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检索条件"任意字段=International Conference on Soft Computing and Pattern Recognition"

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Automatic Embedding Interventions for the Classification of Hematopoietic Cells 26

Automatic Embedding Interventions for the Classification of ...

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

作者： Graebel, Philipp Thull, Julian Crysandt, Martina Klinkhammer, Barbara M. Boor, Peter Bruemmendorf, Tim H. Merhof, Dorit Rhein Westfal TH Aachen Inst Imaging & Comp Vis Aachen Germany Rhein Westfal TH Aachen Univ Hosp Dept Hematol & Oncol Aachen Germany Rhein Westfal TH Aachen Univ Hosp Inst Pathol Aachen Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

The classification of hematopoietic cells is the most essential step in automating the analysis of human bone marrow samples. However, the complex structure of cell classes as well as class imbalance make this a challenging task, even for neural networks. Based on projective latent interventions, we propose automatic interventions that iteratively update a learned embedding with suitable transformations that shift different cell types apart and contract samples of the same type together. We present different ways of applying these: either directly on a higher-dimensional embedding or in a parametric version in two dimensions. We analyze the hyper-parameters and evaluate the proposed approach on a challenging dataset of hematopoietic cells. The results show an improvement of up to 3 percentage points for the classification F-score.

关键词： Representation learning Neural networks Bones pattern recognition Task analysis Contracts

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Generalized Median Computation for Consensus Learning: A Brief Survey 20th

Generalized Median Computation for Consensus Learning: A Bri...

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20th international conference on Computer Analysis of Images and patterns (CAIP)

作者： Jiang, Xiaoyi Nienkoetter, Andreas Univ Munster Fac Math & Comp Sci Munster Germany Sichuan Univ Hongkong Polytech Univ Inst Disaster Management & Reconstruct Chengdu Sichuan Peoples R China

ISBN: (纸本)9783031442360;9783031442377

computing a consensus object from a set of given objects is a core problem in machine learning and pattern recognition. A popular approach is the formulation of generalized median as an optimization problem. The concept of generalized median has been studied for numerous problem domains with a broad range of applications. Currently, the research is widely scattered in the literature and no comprehensive survey is available. This brief survey contributes to closing this gap and systematically discusses the relevant issues of generalized median computation. In particular, we present a taxonomy of computation frameworks and methods. We also outline a number of future research directions.

关键词： pattern recognition

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Single-trial detection and session detection in a rapid serial visual presentation task with covariate, target probability, and concept shifts

Single-trial detection and session detection in a rapid seri...

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international Joint conference on Neural Networks (IJCNN)

作者： Cecotti, Hubert Calif State Univ Fresno Dept Comp Sci Fresno CA 93740 USA

ISBN: (纸本)9798350359329;9798350359312

Brain-computer interfaces (BCIs) that use noninvasive electroencephalography (EEG) have to deal with the non-stationarity of the EEG signal. Capturing variations over time is not an easy task as it is dependent on many factors. In this paper, we propose to create the shift by changing the task from the training session to the test session. We propose a rapid serial visual presentation task with a covariate shift, probability shift, and concept shift. We consider two classification problems: 1) the binary classification of target vs. non-target images in a rapid serial visual presentation task, and 2) the binary classification of baseline session vs. shifted session. In the former, we consider a state-of-the-art discriminant approach using inputs time x space domain while in the latter, we consider a density-based approach with covariance matrices as inputs in different frequency bands. The results highlight the substantial drop in performance that occurs when a data shift happens (AUC approximate to 0.68). The results also show that the sessions can be classified with different frequency bands. The results support the conclusion that covariate and probability shift have a substantial effect on single-trial detection and that the difference between sessions can be discriminated.

关键词： pattern recognition neural network pooling functions

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patterns recognition of Sentimental patterns from the X-Dataset using Deep Learning Algorithm

Patterns Recognition of Sentimental Patterns from the X-Data...

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soft computing for Security Applications (ICSCSA), international conference on

作者： Praveen S R Konduri Department of Data Science School of Computing Mohan Babu University Tirupati India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

This research investigates the application of deep learning techniques to sentiment analysis, a field focused on mining online platforms for subjective assessments. By combining deep neural networks (DNNs) with particle swarm optimization (PSO), we propose a hybrid method to improve feature selection and enhance the accuracy of sentiment classification. The study employs a dataset collected from Twitter using the Ruby Twitter API and evaluates the performance of the proposed method using k-fold cross-validation. The results are compared with the previously used firefly search (FS) method to demonstrate the effectiveness of the hybrid approach. This research contributes to the advancement of sentiment analysis techniques and their application in various domains, such as social media monitoring and market research.

关键词： Deep learning Sentiment analysis Accuracy Social networking (online) Reviews Blogs Artificial neural networks pattern recognition Security Optimization

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Partial Discharge recognition in Real Switchgear Based on soft Scoring Mechanism and Multi-Mode Fusion 6

Partial Discharge Recognition in Real Switchgear Based on So...

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6th IEEE international Electrical and Energy conference, CIEEC 2023

作者： Zhang, Tianyi Di, Zhenguo Li, Simeng Bu, Zewei Wu, Hao Hui, Sisi Ji, Shengchang Offshore Oil Engineering Co. Ltd. Tianjin300451 China Xi'an Jiaotong University State Key Laboratory of Electrical Insulation and Power Equipment Xi'an710049 China

ISBN: (纸本)9798350346671

Partial discharge (PD) in switchgear is closely related to its premature insulation degradation, and accurate identification of PD types is an important means to improve its operation condition and to ensure safety of power system. In this paper, four typical PD types are designed in real switchgear: point discharge, suspension potential discharge, strong vertical component along the surface discharge and weak vertical component along the surface discharge. UHF sensor, TEV sensor, AE sensor and HFCT are combined for detection of switchgear PD, and the detection sensitivity of different sensors under the same defect and the spectral characteristics of different defect types are analyzed. Five characteristic parameters of discharge density distribution and correlation coefficient of contour of the extraction spectrum are combined to form a new feature system. A soft scoring method is proposed to identify the PD pattern of switchgear. This method solves the issues associated with traditional recognition methods that rely solely on single spectral characteristics, resulting in low recognition accuracy and inaccurate recognition types. Moreover, it offers a theoretical framework for online monitoring and fault diagnosis of switchgear equipment. © 2023 IEEE.

关键词： Partial discharges

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soft computing Approach for Student Dropouts in Education System 11th

Soft Computing Approach for Student Dropouts in Education Sy...

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11th international conference on soft computing for Problem Solving, SocProS 2022

作者： Sybol, Sumin Samuel Srivastava, Shilpa Sharma, Hemlata NCR Delhi India Sheffield Hallam University Sheffield United Kingdom

ISBN: (纸本)9789811965241

The education system has increased the number of dropouts in the coming years, decreasing the number of educated people. Education system refers to a group of institutions like ministries of education, local education bodies, teacher training institutes, universities, colleges, schools, and more whose primary purpose is to provide education to all the people, especially young people and children in educational settings. The research aims to improve the student dropout rate in the education system by focusing on students’ performance and feedback. The students’ dropout rate can be calculated based on complexity, credits, attendance, and different parameters. This study involves the extensive study that inculcates student dropout with their performance and other parameters with soft computing approaches. There are various soft computing approaches used in the education system. The approaches and techniques used are sequential pattern mining, sentimental analysis, text mining, outlier decision, correlation mining, density estimation, etc. The approaches and techniques will be beneficial to calculating and decreasing the rate of dropout of students in the education system. The research will make a unique contribution to improved education by calculating the dropout rate of students. In particular, we argue that the dropout rate is increasing, so soft computing techniques can be the solution to improvise/reduce the dropout rate. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Modeling for Stock Trends: A Study of Two-Stage pattern Strategy* 7

Modeling for Stock Trends: A Study of Two-Stage Pattern Stra...

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7th international conference on Image, Vision and computing, ICIVC 2022

作者： He, Yu Zhou, Hong Kimm, Simon Xue, Jiao Zhejiang University of Finance & Economics School of Finance Hangzhou China Renmin University of China School of Finance Beijing China Edith Cowan University School of Arts & Humanities Perth Australia Shanghai Jiao Tong University School of Design Shanghai China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

This paper focuses on how to predict stock trends quantitatively and to differentiate turning point. Through the summarizing of the main theories and methods in security technical analysis, this paper propose the hypothesis that two contiguous white bars or black bars indicate the uptrend or downtrend, which is called "Two-stage pattern strategy (TSPS)". Empirical test supports the hypothesis and shows that when the forecasted short-term trend is consistent with the bullish or bearish long-term trend, the accurate rate of the hypothesis is higher. Trading rules according to the TSPS method have also been discovered and support the method's profitability. © 2022 IEEE.

关键词： pattern recognition

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Performance Comparison of Overlapping Sound Event Detection Methods 14

Performance Comparison of Overlapping Sound Event Detection ...

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14th international conference on Cloud computing, Data Science and Engineering, Confluence 2024

作者： Tyagi, Akshay Singh, Kartikeya S Jaswanth Kumar, P.V. Garg, Prof. Rakesh Yousef Abdullah, Mohammed Edres Amity University Dept. of Computer Science and Engineering Uttar Pradesh India

ISBN: (纸本)9798350344837

Sound event detection is pivotal in various applications, including environmental monitoring and surveillance systems, enhancing situational awareness and response strategies. This paper investigates the intricacies of overlapping sound events, where multiple events occur concurrently, creating complexities in accurate detection. Traditional methods falter when confronted with this challenge, necessitating advanced approaches. This research pursues two primary objectives: comprehensive review of existing overlapping sound event detection methodologies and exploration of innovative solutions to enhance accuracy and robustness. Through meticulous analysis of prior research, this paper offers insights into field progress and identifies unexplored areas. Subsequent sections dissect existing methodologies, evaluating their strengths and limitations, and addressing challenges in common evaluation metrics and datasets. This paper contributes to the advancement of sound event detection technology, particularly in complex audio environments, promoting better solutions for real-world applications. © 2024 IEEE.

关键词： pattern recognition

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Proceedings - 2024 international conference on Electrical Drives, Power Electronics and Engineering, EDPEE 2024

Proceedings - 2024 International Conference on Electrical Dr...

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2024 international conference on Electrical Drives, Power Electronics and Engineering, EDPEE 2024

ISBN: (纸本)9798350395631

The proceedings contain 192 papers. The topics discussed include: application of artificial intelligence and big data in smart buildings;engine detection and online monitoring technology based on image recognition;mechanical parts life prediction and health monitoring system based on deep learning;research on optimal zoning of energy Internet source load matching power grid based on improved K-means algorithm;design of power load forecasting algorithm for distribution network based on machine learning;analysis of sea ice area fluctuation in the arctic circle based on big data and SARIMA model;a redesign method for embroidery pattern graphics based on multiscale image processing technology;and research on data mining of university management decision support archives based on cloud computing.

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Gait pattern recognition via Fusing Static and Dynamic Features in Single Stream Network 15

Gait Pattern Recognition via Fusing Static and Dynamic Featu...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Jain, Isha Navya Reddy, Dharini Pandey, Vijay Kumar Sandhan, Tushar IIT-K EE PIL Kanpur India

ISBN: (纸本)9798350370249

Gait refers to the walking and motion characteristics of an individual. In this paper, we suggest a unique method for analyzing human gait patterns using static as well as dynamic features passing through the same convolutional neural network. Generally, the gaits are processed using contour detection and segmentation to perform time series analysis with extracted features. Furthermore, machine learning models such as decision trees, support vector machine are also utilized for the gait classification. We initially trained our model on both cropped average images and optical flow of each gait independently. Subsequently, we fused the average image of each gait (building static information) with its corresponding optical flow (dynamic information). This novel fusion approach facilitated the training of a single stream convolutional neural network model. Our findings reveal a notable accuracy of 9 8. 7 5% in successfully recognizing individuals based on their gait patterns. Simplicity of the model and succinct fusion of static and dynamic features produce improved results with very less computational time. © 2024 IEEE.

关键词： Convolutional neural networks

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