检索结果-内蒙古大学图书馆

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Haque, Rezaul Sultana, Shayma Prasad, Chandan Hasan, Sayed Fardin, Hasib Sakib, Abdullah Al Malik, Neeru Kaushik, Keshav East West University Department of Cse Dhaka Bangladesh International American University Department of Management Information System United States Pimpri Chinchwad University Department of Computer Science and Engineering Maharashtra Pune India Westcliff University Department of Engineering Management United States Westcliff University Department of Information Technology United States Pimpri Chinchwad University School of Engineering & Technology Maharashtra Pune India Amity University Amity School of Engineering and Technology Punjab India

ISBN: (纸本)9798350350067

The high death rate and rapid spread of pneumonia make it a serious global health concern. The precision of traditional diagnostic techniques is often compromised, particularly in resource-limited settings. Existing studies have predominantly focused on either binary or multi-class classification, often relying on limited datasets, which affects the generalizability and robustness of models. Additionally, the interpretability and transparency of deep learning models remain challenging in clinical environments. Addressing these challenges is crucial for advancing diagnostic methodologies to combat pneumonia. This study explores the use of transfer learning to improve the reliability of pneumonia classification using chest X-ray images. An automated classification system was developed, utilizing a combination of image preprocessing techniques, feature extraction with GLRLM, and various machine learning and transfer learning algorithms. Two datasets were employed: the GWCMCx dataset with 5856 chest X-rays and another dataset comprising 9207 chest X-rays. The system was designed for both binary and multi-class classification scenarios. CapsuleNet emerged as the most effective model, achieving an accuracy of 99.02% in binary classification and 90.43% in multi-class classification, significantly outperforming previous studies. Additionally, a user-friendly web application was developed to facilitate the integration of the classification system into clinical workflows, accelerating clinical decision-making. This study demonstrates the potential of transfer learning to automate and improve pneumonia diagnosis, offering a scalable solution that can alleviate radiologists' workload and improve patient outcomes. © 2024 IEEE.

关键词： Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Neural network-driven parallel accelerated selective image encryption with semantic understanding

引用

The European Physical Journal Special Topics 2025年 1-16页

作者： Song, Wei Fu, Chong Zheng, Yu Chen, Junxin Zhang, Yanfeng School of Computer Science and Engineering Northeastern University Shenyang China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China University of California Irvine Irvine USA School of Software Dalian University of Technology Dalian China

Existing batch image encryption schemes often directly encrypt images without considering their source. However, in real-life scenarios, these images are typically part of a larger collection stored in a directory on a computer or in the cloud. To efficiently protect those sensitive images distributed within the collection, we rethink selective image encryption (SIE) from the perspective of batch image encryption and proposes a novel SIE scheme using deep neural networks (DNN) and parallel computing. First, a DNN method is employed to identify sensitive images by analyzing their semantics. Next, a parallel cross-bitplane permutation algorithm is introduced to shuffle the retrieved images in bit-level. Then, a bi-directional diffusion strategy is employed to spread the encryption influence of a pixel to all images. Experimental results demonstrate the proposed scheme effectively understands the semantic of images, identifies sensitive images, and encrypts them with high permutation and diffusion efficiency. Further security analyses show that the proposed algorithm successfully resists various attacks with desired performance, including achieving high information entropy and low correlation coefficients in statistical analysis, and ideal values of the number of pixels changing rate (NPCR) and the unified averaged changing intensity (UACI) to resist differential attack, as well as high key sensitivity.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Techniques Using the Rapid Miner Tool for Solar Production Forecasting 4

Machine Learning Techniques Using the Rapid Miner Tool for S...

引用

4th International Conference on Digital Age and Technological Advances for Sustainable Development, ICDATA 2023

作者： Ledmaoui, Younes El Fahli, Asmaa Elmaghraoui, Adila El Aroussi, Mohamed Saadane, Rachid Chehri, Abdellah Hassania School of Public Works Casablanca Laboratory Engineering System Morocco Mohammed Vi Polytechnic University Benguerir Morocco Mathematics and Computer Science KingstonONK7K 7B4 Canada

ISBN: (纸本)9798350311112

The Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBOOST), Support Vector Machine (SVM), and Random Forest (RF) machine learning (ML) algorithms are used in this research to give a comparative study of solar energy production forecasting in Morocco. The models were developed, trained, and then assessed. In this paper, two metrics were employed to evaluate the models' performance: Mean absolute error (MAE), and root mean squared error (RMSE). The models' performances show that ANN is the most reliable predictive model for energy forecasting in similar scenarios. © 2023 IEEE.

关键词： Solar energy

来源：评论

学校读者我要写书评

暂无评论

Reveal training performance mystery between Tensor Flow and PyTorch in the single GPU environment

引用

Science China(Information Sciences) 2022年第1期65卷 147-163页

作者： Hulin DAI Xuan PENG Xuanhua SHI Ligang HE Qian XIONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabSchool of Computer Science and Technology Huazhong University of Science and Technology Department of Computer Science University of Warwick

Deep learning has gained tremendous success in various fields while training deep neural networks(DNNs) is very compute-intensive, which results in numerous deep learning frameworks that aim to offer better usability and higher performance to deep learning practitioners. Tensor Flow and Py Torch are the two most popular frameworks. Tensor Flow is more promising within the industry context, while Py Torch is more appealing in academia. However, these two frameworks differ much owing to the opposite design philosophy:static vs dynamic computation graph. Tensor Flow is regarded as being more performance-friendly as it has more opportunities to perform optimizations with the full view of the computation graph. However, there are also claims that Py Torch is faster than Tensor Flow sometimes, which confuses the end-users on the choice between them. In this paper, we carry out the analytical and experimental analysis to unravel the mystery of comparison in training speed on single-GPU between Tensor Flow and Py Torch. To ensure that our investigation is as comprehensive as possible, we carefully select seven popular neural networks, which cover computer vision, speech recognition, and natural language processing(NLP). The contributions of this work are two-fold. First, we conduct the detailed benchmarking experiments on Tensor Flow and Py Torch and analyze the reasons for their performance difference. This work provides the guidance for the end-users to choose between these two frameworks. Second, we identify some key factors that affect the performance,which can direct the end-users to write their models more efficiently.

关键词： deep learning performance comparison TensorFlow PyTorch

来源：评论

学校读者我要写书评

暂无评论

Video key concept extraction using Convolution Neural Network 3

Video key concept extraction using Convolution Neural Networ...

引用

3rd IEEE International Conference on AI in Cybersecurity, ICAIC 2024

作者： Sardar, Tanvir H Amin Hazarika, Ruhul Pandey, Bishwajeet Guru Prasad, M.S. Mahmudul Hassan, Sk Dodmane, Radhakrishna Gohel, Hardik Dept. of Computer Science & Engg Bengaluru India Manipal Institute of Technology Department of Information Technology Bengaluru India Astana It University Department of Intelligent System and Cyber Security Kazakhstan Dept. of Computer Science & Engg. Dehradun India VIT-AP School of Computer Science and Engineering India Nmam Institute of Technology Department of Computer Science & Engg. Nitte India University of Houston Department of Cse Victoria United States

ISBN: (纸本)9798350381856

Objectives: This work aims to develop an automated video summarising methodology and timestamping that uses natural language processing (NLP) tools to extract significant video ***: The methodology comprises extracting the audio from the video, splitting it into chunks by the size of the pauses, and transcribing the audio using Google's speech recognition. The transcribed text is tokenised to create a summary, sentence and word frequencies are calculated, and the most relevant sentences are selected. The summary quality is assessed using ROUGE criteria, and the most important keywords are extracted from the transcript using ***: Our proposed method successfully extracts key points from video lectures and creates text summaries. Timestamping these key points provides valuable context and facilitates navigation within the lecture. Our method combines video-to-text conversion and text summarisation with timestamping key concepts, offering a novel approach to video lecture analysis. Existing video analysis methods focus on keyword extraction or summarisation, while our method offers a more comprehensive approach. Our timestamped key points provide a unique feature compared to other methods. Our method enhances existing video reports by (i) providing concise summaries of key concepts and (ii) enabling quick access to specific information through timestamps. (iii) Facilitating information retrieval through a searchable index. Further research directions: (i) Improve the accuracy of the multi-stage processing pipeline. (ii) Develop techniques to handle diverse accents and pronunciations. (iii) Explore applications of the proposed method to other video genres and ***/Improvements: This approach is practical in giving accurate video summaries, saving viewers time and effort when comprehending the main concepts presented in a video. © 2024 IEEE.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

RGraph: Asynchronous graph processing based on asymmetry of remote direct memory access

RGraph: Asynchronous graph processing based on asymmetry of ...

引用

作者： Chen, Hanhua Yuan, Jie Jin, Hai Wang, Yonghui Wu, Sijie Jiang, Zhihao National Engineering Research Center for Big Data Technology and System Cluster and Grid Computing Lab Services Computing Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

The scale of real-world graphs is constantly growing. To deal with large-scale graphs, distributed graph processing has attracted much research efforts. Existing distributed graph processing systems are commonly built on traditional TCP/IP communication stack, which leads to network bottleneck because of low bandwidth and heavy kernel stack operations. Meanwhile, in real power-law graphs, the average number of mirror vertices after graph partitioning is very large, resulting in significant communication overhead among nodes. The emerging high-performance Remote Direct Memory Access (RDMA) network has the features of low latency, high bandwidth, and low CPU overhead, which brings new opportunities for distributed graph processing systems. Existing RDMA-assisted graph processing systems focus on synchronous execution, which imposes barriers between consecutive iterations. Synchronous execution transfers bulk data among nodes and thus only needs a small number of network transfers. However, synchronous execution is usually less efficient than asynchronous execution because of bulk synchronization. Asynchronous execution accelerates graph processing by eliminating barriers, which in turn requires to transfer a large amount of small size data. In this paper, we propose RGraph, an RDMA-assisted asynchronous distributed graph processing system. RGraph distributes edges into two parts to isolate master and mirror vertices. RGraph exploits the asymmetry of RDMA to accelerate the one-to-many communication between master and mirror vertices. We implement RGraph on top of PowerGraph and conduct comprehensive experiments with large-scale real graphs to evaluate its performance. Results show that compared to existing designs, RGraph reduces the execution time by up to 81%. © 2021 John Wiley & Sons Ltd.

关键词： Mirrors

来源：评论

学校读者我要写书评

暂无评论

Fusion of Natural Language and Knowledge Graph for Multi-hop Reasoning 19th

Fusion of Natural Language and Knowledge Graph for Multi-hop...

引用

19th International Conference on Web Information systems and Applications, WISA 2022

作者： Lu, Xun Zhao, Feng Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031203084

Multi-hop reasoning has been widely studied for its important application values in the domain of intelligent search and question answering. Real-world applications are often dominated by natural language input, and it is difficult to directly model the relation semantics in natural language to fit the multi-hop reasoning model. In addition, the extremely large scale and complex structure of knowledge graphs increase the challenge. We propose a natural language and knowledge graph fusion model (NLKGF) for multi-hop reasoning. NLKGF embeds knowledge graph by fusing natural language semantics during the graph propagation process and adds a relation attention mechanism to enable entity representations to perceive the contribution of different relations. A relation path encoder is designed to encode the relation path by an improved recurrent neural network, the reasoning entity is obtained by calculating the correlation score with the natural language. We tested the performance of the NLKGF model on two datasets requiring multi-hop reasoning. The experimental results show that NLKGF beats advanced benchmark models in multi-hop reasoning tasks, which proves superiority of our model. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Commonsense Knowledge Construction with Concept and Pretrained Model 19th

Commonsense Knowledge Construction with Concept and Pretrain...

引用

19th International Conference on Web Information systems and Applications, WISA 2022

作者： Cai, Hanjun Zhao, Feng Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031203084

Commonsense knowledge (CSK) is the information that people use in daily life but do not often mention. It summarizes the practical knowledge about how the world works. Existing machines have knowledge but lack commonsense because they do not understand and master commonsense knowledge in the same way that humans do. In the latest works, crowdsourcing-based method is costly and has low coverage, knowledge base completion method can highly fit samples, and methods extracted from unstructured data have the defects of low quality. CG &BF is commonsense knowledge construction with a concept-based generator and a BERT-based filter. We utilize semantic search for node matching and entropy encoder for filtering triples with high abstraction. Two algorithms based on concept aggregation and path credibility are proposed to obtain high-quality CSK triples. We subsequently finetuning a BERT to filter incorrect triples. We obtain 500,000 CSK triples based on ConceptNet, which is superior to other construction methods in novelty and quality. In the reading comprehension task, the three-way attention network is selected as the basic model and the knowledge we generate enables the base model to perform better, which proves that the output of CG &BF has higher quality and ease of use. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Deep Conditional Generative Adversarial Networks for Efficient Channel Estimation in AmBC systems

IEEE Transactions on Machine Learning in Communications and ...

引用

IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 805-822页

作者： Zargari, Shayan Tellambura, Chintha Maaref, Amine Li, Geoffrey Ye University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Huawei Canada Ottawa Wireless Advanced System Competency Centre OttawaONK2K 3J1 Canada Imperial College London School of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom

In ambient backscatter communication (AmBC), battery-free devices (tags) harvest energy from ambient radio frequency (RF) signals and communicate with readers. Although reliable channel estimation (CE) is critical, classical pilot-based estimators tend to perform poorly. To address this challenge, we treat CE as a denoising problem using conditional generative adversarial networks (CGANs). A three-dimensional (3D) denoising block leverages spatial and temporal characteristics of pilot signals, considering both real and imaginary components of channel matrices. The proposed CGAN estimator is extensively evaluated against traditional estimators like minimum mean-squared error (MMSE), least squares (LS), convolutional neural network (CNN), CNN-based deep residual learning denoiser (CRLD), and blind estimation. Simulation results show 82% gain of the proposed estimator over CRLD and MMSE estimators at an SNR of 5 dB. Moreover, it has advanced learning capabilities and accurately replicates complex channel characteristics. © 2023 CCBY.

关键词： Channel estimation

来源：评论

学校读者我要写书评

暂无评论

Semantic Reasoning Technology on Temporal Knowledge Graph 19th

Semantic Reasoning Technology on Temporal Knowledge Graph

引用

19th International Conference on Web Information systems and Applications, WISA 2022

作者： Li, Jianuo Zhao, Feng Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031203084

Semantic reasoning techniques based on knowledge graphs have been widely studied since they were proposed. Previous studies are mostly based on closed-world assumptions, which cannot reason about unknown facts. To this end, we propose the Two-Stage Temporal Reasoning Model (TSTR) for reasoning about future facts. In the first stage, probability of future facts occurring is reasoned using repeated information in history. In the second stage, the semantics of the neighborhood nodes are aggregated using the structural encoder and the temporal information is captured using the temporal encoder. The predicted probabilities are obtained by the decoder. Finally, the candidate entity probabilities of the two-stage reasoning are weighted to achieve the prediction of the two-stage fusion. We tested the performance of the TSTR on public datasets and the results demonstrated the effectiveness of the TSTR. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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：