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2nd international conference on intelligent systems and pattern recognition, ispr 2022

ISBN: (纸本)9783031082764

The proceedings contain 32 papers. The special focus in this conference is on intelligent systems and pattern recognition. The topics include: Deep Learning-Based Segmentation of Connected Components in Arabic Handwritten Documents;classifying the Human Activities of Sensor Data Using Deep Neural Network;preface;3D Dense & Scaled Reconstruction Pipeline with Smartphone Acquisition;exploratory Analysis of Driver and Vehicle Factors Associated with Traffic Accidents in Morocco;building a Multilingual Corpus of Tweets Relating to Algerian Higher Education;recursive Feature Elimination Technique for Technical Indicators Selection;document-Based Knowledge Discovery with Microservices Architecture;feature Selection for Credit Risk Classification;parameter Identification and Validation of Multi-innovation Least Squares Lithium Battery for Second-Order Battery Model;bat Echolocation Call Detection and Species recognition by Transformers with Self-attention;Scheduling Techniques for Liver Segmentation: ReduceLRonPlateau vs OneCycleLR;an Explainable Predictive Model for the Geolocation of English Tweets;removing Redundancies in Binary Images;a Genetic Model for Medical Images Reproduction;neural Machine Translation of Low Resource Languages: Application to Transcriptions of Tunisian Dialect;SPIRAL: SPellIng eRror Parallel Corpus for Arabic Language;VMs Migration Mechanism for Underloaded Machines in Green Cloud Computing;TunBERT: Pretrained Contextualized Text Representation for Tunisian Dialect;Road recognition for Autonomous Vehicles Based on intelligent Tire and SE-CNN;malicious Packet Classification Based on Neural Network Using Kitsune Features;an Hybrid Deep Learning Approach for Prediction and Binary Classification of Student’s Stress;a Novel Deep Convolutional Neural Network Architecture for Customer Counting in the Retail Environment;transfer Learning for the Classification of Small-Cell and Non-small-Cell Lung Cancer;attentional Conditional Generative Adversarial Ne

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Computer System of Evaluation of the Mass Exam Results Based on recognition of Handprinted Azerbaijani Characters 2nd

Computer System of Evaluation of the Mass Exam Results Based...

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2nd international conference on Information Technologies and Their Applications

作者： Mustafayev, Elshan Azimov, Rustam Inst Control Syst Lab Recognit Identificat & Methods Optimal Solut Baku Azerbaijan

ISBN: (纸本)9783031734199;9783031734205

The utilization of pattern recognition is on the rise extensively in information systems. The convergence of progress in image processing and the accessibility of open-source libraries enables the implementation of innovative solutions for diverse practical problems. One notable challenge pertains to automatically processing responses in mass large-scale exams. This paper introduces a developed system tailored for recognizing such exam results, showcasing its capacity to deliver dependable, effective, and impartial assessments. This system can be configured on almost any type of form. Its use also allows you to abandon the expensive and difficult to use OMR scanners. To increase productivity of system we propose to use the multicore/multithreading property of modern processors to parallelize processes within a single workstation. As a result of experiments, itwas found that the transition to multi-threaded recognition can increase productivity up to 3.5 times in comparison with single-threaded. To reduce the physical size of exam cards, it is proposed to fill in the answers with handwritten symbols instead of filling in the circles. Multilayer and convolutional neural networks were used as a recognition module. A comparative evaluation of the dependence of recognition results on the architecture of neural networks and the feature extraction algorithm was carried out.

关键词： Evaluation of Exam Results intelligent System Information Processing Multithreading recognition Image Processing OCR

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Deep Reinforcement Active Learning for Stress recognition 2nd

Deep Reinforcement Active Learning for Stress Recognition

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2nd international conference on intelligent systems and Data Science, ISDS 2024

作者： Ngoc, Phan Anh Nguyen, Ky Trung Tran, Thanh-Tung Jayatilake, Senerath Nguyen, Thi Thanh Quynh School of Computer Science and Engineering International University Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam School of Computing and Creative Technologies University of the West of England Bristol United Kingdom The University of Danang - University of Science and Technology Danang Viet Nam

ISBN: (纸本)9789819796151

Stress has become a significantly common mental condition in the current society. It is important for people to be aware of this condition effectively when it occurs. Due to the current improvements in sensor technology, have significantly improved the accuracy and efficiency of collecting data related to human physiological biomarkers. This study focuses on employing Deep learning techniques to accurately determine mental stress states of mental stress based on the WESAD public dataset, a collection of physiological data to detect stress and affect the state. The study has demonstrated the effectiveness of using Long Short-Term Memory (LSTM) and proposed a novel framework namely, Deep Reinforcement Active Learning to enhance the accuracy of LSTM for stress classification. The proposed framework has achieved 93% accuracy, which resulted 4.91% improvement from the original study. This improvement in accuracy was achieved only by employing respiration data, which clearly demonstrates the potential of the proposed framework. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Active learning

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Removing Redundancies in Binary Images 2nd

Removing Redundancies in Binary Images

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2nd international conference on intelligent systems and pattern recognition, ispr 2022

作者： Banaeyan, Majid Batavia, Darshan Kropatsch, Walter G. Pattern Recognition and Image Processing Group 193/03 TU Wien Vienna Austria

ISBN: (纸本)9783031082764

Every day a huge amount of digital data is generated. Processing such big data encourages efficient data structure and parallelized operations. In this regard, this paper proposes a graph-based method reducing the memory requirement of the data storage. Graphs as a versatile representative tool in intelligent systems and pattern recognition may consist of many nonessential edges accumulating memory. This paper defines the structure of such redundant edges in the neighborhood graph of a 2D binary image. We introduce a novel approach for contracting the edges that simultaneously assists in determining the structurally redundant edges. In addition, finding a set of independent edges, the redundant edges are removed in parallel with the complexity O(1 ). Theoretically, we prove that the maximum number of redundant edges is bounded by half of all edges. Practical results show the memory requirement decreases significantly depending on the input data in different categories of binary image data sets. Using the combinatorial map as the data structure, first the topological structure of the graph is preserved. Second, the method can be extended to higher dimensions (nd). © 2022, Springer Nature Switzerland AG.

关键词： Binary images

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Design of A Video-Based Vehicle Speed Measurement System - An Uncertainty Approach 7

Design of A Video-Based Vehicle Speed Measurement System - A...

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Joint 7th international conference on Informatics, Electronics & Vision (ICIEV) / 2nd international conference on Imaging, Vision and pattern recognition (icIVPR)

作者： Javadi, Saleh Dahl, Mattias Pettersson, Mats, I Kulesza, Wlodek J. Blekinge Inst Technol Dept Math & Nat Sci S-37435 Karlshamn Sweden Blekinge Inst Technol Dept Appl Signal Proc S-37179 Karlskrona Sweden

ISBN: (纸本)9781538651636

Speed measurement is one of the key components of intelligent transportation systems. It provides suitable information for traffic management and law enforcement. This paper presents a versatile and analytical model for a video-based speed measurement in form of the probability density function (PDF). In the proposed model, the main factors contributing to the uncertainties of the measurement are considered. Furthermore, a guideline is introduced in order to design a video-based speed measurement system based on the traffic and other requirements. As a proof of concept, the model has been simulated and tested for various speeds. An evaluation validates the strength of the model for accurate speed measurement under realistic circumstances.

关键词： intelligent transportation systems machine vision motion analysis pattern recognition speed measurement

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The method of fuzzy pattern recognition and its application to physical training

The method of fuzzy pattern recognition and its application ...

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2010 2nd international conference on Communication systems, Networks and Applications, ICCSNA 2010

作者： Fang, Li-Hua Hubei Urban Construction Vocational and Technical College Wuhan 430205 China

This thesis adopts the theories of computer programmes and fuzzy pattern recognition, establishes the selecting pattern of swimming athletes according to the principle of choice in recent, and applies the pattern reco... 详细信息

ISBN: (纸本)9781424474769

关键词： pattern recognition

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A color pattern recognition problem based on the multiple classes random neural network model

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NEUROCOMPUTING 2004年第10期61卷 71-83页

作者： Aguilar, J Univ Los Andes Fac Ingn Dept Computac CEMISID Merida Venezuela

Gelenbe has modeled the neural network using an analogy with the queuing theory. Recently, Fourneau and Gelenbe have proposed an extension of this model, called multiple classes random neural network (RNN) model. The purpose of this paper is to describe the use of the multiple classes RNN model to recognize patterns having different colors. We propose a learning algorithm for the recognition of color patterns based upon the non-linear equations of the multiple classes RNN model using gradient descent of a quadratic error function. In addition, we propose a progressive retrieval process with adaptive threshold value. (C) 2004 Elsevier B.V. All rights reserved.

关键词： multiple classes random neural network color pattern recognition learning algorithm image processing retrieval process

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A Review on Methods and Classifiers in Lip Reading 2

A Review on Methods and Classifiers in Lip Reading

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2nd IEEE international conference on Automatic Control and intelligent systems (I2CACIS)

作者： Aran, Leticia Ria Wong, Farrah Yi, Lim Pei Univ Malaysia Sabah Fac Engn Kota Kinabalu Sabah Malaysia

ISBN: (纸本)9781538608463

The idea of lip reading as a visual technique which people may use to translate lip movement into phrases without relying on speech itself is fascinating. There are numerous application areas in which lip reading could provide full assistance. Although there may be a downside to using the lip reading system, whether it may range from problems such as time constraint to minor word recognition mistakes, further development of the system should be an active cycle. Ongoing research into improvement of the lip reading system performance by way of suitable choices of feature extraction and classifiers is essential to track the developing trend in both fields of technology as well as in pattern recognition. This paper discusses a short review on the existing methods and classifiers which have been used in previous work in the field of lip reading.

关键词： lip reading image processing pattern recognition lip detection feature extraction

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A NEW METHOD FOR CLASSIFICATION OF WINES BASED ON PROTON And C-13 NMR-SPECTROSCOPY IN COMBINATION WITH pattern-recognition TECHNIQUES

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CHEMOMETRICS And intelligent LABORATORY systems 1993年第2-3期21卷 249-258页

作者： VOGELS, JTWE TAS, AC VAndENBERG, F VAndERGREEF, J TNO Nutrition and Food Research P.O. Box 360 3700 AJ Zeist Netherlands

Proton and carbon-13 nuclear magnetic resonance (NMR) spectroscopic methods were applied in the differentiation of 53 German white wines from the regions Rheinhessen, Rheingau and Mosel-Saar-Ruwer. The robustness of these differentiations was tested by leaving out one sample at a time and replacing it into the set as a test sample. Small variations in the position of the lines in the spectra, which lowered the classification ability of the tested systems, were successfully eliminated using a novel data pre-processing algorithm called partial linear fit.

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VMs Migration Mechanism for Underloaded Machines in Green Cloud Computing 2nd

VMs Migration Mechanism for Underloaded Machines in Green Cl...

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2nd international conference on intelligent systems and pattern recognition, ispr 2022

作者： Bouchareb, Nassima Zarour, Nacer Eddine LIRE Laboratory Department of Software Technologies and Information Systems Faculty of New Information and Communication Technologies University of Constantine 2 - Abdelhamid Mehri Constantine Algeria

ISBN: (纸本)9783031082764

High cost of Cloud data centers’ energy consumption have inspired numerous researches to provide more efficient virtual machine (VM) management approaches. Migration of VM is one of the important VM management solutions. However, inefficient VMs migration can also increase energy consumption. Thus, it must be handled very cautiously and at least as possible. In this work, a VMs migration mechanism is proposed, particularly the VMs migration of the underloaded machines. The suggested mechanism is founded on Minimum and Maximum thresholds to not have underloaded and overloaded machines, and to have stable hosts. The objective is to reduce the energy use. Finally, we present experimentation results on the simulator: CloudSim, to show the advantages of the adopted solution. © 2022, Springer Nature Switzerland AG.

关键词： Green computing

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