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检索条件"任意字段=3rd International Conference on Pattern Recognition and Machine Learning, PRML 2022"

共 265 条记录，以下是41-50 订阅

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2020 3rd international conference on Intelligent Autonomous Systems, ICoIAS 2020

2020 3rd International Conference on Intelligent Autonomous ...

引用

3rd international conference on Intelligent Autonomous Systems, ICoIAS 2020

ISBN: (纸本)9781728160788

The proceedings contain 25 papers. The topics discussed include: complete scene parsing for autonomous navigation in unstructured environments;semi-supervised meta-learning via self-training;value iteration solver networks;research on pulsar classification based on machine learning;bias and raising threshold algorithm using learning agents for the best proportion-searching problem;a study on layers of deep neural networks;motion-blur-free high-frame-rate vision system with frame-by-frame visual-feedback control for a resonant mirror;complete scene parsing for autonomous navigation in unstructured environments;pattern recognition of dynamic social network;and a comparative study on HSV-based and deep learning-based object detection algorithms for pedestrian traffic light signal recognition.

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Digital Advancement in AgriTech by Using Evolutionary Techniques 3rd

Digital Advancement in AgriTech by Using Evolutionary Techni...

引用

3rd international conference on Smart Computing and Informatics (SCI)

作者： Padhy, Neelmadhab Satapathy, Suresh Chandra GIET Univ Sch Engn & Technol CSE Gunupur Odishsa India KIIT Deemed Univ Bhubaneswar India

ISBN: (纸本)9789813296909;9789813296893

The Strategic decision to take machine learning and AI as a tool to enhance agriculture production is to enrich a farmer's valuable time. It has an immense opportunity to make secured crops and healthy environment in due time. In extreme weather condition and drastic change in a situation many predictive and recognition techniques will improve not only the quality of crops but also their ability to producible. In this context, we have dedicated a proposed model which will predict for a real-time environment which crop will be benefitted on producing and for any particular area what should be the environmental condition for healthy productivity. This is done by using a machine learning algorithm in seasonal trend scenario with predictive analysis and applying a pattern recognition technique for discovering cropping pattern and their evaluation.

关键词： machine learning Predictive analysis pattern recognition

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Impact of Feature Selection and Engineering in the Classification of Handwritten Text 3

Impact of Feature Selection and Engineering in the Classific...

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3rd international conference on Computing for Sustainable Global Development (INDIACom)

作者： Kaushik, Anupama Gupta, Himanshu Latwal, Digvijay Singh Maharaja Surajmal Inst Technol Dept IT Delhi India

ISBN: (纸本)9789380544199

Feature selection forms an important aspect of machine learning and character recognition. It is a process of selecting the most important features (attributes) from the dataset. Accurate feature selection results in significant reduction in the number of irrelevant (constant, redundant) attributes thereby, reducing the processing time and increasing the accuracy of the model without any loss of information. This paper focuses on the impact of feature selection and engineering in the classification of handwritten text by identifying and extracting those attributes of the training dataset that will contribute most towards the classification task using classifiers like J48, NaiveBayes and Sequential Minimal Optimization (SMO). This results in improved accuracy of the classifiers as compared to the work reported earlier. Further, a comparative performance evaluation of the classifiers used for OCR and pattern recognition is done. Initial classification performance of all the classifiers listed above was recorded on the raw dataset. Finally, the dataset was transformed after performing relevant feature selection and engineering on its attributes. The same classifiers were again trained on the transformed dataset and their accuracy was recorded. This paper uses the widely used MNIST dataset of handwritten digits for training the classifiers.

关键词： Character recognition J-48 MNIST Dataset NaiveBayes Optical Character recognition (OCR) Principal Components Analysis (PCA) Sequential Minimal Optimization (SMO) WEKA pattern recognition machine learning

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Using test plans for Bayesian Modeling

引用

3rd international conference on machine learning and Data Mining in pattern recognition

作者： Deventer, R Denzler, J Niemann, H Kreis, O Univ Erlangen Nurnberg Chair Pattern Recognit D-91058 Erlangen Germany Univ Erlangen Nurnberg Chair Mfg Technol D-91058 Erlangen Germany

ISBN: (纸本)3540405046

When modeling technical processes, the training data regularly come from test plans, to reduce the number of experiments and to save time and costs. On the other hand, this leads to unobserved combinations of the input variables. In this article it is shown, that these unobserved configurations might lead to un-trainable parameters. Afterwards a possible design criterion is introduced, which avoids this drawback. Our approach is tested to model a welding process. The results show, that hybrid Bayesian networks are able to deal with yet unobserved in- and output data.

关键词： Bayesian network modeling manufacturing process laserbeam welding

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Research on Automatic Workpiece Sorting System Based on machine Vision 3

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

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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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Accurate Fall Detection Using 3-Axis Accelerometer Sensor And MLF Algorithm 3

Accurate Fall Detection Using 3-Axis Accelerometer Sensor An...

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3rd international conference on pattern Analysis and Image Analysis (IPRIA)

作者： Jahanjoo, Anice Tahan, Marjan Naderan Rashti, Mohammad Javad Shahid Chamran Univ Ahvaz Fac Engn Comp Engn Dept Ahvaz Iran

ISBN: (纸本)9781509064540

Nowadays, with the growing population of elderly people, the number of elderly without caregivers at home has also increased. It is clear that an elderly living alone at home is at higher risk of severe damage, due to potential delays in notifying caregivers and providing care at healthcare facilities. This especially becomes critical in case of high-risk incidents such as stroke or heart attack. To address this issue, an increasing number of methods have been proposed that employ various fall detection algorithms for elderly people. In this paper, we propose a new algorithm to detect falls, using a multi-level fuzzy min-max neural network. The proposed algorithm is compared with three other machine-learning algorithms (MLP, KNN, SVM). The main focus of this paper is on the effect of dimensionality reduction with using the Principal Component Analysis (PCA) method inside the proposed algorithm. The evaluations show that the multi-level fuzzy min-max neural network provides a high level of accuracy with a small number of dimensions. This is in contrast to the other algorithms, where accuracy is further lowered after applying dimensionality reduction. The performance evaluation of this algorithm on a public dataset obtained using accelerometer sensor data with using three dimensions indicates an accuracy of 97.29% for the sensitivity metric and 98.70% for the specifity metric.

关键词： fall detection triaxial accelerometer fuzzy minmax neural networks

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A systematic review of techniques, tools and applications of machine learning 3

A systematic review of techniques, tools and applications of...

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3rd international conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Abhishek Dhankar, Abhishek Gupta, Neha Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (纸本)9780738111834

machine learning is a part of Artificial Intelligence. A branch of artificial Intelligence(AI), that offers the capability to the system by learning on their own and work better from experience without human intervention. By using machine learning (ML), software applications become more precise for outcome prediction with no requirement of explicit programmed. machine learning has a fundamental concept i.e. building of algorithms for accepting input data and with the help of statistical analysis they can predict the output. Nowadays, machine learning has gained vast popularity, and its algorithms are used in every field like object detection, pattern recognition, text interpretation, and different research areas. One of the basic goals of machine learning is to teach computers to use the data to solve a particular problem. A fair range of machine-learning applications include e-mail classification training to distinguish spam from non-spam messages, fraud detection, etc. This paper aims at varied machine learning methods, algorithms, and tools required to run the machine learning projects. It also focuses on the advancements that are administered in order so that the current researchers can be benefited out of it. © 2021 IEEE.

关键词： Reinforcement learning

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Automatic Poincare section 3

Automatic Poincare section

引用

3rd international conference on Applied machine learning, ICAML 2021

作者： Cheng, Jie Wang, Ruike Li, Wenshi School of Electronics and Information En Soochow University Suzhou215006 China Faculty of Science Beijing University of Technology Beijing100124 China

ISBN: (纸本)9781665421256

An attractor-lock-in Poincare section is formed through three space points of the minimum, mean and maximum of the attractor under test, with parameter needless manual selection. The key algorithm steps of automatic Poincare section include (a) digging out three geometry feature points as minimum, mean and maximum;(b) through three basic statistical points making a plane needed;(c) cut-off points counting by close contact measure and close range fusion;(d) using the spectral entropy complexity and construction creep rate in chaos spring test as gold standards of control experiments. Test data are provided by classical Chua’s equation and unified chaotic equation and random number instruction in Matlab tool. The results show that the automatic Poincare section and its cut-off point number can finish three classifying recognition on period, chaos or random states of the test three dimensional data. This case study will promote economically the information geometry, and further be applied to a wide range of pattern recognition fields. © 2021 IEEE

关键词： pattern recognition

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Vehicle Theft Detection Using Neural pattern recognition 3

Vehicle Theft Detection Using Neural Pattern Recognition

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3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Jane Rubel Angelina, J. Subhashini, S.J. Kumar Rapuri, Anil Ravva, Vennela Rasala, Raghavendra Ray Adurgam, Sai Dcepak Kalasalingam Academy of Research and Education Dept of Computer Science and Engineering TamilNadu Krishnankoil India SRM Madurai College for Engineering and Technology Dept of Computer Science and Engineering TamilNadu Sivagangai India

ISBN: (纸本)9798350322842

Finding stolen cars is becoming increasingly important in many urban regions. An automated system for scanning license plates can recognize vehicle numbers without the need for human interaction. This work proposes a vehicle theft detection system based on neural pattern recognition, gaussian filter, and equilibrium optimization. The proposed architecture has significant speedup and higher accuracy rates. The proposed number plate recognition method has a maximum accuracy rate of 94% and an average reduction in the processing time of 32%. pattern recognition is the process of detecting regularities and patterns in data using machine learning. These analogies may now be uncovered via statistical analysis, historical data, or machine-generated knowledge. © 2023 IEEE.

关键词： Image segmentation

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2022 3rd international conference on Issues and Challenges in Intelligent Computing Techniques, ICICT 2022

2022 3rd International Conference on Issues and Challenges i...

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3rd international conference on Issues and Challenges in Intelligent Computing Techniques, ICICT 2022

ISBN: (纸本)9781665482684

The proceedings contain 60 papers. The topics discussed include: web accessibility challenges for disabled and generation of alt text for images in websites using artificial intelligence;developing ERP success model in Indian manufacturing sector;lane line detection using machine learning;real-time sign language to text and speech translation and hand gesture recognition using the LSTM model;a survey of cloud architectures: confidentiality, contemporary state, and future challenges;several categories of energy-efficient routing protocols, features, and security necessities in WSN: a review;face mask detection using convolution neural network;influence of time on efficiency of Indian capital markets: proposition of a recommendation engine;diet recommendation using predictive learning approaches;identification of fraud transactions using Lightgbm technique;machine learning in student health - a review;comparative analysis of rainfall prediction using machine learning and deep learning techniques;energy cognizant scheduling in three-layer cloud architecture: a systematic review;and investigation of thermographic images of photovoltaic modules using deep learning models.

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