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共 1776 条记录，以下是81-90 订阅

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Research on Image recognition based on Reinforcement learning 4

Research on Image Recognition based on Reinforcement Learnin...

引用

4th international conference on Computer Vision, Image and Deep learning, CVIDL 2023

作者： Luo, Jiabin Luo, Rongzhen School of Mathematics and Statistics Northeastern University at Qinhuangdao Hebei China University Baoding Campus of North China Electric Power University Department of Mechanical Engineering Hebei China

ISBN: (纸本)9798350326444

Nowadays, the images recognition has become a fundamental component in computer vision and assist to identify the objectives in the complex images. With the development of images processing methods, the image-level objectives recognition accuracy has been greatly enhanced by utilizing the machine learning models or neural networks. However, existing methods are mainly concentrated on the primary features of input images and concentrate on some certain areas, which ignore the environment features and the deep investigation of the image data-set. In this paper, we propose a novel image recognition method to identify the objectives and obtain the policy gradients for decreasing orders. Furthermore, we compare our proposed models with existing traditional machine learning methods to evaluate the performance of recognition accuracy. From our extensive experimental results, we can conclude that our proposed methods can achieve the subjective detection from numerous images data-set with reasonable computation costs. © 2023 IEEE.

关键词： Image recognition

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Fault Diagnosis Method for Marine Engine under Variable Working Conditions Based on Adversarial Subdomain Adaptation 4

Fault Diagnosis Method for Marine Engine under Variable Work...

引用

4th international conference on machine learning and Intelligent Systems Engineering (MLISE)

作者： Zhang, Xiaorong Zhou, Mingshun Wang, Peng Qingdao Ocean Shipping Mariners Coll Qingdao Shandong Peoples R China

ISBN: (纸本)9798350375084;9798350375077

Marine engines have a high risk of failure during navigation and face issues such as varying operating conditions and complex faults. Existing research mainly focuses on fault diagnosis of marine engines under single operating conditions, neglecting the applicability of these methods under other conditions. To address this issue, this paper employs domain adaptation techniques to study cross-condition fault diagnosis of marine engines. Firstly, a self-calibrating convolutional neural network is used to extract health state features from engine operation data. Meanwhile, by integrating adversarial and subdomain adaptation techniques, the distribution differences in global domains and local subdomains under different operating conditions are reduced, thereby improving the fault diagnosis accuracy under variable conditions. Finally, the effectiveness of the diagnostic model is validated based on simulated marine engine fault data. For the designed one-to-one and one-to-many condition transfer tasks, the average fault diagnosis accuracy reaches approximately 90%, achieving higher recognition accuracy compared to other methods. the research results can provide theoretical reference for fault diagnosis of marine engines under variable operating conditions.

关键词： deep learning intelligent diagnosis transfer learning domain adaptation

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2023 4th international conference on Communication Systems, Computing and IT Applications, CSCITA 2023 - Proceedings

2023 4th International Conference on Communication Systems, ...

引用

4th international conference on Communication Systems, Computing and IT Applications, CSCITA 2023

ISBN: (纸本)9781665459877

the proceedings contain 43 papers. the topics discussed include: deep learning based Marathi sentence recognition using Devanagari character identification;deep learning based Marathi sentence recognition using Devanagari character identification;efficient detection of small and complex objects for autonomous driving using deep learning;implementation of exploratory data analysis on weather data;deep learning model for simulating self-driving car;summarization of video clips using subtitles;reliability stripe coagulation in two failure tolerant storage arrays;efficient video anomaly detection using residual variational autoencoder;situational portfolio forecasting and allocation with deep-learning approach;and recognition of emotions based on facial expressions using bidirectional long-short-term memory and machine learning techniques.

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Alzheimer's disease MRI image recognition method based on deep learning 4

Alzheimer's disease MRI image recognition method based on de...

引用

4th international Signal Processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Jiahao, Huang College of Mechanical and Electronic Engineering Nanjing Forestry University Jiangsu Nanjing China

ISBN: (纸本)9798331528676

Globally, the incidence of Alzheimer's disease is increasing. Although there are many diagnostic methods, traditional methods often rely on professional knowledge and consume much time. therefore, there is an urgent need to introduce automated technology to improve diagnostic efficiency and accuracy. this study used three deep learning models, namely convolutional neural network (CNN), ResNet, and VGGNet. It optimized them according to data characteristics for Alzheimer's MRI image recognition, providing a reference for diagnosing Alzheimer's disease. this study used the dataset from dataset Alzheimer, based on the data preprocessing of Alzheimer's Disease Neuroimaging Initiative (ADNI). the model was trained after the dataset was processed by data augmentation, pixel value scaling and MOTE oversampling. the experimental results indicated that the VGGNet model attained the highest overall accuracy at 76%, and the CNN and ResNet models reached accuracies of 75% and 74%, respectively. Nonetheless, the model's proficiency in reliably identifying categories with limited samples necessitates enhancement. Furthermore, further analysis is necessary to assess the model's generalisation potential and its relevance in clinical environments. Future research may explore more advanced model architectures, incorporating attention processes and generative adversarial networks (GANs), to enhance the model's recognition capabilities and its suitability for therapeutic applications. ©2024 IEEE.

关键词： Adversarial machine learning

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Facial Expression recognition Algorithm Based on Local Feature Extraction 4

Facial Expression Recognition Algorithm Based on Local Featu...

引用

4th IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2024

作者： Du, Huabing Chen, Yaqi Shu, Zhong School of Electronic Information Engineering Jingchu University of Technology Hubei Jingmen China Jingmen Rongmedia Network Technology Co. Ltd Hubei Jingmen China

ISBN: (纸本)9798350359589

this paper employs the Center Symmetric Local Binary pattern (CSLBP) algorithm for extracting fine features in facial expression recognition. Additionally, the Rotation Invariant Local Phase Quantization (RILPQ) algorithm is utilized to capture accurate features for recognizing blurry facial expressions. the proposed approach achieves comprehensive and detailed extraction of different facial expression features. By constructing a multi-nonlinear SVM classifier structure model, the system accomplishes the classification of facial expression features and facilitates facial recognition. the experimental statistical data indicates that our algorithm is most suitable for a 9×9 pixel block grid. the accuracy of facial expression feature extraction is around 92%, and the accuracy of facial expression classification and recognition is also approximately 92%. For images with an 8×8 feature dimension, the algorithm's processing time is approximately 0.23 seconds. © 2024 IEEE.

关键词： Center Symmetric Local Binary pattern Facial Expression recognition Image Feature Extraction Nonlinear Support Vector machine Classifier Rotation Invariant Local Phase Quantizatio

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Comparative Study Using Ensemble Methods and Sampling Techniques for Imbalanced Diabetes data 4th

Comparative Study Using Ensemble Methods and Sampling Techni...

引用

4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Khemisa, Hana Dendani, Nadjette Khedimi, Yasmine Amara, Nour Djihane Azizi, Nabiha Badji Mokhtar Univ Dept Comp Sci Annaba Algeria

ISBN: (纸本)9783031821523;9783031821530

In medical decision making, the efficacy of classification, a vital component of decision support systems, can be significantly compromised by imbalanced class distributions within training data. this discrepancy is especially critical due to the heightened consequences of misclassification, particularly when a minority class is mistakenly classified as a majority class. Diabetes, posing a substantial public health challenge, underscores the urgency of addressing classification issues. the World Health Organization reports that 30% to 40% of diabetes cases remain undiagnosed, contributing to a 3% increase in diabetes-related mortality rates from 2000 to 2019. In 2019 alone, approximately 2 million deaths were attributed to diabetes and related kidney diseases. Consequently, effective intervention strategies are imperative. Managing imbalanced datasets in medical contexts presents significant obstacles to accurate classification, with profound implications for patient outcomes. To address this challenge, this research proposes leveraging machine learning and artificial intelligence techniques. the research comprises two main components: applying boosting and unbalanced bagging to a dataset containing diabetes-related information. Initial findings indicate that boosting yields promising results in classification accuracy. Building upon this success, we compare boosting with three additional technique Synthetic Minority Over-sampling Technique (SMOTE), Edited Nearest Neighbors (ENN), and Tomek links each sequentially applied to the dataset. through rigorous experimentation and analysis, we aim to identify the most effective strategy for handling imbalanced datasets in diabetes classification. By enhancing our understanding of classification methods and their impact on imbalanced datasets, this research aims to contribute to improved decision-making processes in healthcare settings, ultimately leading to better outcomes for patients affected by diabetes.

关键词： machine learning Classification Imbalanced data Diabetes Sampling Ensemble methods

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AI-Infused Research and Development in Universities: Accelerating Scientific Discovery 4

AI-Infused Research and Development in Universities: Acceler...

引用

4th IEEE international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Badrus Salim, Moch. Abdulloh Sari, Ika Novita Azha, Septiana Zulfikri, Moh. Sutantri Tribakti Islamic University Lirboyo Kediri Indonesia

ISBN: (纸本)9798350360165

the past decade has witnessed the role that artificial intelligence (AI) has in university research and development (R&D) processes emerge as a very potent catalyst for accelerating scientific discovery. It thus seeks to explore the transformative impacts that AI-infused R&D is likely to bring in universities, focusing mostly on key mechanisms, challenges, promising avenues for future exploration, and technological breakthroughs in the field. the traditional methodologies of R&D have seen a massive revolution through the entry of AI, which now poses uncharted capabilities in data analysis, pattern recognition, and predictive modeling. the application is able to thoroughly search vast sets of data in an unrealistically short period, so as to reveal latent structures, patterns, and other actionable insights, with the help of machine learning algorithms, neural networks, and natural language processing techniques. this paradigm shift in R&D supports a more agile, data-driven approach to scientific inquiry that allows researchers to better focus their efforts with precision and alacrity on the most vexing, complex problems. the biggest gain to be reaped from infusing AI in R&D is the hastening of the pace of experimentation and hypothesis testing. AI systems thus enable researchers to iterate very quickly by automating repetitive tasks and offering the best designs for experimentation. this will, in effect, bring down the time-todiscovery cycle. Equally, AI simulations allow for the possibility of in silico virtual experiments, thus reducing overreliance on capital-intensive and unwieldy laboratory experiments. © 2024 IEEE.

关键词： Adversarial machine learning

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A Novel Image Deep learning for recognition of Silicon Photonics Redistribution Layer 4

A Novel Image Deep Learning for Recognition of Silicon Photo...

引用

4th IEEE international conference on Electronic Communications, Internet of things and Big data, ICEIB 2024

作者： Fang, Jen-Kuang Hung, Chao-Kai Yang, Peng Lu, Wen-Long Chang, Chien-Wei Advanced Semiconductor Engineering Inc. Kaohsiung Taiwan

ISBN: (纸本)9798350360721

Due to the design requirements for miniaturization and high-density placement of silicon photonics (SiPh) components in integrated circuit (IC) packaging, the system in package (SiP) process and equipment have become important but challenging. Automatic optical inspection (AOI) detection equipment in IC packaging lines is also important in identifying redistribution layer (RDL) anomalies that affect signal transmission. If the AOI misrecognition rate is too high, the production yield and overall equipment performance are lowered. To solve the above problems, we proposed a novel Pix2Pix generative adversarial network (GAN) algorithm based on machine learning methods. the new feature extraction method has demonstrated highly sensitive image detection capabilities after processing tens of thousands of huge SiPh RDL image databases. © 2024 IEEE.

关键词： Deep learning

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Multimodal federated learning framework evaluation for lymph node metastasis in gynecologic malignanciese 4

Multimodal federated learning framework evaluation for lymph...

引用

4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Ma, Ling. Hu, Zhijun. Yue, Ding Wu, Guangyu Shi, Xiaohua Sirejiding, Shalayiding Liu, Kaijiang Library Shanghai Jiao Tong University Shanghai China Shanghai Jiao Tong University Renji Hospital School of Medicine Department of Gynecologic Oncology Shanghai China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China Shanghai Jiao Tong University Renji Hospital School of Medicine Departments of Radiology Shanghai China Shanghai Jiao Tong University Renji Hospital School of Medicine Shanghai China

ISBN: (纸本)9798350324303

Gynecologic malignancies, including cervical cancer, endometrial cancer, and ovarian cancer, represent a significant threat to women's health, and their diagnosis and treatment are crucial aspects of global public health. While imaging techniques play an important role in diagnosing gynecologic tumors, early detection and accurate diagnosis present challenges. Hence, we propose a framework for assessing lymph node metastasis based on multi-modal federated learning. By utilizing MRI images, we develop a hybrid neural network model that effectively predicts lymph node metastasis by combining a multi-layer perceptron (MLP) and a convolutional neural network (CNN). through federated learning, we ensure data privacy while facilitating data sharing and collaborative computation among multiple hospitals. Validation using a dataset from a single hospital illustrates the considerable benefits of the model in enhancing the sensitivity and specificity of lymph node metastasis diagnosis in gynecologic malignancies. this research offers a novel and efficient approach for assessing lymph node metastasis in gynecologic malignancies, showcasing the vast potential of federated learning in the analysis of medical images. © 2023 IEEE.

关键词： Diagnosis

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data and Knowledge Dual-Driven Traffic Sign recognition Algorithm 4th

Data and Knowledge Dual-Driven Traffic Sign Recognition Algo...

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4th international conference on Artificial Intelligence Logic and Applications

作者： Huang, Guopeng Chen, Xinyu Chen, Yixiang East China Normal Univ Natl Engn Res Ctr Trustworthy Embedded Software Shanghai 200062 Peoples R China

ISBN: (纸本)9789819603534;9789819603541

Traffic sign recognition can quickly extract and identify road perception information, making it one of the key technologies in the field of autonomous driving. the reliable recognition of traffic signs is the primary task in achieving safe autonomous driving. However, current machine learning-based traffic sign recognition technologies still suffer from low interpretability and an excessive dependence on the sample space. therefore, this paper proposes a data and Knowledge Dual-Driven Traffic Sign recognition Algorithm that leverages the convenience of data-driven methods and the reliability of knowledge-driven approaches. the algorithm integrates data-driven methods such as color recognition, shape recognition, and convolutional neural networks, along with knowledge-driven methods that involve reasoning based on traffic sign knowledge base, to accomplish the task of traffic sign recognition. the experimental results demonstrate that compared to other methods, the dual-drive approach proposed in this paper can identify traffic signs with greater accuracy and reliability.

关键词： Traffic Sign recognition Convolutional Neural Networks Siamese Neural Networks Anomaly recognition

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