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Classification of Multi-view mammogram images using a parallel pre-trained models system

Classification of Multi-view mammogram images using a parall...

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international conference on Image and Signal processing and their Applications (ISPA)

作者： Abdelhafidh Kacher Medjeded Merati Saïd Mahmoudi Computer Science Department LIM Research Laboratory University of Tiaret Algeria ILIA Department Faculty of Engineering University of Mons Belgium

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Breast cancer is the main lead of women’s cancer-related mortalities. therefore, early detection is imperative for preventing breast cancer from developing to advanced stages. Moreover, the emergence of Computer-aided diagnosis systems combined with Deep Learning techniques improve breast cancer diagnosis at its early stages. this study proposes a system for classifying mammogram images scanned from different views based on Deep learning techniques. In this paper, we propose combining four pre-trained models in one system employing the Transfer Learning technique with different input images of the same lesion. this proposed system provides a binary classification of the lesion into malignant or benign classes. the Mini-DDSM dataset images were used in this study; the images were pre-processed and augmented to achieve the best results. Several well-known pre-trained models for image classification (ResNet, Xception, DenseNet, MobileNet, and Inception) were experimented with for the proposed model construction. the study results showed that our proposed model achieved an accuracy of 94.18%, a precision of 94.26%, a recall of 94.16%, and an F1-Score of 94.21%, outperforming the pre-trained model on classifying mammogram images of the same dataset.

关键词： Deep learning Computational modeling Transfer learning Signal processing Lead Mammography Breast cancer

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Optimized Stochastic Approaches Based on Sobol Quasirandom Sequences for Fredholm Integral Equations of the Second Kind 10th

Optimized Stochastic Approaches Based on Sobol Quasirandom ...

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10th international conference on Numerical Methods and Applications, NMA 2022

作者： Todorov, Venelin Dimov, Ivan Georgieva, Rayna Ostromsky, Tzvetan Department of Information Modeling Institute of Mathematics and Informatics Bulgarian Academy of Sciences Acad. Georgi Bonchev Str. Block 8 Sofia1113 Bulgaria Department of Parallel Algorithms Institute of Information and Communication Technologies Bulgarian Academy of Sciences Acad. G. Bonchev Str. Block 25A Sofia1113 Bulgaria

ISBN: (纸本)9783031324116

In this paper three possible approaches to compute linear functionals of the solution of the Fredholm integral equation of the second kind are under consideration: a biased Monte Carlo method based on evaluation of truncated Liouville-Neumann series;transformation of this problem into the problem of computing a finite number of integrals, and an unbiased stochastic approach. In the second case several Monte Carlo algorithms for numerical integration have been applied including optimized stochastic approaches developed in our previous studies. the unbiased stochastic approach has been applied to a multidimensional numerical example. A comprehensive analysis about the reliability and the efficiency of the algorithms has been done. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Monte Carlo methods

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1-bit LAMB: Communication Efficient Large-Scale Large-Batch Training with LAMB's Convergence Speed 29

1-bit LAMB: Communication Efficient Large-Scale Large-Batch ...

引用

29th Annual IEEE international conference on High Performance Computing, Data, and Analytics (HiPC)

作者： Li, Conglong Awan, Ammar Ahmad Tang, Hanlin Rajbhandari, Samyam He, Yuxiong Microsoft Redmond WA 98052 USA Univ Rochester Rochester NY 14627 USA

ISBN: (纸本)9781665494236

To train large machine learning models (like BERT and GPT-3) on hundreds of GPUs, communication has become a significant bottleneck, especially on commodity systems with limited-bandwidth TCP networks. On one side, large batch-size optimization such as the LAMB algorithm was proposed to reduce the frequency of communication. On the other side, communication compression algorithms such as 1-bit Adam help to reduce the volume of each communication. However, we find that simply using one of the techniques is not sufficient to solve the communication challenge, especially under low network bandwidth. Motivated by this we aim to combine the power of large-batch optimization and communication compression but we find that existing compression strategies cannot be directly applied to LAMB due to its unique adaptive layerwise learning rates. To this end, we design a new communication-efficient optimization algorithm, 1-bit LAMB, which introduces a novel way to support adaptive layerwise learning rates under compression. In addition to the algorithm and corresponding theoretical analysis, we propose three novel system implementations in order to achieve actual wall clock speedup: a momentum fusion mechanism to reduce the number of communications, a momentum scaling technique to reduce compression error, and a NCCL-based compressed communication backend to improve both usability and performance. For the BERT-Large pre-training task with batch sizes from 8K to 64K, our evaluations on up to 256 GPUs demonstrate that our optimized implementation of 1-bit LAMB is able to achieve up to 4.6x communication volume reduction, up to 2.8x end-to-end time-wise speedup, and the same sample-wise convergence speed (and same fine-tuning task accuracy) compared to uncompressed LAMB. Furthermore, 1-bit LAMB achieves the same accuracy as LAMB on computer vision tasks like ImageNet and CIFAR100.

关键词： communication compression distributed deep learning language model pre training natural language processing optimization

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Comparison of the Genetic Algorithm and the Particle Swarm Optimization for Minimizing Fuel Consumption for UAVs

Comparison of the Genetic Algorithm and the Particle Swarm O...

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Innovative Research in applied Science, Engineering and Technology (IRASET),conference on

作者： Vincent Roberge Mohammed Tarbouchi Gilles Labonté Department of Electrical and Computer Engineering Royal Military College of Canada Kingston Canada Department of Mathematics and Computer Science Royal Military College of Canada Kingston Canada

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

this paper compares the efficiency of two metaheuristics, namely the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO), for the problem of fuel minimization for uncrewed aerial vehicles (UAVs). When flying search-and-rescue or surveillance missions, it is critical to minimize fuel consumption in order to increase the autonomy of the vehicles. the method proposed consists of using the metaheuristic to compute optimized power settings for the UAV along the trajectory. the approach involves path smoothing in 3D using circular arcs for fixed-wing UAVs. Equations of motion and Newton's equation are used to calculate fuel consumption based on power settings. the metaheuristics are used to compute optimized power settings while adhering to physical constraints. the method is parallelized on a multicore processor for faster computation and optimization in case of trajectory changes. Results show that both the GA and the PSO are efficient at minimizing fuel consumption for the UAV with the PSO being slightly superior. the proposed method can reduce fuel consumption by up to 15% and the parallel implementation is 13.8× faster than a sequential implementation on the CPU.

关键词： Smoothing methods Multicore processing Surveillance Metaheuristics Autonomous aerial vehicles Minimization Trajectory Fuels Particle swarm optimization Genetic algorithms

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Pre-processing Block Design for the Electroencephalography Signal by Using Notch Filter and Blind Source Separation Technique 8th

Pre-processing Block Design for the Electroencephalography S...

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8th international conference on the Development of Biomedical Engineering, BME8 2020

作者： Pham, Minh Bao Phan, Xuan Yen Nhi Le, Quoc Khai Huynh, Quang Linh Biomedical Engineering Department HCMUT-VNU 146 Ly Thuong Kiet Street Ho Chi Minh City70000 Viet Nam

ISBN: (纸本)9783030755058

the digital filters play an indispensable role in recording and displaying EEG data. Without filters, many segments of EEG would be virtually unreadable. the main benefit of filters is that it makes the EEG tracing cleaner, easier to interpret and observe. Specific filter settings can also be used to accentuate particular types of EEG activity. Filters can, however, be misused, and at times their use can lead to unintended consequences. In this study, we present and compare the conventional filter design methods applied to significant types of noises. We built a program with a friendly interface with effective noise filtering. © 2022, Springer Nature Switzerland AG.

关键词： Blind source separation

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Improving Single Image-based Face Morphing Attack Detection Rate with Image Pre-processing

Improving Single Image-based Face Morphing Attack Detection ...

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international conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： R ChaliniG K.V. Kanimozhi Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Morphing attack detection is nothing but person who can cross the border without any valid identity. Face morphing attack detection can be divided into two categories: S-MAD for Single Morph Attack Detection and D-MAD for Differential Morph Attack Detection. the proposed study in this research is mainly focused on pre-processing methods using various filtering approaches. Several filtering approaches are used to decrease noise from facial images in order to distinguish changing faces. In this paper, the non-local means filter, the gaussian filter, the low-pass filter, and a bilateral filter are analysed and compared. the performance and quality of Feature Extraction, Optimization, and Classification improves when the bilateral filter technique is applied for image preprocessing. the AMSL Face Morph picture dataset is collected via the Github Repository, and an experiment is carried out to evaluate the efficiency of bilateral filtering in removing noise from the image.

关键词： Filtering Noise Low-pass filters Aerospace electronics Feature extraction Proposals Image preprocessing Faces Optimization Software development management

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parallel Matching for Graph Partitioning on Shared Memory

Parallel Matching for Graph Partitioning on Shared Memory

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Anton A. Rakitskiy Kirill V. Trusov Siberian State University of Telecommunications and Information Sciences Institute of Computational Technologies SB RAS Novosibirsk State University Novosibirsk Russia Dept. of Applied Mathematics and Cybernetics Siberian State University of Telecommunications and Information Science Novosibirsk Russia

Graph partitioning may require a considerable amount of time for large-scale problems and one of the most time-consuming phases is finding maximal matching for graph coarsening. Approaches to find approximately maximal matching uses heuristic strategies to obtain balance between quality and performance, for example, there are used random matching and heavy edge matching approaches for unweighted and weighted undirected graphs respectively. In this paper we present the parallel algorithms for these techniques to increase the performance and maintain matching quality for multi-core systems. the main idea of this approach is to distribute vertices into subsets, perform matching in parallel and launch the serial matching algorithm for unmatched vertices to obtain better quality. Serial phase requires much less time, because after parallel phase number of vertices that are checked for possible matching considerably decreases. Setting minimum size of the subsets helps to reduce number of vertices for processing in serial phase. the potential acceleration is up to number of used processes.

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Gaussian-Weighted Trend-Seasonal Decomposition Interactive Attention LSTM for Wind Turbine Drivetrain State Forecasting

Gaussian-Weighted Trend-Seasonal Decomposition Interactive A...

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

作者： Hui-Ni Sun Hao Zheng Bai Shuang School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Accurate prediction of the operational states of wind turbines is crucial for improving the stability of their operation and the utilization efficiency of wind power. Currently, most data-driven studies focus primarily on forecasting the output power of wind turbines, without addressing other important operational data such as rotor loads. therefore, this paper proposes the Gaussian-Weighted Trend-Seasonal Decomposition Interactive Attention LSTM model for predicting the operational states of wind turbines. Given the diverse and complex nature of wind farm data, the Gaussian-Weighted Trend-Seasonal Decom-position is designed to decompose the features of different time patterns within the wind farm data, with Gaussian weighting applied to emphasize information from adjacent time points. Additionally, the Interactive Attention LSTM is designed to separately predict the trend and seasonal components, using the attention mechanism to enable interaction between the current states and historical time points, thus enhancing the contribution of historical information to future feature predictions. Experimental results demonstrate that the proposed model outperforms other models in terms of prediction accuracy for multiple wind turbine states.

关键词： Accuracy Wind speed Predictive models Wind farms Wind power generation Signal processing Stability analysis Wind turbines Forecasting Long short term memory

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Single DXCCTA based Charge Controlled Floating Incremental/Decremental Memristor Emulator

Single DXCCTA based Charge Controlled Floating Incremental/D...

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international conference on Signal processing and Communication (ICSC)

作者： Navnit Kumar Manjeet Kumar Neeta Pandey Department of Electronics and Communication Engineering Delhi Technological University Delhi India

ISBN: (纸本)9781665454315

this article offers a charge controlled floating incremental/decremental memristor emulator using Dual-X Current Conveyor Transconductance Amplifier (DXCCTA). the proposed circuit includes one DXCCTA, four resistors, one grounded capacitor and two MOS switches. Incremental and decremental mode of operation depends upon setting of MOS switch. this paper avoids the utilization of analog multiplier circuit. Pinch hysteresis curves of proposed circuit are hold up to 60 MHz. Moreover, two memristor combinations are connected in series and parallel to examine the functionality of the proposed memristor emulator, and the intended results are acquired. Performance of the designed circuit is evaluated, at schematic level 90 CMOS process technology, are presented.

关键词： Digital-analog conversion Capacitors Memristors Low-pass filters Switches Signal processing Fingerprint recognition

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parallel Implementation of Luhn’s Algorithm for Credit Card Validation Using MPI and CUDA 8th

Parallel Implementation of Luhn’s Algorithm for Credit Card...

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8th international conference on Frontiers of Intelligent Computing: theory and Applications, FICTA 2020

作者： Kudva, P. Karthik G. Shreyas, M.L. Rao, B. Ashwath Rai, Shwetha Kini, N. Gopalakrishna Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education ManipalKarnataka576104 India

ISBN: (纸本)9789811557873

the Luhn’s algorithm is the first line of defense in various e-commerce sites and is utilized to validate credit card numbers. With increase in usage of credit cards validation process also needs to be faster. this fast processing is achievable by parallel processing. this paper intends to make use of MPI and CUDA programming to enhance the computation time of Luhn’s algorithm of validation of multiple credit cards in parallel. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： parallel programming

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