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检索条件"任意字段=6th IAPR International Conference on Pattern Recognition in Bioinformatics"

共 1869 条记录，以下是201-210 订阅

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Multi-CNN Model for Multi-Classification of Cultural Heritage Monuments

Multi-CNN Model for Multi-Classification of Cultural Heritag...

引用

international conference on pattern Analysis and Intelligent Systems (PAIS)

作者： Toufik Djelliout Hassina Aliane Department of Computer Science Faculty of Exact Sciences University of Bejaia Algeria Research Center for Scientific and Technical Information Algiers Algeria

ISBN: (数字)9798350350265

ISBN: (纸本)9798350350272

the use of convolutional neural networks (CNN) in the preservation of cultural heritage monuments, especially in conflict-affected regions such as Gaza, Ukraine, Iraq and others, represents a significant advancement in heritage conservation efforts. this paper presents an approach that uses a Multi-CNN model to classify images of cultural heritage monuments into various categories, encompassing period, monument type and location. By leveraging the capabilities of CNNs, this model demonstrates a high level of accuracy in categorizing heritage monuments based on multiple attributes. the study highlights the superior performance of the Multi-CNN model compared to other popular models such as DenseNet169, GoogleNet and MnasNet, highlighting its effectiveness in accurately classifying images of cultural heritage monuments in various dimensions. According to the evaluation results, the top-performing multi-CNN model achieves a classification accuracy of 94.52%, outperforming the single CNN models. the DenseNet196 model achieves 93.70% accuracy, the MnasNet model achieves 92.80% accuracy, and the GoogleNet model achieves 88.18% accuracy.

关键词： Analytical models pattern recognition Cultural differences Convolutional neural networks pattern analysis Intelligent systems

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Adaptive Scaling for Archival Table Structure recognition 16th

Adaptive Scaling for Archival Table Structure Recognition

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16th iapr international conference on Document Analysis and recognition (ICDAR)

作者： Li, Xiao-Hui Yin, Fei Zhang, Xu-Yao Liu, Cheng-Lin Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit 95 Zhongguancun East Rd Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing Peoples R China

ISBN: (纸本)9783030865498

Table detection and structure recognition from archival document images remain challenging due to diverse table structures, complex document layouts, degraded image qualities and inconsistent table scales. In this paper, we propose an instance segmentation based approach for archival table structure recognition which utilizes both foreground cell content and background ruling line information. To overcome the influence from inconsistent table scales, we design an adaptive image scaling method based on average cell size and density of ruling lines inside each document image. Different from previous multi-scale training and testing approaches which usually slow down the speed of the whole system, our adaptive scaling resizes each image to a single optimal size which can not only improve overall model performance but also reduce memory and computing overhead on average. Extensive experiments on cTDaR 2019 Archival dataset show that our method can outperform the baselines and achieve new state-of-the-art performance, which demonstrates the effectiveness and superiority of the proposed method.

关键词： Archival document Table detection Table structure recognition Adaptive scaling

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Gas-insulated Switchgear Discharge pattern recognition Based on KPCA and Multilayer Neural Network 6

Gas-insulated Switchgear Discharge Pattern Recognition Based...

引用

6th international conference on Communication, Image and Signal Processings, CCISP 2021

作者： He, Nan Liu, Hongjing Qian, Mengdi Miao, Wang Liu, Kewen Wu, Linlin SGCC Beijing Electric Power Research Institute Beijing China State Grid Beijing Electric Power Company Beijing China

ISBN: (纸本)9781665432795

Identifying the discharge type based on partial discharge signals in Gas-insulated Switchgear (GIS) equipment is very important for fault diagnosis and early warning of GIS equipment. this paper proposes a method to recognize different typical discharge types of GIS equipment through experiment. Firstly, different discharge defect models of GIS are built, and UHF sensors are used to detect and obtain partial discharge signals of discharge defects. then, 3-dimentional PRPS patterns of different defects are drawn based on discharge signals. After that, 2-dimentional Gabor filter is applied to these patterns and texture and shape features are obtained. the dimension of the characteristics of discharge patterns is reduced by Kernel Principal Component Analysis (KPCA), and a multi-layer neural network is built to train and test the samples of discharge data. the results show that the recognition accuracy of three discharge types by using the multi-layer neural network can reach 83.3%, which is better than other algorithms, and can accurately diagnose the defect types of GIS. © 2021 IEEE.

关键词： Partial discharges

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Design of an Edge Detected CMOS Image Sensor for an Intelligent pattern recognition

Design of an Edge Detected CMOS Image Sensor for an Intellig...

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international conference on Information Technology (INCIT)

作者： Hohyeon Lee Soo Youn Kim Minkyu Song Dept. of Semiconductor Science Dongguk University Seoul Korea

Normally, a pattern recognition has been studied in the field of digital signal processing with digital codes. Even though it has very accurate results, it needs a lot of power consumption, very long operating time, and huge hardware bundles. In this paper, an edge detected CMOS image sensor for an intelligent pattern recognition is proposed. Further, an efficient edge detection algorithm is also described. With the new techniques, a low power and high speed pattern recognition is available in the field of CMOS image sensor. the image sensor is composed of photodiodes, a two-step single-slope 8-bit analog-to-digital converter, static random memories, and etc. the testing sensor has been implemented with an 180nm technology. It has a very excellent measured results, compared to other published chips.

关键词： Semiconductor device measurement Power demand Image edge detection Signal processing algorithms CMOS image sensors pattern recognition Photodiodes

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Minimization of the Average Risk in pattern recognition for Smart Grid Systems 6

Minimization of the Average Risk in Pattern Recognition for ...

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6th international conference on Computational Linguistics and Intelligent Systems, COLINS 2022 - Volume I: Main

作者： Mammadov, Rahim Aliyev, Timur Mammadov, Gurban Azerbaijan State Oil and Industry University Azadliq av. 16/21 BakuAZ1010 Azerbaijan Azerbaijan State Scientific Research Institute for Labor Protection and Occupational Safety Tabriz st.108 BakuAZ1008 Azerbaijan

In problems of object and signal recognition, each of the errors of the first and second kind has its own cost, which takes on non-negative values. If they are equal, then the problem is relatively easy to solve. Since, after some transformations, the equation is transformed so that the Laplace function can be applied to it and the approximate values can be found. However, finding more accurate values, with inequality of errors of the first and second kind, and minimizing the average risk is in demand and necessary. In the course of the study, a method was developed for finding the minimum value of the average risk for two functions that have a normal distribution, as well as an independent mathematical expectation and standard deviation. the obtained theoretical results are simulated on a computer. In the course of modeling, various combinations of the probabilities of errors of the first and second kind were set, in the course of which the tendency of change in the average risk was determined. the results of computer modeling show the effectiveness of the proposed technique. A mathematical model is built to estimate the errors of the measure of proximity between objects when solving problems of pattern recognition when recognizing signals, and the conditions for minimizing errors of the measure of proximity between objects are derived from it. the fulfillment of these conditions allows two to four times to reduce the errors in estimating the measure of proximity between objects. © 2022 Copyright for this paper by its authors.

关键词： Errors

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Enhancing Typing Dynamics Emotion recognition: A Multi-Class XGBoost Approach for Accurate Sentiment Detection

Enhancing Typing Dynamics Emotion Recognition: A Multi-Class...

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Advancements in Computing (ICAC), international conference on

作者： Sudesh Bandara tharindu Dasath Senula Nanayakkara Samadhi Rathnayake thusithanjana thilakarthna Faculty of Computing Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

this research paper aims to enhance the accuracy and efficiency of emotion recognition through keystroke dynamics by utilizing a multi-class XGBoost model. the study addresses the limitations observed in previous models, such as Support Vector Machines and Logistic Regression, which struggled with the complex, high-dimensional nature of keystroke data. the primary objective was to develop a robust model capable of capturing the subtle variations in typing behavior associated with different emotional states. To achieve this, a comprehensive approach was adopted, involving the collection of detailed keystroke datasets, advanced feature engineering, and systematic hyperparameter tuning. the XGBoost model was chosen for its superior ability to manage complex interactions within large datasets. through rigorous evaluation, the XGBoost model outperformed traditional models in accuracy, precision, and recall, demonstrating significant improvements in emotion classification tasks. the findings of this study reveal that XGBoost, when combined with meticulous feature engineering and hyperparameter optimization, offers a substantial advancement in the field of emotion recognition via keystroke dynamics. the research not only confirms the effectiveness of XGBoost in this domain but also provides new insights into optimizing machine learning models for emotion detection. these results have broad implications for the development of more emotionally aware computing systems, potentially improving user interaction across various applications.

关键词： Support vector machines Emotion recognition Logistic regression Adaptation models Accuracy Systematics Computational modeling Keystroke dynamics pattern recognition Tuning

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Physical Exergames Movements and pattern recognition using Convolutional Neural Network

Physical Exergames Movements and Pattern Recognition using C...

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international conference on Advancements in Computational Sciences (ICACS)

作者： Tanvir Fatima Naik Bukht Ahmad Jalal Faculty of Computing & Artificial Intelligence Air University Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

this paper proposes a novel framework for accurately recognizing physical exergames interaction in video sequences. the developed approach uses image processing and machine learning tools to correctly extract and classify the features. the framework comprises five main steps: Silhouette extraction, preprocessing, feature extraction, feature optimization and classification. Image contrast is improved by power law transformation, silhouette is extracted by the Multiple Object Tracking (MOT) algorithm and graph-based segmentation. We extract ORB and geometric skeleton based keypoints to capture discriminative features, and apply Fast Independent Component Analysis (FICA) to optimize feature representation. thirdly, the Convolutional Neural Network algorithm is used to classify the optimized features, with a macro average accuracy of 0.85 and a weighted average accuracy of 0.87%. the proposed framework effectively recognizes physical exergames interaction and can be applied in many domains, such as human-human interaction and surveillance.

关键词： Image segmentation Machine learning algorithms Accuracy Surveillance Video sequences Independent component analysis Feature extraction Classification algorithms Convolutional neural networks Optimization

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An Approach to Recognize Human Activities based on ConvLSTM and LRCN

An Approach to Recognize Human Activities based on ConvLSTM ...

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international conference on Information Systems and Computer Networks (ISCON)

作者： Shradha Bhatia Tushar Chauhan Sumita Gupta Sapna Gambhir Jitesh H. Panchal Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Noida Department of Computer Engineering J. C Bose University of Engineering & Technology Faridabad Department of Mechanical Engineering Purdue University

In recent times, approaches based on deep learning (DL) have been effectively used to predict a variety of human actions using time series data from smartphones and wearable sensors. Time series data handling remains a barrier for DL-based techniques, even though they did quite well in activity detection. Traditional pattern recognition techniques have achieved significant advancements in recent years. However, the performance of the generalization model may be hampered by the approaches’ heavy reliance on human feature extraction. Deep learning methods are becoming more and more successful, and employing these approaches to understand human behaviours in mobile and wearable computing situations or using vision-based technologies has garnered a lot of interest. ConvLSTM and LRCN which is a combination of Convolutional Neural Network (CNN) and Long shirt-term memory (LSTM) are the machine learning methods we employed in this research. With the help of CNNLSTM, it is possible to anticipate human actions more accurately while also simplifying the model and doing away with the necessity for sophisticated feature engineering. Both in terms of space and time, the CNN-LSTM network is deep. In this paper, the LRCN model gets 92% accuracy when we compare the performance of all the models that were utilized against on each other.

关键词： Deep learning Computational modeling Wearable computers Time series analysis Feature extraction pattern recognition Convolutional neural networks

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RIME-SVM for Electromagnetic Radiation Signal Identification in Coal Mining Environment

RIME-SVM for Electromagnetic Radiation Signal Identification...

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IEEE international conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Pengxue Zhu Shiqi Huang Le Liu Zheng Zhang China West Normal University Nanchong China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

In this study, an innovative signal classification and recognition system is proposed for the early warning needs of disasters such as impact ground pressure in coal mining. the RIME-SVM model is introduced, which combines the Rime Optimization Algorithm and Support Vector Machine to optimize the classifier parameter configuration and significantly improve the classification performance. Experiments have demonstrated that the model has excellent signal classification ability in industrial environments, which is especially suitable for the identification of electromagnetic radiation signals in coal mine environments. Compared with the traditional method, RIME-SVM significantly improves the classification accuracy, demonstrates the efficiency and accuracy of processing complex industrial signal data, and brings important progress to the field of coal mine safety monitoring technology.

关键词： Support vector machines Time-frequency analysis Accuracy pattern classification Electromagnetic radiation Data models Coal mining Classification algorithms Optimization Monitoring

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Analysis of Complex Network Topological Features for EEG Signals Classification

Analysis of Complex Network Topological Features for EEG Sig...

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Data-driven Optimization of Complex Systems (DOCS), international conference on

作者： Ping Zhen Yuanyuan Yue Qingshan Liu School of Mathematics Southeast University Nanjing Jiangsu China

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

Electroencephalogram (EEG) signals provide direct insights into the physiological activities of brain, making EEG analysis valuable for understanding cognitive processes and neurological disorders. the main objective of this study is to investigate the effectiveness of extracting combined topological features for epilepsy recognition based on EEG data. We utilize visibility graphs and persistent diagrams to extract both graph features and homological features. these features are then combined to enhance the classification performance, surpassing the results achieved by using graph features alone. Notably, our approach achieves high accuracy rates of 100% for classifying Z and S, 99.75% for N and S, and 97.88% for F and S. these findings significantly contribute to the field of EEG-based epilepsy classification and provide valuable insights for further research and investigation.

关键词： Neurological diseases Time series analysis Epilepsy pattern classification Feature extraction Electroencephalography Physiology Data mining Signal analysis Optimization

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