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Machine Learning Techniques for pattern recognition in High-Dimensional Data Mining

Machine Learning Techniques for Pattern Recognition in High-...

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Electronic Information Engineering and Computer Communication (EIECC), international conference on

作者： Pochun Li Northeastern University Boston USA

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

this paper proposes a frequent pattern data mining algorithm based on support vector machine (SVM), aiming to solve the performance bottleneck of traditional frequent pattern mining algorithms in high-dimensional and sparse data environments. By converting the frequent pattern mining task into a classification problem, the SVM model is introduced to improve the accuracy and robustness of pattern extraction. In terms of method design, the kernel function is used to map the data to a high-dimensional feature space, so as to construct the optimal classification hyperplane, realize the nonlinear separation of patterns and the accurate mining of frequent items. In the experiment, two public datasets, Retail and Mushroom, were selected to compare and analyze the proposed algorithm with traditional FP-Growth, FP-Tree, decision tree and random forest models. the experimental results show that the algorithm in this paper is significantly better than the traditional model in terms of three key indicators: support, confidence and lift, showing strong pattern recognition ability and rule extraction effect. the study shows that the SVM model has excellent performance advantages in an environment with high data sparsity and a large number of transactions, and can effectively cope with complex pattern mining tasks. At the same time, this paper also points out the potential direction of future research, including the introduction of deep learning and ensemble learning frameworks to further improve the scalability and adaptability of the algorithm. this research not only provides a new idea for frequent pattern mining, but also provides important technical support for solving pattern discovery and association rule mining problems in practical applications.

关键词： Support vector machines Deep learning Machine learning algorithms Accuracy Scalability Robustness Classification algorithms Association rule learning Data mining Random forests

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Keynote Talk 4: Virtual and Augmented Reality Animals in Smart and Playful Cities

Keynote Talk 4: Virtual and Augmented Reality Animals in Sma...

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international conference on Informatics, Electronics & Vision (ICIEV)

作者： Anton Nijholt University of Twente

ISBN: (数字)9781728193311

ISBN: (纸本)9781728193328

Our future urban environments are smart. Sensors and actuators are embedded in these environments and their inhabitants. We have an Internet of things, where the `things' include objects, cars, tools, buildings, street furniture, and w hatever can be equipped with sensors and actuators, including human and non non-human animals. Augmented humans and augmented animals have their senses extended with digital technology. their smart wearables connected with the smart environment make humans and animals smarter. Rather than on living animals, in this survey paper we focus on non non-living virtual and augmented reality non non-human animals that will inhabit our smart and playable urban environments. they will co co-exist with robotic animals and (digitally augmented) humans and nonhuman animals. We include observations on augmented humans interacting with virtual and augmented reality animals. the paper is meant to raise awareness for the possibilities of augmented reality to introduce virtual animals for s ocial, entertainment, and educational reasons.

关键词： Animals Urban areas Augmented reality Computer science Brain-computer interfaces Intelligent sensors Entertainment industry

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High performance data mining

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5th international conference on High Performance Computing for Computational Science, VECPAR 2002

作者： Kumar, Vipin Joshi, Mahesh V. Han, Eui-Hong Sam Tan, Pang-Ning Steinbach, Michael University of Minnesota 4-192 EE/CSci Building 200 Union Street SE MinneapolisMN55455 United States

ISBN: (纸本)3540008527

Recent times have seen an explosive growth in the availability of various kinds of data. It has resulted in an unprecedented opportunity to develop automated data-driven techniques of extracting useful knowledge. Data mining, an important step in this process of knowledge discovery, consists of methods that discover interesting, non-trivial, and useful patterns hidden in the data[SAD+93, CHY96]. the field of data mining builds upon the ideas from diverse fields such as machine learning, pattern recognition, statistics, database systems, and data visualization. But, techniques developed in these traditional disciplines are often unsuitable due to some unique characteristics of today’s data-sets, such as their enormous sizes, high-dimensionality, and heterogeneity. there is a necessity to develop effective parallel algorithms for various data mining techniques. However, designing such algorithms is challenging, and the main focus of the paper is a description of the parallel formulations of two important data mining algorithms: discovery of association rules, and induction of decision trees for classification. We also briefly discuss an application of data mining to the analysis of large data sets collected by Earth observing satellites that need to be processed to better understand global scale changes in biosphere processes and patterns. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Data mining

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An Enhanced Coarse-to-Fine Framework for the Segmentation of Clinical Target Volume 23rd

An Enhanced Coarse-to-Fine Framework for the Segmentation of...

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Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images Challenge, ABCs 2020, Learn2Reg Challenge, L2R 2020 and thyroid Nodule Segmentation and Classification in Ultrasound Images Challenge, TN-SCUI 2020 held in conjunction with 23rd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Chen, Huai Qian, Dahong Liu, Weiping Li, Hui Wang, Lisheng Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai200240 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Algorithm and Research Shanghai Aitrox Technology Co. Ltd. Shanghai China National Key Laboratory of Science and Technology on Nano/Micro Fabrication Key Laboratory for Thin Film and Micro Fabrication of the Ministry of Education Institute of Micro-Nano Science and Technology Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9783030718268

In radiation therapy, obtaining accurate boundary of the clinical target volume (CTV) is the vital step to decrease the risk of treatment failures. However, it is a time-consuming and laborious task to obtain the delineation by hand. therefore, an automatic algorithm is urgently needed to realize accurate segmentation. In this paper, we propose an enhanced coarse-to-fine frameworkto automatically fuse the information of CT, T1 and T2 images to get the target region. this framework includes a coarse-segmentation stage to identify the region of interest (ROI) of targets and a fine-segmentation stage to iteratively refine the segmentation. In the coarse-segmentation stage, the F-loss is proposed to keep the high recall rate of the ROI. In the fine segmentation, the ROI of target will be first cropped according to the ROI obtained by coarse-segmentation and be fed into a 3D-Unet to get the initial results. then, the prediction and medium features will be set as additional information for the next one network to refine the results. When evaluated on the validation dataset of challenge of Anatomical Brain Barriers to Cancer Spread (ABCs), our method won the 3th place in the public leaderboard. © 2021, Springer Nature Switzerland AG.

关键词： Iterative methods

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Supervoxels for human and clothes in RGB-D image

Supervoxels for human and clothes in RGB-D image

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international conference on Wireless Communication, Vehicular Technology, Information theory and Aerospace and Electronic Systems Technology (Wireless VITAE)

作者： Feng Qi Honggang Zhang Bo Zhang Zhanyu Ma Na Liu Pattern Recognition and Intelligent System Lab. Beijing University of Posts and Telecommunications Beijing China

In order to reconstruct human body and remodel clothes in virtual try on and size-fitting scenario, we preprocess the input point cloud into supervoxels through over-segmentation. Over-segmentation of an image into superpixels is a widely used preprocessing approach in image segmentation. Recently, over-segmentation algorithm, which clusters points with similar feature into a small group, known as supervoxel here, has been used on RGB-D image or the combination of observations from several calibrated RGB-D cameras to reduce occurrence of clusters crossing object boundaries. this method makes use of both color information and the 3D geometric relationships to describe features and segmented regions. We apply this method to RGB-D images of human body and clothes, so that largely reduce the number of regions that must be considered in following remodel process. Experiments were taken on point clouds captured by Kinect. Because the final goal is to predict object boundaries that represent different part of human and clothes, we further compare the recall with SLIC method for boundaries we have marked previously, and give a suggestion on choosing a better seeding radius in our scenario.

关键词： three-dimensional displays Image segmentation Image color analysis Clothing Cameras Image reconstruction Clustering algorithms

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Neural Network Models for Classifying Immune Cell Subsets

Neural Network Models for Classifying Immune Cell Subsets

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IEEE international conference on bioinformatics and Biomedicine

作者： Yongguo Mei Raquel Hontecillas Xiaoying Zhang Adria Carbo Josep Bassaganya-Riera The Center for Modeling Immunity to Enteric Pathogens Nutritional Immunology and Molecular Medicine Laboratory Virginia Bioinformatics Institute Virginia Tech Blacksburg VA 24061 USA

ISBN: (纸本)9781479913114

the immune system is composed of heterogeneous cell populations and it includes several hundreds of distinct cells types such as neutrohils, eosinophils, basophils, macrophages, dendritic cells, CD4+ and CD8+ T cells, γδ T cells, mast cells, and B cells and each main cell type can be further differentiated into subsets with unique and overlapping functions. For example CD4+ T cells can be differentiated into T helper (th)1, th2, thl7, and regulatory T cell (Treg) subsets. To study molecular mechanisms of cell differentiation, Systems Biology Markup Language (SBML) based Ordinary Differential Equation (ODE) models can be used for representing such processes. these intracellular signaling models often require many equations to accurately represent intracellular pathways and biochemical reactions. Another challenge in studying the immune system and immune responses is the need for integration of complex processes that occur at different time and space scales (i. e., populations, whole organism, tissue level, cellular and molecular) through multi-scale modeling. this study presents two novel neural network models for modeling CD4+ T cell differentiation and immune cell subset classification. the first model reduces the complex ODE intracellular model by focusing on the input and output cytokines and the second model establishes an automated subset classification based on molecular patterns expressed in immune cells. After training, the first model achieves small prediction errors of cytokine concentrations and the second model achieves 98% prediction rate for subset classification. Neural network algorithm and models have been widely used for many data mining tasks such as classification and pattern recognition. However, to the best of our knowledge this study is the first one applying the neural network algorithm for immune cell differentiation and subset classification. In addition, these novel neural network models can be computationally efficiently integrated into m

关键词： Classifying Immune ODE SBML

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Research on Traffic Sign Detection Algorithm Based on YOLO 21

Research on Traffic Sign Detection Algorithm Based on YOLO

引用

Proceedings of the 2021 4th international conference on Artificial Intelligence and pattern recognition

作者： Feng Min Ma Wenzhe Hou Zeming Wuhan Institute of Technology China

ISBN: (纸本)9781450384087

In view of the large amount of network parameters and calculations of the current two-stage target detection algorithm, and the low recognition accuracy of the single-stage target detection algorithm, an improved traffic sign detection algorithm based on yolov4-tiny is *** algorithm uses the SSD structure idea to divide the ordinary convolution into two steps, which not only reduces the computing resources and the number of parameters, but also adds a new feature extraction structure to obtain more target ***, bottom-up multi-scale fusion is used, combined with low-level information to enrich the feature level of the network to improve feature ***, CIoU is used as the bounding box regression loss function to speed up model convergence and improve the accuracy of traffic sign *** experimental results on the CCTSDB data set show that, compared with the original YOLOv4-tiny, the mAP is increased by about 12%, and the recognition speed is 10 ms. It indicates that the method proposed in this paper has a faster speed and higher accuracy.

关键词： Convolution neural network Multi-scale feature maps Small target detection Traffic signs YoLoV4-Tiny

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A Proficient Model to Classify Bangladeshi Bank Notes for Automatic Vending Machine Using a Tıny Dataset with One-Shot Learning & Siamese Networks

A Proficient Model to Classify Bangladeshi Bank Notes for Au...

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international conference on Computing and Networking Technology (ICCNT)

作者： Md. Ekram Hossain Arni Islam Md. Sanzidul Islam Daffodil International University Dhaka Bangladesh

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

Automatic vending machine is a necessity at this technological era. It's a step to go toward the vendor-less shop management, which supports the commandment of the 4th Industrial Revolution. But image recognition system needs a lot of data to get the pattern. Deep learning applications is very computationally extravagant for getting good features and find in many cases that little data cannot give good learning features. We have used and reworked the architecture of Siamese Neural Network for One-shot learning to recognize the Bangladeshi bank notes with a tiny dataset. In this article we analyzed 20 images only for 5 different Bangladeshi bank notes what people used regularly. this research will help general people to get better experience with vending machines which can recognize notes with one data example only. We used 5 notes (5,10,20,50,100 TAKA) and get excellent result with 97.38% accuracy using help of convolutional architecture.

关键词： Image recognition Training Lakes Currencies Standards Classification algorithms Biological neural networks

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A Chinese-English organization name translation system using heuristic web mining and asymmetric alignment 09

A Chinese-English organization name translation system using...

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Proceedings of the Joint conference of the 47th Annual Meeting of the ACL and the 4th international Joint conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1

作者： Fan Yang Jun Zhao Kang Liu National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781932432459

In this paper, we propose a novel system for translating organization names from Chinese to English with the assistance of web resources. Firstly, we adopt a chunking-based segmentation method to improve the segmentation of Chinese organization names which is plagued by the OOV problem. then a heuristic query construction method is employed to construct an efficient query which can be used to search the bilingual Web pages containing translation equivalents. Finally, we align the Chinese organization name with English sentences using the asymmetric alignment method to find the best English fragment as the translation equivalent. the experimental results show that the proposed method outperforms the baseline statistical machine translation system by 30.42%.

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), international conference on

作者： Dhruvi Srishwal Shibu PS Harsha Vardan Sajjad Ibrahim. A Gouthaman. P Department Of Networking And Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department Of Networking And Communications Faculty of Engineering and Technology and Technology SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Heart arrhythmias, if not detected early, can lead to severe conditions like stroke or heart failure, making timely and accurate classification critical. Traditional methods of analyzing Electrocardiogram (ECG) data are manual, time-consuming, and prone to errors due to the complexity of ECG signals. this paper presents an automated approach for arrhythmia classification using vector databases and similarity search algorithms. By converting raw ECG time-series data into vectorized representations, the system enables efficient pattern recognition and real-time analysis. Machine learning models applied to the vectorized data enhance classification accuracy, while the use of a vector database allows for quick similarity searches, facilitating real-time comparison of patient data with known arrhythmia patterns. the proposed method significantly reduces analysis time and increases precision, offering a robust framework for real-time monitoring and early detection, thereby improving patient outcomes and enabling timely medical interventions.

关键词： Heart Accuracy Databases Arrhythmia Electrocardiography Ubiquitous computing Vectors Real-time systems pattern recognition Monitoring

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