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

共 3325 条记录，以下是31-40 订阅

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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...

引用

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...

引用

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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Text Classification Experiments on Contextual Graphs Built by N-Gram Series 4th

Text Classification Experiments on Contextual Graphs Built b...

引用

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

作者： Sen, Tarik Uveys Yakit, Mehmet Can Gumus, Mehmet Semih Abar, Orhan Bakal, Gokhan Abdullah Gul Univ TR-38080 Kayseri Turkiye Osmaniye Korkut Ata Univ TR-80000 Osmaniye Turkiye

ISBN: (纸本)9783031821493;9783031821509

Traditional n-gram textual features, commonly employed in conventional machine learning models, offer lower performance rates on high-volume datasets compared to modern deep learning algorithms, which have been intensively studied for the past decade. the main reason for this performance disparity is that deep learning approaches handle textual data through the word vector space representation by catching the contextually hidden information in a better way. Nonetheless, the potential of the n-gram feature set to reflect the context is open to further investigation. In this sense, creating graphs using discriminative ngram series with high classification power has never been fully exploited by researchers. Hence, the main goal of this study is to contribute to the classification power by including the long-range neighborhood relationships for each word in the word embedding representations. To achieve this goal, we transformed the textual data by employing n-gram series into a graph structure and then trained a graph convolution network model. Consequently, we obtained contextually enriched word embeddings and observed F1-score performance improvements from 0.78 to 0.80 when we integrated those convolution-based word embeddings into an LSTM model. this research contributes to improving classification capabilities by leveraging graph structures derived from discriminative n-gram series.

关键词： text-graph transformation graph convolution network deep learning text mining

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An unsupervised approach to creating a restaurant recommendation system 4th

An unsupervised approach to creating a restaurant recommenda...

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

作者： Srujan, Metta Venkata Viswam, Rohit Raghavendra, S. Ramyashree Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9781032426853

Over the last few years, recommender systems have become increasingly popular due to the technological advancements occurring in the fields of data mining, predictive analysis, and machine learning. In this, we try to use an unsupervised learning approach to cluster restaurants that are similar and recommend restaurants to users according to the ones that they have ordered from in the past. the models used in this project gave us average results given that clustering techniques have their problems and are not the most novel architectures available. However, working on a real-life data set lets us take a deep dive into the nuances of mining data and making predictions in big projects taken up by companies. DBSCAN seemed to work better than K-Means given the latter is very susceptible to outliers. Motivation to use DBSCAN is, this method is able to represent clusters of arbitrary shape and better to handle noise. However, the models that we have built still need a lot of work such as hyperparameter tuning, cross-validation, etc. to increase their accuracy and to be used in real life. © 2024 the Author(s).

关键词： data mining

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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...

引用

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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Active Prewarning Technology for Smart Grid Based on Unsupervised learning 4

Active Prewarning Technology for Smart Grid Based on Unsuper...

引用

4th international conference on machine learning and Intelligent Systems Engineering (MLISE)

作者： Qu, Ruiting Tian, Xiaolei Chen, Shuo State Grid Liaoning Informat & Commun Co Shenyang Peoples R China

ISBN: (纸本)9798350375084;9798350375077

this paper proposes an early warning technology for power grid cloud platform microservice architecture alarm information based on unsupervised learning, discovers potential abnormal patterns and rules, sends out early warning signals in a timely manner, and improves the ability to identify and process power grid cloud platform microservice architecture alarms. According to the above steps, based on the establishment of the power grid business microservice operation alarm log alarm information knowledge base, this article will study the cloud platform operation monitoring data scoring algorithm, classify the cloud platform monitoring information, and then filter out the suspected abnormal data. For different business types of complex scenarios, research on abnormal information classification mining technology based on statistical models, proximity, clustering and classification, apply improved K-means clustering[1] and LOF outlier detection technology[2] to establish a combined detection model, and detect anomalies the data is clustered and outliers detected to achieve active early warning technology of unsupervised learning.

关键词： smart grid unsupervised learning K-means clustering LOF

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4th Crowd Science Workshop - CANDLE: Collaboration of Humans and learning Algorithms for data Labeling 23

4th Crowd Science Workshop - CANDLE: Collaboration of Humans...

引用

16th international conference on Web Search and data mining

作者： Ustalov, Dmitry Savage, Saiph van Berkel, Niels Liu, Yang Toloka Belgrade Serbia Northeastern Univ Boston MA USA Aalborg Univ Aalborg Denmark Univ Calif Santa Cruz Santa Cruz CA USA

ISBN: (纸本)9781450394079

Crowdsourcing has been used to produce impactful and large-scale datasets for machine learning and Artificial Intelligence (AI), such as ImageNET, SuperGLUE, etc. Since the rise of crowdsourcing in early 2000s, the AI community has been studying its computational, system design, and data-centric aspects at various angles. We welcome the studies on developing and enhancing of crowdworker-centric tools, that offer task matching, requester assessment, instruction validation, among other topics. We are also interested in exploring methods that leverage the integration of crowdworkers to improve the recognition and performance of the machine learning models. thus, we invite studies that focus on shipping active learning techniques, methods for joint learning from noisy data and from crowds, novel approaches for crowd-computer interaction, repetitive task automation, and role separation between humans and machines. Moreover, we invite works on designing and applying such techniques in various domains, including e-commerce and medicine.

关键词： crowdsourcing data labeling human-in-the-loop human-machine collaboration fairness and inclusion

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Applying machine learning Approaches with Integrated Internet of things for Water Management System 4th

Applying Machine Learning Approaches with Integrated Interne...

引用

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

作者： Belmir, Meroua Difallah, Wafa Ghazli, Abdelkader Tahri Mohammed Univ Bechar Fac Exact Sci Dept Math & Comp Sci Bechar Algeria Coding & Informat Secur Lab LACOSI Oran Algeria Lab Energet Arid Zones ENERGARID Bechar Algeria

ISBN: (纸本)9783031821523;9783031821530

the challenges of water loss and inadequate scheduling in traditional irrigation methods hinder the ability to meet the increasing demand for food production while also conserving valuable water resources. In order to overcome these issues, this study introduces an advanced smart irrigation system that harnesses the capabilities of the Internet of things (IoT), data analysis, and machine learning. the primary objective is to determine the most effective method for applying and scheduling irrigation, thus optimizing resource usage. this innovative system relies on a meticulously curated dataset, "INTELLIGENT IRRIGATION SYSTEM" obtained from Kaggle. the dataset encompasses four essential attributes, extensively detailed in the metadata, providing foundational information crucial for precise irrigation management. this paper proposes an intelligent irrigation approach that can be deployed in different contexts. this approach is based on machine learning algorithms. Multiple models, including K-Nearest Neighbors (KNN), Logistic Regression (LR), and Gaussian process (GP) classifier, were employed in the development of this approach. the results showed that LR and GP are better with an accuracy of 0.9833, precision of 0.9782, F1 score of 0.9890, specificity of 0.9333, and sensitivity of 1.0 compared to KNN model.

关键词： Smart irrigation system machine learning smart agriculture Internet of things

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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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