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Bangla Sign Language Recognition using YOLOv5 8

Bangla Sign Language Recognition using YOLOv5

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8th IEEE international conference for Convergence in Technology, I2CT 2023

作者： Karim, Mainul Ali, Maun Hasan, Md. Ferdous Fatema-Tuj-Jahra, Most Sultana, Syeda Sumaia Farid, Dewan Md. United International University United City Department of Computer Science and Engineering Madani Avenue Badda Dhaka1212 Bangladesh

ISBN: (纸本)9798350334012

Bangla Sign Language (BdSL), also known as Bengali or Bangladeshi Sign Language, is the sign language that is commonly used for communication with the deaf people in Bangladesh and some parts of India, e.g., West Bengal (formally known as Calcutta), Assam, Tripura, and the Andaman and Nicobar Islands. Bangla is one of the sweetest languages in the world, according to the UNESCO survey, and the 7th most spoken language in the world. In Bangladesh, about 13.7 million people have a hearing problem, and the Bangladesh National Federation of the Deaf (BNFD) was established on December 25, 1963, to provide training, skill development, and education to deaf people. In this study, we have applied transfer learning in machine learning for BdSL recognition that reuses a pre-trained classifier named YOLOv5 to detect and classify BdSL. Transfer learning speeds up the training process with a minimum number of instances and improves classification performance. Transfer learning for deep learning has become very popular for solving computer vision and natural language processing (NLP) tasks nowadays. Initially, we collected a 24.2 GB video of BdSL from SignBD-word and extracted images from the video using the FFmpeg multimedia framework. then, we annotated the images using the LalelImg image annotation tool. Next, we checked the image annotation and built the model using YOLOv5. YOLOv5 is a family of compound-scaled object detection models, which stands for "You Only Look Once", commonly used for detecting objects by separating images into a grid structure. Finally, we have implemented the prototype with an average accuracy of 91.62%. © 2023 IEEE.

关键词： Image annotation

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An Efficient parallel Model for Coupled Open-Porous Medium Problem applied to Grain Drying processing 8th

An Efficient Parallel Model for Coupled Open-Porous Medium P...

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8th Latin American High Performance Computing conference (CARLA)

作者： Silva, Higor Uelinton Schepke, Claudio da Cruz Cristaldo, Cesar Flaubiano de Oliveira, Dalmo Paim Lucca, Natiele Fed Univ Pampa Alegrete RS Brazil

ISBN: (纸本)9783031042096;9783031042089

Does estimated that between 10% to 25% of the loss of the grain crop occurs post-harvest. Drying grains is one of the most critical steps in grain processing for its proper conservation after harvest. Considering that grain mass is an amount of solid and empty spaces (holes) through which a fluid can pass, grain drying can be assumed to be a coupled open-porous medium problem. In this paper is proposed a mathematical and computer simulation model. It describes convection in a free flow with a porous obstacle applied to grain drying. A computational fluid dynamics scheme was implemented in FORTRAN using Finite Volume to simulate and compute the numerical solutions. the code is parallel implemented using OpenMP programming interface. there was a significant reduction in processing time. the total simulation time was eight times less for a multicore architecture (16 physical cores).

关键词： Porous materials

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Binary Grey Wolf Optimizer for Mapping Real Time Applications on MPSOCs Architecture 7

Binary Grey Wolf Optimizer for Mapping Real Time Application...

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7th international conference on Informatics and applied mathematics, IAM 2024

作者： Boumaza, Farid Zouache, Djaafar Belazzoug, Mouhoub Hadji, Atmane Aroui, Abdelkader Computer Science Department University of Mohamed El Bachir El Ibrahimi Bordj Bou Arreridj34030 Algeria Laboratory of Parallel Embedded architectures and Intensive Computing University of Oran1 Oran31000 Algeria LISI Laboratory Computer Science Department University Center A. Boussouf Mila Mila43000 Algeria Center for Space Techniques Palestine Avenue Arzew Oran31200 Algeria

With the growing complexity of real-time applications, Multi-Processor Systems-on-Chip (MPSoCs) have become a vital solution for meeting stringent performance, power, and scalability requirements. Efficient task mapping plays a crucial role in optimizing the performance of such systems, particularly for real-time applications that demand strict timing constraints. Traditional mapping techniques, including static and dynamic strategies, struggle with balancing execution time, energy efficiency, and communication overhead in heterogeneous MPSoC architectures. In this paper, we propose a novel approach for optimizing task mapping in MPSoCs, based on the Grey Wolf Optimizer (GWO), a bio-inspired metaheuristic renowned for its effectiveness in solving complex optimization problems. this methodology aims to minimize task execution times and communication delays by intelligently mapping real-time tasks onto heterogeneous processing elements (PEs), while also adhering to real-time constraints and reducing energy consumption. the results confirm that the improved GWO algorithm is a powerful tool for addressing the challenges of real-time task mapping in MPSoCs, providing a robust and scalable solution for future embedded systems. © 2024 Copyright for this paper by its authors.

关键词： System-on-chip

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Error Analysis of the Cable-Driven parallel Robot in the Upgraded Feed Cabin of FAST 8

Error Analysis of the Cable-Driven Parallel Robot in the Upg...

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8th IEEE international conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Li, Zhaoyang Zhang, Bin Gao, Xinyu Wang, Xiaoyan Yao, Rui Li, Hui Yin, Jia-Ning Yang, Qingge Li, Qingwei Shang, Weiwei University of Science and Technology of China Department of Automation Hefei230027 China National Astronomical Observatories Chinese Academy of Sciences CAS Key Laboratory of FAST Beijing100101 China

ISBN: (纸本)9798350300178

the five-hundred-meter aperture spherical radio telescope, also called FAST, is known as one of the most famous cable-driven parallel robot (CDPR) systems in the world that has pioneered the application of CDPRs to large-scale radio telescopes. the FAST feed cabin currently uses a rigid-link structure to rotate the observation platform. In consequence, problems such as high self-magnitude and low observation angle always exist. After the preliminary research in this paper, a prototype of a CDPR with 9 cables can be used to replace the original rigid-link structure in the feed cabin and then improve the observation performance of FAST. Aiming at the new CDPR in the feed cabin, the error transfer model is established to analyze the influence of factors on the position and orientation error of the observation platform. In the simulation, the error distribution of the platform under static conditions is given, and the error control level is preliminarily analyzed. As a result, we have verified that the static error performance of the new observation platform can meet the designed expectations. © 2023 IEEE.

关键词： Cables

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Character Recognition Using Convolutional Neural Networks Based on CUDA

Character Recognition Using Convolutional Neural Networks Ba...

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

作者： Set Paing Tun Applied Mathematics and Artificial Intelligence Department National Research University “MPEI” Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

the article explores character recognition using convolutional neural networks (CNNs) optimized with the CUDA platform to enhance computational efficiency. It outlines the CNN architecture, methods for leveraging GPU-based parallel data processing, and presents experimental results derived from the MNIST dataset. the study highlights that implementing CUDA drastically reduces processing time while maintaining a high level of predictive accuracy. the findings emphasize the potential of GPU acceleration in handling intensive computational tasks, making it a promising approach for real-time applications in image recognition and machine learning.

关键词： Deep learning Training Energy consumption Graphics processing units parallel processing Real-time systems Energy efficiency Convolutional neural networks Character recognition Optimization

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parallel Extremely Randomized Decision Forests on Graphics Processors for Text Classification 14th

Parallel Extremely Randomized Decision Forests on Graphics ...

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14th international conference on parallel processing and applied mathematics, PPAM 2022

作者： Pires, Julio Cesar Batista Martins, Wellington Santos Federal University of Mato Grosso do Sul MS Campo Grande79070-900 Brazil Federal University of Goiás GO Goiânia74690-900 Brazil

ISBN: (纸本)9783031304415

the amount of readily available on-line text has grown exponentially, requiring efficient methods to automatically manage and sort data. Automatic text classification provides means to organize this data by associating documents with classes. However, the use of more data and sophisticated machine learning algorithms has demanded an increasingly computing power. In this work we accelerate a novel Random Forest-based classifier that has been shown to outperform state-of-art classifiers for textual data. the classifier is obtained by applying the boosting technique in bags of extremely randomized trees (forests) that are built in parallel to improve performance. Experimental results using standard textual datasets show that the GPU-based implementation is able to reduce the execution time by up to 20 times compared to an equivalent sequential implementation. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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YOLOv8 Image processing for Evaluation of Stability Algorithms Based on Neural Networks: A Sports Use Case 8th

YOLOv8 Image Processing for Evaluation of Stability Algorith...

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8th international conference on Inventive Communication and Computational Technologies, ICICCT 2024

作者： Rahman, Md. Habibur Mohiul Islam, A.S.M. Hasan, Abdullah Ibnah Uddin, Mahtab Ahmed, Ashek Miazee, Asif Ahammad Hossain, Yamin Department of Mathematics University of Chittagong Chittagong Bangladesh Dhaka College Dhaka Bangladesh Institute of Natural Sciences United International University Dhaka Bangladesh Department of Computer Science Maharishi International University FairfieldIA United States Department of Computer Science and Engineering VIT-AP University Amaravati India

ISBN: (纸本)9789819777099

Sports image classification is a complex problem with many different sports involved. It has subpar detection performance and challenges with feature recognition. the issue of classifying 110 different sports image categories is tackled by this study using four pre-trained models: Residual Network-50, EfficientNet-B7, Densely Connected Convolutional Network-121 (DenseNet-121), and You Only Look Once version 8 (YOLOv8). this dataset, which includes 12,500 sports images, provides a solid test base for this research. Comparing their results, ResNet-50 shows excellent performance on the training set, with an accuracy of 94.90 and 91.75% on the validation set. the EfficientNet-B7 model has an estimated accuracy of 65.48% in inference and 41.49% in training. Its underperformance may be due to its extraordinary performance and inability to represent specific gaming images accurately during the image segmentation task. DenseNet-121 performs 75.80% accuracy on the training set and 88.24% on the validation set. It does better than EfficientNet-B7 for capturing features from reflection images. the Geolovin-8 model performed well and averaged 94.90% accuracy on the training set and 97.87% on the validation set in most cases. these results demonstrate that Geolovin-8 has good image recognition and classification performance in the game. this article compares the performance of complex image classification algorithms concerning the same confidential image classification capabilities so that the required information is available. Knowing the advantages and disadvantages of these qualitative features may be necessary to understand the challenges of latent image classification and may suggest future research efforts. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

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RDBMS Speculative Support Improvement by the Use of the Query Hypergraph Representation 14th

RDBMS Speculative Support Improvement by the Use of the ...

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14th international conference on parallel processing and applied mathematics, PPAM 2022

作者： Sasak-Okoń, Anna Tudruj, Marek University of Maria Curie Sklodowska in Lublin Pl. Marii-Curie Sklodowskiej 5 Lublin20-031 Poland Institute of Computer Science Polish Academy of Sciences ul. Jana Kazimierza 5 Warsaw01-248 Poland Polish-Japanese Academy od Information Technology ul. Koszykowa 83 Warsaw02-008 Poland

ISBN: (纸本)9783031304415

the paper concerns the methodology for speculative support for query execution in Relational Database Management Systems (RDBMSs). It discusses and develops our proposal of supporting the RDBMS query execution based on a graph-based analytic approach. this approach assumes using the results of speculative queries defined by analyzing a multigraph representation of a stream of input queries arriving to a RDBMS. the queries from the RDBMS input stream are permanently analysed using a Speculation Window moving on the query stream to define the optimized speculative queries for execution. More specifically, the current paper develops the basic idea of the speculative query support and shows how the use of the speculative query results by the awaiting input user queries can be improved by the analysis of a query hypergraph representation. the analysis of a joint hypergraph representation of the speculative and user input queries has been employed to better organize the use of the speculative query results in the stream of input user queries of a RDBMS. the advantages of the proposed approach are positively assessed based on the experiments with the simulated execution of the testbed sets of queries. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Query processing

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An Efficient LRnLA Algorithm and Data Structure for Manycore and Multicore Computers with Hierarchical Cache 17th

An Efficient LRnLA Algorithm and Data Structure for Manyco...

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17th international Scientific conference on parallel Computational Technologies, PCT 2023

作者： Levchenko, Vadim Perepelkina, Anastasia Keldysh Institute of Applied Mathematics Moscow Russia

ISBN: (纸本)9783031388637

According to the Roofline model, low arithmetic intensity is the main performance bottleneck for multidimensional simulation with stencil data access. this bottleneck can be eliminated using LRnLA (locally recursive nonlocally asynchronous) algorithms, which take advantage of all levels of cache. We introduce the new algorithm FArShFold, a development of previously published Torre-type LRnLA algorithms by using data arrays aligned with a space-time wavefront. the novelty is in the distribution of data between threads and the decomposition in the time axis for parallelism. In this paper, we present the implementation of the algorithm for the solution of fluid dynamics problems with the LBM method and its scaling on AMD zen2/3 processor architectures. the obtained performance for single precision D3Q19 LBM is more than 1.4 GLUps on the AMD Ryzen R9 5950X processor. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphics processing unit

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Balance Multi-Head Attention based on Software and Hardware Co-design 9

Balance Multi-Head Attention based on Software and Hardware ...

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9th IEEE international conference on Cyber Security and Cloud Computing and 8th IEEE international conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2022

作者： Xu, Dian Hu, Wei Liu, Fang Fan, Zimeng Shi, Qingsong Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System College of Computer Science Wuhan China Wuhan University Department of Information Engineering Wuhan Institute of City School of Computer Science Wuhan China Zhejiang University College of Computer Science Hangzhou China

ISBN: (数字)9781665480666

ISBN: (纸本)9781665480666

Recently, the Transformer-based models have achieved leading results in many research areas such as natural language processing and computer vision. However, since Transformer-based models have a huge computational complexities, optimization for Transformer is the focus of current research. the core of Transformer models is the attention module, and in this paper we propose a software and hardware co-designed attention module, which reduces the computational units of the multi-head attention module by balancing the the inference time of the attention module is reduced by balancing the computational units of multiple attention modules. At the same time, we design the corresponding structure on FPGA, which accelerate 14.6x inference time in comparison with the original attention module implementation on GPU. We applied this attention module to multiple visual Transformer models and tested the accuracy difference between the balanced multi-head attention module-based model and the original model on CIFAR-10, and obtained a result of 80.46%, which lower from the original version by 0.18%. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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