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检索条件"任意字段=4th International Conference on Machine Learning and Data Minining in Pattern Recognition"

共 1776 条记录，以下是11-20 订阅

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AI-Based Identification of Wear and Corrosion on Steel Surfaces 24

AI-Based Identification of Wear and Corrosion on Steel Surfa...

引用

4th international conference on Internet of things and machine learning

作者： Zhu, Yuze Nanjing Univ Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9798400710353

this study utilizes deep learning technology to automatically identify wear and corrosion on steel surfaces. A total of 1,770 high-resolution images of steel surfaces were obtained, and after preprocessing and data augmentation, 1239 images were selected as the training set and 531 images as the test set. Five models, including ResNet, DenseNet121, InceptionV3, MobileNet, and VGG, were compared, with results indicating that ResNet performed best in terms of accuracy and stability. the study also explored the impact of optimizers, learning rates, and attention mechanisms on model performance as well as found that the introduction of attention mechanisms and transfer learning significantly improved recognition capabilities. the findings provide a reliable technical foundation for steel damage detection, with broad industrial application potential. Future work will focus on further optimizing the model and reducing reliance on labeled data to enhance detection efficiency and accuracy.

关键词： Deep learning Steel Surface Inspection Attention Mechanisms Transfer learning

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Survey on Advanced Techniques for Cardiac Arrest Prediction Using machine learning 4

Survey on Advanced Techniques for Cardiac Arrest Prediction ...

引用

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

作者： Periasamy, J.K. Raagavi, K. Tarun Ashwanth, P. Sri Sai Ram Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

Cardiac arrest has been a leading cause of mortality worldwide, with limited opportunities for intervention. this project introduces a novel machine-learning approach to predict and process cardiac arrest risk in high-risk patients. Our system integrates 1. Advanced signal processing techniques for ECG analysis 2. Deep learning algorithms for pattern recognition 3. Real-time monitoring and alert system. By analyzing ECG data, medical history, and vital signs, our system provides personalized risk scores, enabling early intervention and improving patient outcomes. Our approach has the potential to revolutionize cardiac arrest prediction, saving lives and reducing healthcare costs. © 2024 IEEE.

关键词： Electrocardiography

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4th Annual international conference on data Science, machine learning and Blockchain Technology, AICDMB 2023

4th Annual International Conference on Data Science, Machine...

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4th Annual international conference on data Science, machine learning and Blockchain Technology, AICDMB 2023

ISBN: (纸本)9781032426853

the proceedings contain 50 papers. the special focus in this conference is on data Science, machine learning and Blockchain Technology. the topics include: An unsupervised approach to creating a restaurant recommendation system;Classification of alzheimer’s disease using D-DEMNET framework;comparison of machine learning and deep learning methods for detection of liver abnormality;soil micronutrient detection using machine learning;a review of tracking concept drift detection in machine learning;wearable electrogastrogram perspective for healthcare applications;computer vision based home automation;Early autism detection using ML on behavioural pattern;implementation of application prototypes for human-to-computer interactions;recommendation system for anime using machine learning algorithms;predicting bitcoin price fluctuation by Twitter sentiment analysis;microarchitecture design and verification of co-processor for floating point operation;blockchain based higher education ecosystem;document verification using blockchain;blockchain-based traceability system for readymade food products;a cloud based interactive framework for emergency medical data sharing;the analysis and interpretation of higher education teachers based on student and teachers feedback;Implementing AI on microcontrollers in fog and edge architectures;abnormality detection in chest radiograph using deep learning models;a comprehensive review on hate speech recognition utilizing natural language processing and machine learning;prevalence of migraine among collegiate students in greater Noida;Indoor navigation using BLE beacons;strategic health planner and exercise suggester;perspective of deep learning strategies for analysis of 1D biomedical signals;revolution in agriculture sector using blockchain technology;customer churn prediction using ensemble learning with neural networks;Securing crime case summary and E-FIR using blockchain concept.

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Predictive Flood Management Using Environmental and Infrastructural data: A machine learning Approach 4

Predictive Flood Management Using Environmental and Infrastr...

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4th IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Ezhilarasan, D. Bhavani, N.P.G. Saveetha School of Engineering Chennai India

ISBN: (纸本)9798350352931

Flood management has more recently become highly essential in countering the destructive impact of climatic changes, especially in vulnerable regions. this research focuses on developing a predictive framework for flood management through environmental and infrastructural data by applying machine learning methods. the proposed system will be based on multiple data sources, including meteorological data, river water levels, terrain features, land use patterns, and urban infrastructure, to enhance the accuracy and timeliness of flood warning predictions. With this paper, the author expects to develop a model that precisely predicts flood events, so that actionable insights are offered to support proactive disaster management strategies. High-dimensional data processing and pattern recognition are conducted using advanced machine learning algorithms, such as neural networks and ensemble models, that optimize the accuracy of the predictions. Real-time data, along with historical flood records, is used for training and validation. Reliability of the model is tested for various flood-prone areas through the metrics of evaluation applied: prediction accuracy, response time, and false alarm rates ensuring robustness and feasibility in operations. the expected outcomes will depict precisely how data-driven approaches might be incorporated into flood management systems that can contribute effectively to the development of resilient infrastructure and community preparation. this research underscores a scalable approach well-suited to different kinds of geographic contexts, especially the potential of machine learning toward environmental resilience. © 2024 IEEE.

关键词： Adversarial machine learning

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Research and Application of Fault Diagnosis and Localization Techniques for Power Communication Optical Cables Based on Multimodal data Analysis 4

Research and Application of Fault Diagnosis and Localization...

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4th IEEE international conference on data Science and Computer Application, ICDSCA 2024

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

ISBN: (纸本)9798350368239

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. ©2024 IEEE.

关键词： Optical fibers

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Deep learning Algorithms for Colon Cancer Detection: A Comparative Study with Traditional machine learning Methods 4th

Deep Learning Algorithms for Colon Cancer Detection: A Compa...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Nabti, Ilhem Kouari, Zakaria Ferradji, Mohamed Abderraouf Merabet, Asma Univ Ferhat Abbes Setif 1 Setif Algeria Univ Larbi Ben Mhidi Oum El Bouaghi LIAOA Lab Oum El Bouaghi Algeria

ISBN: (纸本)9783031821523;9783031821530

this work aims to highlight the distinctions between deep learning models and traditional machine learning methods in colon cancer detection. Based on the LC25000 dataset, we analyze the performance of Support Vector machine (SVM) and Random Forest (RF) compared to advanced deep learning models such as InceptionV3, VGG19, and EfficientNetV2. Our methodology involves a detailed analysis of each algorithm's Recall, F1 score, Precision, and accuracy. the results of this research show that deep learning algorithms, and EfficientNetV2 in particular, have outperformed traditional machine learning methods. these results highlight the need to enhance colon cancer detection accuracy by adopting deep learning technology in medical imaging diagnosis.

关键词： colon cancer detection deep learning machine learning SVM Random Forest VGG19 InceptionV3 EfficientNetV2

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Social Media Analysis for Criminal Behavior Detection: Methods, Application and Challenge 4

Social Media Analysis for Criminal Behavior Detection: Metho...

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4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Patel, Prachi Bhushanwar, Kush Patel, Hemlata Piet Parul University Computer Engineering Department Gujarat Vadodara India Piet Parul University Computer Science & Engineering Department Gujarat Vadodara India

ISBN: (纸本)9798331523923

Social media websites have provided rich contextual data but its misuse for criminal activities creates lot of problems. this paper seeks to solve the problem of criminal behavioral analysis on social media platforms using machine learning, deep learning and Natural Language Processing (NLP) methodologies. the goal is to assess the effectiveness of classification algorithms such as SVM, Navier Bayes, Random Forest, Convolutional Neural Network, Recurrent Neural Network, and the Natural Language Processing (NLP) methodology that include sentiment analysis, topic modelling and entity recognition for detecting irregularities. Specifically, this research study discusses about the recent progress in these methods while considering essential problems, including data accessibility, model flexibility, and the topic of ethics. © 2025 IEEE.

关键词： Adversarial machine learning

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Gesture recognition Technology in Smart Gloves Enhanced by machine learning 4

Gesture Recognition Technology in Smart Gloves Enhanced by M...

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4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： thangam, S. Kandi, Nithin Charan, Dorai Sai Gosu, Vijay Kumari, J. Jesy Janet Virupaxappa, G. Amrita School of Computing Amrita Vishwa Vidhyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331529635

Gesture recognition systems are changing how people interact with devices, promising natural and intuitive hands-free control in various applications. Based on the ESP32 microcontroller and machine learning techniques, the proposed work is developing a compact, power-efficient system for recognizing hand gestures from data gathered by flex sensors and accelerometers. the entire system fits within memory constraints, as lightweight machine learning models are first trained on a computer using Python and then ideally deployed in optimized versions onto the ESP32. In this respect, the stream of pre-processed sensor data happens to be fed to the ML model for inference, sending back results to the ESP32, which hence triggers specific actions or commands. this fundamentally new approach shows the feasibility of machine learning on resource-constrained platforms, opening the road for wearable technologies that will be more clever and reactive. © 2024 IEEE.

关键词： Accelerometers

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Enhancing Forest Fire Classification with Feature Selection and machine learning Based on PRISMA Hyperspectral data 4th

Enhancing Forest Fire Classification with Feature Selection ...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Meroufel, Hanifi Berrichi, Faouzi Amri, Mohammed Bilel Larabi, Mohammed El Amin Ctr Tech Spatiale Agence Spatiale Algerienne Arzew Algeria Algerian Space Agcy Algiers Algeria

ISBN: (纸本)9783031821554;9783031821561

Forest fires, a significant global threat causing vast annual devastation where each year millions of hectares are destroyed over the world. Burned areas necessitate early and accurate detection for effective response. this study investigates the application of Feature Selection (FS) to address data complexity in forest fire detection utilizing PRISMA hyperspectral imagery (HSI). FS is employed to select the most relevant information, thereby reducing complexity and facilitating the interpretation of results. the effectiveness of Extreme Gradient Boosting (XGBoost), Light Gradient Boosting machine (LGBM), Random Forest (RF), and Support Vector machine (SVM) algorithms is investigated. Analysis reveals that while the raw 229-band data yielded a reasonable accuracy with RF, applying FS through wrapper methods only select 13 bands and improve the performance of LGBM, XGBoost, and SVM. Notably, the best model achieves a classification precision of 94% and recall of 91%, demonstrating the effectiveness of FS in enhancing forest fire detection and mapping.

关键词： Forest fires Feature selection XGBoost LGBM Random Forest Support Vector machine

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Improving the quality of Indian celebrity data for machine learning applications 4th

Improving the quality of Indian celebrity data for machine l...

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4th international conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Kesarwani, Jyoti Himanshu Rai United University Prayagraj India

ISBN: (纸本)9781032911571

this paper presents a comparative study of various convolutional neural network (CNN) models like Inception, ResNet, and VGG16 for face recognition on the Indian Celebrity dataset. the work highlights the importance of an Indian celebrity face dataset to improve facial recognition performance globally, given the massive and multicultural population in India. the paper discusses different data preprocessing techniques applied to the Indian Celebrity dataset including data cleaning, reduction, and transformation methods like normalization. It investigates how these preprocessing steps can enhance the data quality and model performance. the CNN models are evaluated on tasks such as facial attribute prediction and face recognition using the preprocessed Indian Celebrity dataset. the paper aims to provide insights into effectively utilizing CNNs and data preprocessing for face analysis on a diverse Indian celebrity face dataset. © 2025 the Author(s).

关键词： data assimilation

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