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检索条件"任意字段=6th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 2812 条记录，以下是241-250 订阅

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Speeding up Heterogeneous Federated learning with Sequentially Trained Superclients 26

Speeding up Heterogeneous Federated Learning with Sequential...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Zaccone, Riccardo Rizzardi, Andrea Caldarola, Debora Ciccone, Marco Caputo, Barbara Politecnico Torino Turin Italy

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Federated learning (FL) allows training machine learning models in privacy-constrained scenarios by enabling the cooperation of edge devices without requiring local data sharing. this approach raises several challenges due to the different statistical distribution of the local datasets and the clients' computational heterogeneity. In particular, the presence of highly non-i.i.d. data severely impairs both the performance of the trained neural network and its convergence rate, increasing the number of communication rounds requested to reach a performance comparable to that of the centralized scenario. As a solution, we propose FedSeq, a novel framework leveraging the sequential training of subgroups of heterogeneous clients, Le. superclients, to emulate the centralized paradigm in a privacy-compliant way. Given a fixed budget of communication rounds, we show that FedSeq outperforms or match several state-of-the-art federated algorithms in terms of final performance and speed of convergence. Finally, our method can be easily integrated with other approaches available in the literature. Empirical results show that combining existing algorithms with FedSeq further improves its final performance and convergence speed. We test our method on CIFAR-10 and CIFAR-100 and prove its effectiveness in both i.i.d. and non-i.i.d. scenarios.(1)

关键词： Training Performance evaluation Federated learning Computational modeling Neural networks Statistical distributions data models

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Patch Shuffle and Pixel Contrast: Dual Consistency learning for Semi-supervised Lung Tumor Segmentation 6th

Patch Shuffle and Pixel Contrast: Dual Consistency Learning ...

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6th Chinese conference on pattern recognition and Computer Vision (PRCV)

作者： Cai, Chenyu He, Jianjun Zhang, Manlin Hu, Yanxu Li, Qiong Ma, Andy J. Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Sun Yat Sen Univ Dept Radiol Ctr Canc Guangzhou Peoples R China Guangdong Prov Key Lab Informat Secur Technol Guangzhou Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China

ISBN: (纸本)9789819984688;9789819984695

Semi-supervised learning is a promising approach for medical image segmentation with limited labeled data. though existing consistency learning based SSL methods achieve convincing results, they neglect the finer-grained information. In this work, we propose a novel dual consistency learning (DCL) method based on characteristics of medical images for semi-supervised lung tumor segmentation. For patch shuffle consistency learning, image patches are shuffled as a strong-augmented view to improve both the student and teacher models in the mean teacher framework. For pixel contrast consistency learning, we construct a memory bank by high-quality pixel features updated with reservation and obtain anchors from wrongly classified tumor and high-confident background features, making the pixel-level feature space more discriminative. Experiments on three lung tumor datasets demonstrate the effectiveness of our method for semi-supervised medical image segmentation.

关键词： Tumor Segmentation Semi-supervised learning Patch Shuffle Consistency Pixel Contrast Consistency

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Face Expression recognition with Local Ternary pattern Images using Convolutional Neural Network and Extreme learning machine 6

Face Expression Recognition with Local Ternary Pattern Image...

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6th international conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022

作者： Krisnahati, Ice Suciati, Nanik Chusnul Hidayati, Shintami Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia

ISBN: (纸本)9798350399615

Facial Expression recognition (FER) performed computationally is an exciting task to explore in computer vision. Some methods have been proposed to handle variants of illumination in FER. Based on research, Local Ternary pattern (LTP) as a feature extractor can handle the variation of lights. However, LTP is a traditional feature extractor that needs to be processed manually. Unlike LTP, Convolution Neural Network (CNN) architecture has an automatic feature extractor. therefore, this study proposes LTP images as input into CNN architecture to handle light variations and keep the feature extraction automatically. Afterward, in the classification layer, Extreme learning machine (ELM) is employed as a classifier to improve the training speed of the original CNN classifier. the proposed model performance for the KDEF dataset with 10-fold cross-validation yields an accuracy of 85.51% © 2022 IEEE.

关键词： Convolutional Neural Network Extreme learning machine Facial Expression recognition KDEF Local Ternary pattern

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An in-Depth Analysis for machine-Vision-Based Mango Leaf Diseases recognition 6

An in-Depth Analysis for Machine-Vision-Based Mango Leaf Dis...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Das, Prothoy Rahman, Md. Ataur Habib, Md. Tarek Khan, Md. Abbas Ali Islam, Mohammad Monirul Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh

ISBN: (纸本)9798331522667

Many leaf diseases that affect crop health cause severe mango farming concerns. this study employed deep learning techniques to analyze mango leaf disease categorization comprehensively. this study looks at the classification of five different mango leaf diseases using convolutional neural networks (CNNs): powdery mildew, dieback, gall midge, anthracnose, and healthy leaves. A dataset of 661 raw images was first gathered, and these raw images were augmented to create a final dataset of 2678 images. the study examines how preprocessing methods affect classification accuracy using five CNN models before and after data preprocessing. the outcomes show notable gains in accuracy following preprocessing, highlighting its critical function in improving model performance. Additionally, a new custom model is proposed and evaluated, demonstrating its effectiveness in reaching an impressive accuracy of 98.50%. this comparative study shows that the proposed model outperforms current CNN architectures, indicating that it has the potential to classify mango leaf diseases accurately. Our research aims to highlight the importance of preprocessing techniques and creating customized deep-learning models by presenting an effective method for automated mango leaf disease identification. © 2025 IEEE.

关键词： Fruits

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Physics-Informed machine learning for State of Charge Estimation of Lithium-Ion Batteries 6

Physics-Informed Machine Learning for State of Charge Estima...

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6th international conference on Robotics and Automation in Industry, ICRAI 2024

作者： Jawaad, Hafiz Muhammad Saeed, Muhammad Malik, Anjum Naeem Khalatbarisoltani, Arash Elahi, Hassan Khan, Umar Shahbaz National University of Sciences and Technology Dept. of Mechatronics Engg. Islamabad Pakistan School of Electrical Enigneering Chonqing University Chongqing400044 China College of Mechanical and Vehicle Engineering Chongqing University Chongqing400044 China

ISBN: (纸本)9798350355703

An accurate estimation of battery state of charge (SOC) is crucial to ensure the safety of Li-ion batteries (LIBs), and the recent boom in machine learning (ML) has illustrated a strong potential for SOC estimation. However, the ignorance of LIB internal dynamics greatly limits the performance of ML-based methods. therefore, this work incorporates multiple battery internal dynamics into the ML-based method to ensure enhanced performance. First, using a simple mechanism based on the LIB model, the open circuit voltage, ohmic response, and temperature-corrected pattern are extracted from the measured data and provided as inputs to the neural networks (NNs). then, to ensure time adaptability and accuracy, the predictions of the proposed design are adjusted using short-term Ampere-hour results. the proposed technique is validated using different real-world driving profiles. the comparative results with benchmark NNs-based methods verify a >60% reduction in root mean square error, demonstrating the effectiveness of incorporating battery internal dynamics into NNs. © 2024 IEEE.

关键词： State of charge

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Utilizing machine learning for Gene Expression data: Incorporating Gene Sequencing, K-Mer Counting and Asymmetric N-Grams Features 6th

Utilizing Machine Learning for Gene Expression Data: Incorpo...

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6th international conference on Soft Computing and data mining (SCDM)

作者： Chuah, Chai-Wen He, WanXian Huang, De-Shuang Alawatugoda, Janaka Guangxi Acad Sci Nanning Peoples R China Guangdong Univ Sci & Technol Dongguan Guangdong Peoples R China Rabdan Acad Res & Innovat Ctr Div Abu Dhabi U Arab Emirates Griffith Univ Inst Integrated & Intelligent Syst Nathan Qld Australia

ISBN: (纸本)9783031669644;9783031669651

Comprehending the sequence of deoxyribonucleic acid sequence is a pivotal aspect of bio-informatics research. With the substantial surge in biological data, there arises a necessity of effective methodologies to address the crucial challenge within the overarching computation framework of deoxyribonucleic acid sequence classification. Numerous machine learning's can be used to complete these tasks compared to a manual technique that is followed for ages. the aim of this project is to perform effective approaches for pre-processing deoxyribonucleic acid sequences (K562 ChIP-seq) and use machine learning's to train the model by using deoxyribonucleic acid sequences to create judgments, predictions and classifications deoxyribonucleic acid sequences into known categories. As the machine learning may analyze large and complex datasets to identify patterns and trends of deoxyribonucleic acid sequences that may not be apparent to humans. In this study, the pre-processing methods are k-mers and N-grams. the machine learning classifiers, we employ Naive Bayes classifier, K-nearest neighbors classifier and Random Forest classifier to train model and to evaluate the accuracy of predicting the K562 ChIP-seq. All the classifiers achieve overall accuracy over 80%. We show that Na ve Bayes classifier based on k-mers encoding which gives good result for both symmetric and asymmetric N-grams. the Na ve Bayes classifier based on 3-mers until 6-mers encoding have the highest accuracy of all the classifiers tested at 89.8% for symmetric N-grams and 90% for asymmetric N-grams. the Random Forest based on 3-mers and (2,3)-grams achieves the second highest accuracy in predicting the K562 ChIP-seq with 87.1%. the highest accuracy for K-nearest neighbors classifier based on 2-mers until 6-mers encoding for asymmetric N-grams tested at 86.9%. the results also reveal that the performance of the novelty classifiers' finding based on asymmetric N-grams are better if compared with symmetric N-gr

关键词： DNA sequencing machine learning Na ve Bayes Classifiers K-nearest neighbors Classifiers Random Forest Classifiers k-mers N-grams

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Research on Weather data Visualization Algorithms Based on AI-Assisted Bayesian Networks 6

Research on Weather Data Visualization Algorithms Based on A...

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6th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2024

作者： Wang, Han Li, Ling Meteorological Comprehensive Support Centre Meteorological Bureau of Hulunbuir Hulunbuir China Meteorological Observatory Hailar District Meteorological Bureau of Hulunbuir Hulunbuir China

ISBN: (纸本)9781837242672

As global climate change intensifies and extreme weather events become more frequent, the importance of accurate weather forecasting and advanced meteorological data visualization is becoming crucial. However, traditional weather data visualization algorithm models often encounter challenges such as low accuracy and insufficient processing capabilities, which result in visual outputs that do not accurately depict actual meteorological conditions. To tackle these issues, this paper introduces a weather data visualization algorithm based on an enhanced Bayesian network. this innovative algorithm employs machine learning techniques to build a comprehensive corpus containing tens of thousands of meteorological data points, which facilitates automated data retrieval. Furthermore, this paper establishes a sophisticated weather data visualization intelligent recognition model and outlines its implementation through systematic data collection, processing, and output strategies. the proposed method meticulously collects and processes input signals from weather data, extracting feature parameters that enable intelligent recognition capabilities. An extensive experimental analysis of the developed weather data visualization model is conducted, with experimental data meticulously recorded and analyzed. the results conclusively demonstrate that the model achieves significantly higher accuracy in weather data visualization, effectively fulfilling the stringent requirements of complex meteorological data analysis tasks. © the Institution of Engineering & Technology 2024.

关键词： Bayesian network Climate change data visualization machine learning weather forecasting

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Criminal Identification and Comprehensive Analysis Using Decision Tree Classifier 6th

Criminal Identification and Comprehensive Analysis Using Dec...

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6th international conference on Communications and Cyber-Physical Engineering, ICCCE 2023

作者： Sehgal, Laveena Bharti, P.K. Sharma, Mahendra Computer Science and Engineering Shri Venkateshwara University Gajraula India Shri Venkateshwara University Gajraula India Information Technology IIMT College of Engineering UP Greater Noida India

ISBN: (纸本)9789819971367

machine learning is the process of deriving insights or new knowledge from large data sets. During a criminal investigation, cops collect a mountain of data, but only the most pertinent pieces need to be analysed. So, it’s possible to use machine learning for this function. the results produced are more sensitive to the choice of machine learning technique. this is the main justification for comparing and choosing the best performing machine learning algorithm. Anybody can grasp the fundamental idea of utilising a Decision Tree to categorise data. In addition, this paper uses Decision-Tree algorithms and face pattern analysis tools to study criminal faces. this was achieved in MATLAB by using face pattern tools that were embedded into the DT algorithms. the face detection algorithm in the MATALAB platform has also been upgraded to work in real time. Prior work has mostly focused on criminal event data sets but has made no adjustments for real-time functionality. the images of actual criminals have been used to train and test the face data sets that have been stored. In our experiments, the proportion of correct predictions of faces has increased by 2%. the test accuracy provided by the prior work is 78%, while the proposed method is 80%. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： machine learning

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Weather Prediction Platform using data mining 6

Weather Prediction Platform using Data Mining

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6th international conference on Computing and Informatics, ICCI 2024

作者： Elsonbaty, Haya K. Ibrahim, Manar A. Elnakeib, Noure A. Noh, Eman A. Fayez, Haneen I. Elbanhawy, Magy Mazrua, Ahmed T. Moawad, Ibrahim F. Elzeki, Omar M. Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Faculty of Engineering Portsaid University Egypt Mansoura University Mansoura Egypt Faculty of Computer and Information Sciences Ain shams University Cairo Egypt

ISBN: (纸本)9798350373875

this study focuses on using the Weka toolkit's machine learning algorithms to forecast weather conditions based on a dataset that includes columns for precipitation, temperature_max, temperature_min, and wind. Forecasting various weather conditions, such as drizzle, rain, sun, snow, and fog, is the goal. A thorough examination has been carried out utilizing a range of Weka algorithms, covering models with higher performance and lesser accuracy. After extensive testing, the best performing logistic algorithms meta. logitboost, *** Classifier, ***, trees.j48, and functions. Logisticwere able to predict the weather with exceptional accuracy. these models demonstrated how well they could comprehend and predict weather conditions using the parameters from the given dataset. On the other hand, weather state predictions were relatively less accurate for models using the meta. classificationvia Regression, meta .bagging, ***, misc. input, and *** algorithms. In order to furnish stakeholders with practical and captivating insights, the outcomes were condensed and shown via user-friendly and aesthetically pleasing Google Looker dashboards. With the help of these dashboards, stakeholders may interactively examine and understand the predicted weather conditions that are based on the reliable machine learning algorithms. this research attempts to provide decision-makers with easily accessible, data-driven weather forecasts by combining these predictive algorithms with Google Looker's visualization capabilities. the dashboard system's extensive analysis and visualization are intended to improve the comprehension and use of machine learning in weather prediction, promoting well-informed decision-making across a range of industries dependent on weather-sensitive operations. © 2024 IEEE.

关键词： Decision making

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Modulation pattern recognition based on ShuffleNet neural network 23

Modulation pattern recognition based on ShuffleNet neural ne...

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6th international conference on Signal Processing and machine learning, SPML 2023

作者： Guohao, Shi Z., Rui C., Jiuxiao B., Mengmeng Xijing University China

ISBN: (纸本)9798400707575

Modulation pattern recognition is widely used in civil and military fields. In this paper, a modulation pattern recognition technology based on convolutional neural networks is proposed and the accuracy of modulation signal recognition based on three kinds of convolutional neural networks is compared to analyze the performance of the neural network model. the experimental results show that the convolutional neural network can achieve feature extraction and classification recognition at the same time. the simulation results show that ShuffleNet neural network has higher recognition accuracy and classification consistency for modulation pattern recognition, and its feature extraction performance is better than that of VGG and ResNet neural network. During the experiment, modulation pattern recognition based on VGG neural network model has high computational complexity and model size, which requires high hardware level. In this paper, modulation pattern recognition technology based on ShuffleNet neural network is proposed. Combined with the characteristics of neural network: point-to-point deep convolution and channel random rearrangement, the network model is constructed. the theoretical analysis and simulation results show that the modulation pattern recognition method based on ShuffleNet network model has lower computational complexity, fewer model parameters and smaller model size while ensuring the recognition accuracy. © 2023 ACM.

关键词： Feature extraction

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