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Constructing an Automated Method for Interpreting Alzheimer’s Disease Based on Deep Learning Models and Image processing 24

Constructing an Automated Method for Interpreting Alzheimer...

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8th international conference on Medical and Health Informatics, ICMHI 2024

作者： Yi, Chia-Pin Ting, Hsien-Wei Yang, Tzu-Fang Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare New Taipei City242 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan TaiwanInnovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Alzheimer’s disease (AD) often presents only mild symptoms in its early stages, and as there is no direct diagnostic method currently available, many patients are diagnosed only after the condition has worsened. Considering that magnetic resonance imaging (MRI) can provide key information in the diagnosis of AD, but the condition may go undiagnosed under manual interpretation due to the subtleness of symptoms and the reliance on radiologists to interpret MRI images, this study aimed to develop an automated method for interpreting brain MR images, hoping to screen for potential AD patients. By combining image processing, the U-Net model, YOLOv8 model, various CNN model architectures, and ensemble data statistics, our method was able to identify features related to AD and predict whether a patient has the disease. Our method can process a complete set of MR images and automatically extract important slices for prediction, making it practical for medical applications. Furthermore, we tested the accuracy and reliability of the method with additional datasets. © 2024 Copyright ACM.

关键词： Magnetic resonance imaging

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swDarknet: A Heterogeneous parallel Deep Learning Framework Suitable for SW26010 Pro Processor 20th

swDarknet: A Heterogeneous Parallel Deep Learning Framework ...

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20th IFIP WG 10.3 international conference on Network and parallel Computing, NPC 2024

作者： Liu, Huazeng Tian, Min Wu, Xiaoming Yang, Meihong Ren, Zenghui Li, Yanlong Jinan China Jinan Key Laboratory of High Performance Computing Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9789819628292

As the size of AI models grows dramatically to achieve higher accuracy, more and more models are trained on supercomputers with massive computing and memory. However, it is still challenging to accelerate deep learning applications on supercomputer systems with many-core architectures. In this paper, we propose swDarknet: a Darknet-based heterogeneous parallel deep learning framework on the SW26010 Pro processor. First, We designed three heterogeneous parallel DNN layers: adaptive block-splitting strategy for matrix-multiplication layer, optimized memory access for convolutional layer, and block-based parallel pooling layer. Second, we proposed a model averaging (MA) method to efficiently scale swDarknet across many processors. Finally, we evaluated the performance of swDarknet on a core group. As compared with darknet, the convolutional layers achieved an average speedup of 32x. When training AlexNet and VGG-16, swDarknet can achieve up to 36.79× and 181.35× speedup, respectively. © IFIP international Federation for Information processing 2025.

关键词： Supercomputers

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Implementation of the QGD Algorithm Using AMR Technology and GPU parallel Computing 24th

Implementation of the QGD Algorithm Using AMR Technology and...

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24th international conference on Computational Science (ICCS)

作者： But, Ivan Epikhin, Andrey Kirushina, Maria Elizarova, Tatiana RAS Ivannikov Inst Syst Programming Moscow 109004 Russia RAS Keldysh Inst Appl Math Moscow 125047 Russia

ISBN: (纸本)9783031637858;9783031637834

the paper presents an algorithm based on the quasi-gasdynamic approach for the solution of unsteady compressible flows over a wide range of Mach numbers. It is implemented on the AMReX open platform, which uses adaptive mesh refinement technology to facilitate parallelization of computations on GPU architectures. To validate its effectiveness, the developed solver is applied to the numerical simulation of the shock-vortex interaction problem with flow parameter values of M-v = 0.9 and M-s = 1.5. Cross-validation to assess its performance is conducted with OpenFOAM-based solvers, specifically rhoCentralFoam and QGDFoam. Schlieren fields are used to evaluate oscillations of the numerical schemes and algorithms, while resolution capabilities of the algorithm are assessed by comparing density fields in five cross-sections with the reference values.

关键词： Shock-vortex interactions Compressible flow Quasi-gas dynamic equations OpenFOAM AMReX

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parallel, Portable algorithms for Distance-2 Maximal Independent Set and Graph Coarsening 36

Parallel, Portable Algorithms for Distance-2 Maximal Indepen...

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36th IEEE international parallel and Distributed processing Symposium (IEEE IPDPS)

作者： Kelley, Brian Rajamanickam, Sivasankaran Sandia Natl Labs POB 5800 Albuquerque NM 87185 USA

ISBN: (纸本)9781665481069

Given a graph, finding the distance-2 maximal independent set (MIS-2) of the vertices is a problem that is useful in several contexts such as algebraic multigrid coarsening or multilevel graph partitioning. Such multilevel methods rely on finding the independent vertices so they can be used as seeds for aggregation in a multilevel scheme. We present a parallel MIS-2 algorithm to improve performance on modern accelerator hardware. this algorithm is implemented using the Kokkos programming model to enable performance portability. We demonstrate the portability of the algorithm and the performance on a variety of architectures (x86/ARM CPUs and NVIDIA/AMD GPUs). the resulting algorithm is also deterministic, producing an identical result for a given input across all of these platforms. the new MIS-2 implementation outperforms implementations in state of the art libraries like CUSP and ViennaCL by 3-8x while producing similar quality results. We further demonstrate the benefits of this approach by developing parallel graph coarsening scheme for two different use cases. First, we develop an algebraic multigrid (AMG) aggregation scheme using parallel MIS-2 and demonstrate the benefits as opposed to previous approaches used in the MueLu multigrid package in Trilinos. We also describe an approach for implementing a parallel multicolor "cluster" Gauss-Seidel preconditioner using this MIS-2 coarsening, and demonstrate better performance with an efficient, parallel, multicolor Gauss-Seidel algorithm.

关键词： Distributed processing Clustering algorithms Programming Libraries Hardware Partitioning algorithms

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Retinal Image Analysis for Detection of Diabetic Macular Edema Using Transfer Learning 8

Retinal Image Analysis for Detection of Diabetic Macular Ede...

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8th international conference on Data Science and Machine Learning Applications, CDMA 2025

作者： Asim, Tayyaba Akbar, Shahzad Shahzore, Usama Rehman, Amjad Saba, Tanzila Ayesha, Noor Riphah College of Computing Riphah International University Faisalabad Pakistan Prince Sultan University AIDA Lab. CCIS Riyadh Saudi Arabia Zhengzhou University Zhengzhou China Henan450001 China

ISBN: (纸本)9798331539696

Diabetic Macular Edema (DME) is a disease of the eye's retina and it's a major factor of causing vision problems and leads towards blindness if it is undiagnosed. Early detection of DME can prevent vision loss and may reduce diabetic-related problems like cardiovascular issues. therefore, this study presents a method for detecting DME through Optical Coherence Tomography (OCT) and fundus images using transfer learning. the proposed method is based on four stages: Pre-processing, augmentation, segmentation through DeepLabV3+, and binary classification of DME by using two (02) publicly available datasets;the first dataset is Messidor-2, which contains fundus images, and the second dataset is Retinal Images of Optical Coherence Tomography (OCT). In the Messidor-2 dataset, the total number of images is 1744, and Retinal OCT Images dataset consists of 84,495 images total, separated into four categories (CNV, DME, DRUSEN, NORMAL). In the proposed method, Convolutional Neural Networks (CNN) architectures ResNet50 and VGG-19 have been used for the detection of the DME. Convolutional Neural Networks (CNNs) have been extensively used in medical imaging analysis and classification. Using the well-known ResNet50 architecture for classification of each dataset, the proposed model yielded an accuracy of 98.79%, 99% of F1 Score, 98.43% of Precision, and recall of 98.89%. By using VGG-19, the proposed model gives an accuracy of 98.81%, 98.94% of Fl Score, 98.1 % of Precision and recall of 98.73%. When both models (ResNet50 and VGG-19) were compared, the VGG-19 gave the best accuracy. © 2025 IEEE.

关键词： Convolutional neural networks

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Optimizing Yinyang K-Means Algorithm on ARMv8 Many-Core CPUs 22nd

Optimizing Yinyang K-Means Algorithm on ARMv8 Many-Core CPU...

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22nd international conference on algorithms and architectures for parallel processing, ICA3PP 2022

作者： Zhou, Tianyang Wang, Qinglin Yin, Shangfei Hao, Ruochen Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China School of Computer Science National University of Defense Technology Changsha410073 China

ISBN: (纸本)9783031226762

K-Means algorithm is one of the most common clustering algorithms widely applied in various data analysis applications. Yinyang K-Means algorithm is a popular enhanced K-Means algorithm that avoids most unnecessary calculations using triangle inequality. However, Yinyang K-Means algorithm is time-consuming when the problem size is large. Due to the influence of performance and energy-efficiency, ARM CPUs have appeared in high performance computing. therefore, it is very interesting to accelerate Yinyang K-Means algorithm on ARM CPUs. In this paper, we propose an efficient parallel implementation of Yinyang K-Means algorithm on ARMv8 many-core CPUs by means of vectorization, NUMA affinity memory optimization and data layout optimization. the experiment on two ARMv8 many-core CPUs has shown that our implementation can achieve up to 5.6 times faster than the open-source multi-threaded one of Yinyang K-Means algorithm. To the best of our knowledge, this is the first work that studies the optimization of Yinyang K-Means algorithms on ARMv8 CPUs. © 2023, Springer Nature Switzerland AG.

关键词： K-means clustering

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A Mini-model can Make Machine Unlearning Better 24th

A Mini-model can Make Machine Unlearning Better

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Zhao, Mingkang Yu, Weng Zhu, Congcong China University of Geosciences Wuhan China Ministry of Education Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance School of Information Engineering Beijing China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan572499 China City University of Macau 999078 China

ISBN: (纸本)9789819615247

In recent years, with the development of machine learning, plenty of personal data have been utilized in the training process of the models which incurs severe privacy leakage in the field. Current regulations mandate the removal of private user information from both databases and machine learning models upon specific deletion requests. While wiping data records from memory storage is a straightforward task, eliminating the influence of specific data samples from an already-trained model is challenging. Numerous studies in machine unlearning focus on mitigating the impact of target data by adjusting model parameters in deep learning. However, existing methods for data removal in deep learning often fall short of meeting real-world practicality. the intricacies of deep neural networks make unlearning in deep learning a less expedient process than retraining from scratch. For example, some methods require a ton of matrix calculations or even more complicated computations, and some algorithms need a lot of space, which ends up hindering their effectiveness in real-world applications. Accordingly, we’ve come up with a more practical solution. Our method just needs a small part of the validation dataset to pre-train a mini-model to update the parameters of the target model quickly. With this assistant model, we can quickly and accurately unlearn target classes and items. We prove that our proposed method is highly effective in unlearning tasks with not very large amounts of data. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Information leakage

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Fake News Detection Using Machine Learning and Natural Language processing 8th

Fake News Detection Using Machine Learning and Natural Langu...

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8th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Suthar, Sanket Dalwadi, Bijal Bagadia, Dharma Goplani, Harshvardhan Changa India Department of Information Technology Birla Vishwakarma Mahavidyalaya Engineering College Vallabh Vidyanagar India

ISBN: (纸本)9789819766741

the study addresses the challenge of fake news in the digital era by employing a variety of machine-learning techniques, including logistic regression, decision trees, gradient boosting, LSTM, and BERT models. It uses a dataset from Kaggle consisting of news articles from different sources to develop methods for distinguishing between genuine and fabricated information. through natural language processing (NLP), linguistic features such as textual patterns and sentiment analysis are extracted from the news content. these features are then utilized to train and evaluate the performance of various machine-learning models in identifying fake news. By combining traditional statistical methods with advanced deep learning architectures, the research aims to gain a comprehensive understanding of fake news characteristics and create robust detection mechanisms. the study emphasizes the importance of adopting a multi-dimensional approach to combat misinformation online, highlighting the necessity for a comprehensive strategy in addressing the proliferation of fake news on popular online platforms. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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High-Performance Matrix Multiplication on the New Generation Shenwei Processor 24

High-Performance Matrix Multiplication on the New Generation...

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24th IEEE international conference on High Performance Computing and Communications, 8th IEEE international conference on Data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Xu, Le An, Hong Chen, Junshi Zhang, Pengfei School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9798350319934

As a critical operation in numerical computing, matrix multiplication is widely used in many high-performance applications. the new generation Shenwei processor is the newest many-core processor with powerful computing capacity, thus it's necessary to enable highly efficient matrix multiplication on it. In this paper, we present a detailed implementation of double-precision matrix multiplication on Shenwei processor and further perform deep optimizations in terms of both memory access and computing. We employ a three-level task partitioning scheme mapping blocked matrix multiplication to each mem-ory hierarchy to improve computational parallelism and data reusability. And the dual broadcasting algorithm enables efficient parallel computing on chip. For further optimization, we present a three-level latency hiding strategy to bridge the performance gap between computing and memory access, where the hybrid data prefetch effectively hides DMA memory access and RMA communication costs. And the adaptive blocking algorithm is proposed for sake of the generality of algorithms towards various matrix sizes and shapes. High-performance matrix multiplication is highly correlated with the compute kernel, and we propose the register-buffering instruction scheduling with latency hiding strategy to maximize pipeline efficiency. the experiments show that the performance of our algorithm can reach 2 TFLOPS at large scales, near the theoretical peak performance. And the hybrid prefetch can improve performance by 82 %, while the adaptive blocking not only improves performance but also smoothes the performance jitter of irregular matrix multi-plications. the compute kernel with instruction scheduling is highly optimized and can achieve more than 380% performance improvement. © 2022 IEEE.

关键词： parallel processing systems

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Traffic Sign Recognition by Image Preprocessing and Deep Learning 15th

Traffic Sign Recognition by Image Preprocessing and Deep Lea...

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15th international conference on Intelligent Human-Computer Interaction (IHCI)

作者： Khamdamov, U. R. Umarov, M. A. Khalilov, S. P. Kayumov, A. A. Abidova, F. Sh. Univ Management & Future Technol Tashkent Uzbekistan Tashkent Univ Informat Technol Samarkand Branch Tashkent Uzbekistan Tashkent Univ Informat Technol Tashkent Uzbekistan

ISBN: (纸本)9783031538292;9783031538308

Due to the improvement in the car manifacture, the rate of road traffic accidents is increasing. To solve these problems, there is loads of attention in research on the development of driver assistance systems, where the main innovation is traffic sign recognition (TSR). In this article, a special convolutional neural network model with high accuracy compared to traditional models is used for TSR. the Uzbek Traffic Sign Dataset (UTSD) applied in the zone of Uzbekistan was created, consisting of 21.923 images belonging to 56 classes. We proposed a parallel computing method for real-time processing of video haze removal. Our utilization can process the 1920 x 1080 video series with 176 frames per second for the dark channel prior (DCP) algorithm. 8.94 times reduction of calculation time compared to the Central processing Unit (CPU) was achieved by performing the TSR process on the Graphics processing Unit (GPU). the algorithms used to detect traffic signs are improved YOLOv5. the results showed a 3.9% increase in accuracy.

关键词： Traffic sign recognition deep learning image dehazing UTSD dataset parallel processing data augmentation

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