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applied CNN for Automatic Diabetic Retinopathy Assessment Using Fundus Images 6th

Applied CNN for Automatic Diabetic Retinopathy Assessment Us...

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6th international conference on Business Intelligence, CBI 2021

作者： El Hossi, Amine Skouta, Ayoub Elmoufidi, Abdelali Nachaoui, Mourad Mathematics Applications Laboratory Faculty of Sciences and Technics Sultan Moulay Slimane University Beni Mellal Morocco Computer Science Modeling Systems and Decision Support Laboratory Faculty of Science Aïn Chock Hassan II University Casablanca Morocco Information Processing and Decision Support Laboratory Faculty of Sciences and Technics Sultan Moulay Slimane University Beni Mellal Morocco

ISBN: (纸本)9783030765071

In the area of ophthalmology, diabetic retinopathy affects an increasing number of people. Early detection avoids severe diabetic proliferative retinopathy complications. In this paper, we propose a method for binary classification of retinal images using convolutional neural networks architecture. this method is formed to recognize and classify a retinal image as normal or abnormal retina. the paper setup is, first of all a preprocessing step is applied, next by data augmentation, and then a CNN formed, and applied. To train, validate and test the proposed model, we have used a public dataset "Resized version of the Diabetic Retinopathy Kaggle competition dataset" from Kaggle web site. Proposed model has trained using 4000 images of the normal retina and 4000 images of abnormal diabetic retina, and 500 images of the normal retina and 500 images of abnormal diabetic retina for testing. the accuracy Achieves 89% in 100 images of single prediction words. © 2021, Springer Nature Switzerland AG.

关键词： Ophthalmology

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Application of Image Recognition Technology in Automatic Teaching of Humanoid Educational Robots 2020

Application of Image Recognition Technology in Automatic Tea...

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8th international conference on Information and Education Technology (ICIET)

作者： Tang Xinning Ren Bingyin Zhao Xu Zhao Shuying Harbin Inst Technol 92 Xidazhi St Harbin Peoples R China Northeastern Univ 3-11 Wenhua Rd Shenyang Peoples R China

ISBN: (纸本)9781450377058

In recent years, more and more robots are being applied to school and home widely. Teaching and playback are a universal method to develop robot functions. For purpose of simplifying the process of teaching humanoid educational robots, a method of human motion and gesture recognition, based on digital-image processing technology, is proposed for automatic teaching of robots. In this method, raw images taken by camera are converted to human body gesture binary images by using the technology of image subtraction and image preprocessing. then, the body gesture images are mapped to body mathematical models by the skeletonization and recognition algorithms. Finally, the control commands of robots are calculated and drive robots to do the similar actions. Compared with other methods of robot teaching, the teaching method by using digital-image processing and recognition technology has an easier usage and lower cost. By series of experiments, it is seen that this method has a good performance on teaching robots.

关键词： educational robots automatic teaching image processing gesture recognition robot control

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A High-Speed parallel Accessing Scheduler of Space-Borne Nand Flash Storage System 8th

A High-Speed Parallel Accessing Scheduler of Space-Borne Nan...

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8th international conference on Communications, Signal processing, and Systems, CSPS 2019

作者： Li, Xin Yu, Ji-Yang Li, Ke Chen, Mei-Shan Ma, Jin-Yuan Beijing Institute of Spacecraft System Engineering Beijing China

ISBN: (纸本)9789811394089

A parallel accessing schedule of multi-channel Nand Flash for signal processing system on board is proposed, which obtains high-performance in throughput rate with the combine of intra-channel pipeline and inter-channel interleaving. the massive data storage efficiency assessment model is established to calculate the bandwidth formula of the proposed method. the parameters which affect the parallel access rate of multi-channel Nand Flash memory are analyzed. the proposed architecture is implemented in a Virtex II FPGA, and is applied in the signal processing system on board. With 8-stage intra-channel interleaving and 4-stage inter-channel pipeline, the throughput of Nand Flash storage system can reach about 560 MBps for store and about 220 MBps for playback, which is more efficient compared with previous literatures. the results of the experiments verify the advantages and feasibility of the proposed method. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Pipelines

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Computational parallel of K-Nearest Neighbor on Page Blocks Classification Dataset 8

Computational Parallel of K-Nearest Neighbor on Page Blocks ...

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8th international conference on Information and Communication Technology, ICoICT 2020

作者： Zaky, Damar Gunawan, P.H. Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9781728161426

K-Nearest Neighbor (KNN) is considered as one of the simplest machine learning algorithms. While the implementation is quite simple, KNN is actually computationally expensive that makes it take a lot of time when it tries to predict. KNN has been known to be a lazy learning machine learning method that means that this method doesn't generalize the data, instead it has to memorize the training data, even when testing. this paper aims to optimize the KNN classifier to solve page blocks classification by making the algorithm parallel. the part of the KNN algorithm that is changed to become parallel is the outer part where the task for each test data is divided according to the number of processors. In this work, we use parallel KNN to classify page blocks. Page blocks are any blocks of a page layout that are detected by using a segmentation technique, the KNN is trained to classify whether a block is a vertical line, picture, text, horizontal line or graphic. the experiment shows that the KNN classifier obtains an accuracy of 93.51% and by using parallel KNN, a speedup of 4.64 times faster and an efficiency of 57.96% can be obtained by using 8 processors and an increasing number of grids up to 6040 while it obtains the same accuracy as serial. © 2020 IEEE.

关键词： Classification (of information)

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Fitting the OCV-SOC relationship of a battery lithium-ion using genetic algorithm method

Fitting the OCV-SOC relationship of a battery lithium-ion us...

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2020 international conference on Innovation, Modern applied Science and Environmental Studies, ICIES 2020

作者： Elmahdi, Fadlaoui Ismail, Lagrat Noureddine, Masaif Laboratory of Electronic Systems Information Processing Mechanics and Energy Ibn Tofail University Kenitra Morocco Laboratory of Advanced Systems Engineering National Schools of Applied Sciences Ibn Tofail University Kenitra Morocco

In response to the need of reducing fossil fuel dependence and environmental impacts for ground transportation, electric vehicles (EVs) powered by lithium-ion batteries (LIBs) are being intensively researched and they have placed on the forefront as alternative vehicles. the state of charge (SOC) is one of the most important states of LIBs that is monitored online. However, the model-based method state of charge estimation requires an accurate Open circuit voltage (OCV), which is an important characteristic parameter of lithium-ion batteries, that is used to estimate battery state of charge (SOC). therefore, accurate OCV modeling is a great significance for lithium-ion battery management. the polynomial OCV model uses the polynomial function to establish the relationship between OCV and SOC mapping. In this paper,8th degree polynomial fitting curve is considered and the genetic algorithm optimization method is proposed for estimating the parameters. the results show that the root mean square error can be decreased to 0.002. However, the best fitting OCV-SOC curve can increase the accuracy of the model and improve the accuracy of battery state estimation. © 2021 the Authors, published by EDP Sciences.

关键词： Open circuit voltage

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Identification of Patients with Immune-related Adverse Events from Clinical Notes using Machine Learning 8

Identification of Patients with Immune-related Adverse Event...

引用

8th IEEE international conference on Healthcare Informatics (ICHI)

作者： Gupta, Samir Belouali, Anas Shah, Neil Atkins, Michael Madhavan, Subha Georgetown Univ ICBI Washington DC 20057 USA Mem Sloan Kettering Canc Ctr New York NY USA MedStar Georgetown Univ Hosp Lombardi Comprehens Canc Ctr Washington DC USA

ISBN: (纸本)9781728153827

Although Immune Checkpoint Inhibitors (ICIs) have substantially improved survival in patients with advanced malignancies, they are associated with a unique spectrum of side effects termed Immune-Related Adverse Events (irAEs). To ensure treatment safety, research efforts are needed to comprehensively detect and understand irAEs. the goal of this work is to evaluate a Machine Learning-based phenotyping approach that can identify patients with irAEs from a large volume of retrospective clinical notes. We applied different shallow and deep Machine Learning (ML) models using different feature representations (frequency-based, distributed word embeddings) on the reduced text to classify the presence or absence of any irAE. Evaluation shows promising results with an average F1-score=0.75 and AUC-ROC=0.78.

关键词： natural language processing machine learning clinical notes adverse events cancer

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Solving the three-dimensional linear magnetometry problem on GPU 19

Solving the three-dimensional linear magnetometry problem on...

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19th international conference on Geoinformatics: theoretical and applied Aspects, Geoinformatics 2020

作者： Akimova, E.N. Misilov, V.E. Miftakhov, V.O. Krasovskii Institute of Mathematics and Mechanics of UrB RAS Ural Federal University Russia

the paper is devoted to construction and implementation of the cost-efficient algorithm for solving the three-dimensional problem of finding the vertical component of the magnetic field generated by a rectangular parallelepiped with a variable unidirectional magnetization. the algorithm uses the Toeplitz-block-Toeplitz structure of the matrix of the discretized problem to reduce the amount of calculations and computing time. the algorithm was implemented on GPU using the CUDA technology. A series of numerical experiments were performed to study the efficiency of the parallel algorithm. © Geoinformatics 2020 - XIXth international conference "Geoinformatics: theoretical and applied Aspects". All rights reserved.

关键词： Graphics processing unit

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Development of a Machine Learning-Based Robotic System for Medical-Targeted Drug Delivery System 8th

Development of a Machine Learning-Based Robotic System for M...

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8th National conference on New Trends in Information and Communications Technology Applications, NTICT 2024

作者： Simango, Doubt Manyanye, Trish Aldhaibani, Jaafar A. Yahya, Abid Mushiri, Tawanda Ndaimani, Augustine Department of Mechatronic Engineering School of Engineering Sciences and Technology Private Bag Off-Chirundu Road Chinhoyi7724 Zimbabwe College of Engineering University of Information Technology and Communication Baghdad Iraq Botswana International University of Science and Technology Private Bag 16 Palapye Botswana Department of Industrial and Mechatronics Engineering Faculty of Engineering and the Built Environment University of Zimbabwe 630 Churchill Avenue Mt Pleasant Harare Zimbabwe Department of Biomedical Informatics and Biomedical Engineering University of Zimbabwe 630 Churchill Avenue Mt Pleasant Harare Zimbabwe

ISBN: (纸本)9783031870750

Medical robotics is a field that combines engineering and artificial intelligence to create robotic systems used in healthcare. this study aims to delve into the exciting field of developing medical-targeted drug delivery systems, which hold immense potential for revolutionizing how we administer therapeutic substances. By precisely delivering drugs to specific sites within the body, these innovative systems offer improved treatment outcomes, reduced side effects, and enhanced patient compliance. therefore, an image processing algorithm for visible target detection was designed, a system to determine the coordinates of the exact location of identified regions of the target was developed, and a trajectory plan for the robotic arm end effector for precise drug delivery was also designed. the integration of robotic systems into MRI scanners, the development of image processing algorithms, and also real-time feedback control systems are mechatronics-based processes. Medical image navigation technology has many uses in minimally invasive robots, mainly image acquisition techniques such as Magnetic Resonance Imaging (MRI) to acquire equivalent image data;however, medical imaging techniques have inefficient visibility in robotic-assisted surgeries of small and confined incisions. therefore, image processing methods are applied to precisely regulate the operation’s location and visibility and improve the procedure’s efficacy, accuracy, and safety. To develop an advanced medical-targeted drug delivery system. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： End effectors

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12th international conference on parallel processing and applied mathematics, PPAM 2017

12th International Conference on Parallel Processing and App...

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12th international conference on parallel processing and applied mathematics, PPAM 2017

ISBN: (纸本)9783319780238

the proceedings contain 101 papers. the special focus in this conference is on parallel processing and applied mathematics. the topics include: Performance and energy analysis of scientific workloads executing on LPSoCs;Energy Efficient dynamic load balancing over multiGPU heterogeneous systems;scheduling data gathering with maximum lateness objective;Fair scheduling in grid VOs with anticipation heuristic;a security-driven approach to online job scheduling in IaaS cloud computing systems;dynamic load balancing algorithm for heterogeneous clusters;multi-objective extremal optimization in processor load balancing for distributed programs;pardis: A process calculus for parallel and distributed programming in haskell;towards high-performance python;Using GPGPU accelerated interpolation algorithms for marine bathymetry processing with on-premises and cloud based computational resources;actor model of a new functional language - Anemone;Almost optimal column-wise prefix-sum computation on the GPU;A combination of intra- and inter-place work stealing for the APGAS library;Benchmarking molecular dynamics with OpenCL on many-core architectures;efficient language-based parallelization of computational problems using cilk plus;a taxonomy of task-based technologies for high-performance computing;Interoperability of GASPI and MPI in large scale scientific applications;Evaluation of the parallel performance of the java and PCJ on the intel KNL based systems;Fault-tolerance mechanisms for the java parallel codes implemented with the PCJ library;Exploring graph analytics with the PCJ toolbox;Relaxing the correctness conditions on concurrent data structures for multicore CPUs. A numerical case study;Big data analytics in java with PCJ library: Performance comparison with hadoop;parallel exact diagonalization approach to large molecular nanomagnets modelling.

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12th international conference on parallel processing and applied mathematics, PPAM 2017

12th International Conference on Parallel Processing and App...

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12th international conference on parallel processing and applied mathematics, PPAM 2017

ISBN: (纸本)9783319780535

the proceedings contain 45 papers. the special focus in this conference is on parallel processing and applied mathematics. the topics include: Performance and energy analysis of scientific workloads executing on LPSoCs;Energy Efficient dynamic load balancing over multiGPU heterogeneous systems;scheduling data gathering with maximum lateness objective;Fair scheduling in grid VOs with anticipation heuristic;a security-driven approach to online job scheduling in IaaS cloud computing systems;dynamic load balancing algorithm for heterogeneous clusters;multi-objective extremal optimization in processor load balancing for distributed programs;pardis: A process calculus for parallel and distributed programming in haskell;towards high-performance python;Using GPGPU accelerated interpolation algorithms for marine bathymetry processing with on-premises and cloud based computational resources;actor model of a new functional language - Anemone;Almost optimal column-wise prefix-sum computation on the GPU;A combination of intra- and inter-place work stealing for the APGAS library;Benchmarking molecular dynamics with OpenCL on many-core architectures;efficient language-based parallelization of computational problems using cilk plus;a taxonomy of task-based technologies for high-performance computing;Interoperability of GASPI and MPI in large scale scientific applications;Evaluation of the parallel performance of the java and PCJ on the intel KNL based systems;Fault-tolerance mechanisms for the java parallel codes implemented with the PCJ library;Exploring graph analytics with the PCJ toolbox;Relaxing the correctness conditions on concurrent data structures for multicore CPUs. A numerical case study;Big data analytics in java with PCJ library: Performance comparison with hadoop;parallel exact diagonalization approach to large molecular nanomagnets modelling.

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