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One-shot Distributed Algorithm for Generalized Eigenvalue Problem 22

One-shot Distributed Algorithm for Generalized Eigenvalue Pr...

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8th international conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Lv, Kexin Sun, Zheng He, Fan Huang, Xiaolin Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Quality and Business Operation Department Saic Motor Corporation Limited 489 Wei Hai Road Shanghai200041 China

ISBN: (纸本)9781450396110

Nowadays, more and more datasets are stored in a distributed way for the sake of memory storage or data privacy. the generalized eigenvalue problem (GEP) plays a vital role in a large family of high-dimensional statistical models. However, the existing distributed method for eigenvalue decomposition cannot be applied in GEP for the divergence of the empirical covariance matrix. Here we propose a general distributed GEP framework with one-shot communication for GEP. If the symmetric data covariance has repeated eigenvalues, e.g., in canonical component analysis, we further modify the method for better convergence. the theoretical analysis on approximation error is conducted and the relation to the divergence of the data covariance, the eigenvalues of the empirical data covariance, and the number of local servers is analyzed. Numerical experiments also show the effectiveness of the proposed algorithms both in the synthetic dataset and real-world datasets. © 2022 ACM.

关键词： Covariance matrix

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8th international conference on Cognitive Systems and Information processing, ICCSIP 2023

8th International Conference on Cognitive Systems and Inform...

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8th international conference on Cognitive Systems and Information processing, ICCSIP 2023

ISBN: (纸本)9789819980208

the proceedings contain 52 papers. the special focus in this conference is on Cognitive Systems and Information processing. the topics include: Exploring Model Depth Adaptation in Image Super-Resolution for Efficient Inference;RGB to L∗a∗b∗ Color Prediction Model Based on Color Cards;a Diverse Environment Coal Gangue Image Segmentation Model Combining Improved U-Net and Semi-supervised Automatic Annotation;RB-LIO: A SLAM Solution applied to Large-Scale Dynamic Scenes with Multiple Moving Objects;object Detection with Depth Information in Road Scenes;Mobilenetv2-SSD Target Detection Method Based on Multi-scale Feature Fusion;no-Reference Point Cloud Quality Assessment via Contextual Point-Wise Deep Learning Network;measurement of Solder Wetting Angle Based on Image processing and Polynomial Fitting;infrared Aircraft Anti-interference Recognition Based on Feature Enhancement CenterNet;Obstacle Avoidance Control Method for Robotic Assembly Process Based on Lagrange PPO;MHRN-ST: Go Board Recognition With Multi-stage Highlight Removal Network Based On Swin Transformer;construction of Minecraft Virtual Reality Scene Based on Voxel Point Cloud;zero-Shot Sim-To-Real Transfer of Robust and Generic Quadrotor Controller by Deep Reinforcement Learning;robot Manipulation Strategy for Explosives Disposal in Cluttered Scenes;Adaptive Neural Composite Learning Finite-Time Control for a QUAV with Guaranteed Tracking Performance;multimodal Wearable Device Signal Based Epilepsy Detection with Multi-scale Convolutional Neural Network;Improving EEG-Based Continuous Grip Force Decoding in Grasp-Lift Tasks by Considering Grip Force Levels;3D Path Planning and Tracking of Quadrotors Based on Improved Whale Optimization Algorithm;multimodal Pneumatic Control System for Soft Robot.

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An Association Rule Mining Algorithm for Clinical Decision Support 22

An Association Rule Mining Algorithm for Clinical Decision S...

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8th international conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Cui, Jiaming Zhao, Shuo Sun, Xin School of Computer Science Fudan University Shanghai200433 China School of Computer Science and Technology Beijing Institue of Technology Beijing100081 China Beijing Eng. Res. Ctr. of High Vol. Language Information Processing and Cloud Computing Applications Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781450396110

the use of Machine Learning (ML) in healthcare has enormous potential for improving disease detection, clinical decision support. Association Rule Mining (ARM), as a classical machine learning method, provides researchers with important opportunities for data analysis and potential relationship discovery. In this paper, the weighted Apriori algorithm (MW-Apriori) is proposed to discovery the association rules from the disease diagnostic data. Furthermore, in order to obtain more accurate associations on a higher level, we design an improved k-nearest neighbor (CW-KNN) algorithm applied as a pre-step, which can divide the large number of medical texts into specific classification. MW-Apriori, can mine the hidden and potentially useful knowledge and rules behind huge amount of uncertain information. Based on the high correlation of classified data, we have also obtained high-quality association rules between symptoms and diseases. Experiment results on the multiple data sets have proved the superior performance of MW-Apriori algorithm. © 2022 ACM.

关键词： Association rules

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Distance Estimation by Fusion of Monocular Ranging and MMW Radar Information

Distance Estimation by Fusion of Monocular Ranging and MMW R...

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international conference on Vision, Image and Signal processing (ICVISP)

作者： QiRui Cui Ye Chen ShunGeng Wang School of engineer Huzhou University Huzhou Zhejiang China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

A single sensor often fails to provide comprehensive and reliable information in complex environments. However, multi-sensor fusion leverages the complementary strengths of different sensors, fully utilizing their measurement capabilities and effectively mitigating the limitations of individual sensors. In practical applications, sensor data is frequently influenced by environmental noise, resulting in increased measurement errors. To overcome this challenge, this paper presents a monocular distance measurement model based on object detection algorithms, which is combined with millimeter-wave radar for sensor fusion, aiming to enhance measurement accuracy and system robustness. the proposed method first constructs a geometric model for monocular distance measurement based on object detection algorithms. Subsequently, a dynamic error model is introduced to address the issue of sensor errors varying with distance. Finally, Kalman filtering is applied to fuse data from multiple sensors. Simulation results demonstrate that the proposed approach effectively reduces sensor measurement noise, significantly improves distance measurement accuracy, and meets the requirements of practical applications.

关键词： Accuracy Fuses Heuristic algorithms Geometric modeling Signal processing algorithms Radar detection Object detection Millimeter wave radar Distance measurement Kalman filters

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An Optical and SAR Sensor Image Fusion Method Based on Improved Auto-Encoder

An Optical and SAR Sensor Image Fusion Method Based on Impro...

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international conference on Vision, Image and Signal processing (ICVISP)

作者： Junyu Wei Shaojing Su Siyang Huang Liushun Hu Zhen Zuo Zongqing Zhao Xiaozhong Tong College of Intelligence Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

there are significant differences between optical and synthetic aperture radar (SAR) sensor image data, and extracting complementary features useful for subsequent tasks from image data acquired by these two different types of sensors is a key issue. In this paper, we propose an optical and SAR sensor image fusion method based on improved Auto-Encoder (AE). the method employs a two-branch feature extraction network constructed using Convolutional Neural Network (CNN) and Transformer network to extract detail and base features of the image, respectively. Furthermore, Lite Transformer (LT) and DropKey are introduced to enhance the computational efficiency of the network and base feature extraction. the Convolutional Block Attention Module (CBAM) enables the CNN to extract detail features with greater efficiency. Finally, the concept of correlation is applied in the fusion stage, which facilitates the comprehensive extraction of base and detail features. the experimental results demonstrate that the algorithm proposed in this paper is capable of generating superior optical and SAR fused imagery.

关键词： Integrated optics Feature extraction Optical imaging Transformers Adaptive optics Optical sensors Convolutional neural networks Optical signal processing Synthetic aperture radar Image fusion

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Image processing and Temperature Analysis Algorithms for Quality Monitoring of Lettuce Plants

Image Processing and Temperature Analysis Algorithms for Qua...

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international conference on Vision, Image and Signal processing (ICVISP)

作者： Zhengda Cui Huishan Yu Wenbo Gou Liaocheng University Liaocheng China Information Technology Research Center Beijing Academy of Agriculture and Forestry Sciencess Beijing China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

To ensure high-quality growth of lettuce plants, it is essential to acquire the temperature data of the plants and investigate the distribution of canopy temperature. this study focuses on the thermal image processing and temperature analysis algorithms. the SAM (Segment Anything Model) algorithm is used to remove the background in RGB images and segment individual lettuce plants, which are then mapped to CSV files containing temperature data. the temperature of each pixel in the segmented lettuce plant is analyzed, and several indicators, including the mean temperature $T_{mean}$ , temperature standard deviation $std$ , and coefficient of variation $C_{v}$ , are computed. A temperature distribution histogram and kernel density curve are generated to visualize the temperature distribution across the lettuce plant. the temperature standard deviation $std$ and coefficient of variation $C_{v}$ are utilized to evaluate the quality of the lettuce plants, and K-means clustering is applied to categorize the lettuce plants into three quality levels: high-quality, medium-quality, and low-quality. Experimental results demonstrate that the developed image processing and temperature analysis algorithms efficiently capture the temperature information of lettuce plants, providing valuable technical support for quality assessment and management in precision agriculture.

关键词： Temperature distribution Image segmentation Plants (biology) Crops Production Quality assessment thermal analysis Genotypes Standards thermal stability

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Diabetes Disease Prediction Using Various Metaheuristic Optimization Algorithms

Diabetes Disease Prediction Using Various Metaheuristic Opti...

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international conference for Convergence of Technology (I2CT)

作者： J. Vijaya Sanskriti Chandra Pallavi Baghel Department of DSAI International Institute of Information Technology Naya Raipur Chhattisgarh

Big data is increasingly being applied in a wide variety of fields, including industry, finance, and medicine, because of its ability to handle the hurdles of processing enormous amounts of data. the Map Reduce model is one of the data mining strategies that are frequently and efficiently used to classify huge data. In this research, a strategy for big data categorization is created using PSO, GA, ACO, FireFly, Whale, Cuckoo, and MayFly as well as a compared result of these 7 optimization classifications with 8 base classifications (RF, LR, Extra Tree, KNN, LinearSVC, GaussianNB, SVC, DT) in diabetes disease prediction. the major purpose of this work is to discover the best outcomes for diabetes illness prediction in terms of accuracy and computational time. In this project, we used seven optimization algorithms PSO, GA, ACO, FireFly, Whale, Cuckoo, and MayFly for the diabetes prediction model. We have also included the performance evaluation of the models with different evaluation metrics like precision, recall, and F1-score. We obtained an overall accuracy of 94% in the MayFly algorithm and 95.05% accuracy using the Whale Algorithm.

关键词： Radio frequency Computational modeling Whales Static VAr compensators Big Data Predictive models Prediction algorithms

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A Survey on Event Extraction from Webpage

A Survey on Event Extraction from Webpage

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Contemporary Information Technology and mathematics (ICCITM), international conference on

作者： Rasha Ali Ban Sharief Mustafa Computer Science Dep. Mosul University Mosul Iraq

Event extraction (EE) is one of most important researches in the field of Natural Language processing (NLP). Numerous significant events occur every day all over the world, and it published in various media outlets with varying narrative styles. A primary job in EE is to determine if events in the real world have been reported in articles and posts driven by the 4Ws’ (what, who, when, and where). A complex relationship between people can be explained by an event, place, actions, and objects. An event-centered model captures the dynamic and semantic aspects of an event representation of event facts. An updated and comprehensive survey is needed due to the proliferation of methods, datasets, and evaluation metrics in the literature. In this paper, a survey on extracting events from websites and defining their types and applications in several different fields has been discussed. In addition, the study presents the strengths and weaknesses of event extraction systems for different types of models

关键词： Training Text mining Measurement Semantics Web pages Media Natural language processing

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Robotic Manipulation of Pathological Slides Powered by Deep Learning and Classical Image processing

Robotic Manipulation of Pathological Slides Powered by Deep ...

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international Symposium on Computational Intelligence and Informatics

作者： Artúr I. Károly Sebestyén Tirczka Tamás Piricz Péter Galambos Antal Bejczy Center for Intelligent Robotics Óbuda University Bécsi u. 96/B. H-1034 Budapest Hungary Doctoral School of Applied Informatics and Applied Mathematics Óbuda Universty

ISBN: (纸本)9798350398830

Digital pathology has many advantages, so the need for digitizing already existing archives naturally arises. However, the fact that there is no standard way of storing pathology archives makes it difficult to provide an automated solution. In this paper, we tackle this problem with a robotic system, which uses a deep convolutional neural network and traditional image processing methods to automatically detect and localize the pathology samples and perform pick and place to organize the samples in a rack that can be directly inserted into the whole slide imaging (WSI) scanner. We were able to achieve a 90% success rate for the pick and place process. this paper introduces the hardware setup and software components that we used for our system and briefly explains the detection procedure.

关键词： Training Pathology Image processing Glass Containers Software Convolutional neural networks

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Emotion Recognition from WhatsApp Text Messages Using Unsupervised Machine Learning

Emotion Recognition from WhatsApp Text Messages Using Unsupe...

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international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Roshni Padate Ashutosh Gupta Prasun Chakrabarti Arvind Sharma Computer Engineering Fr. Conceicao Rodrigues College of Engineering Mumbai India Computer Science and Engineering Sir Padampat Singhania University Udaipur India Department of Mathematics Sir Padampat Singhania University Udaipur India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

With the widespread use of instant messaging applications like WhatsApp, there is an increasing demand for systems that can analyze and interpret user emotions based on textual communication. this research introduces a novel system for detecting emotions from WhatsApp text messages using unsupervised machine learning techniques. the system leverages natural language processing (NLP) methods to preprocess text data and applies a Pointwise Mutual Information (PMI)-based algorithm to classify messages into six basic emotion categories: joy, anger, sadness, fear, disgust, and surprise. the model’s effectiveness is evaluated using a corpus of WhatsApp messages, demonstrating a precision rate of 92.75%. this approach shows promising results in handling the informal language and slang common in WhatsApp messages, offering significant potential for real-world applications in emotion detection from textual data.

关键词： Freeware Emotion recognition Machine learning algorithms Text recognition Machine learning Instant messaging Natural language processing Classification algorithms Internet telephony Mutual information

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