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Toward an Efficient Deep Learning Model for Lungpathologies DetectionLung pathologies Detection In X -ray Images 18

Toward an Efficient Deep Learning Model for Lungpathologies ...

引用

18th IEEE international Wireless Communications and Mobile Computing conference (IWCMC)

作者： Souid, Abdelbaki Sakli, Nizar Sakli, Hedi Univ Gabes MACS Lab RL16ES22 ENIG Gabes Tunisia EITA Consulting 5 Rue Chants Oiseaux F-78360 Montesson France

ISBN: (纸本)9781665467490

Medical imaging methods identify and record anomalies in the human body. these techniques are critical for assessing, diagnosing, and treating lung illnesses. Chest radiography (chest X-ray) is a low-cost yet effective medical imaging technology. However, a scarcity of trained radiologists may significantly restrict the technique's use. To detect abnormality in chest radiographs, emerging technologies such as deep learning should be applied to improve diagnostic performance and accuracy. CNN-based deep learning algorithms have made significant progress. the success of CNN in image classification has led researchers to investigate its utility as a diagnostic method for identifying and characterizing lung diseases. To achieve the defined goal, we leverage and extend the EfficientNet family of deep artificial neural networks knowns for their high accuracy and small footprint in other applications. A collection of three datasets is used to train the proposed approach. the results show that the proposed approach was able to produce a high-quality model, with an overall AUC of 0.871, and the overall sensitivity of 79.4%, while having 5 to 30 times fewer parameters than other architectures.

关键词： Image Classification Deep learning Transfer learning Medical image processing Computer vision Frontal chest x-ray Introduction

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Edge-Cloud Co-Evolutionary Task Offloading for Faulty Edge Microservices

Edge-Cloud Co-Evolutionary Task Offloading for Faulty Edge M...

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Information Science, parallel and Distributed Systems (ISPDS), international conference on

作者： Shan Zhang Linru Ma Yingchao Wang Institute of Systems Engineering Academy of Military Science Beijing China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

Edge microservices encounter failures that affect the stability and availability of the entire edge microservice system. Some studies are devoted to developing efficient edge-edge offloading methods that offload faulty microservices' computing tasks to other healthy microservices. However, in the face of a large-scale microservice failure, it may become necessary to temporarily solicit assistance from the edge microservices of different regions, which complicates the offloading optimization problem and poses additional challenges to its efficient resolution. therefore, this paper proposes an edge-cloud collaborative task offloading optimization model for faulty edge microservices to minimize task offloading delay and designs an edge-cloud twostage task offloading algorithm based on the WOSCA algorithm. the example results show that compared with the cloud-based offloading method, the edge-cloud collaborative offloading effectively reduces the task offloading delay by an average of 7.94%, ensuring the reliability of the edge microservice system.

关键词： Information science Adaptation models Optimization models Microservice architectures Collaboration Stability analysis Delays

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Chinese Named Entity Recognition Based on Bi-directional Quasi-Recurrent Neural Networks improved with BERT new method to solve chinese ner 2021

Chinese Named Entity Recognition Based on Bi-directional Qua...

引用

5th international conference on Innovation in Artificial Intelligence (ICIAI)

作者： Liu, Kang Yu, Qingsong Zhong, Shanhao East China Normal Univ Shanghai Peoples R China

ISBN: (纸本)9781450388634

Named Entity Recognition is a basic and important task in the field of natural language processing. In the field of Chinese NER, the BiLSTM-CRF model has always been favored by many researchers due to its excellent information extraction capabilities. However, with the advent of the big data era, LSTM cannot be calculated in parallel, exposing the shortcomings of low training efficiency. In this paper, we focused on model training efficiency, using Bi-directional Quasi-Recurrent Neural Networks (BiQRNN) to replace BiLSTM, the experimental results show that the model we proposed achieves good results and increases the training speed of the model by 35%.

关键词： parallel computing Chinese named entity recognition QRNN

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An Image processing-Based Approach for Fuzzy Image Recognition

An Image Processing-Based Approach for Fuzzy Image Recogniti...

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international conference on Electronic Engineering and Informatics (EEI)

作者： Zhenyu Song Tao Liang Bin Zhang Mengyi Zhao Lvxing Zhao Xiangmin Ren College of Information Engineering Taizhou University Taizhou Jiangsu China

Developing in parallel with image processing technology, image recognition technology becomes an important sector in the artificial intelligence domain and is widely adopted in different fields. However, the existence of various fuzzy images in real life brings new challenges to image recognition. To effectively improve the feature matching rate between motion-blurred and original images, this paper first carries out a blurring simulation on the image with Gaussian noise, then performs a recovery operation on the re-blurred image by inverse filtering and Wiener filtering (WF), and later uses the ORB algorithm to perform feature point matching between the recovered image and the original image. the experimental results verify that the WF-based approach achieves satisfactory results in processing the recovery of simulated motion blur images with added noise. It is clear that the WF-based approach can contribute to the higher recognition rates motion blur image recognition.

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High-Precision Computing based on the CUDA Architecture in Residual Number Systems

High-Precision Computing based on the CUDA Architecture in R...

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international Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Vladimir A. Chumychkin Nataliya A. Galanina Information Systems Math and Hardware Department Chuvash State University Cheboksary Russian Federation

Residual Number Systems (RNS) are recognized for their parallel arithmetic capabilities. In recent years, they have been employed in various demanding applications, including digital signal processing, high-precision computing, cryptography, and large neural networks. With the advancement of multiprocessor systems on general-purpose platforms, the utilization of RNS for the effective parallelization of arithmetic computational operations with a large dynamic range of up to several tens of bits becomes a crucial aspect. To this end, modern Graphics processing Units (GPUs) with several thousands of computational cores are ideal platforms. this study evaluates the efficacy of using RNS for implementing multiple precision arithmetic on NVIDIA GPUs using the CUDA hardware and software parallel computing architecture. the results of the experiments demonstrate the high efficiency of RNS for the multiplication operation. A configuration of 256 modules providing a 6400-bit dynamic range results in a computation time that is 7.92 times faster compared to the implementation on the standard GMP MP variant.

关键词： Power engineering Neural networks Graphics processing units Computer architecture Dynamic range Software Libraries

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A Statistical-based Precursor Operator Analysis Method

A Statistical-based Precursor Operator Analysis Method

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Information Science, parallel and Distributed Systems (ISPDS), international conference on

作者： Pengteng Huang Guanyu Chen Jun Nie Yantao He Department of Computer Science Guangdong University of Science and Technology Dongguan China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

this paper explores the execution relation between database operators, specifically precursor relations, and their impact on optimizing computer system performance. the study defines these precursor relations and discovers that they significantly influence the data transfer volume between the host and storage ends, thereby affecting query execution efficiency. Given that precursor relations can vary with changes in query statements or optimization strategies, this paper introduces a statistical method for analyzing precursor operators. this method generates operator precursor probability tables for specific databases, allowing for the determination of the priority of database operators delegated to the storage end.

关键词： Information science Statistical analysis System performance Query processing Distributed databases Probability Data transfer

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the Problem of Generating Psychological Tests using Artificial Intelligence

The Problem of Generating Psychological Tests using Artifici...

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Machine Learning and Human-Computer Interaction (MLHMI), international conference on

作者： Yelizaveta Vitulyova Dinara Matrassulova Mariya Kostsova Anastasiya Grishina Sherniyaz Kabdushev Ibragim Suleimenov al-Farabi Kazakh National university Almaty Kazakhstan Sevastopol State University Sevastopol Crimea V.l. Vernadsky Crimean Federal University Sevastopol Economic and Humanitarian Institute Sevastopol Crimea

ISBN: (数字)9798350375763

ISBN: (纸本)9798350375770

the paper formulates the problem of generating psychological tests using artificial intelligence and proposes a general principle of its functioning. this method of generating psychological tests (obtaining them automatically) by processing an array of experimental data is primarily intended for completing works of fiction (in particular, science fiction novels). the combination of psychological testing with works of art, in turn, is aimed at analyzing the psychological state of the population on a mass scale. the principle of operation of the proposed system is based on the use of permutation tests, in which the respondent ranks certain images based on his personal preferences, as well as on identifying a set of reference sequences (substitutions in terms of group theory). the latter are selected as the basis for the generated test. the selection of reference sequences is carried out by minimizing errors (deviations) between them and the sequences obtained during experiments. the work also proposes algorithms that make it possible to compact the information obtained through the use of permutation tests, based on the use of functions that take values in Galois fields.

关键词： Human computer interaction Art Codes Neural networks Psychology Machine learning parallel processing Software Galois fields Testing

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Research on Point Cloud Data processing in Multiple Scenarios

Research on Point Cloud Data Processing in Multiple Scenario...

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Information Science, parallel and Distributed Systems (ISPDS), international conference on

作者： Jinkui Huang Xiangyao Ma Jingyuan Wang Jianhua Li College of Communication Engineering Army Engineering University of PLA Nanjing China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

With the development of 3D laser scanning technology, point cloud data has been widely used in various fields. As the most critical link after the point cloud data collection, the denoising process of point cloud data directly affects the subsequent point cloud data splicing, 3D reconstruction and other links. At present, there are still gaps in the research of point cloud data processing methods under different application scenarios. therefore, this paper studies the statistical filtering and radius filtering methods of point cloud data in different scenarios and environments, and combines the characteristics of the two filtering methods to obtain a point cloud data filtering method suitable for different scenarios.

关键词： Point cloud compression Information science three-dimensional displays Splicing Oceans Noise reduction Noise

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Evolutionary Multi-Objective Neural Architecture Search for Generalized Cognitive Diagnosis Models

Evolutionary Multi-Objective Neural Architecture Search for ...

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Data-driven Optimization of Complex Systems (DOCS), international conference on

作者： Shangshang Yang Cheng Zhen Ye Tian Haiping Ma Yuanchao Liu Panpan Zhang Xingyi Zhang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

Cognitive diagnosis models (CDMs) with high generalization are essential for intelligent education systems to reveal students' knowledge states in multiple datasets. However, existing CDMs' architectures are designed dependent on researcher expertise and experience from observing and summarizing partial students' learning behaviors, which makes handcrafted models not cover all learning behaviors well and thus limits their generalization. To develop generalized CDMs, this paper proposes an evolutionary neural architecture search to design CDMs' architectures effective on multiple datasets automatically. Specifically, we first formulate the search task as a multi-objective optimization problem (MOP), which maximizes model performance on multiple datasets containing learning behaviors as many as possible to ensure model generalization. then, an expressive search space is devised to contain as many potential architectures as possible, where each architecture is denoted by a unified form, taking three given inputs and integrating them in a linear or no-linear manner for prediction. Finally, an evolutionary algorithm with a tailored deduplication strategy is employed to solve the MOP, where each architecture is further represented by a single-root computation tree for easy optimization. Experiments on multiple datasets demonstrate the generalization and effectiveness of the best architecture searched by the proposed approach, and the searched architecture also holds as good interpretability as handcrafted architectures.

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Multi-core Processor Chip Design for Complex parallel Architecture

Multi-core Processor Chip Design for Complex Parallel Archit...

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Clean Energy and Electric Power Engineering (ICCEPE), international conference on

作者： Yu Wang Mingyong Xin Changbao Xu Jianyang Zhu Electric Power Research Institute of Guizhou Power Grid Co. Ltd. Guiyang Guizhou China

ISBN: (数字)9798350390315

ISBN: (纸本)9798350390322

the current research on multi-core processor chips oriented to complex architectures has too much calculations when performing logic operations, and the problem of excessive power consumption leads to excessive power consumption. In order to solve the above problems, this paper studies a new multi-core processor chip oriented to complex architecture. By setting up 8 general-purpose computing units and the chip structure of the processor bus on the multi-core processor chip, using hyper-threading technology to achieve logical operations to increase the amount of calculation, calculating the power consumption of the multi-core processor chip, and analyzing the principles of power management control technology to achieve power Control, use the clock gating device to reduce the power consumption of the microprocessor chip, and complete the low-power design. And analyzed the power consumption of the chip in the logic operation process, and finally designed a low-power multi-core processor chip through the methods of power consumption calculation, power management control and dual-level clock gating of the multi-core processor chip. the experimental results show that the multi-core processor chip oriented to the complex parallel architecture studied in this paper can effectively reduce the amount of calculation when performing logic operations. Compared with before optimization, the power consumption is reduced to 1.5W, which has better practical application significance.

关键词： Power demand Multicore processing Power system management Microprocessor chips Power control Process control Logic Transistors Optimization Clocks

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