed to learn the matrix. We develop two compressed learning models based on the variational autoencoders (VAE) framework, reducing the problem of learning the elements of the matrix to learning the distributional parameters of the matrix. this method significantly reduces the number of parameters to learn, and also offers robustness to small perturbation in data. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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