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2022 1st international conference on Artificial Intelligence Trends and pattern recognition, ICAITPR 2022

2022 1st International Conference on Artificial Intelligence...

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1st international conference on Artificial Intelligence Trends and pattern recognition, ICAITPR 2022

ISBN: (纸本)9781665425216

The proceedings contain 49 papers. The topics discussed include: analysis of customer reviews using deep neural network;a comparative investigation on the use of machine learning techniques for currency authentication;a diagnostic survey on sybil attack on cloud and assert possibilities in risk mitigation;e-health security on could computing and its challenges;high speed multiplier using embedded approximate 4-2 compressor for image multiplication;a review on self-stabilizing platform in scope of merchant navy applications;study on information management system that connects students and instructor through chatting;machine learning approaches for microscopic image analysis and microbial object detection(MOD) as a decision support system;and literature survey on video surveillance crime activity recognition.

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Multimodal Attention Networks for Human Activity recognition From Earable Devices

Multimodal Attention Networks for Human Activity Recognition...

引用

2022 ACM international Joint conference on Pervasive and Ubiquitous computing and the 2022 ACM international Symposium on Wearable Computers, UbiComp/ISWC 2022

作者： Stuchbury-Wass, Jake Ferlini, Andrea Mascolo, Cecilia University of Cambridge Cambridge United Kingdom Nokia Bell Labs Cambridge United Kingdom

ISBN: (纸本)9781450394239

Earables (a.k.a ear-worn wearable devices) are gaining traction in the wearables ecosystem for monitoring user health. Human activity recognition (HAR) is a promising use case of earables due to their placement on the head and the combination of sensors. In this paper, we explore using multimodal attention-based neural networks for HAR from the ear. Attention networks have had a large impact on other disciplines' machine learning tasks and we believe they present opportunities in HAR from earable data. Different methods of utilising attention mechanisms in the literature are discussed as well as the benefits and challenges of using such networks in the context of HAR on real systems. © 2022 Owner/Author.

关键词： pattern recognition

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Character Classification and Actor recognition in Yakshagana Images Using Machine Learning Techniques and Facial Makeup pattern Analysis 8

Character Classification and Actor Recognition in Yakshagana...

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8th IEEE international conference on Distributed computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Murthy, Anantha Prathwini Kulkarni, Sanjeev Savitha, G. Nitte Karkala Institute of Computer Science and Information Science Srinivas University Department of Master of Computer Applications India Department of Master of Computer Applications Karkala India Srinivas University Institute of Engineering and Technology Department of Computer Science and Engineering Mangalore India Manipal Institute of Technology Manipal Academy of higher Education Manipal Department of Data Science and Computer Applications India

ISBN: (纸本)9798350350593

Yakshagana, a traditional theater form from Karnataka, India, features a unique combination of vibrant costumes, dynamic dance movements, and elaborate facial makeup, making character and actor identification a challenging task for automated systems. This research work presents a novel approach to classify Yakshagana characters into two primary categories, Vaishnava and Shaiva, and to identify the actors performing these characters using advanced machine learning techniques. Our research employs a Cyclic Gated Recurrent Neural Network (Cyclic GRNN) for classification and identification tasks. For character category classification, we integrate YOLOv3 and Faster R-CNN models, achieving an accuracy of 92.85% with YOLOv3 and 88.285% with Faster R-CNN. The categorization is specifically focused on distinguishing between Vaishnava and Shaiva characters. Additionally, for actor name identification, we utilize a RESNET-50 model, attaining a high accuracy of 95.60%. The results demonstrate the efficacy of Cyclic GRNN combined with state-of-the-art object detection and image classification models in accurately recognizing and categorizing Yakshagana characters and actors. This research contributes to the preservation and digital documentation of Yakshagana by providing robust tools for automated identification, thereby facilitating cultural heritage studies and enhancing audience engagement with this traditional art form. © 2024 IEEE.

关键词： Recurrent neural networks

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patternGPT :A pattern-Driven Framework for Large Language Model Text Generation 12

PatternGPT :A Pattern-Driven Framework for Large Language Mo...

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12th international conference on computing and pattern recognition, ICCPR 2023

作者： Xiao, Le Shan, Xin Chen, Xiaolin College of Information Science and Engineering Henan University of Technology Zhengzhou China

ISBN: (纸本)9798400707988

Large language models(LLMS) have shown excellent text generation capabilities, capable of generating fluent human-like responses for many downstream tasks. However, applying large language models to real-world critical tasks remains challenging due to their susceptibility to hallucinations and inability to directly use external knowledge. To cope with the above challenges, this paper proposes patternGPT, a pattern-driven text generation framework for Large Language Models. Firstly, the framework utilizes the extraction capability of Large Language Models to generate rich and diversified structured and formalized patterns, which facilitates the introduction of external knowledge to do the computation, and then draws on the idea of federated learning to use multiple agents to achieve the sharing in order to obtain more diversified patterns, and finally uses judgment criteria and optimization algorithm to search for high-quality patterns to guide the generation of models. Finally, external knowledge such as judgment criteria and optimization algorithms are used to search for high-quality patterns, and the searched patterns are used to guide model generation. This framework has the advantages of generating diversified patterns, protecting data privacy, combining external knowledge, and improving the quality of generation, which provides an effective method to optimize the text generation capability of large language models, and make it better applied to the field of intelligent dialogue and content generation. © 2023 ACM.

关键词： Data privacy

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Performance Evaluation of AE-RBM Based Pedestrian Detectors in Autonomous Vehicles: Substantial Issues and Challenges 1

Performance Evaluation of AE-RBM Based Pedestrian Detectors ...

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1st IEEE international conference on Electronics, computing, Communication and Control Technology, ICECCC 2024

作者： Reddy, Abhilash Reddy Pabbath Lal, Madan Boobalan, M. Sapkal, Narayan Pandurang Swetha Reddy, A. Kumar, A.Saran Infobuilders inc Department of IT Georgia University College of Engineering and Technology Department of Mechanical Engineering Bikaner India K S R Institute For Engineering And Technology Department of CSE Tamilnadu Tiruchengode India Dr. D.Y. Patil Institute of Technology Mechanical Engineering Dr. D. Y. Patil Unitech Society's Maharashtra Pune India R.M.K. College of Engineering and Technology Electronics and Communication Engineering Tamilnadu Chennai India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Sathyamangalam India

ISBN: (纸本)9798350371802

Autonomous vehicles' rising profile is a reflection of the widespread interest in them. Improved dependability, reduced fuel consumption, cost savings, and enhanced passenger convenience are all benefits of these cars. However, autonomous vehicles must first have their functionality problems resolved before they can be extensively used on major routes. Pedestrian detection is a crucial component in autonomous vehicles that helps to avoid accidents. Preprocessing, feature extraction, and model training all rely on proper sequencing. A number of filters, such as the Median, Histogram, Dilation and Erosion, Gaussian, mean, and others, can be used to smooth out images before lane marking recognition. Haar wavelet and LBP are the components that make up feature extraction. In order to train for AE-RBM, one must have precise control over the attributes. This method looks so much more modern than the RBM and AE algorithms of today. The results demonstrated a notable enhancement in accuracy, reaching 95.84%. © 2024 IEEE.

关键词： Local binary pattern

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A Novel Implementation of Esophagus Diagnosis Using Deep Learning 2

A Novel Implementation of Esophagus Diagnosis Using Deep Lea...

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2nd international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Janaki, R. Lakshmi, D. Sri Sai Ram Institute of Technology Department of ECE Chennai India

ISBN: (纸本)9781665437899

The presented work is a systematic review on recent technologies in deep learning for Barrett's esophagus (BE), a disease which affects the food pipe. Evaluation of this disorder can be made easy with the help of model developed with deep learning and artificial intelligence. The dysplasia and adenocarcinoma exhibits a complex pattern for detecting through endoscopic diagnosis. The diagnosis and automatic detection using computer analysis is beneficial for assisting the endoscopy procedure. The deep learning technique developed through manual and automated segmentation for the evaluation of BE disorders. The review is done by compilation of works published in Springer, Binda Wi, IEEEXplore, and Association for computing Machinery, Science Direct and Pubmed on the category of automatic detection of regions for classification purposes. The problem statement, methodology, objective and result of the selected work have been analyzed. © 2022 IEEE.

关键词： pattern recognition

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IProceedings of the 4th international conference on Artificial Intelligence and Computer Engineering, ICAICE 2023

IProceedings of the 4th International Conference on Artifici...

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4th international conference on Artificial Intelligence and Computer Engineering, ICAICE 2023

ISBN: (纸本)9798400708831

The proceedings contain 200 papers. The topics discussed include: heat-aware graph data placement strategy for NVM;parameters estimation of photovoltaic models via an improved differential evolution algorithm;study on the extraction of law enforcement relationships in administrative law enforcement instrument data;online algorithm for exploring a grid polygon with two robots;research on gesture recognition method by improving dung beetle algorithm to optimize BP neural network;an improved algorithm for frequent sequence pattern mining based on PrefixSpan-ComplexPrefixSpan;machine learning-based research on reserve prediction of natural-gas-hydrates;enhancing coal mine safety monitoring algorithm using graph computing techniques;reverse distillation support vector data description for unsupervised anomaly detection;and few-shot object counting model based on self-support matching and attention mechanism.

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Transfering RGB-D Models to RGB by Multi-View Embedding Space Learning for Object Detection 4

Transfering RGB-D Models to RGB by Multi-View Embedding Spac...

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4th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2022

作者： Liu, Jinde Zhang, Zhang University of Chinese Academy of Sciences Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665463997

In this paper, we propose a new framework for detecting objects in RGB images captured by conventional cameras by leveraging a set of labeled RGB-D data. We formulate this problem into a new multi-view learning problem, where we have the depth information in the training set that is not available in the testing set. To solve this problem, we use a three-view CCA learning method which combines visual features, depth features, and label information together to get the projection matrices. Sufficient experiments are done to demonstrate the effectiveness of our method, and improvement measures in the future are also proposed. © 2022 IEEE.

关键词： Object detection

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Attention-guided Unsupervised Semantic Fusion for Infrared and Visible Images 15

Attention-guided Unsupervised Semantic Fusion for Infrared a...

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15th international conference on Graphics and Image Processing, ICGIP 2023

作者： Qi, Qian Kong, Quan Liao, Qing Hu, Ziming Li, Jinglei Zhang, Jialian Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology University China School of Art & Design Wuhan Institute of Technology University China

ISBN: (纸本)9781510675018

Infrared and visible image fusion aims to synthesize a new image with complementary information of the source images such as the thermal radiation information and detailed texture information. However, the existing methods applied in the image fusion tasks tend to pay more attention to the improvements of visual quality and multiple evaluation indicators, while ignoring the importance of semantic information in high-level missions of image processing. In this paper, we present a novel approach to image fusion, where the synthesis of fused images is achieved through the utilization of semantic layouts generated via unsupervised learning. With the introduction of Attention Mechanism, the relationships between each pair of pixel points are gained to construct soft semantic layouts and further capture the global context information to make the same kind of semantics show the same fusion effect. By leveraging the semantic information, our method enables the automatic learning of fusion weights for two source images at the same spatial position. In comparison with other state-of-the-art image fusion methods, our experimental results obtained excellent performance both on qualitative results and quantitative indicators. Moreover, our method retains the high-level semantic information to the greatest extent, which is one of our outstanding characteristics. © 2024 SPIE. All rights reserved.

关键词： Image fusion

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Meta soft Label Generation for Noisy Labels 25

Meta Soft Label Generation for Noisy Labels

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25th international conference on pattern recognition (ICPR)

作者： Algan, Gorkem Ulusoy, Ilkay Middle East Tech Univ Elect Elect Engn Ankara Turkey ASELSAN Yenimahalle Ankara Turkey

ISBN: (纸本)9781728188089

The existence of noisy labels in the dataset causes significant performance degradation for deep neural networks (DNNs). To address this problem, we propose a Meta soft Label Generation algorithm called MSLG, which can jointly generate soft labels using meta-learning techniques and learn DNN parameters in an end-to-end fashion. Our approach adapts the meta-learning paradigm to estimate optimal label distribution by checking gradient directions on both noisy training data and noise-free meta-data. In order to iteratively update soft labels, meta-gradient descent step is performed on estimated labels, which would minimize the loss of noise-free meta samples. In each iteration, the base classifier is trained on estimated meta labels. MSLG is model-agnostic and can be added on top of any existing model at hand with ease. We performed extensive experiments on CIFAR10, Clothing1M and Food101N datasets. Results show that our approach outperforms other state-of-the-art methods by a large margin. Our code is available at https://***/gorkemalgan/MSLG_noisy_label.

关键词： deep learning label noise noise robust noise cleansing meta-learning

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