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

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3rd international conference on Communication, Devices and computing, ICCDC 2021

3rd International Conference on Communication, Devices and C...

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3rd international conference on Communication, Devices and computing, ICCDC 2021

ISBN: (纸本)9789811691539

The proceedings contain 70 papers. The special focus in this conference is on Communication, Devices and computing. The topics include: Accurate Prediction of Petermann I and II Spot Sizes of First Higher Order Mode for Kerr Type Nonlinear Graded Index Fiber by a Simple Method;Analysis of All-Optical XNOR Gate Using Quantum Dot Semiconductor Optical Amplifier (QDSOA);New COVID-19 Normal: An Experimental Prototype of Smart Face Mask Vending Machine—An Indispensable Kiosk;Arduino and ESP32-CAM-Based Automatic Touchless Attendance System;speech Emotion recognition of Tamil Language: An Implementation with Linear and Nonlinear Feature;radiation-Resilient GaN/InxGa1-xN Multi-junction Solar Cells with Varying in Contents;A Simple but Accurate Mathematical Formalism for Prediction of the Far Field pattern of First Higher Order (LP11) Mode in Kerr Type Nonlinear Step Index Optical Fiber;A Priority Based Routing Protocol to Improve Quality of Service (QoS) in Wireless Body Area Network for COVID and Diabetic Patients;accurate Estimation of Dimensionless Vector and Scalar Propagation Constants for First Higher Order Mode of Kerr Type Nonlinear Graded Index Fiber by Simple Mathematical Formalism;Performance Analysis of Berlekamp–Massey-Based KES Block for 3-Byte RS Decoder;a Simple Method for Accurate Prediction of Splice Loss for First Higher-Order Mode of Step-Index Fiber in Presence of Kerr Nonlinearity;issues of Knowledge Management in Deep Web and Its Graph-Based Analysis;a Generic Approach for Interpolation and Image Fusion to Obtain Pan-Sharpening;fastResponse—A Smart Ambulatory System for Monitoring Accident/Disaster Victims Along with Live Tracking Facility;COVID-19 Data Forecasting Using soft computing Technique;Analyze DGS Antenna Structure.

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Fake Currency Detection Using pattern recognition Algorithm

Fake Currency Detection Using Pattern Recognition Algorithm

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Trends in Quantum computing and Emerging Business Technologies (TQCEBT), international conference on

作者： N. S Yoga Ananth S. Suba Lakshmi B. Narmatha G. Sundari Electronics and Communication Engineering P.S.R Engineering College Sivakasi Tamilnadu India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

The creation of numerous counterfeit banknotes in recent years has had a detrimental effect on society and resulted in significant losses. Thus, it is now imperative to establish a technique for identifying fake cash. To combat this problem, the article uses a pattern recognition algorithm to provide a strong solution for fake currency identification. The aim is to create a precise and effective system that can differentiate between real currency and fake notes. In order to examine and distinguish between the complex characteristics and security elements incorporated in real money notes, The method retrieves important visual properties, including watermarks, security threads, holograms, and other security aspects, by using sophisticated image processing algorithms. After that, a machine-learning model for categorization is trained using these features. The suggested solution incorporates a broad dataset of real and fake banknotes. It uses a diverse dataset of authentic and counterfeit notes to train a machine-learning model. The system's accuracy and efficiency are evaluated against real-world counterfeit samples, resulting in a high detection rate and a low false-positive rate. This technology enhances financial system security and integrity. The system's effectiveness, reliability, and adaptability make it a crucial tool in combating counterfeit currency, safeguarding economic stability, and promoting trust in financial transactions

关键词： Visualization Quantum computing Machine learning Watermarking Market research Stability analysis pattern recognition

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Integrating Bi-LSTM and Extended Geological Constraints Method for Automatic Logging Curve Stratification

Integrating Bi-LSTM and Extended Geological Constraints Meth...

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Cloud computing, Big Data Applications and software Engineering (CBASE), international conference on

作者： Xiang Li XiaLin Zhang ZhangLin Li ZhengPing Weng School of Computer Science China University of Geosciences Wuhan China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

Logging curve stratification plays a crucial role in the interpretation of geological data. As the volume of logging data increases, logging curve stratification becomes increasingly challenging and time-consuming. Current automated log stratification methods primarily rely on pattern recognition techniques, which are limited to processing single curves and overlook the complex nonlinear relationships between different logging curves, resulting in suboptimal performance in thin layer stratification. In this study, we propose a deep learning-based approach that simultaneously handles multiple logging curves while incorporating geological constraints to enhance the impact of spatial features on logging curve stratification. Furthermore, we optimize the loss function and introduce a new evaluation metric to better assess the model's performance. Experimental results demonstrate that our proposed method performs better in terms of stratification error and thin layer detection.

关键词： Measurement Deep learning Cloud computing Accuracy Geology Computational modeling Big Data applications Data models pattern recognition software engineering

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Brain-like Combination of Feedforward and Recurrent Network Components Achieves Prototype Extraction and Robust pattern recognition 8th

Brain-like Combination of Feedforward and Recurrent Network ...

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8th Annual international conference on Machine Learning, Optimization and Data science (LOD)

作者： Ravichandran, Naresh Balaji Lansner, Anders Herman, Pawel KTH Royal Inst Technol Sch Elect Engn & Comp Sci Div Computat Sci & Technol Stockholm Sweden Stockholm Univ Dept Math Stockholm Sweden KTH Royal Inst Technol Digital Futures Stockholm Sweden

ISBN: (纸本)9783031258909;9783031258916

Associative memory has been a prominent candidate for the computation performed by the massively recurrent neocortical networks. Attractor networks implementing associative memory have offered mechanistic explanation for many cognitive phenomena. However, attractor memory models are typically trained using orthogonal or random patterns to avoid interference between memories, which makes them unfeasible for naturally occurring complex correlated stimuli like images. We approach this problem by combining a recurrent attractor network with a feedforward network that learns distributed representations using an unsupervised Hebbian-Bayesian learning rule. The resulting network model incorporates many known biological properties: unsupervised learning, Hebbian plasticity, sparse distributed activations, sparse connectivity, columnar and laminar cortical architecture, etc. We evaluate the synergistic effects of the feedforward and recurrent network components in complex pattern recognition tasks on the MNIST handwritten digits dataset. We demonstrate that the recurrent attractor component implements associative memory when trained on the feedforward-driven internal (hidden) representations. The associative memory is also shown to perform prototype extraction from the training data and make the representations robust to severely distorted input. We argue that several aspects of the proposed integration of feedforward and recurrent computations are particularly attractive from a machine learning perspective.

关键词： Attractor Associative memory Unsupervised learning Hebbian learning Recurrent networks Feedforward networks Brain-like computing

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GomokuPro: An Implementation of Enhanced Machine Learning Algorithm Utilizing Convolutional Neural Network in Gomoku Strategy and Predictions Model 7

GomokuPro: An Implementation of Enhanced Machine Learning Al...

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7th international conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Fu, Xiang Nansha College Preparatory Academy 180 Gangqian S Ave Nansha District Guangzhou511458 China

ISBN: (纸本)9781665478571

In this paper, the researcher proposes a machine learning Gomoku model, GomokuPro, which is based on CNN (Convolutional Neural Network) as well as Monte Carlo tree search. The purpose of this research aggregation is to solve the previous problem of artificial intelligence algorithms taking too long to compute in board games. The researchers turned a more multidimensional fractional model into an adaptive model based on a single machine learning strategy previously developed by the researchers and capable of making judgments and predictions based on the following human algorithms. Due to the limitations of the hardware used for testing, the researchers found that the results were not applicable to some specific cases. Although this algorithm significantly reduces the computational effort, it still does not provide robust performance in some cases. The computational effort of convolutional neural networks is also influenced by the type of data. However, most of the time, machine learning models are still able to make master predictions. © 2022 IEEE.

关键词： pattern recognition

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EEG-Based Cognitive State Assessment Using Deep Ensemble Model and Filter Bank Common Spatial pattern 25

EEG-Based Cognitive State Assessment Using Deep Ensemble Mod...

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25th international conference on pattern recognition (ICPR)

作者： Das Chakladar, Debashis Dey, Shubhashis Roy, Partha Pratim Iwamura, Masakazu Indian Inst Technol Roorkee Roorkee Uttar Pradesh India Inst Engn & Management Kolkata Kolkata India Osaka Prefecture Univ Osaka Japan

ISBN: (纸本)9781728188089

Electroencephalography (EEG) is the most used physiological measure to evaluate the cognitive state of a user efficiently. As EEG inherently suffers from a poor spatial resolution, features extracted from each EEG channel may not be efficiently used for the cognitive state assessment. In this paper, the EEG-based cognitive state assessment has been performed during the mental arithmetic experiment, which includes two cognitive states (task and rest) of a user. To obtain the temporal as well as the spatial resolution of the EEG signal, we combined the Filter Bank Common Spatial pattern (FBCSP) method and Long Short-Term Memory (LSTM)-based deep ensemble model for classifying the cognitive state of a user. Subject-wise data distribution has been performed due to the execution of a large volume of data in a low computing environment. In the FBCSP method, the input EEG is decomposed into multiple equal-sized frequency bands, and spatial features of each frequency bands are extracted using the Common Spatial pattern (CSP) algorithm. Next, a feature selection algorithm has been applied to identify the most informative features for classification. The proposed deep ensemble model consists of multiple similar structured LSTM networks that work in parallel. The output of the ensemble model (i.e., the cognitive state of a user) is computed using the average weighted combination of the individual model prediction. This proposed model achieves 87% classification accuracy, and it can also effectively estimate the cognitive state of a user in a low computing environment.

关键词： Electroencephalography

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Algebraic Bayesian Networks: the Exact Generation of the Knowledge pattern Canonical Representation

Algebraic Bayesian Networks: the Exact Generation of the Kno...

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international conference on soft computing and Measurements (SCM)

作者： Artyom A. Vyatkin Maksim V. Abramov St. Petersburg Federal Research Center of the Russian Academy of Sciences St. Petersburg Russia

ISBN: (数字)9798350363708

ISBN: (纸本)9798350363715

Among probabilistic graphical models, the class of algebraic Bayesian networks stands out. The theory of algebraic Bayesian networks is based on the decomposition of knowledge into knowledge patterns represented as sets of statements. Knowledge patterns are formalized, in particular, by means of their representation as a set of quanta with scalar or interval estimates of truth probability. A distinctive aspect of the practical application of algebraic Bayesian networks is that the work of algorithms with interval estimations takes several orders of magnitude longer than with scalar ones. Therefore, when time or computational resources are scarce, it may be relevant to construct a knowledge pattern with scalar estimates that best characterizes the knowledge pattern with interval estimates, i.e., to construct a canonical representation of the knowledge pattern. Previously, an approach to approximate construction of the canonical representation of the knowledge pattern was proposed, but the exact generation for knowledge patterns of small cardinality can give a gain in time, which is the subject of this paper. It was shown that the algorithm of exact generation in the case of knowledge pattern of cardinality 1 works almost instantaneously, in the case of knowledge pattern of cardinality 2–30 times faster, for cardinality 3 — faster by 1.5–2 times. This approach is all the more relevant because it is the knowledge patterns of small cardinality that are supposed to be used in the practical application of algebraic Bayesian networks.

关键词： Knowledge engineering Graphical models Accuracy Computational modeling Estimation Approximation algorithms Probabilistic logic

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Dynamic Updating of Partial Feature Maps for Image-based Real-Time recognition of Online Handwritten Chinese Text 22

Dynamic Updating of Partial Feature Maps for Image-based Rea...

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8th international conference on computing and Artificial Intelligence, ICCAI 2022

作者： Wu, Yuefeng Zhong, Ying Wang, Da-Han Chi, Xueke School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China

ISBN: (纸本)9781450396110

Online handwritten Chinese text recognition (OHCTR) has made certain progress in recent years. Most methods currently used are image-based text recognition which has achieved some success. However, it should be noticed that, it is difficult to smoothly recognize online handwritten texts in real-time, especially for long text lines. For real-time recognition of OHCT, the feature extraction of ever-extending text lines will be time-consuming and make the system un-humanization. Moreover, the feature extraction of the non-written part will be performed repeatedly, which is not suitable and has not been addressed by previous works. In this paper, we propose a method to dynamically update partial feature maps of the newly written part for real-time OHCTR. The basic idea is to update the feature maps according to the newly written stroke, which can avoid repeated convolutional computations and thus improve the recognition speed. The proposed method can handle the case of delayed strokes, and can greatly improve the speed of real-time recognition without any loss of accuracy. Experiments on the CASIA-OLHWDB and the ICDAR-2013 competition database demonstrate that our real-time recognition speed are multiplier increased compared with traditional text line recognition methods. © 2022 Association for computing Machinery. All rights reserved.

关键词： Convolution

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Development an Object Tracking Method Based on Deep Learning

Development an Object Tracking Method Based on Deep Learning

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pattern recognition and Artificial Intelligence (PRAI), international conference on

作者： Ahmad Ismail Sergey Vishnyakov Department of Computing Machines Systems and Networks Moscow Power Engineering Institute Moscow Russia

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

Object tracking based on deep learning is a challenging task. Accurate object detection and tracking requires a neural network that is deeper than a typical network. Therefore, it requires more levels of processing and takes more time. The YOLO model is considered one of the most state-of-the-art neural networks. Its wide application in object tracking depends on the speed of processing and extracting object features. In this article, we develop the YOLO model by changing the final layer from a fully connected layer to a fully convolutional layer, changing the activation function, and adding a batch normalization layer. A comparison of the results with the old YOLO model shows improved accuracy and speed. Additionally, the developed model is less sensitive to noisy environments.

关键词： YOLO Accuracy Target tracking Neural networks Feature extraction pattern recognition Object tracking Noise measurement Artificial intelligence Batch normalization

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Analysis of Distracted Driving Detection Based on Deep Learning Human Posture 23

Analysis of Distracted Driving Detection Based on Deep Learn...

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9th international conference on computing and Artificial Intelligence, ICCAI 2023

作者： Li, Yanjun Ling, Xufeng Yang, Jie Shanghai Normal University Tianhua College AI School No. 1661 North Sheng Xin Road China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University 800 Dongchuan Road 200240 China

ISBN: (纸本)9781450399029

Distracted driving is a phenomenon that drivers’ attention points to activities unrelated to normal driving, which leads to the decline of driving operation ability. With the popularity of smart phones, traffic accidents caused by distracted driving are on the rise. Traffic accidents caused by driver’s inattention due to vision deviation or distraction are particularly obvious in rear end collisions. The results of the distracted driving questionnaire show that almost all drivers have experienced distracted driving. In order to detect whether drivers are distracted when driving, this paper proposes a distracted driving analysis based on deep learning of human posture. This method uses the MMDetection framework in depth learning to build a target detection network to achieve target detection, then uses the depth neural network to estimate the geometric transformation features, and uses the stacked hourglass network to accurately predict the state features of drivers in the graph. The experimental results show that the measurement method has good accuracy, fast speed, good robustness and consistency, more accurate extraction of human posture related features, strong generalization ability and certain practical value. © 2023 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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