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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 3282 条记录，以下是701-710 订阅

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Large-scale Face Clustering Method Research Based on Deep learning 3

Large-scale Face Clustering Method Research Based on Deep Le...

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3rd international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Wen, Zixin Hebei Agr Univ Dept Comp Sci Baoding Peoples R China

ISBN: (纸本)9781665417907

Face clustering is a pre-processing stage for face detection and face recognition tasks, alleviating the heavy manual labeling work. The goodness of clustering depends on the data features and clustering algorithm. The current data is so complex and diverse that traditional machine learning clustering algorithms can no longer handle such large-scale and high latitude data. Therefore, this paper introduces two types of face clustering algorithms based on deep learning, face clustering algorithms based on deep subspace and face clustering algorithms based on graph convolutional networks, and introduces each algorithm and compares its advantages and shortcomings.

关键词： face clustering deep learning large-scale data

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Dynamic Prediction and Optimization of Tight Reservoir Development Based on machine learning

Dynamic Prediction and Optimization of Tight Reservoir Devel...

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data Analytics, Computing and Artificial Intelligence (ICDACAI), international conference on

作者： Yude Gao No.10 Oil Production Plant Daqing Oilfield Co. Ltd. Daqing China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

With the continuous rise in global energy demand, the development of tight reservoirs has become a key focus in oil and gas research. However, due to the characteristics of tight reservoirs, such as significant heterogeneity and low permeability, conventional development methods face challenges in accurately predicting dynamic changes and achieving optimized extraction. In recent years, machine learning technology, known for its robust data processing and pattern recognition capabilities, has demonstrated promising potential in addressing the complexities of tight reservoir development. This paper proposes a comprehensive solution leveraging machine learning to address the challenges of dynamic prediction and optimization in tight reservoir development. Initially, a dynamic prediction model for tight reservoirs is established, with an appropriate machine learning algorithm selected for training and validation using real-world data. Following this, an optimization algorithm is applied based on the prediction outcomes, enhancing the development strategy to improve operational efficiency and economic returns. The efficacy and suitability of the proposed approach are validated through case studies. The research presented in this paper offers innovative insights and technical support for the efficient exploitation of tight oil reservoirs.

关键词： machine learning algorithms Oils Heuristic algorithms machine learning Predictive models Reservoirs Prediction algorithms data models Permeability Optimization

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Musical Instrument recognition Using machine learning Approaches 3

Musical Instrument Recognition Using Machine Learning Approa...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Chethan, B.K. Bharath, K.N. Puttegowda, Kiran Manjula, A.V. Sunil Kumar, D.S. Anitha, K. Yathiraj, G.R. Mysuru India Dayananda Sagar Academy of Technology and Management Department of Ece Bengaluru India Vidyavardhaka College of Engineering Department of Ece Mysuru India The National Institute of Engineering Department of Ece Mysuru India East Point College of Higher Education Department of Computer Applications Bengaluru India Gsss Institute of Engineering Technology for Women Department of Ece Mysuru India Coorg Institute of Technology Department of Cse Ponnampet India

ISBN: (纸本)9798331508456

This work focuses on musical instrument recognition in miscellaneous audio sources, a fundamental issue of the machine auditory system. We contrast traditional machine learning techniques against deep learning strategies in a clinical dataset solved under a supervised multi-label, multi-output classification paradigm. To enhance the performance of the system, we adjust the analysis parameters for window size, hop size, and label threshold levels. Furthermore, we explore the benefits of auxiliary feature sets as alternative data representations, specifically: Auditory features are based on two categories: (1) four-band spectral decomposition of stereo sound data, left, right, mid, and side channels and (2) the harmonic and residual model of the audio signal. Two methods for fusing the models trained on these representations are suggested and we use the best of these methods to increase recognition accuracy. These enhancements lead to a vast improvement;with a 14.25 % increase M-F score for the macro average and a 24.17 % improvement in the exact match ratio overall compared to the baseline. © 2025 IEEE.

关键词： Deep learning

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A Novel Intrusion Detection System based on machine learning for Internet of Things (IoT) Devices

A Novel Intrusion Detection System based on Machine Learning...

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Smart data Intelligence (ICSMDI), international conference on

作者： Dhwanil Chauhan Margi Shah Harshil Joshi Devang Patel Institute of Advance Technology and Research Charotar University of Science and Technology Changa Gujarat

An exponential increase has been observed in the amount of IOT devices. The demand for an intrusion detection system has increased with the proliferation of IOT devices. An intrusion detection system is made of machine learning algorithms or a combination of machine learning algorithms. These algorithms are used to identify and classify intrusions. This study compares the results obtained by applying Support Vector Classifier, Decision Tree Classifier and Random Forest Classifier on the CICIDS -17.

关键词： Training machine learning algorithms Support vector machine classification Intrusion detection Boosting Internet of Things Object recognition

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Dance pose capture and recognition based on heterogeneous sensors 3

Dance pose capture and recognition based on heterogeneous se...

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

作者： Miao, Yan Baotou Teachers' College Baotou014030 China

In order to capture the movement posture of dancer in real time and effectively, a dancer motion and posture capture system based on heterogeneous sensors is proposed. Starting from the data processing of heterogeneous sensors in a single node, the single attitude measurement node of the system can be used to collect the initial posture data in the process of dancer motion in real time, achieve accurate capture of dancer movements, and filter and fuse the original motion data. In order to verify the performance and accuracy of the designed dancer movements capture system, relevant experiments are designed to verify it. The experimental results show that the algorithm designed in this experiment basically solves the interference problem of magnetic field and acceleration on attitude solution, and the attitude solution accuracy can basically meet the requirements. The fitting degree of the comparison between the extracted angle of the video sequence and the output angle of the sensor can basically meet the requirements, and the angle deviation is kept within 1 degree. The system can capture the dancer movements of the whole body and the hand, and the experimental results can meet the requirements basically, which has certain reference value. © 2023 The Authors. Published by Elsevier B.V.

关键词： data fusion

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SuperView-1 satellite image-based winter wheat spatial distribution information refined extraction using the fusion of machine learning and deep learning 3

SuperView-1 satellite image-based winter wheat spatial distr...

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3rd international conference on Remote Sensing, Surveying, and Mapping, RSSM 2024

作者： Zhen, Haocheng Li, Ting Ji, Min He, Yawen College of Geodesy and Geomatics Shandong University of Science and Technology Qingdao0532-86057276 China College of Oceanography and Space Informatics China University of Petroleum Qingdao0532-86980518 China

ISBN: (数字)9781510680081

ISBN: (纸本)9781510680074

Wheat is one of the most important cereal crops in the world. Efficiently and accurately extracting spatial distribution information of winter wheat from high-resolution remote sensing images has become crucial research and application fields in agricultural monitoring. However, the complex texture features and spatial heterogeneity in high-resolution imagery often require deep learning models to be trained with a substantial number of samples to achieve the desired outcomes. In response to the challenges posed by insufficient sample volumes, which impair the accuracy of recognition in the field of extracting spatial distribution information of winter wheat, this paper introduces a new methodology that synergizes deep learning neural networks with machine learning technologies to address these issues. The new process first applied a Grey-Level Co-occurrence Matrix (GLCM) to enhance the image texture. It then utilized a random forest (RF) model to generate a confidence map of winter wheat distribution. Next, the method recommended a supervised correction to this map. It integrated the corrected map into an improved Segformer model as a priori knowledge to increase the weight of the high-confidence regions to achieve high extraction accuracy and robustness. This study selected Shouguang City, Shandong Province, as the case study and the SuperView-1 satellite data as the input image. This study compared the proposed GLCM-RF-Segformer model with ResUnet, DeepLabV3Plus, Hrnet, FCN8S, and RF to validate the experiment's effectiveness. The experimental results have shown that the new model has achieved average metrics of 91.387% for Precision, 94.467% for Recall, 94.180% for Overall Accuracy (OA), 87.053% for mean Intersection over Union (mIoU), 85.087% for Kappa coefficient, and 92.630% for F1-Score. These accuracy statistics surpass those of the five other comparative models, highlighting the model's high accuracy and robustness in extracting the spatial distribution of

关键词： Remote sensing

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A Latent Dirichlet Allocation Technique for Opinion mining of Online Reviews of Global Chain Hotels 3

A Latent Dirichlet Allocation Technique for Opinion Mining o...

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3rd international conference on Intelligent Engineering and Management, ICIEM 2022

作者： Afaq, Anam Gaur, Loveleen Singh, Gurmeet Amity University Amity International Business School Noida India The University of the South Pacific School of Business and Management Suva Fiji

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

The hospitality industry has faced unprecedented challenges with the outbreak of Covid-19, which has changed customers' expectations. Therefore, it is essential to identify customers' new perceptions and expectations that lead to positive and negative opinions towards the service providers. Accordingly, this study aims to perform topic modeling and sentiment analysis on 94,200 online reviews of five global chain hotels in South Asia. Topic modeling as a text mining, unsupervised machine learning technique can decipher topics from a corpus such as online reviews, online reports, news covers, etc. In this study, the data is extracted from Trip Advisor through web scraping. Topic modeling is performed using the Latent Dirichlet Approach (LDA) on the extracted data set to analyze the key topics mentioned by the customers in the online reviews. The analysis depicted that cleanliness, food, staff, and service were the main concerns of the hotel guests. Furthermore, the findings represented that the main issues impacting the hotel guests were service delays. However, food and services were the keywords with the maximum word count as depicted by topic modeling. © 2022 IEEE.

关键词： Sentiment analysis

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data Analysis of College Practice Teaching Based on machine learning Algorithm 25

Data Analysis of College Practice Teaching Based on Machine ...

引用

Proceedings of the 2025 3rd international conference on Communication Networks and machine learning

作者： Xinwu Wang Dalian University of Finance and Economics Dalian Liaoning China

ISBN: (纸本)9798400713231

With the rapid development of information technology, especially the wide application of big data and artificial intelligence technology, the field of education is undergoing unprecedented changes. Among them, practical teaching in colleges and universities, as an important part of modern education system, is facing severe challenges. However, the traditional practice teaching mode often has some problems, such as uneven distribution of resources, disjointed teaching content and market demand, and single teaching method, which limit the improvement of practice teaching effect. In this regard, based on the actual needs, this paper will deeply analyze the application feasibility of artificial intelligence and machine learning in the reform of practical teaching in colleges and universities, and put forward a set of data analysis and processing scheme of practical teaching in colleges and universities, aiming at improving the effectiveness and personalized level of practical teaching through the integration, mining and analysis of multi-source data. Practice has proved that machine learning algorithms such as fuzzy C-means (FCM) clustering analysis and frequent pattern growth (FP-growth) correlation analysis are adopted in the scheme, and the data involved in college practice teaching are deeply analyzed, and the feasibility and effectiveness of the model are verified by practical tests, which provides scientific basis and decision support for college practice teaching.

关键词： association rule analysis

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Automated Detection of Poultry Diseases using Implicit pattern mining over Explicit Image Analysis

Automated Detection of Poultry Diseases using Implicit Patte...

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IEEE international conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES)

作者： Khushi Donthi Kuval Kiran Gowri Srinivasa Dept. of CSE PES Center for Pattern Recognition PES University Bangalore India

ISBN: (数字)9798350376135

ISBN: (纸本)9798350376142

Early detection of poultry diseases is crucial for poultry farmers, but traditional identification methods are often slow and tedious due to limited facilities. We present a comprehensive evaluation of implicit pattern mining techniques using lightweight, CNN-based deep learning models such as, MobileNetV2, InceptionV3, and EfficientNetB5 that are amenable for implementation on edge devices. We contrast them with traditional machine learning approaches applied to analyse chicken fecal image data to classify images of healthy samples from unhealthy ones. We use model interpretation to gain insight to the patterns in the input image that the deep learning models use to make predictions. This facilitates corroborating domain understanding with model performance. This study demonstrates the viability of deploying deep learning based methods for poultry disease detection on handheld devices, highlighting its potential for deployment in the field.

关键词： Deep learning Support vector machines Accuracy machine learning Predictive models Feature extraction data mining Random forests Diseases Testing

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Emotion recognition Using a Hybrid Model of Transformer and CNN-LSTM for Improved Performance 3

Emotion Recognition Using a Hybrid Model of Transformer and ...

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3rd international conference on Disruptive Technologies, ICDT 2025

作者： Palak Chadha, Sukriti Sharma, Palvi Chilluri, Venkata Suresh Babu Model Institute of Engineering & Technology Dept. of Cse Jammu & Kashmir Jammu India Mountain View CA94043 United States

ISBN: (纸本)9798331519582

Emotion recognition is a crucial aspect of AI technology that fills in the gap of human-machine interaction, allowing systems to react to human emotions effectively. The traditional models often struggle with precision and robustness when it comes to noisy data along with the complexity of analyzing human emotions. This paper proposes a hybrid model architecture by combining the Transformer model and CNN-LSTMs in order to maximize emotion recognition. The Transformer module captures input data's long-range dependencies, whereas the CNN-LSTM architecture extracts spatial and temporal features from voice signals, with potential extension to multimodal inputs such as facial expressions and physiological data. This work shows the capability of advanced hybrid architecture in developing emotionally intelligent systems in healthcare, education, customer service, and other applications. The findings pave the way for future work on multimodal data integration and generalization in complicated environments. © 2025 IEEE.

关键词： Deep learning

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