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GRC-Net: Grouping Guided R-band Chromosome recognition Network 24

GRC-Net: Grouping Guided R-band Chromosome Recognition Netwo...

引用

10th international conference on computing and Artificial Intelligence, ICCAI 2024

作者： Xia, Chao Liu, Wei Liu, Zhao Jiang Chen, Bing Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Shanghai Institute of Hematology State Key Laboratory of Medical Genomics National Research Center for Translational Medicine at Shanghai Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China

ISBN: (纸本)9798400717055

Chromosome recognition is a critical and time-consuming process in karyotyping, especially for R-band chromosomes with poor visualization quality. In this paper, we propose an end-to-end grouping guided R-band chromosome recognition method GRC-Net. GRC-Net serves the chromosome recognition task as the main task and takes the chromosome length grouping task and centromere position grouping task as auxiliary tasks. Two auxiliary modules, Chromosome Length Grouping Module (CLGM) and Centromere Position Grouping Model (CPGM), are designed to extract the task-specific feature and refine the feature map of the main task. A large-scale R-band chromosome dataset with 1735 cases was collected. Experiment results on the 423 testing cases show that the proposed GRC-Net gets the highest accuracy of 96.87%, outperforming the baseline by 2.24%. With grouping task-guided feature extraction, GRC-Net reduces approximately 50% of the inter-group misclassification. The proposed GRC-Net can serve as a general framework for incorporating the domain knowledge into the process of feature learning, meanwhile, a powerful tool to assist clinical chromosome karyotyping. © 2024 ACM.

关键词： Feature extraction

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7th international conference on soft computing: Theories and Applications, SoCTA 2023

7th International Conference on Soft Computing: Theories and...

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7th international conference on soft computing: Theories and Applications, SoCTA 2023

ISBN: (纸本)9789819720880

The proceedings contain 36 papers. The special focus in this conference is on soft computing: Theories and Applications. The topics include: Detection of Duplicate Question Pairs by Applying Proposed BoW, TF-IDF, and USE Approach;the Hand Glove Enabling Voice and Text Communication;advancing Rheumatoid Arthritis Care: Exploring Technological Breakthroughs and Future Directions;twitter Trolling Detection Using Machine Learning;HMHDTML: Human Mental Health Detection Using Text and Machine Learning Model;analyzing the Growth Profile of Brain Tumor with Caputo Fractional Operator via Sumudu Transform;multi-labelled Topic Classification of Research Articles Using Machine Learning;security in Mobile Ad Hoc Networks: Impact of Attacks and Countermeasure Approaches;optimization of Vibrational Frequencies for Orthotropic Parallelogram Plates With Circular Variations in Tapering at Simply Supported Boundary;drowsiness Detection Using Adaboost Method and Haar Cascade Classifier to Improve Safety of Drivers;early Detection of Colorectal Cancer from Polyps Images Using Deep Learning;motion Control of Underactuated Cart-Double-Pendulum System Via Fractional-Order Sliding Mode Controller;a Comparative Study of Pedestrian Detection Techniques Over the Last Decade;approximation Properties of Modified-Bernstein Operators Having Szász Weight Functions;fourier-Laguerre Expansion of Signals by Composite Summable Technique;a Multiple Linear Regression Model to Estimate Global, Direct and Diffuse Irradiance in Gurugram, India, Using Python;supervised Machine Learning Approaches for Customer Reviews Sentiment Analysis;stock Price Prediction on Indian Share Market Using Machine Learning;transparent Price Forecasting for Basic Food Commodities in a Developing Economy;Parallel Deep Convolution Neural Network (P-DCNN) Prediction of Paddy Crop Disease;Convolutional-LSTM Network for Emotion recognition Using EEG Data in Valence-Arousal Dimension;Analysis of Multiply-Accumulate (MAC) Unit Using C

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Optimized Non-Maximum Suppression for Multi-Source Local Discharge PRPD pattern Identification 14

Optimized Non-Maximum Suppression for Multi-Source Local Dis...

引用

14th international conference on Electronics, Communications and Networks, CECNet 2024

作者： Fang, Yizhi Lin, Yuling Li, Tianshu Qiu, Zicong Huang, Yuhua Yi, Xiabo Zhuhai Power Supply Bureau Guangdong Power Grid Corporation Guangdong Province Zhuhai519000 China Zhuhai Yite High Tech Co. Ltd Guangdong Zhuhai519000 China

ISBN: (纸本)9781643685632

The multi-source partial discharge PRPD pattern can realize the pattern recognition of multi-source partial discharge types through the target detection algorithm training and identification of shape features. However, when the characteristics of different discharge pattern overlap, the small target is easily blocked by the large target, resulting in false detection and missed detection. Therefore, this paper proposes a multi-source partial discharge PRPD pattern identification algorithm with optimized non-maximum suppression. The soft-NMS algorithm was introduced to solve the missed detection caused by overlapping targets;GIoU was used to replace the traditional IoU to calculate the similarity between targets and the loss function was optimized;the YOLOv7 network model was further built to identify the PRPD pattern of four typical discharges shape features. After cross-validation between simulation experiments and charged field data, the results prove that the average detection accuracy of the algorithm can reach 98.2% in simulation experiments and 88.4% in field experiments, effectively reducing the false detection rate and successfully identifying the characteristics of multi-source local discharge PRPD pattern when the targets overlap. © 2024 The Authors.

关键词： Multi-source partial discharge PRPD pattern soft-NMS Target detection YOLOv7

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BONE POROSITY AND THICKNESS EVALUATION BY ULTRASONIC SIGNALS USING pattern recognition 18

BONE POROSITY AND THICKNESS EVALUATION BY ULTRASONIC SIGNALS...

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18th international conference on Interfaces and Human Computer Interaction 2024, IHCI 2024, the 17th international conference on Game and Entertainment Technologies 2024, GET 2024 and the 18th international conference on Computer Graphics, Visualization, Computer Vision and Image Processing 2024, CGVCVIP 2024, Part of the 18th Multi conference on Computer Science and Information Systems 2024, MCCSIS 2024

作者： Mamontovs, Maksims Sisojevs, Aleksandrs Tatarinov, Alexey Riga Technical University 6A Ķīpsalas Str. Riga Latvia Institute of Electronics and Computer Science 14 Dzerbenes Str. Riga Latvia

ISBN: (纸本)9789898704580

The fragility of long bones in osteoporosis is linked to a decrease in cortical layer thickness (CTh) and the development of internal porosity (P) within the cortex. This study is dedicated to investigate whether it is possible and how accurately P and CTh can be determined using a pattern recognition methods, utilizing discrete Fourier transform (DFT) analysis of ultrasonic signals transmitted through a soft tissue layer. As input data, ultrasound scans of phantom of cortical bone with different grades of CTh and P were used. Each of the phantom scans was chosen in turn as a test object, while other specimens composed the training set. For analysis, three different existing approaches were applied firstly and the later one was developed by improving the existing ones. The analysis was carried out in three categories: P and CTh ratio, P only and CTh only. Based on the results of each analysis, a method was obtained that predicts P and CTh better than other applied ones. A comparison of the methods for P and CTh determining and choosing the most effective ones are shown. © 2024 Interfaces and Human Computer Interaction. All Rights Reserved.

关键词： Discrete Fourier transforms

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7th international conference on soft computing: Theories and Applications, SoCTA 2023

7th International Conference on Soft Computing: Theories and...

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7th international conference on soft computing: Theories and Applications, SoCTA 2023

ISBN: (纸本)9789819720309

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S-FINCH: An Optimized Streaming Adaptation to FINCH Clustering 26

S-FINCH: An Optimized Streaming Adaptation to FINCH Clusteri...

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

作者： Cunningham, James Davis, Jim Tarplee, Kyle Vasquez, Juan Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA Univ Dayton Res Inst Dayton OH 45469 USA Air Force Res Lab Wright Patterson AFB OH 45433 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Real-world datasets are growing ever larger and more dynamic, motivating the need for efficient online data exploration algorithms. In this work, we present S-FINCH, a streaming domain optimization of the recent FINCH clustering method. The original FINCH approach demonstrated state-of-the-art offline performance while avoiding sensitive hyperparameters but was not designed for the online data streaming domain. The proposed S-FINCH method is an exact, incremental update to the FINCH approach which generates high-quality clusters while avoiding the potential sensitivities of other online clustering methods. We also provide alternative cluster tree representatives for faster empirical cluster times. Experiments are shown comparing the original FINCH approach to the proposed S-FINCH method in a streaming domain with multiple benchmark synthetic and real datasets. The S-FINCH method leverages the ability to make local changes for efficient, real time updates and can be applied to multiple data streaming scenarios.

关键词： Sensitivity Heuristic algorithms Clustering methods soft sensors Clustering algorithms Data science Benchmark testing

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Optical-Electrical Nonlinear Feedback Assisted Photonic Circuits for Temporal pattern recognition 27

Optical-Electrical Nonlinear Feedback Assisted Photonic Circ...

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27th OptoElectronics and Communications conference/international conference on Photonics in Switching and computing, OECC/PSC 2022

作者： Cong, Guangwei Yamamoto, Noritsugu Yamada, Koji Japan

We have proposed projection-based photonic circuits for nonlinear classification and here we extend our work to temporal pattern recognition by adding optical-electrical nonlinear feedback module to this photonic circ... 详细信息

ISBN: (数字)9784885523366

ISBN: (纸本)9784885523366

关键词： Timing circuits

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6th international conference on soft computing and Data Mining, SCDM 2024

6th International Conference on Soft Computing and Data Mini...

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6th international conference on soft computing and Data Mining, SCDM 2024

ISBN: (纸本)9783031669644

The proceedings contain 41 papers. The special focus in this conference is on soft computing and Data Mining. The topics include: Minimal Data for Maximum Impact: An Indonesian Part-of-Speech Tagging Case Study;alleviating Sparsity to Enhance Group Recommendation with Cross-Linked Domain Model;evaluating Deep Transfer Learning Models for Detecting Various Face Mask Wearings;classification of Stunting Events: Case Study in West Java, Indonesia;the Effects of Data Reduction Using Rough Set Theory on Logistic Regression Model;robust Heart Disease Prognosis: Integrating Extended Isolation Forest Outlier Detection with Advanced Prediction Models;overlapping Granular Clustering: Application in Fuzzy Rule-Based Classification;improved Rough-Multiple Regression for Unemployment Rate Model in Indonesia;utilizing Machine Learning for Gene Expression Data: Incorporating Gene Sequencing, K-Mer Counting and Asymmetric N-Grams Features;Text Sentiment Analysis on VIX’s Impact on Market Sentiment Dynamics;multilevel Monte Carlo Simulation Model for Air Pollution Index Prediction of a Smart Network;an In-Depth Strategy using Deep Generative Adversarial Networks for Addressing the Cold Start in Movie Recommendation Systems;predicting Undergraduate Academic Success with Machine Learning Approaches;comparative Assessment of Facial Expression recognition Models for Unraveling Emotional Signals with Convolutional Neural Networks;Evaluating Path-Finding Algorithms for Real-Time Route Recommendation System Built using FreeRTOS;machine Learning-Based Phishing Website Detection: A Comparative Analysis and Web Application Development;Comparative Performance of Multi-level Pre-trained Embeddings on CNN, LSTM and CNN-LSTM for Hate Speech and Offensive Language Detection;improved Classifier Chain Method Based on Particle Swarm Optimization and Genetic Algorithm for Multilabel Classification Problem;sentiment Analysis on Umrah Packages Review in Malaysia.

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nuggets: Data pattern Extraction Framework in R 21st

nuggets: Data Pattern Extraction Framework in R

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21st international conference on Modeling Decisions for Artificial Intelligence (MDAI)

作者： Burda, Michal Univ Ostrava Inst Res & Applicat Fuzzy Modeling CE IT4Innovat 30 Dubna 22 Ostrava 70103 Czech Republic

ISBN: (纸本)9783031682070;9783031682087

nuggets is a framework for subgroup discovery, contrast and emerging patterns, association rules, and more. Developed as a package for the R statistical environment, nuggets provides a novel and extensible toolkit for performing rule-based analyses. Both crisp (Boolean) and fuzzy data are supported. The package generates conditions in the form of elementary conjunctions, evaluates them on a dataset, and checks the induced sub-data for interesting statistical properties. A user defined function may be evaluated on generated sub-dataset, which provides a novel generality. The aim of this paper is to present that free software to the soft computing community, as the tool could be useful to both researchers and analysts in the domain of pattern mining, as, besides searching for various existing pattern types, brand new ideas may be easily implemented and evaluated within that framework.

关键词： fuzzy association rules contrast patterns subgroup discovery emerging patterns R package

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Artificial Intelligence and Image Processing Techniques for Blood Group Prediction 5

Artificial Intelligence and Image Processing Techniques for ...

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5th IEEE international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Gupta, Tannmay Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350383522

The classification and prediction of blood group is most important aspect for the transfusion of blood. In present situations, they are done in laboratory using manual process. This is a time-consuming process and hence need manual energy. To overcome the constraints in the prediction of conventional methods in blood group, the artificial intelligence is implemented. This includes the image processing techniques with segmentation process to detect the classification of blood group. They are done through MATLAB simulations to detect the blood components. Through collecting the blood samples and processing and classified the images with feature extraction leads to govern the variety of blood based on ABO and Rh group systems. To overcome the drawbacks in the conventional process, the developed methodology is implemented. This reduces various manual errors. Thus, the image processing technique with artificial intelligence helps to determine the classification of blood rapidly without any errors. © 2024 IEEE.

关键词： artificial intelligence blood group prediction feature extraction image processing pattern recognition segmentation Transfusion

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