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检索条件"任意字段=5th International Conference on Pattern Recognition in Bioinformatics"

共 1408 条记录，以下是251-260 订阅

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pattern recognition of Time-Varying Signals Using Ensemble Classifiers 5th

Pattern Recognition of Time-Varying Signals Using Ensemble C...

引用

5th international conference on Microelectronics, Electromagnetics and Telecommunication, ICMEET 2019

作者： Subbarao, M. Venkata Terlapu, Sudheer Kumar Chakravarthy, V.V.S.S.S. Satapaty, Suresh Chandra Department of Electronics and Communication Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Electronics and Communication Engineering Raghu Institute of Technology Visakhapatnam India Department of Computer Science and Engineering KIIT Bhubaneswar India

ISBN: (纸本)9789811538278

A new classification approach for time-varying power quality (PQ) signals using ensemble classifiers (EC) is proposed in this paper. To achieve high performance, existing expert systems require several signal features so that these systems have more computational complexity. In order to reduce the computational cost and to improve the accuracy further, a new set of features called moments and cumulants are introduced in this paper to classify PQ events. Further, the performance of various ensemble classifiers is analyzed with the proposed feature set. Moreover, the analysis is carried out with different training and testing rates. Finally, the performance comparison is made with that of the existing techniques to prove the superiority of the proposed features and classifiers. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Expert systems

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Artificial Intelligence approaches for Predicting Hypertension Diseases: Open Challenges and Research Issues

Artificial Intelligence approaches for Predicting Hypertensi...

引用

international conference on Contemporary Computing and Informatics (IC3I)

作者： Simranjit Kaur Khushboo Bansal Yogesh Kumar Desh Bhagat University Mandi Gobindgarh Punjab Computer Science Engineering Pandit Deendayal Energy University Gandhinagar India

Despite improved therapies and decades of research over the last century, hypertension remained the world's leading avoidable cause of death. Machine learning (ML) is a subfield of artificial intelligence that integrates computer science, statistics, and decision theory to recognize complicated patterns in vast amounts of data. Because hypertension may induce a rapid rise in blood pressure, artificial intelligence approaches are used to anticipate the start of hypertension the study's main goal is to use artificial intelligence technologies like deep learning (DL) and machine learning (ML) to predict hypertension. this research will analyze the current state, challenges, and potential of adopting technologies to detect and predict hypertension diseases. thus, AI-integrated hypertension care has the ability to revolutionize clinical practice by introducing individualized approaches to prevention and treatment, such as the establishment of optimal and patient-specific blood pressure (BP) goals, and the development of interventions focusing on modifiable risk factors. Prior research has showed that machine learning has the ability to improve all areas of patient care, from research and development to everyday clinical practice and global health. the purpose of this paper is to illustrate the potential of artificial intelligence for treating hypertension by analyzing existing evidence.

关键词： Hypertension Deep learning Decision theory Medical treatment Focusing Pressure measurement pattern recognition

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A Ternary Bi-Directional LSTM Classification for Brain Activation pattern recognition Using fNIRS 5

A Ternary Bi-Directional LSTM Classification for Brain Activ...

引用

5th international conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)

作者： Wickramaratne, Sajila D. Mahmud, Md Shaad Univ New Hampshire Elect & Comp Engn Durham NH 03824 USA

ISBN: (纸本)9781728180229

Functional near-infrared spectroscopy (fNIRS) is a non-invasive, low-cost method used to study the brain's blood flow pattern. Such patterns can enable us to classify performed by a subject. In recent research, most classification systems use traditional machine learning algorithms for the classification of tasks. these methods, which are easier to implement, usually suffer from low accuracy. Further, a complex pre-processing phase is required for data preparation before implementing traditional machine learning methods. the proposed system uses a Bi-Directional LSTM based deep learning architecture for task classification, including mental arithmetic, motor imagery, and idle state using fNIRS data. Further, this system will require less pre-processing than the traditional approach, saving time and computational resources while obtaining an accuracy of 81.48%, which is considerably higher than the accuracy obtained using conventional machine learning algorithms for the same data set.

关键词： fNIRS deep learning LSTM mental arithmetic

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DarkGAN: Night Image Enhancement Using Generative Adversarial Networks 5th

DarkGAN: Night Image Enhancement Using Generative Adversaria...

引用

5th international conference on Computer Vision and Image Processing, CVIP 2020

作者： Alaspure, Prasen Hambarde, Praful Dudhane, Akshay Murala, Subrahmanyam Computer Vision and Pattern Recognition Lab IIT Ropar Rupnagar India

ISBN: (纸本)9789811610851

Low light image enhancement is one of the challenging tasks in computer vision, and it becomes more difficult when images are very dark. Recently, most of low light image enhancement work is done either on synthetic data or on the images which are considerably visible. In this paper, we propose a method to enhance real-world night time images, which are dark and noisy. the proposed DarkGAN consists of two pairs of Generator - Discriminator. Moreover, the proposed network enhances dark shades and removes noise up to a much extent, with natural-looking colors in the output image. Experimental results evaluation of the proposed method on the "See In the Dark" dataset demonstrates the effectiveness of the proposed model compared with other state-of-the-art methods. the proposed method yields comparable better results on qualitative and quantitative assessments when compared with the existing methods. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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One pattern recognition Algorithm Based on Centroids of Fuzzy Hyper-Pyramid Numbers 15th

One Pattern Recognition Algorithm Based on Centroids of Fuzz...

引用

15th international conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th international conference on Harmony Search, Soft Computing and Applications, ICHSA 2019

作者： Xu, Yifeng Wang, Guixiang Shen, Chenjie Institute of Operations Research and Cybernetics Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)9783030324551

In this paper, a pattern recognition algorithm is given based on centroids of fuzzy hyper-pyramid numbers which are special type fuzzy n-cell numbers. the specific calculation formula (which can be easy calculated by computer program in applications) is obtained for fuzzy hyper-pyramid numbers. Using the results obtained by us, we propose a algorithm of pattern recognition. At last, a practical example is given to demonstrate the application of the algorithm given by us in recognizing uncertain or imprecise multidimensional digital information. © Springer Nature Switzerland AG, 2020.

关键词： pattern recognition

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Improving the prediction accuracy of Parkinson's Disease based on pattern techniques 5

Improving the prediction accuracy of Parkinson's Disease bas...

引用

5th international conference on Devices, Circuits and Systems, ICDCS 2020

作者： Jeba Priya, S. Rani, Arockia Jansi Ubendran, Neha Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Manonmaniam Sundaranar University Research Scholar Department of Computer Science and Engineering Tirunelveli India

ISBN: (纸本)9781728163680

Parkinson's Disease is a locomotive disorder commonly found among elders and causes various physical prodromes in an affected personnel. Freezing of Gait is a prominent symptom and gait data may be used to identify the occurrence of this event. the proposed study employs binary pattern recognition such as Local Binary pattern and Extended Local Binary pattern to transform gait data into a normal distribution and then extract statistical features such as skewness, kurtosis and etc. the procured features were then classified by different classifiers to determine the model with highest performance. the performance metrics evaluated were accuracy, precision and recall, and an accuracy of 98.82% were achieved for the classifier Logistic Regression. © 2020 IEEE.

关键词： Local binary pattern

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Characterizing the Role of a Single Coupling Layer in Affine Normalizing Flows 1

引用

42nd German conference on pattern recognition, DAGM GCPR 2020 held in parallel with 25th international Symposium on Vision, Modeling, and Visualization, VMV 2020 and 10th Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2020

作者： Draxler, Felix Schwarz, Jonathan Schnörr, Christoph Köthe, Ullrich Heidelberg Collaboratory for Image Processing Heidelberg University Heidelberg Germany Visual Learning Lab Heidelberg University Heidelberg Germany Image and Pattern Analysis Group Heidelberg University Heidelberg Germany

ISBN: (数字)9783030712785

ISBN: (纸本)9783030712778

Deep Affine Normalizing Flows are efficient and powerful models for high-dimensional density estimation and sample generation. Yet little is known about how they succeed in approximating complex distributions, given the seemingly limited expressiveness of individual affine layers. In this work, we take a first step towards theoretical understanding by analyzing the behaviour of a single affine coupling layer under maximum likelihood loss. We show that such a layer estimates and normalizes conditional moments of the data distribution, and derive a tight lower bound on the loss depending on the orthogonal transformation of the data before the affine coupling. this bound can be used to identify the optimal orthogonal transform, yielding a layer-wise training algorithm for deep affine flows. Toy examples confirm our findings and stimulate further research by highlighting the remaining gap between layer-wise and end-to-end training of deep affine flows. © 2021, Springer Nature Switzerland AG.

关键词： Maximum likelihood estimation

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2020 5th international conference on Renewable Energies for Developing Countries, REDEC 2020

2020 5th International Conference on Renewable Energies for ...

引用

5th international conference on Renewable Energies for Developing Countries, REDEC 2020

ISBN: (纸本)9781728155951

the proceedings contain 93 papers. the topics discussed include: comparative study between the different MPPT techniques;sliding mode observer design for battery state of charge estimation;effect of PEF and HVED on polyphenol extraction from pomegranate peels;EVs charging and discharging model consisted of EV users behavior;comparison between LCL and LLCL filters for a grid connected inverter using selective harmonic modulation;fault pattern recognition in power distribution integrated network with renewable energy source;enhancing small-signal stability of intermittent hybrid distributed generations;modeling of the lightning current on towers connected to a malt grid;and thermal performance and adherence of local materials to the Moroccan building code requirements.

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Research on the Cost Control Model of Power Gird Maintenance Based on Fuzzy pattern recognition theory 5

Research on the Cost Control Model of Power Gird Maintenance...

引用

5th international conference on Green Materials and Environmental Engineering (GMEE)

作者： Ma, Xingbin Zhang, Suiyu Li, Moxiao Shandong Univ Sci & Technol Finance Dept Qingdao Peoples R China

the power grid lines and equipment maintenance of power enterprises is a complicated construction process, the cost of which is affected by meteorological and geographical factors, and the influence mode is uncertain. Using fuzzy clustering method and the threshold intervals of the objective function in clusters, this paper builds a predictive control model to control the project cost. this model uses relative fuzzy operator to build fuzzy matrix, construct correlation between factors, and describe the factors' effect. Extracting the cluster's eigenfunction, and defining the boundaries of various clusters, we determined the type of the predicted points and the range of the objective function. When the actual cost of the maintenance project is within the range calculated by the cost model, then it is normal. If the actual cost exceeds this range, then further analysis of all the aspects of the cost is needed to find out the reason.

关键词： Cost management predictive control fuzzy clustering control interval

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Keypoint Context Aggregation for Human Pose Estimation 11th

Keypoint Context Aggregation for Human Pose Estimation

引用

11th international conference on Image and Graphics, ICIG 2021

作者： Wu, Wenzhu Wang, Weining Guo, Longteng Liu, Jing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030873578

Human pose estimation has drawn much attention recently, but it remains challenging due to the deformation of human joints, the occlusion between limbs, etc. And more discriminative feature representations will bring more accurate prediction results. In this paper, we explore the importance of aggregating keypoint contextual information to strengthen the feature map representations in human pose estimation. Motivated by the fact that each keypoint is characterized by its relative contextual keypoints, we devise a simple yet effective approach, namely Keypoint Context Aggregation Module, that aggregates informative keypoint contexts for better keypoint localization. Specifically, first we obtain a rough localization result, which can be considered as soft keypoint areas. Based on these soft areas, keypoint contexts are purposefully aggregated for feature representation strengthening. Experiments show that the proposed Keypoint Context Aggregation Module can be used in various backbones to boost the performance and our best model achieves a state-of-the-art of 75.8% AP on MSCOCO test-dev split. © 2021, Springer Nature Switzerland AG.

关键词： Computers

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