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检索条件"任意字段=5th International Conference on Machine Learning and Data Mining in Pattern Recognition"

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A Two-stage Heterogeneous Ensemble learning Algorithm Based on MLP-SVR-LGBM

A Two-stage Heterogeneous Ensemble Learning Algorithm Based ...

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international conference on Industrial Artificial Intelligence (IAI)

作者： Chunpeng Yang Xurui Zhang Yiping Wang Jianjun Zhao Kai Sun School of Information and Automation Qilu University of Technology (Shandong Acadamy of Sciences) Jinan China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

A single machine learning approaches often encounter numerous challenges and limitations when addressing complex problems. In this study, we propose a novel stacking ensemble learning model for heterogeneous soft sensors, utilizing multi-layer perceptron (MLP), support vector regression (SVR), and light gradient boosting machine (LGBM) as base learners, with gradient boosting decision tree (GBDT) as the meta-learner. First, the proposed heterogeneous soft sensor model fully considers the diversity of the base learners and their learning performance to effectively exploit the advantages of various machine learning methods. Second, the meta-learner, GBDT, is an integrated learning algorithm that can better handle different data of the base learners and improve the performance of the overall model. Subsequently, we demonstrate the superiority of the proposed method is confirmed through an artificial dataset case study.

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Human Kinematics Analysis by Markerless Vision Based on OpenSim

Human Kinematics Analysis by Markerless Vision Based on Open...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Chi Zhang Yurong Li Wangwei Ye Gaohua Huang College of Electrical Engineering and Automation Fuzhou University Fuzhou Fujian China Fujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology Fuzhou China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Human Kinesiology analysis is essential for understanding biomechanical loads in rehabilitation, injury prevention, and diagnosis. However, traditional marker-based motion capture systems are suboptimal due to high equipment and time costs, as well as the need for specialized expertise. Although OpenSim can perform detailed kinematic analyses using musculoskeletal models, integrating these analyses seamlessly with sparse human key points derived from computer vision remains challenging. Additionally, the current triangulation methods based on direct linear transformation (DLT) have limitations in accuracy and generalization ability. While learnable triangulation methods can extract complex features from images and significantly improve the accuracy of identifying and locating human nodes, they still lack limb length constraints and require calibration for each inference. In order to solve the above problems, in this work, we apply a triangulation method combining graph convolutional network with joint context constraints and camera pose distribution to human kinematics analysis, which fully considers the bone length constraints and joint connection relations, and improves the reasoning ability of the model under different camera parameter datasets. In addition, we fine-tuned the backbone network using relevant data with foot markers to suit the needs of kinesiology analysis. Finally, the output key points of the triangulation network is sent into the deep learning network to obtain the corresponding anatomical markers, and the results in the visual field are better combined with the musculoskeletal model provided by OpenSim, and more accurate kinematic parameters are obtained.

关键词： Analytical models Musculoskeletal system Visualization Accuracy Prevention and mitigation Cameras Feature extraction Motion capture pattern recognition Context modeling

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Research on three-Dimensional Failure Envelope Surface of Composite Materials Based on data-driven

Research on Three-Dimensional Failure Envelope Surface of Co...

引用

Intelligent Computing and Human-Computer Interaction (ICHCI), international conference on

作者： Jingchao Wei Su Cao Zhe Wang Yu Yang National Key Laboratory of Strength and Structural Integrity Aircraft Strength Research Institute of China Xi'an China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

data-driven methods based on machine learning have been successfully used in composite material design, structural analysis, data processing, material performance characterization, prediction and testing, and other fields. there is no theory in the field of composite material applications that can provide high-precision failure analysis and prediction results in all situations. this paper designed a biaxial compression-compression test of composite materials to obtain multi-variable data such as DIC digital speckle data, testing machine load-displacement data, and strain data. For the multidimensional data generated by the biaxial test, a fusion algorithm based on machine learning was used to construct a three-dimensional failure envelope of the composite material. the results show that the data mining methods can identify correlations in failure data and express them in the form of three-dimensional stress function equations. data-driven methods based on physical information can further improve the accuracy of composite material failure behavior prediction and provide a technical basis for failure analysis and strength prediction of composite material structures.

关键词： Correlation Accuracy Composite materials Failure analysis machine learning Speckle Prediction algorithms data mining Stress Strain

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Comparative evaluation of machine learning classifiers with Obesity dataset 5

Comparative evaluation of machine learning classifiers with ...

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5th international conference on Computing Sciences (ICCS)

作者： Ramya, A. Rohini, K. Vels Inst Sci Technol & Adv Studies Dept Comp Sci Chennai Tamil Nadu India

ISBN: (纸本)9781665494458

datamining is very important in modern world. Collection of many types of data we find (knowledge discovery process) the essential information from hidden things. So, data mining is very important to extract the essential hidden data. data mining with machine learning algorithmsis effective to mine the essential data and it is very fast-growing technology. Few ML algorithms are compared using BMI based. Obesity is BMI level is equal to 30 or above 30, so this disease is very complex. Obesity will affect the quality of life like depression, lower work achievement, disability. In this paper we applied classification machine learning algorithms like KNN, XGB, Logistic Regression, DT and compared those algorithms in obesity data.

关键词： BMI-Body mass index ML - machine learning KNN - K nearest neighbors XGB - Extreme gradient boost DT-Decision Tree

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Research on Multimodality Tibetan-Chinese machine Translation of Text and Image Based on a Dual-Layer Attention Mechanism Decoder

Research on Multimodality Tibetan-Chinese Machine Translatio...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Quzong Gesang Chenghao He Mingjun Zhou Gadeng Lobsang Nuo Qun Tashi Nyima School of Information Science and Technology Tibet University Collaborative Innovation Center for Tibet Informatization by MOE and Tibet Autonomous Region Lhasa China School of Information Science and Technology Tibet University Lhasa China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Current Tibetan-Chinese machine translation based on text has achieved significant progress, yet it still faces numerous challenges. One of the major technical challenges is addressing ambiguity in translation. To tackle this issue, this paper proposes a dual-layer attention mechanism decoder-based method for the fusion of text and image multimodal information. In this method, the decoder, while generating words in the target language, learns to independently focus on parts of the source language words and the image through two distinct attention mechanisms. Specifically, the model effectively integrates two separate attention mechanisms: one for the source language words and another for different regions of the image. the empirical findings indicate that the approach introduced within this study has achieved a marked enhancement in translation quality, outperforming the Transformer and other RNN for pure text translation by 2.6 and 4.2 points on the BLEU scale, respectively. Additionally, the method exhibits a robust capacity to assimilate visual information and discern ambiguities within the translation process. Such advancements are conducive to refining the current neural Tibetan-to-Chinese machine translation systems, thereby augmenting the fidelity and applicability of the translations produced. It is hoped that the research presented in this paper can serve as a proof of concept to inspire future studies on Tibetan- Chinese text and image multimodal machine translation.

关键词： Visualization Attention mechanisms Image resolution Refining Transformers Decoding pattern recognition machine translation Faces

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Research and Analysis of Facial Emotion recognition Based on Convolutional Neural Network

Research and Analysis of Facial Emotion Recognition Based on...

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Computer Vision, Image and Deep learning (CVIDL), international conference on

作者： Huafeng Qu Fang Jing Yunnan College of Business Management Yunnan China

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

Product neural network is one of the most popular deep learning models, because of its good feature extraction ability and classification prediction effect, it is widely used in the field of image recognition, face recognition and natural language processing, etc. the CNN’s local connectivity, weight sharing and downsampling are the significant features that differentiate it from other network models. User emotion recognition is the basis of human-machine emotional interaction, for in the welcoming scene, the user emotion performance in the interaction process is mostly composite emotions, so the recognition of the basic emotion category can not satisfy the robot for the characterization of any user emotion, for this kind of problem, this paper proposes an emotion cognition method based on the characterization of facial features, and under the role of convolutional neural network, the human facial emotion recognition is carried out. research and exploration. Combined with the research of human facial emotion recognition under big data technology is a new field of big data used in human research, this study is based on the research foundation of previous scholars, and carries out in-depth exploration and analysis, which plays a certain role in promoting the research of human facial emotion recognition.

关键词： Deep learning Emotion recognition Image recognition Face recognition Predictive models Big data Brain modeling

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Tri-Axial Accelerometer data-Driven Senior Activity recognition: Multi-Class machine learning Approaches

Tri-Axial Accelerometer Data-Driven Senior Activity Recognit...

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Intelligent Design (ICID), international conference on

作者： Yawen Zheng Junping Wei Jinan University-University of Birmingham Joint Institute Jinan University Guangzhou China

ISBN: (数字)9798331541491

ISBN: (纸本)9798331541507

As global aging intensifies, the demand for health monitoring and services for the elderly has significantly increased. Human Activity recognition (HAR) technology is considered a key solution to addressing these challenges. this study aims to establish a standard paradigm for HAR using the HAR70+ dataset from the University of California, Irvine machine learning Repository. the dataset is extensive, and various preprocessing methods are employed. Cross-validation is used to split the training and validation sets, and multiple metrics are applied to assess model performance. the models utilized in this study include statistical, machine learning, and deep learning algorithms such as logistic regression, random forests, and long short-term memory (LSTM) networks. A comparative analysis is conducted by evaluating overall prediction accuracy, label-wise accuracy, variable combination accuracy, and loss curves. those analyses help identify the optimal algorithm for the dataset and explore the best variable combinations. the preprocessing workflows, comparison methods, and analytical approaches adopted in this study contribute to creating a standard paradigm for HAR technology.

关键词： Deep learning Adaptation models Accuracy machine learning algorithms Prediction algorithms Human activity recognition Older adults Standards Monitoring Long short term memory

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Intelligent DoS and DDoS Detection: A Hybrid GRU-NTM Approach to Network Security

Intelligent DoS and DDoS Detection: A Hybrid GRU-NTM Approac...

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Smart Electronics and Communication (ICOSEC), international conference on

作者： Caroline Panggabean Chandrasekar Venkatachalam Priyanka Shah Sincy John P Renuka Devi Shanmugavalli Venkatachalam Departement of CSE (AI) JAIN (Deemed – to – be University) Bangalore Karnataka Departement of CSE (AIML) JAIN (Deemed – to – be University) Bangalore Karnataka Department of CSE KSR College of Engineering Namakkal Tamil Nadu

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Detecting Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks remains a critical challenge in cybersecurity. this research introduces a hybrid deep learning model combining Gated Recurrent Units (GRUs) and a Neural Turing machine (NTM) for enhanced intrusion detection. Trained on UNSW-NB15 and BoT-IoT datasets, the model employs GRU layers for sequential data processing and an NTM for long-term pattern recognition. the model achieves 99% accuracy in distinguishing between normal, DoS, and DDoS traffic. this approach offers promising advancements in realtime threat detection, potentially improving network security across various domains.

关键词： Accuracy Turing machines Network intrusion detection Network security Denial-of-service attack threat assessment Real-time systems pattern recognition Resource management Computer crime

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Short Analysis of machine learning and Deep learning Techniques used for Glaucoma Detection

Short Analysis of Machine Learning and Deep Learning Techniq...

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international conference on Smart Systems and Inventive Technology (ICSSIT)

作者： E. Archana S. Geetha G.Victo Sudha George Department of Computer Science and Engineering Dr.M.G.R.Educational and Research Institute

Glaucoma is a neurodegenerative and chronic ocular disease derived from optic neuropathy and caused by visual disturbances regarding degeneration of optic nerve fiber and cupping in the optic disc. Recently, glaucoma detection and monitoring need precise eye evaluation and additional testing. Owing to the advance of artificial intelligence, deep learning methods require to process the availability of huge-scale datasets and recent progressions in computing technology. Moreover, these deep learning methods produce higher performance in pattern recognition and image classification tasks. the clinical studies are hugely derived based on the auxiliary imaging tests, where the utilization of deep learning and machine learning techniques helps analyze the huge range of medical data attained from the imaging tests particularly for glaucoma to evaluate the macula and optic nerve. Hence, this work aims to make an analysis of deep learning and machine learning algorithms used for detecting glaucoma diseases by considering the recently developed works. this review includes the algorithmic classification, datasets used, performance measures,and also the tools used for their implementation. In addition, the chronological review was also added. Finally, the current research gaps are discussed to understand the complexity of findingsuitable solutions for glaucoma detection in practical applications.

关键词： Deep learning Optical fibers Integrated optics Visualization machine learning algorithms Biomedical optical imaging Current measurement

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data Selection Using Genetic Algorithm to Improve Transfer learning Efficiency in Brain-machine-Interface Systems

Data Selection Using Genetic Algorithm to Improve Transfer L...

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international conference on Control and Robots (ICCR)

作者： Shuichi Suguro Goragod Pongthanisorn Shin-ichiro Kaneko Genci Capi Dept. of Mechanical Eng. Graduate School of Science and Engineering Hosei University Tokyo Japan Department of Electrical and Control Systems Engineering National Institute of Technology Toyama College Toyama Japan

data selection has a great effect on the performance of trained Convolutional Neural Networks (CNNs) for Brain machine Interface systems. In this paper, we combine transfer learning and Genetic Algorithms to optimize the training data of CNNs. We implement transfer learning between different subjects. the data selected by GA to improve the performance of the CNN after transfer learning can be used to compare the similarity of the brain activity between subjects. the results show that brain data selected by GA improves the CNN recognition accuracy and reduces the training time. In addition, the performance of trained CNN using transfer learning on different days shows that although the performance deteriorates still it is acceptable.

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