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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 2833 条记录，以下是171-180 订阅

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Modeling of Metaheuristics with Deep Transfer learning Based Visual data mining for the Healthcare Sector 6

Modeling of Metaheuristics with Deep Transfer Learning Based...

引用

6th Iraqi international conference on Engineering Technology and its Applications, IICETA 2023

作者： Abed, Z. Shnawa, Ammar H. Hadi, M.R. Ibrahim, Karrar Adnan, Myasar Mundher Hameed, Waleed Ahl Al Bayt University Karbala Iraq National University of Science and Technology Dhi Qar Iraq Altoosi University College Najaf Iraq Al-Zahraa University for Women Karbala Iraq The Islamic University Najaf Iraq Medical Technical College Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350303339

data mining is a process that interacts with a large dataset to determine complex interesting patterns from unknown structured data. Visual data mining (VDM) is considered a combination of two disciplines: data mining and visualization to explore useful and implicit knowledge from a huge dataset. Also, it is strongly associated with multimedia systems, high-performance computing, computer graphics, human-computer interaction, and pattern recognition. Recently, VDM approaches find useful in the healthcare sector to aid the decision-making process. In addition, the design of VDM approaches for healthcare applications needs special consideration to make sure security of data. Artificial intelligence (AI) methods play a vital role in achieving scalability and accurate analysis from real-time environments. therefore, this study develops a blockchain-assisted quantum bacterial colonial optimization with deep learning (BAQBCO-DL) algorithm for VDM in the healthcare environment. the proposed BAQBCO-DL model exploits BC technology for secured data communication in the healthcare sector. In addition, the BAQBCO-DL model designs a U-Net-based segmentation with an optimal densely connected network (DenseNet) model for feature extraction, and the hyperparameter tuning process is performed using the QBCO algorithm. Finally, a probabilistic neural network (PNN) classifier is utilized to define appropriate class labels. An extensive experimental analysis is conducted to report the superior performance of the BAQBCO-DL technique and the outcomes are measured under several aspects. the simulation outcomes underlined the supremacy of the BAQBCO-DL algorithm over recent methodologies. © 2023 IEEE.

关键词： data mining

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Efficient machine learning Approach for Diabetes Mellitus Disease Prediction 6

Efficient Machine Learning Approach for Diabetes Mellitus Di...

引用

6th international conference on pattern Analysis and Intelligent Systems, PAIS 2024

作者： Oualid, Mecili Hadj, Barkat Nouioua, Farid Attia, Abdelouahab Samir, Akhrouf University Mohamed El Bachir El Ibrahimi of Bordj Bou Arreridj Algeria Aix-Marseille University Lis UMR-CNRS 7020 Marseille France University Mohamed El Bachir El Ibrahim of Bordj Bou Arreridj Lmse Laboratory Laboratory Materials And Electronic System Algeria Mohamed Boudiaf University M'sila Algeria

ISBN: (纸本)9798350350265

Diabetes mellitus, a widely recognized chronic disease often termed a silent killer, disrupts insulin production, leading to elevated blood sugar levels and complications affecting organs including the eyes, kidneys, and nerves. Despite significant research interest, diabetes classification-based machine learning methods suffer from low performance due to missing data and imbalanced class data. thus, this paper introduces a new method for Diabetes Mellitus classification for Imbalanced data with Missing values (DMIM). the proposed approach involves these key steps. Firstly, missing values are preprocessed using the Isolation Forest method that provides data normalization. then, an adaptive synthetic sampling technique, Smote T-link, is employed to counter the impact of class imbalance. Finally, we utilized a Random Forest (RF) classifier and conducted experiments on the Pima Indians diabetes dataset from the University of California. Our proposed approach yielded highly accurate predictions, as evidenced by impressive performance metrics such as an Area Under the Curve (AUC) of 97.2%, Specificity (SP) of 91.7%, and Recall (Rec) of 92.3%, Further comparison between other ML methods including k-nearest Neighbors, Decision Trees, Random Forest, AdaBoost, Naive Bayes, XGBoost, Extra Trees. the results demonstrate the effectiveness and superiority of our proposed method and improved diabetes classification performance compared to existing techniques. © 2024 IEEE.

关键词： Forecasting

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Seismic Magnitude Determination Based on Deep learning 6

Seismic Magnitude Determination Based on Deep Learning

引用

6th international conference on Communications, Information System and Computer Engineering, CISCE 2024

作者： Mao, Fei Huang, Yongming College of Software Southeast University Nanjing China College of Automation Southeast University Nanjing China

ISBN: (纸本)9798350353174

Earthquakes are a natural disaster that have serious impacts on human society. Accurately determining the magnitude of earthquakes is crucial for understanding their danger and taking appropriate measures. Traditional methods for seismic magnitude determination face challenges such as data complexity and computational complexity. With the development of deep learning technology, convolutional neural networks have been introduced as a powerful tool for pattern recognition in the field of seismology. this study aims to investigate the effectiveness of seismic magnitude determination methods based on convolutional neural networks. In this study, we designed a deep convolutional neural network model to automatically extract features from seismic waveform data and estimate magnitudes. We conducted experiments using a large-scale earthquake dataset from seismic monitoring networks for validation. the experimental results demonstrate that compared to traditional methods, seismic magnitude determination based on convolutional neural networks achieves significant improvements in accuracy and generalization ability. © 2024 IEEE.

关键词： Convolutional neural networks

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Improved R3D for Human Action recognition Based on Transfer learning 6

Improved R3D for Human Action Recognition Based on Transfer ...

引用

6th international conference on Robotics and Computer Vision, ICRCV 2024

作者： Han, Xiaowei Guo, Qiuyang Lu, Yunjing Liu, Jiwu Fei, Chaolong Harbin University of Commerce Postdoctoral research workstation of Northeast Asia Service Outsourcing Research Center Station of Applied Economics Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbi China Harbin University of Commerce Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin China Guangdong Technology College Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Zhaoqing China

ISBN: (纸本)9798331527426

Human action recognition is an important research direction in the field of computer vision and machine learning, which is widely used in intelligent surveillance, human-computer interaction, sports analysis and other fields. Traditional action recognition methods usually rely on large-scale labeled datasets and complex feature extraction processes, which have problems such as high data acquisition cost and long model training time, as an effective solution, transfer learning can significantly improve the performance of the model and reduce the dependence on labeled data by transferring the features learned by the pre-trained model on large-scale datasets to the target task. In this paper, the R3D model is used as the backbone network, and the model is improved by strengthening the channel and temporal feature extraction capabilities. the model achieves good performance on Shopping Action_ v l by transfer learning based on HMDB51 and UCF101 datasets, and obtains 89.53% and 94.14% of the optimal Top-1 and Top-5 indicators after pre-training on UCF101 dataset. In summary, the C-TSAB ResNets model is proposed in this paper performs well in accuracy and robustness on human action recognition tasks through transfer learning, which verifies the actual effect of the innovation in the research work of this topic. © 2024 IEEE.

关键词： Convolutional neural networks

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Predictive Monitoring of Business Process Execution Delays 6th

Predictive Monitoring of Business Process Execution Delays

引用

6th international conference on Information and Knowledge Systems (ICIKS)

作者： Ben Fradj, Walid Turki, Mohamed Univ Sfax MIRACL Lab ISIMS POB 242 Sfax 3021 Tunisia

ISBN: (纸本)9783031516634;9783031516641

Nowadays, organizations are becoming increasingly aware of the importance of better utilizing their knowledge assets and adopting a quality management model based on a process approach. this can be achieved by adopting a multidisciplinary approach that combines the fields of Knowledge Management, Business Process Management and Process mining. therefore, to improve their performance and increase their responsiveness, organizations must identify, manage and monitor all the business processes (BP) that are likely to mobilize crucial knowledge. In fact, implementing an IT system that automates business processes is necessary to achieve these goals. In this context, we propose a new method for predicting the execution times of business processes baptized BPETPM. this method is based on the CRISP-DM approach. We used a Process mining techniques particularly the machine learning to exploit the execution data of a workflow engine. In order to prove the applicability of this method we have developed an intelligent system for predicting execution time of BP named iBPMS4PET. the applicative framework of this research work is the incoming mail management process as part of a group health insurance.

关键词： Business Process Management Process mining Knowledge Management machine learning CRISP-DM

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Classification and recognition Method of Bearing Fault Based on SDP-CNN 6th

Classification and Recognition Method of Bearing Fault Based...

引用

conference of the-Efficiency-and-Performance-Engineering-Network (TEPEN) / 6th international conference on Maintenance Engineering (IncoME)

作者： Wang Xing-he Wang Hong-jun Cui Ying-jie Liu Ze-rui Beijing Informat Sci & Technol Univ Mech Elect Engn Sch Beijing 100192 Peoples R China Beijing Int Sci Cooperat Base High End Equipment Beijing 100192 Peoples R China MOE Key Lab Modern Measurement & Control Technol Beijing 100192 Peoples R China

ISBN: (纸本)9783030990756;9783030990749

In view of the problems that the signal features are difficult to extract when a fault occurs in a rolling bearing, and the time domain image of the original vibration signal cannot obviously show the feature differences of different faults, and the direct deep feature learning and recognition will have a large impact on the system performance, etc., a bearing fault classification and recognition method based on symmetry dot pattern-convolutional neural network (SDP-CNN) is proposed. First, the SDP method is used to analyze the vibration signals of different faults, and the signal SDP images obtained can clearly show the feature differences of different faults;then, the SDP images are input into the CNN network for feature learning and state recognition;finally, Validation was performed using the Case Western Reserve University (CWRU) bearing dataset. the results show that the recognition accuracy of this method is 97.5%, which further verifies that the deep learning algorithm can adaptively extract the features of the SDP image and effectively identify bearing faults.

关键词： Symmetry dot pattern (SDP) Convolutional neural network (CNN) Feature learning State recognition

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A Pre-game Item Recommendation Method Based on Self-Supervised learning 6

A Pre-game Item Recommendation Method Based on Self-Supervis...

引用

6th IEEE international conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Bao, Zhenshan Sun, Xiaochuan Zhang, Wenbo Beijing University of Technology Faculty of Information Technology Beijing China

ISBN: (纸本)9798350325485

With the development of e-sports, Multiplayer Online Battle Arena (MOBA) has become the most popular game type in the world. In addition to bringing huge commercial value, it also produces a large amount of rich public game data, which attracts researchers to deeply combine deep learning technology with MOBA. Among them, item recommendation is the core task, including pre-game item recommendation and in-game item recommendation. Compared with in-game item recommendation, pre-game recommendation faces serious data sparsity and missing data labels, which makes pre-game item recommendation a hard challenge. Existing methods pay more attention to conventional indicators such as precision and recall, but ignore the usability of results in MOBA, and have not fully considered the relationship between items. In this paper, we propose a pre-game item recommendation method based on self-supervised learning. While repairing the missing labels, the Transformer encoder is used to learn the relationship between items. Experimental results on a public dataset show that our method outperforms previous methods in terms of precision and usability indicators. © 2023 IEEE.

关键词： Deep learning

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Adv-Cut Paste: Semantic adversarial class specific data augmentation technique for object detection 26

Adv-Cut Paste: Semantic adversarial class specific data augm...

引用

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

作者： Arun, Kumar S. Pal, Abhijit Mopuri, Konda Reddy Gorthi, Rama Krishna Indian Inst Technol Tirupati Tirupati Andhra Pradesh India Indian Inst Technol Guwahati Gauhati India

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

data augmentation has been a prevalent approach in improving the performance of deep learning models against slight variations in data. Adversarial learning is one such form of data augmentation. In this work, we aim to introduce a framework to generate harder examples for a specific object class and an adversarial attack for the object detection task. We have also presented our study on the effect of training against such generated harder examples and adversarial samples in object detection. We have applied this adversarial learning technique to a YOLOv3 model and due to the nature of the attack, we demonstrated a substantial improvement in average precision (AP) for a single class of the COCO dataset. As per the literature, we are the first to introduce this kind of class-specific data augmentation strategy in object detection. With our approach, we have shown an improvement of 23.34% in AP for Cat class and 3.1% on overall mAP of YOLOv3 model on clean validation data, while 43.5% improvement in AP for the Cat class on the composite images with class-specific adversarial samples.

关键词： Training Deep learning Semantics Object detection data models Adversarial machine learning pattern recognition

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Multi-Class Hypersphere Anomaly Detection 26

Multi-Class Hypersphere Anomaly Detection

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

作者： Kirchheim, Konstantin Filax, Marco Ortmeier, Frank Otto von Guericke Univ Dept Comp Sci Magdeburg Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

machine learning-based classification algorithms typically operate under assumptions that assert that the underlying data generating distribution is stationary and draws from a finite set of categories. In some scenarios, these assumptions might not hold, but identifying violating inputs - here referred to as anomalies - is a challenging task. Recent publications propose deep learning-based approaches that perform anomaly detection and classification jointly by (implicitly) learning a mapping that projects data points to a lower-dimensional space, such that the images of points of one class reside inside of a hypersphere, while others are mapped outside of it. In this work, we propose Multi-Class Hypersphere Anomaly Detection (MCHAD), a new hypersphere learning algorithm for anomaly detection in classification settings, as well as a generalization of existing hypersphere learning methods that allows incorporating example anomalies into the training. Extensive experiments on competitive benchmark tasks, as well as theoretical arguments, provide evidence for the effectiveness of our method. Our code is publicly available(1).

关键词： learning systems Training Codes Performance gain Benchmark testing Linear programming Classification algorithms

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Surface Defect Detection Based on machine learning and data Augmentation 6

Surface Defect Detection Based on Machine Learning and Data ...

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6th IEEE international conference on Civil Aviation Safety and Information Technology, ICCASIT 2024

作者： Liu, Hengxu Xu, Yuqing Huang, Jiwang Bao, Qiaozhi Wen, Zhenhua Qinghai University Xining China Qingdao Agricultural University Qingdao China Anhui Agricultural University Hefei China North Carolina State University Raleigh United States Guangzhou Vocational and Technical University of Science and Technology Guangzhou China

ISBN: (纸本)9798350389418

the development of industrial automation and intelligent manufacturing has promoted the research of computer vision and machine learning in the automated detection of surface defects in metal and plastic products, to solve the problems of low efficiency and high inconsistency in traditional manual detection. In response to such demandsthis paper proposes a lightweight model that can be used for high-precision industrial defect detection. data Preprocessing First, we use Python to write programs to read images. Secondly, image features are extracted. In this paper, the HOG algorithm is used to extract the features of the image, and the principal component analysis (PCA) is used to reduce the dimension of the feature. Finally, we trained a defect target detection model based on grid division and LightGBM, which we call the Grid-HOG-PCA-LightGBM model. the recognition accuracy of the model can reach 98%, and it only takes up 4000B memory size. Our models not only enable automated inspection, increasing inspection efficiency and accuracy;they also reduce costs in the production process by enabling continuous and efficient inspection. © 2024 IEEE.

关键词： Plastic products

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