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检索条件"任意字段=International Conference on Soft Computing and Pattern Recognition"

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pattern recognition Search: An Advancement Over Interpolation Search

Pattern Recognition Search: An Advancement Over Interpolatio...

引用

IEEE international conference on Big Data and Smart computing (BigComp)

作者： Yilmaz, Shahpar Nadeem, Yasir Mehdi, Syed Ali Jamia Hamdard New Delhi India

ISBN: (纸本)9781665421973

Interpolation Search is an algorithm that performs the task of searching for a data through a sorted array by opting a divide and conquer approach, meaning filtering out data to reduce the sample size through unique deductions. In the case of interpolation search specifically, the procedure is to assume the array to be in the form of a perfect arithmetic progression series and then predict the ideal position of the key element. This paper proposes a new searching technique which is an advancement of interpolation search, in the name of 'attern recognition Search'. We look to highlight some of the key drawbacks of interpolation search which are accounted for in the pattern recognition Search.

关键词： Sorting Time Space Complexity Array Index

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Skin texture recognition of leg images based on chaotic mapping reorganization features 3

Skin texture recognition of leg images based on chaotic mapp...

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3rd international conference on Intelligent computing and Human-Computer Interaction, ICHCI 2022

作者： Zhang, Tianshu Tang, Chaoying Huang, Qiaoyue Nanjing University of Aeronautics and Astronautics Automation College Nanjing China

ISBN: (纸本)9781510661301

In criminal and victim identification, when information such as fingerprint and facial images cannot be obtained, the development of other new bio-metric recognition has become an important task. It is found from investigations that the lower leg is often exposed in an evidence image. If we can use the information to identify the identity of the suspect, it will be a great break-through for criminal investigation. Unlike faces or fingerprints, the image of a lower leg does not have many discriminative features. Therefore, capturing more information from an image has become the key to the success of recognition. Despite the rapid development of deep learning in recent years and the wide application of convolutional neural networks in biometric recognition, they are very dependent on a large amount of training data. In this paper, we propose a recognition method based on the combination of chaotic mapping and local binary pattern (LBP), which captures the nonlinear features of the image. Experimental results show that the method proposed in this paper has a better recognition rate and an encouraging performance. © 2023 SPIE.

关键词： Local binary pattern

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Machine Learning Approach for Mixed type Wafer Defect pattern recognition by ResNet Architecture 5

Machine Learning Approach for Mixed type Wafer Defect Patter...

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5th international conference on Control, Communication and computing, ICCC 2023

作者： Remya, K.P. Sajith, V. ISRO Liquid Propulsion Systems Centre Trivandrum India College of Engineering Trivandrum Department of Electronics and Communication Trivandrum India

ISBN: (纸本)9798350334128

Semiconductor manufacturing process involves various steps: fabrication of Wafers, testing of wafers, assembly of each single die and package level final test. Out of which, testing of wafer for defect detection and classification is the most important step where defective dies are eliminated. This improves the efficiency of the overall manufacturing process. Semiconductor wafer defects can be basic defects or mixed defects containing two or more basic defects. Since these defects varied from wafer to wafer and are complex, the accuracy obtained from traditional deep learning model is poor. To address this problem, a semiconductor mixed defect pattern recognition based on Residual Network (ResNet) Architecture is attempted. Among the ResNet architectures, ResNet50 is selected for wafer defect pattern recognition which has a 50 layer structure. The model is evaluated based on data set with 15 mixed defect patterns which were generated from WM-811K wafer map data set. Training, testing and validation of the data set is done. The performance parameters are generated and compared the performance with four different architectures including Convolutional Neural Network(CNN). The training results indicate that the ResNet50 shows a better performance in terms of accuracy, precision, recall and F1 score. © 2023 IEEE.

关键词： Scanning electron microscopy

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2024 international conference on Advances in computing Research on Science Engineering and Technology, ACROSET 2024

2024 International Conference on Advances in Computing Resea...

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2024 international conference on Advances in computing Research on Science Engineering and Technology, ACROSET 2024

ISBN: (纸本)9798350388800

The proceedings contain 197 papers. The topics discussed include: analyzing the human element in cybersecurity breaches with a focus on social engineering tactics and the risks posed by insider threats;design and development of real-time image based fire detection using OpenCV and HSV;the role of low-dimensional neural networks in solving complex inverse problems;transformative potential IoT sensor frameworks for real-time Alzheimer's detection and monitoring in elderly populations;decoding the Devanagari - handwritten Hindi recognition using deep learning methodology;countering the rise of AI-generated content with innovative detection strategies and large language models;a method to reduce the incidence of wheat leaf diseases;and an extensive analysis of pattern identification and categorization.

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A spiking neural network that extracts frequent symbol patterns from time-series data with different symbol appearance intervals 12

A spiking neural network that extracts frequent symbol patte...

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Joint 12th international conference on soft computing and Intelligent Systems / 23rd international Symposium on Advanced Intelligent Systems (SCIS and ISIS)

作者： Morita, Kenta Morita, Naoki Suzuka Univ Med Sci Fac Med Engn Tsu Mie Japan Tokai Univ Sch Informat Telecommun Engn Tokyo Japan

ISBN: (纸本)9781665499248

In this research, we aim to extract frequent symbol patterns from symbol sequences. In the target time series data, the appearance timing of symbols is different. The purpose is to absorb the deviation of the appearance timing and extract frequent symbol patterns. This paper proposes a spiking neural network that extracts frequent symbol patterns. The structure of the proposed network grows automatically each time a symbol is given. By learning the network, the output unit will only fire when given a frequent symbol pattern, and the network will extract the frequent symbol patterns. As a result of a simple experiment, it was confirmed that the proposed network can extract frequent symbol patterns from the target time series data.

关键词： Spiking neural network Time-series information processing pattern mining

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Design and Implementation of a Wireless Monitoring Application for Pregnant Women's Fetal Movement

Design and Implementation of a Wireless Monitoring Applicati...

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2025 Asia-Europe conference on Cybersecurity, Internet of Things and soft computing, CITSC 2025

作者： Qin, Manping Xu, Yong Chan, Choon Kit Chen, Zhiling Zhu, Caitong School of Artificial Intelligence and Smart Manufacturing Hechi University Hechi China Inti International University Faculty of Engineering and Quantity Surveying Negeri Sembilan Nilai Malaysia

ISBN: (纸本)9798331504205

The fetal movement signal represents the most intuitive indicator of the health of the fetus and the mother. In order to effectively monitor the health status of the fetus and the pregnant woman in the third trimester, an application was designed based on the Android operating system for real-time monitoring of fetal movement in pregnant women. Using Android Studio as the development platform, the SQLite database was utilized to facilitate user registration and login. The low-power Bluetooth API is utilized to establish the connection between the mobile terminal and the wireless fetal movement monitor for pregnant women, enabling the collection of raw fetal movement data. Real-time transmission of data to the smartphone via low-power Bluetooth is achieved, followed by the processing and recognition of fetal movement signals on the app, culminating in the visualization of the recognition results. Through rigorous testing and application, the system facilitates the visualization of fetal health data, addressing family concerns about the health of pregnant women and fetuses, and is applicable in the field of fetal health monitoring. © 2025 IEEE.

关键词： Fetal monitoring

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An Improved Three-dimensional Indoor Positioning Algorithm Based on Pedestrian Dead Reckoning 8

An Improved Three-dimensional Indoor Positioning Algorithm B...

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8th international conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Dong, Xuezhi Xiao, Jiaxun Central South University School of Physics and Electronics Changsha China

ISBN: (纸本)9798350302455

Due to the complex indoor environment of the buildings, Two-dimensional (2D) indoor positioning is difficult to meet the needs of people. Therefore, it is necessary to make some improvements in three-dimensional (3D) indoor positioning. In this paper, a 3D indoor positioning algorithm based on improved pedestrian dead-reckoning (PDR) is proposed. The PDR was improved in motion pattern recognition and step detection. For motion pattern recognition, an improved support vector machine (SVM) method is proposed, which use the confidence threshold to improve the recognition accuracy of the SVM in continuous motion state. For step detection, an improved step detection method is proposed, which sets the peak threshold and valley threshold for acceleration, and the idea of zero-crossing detection method is also used to improve the accuracy of peak detection method. The experimental result of 3D environment shows that the accuracy of motion pattern recognition and step detection is 96.43% and 99.87% respectively and the mean error of the positioning result is 0.63m, which has a reduction of 75.8% compared to the traditional PDR algorithm. © 2023 IEEE.

关键词： pattern recognition

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CHART-Info 2024: A Dataset for Chart Analysis and recognition 27th

CHART-Info 2024: A Dataset for Chart Analysis and Recognit...

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27th international conference on pattern recognition, ICPR 2024

作者： Davila, Kenny Lazarus, Rupak Xu, Fei Rodríguez Alcántara, Nicole Setlur, Srirangaraj Govindaraju, Venu Mondal, Ajoy Jawahar, C.V. School of Computing DePaul University ChicagoIL60604 United States International Institute of Information Technology Hyderabad India CSE University at Buffalo BuffaloNY14260 United States Facultad de Ingenieria Universidad Tecnológica Centroamericana Tegucigalpa Honduras

ISBN: (纸本)9783031784941

Charts are tools for data communication used in a wide range of documents. Recently, the pattern recognition community has shown interest in developing methods for automatically processing charts found in the wild. Following previous efforts on ICPR’s CHART-Infographics competitions, here we propose a newer, larger dataset and benchmark for analyzing and recognizing charts. Inspired by the steps required to make sense of a chart image, the benchmark is divided into 7 different tasks: chart image classification, chart text detection and recognition, text role classification, axis analysis, legend analysis, data extraction, and end-to-end data extraction. We also show the performance of different baselines for the first five tasks. We expect that the increased scale of the proposed dataset will enable the development of better chart recognition systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Charts Dataset Graphic recognition

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

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

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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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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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