检索结果-内蒙古大学图书馆

11th IEEE International Conference on Intelligent computing and Information Systems, ICICIS 2023

作者： Abdelhamid, Mohamed Mahmoud Wael Abdelmawgod, Amr Al-Metwally, Mohamed Tharwat Mohamed Hossien, Abdelrahman Shedeed, Howida A. Saeed Fouly, Eslam Naser, Aya Ain Shams University Faculty of Computer and Information Science Scientific Computing Department Cairo Egypt Ain Shams University Faculty of Computer and Information Science Scientific Computing Department Egypt

ISBN: (纸本)9798350322101

Classifying various precise manual movements based on electroencephalogram (EEG) signals presents an important research obstacle, particularly in the category of motor rehabilitation within brain-computer interface (BCI) applications. This study focuses on classifying EEG signals into six hand movement classes (forward, backward, up, down, right, left) utilizing machine learning and deep learning methods. The goal is to detect the person's current thoughts and intentions, including motor imagery and real movements, to assist individuals with physical disabilities or limb loss in controlling their robotic arms. After data collection and preprocessing, feature extraction is performed on the EEG signals obtained from the five signal bands (alpha, beta, theta, delta, gamma). Deep learning techniques include the use of recurrent neural networks with long short-term memory (RNN-LSTM) and convolutional neural networks (CNN), in addition to more traditional classifiers like Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA), an overall best accuracy 70% is achieved in detecting the 6 hand movements classes from one subject. DWT with Logistic Regression, also CSP using SVM (one vs rest) achieved the same accuracy. EEG-Net also got 70% accuracy on the 6 classes. 93% accuracy is achieved for binary classification (2 classes) with CSP as a feature and Random Forest classifier also DWT using LSTM achieved the same accuracy using a 60-channel EEG system, the results that overcome the results from literature review on the same dataset. Cross subjects' technique is experimented also in this study. Using cross subjects' data from 4 classes, CSP using LSTM shows high accuracy of 61%. The use of a 60-channel EEG system and the adoption of deep learning techniques resulted in the best accuracy for hand movement recognition in this study. © 2023 IEEE.

关键词： Brain computer interface

来源：评论

学校读者我要写书评

暂无评论

DeepFake Detection System using Deep Learning 11

DeepFake Detection System using Deep Learning

引用

11th IEEE International Conference on Intelligent computing and Information Systems, ICICIS 2023

作者： Ahmed, Omar Fekry, Sameh Metwally, Abdullah Abdelwahid, Abdelrahman Khattab, Dina Abdulelmagid, Amer Hossieny, Radwa Ain Shams University Faculty of Computer and Information Science Scientific Computing Department Cairo Egypt

ISBN: (纸本)9798350322101

Due to the growing usage of DeepFake technology to produce fake photos and videos, the problem of DeepFake identification has become a serious concern in recent years. With the use of this technology, fake material that is difficult to tell apart from real content may be produced. Therefore, the development of accurate DeepFake detection algorithms is essential to identify and prevent the spread of manipulated content. However, current publicly available DeepFake detection datasets suffer from a lack of diversity, with only a few actors appearing in multiple videos. This results in an oversampled training dataset, leading to model overfitting and inadequate performance when tested on new data. The overfitting problem can cause deep neural networks to focus more on facial features than on specific traits of DeepFake content, thereby reducing the model's ability to generalize. To address this issue and broaden the training dataset's variety, we suggest applying data augmentation methods like Face Cutout and Random Erase. The Face Cutout technique randomly removes a rectangular area of an image containing the face, while the Random Erase technique randomly removes a rectangular area of the image. These techniques introduce variations in the images, making it more challenging for the model to overfit and focus on specific traits of DeepFake content. We evaluated our approach using several models and achieved significant accuracy improvements using the EfficientnetV2B0 model and Random Erase augmentation. © 2023 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Exploring Multiscale Navigation of Homogeneous and Dense Objects with Progressive Refinement in Virtual Reality 32

Exploring Multiscale Navigation of Homogeneous and Dense Obj...

引用

32nd IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2025

作者： Pavanatto, Leonardo Giovannelli, Alexander Giera, Brian Bremer, Timo Miao, Haichao Bowman, Doug A. Virginia Tech Center for Human-Computer Interaction Department of Computer Science United States Lawrence Livermore National Laboratory Center for Applied Scientific Computing United States

ISBN: (纸本)9798331536459

Locating small features in a large, dense object in virtual reality (VR) poses a significant interaction challenge. While existing multiscale techniques support transitions between various levels of scale, they are not focused on handling dense, homogeneous objects with hidden features. We propose a novel approach that applies the concept of progressive refinement to VR navigation, enabling focused inspections. We conducted a user study where we varied two independent variables in our design, navigation style (STRUCTURED vs. UNSTRUCTURED) and display mode (SELECTION vs. EVERYTHING), to better understand their effects on efficiency and awareness during multiscale navigation. Our results showed that unstructured navigation can be faster than structured and that displaying only the selection can be faster than displaying the entire object. However, using an everything display mode can support better location awareness and object understanding. © 2025 IEEE.

关键词： Virtual reality

来源：评论

学校读者我要写书评

暂无评论

POSTER: Learning to Parallelize in a Shared-Memory Environment with Transformers 28

POSTER: Learning to Parallelize in a Shared-Memory Environme...

引用

28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, PPoPP 2023

作者： Harel, Re'Em Pinter, Yuval Oren, Gal Department of Computer Science Ben-Gurion University of the Negev Department of Physics Scientific Computing Center Nuclear Research Center Negev Israel Department of Computer Science Ben-Gurion University of the Negev Israel Department of Computer Science Technion - Israel Institute of Technology Scientific Computing Center Nuclear Research Center Negev Israel

ISBN: (纸本)9798400700156

In past years, the world has switched to multi and many core shared memory architectures. As a result, there is a growing need to utilize these architectures by introducing shared memory parallelization schemes, such as OpenMP, to applications. Nevertheless, introducing OpenMP work-sharing loop construct into code, especially legacy code, is challenging due to pervasive pitfalls in management of parallel shared memory. To facilitate the performance of this task, many source-to-source (S2S) compilers have been created over the years, tasked with inserting OpenMP directives into code automatically. In addition to having limited robustness to their input format, these compilers still do not achieve satisfactory coverage and precision in locating parallelizable code and generating appropriate directives. In this work, we propose leveraging recent advances in machine learning techniques, specifically in natural language processing (NLP), to suggest the need for an OpenMP work-sharing loop directive and data-sharing attributes clauses - the building blocks of concurrent programming. We train several transformer models, named PragFormer, for these tasks and show that they outperform statistically-trained baselines and automatic source-to-source (S2S) parallelization compilers in both classifying the overall need for an parallel for directive and the introduction of private and reduction clauses. In the future, our corpus can be used for additional tasks, up to generating entire OpenMP directives. The source code and database for our project can be accessed on GitHub 1 and HuggingFace 2. © 2023 Owner/Author.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

***: A Deep Learning System for Digital Smile Design 11

***: A Deep Learning System for Digital Smile Design

引用

11th IEEE International Conference on Intelligent computing and Information Systems, ICICIS 2023

作者： Khaled Ali, Ganna Allah Abdelbadei, Khloud Aboelhasan Essam, Mohammad Gamal, Riham Essam Mohamed, Taghreed Samir Al-Berry, Maryam Nabil Nabil, Sarah Hatem Said, Amr Mohamed Ain Shams University Bioinformatics Department Computer and Information Science Cairo Egypt Ain Shams University Scientific Computing Department Computer and Information Science Cairo Egypt

ISBN: (纸本)9798350322101

Digital Smile Design (DSD) is an advanced technique that enables dentists to create customized treatment plans for each patient by creating a digital representation of their desired smile. This approach promotes collaboration between dentists and patients to meet individual needs and goals. Special software like DSD allows dentists to plan treatments more precisely and achieve the patient's desired outcome. *** is an application inspired by DSD, which utilizes machine learning, deep learning, and image processing to simulate a desired *** helps patients participate in the smile creation process and allows for more predictable outcomes, improving patient acceptance of suggested treatment plans. *** also helps dentists to simplify the connection with their patients, educate them about the benefits of treatment, and trying to reduce patient reluctance. The first approach is based on image segmentation using a segmentation model. Unet++, MAnet, DeepLabv3+, FPN, while the second approach is based on ControlNet, ControlNet is a type of neural network architecture that controls diffusion models by introducing additional constraints. This innovation represents a major advance in the field of AI imaging as it allows excellent control over stable diffusion ControlNet is growing in popularity for several reasons. First, it produces high-quality output with better control compared to other methods like depth-to-image. Second, it is highly efficient and requires significantly less training time than traditional approaches. Finally, ControlNet prevents overfitting. Overall, *** is a revolutionary tool that can improve the quality of dental treatments and patient satisfaction. © 2023 IEEE.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Testing the Unknown: A Framework for OpenMP Testing via Random Program Generation

Testing the Unknown: A Framework for OpenMP Testing via Rand...

引用

2024 Workshops of the International Conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Laguna, Ignacio Chapman, Patrick Parasyris, Konstantinos Georgakoudis, Giorgis Rubio-Gonzalez, Cindy Lawrence Livermore National Laboratory Center for Applied Scientific Computing United States University of California Department of Computer Science Davis United States

ISBN: (纸本)9798350355543

We present a randomized differential testing approach to test OpenMP implementations. In contrast to previous work that manually creates dozens of verification and validation tests, our approach is able to randomly generate thousands of tests, exposing OpenMP implementations to a wide range of program behaviors. We represent the space of possible random OpenMP tests using a grammar and implement our method as an extension of the Varity program generator. By generating 1,800 OpenMP tests, we find various performance anomalies and correctness issues when we apply them to three OpenMP implementations: GCC, Clang, and Intel. We also present several case studies that analyze the anomalies and give more details about the classes of tests that our approach creates. © 2024 IEEE.

关键词： differential testing OpenMP random program generation software testing

来源：评论

学校读者我要写书评

暂无评论

Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

引用

IEEE Transactions on Electron Devices 2024年第1期71卷 246-253页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation The Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program The Institute of Communications Engineering the Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

Enhancing single-lead electrocardiogram arrhythmia detection with empirical mode decomposition

引用

Neural computing and Applications 2025年 1-23页

作者： Issa, Mohamed F. Yousry, Ahmed Tuboly, Gergely Wang, Zeyu Zoltan, Juhasz Selim, Mazen M. AbuEl-Atta, Ahmed H. Department of Scientific Computing Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Electrical Engineering and Information Systems University of Pannonia Veszprem8200 Hungary Department of Mechatronics Delta University for Science and Technology Gamasa35712 Egypt

Cardiac arrhythmias pose a significant challenge to health care, requiring accurate and reliable detection methods to enable early diagnosis and treatment. However, traditional ECG beat classification methods often lack robustness and fail to generalize effectively to diverse clinical scenarios, particularly when using single-lead-I recordings, which are less studied compared to lead II. To address this gap, this paper proposes a novel method that combines deep learning and empirical mode decomposition (EMD) for arrhythmia detection using single-lead-I ECG recordings obtained with electrodes on each hand. The hybrid model comprises 149 layers of one-dimensional convolution, max pooling, and residual blocks. By leveraging EMD, it classifies ECG beats into normal beats, premature ventricular contractions, and atrial premature beats with high sensitivity (99.86%, 99.72%, and 99.84%, respectively) using the first four intrinsic mode functions (IMFs). The proposed method is applicable in clinical and out-of-hospital monitoring scenarios, and an accompanying web application provides real-time analysis and diagnostic results. This approach addresses a critical gap in lead-I ECG analysis, enhancing diagnostic precision and expanding the scope of ECG applications in health care. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Empirical mode decomposition

来源：评论

学校读者我要写书评

暂无评论

Text-to-Image Synthesis using Generative Adversarial Networks 11

Text-to-Image Synthesis using Generative Adversarial Network...

引用

11th IEEE International Conference on Intelligent computing and Information Systems, ICICIS 2023

作者： Ibrahim, Mostafa Samy Nabil El-Berry, Mariam Fahmy Tolba, Mohamed Fadel Bahgat, Sayed Faculty of Computer and Information Sciences-Ain Shams University Bachelor of Computer and Information Science Egypt Faculty of Computer and Information Sciences-Ain Shams University Maryam Lecturer in Scientific Computing Department Egypt Faculty of Computer and Information Sciences-Ain Shams University Scientific Computing Department Egypt

ISBN: (纸本)9798350322101

Text-to-image synthesis is considered a unique problem in image synthesis, which is the task of generating image from input text. Previously, the purpose of text-to-image synthesiswas to match words and images using retrieval methods basedon a phrase or collection of keywords. With the evolution ofdeep learning, particularly the usage of generative models, image synthesis has made potentially significant advancements. One of the most significant generative models are Generative Adversarial Networks (GANs), and they have been widely successfully utilized in various domains. In this study proposes our model, examines and summarizes recent work on GAN-based text-to-image synthesis, as well as providing an overview of the evolution of both classic and cutting-edge models. The usual structure of each category is described in depth. The survey presents a thorough review of the development of GAN-based text to image synthesis. Finally, Dynamic Memory GAN with Contrastive Learning (DMGAN+CL) with prompt engineering, which optimizes and acquires consistent textual representations for captions using the contrastive learning technique. We tested our technique on the CUB-200 dataset, and the findings demonstrate that our framework achieved comparable results with other typical GANs architectures in text-to-image synthesis. © 2023 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

On the Scalability of computing Genomic Diversity Using SparkLeBLAST: A Feasibility Study

On the Scalability of Computing Genomic Diversity Using Spar...

引用

2024 IEEE High Performance Extreme computing Conference, HPEC 2024

作者： Prabhu, Ritvik Moussad, Bernard Youssef, Karim Vatai, Emil Feng, Wu-Chun Virginia Tech Department of Computer Science United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States Riken Center for Computational Science Japan

ISBN: (纸本)9798350387131

Studying the genomic diversity of viruses can help us understand how viruses evolve and how that evolution can impact human health. Rather than use a laborious and tedious wet-lab approach to conduct a genomic diversity study, we take a computational approach, using the ubiquitous NCBI BLAST and our parallel and distributed SparkLeBLAST, across 53 patients (40,000,000 query sequences) on Fugaku, the world's fastest homogeneous supercomputer with 158,976 nodes, where each code contains a 48-core A64FX processor and 32 GB RAM. To project how long BLAST and SparkLeBLAST would take to complete a genomic diversity study of COVID-19, we first perform a feasibility study on a subset of 50 query sequences from a single COVID-19 patient to identify bottlenecks in sequence alignment processing. We then create a model using Amdahl's law to project the run times of NCBI BLAST and SparkLeBLAST on supercomputing systems like Fugaku. Based on the data from this 50-sequence feasibility study, our model predicts that NCBI BLAST, when running on all the cores of the Fugaku supercomputer, would take approximately 26.7 years to complete the full-scale study. In contrast, SparkLeBLAST, using both our query and database segmentation, would reduce the execution time to 0.026 years (i.e., 22.9 hours) - resulting in more than a 10,000× speedup over using the ubiquitous NCBI BLAST. © 2024 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：