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检索条件"任意字段=3rd IAPR International Conference on Pattern Recognition in Bioinformatics"

共 2191 条记录，以下是31-40 订阅

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Movement pattern recognition in Boxing Using Raw Inertial Measurements 3rd

Movement Pattern Recognition in Boxing Using Raw Inertial Me...

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3rd international conference on Optimization, Learning Algorithms and Applications (OL2A)

作者： Puchalski, Radoslaw Giernacki, Wojciech Poznan Univ Tech Poznan Poland

ISBN: (纸本)9783031530357;9783031530364

In the paper, a new machine-learning technique is proposed to recognize movement patterns. The efficient system designed for this purpose uses an artificial neural network (ANN) model implemented on a microcontroller to classify boxing punches. Artificial intelligence (AI) enables the processing of sophisticated and complex patterns, and the X-CUBE-AI package allows the use of these possibilities in portable microprocessor systems. The input data to the network are linear accelerations and angular velocities read from the sensor mounted on the boxer's wrist. By using simple time-domain measurements without extracting signal features, the classification is performed in real-time. An extensive experiment was carried out for two groups with different levels of boxing skills. The developed model demonstrated high efficiency in the identification of individual types of blows.

关键词： activity recognition artificial intelligence artificial neural networks edge computing microcontrollers

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Development of an emotion and age recognition model based on the combination of local binary patterns and squeeze-and-excitation block method 3

Development of an emotion and age recognition model based on...

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3rd international conference on Digital Technologies, Optics, and Materials Science, DTIEE 2024

作者： Soma, Guedes Kopanitsa, Georgy ITMO University St. Petersburg197101 Russia

ISBN: (纸本)9781510681590

This paper presents models for improving accuracy and efficiency in recognizing a person's emotional state and age based on facial expressions using the combination of Local Binary patterns and Squeeze-and-Excitation Block methods. The proposed models were trained on the AffectNet dataset for emotion recognition and the Adience dataset for age recognition with four classes [angry, happy, neutral, sad] and [6-20, 25-30, 42-48, 60-98]. The accuracy and speed of these models, achieved after training, were compared with the results described in scientific articles published in the Scopus database. According to the obtained experimental data, the first and second developed models achieved validation accuracy of 81.5% and 72.3%, respectively. © 2024 SPIE.

关键词： Local binary pattern

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Research on Automatic Workpiece Sorting System Based on Machine Vision 3

Research on Automatic Workpiece Sorting System Based on Mach...

引用

2023 IEEE 3rd international conference on Data Science and Computer Application, ICDSCA 2023

作者： Ji, Linfeng Zhenjiang Vocational Technical College Zhenjiang China

ISBN: (纸本)9798350341546

This paper expounds the automatic recognition method of parts based on computer vision. The feature database of the processed parts is constructed by using machine learning method. Image preprocessing, threshold segmentation, edge and contour extraction are used to analyze the features of the image of the inspected part to judge whether the inspected part is qualified or not. Aiming at the attitude error generated when the workpiece is placed, a method for positioning the center of gravity and rotation angle of the workpiece based on boundary detection and contour tracking is proposed. Use the similarity measure to determine the characteristics of the object to be processed. Simulation results show that the method proposed in this paper is effective. © 2023 IEEE.

关键词： image processing Machine vision pattern recognition workpiece

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Machine Learning Based pattern recognition with HOG Features for Detection of Islanding 3

Machine Learning Based Pattern Recognition with HOG Features...

引用

3rd IEEE international conference on Electrical Power and Energy Systems, ICEPES 2024

作者： Pal, Kumaresh Goswami, Subhadip Bhuyan, Anshuman Koley, Santanu Bera, Bidisha Basu, Sarmistha National Institute of Technology Department of Electrical Engineering Jamshedpur India National Institute of Technology Department of Electrical Engineering Arunachal Pradesh India Gandhi Institute for Education & Technology Department of Electrical Engineering Bhubaneswar India Haldia Institute of Technology Department of Computer Science Engineering Haldia India Global Institute of Science and Technology Department of Computer Science and Technology Haldia India Calcutta Institute of Engineering and Management Department of Physics Kolkata India

ISBN: (纸本)9798350390728

Islanding is a significant difficulty that arises from the high integration of distributed generation sources. Islanding could potentially damage the clients and their equipment. According to the IEEE 1547 DG interconnection requirements, islanding must be detected within two seconds, and the DG must be shut down. This research introduces a new method for identifying islanding using image pattern recognition utilising a Machine Learning classification algorithm. The suggested technique utilises the Random Forest (RF) classification algorithm. Images are utilised to extract the histogram of oriented gradient (HOG) features in order to detect both non-islanding and islanding occurrences. The spectrogram images are formed from the time-series signal collected from the point of common coupling. The images are utilised to extract features from histogram of oriented gradient, which are subsequently used as feature vector inputs for training and testing the RF classifiers. The spectrogram image is derived using the rate of change of the voltage parameter. The efficacy of the RF classifier is evaluated using 5, 10, 20, and 25-fold cross-validations. The classification findings indicates that islanding detection utilising a Random Forest classifier and Histogram of Oriented Gradients (HOG) feature for image pattern recognition obtained outstanding performance with 98.75% accuracy and a detection time of 192 milliseconds. © 2024 IEEE.

关键词： Spectrographs

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Spark-KNN Parallel pattern recognition Technique for Substation Secondary Cable Data 3

Spark-KNN Parallel Pattern Recognition Technique for Substat...

引用

3rd international conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Lu, Weizhou Huang, Haiying Luo, Huan Guangdong Power Grid Maoming Power Supply Bureau China

ISBN: (纸本)9798350382426

With the rapid development of smart grid, the substation secondary cable condition monitoring data is growing exponentially and gradually constitutes the secondary circuit condition monitoring big data. The traditional computing architecture can no longer meet the computing performance demand. Combining Spark big data processing technology and AliCloud E-MapReduce cloud computing platform, we propose a parallel pattern recognition method for substation secondary cable condition monitoring big data, aiming to improve the ability of the secondary cable online monitoring system to quickly batch analyze the alarm monitoring data that suddenly increase in a short period of time. Spark-KNN, a parallelized K-nearest neighbor classification algorithm based on Spark, is designed to realize parallel pattern recognition of massive secondary cable monitoring data. The experimental results show that the average performance of Spark-KNN is 3.17 times higher than that of Hadoop MapReduce implementation, which is more suitable for performing real-time processing tasks of secondary cable monitoring big data. © 2023 IEEE.

关键词： Online monitoring Secondary cable data Spark-KNN

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Towards the Detectability of Image Steganography Using NNPRTOOL (Neural Network for pattern recognition) 3rd

Towards the Detectability of Image Steganography Using NNP...

引用

3rd international conference on Intelligent Systems and pattern recognition, ISPR 2023

作者： Alharbi, Ayidh School of Computer Science University of Tabuk Tabuk Saudi Arabia

ISBN: (纸本)9783031463341

The term steganography represent the science of hiding secret information in a carrier medium like Image, Audio, Video, etc. This field has been participated in multidisciplinary applications and different sectors. For instance, in Healthcare, E-commerce, Access control, and Databases. According to literature, some of those applications, and their analytical, classification data and results are studied. This include current challenges of detectability which can be done with or without using a machine learning classifier. In this research, an additional experiment of Neural Network pattern recognition (NNPRTOOL) is applied to classify original and stego images belong to two main classes and directories. This experiment contributes in the robustness of steganography in general. Moreover, it highlights the evolution of image steganalysis research using a machine learning classifier. However, the potential of such tools like NNPRTOOL, Support Vector Machine (SVM), and ENSEMBLE Classifier have shown significant results in image classifications. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Steganography

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Research on Automatic recognition Model of Reconnaissance Target based on Fuzzy pattern 24

Research on Automatic Recognition Model of Reconnaissance Ta...

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3rd international conference on Computer, Artificial Intelligence and Control Engineering, CAICE 2024

作者： Du, Jian Liu, Ying Du, Yichao Du, Lijuan Department of Fundament Army Academy of Armored Force Beijing100072 China Center for Innovation and Application China Academy of Aerospace Aerodynamics Beijing100071 China College of Aircraft Engineering Nanchang Hangkong University Beijing100091 China College of Humanities and Development Studies China Agricultural University Beijing100091 China

ISBN: (纸本)9798400716942

In the operation under the condition of information technology and intelligence, the correct analysis and processing of reconnaissance information determines the initiative of the battlefield in a certain sense. In order to solve the problem of fuzziness in target recognition, the fuzzy theory is used to establish the target recognition model, extract and classify the target attributes obtained from the battlefield, establish the target recognition model, and identify the types of urban offensive combat targets. Based on the information obtained from reconnaissance observation, the model determines the membership degree of five types of equipment as 0.87, 0.14, 0.53, 0 and 0.13 respectively. According to the principle of maximum membership, the coordinate position of the target is scientifically determined, which greatly improves the accuracy of target identification and provides a quantitative basis for command decision-making. © 2024 ACM.

关键词： Automatic target recognition

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A New Federated Learning Framework for Plant Leaf Disease Diagnosis 3

A New Federated Learning Framework for Plant Leaf Disease Di...

引用

3rd international conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Singh, Aadarsh Kumar Chattopadhyay, Pratik Singh, Lokesh Pattern Recognition Lab Dept. of Cse Varanasi India Reinventbio.io Varanasi India

ISBN: (纸本)9798331528201

Several Deep Learning-based approaches have recently been proposed for plant leaf disease detection. Traditional learning-based models necessitate centralizing large image datasets, requiring huge volumes of storage space. It appears that a better predictive model can be developed if the available data at multiple data sites can be effectively utilized using a collaborative learning strategy. Federated Learning (FL) offers a decentralized alternative, enabling collaborative model training by allowing multiple participants to update a global model using their local data without sharing high volumes of raw image datasets. Our work presents an FL framework to train a Vision Transformer-based model with an improved Federated Averaging (FedLossAvg) algorithm. Experimental results demonstrate our model's capability to handle large, varied datasets. Using four clients, our global model achieved an F1-score of 95.62% and an accuracy of 99.89% on the PlantVillage data. © 2024 IEEE.

关键词： Federated learning

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TCPNet: A 3D Point Cloud Classification Model Based on the Advanced CNN and Transformer 3

TCPNet: A 3D Point Cloud Classification Model Based on the A...

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3rd international conference on Artificial Intelligence and Computer Information Technology, AICIT 2024

作者： Wu, Jinmeng Ma, Jingwei Hong, Hanyu Hao, Yanbin Ma, Lei Wang, Lei Wuhan Institute of Technology Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan China School of Information Science and Technology University of Science and Technology of China Anhui China

ISBN: (纸本)9798350365443

Transformer and Convolutional Neural Network (CNN) are currently two important models in the field of deep learning. Among them, Transformer has strong global perception ability but weak local perception ability, and CNN precisely compensates for this. CNN has strong local perception ability, so in order to combine the advantages of both, we propose a 3D classification model TCPNet based on Transformer and CNN. We first use Transformer to extract the global features of the point cloud, and then concatenate a CNN network to enhance the detail perception ability, so that the network can better understand the 3D point cloud. The experimental results on the ModelNet40 and ScanObjectNN datasets show that our method can effectively achieve 3D classification tasks. On ModelNet40, an accuracy of 93.6% can be achieved. On the most difficult variant PB-T50-RS of ScanObjectNN, an accuracy of 83.93% can be achieved. © 2024 IEEE.

关键词： Convolutional neural networks

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Identification Method of Dress pattern Drawing based on Machine Vision Algorithm 3

Identification Method of Dress Pattern Drawing based on Mach...

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3rd international conference on Computer Vision, Image and Deep Learning and international conference on Computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Lyu, Ke Yan, Haizhang Zhejiang Sci-Tech University School of International Education Zhejiang310000 China Xidian University School of Computer Science and Technology Xi'an71000 China

ISBN: (纸本)9781665459112

This paper uses the machine vision method to identify the skirt module. We have constructed three kinds of machine recognition models of skirt profile processing, structure analysis of style drawing, and size estimation. The author constructs a relatively complete image recognition system for dress pattern drawing. In addition, we conducted an effect evaluation with a certain number of samples at the later stage of the experiment. This study has A good effect in distinguishing an A-type skirt from an H-type skirt, identifying the reasonable degree and length of the skirt, and determining the quantity statistics of each component element in the skirt pattern diagram. © 2022 IEEE.

关键词： Image recognition

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