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检索条件"任意字段=4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023"

共 108 条记录，以下是91-100 订阅

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Detection of Plant Diseases and Recommendation of Plant Cycle using machine learning 4

Detection of Plant Diseases and Recommendation of Plant Cycl...

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4th international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Divya, B. Alivn Ancy, A. Vivekanandan, G. Swapna, A. Kaniha, P. Indhu Priyadharshini, S. Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India Chennai Institute of Technology Dept of Electronics and Communication Engineering India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

Plant diseases create significant challenges to productivity in agriculture, food security, and ecological stability. Pathogens and environmental variables interact to cause plant diseases. Pathogens include various microorganisms like bacteria, fungi, and viruses, each with unique modes of spread. Environmental conditions such as temperature, humidity, and soil quality impact disease development and severity. the impacts of plant diseases are far-reaching. It affects crop yields and quality. Outbreaks of plant diseases can lead to economic losses, and food shortages which ultimately lead to hunger and poverty. Effective management of plant diseases requires an integrated approach that combines detection, preventive measures, cultural practices, and chemical interventions. the solution has two modules. the first module focuses on detection of plant diseases and its management. the second module focuses on recommendations of the plant cycle. Our proposed system utilizes CNN algorithms trained on a large amount of datasets. Using image analysis the features are extracted and patterns are recognized. therefore, the system can quickly identify the disorders.. the system also includes suggestions for preventive measures. the system integrates modules for recommending optimal plant cycles to the farmers and gardeners. It generates personalized recommendations for planting schedules and crop varieties. this system increases crop yields and decreases the risk of disease outbreaks. Our solution offers a holistic approach to plant health monitoring and management. © 2024 ieee.

关键词： Productivity Plant diseases Pathogens Schedules Soil Feature extraction Stability analysis pattern recognition Crop yield thermal stability

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Spatiotemporal Visual Tracker Based on Deformable Attention

Spatiotemporal Visual Tracker Based on Deformable Attention

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pattern recognition and machine learning (prml), ieee international conference on

作者： Zhuo Wu Askar Hamdulla Yucheng Huang School of Computer Science and Technology(School of Cyberspace Security) Xinjiang University Urumqi China School of Intelligence Science and Technology(School of Future Technology) Xinjiang University Urumqi China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

At present, the visual tracking method based on Transformer has made significant progress, and has shown excellent performance on various data sets. However, the traditional Transformer tracker pays too much attention to global features, and its intensive attention calculation will increase the space-time complexity of the tracking algorithm. And the features of the relevant region will be affected by the features of the non-relevant region, resulting in a decrease in tracking accuracy. In addition, the Transformer tracker lacks the ability to utilize the spatiotemporal information between frames, which further hinders the improvement of its tracking performance. To solve these problems, this paper proposes a new tracker SVT-DA based on deformable attention. the tracker captures local and global important features through CNN and deformable attention. In addition, it uses a cross-attention module nested with self-attention to achieve deep semantic information interaction between template features and search features. In order to verify the effectiveness of the proposed method, we conduct experiments on LaSOT, GOT-10K, OTB100 and VAU123 datasets. the result indicators are 4%~5% higher than the baseline method. the experimental results show that our method improves the tracking performance while reducing the number of model parameters, and is highly competitive with other state-of-the-art methods.

关键词： Visualization Target tracking Semantics Transformers Feature extraction Spatiotemporal phenomena pattern recognition Convolutional neural networks Information exchange Resilience

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Text-to-CQL Based on Large Language Model and Graph pattern Enhancement

Text-to-CQL Based on Large Language Model and Graph Pattern ...

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pattern recognition and machine learning (prml), ieee international conference on

作者： Xinke Zhao Hankiz Yilahun Askar Hamdulla School of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

With the wide application of knowledge graphs in various scenarios and Neo4j graph databases becoming excellent knowledge graph carriers, Cypher (CQL for short) has become the most popular graph database query language. However, when performing graph database retrieval, the complex pattern and syntax make constructing CQL statements a complicated and time-consuming task. therefore, similar to Text-to-SQL, it is necessary and urgent to study an effective method for end-to-end transformation of natural languages into CQL. there have been many advances in Text-to-SQL for traditional relational databases, but these methods cannot be well applied to Text-to-CQL, so there is still a lack of effective work on Text-to-CQL for graph databases. In recent years, as large language models have been widely applied to different tasks with good results, and considering that the fundamental difference between CQL and SQL is the representation of graph patterns. We propose in this paper PA-LLM, a Text-to-CQL method that utilizes large language models combined with graph patterns enhancement, which combines large language models and subdivides the graph patterns into three categories according to their respective characteristics. they are simple query patterns, multi-hop query patterns, and function query patterns. For different graph patterns, the method optimizes the process of generating CQL for the model, which can be subdivided into four sub-methods, simple pattern enhancement method, multi-hop pattern enhancement method, function pattern enhancement method, and entity-relationship enhancement method. the experimental results show that the method improves the quality of the CQL statements generated by the model, including the logical accuracy ACC LX and the execution result accuracy ACC EX , and achieves better results on the SpCQL dataset proposed by the National Defense University of Science and Technology (NDUST). It provides an effective solution for the task of converti

关键词： Structured Query Language Accuracy Quantization (signal) Large language models Natural languages Knowledge graphs Relational databases Syntactics Real-time systems pattern recognition

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Automated Quality Control for Proton Magnetic Resonance Spectroscopy Data Using Convex Non-negative Matrix Factorization 4th

Automated Quality Control for Proton Magnetic Resonance Spec...

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4th international Work-conference on Bioinformatics and Biomedical Engineering (IWBBIO)

作者： Mocioiu, Victor Kyathanahally, Sreenath P. Arus, Carles Vellido, Alfredo Julia-Sape, Margarida Univ Autonoma Barcelona Dept Bioquim & Biol Mol E-08193 Barcelona Spain Univ Bern Dept Radiol & Clin Res Bern Switzerland Univ Politecn Cataluna BarcelonaTech Dept Ciencies Comp Campus Nord ES-08034 Barcelona Spain Ctr Invest Biomed Red Bioingn Biomat & Nanomed CI Barcelona Spain

ISBN: (纸本)9783319317441;9783319317434

Proton Magnetic Resonance Spectroscopy (H-1 MRS) has proven its diagnostic potential in a variety of conditions. However, MRS is not yet widely used in clinical routine because of the lack of experts on its diagnostic interpretation. Although data-based decision support systems exist to aid diagnosis, they often take for granted that the data is of good quality, which is not always the case in a real application context. Systems based on models built with bad quality data are likely to underperform in their decision support tasks. In this study, we propose a system to filter out such bad quality data. It is based on convex Non-Negative Matrix Factorization models, used as a dimensionality reduction procedure, and on the use of several classifiers to discriminate between good and bad quality data.

关键词： Brain tumors Magnetic resonance spectroscopy Convex non-negative matrix factorization pattern recognition Quality control machine learning

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Pre-processing aspects for complexity reduction of the QSAR problem

Pre-processing aspects for complexity reduction of the QSAR ...

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international ieee conference on Intelligent Systems, IS

作者： L. Dumitriu C. Segal M-V. Craciun A. Cocu Compute Science Department Dunarea de Jos University of Galati Galati Romania the Computer Science Department Galati Romania

Predictive Toxicology (PT) is one of the newest targets of the Knowledge Discovery in Databases (KDD) domain. Its goal is to describe the relationships between the chemical structure of chemical compounds and biological and toxicological processes. In real PT problems there is a very important topic to be considered: the huge number of the chemical descriptors. Irrelevant, redundant, noisy and unreliable data have a negative impact, therefore one of the main goals in KDD is to detect these undesirable proprieties and to eliminate or correct them. this assumes data cleaning, noise reduction and feature selection because the performance of the applied machine learning algorithms is strongly related with the quality of the data used. In this paper, we present some of the issues that can be taken into account for preparing data before the actual knowledge discovery is performed.

关键词： Chemical compounds Data mining Toxicology pattern recognition Principal component analysis Databases Noise reduction Neural networks Statistical analysis pattern analysis

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Artificial immune system based machine learning for voltage stability prediction in power system

Artificial immune system based machine learning for voltage ...

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international Power Engineering and Optimization conference (PEOCO)

作者： S. I. Suliman T. K. Abdul Rahman Faculty of Electrical Engineering Universiti Teknologi Mara Malaysia

Voltage instability has recently become a challenging problem for many power system operators. this phenomenon has been reported to be responsible for severe low voltage condition leading to major blackouts. this paper presents the application of Artificial Immune Systems (AIS) for online voltage stability evaluation that could be used as early warning system to the power system operator so that necessary action could be taken in order to avoid the occurrence of voltage collapse. Key features of the proposed method are the implementation of clonal selection principle that has the capability in performing pattern recognition task. the proposed technique was tested on the ieee 30 bus power system and the results shows that fast performance with accurate evaluation for voltage stability condition has been obtained.

关键词： Stability criteria Power system stability Artificial neural networks Indexes pattern recognition

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Research and Application of Fault Diagnosis and Localization Techniques for Power Communication Optical Cables Based on Multimodal Data Analysis

Research and Application of Fault Diagnosis and Localization...

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Data Science and Computer Application (ICDSCA), ieee international conference on

作者： Xuguang Zhang Bo Zhang Bo Guo Hang Ren Wanxin Wang Chao Ma State Grid Jilin Electric Power Co. Ltd. Tonghua Power Supply Company Jilin China

ISBN: (数字)9798350368239

ISBN: (纸本)9798350368246

Aiming at the problems of low accuracy of power cable fault pattern recognition and large error of fault location, a method of power cable fault diagnosis and location based on Beluga Whale Optimization-Extreme learning machine (BWO-ELM) algorithm and GIS system is proposed. First, the beluga whale optimization (BWO) algorithm is used to optimize the initial parameters of the extreme learning machine (ELM), and the BWO-ELM multi classification Optical Time Domain Reflectometry (OTDR) curve analysis fault pattern recognition method is constructed, obtain the straight-line distance and type of the optical fiber fault point to lay the foundation for fault location. Secondly, the accurate positioning method of optical cable fault based on GIS is proposed, which converts the straight-line distance of the optical fiber fault point into the optical cable distance, and matches it with the actual geographical location to obtain the coordinates of the actual fault point, which are intuitively displayed in the GIS system. Finally, the application effect of the proposed method is verified by simulation experiments. the results show that the proposed method achieves a fault pattern recognition accuracy of about 98.66%, the fault location error is floating up and down 3M, and the average error is 1.481%. It has higher performance and accuracy than other recognition models and fault location methods.

关键词： Optical fibers Integrated optics Accuracy Power cables Optical fiber cables Fault location Adaptive optics pattern recognition Optical reflection Geographic information systems

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A real-time hand tracking and multimedia-based system for breast self-examination: the simplified 3D hand model

A real-time hand tracking and multimedia-based system for br...

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ieee EMBS international conference on Information Technology Applications in Biomedicine (ITAB)

作者： Yuqin Hu R.N.G. Naguib A.G. Todman S. Amin A. Oikonomou H. Alomishy BIOCORE School of Mathematical and Information Sciences Coventry University Coventry UK Walsgrave General Hospital Coventry UK

Breast self-examination (BSE) is a non-invasive, self-administered and simple screening procedure for detecting breast cancer at an early stage. this procedure can be performed in private and at any time. A variety of leaflets and websites exist, which attempt to train women on how to perform BSE. there are also some learning systems consisting of videos and audio cassettes. However, there are no fully interactive systems in existence and no real-time feedback is given to a user on whether she is correctly performing the procedure. We aim to develop an intelligent interactive multimedia system incorporating pattern recognition and machine vision techniques, and provide real-time feedback to assist and guide women to perform BSE accurately. Using her hand in a specific configuration to conduct palpation of the breasts is the basic means available for a woman to perform BSE. However, a human hand is highly articulated and deformable with 27 degree-of-freedom parameters according to its anatomy. Recognising hand gestures and postures is a challenging task that has been studied in many areas and applications. In this paper, the simplified three-dimensional (3D) hand model is presented, which has only 8 degree-of-freedom parameters and is especially adapted for use with the breast self-examination system. this model will be a potentially effective simulation and tracking tool that will contribute to BSE learning and thus to the development of an intelligent fully interactive BSE system.

关键词： Real time systems learning systems Feedback Cancer detection Breast cancer Videos Interactive systems Intelligent systems Multimedia systems pattern recognition

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Comparative Analysis of machine learning Techniques for Concrete Compressive Strength Prediction

Comparative Analysis of Machine Learning Techniques for Conc...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), international conference on

作者： Fuad Musleh Ranyah Taha Abdel Rahman Musleh Civil Engineering Department University of Bahrain Isa Town Kingdom of Bahrain Computer Department Al Iman School Isa Town Kingdom of Bahrain Electrical Engineering Department University of Bahrain Isa Town Kingdom of Bahrain

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

the compressive strength of concrete is of utmost importance in ensuring the structural stability and integrity of construction projects. With increasing importance placed on this aspect, there is a demand for intelligent and efficient techniques to accurately predict and evaluate compressive strength. machine learning methods (ML) present a promising approach to developing accurate models for assessing the compressive strength of concrete. By leveraging the power of data analysis and pattern recognition, these techniques enable the construction industry to obtain reliable and robust concrete for a wide range of applications. ML models can identify the key factors influencing compressive strength, allowing for more informed decision-making, and ensuring the construction of structurally sound and durable projects. this article explores the assessment and comparison of four machine learning (ML) methods, namely Gradient Boosting, Categorical Gradient Boosting CatBoost, Random Forest (RF), and extreme gradient boosting (XGBoost) regressors, for predicting concrete compressive strength. Among these techniques, the CatBoost Regressor demonstrated superior performance in terms of accuracy, R-squared coefficient of determination, and Root Mean Square Error RMSE, surpassing the other three methods.

关键词： Radio frequency Data analysis Accuracy Boosting Concrete Structural engineering pattern recognition

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COST-SENSITIVE SPARSE REPRESENTATION BASED CLASSIFICATION

COST-SENSITIVE SPARSE REPRESENTATION BASED CLASSIFICATION

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2016 4th ieee international conference on Cloud Computing and Intelligence Systems（ieee CCIS2016）

作者： Zhenbing Liu Chunyang Gao Huihua Yang Qijia He School of Computer and Information Security Guilin University of Electronic Technology School of Electronic Engineering and Automation Guilin University of Electronic Technology School of Automation Beijing University of Posts and Telecommunications

ISBN: (纸本)9781509012572

Sparse representation has been successfully used in pattern recognition and machine learning. However, most existing sparse representation based classification(SRC) methods are to achieve the highest classification accuracy, assuming the same losses for different misclassifications. this assumption, however, may not hold in many practical applications as different types of misclassification could lead to different losses. To address this problem, we propose a novel cost-sensitive sparse representation based classification(CSSRC) method by using probabilistic modeling. Unlike traditional methods, we predict the class label of test samples by minimizing the misclassification losses, which are obtained via computing the posterior probabilities. Experimental results on the UCI databases validate the efficacy of the proposed approach on average misclassification cost, positive class misclassification rate and negative class misclassification rate. In addition, the experiments show that our proposed method performs competitively compared to SRC, CSSVM and CS4 VM.

关键词： Sparse representation based classification(SRC) Cost-sensitive Probabilistic modeling Loss function

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