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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3244 条记录，以下是331-340 订阅

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Enhancing Forest Fire Detection and Monitoring Through Satellite Image recognition: A Comparative Analysis of Classification Algorithms Using Sentinel-2 data 3rd

Enhancing Forest Fire Detection and Monitoring Through Satel...

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3rd international conference on Optimization, learning Algorithms and Applications (OL2A)

作者： Brito, Thadeu Pereira, Ana I. Costa, Paulo Lima, Jose Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Campus Santa Apolonia P-5300253 Braganca Portugal Inst Politecn Braganca Lab Sustentabilidade & Tecnol Reg Montanha SusTEC Campus Santa Apolonia P-5300253 Braganca Portugal INESC TEC INESC Technol & Sci Porto Portugal Univ Porto Fac Engn Porto Portugal

ISBN: (纸本)9783031530357;9783031530364

Worldwide, forests have been harassed by fire in recent years. Either by human intervention or other reasons, the history of the burned area is increasing considerably, harming fauna and flora. It is essential to detect an early ignition for fire-fighting authorities can act quickly, decreasing the impact of forest damage impacts. The proposed system aims to improve nature monitoring and improve the existing surveillance systems through satellite image recognition. The soil recognition via satellite images can determine the sensor modules' best position and provide crucial input information for artificial intelligence-based systems. For this, satellite images from the Sentinel-2 program are used to generate forest density maps as updated as possible. Four classification algorithms make the Tree Cover Density (TCD) map, consisting of the Gaussian Mixture Model (GMM), Random Forest (RF), Support Vector machine (SVM), and K-Nearest Neighbors (K-NN), which identify zones by training known regions. The results demonstrate a comparison between the algorithms through their performance in recognizing the forest, grass, pavement, and water areas by Sentinel-2 images.

关键词： machine learning Classification algorithm Satellite Imagery Wildfires Tree Cover Density

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machine learning Approaches for Early Detection of Breast Cancer 3

Machine Learning Approaches for Early Detection of Breast Ca...

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Priya, B.Pandi Ramani, R. Psr Engineering College Tamilnadu Sivakasi India

ISBN: (纸本)9798350361186

A crucial concern in biomedical research revolves around forecasting genome disorders, which contribute to diverse and severe ailments such as cancer, dementia, diabetes, cystic fibrosis, and leigh syndrome, resulting in significant global mortality rates. Historically, theoretical and explanatory methods were employed to anticipate genome disorders. Progressions in innovation prompted upgraded hereditary information inclusion, traversing almost the whole genome and protein, provoking the presentation of machine and profound learning approaches for genome jumble expectation. Simultaneously, parallel machine and deep learning strategies emerged as part of this technological evolution. Past exploration broadly investigated genome jumble forecast utilizing directed, solo, and semi-regulated learning procedures, with a dominating spotlight on twofold issue expectation utilizing hereditary grouping information. machine learning has become a prevalent tool in medical applications, particularly in identifying cancer cell types. Breast cancer, a prominent cause of female mortality, can be mitigated through early detection of cancerous cells. Diagnostic tests such as MRI, mammogram, ultrasound, and biopsy are commonly employed for this purpose. In the domain of AI and information mining, analysts mean to recognize the best strategy for supporting enormous data sets with high expectation exactness. The essential goal of our methodology is to distinguish and anticipate bosom malignant growth in view of side effects inside the information dataset. Our system utilizes a combination of machine learning algorithms to achieve accurate breast cancer predictions. © 2024 IEEE.

关键词： Diseases

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Research on vehicle speech recognition algorithm based on multidimensional data 3

Research on vehicle speech recognition algorithm based on mu...

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3rd international conference on Computer Vision and pattern Analysis, ICCPA 2023

作者： Wang, Zengxi Pan, Xia Zong, Yan CATARC Intelligent and connected technology Co. LTd China China Automotive Technology & Research Center Co. Ltd China

ISBN: (纸本)9781510667563

With the progress of intelligent computers, the auto speech recognition function has developed rapidly. However, at present, the automatic recognition efficiency of on-board speech interaction is low and the recognition rate is poor. Even some vehicles do not display speech characters, which makes it impossible to verify whether the speech recognition is accurate. This paper proposes a multi-dimensional data method based on voice, picture data, text data, ADB data and internal changes of vehicle and machine, and improves the accuracy of vehicle borne voice recognition verification through multi-mode information fusion technology. Then, based on the parameters of speech wake-up rate recognition, false wake-up rate recognition, average wake-up time recognition, speech command response time recognition and speech command function recognition, a speech performance evaluation index model is established. Finally, the experimental results show that the verification method of multi-dimensional data speech recognition can verify the speech recognition rate and the execution rate of the vehicle in an all-round way. © 2023 SPIE.

关键词： Speech recognition

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Image Classification using Deep learning: A Comparative Study of VGG-16, InceptionV3 and EfficientNet B7 Models 3

Image Classification using Deep Learning: A Comparative Stud...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Aggarwal, Shivam Sahoo, Ashok Kumar Bansal, Chetan Sarangi, Pradeepta Kumar Chitkara University Institute of Engineering & Technology Chitkara University Punjab India Graphic Era Hill University Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

Image classification is the process of identification and classification of an input image or visual from a predetermined set of labeled images. This work comes under computer vision and machine learning. This work is dedicated towards image classification using deep learning by comparing various classification models based on their architecture performance and accuracy. Convolutional Neural Network (CNN) is a type of Artificial Neural Network (ANN) that learns the structural representation of an image and make predictions about its contents. The main layers of the model, known as the Convolutional layers. It detects and analyzes specific patterns in the image using filters. In this research development, image classification is done on several images of butterflies and spiders. The data set has been acquired from the ImageNet dataset. The technological progress and innovation include the observations of different CNN models to find out which one gives the most accurate outputs, i.e., identifying the type of insect in the input image. The focus has been on image recognition, data set size, classification techniques, and recognition accuracy. Three machine learning models such as VGG-16, InceptionV3 and EfficientNetB7 have been implemented in this work and the accuracies observed from the models are 97.67 %, 97.2 % and 99 % respectively. From the results it can be seen that EfficientNetB7 was found to be the best among the three models. © 2023 IEEE.

关键词： Contrastive learning

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Detection of Early Fault in Power Electronic Converters through machine learning and data mining Techniques 3

Detection of Early Fault in Power Electronic Converters thro...

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3rd international conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Pradeep, K.V. Gowrishankar, K. Sivanantham, E. Rao, Katta Subba Kiran, Nagulapati Vimal, A. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India Amet University Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Medak India Anil Neerukonda Institute of Technology and Sciences Department of Electrical and Electronics Engineering Andhra Pradesh Visakhapatnam India Sri Eshwar College of Engineering Department of Mechanical Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9781665462167

The Power electronic system plays a significant role in versatile applications. The power electronic converters are largely used in energy conversion mechanisms. A fault is defined as the abnormal condition of the system that results in various consequences. The important constraints in the modelling of power electronic systems involve losses, Electromagnetic Interference (EMI) and harmonics. This includes the fault detection in the power electronic converters that includes three phase rectifier, d-dc converter and single-phase inverter. These parameter affects the overall efficiency and quality of the system. To overcome the fault in the power electronic converters, the machine learning with data mining techniques is adopted. This helps to predict the early fault and helps to increase the efficiency of the system. © 2023 IEEE.

关键词： Signal interference

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Detection of Heart Disease Using machine learning 1

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3rd international conference on Artificial-Business Analytics, Quantum and machine learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Raheja, Supriya Ray, Navya Amity University Noida India

ISBN: (数字)9789819725083

ISBN: (纸本)9789819725076

Heart disease is one of the key causes of death worldwide, and it is important to detect in its early stage which helps in halting its progression. Because of its superiority in pattern identification and categorization, machine learning (ML) supports decision making and risk assessment from the massive amount of data produced by the healthcare industry on cardiac disorders. This research paper examines the ML techniques which are being used to detect heart diseases. This study made use of a dataset from a cardiovascular study of residents of Framingham, Massachusetts. The classification goal is to predict if the patient will develop coronary heart disease in the next ten years. The goal of this paper is to present the comparison among six different machine learning models namely Logistic Regression, Support Vector machine, Decision Tree, AdaBoost, KNN and Random Forest to predict the 10-year risk of coronary heart disease. The paper presents the performance comparison evaluation on the basis of accuracy, precision, recall and F1-Score. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Logistic regression

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Remote data Security Monitoring Technology for Computer Networks Based on machine learning Algorithms 3

Remote Data Security Monitoring Technology for Computer Netw...

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3rd international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Wang, Fengzheng Shandong Transport Vocational College Shandong Weifang China

In the field of computer network communication, there are various types of data, and these data have strong commercial value. Therefore, it is particularly important to apply them to relevant security monitoring. This article analyzes and studies the problems in remote detection of computer networks using machine learning algorithms. Firstly, three key technologies based on classifiers, feature extraction, and association rule recognition were introduced. Secondly, how to use machine learning methods to evaluate the performance indicators and operational effectiveness data statistics of computer network security monitoring systems was explained. Finally, this article tests the monitoring technology of the system, and the test results show that the security of the technology for data monitoring can reach over 94%. © 2023 The Authors. Published by Elsevier B.V.

关键词： Remote control

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Correlation Analysis Between Wind-Induced Vibration Disasters and Influencing Factors of Transmission Lines Based on machine learning Technology 3

Correlation Analysis Between Wind-Induced Vibration Disaster...

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3rd international conference on Advanced Manufacturing Technology and Electronic Information, AMTEI 2023

作者： Han, Jingshan Su, Hongzhi Zhang, Lei Wang, Yi Liu, Guiqing Xu, Zhongyang China Electric Power Research Institute Beijing China North China Branch of State Grid Corporation of China Beijing China

ISBN: (纸本)9781510674806

With the continuous expansion of power grid scale and the improvement of power grid informatization level, China has accumulated a certain amount of wire wind vibration data in years of transmission line operation, including structured and unstructured data such as text, statistical charts, images, videos, etc, providing strong data support for data-driven wire wind vibration disaster mechanism mining. Therefore, it is necessary to integrate multi-disciplinary knowledge such as computer, mathematics, information processing, mechanics, and biology based on data mining and digital machine learning techniques, effectively mine and deeply analyze the historical records of wind induced vibration disasters on transmission lines, meteorological and geographical environmental parameter information, multi-source and wide-area wind induced vibration monitoring information, and line structural parameters, in order to clarify the main influencing factors and disaster mechanisms of wind induced vibration disasters on transmission lines, Improve the prevention and control technology of wind induced vibration disasters on transmission lines, enhance the identification level of wind induced vibration disasters in power grids, and provide decision-making support for the safe operation of power grids. © 2024 SPIE.

关键词： Electric lines

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3rd international conference on data Science, machine learning and Applications, ICDSMLA 2021

3rd International Conference on Data Science, Machine Learni...

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3rd international conference on data Science, machine learning and Applications, ICDSMLA 2021

ISBN: (纸本)9789811959356

The proceedings contain 85 papers. The special focus in this conference is on data Science, machine learning and Applications. The topics include: Design of QCA-Based XOR/XNOR Structures;Design of QCA-Based 1-Bit Magnitude Comparator;a Novel Multimodal Anatomical Medical Image Fusion Using Structure Extraction;Parametric Analysis for Channel Estimation in Massive MIMO Systems with 1-Bits ADCs;skin Cancer Classification Using Deep learning;image Dehazing Using Improved Dark Channel and Vanherk Model;A Novel Bayesian Fusion Model for IR and Visible Images;Retinal Boundary Segmentation in OCT Images Using Active Contour Model;Spectral Efficiency for Multi-bit and Blind Medium Estimation of DCO-OFDM Used Vehicular Visible Light Communication;An Efficient Retinal Layer Segmentation Based on Deep learning Regression Technique for Early Diagnosis of Retinal Diseases in OCT and FUNDUS Images;Design of QCA-Based BCD Adder;crop Yield Prediction Using Deep learning;preface;road Accident Detection and Indication System;real-Time Tweets Streaming and Comparison Using Naïve Bayes Classifier;smart Shopping Trolley for Billing System;a Survey on IoT Protocol in Real-Time Applications and Its Architectures;safe Characteristic Signature Systems with Different Jurisdiction Using Blockchain in E-Health Records;web-Based Trash Segregation Using Deep learning Algorithm;home Automation Using Face recognition for Wireless Security;Hybrid-Network Intrusion Detection (H-NID) Model Using machine learning Techniques (MLTs);impact of Using Partial Gait Energy Images for Human recognition by Gait Analysis;Several Routing Protocols, Features and Limitations for Wireless Mesh Network (WMN): A Review;a Deep Meta-model for Environmental Sound recognition;design of Progressive Monitoring Overhead Water Tank.

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DBGAN: A data Balancing Generative Adversarial Network for Mobility pattern recognition 25th

DBGAN: A Data Balancing Generative Adversarial Network for M...

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25th international conference on Big data Analytics and Knowledge Discovery (DaWaK)

作者： Zhang, Ke Liu, Hengchang Clarke, Siobhan Trinity Coll Dublin Dublin D02 R123 Ireland Univ Elect Sci & Technol China Chengdu 611731 Sichuan Peoples R China

ISBN: (纸本)9783031398308;9783031398315

Mobility pattern recognition is a central aspect of transportation and data mining research. Despite the development of various machine learning techniques for this problem, most existing methods face challenges such as reliance on handcrafted features (e.g., user has to specify a feature such as "travel time") or issues with data imbalance (e.g., fewer older travelers than commuters). In this paper, we introduce a novel data Balancing Generative Adversarial Network (DBGAN), which is a specifically designed attention mechanism-based GAN model to address these challenges in mobility pattern recognition. DBGAN captures both static (e.g., travel locations) and dynamic (e.g., travel times) features of different passenger groups, and avoids using handcrafted features that may result in information loss, based on a sequence-to-image embedding method. Our model is then applied to overcome the data imbalance issue and perform mobility pattern recognition. We evaluate the proposed method on real-world public transportation smart card data from Suzhou, China, and focus on recognizing two different passenger groups: older people and students. The results of our experiments demonstrate that DBGAN is able to accurately identify the different passenger groups in the data, with the detected mobility patterns being consistent with the ground truth. These results highlight the effectiveness of DBGAN in overcoming data imbalance in mobility pattern recognition, and demonstrate its potential for wider use in transportation and data mining applications.

关键词： Mobility pattern recognition data imbalance Kernel embedding Sequence to image Generative adversarial networks Self-attention

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